Next Article in Journal
Bovine HOXA11 Gene Identified from RNA-Seq: mRNA Profile Analysis and Genetic Variation Detection Using ME Method and Their Associations with Carcass Traits
Next Article in Special Issue
ZnT8 Loss of Function Mutation Increases Resistance of Human Embryonic Stem Cell-Derived Beta Cells to Apoptosis in Low Zinc Condition
Previous Article in Journal
GEF-H1 Transduces FcεRI Signaling in Mast Cells to Activate RhoA and Focal Adhesion Formation during Exocytosis
Previous Article in Special Issue
The Breakthroughs and Caveats of Using Human Pluripotent Stem Cells in Modeling Alzheimer’s Disease
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Transition from Animal-Based to Human Induced Pluripotent Stem Cells (iPSCs)-Based Models of Neurodevelopmental Disorders: Opportunities and Challenges

by
Sara Guerreiro
1,2 and
Patrícia Maciel
1,2,*
1
ICVS—Life and Health Sciences Research Institute, School of Medicine, University of Minho, 4710-057 Braga, Portugal
2
ICVS/3Bs—PT Government Associate Laboratory, 4710-057 Braga, Portugal
*
Author to whom correspondence should be addressed.
Cells 2023, 12(4), 538; https://doi.org/10.3390/cells12040538
Submission received: 31 December 2022 / Revised: 25 January 2023 / Accepted: 2 February 2023 / Published: 7 February 2023

Abstract

:
Neurodevelopmental disorders (NDDs) arise from the disruption of highly coordinated mechanisms underlying brain development, which results in impaired sensory, motor and/or cognitive functions. Although rodent models have offered very relevant insights to the field, the translation of findings to clinics, particularly regarding therapeutic approaches for these diseases, remains challenging. Part of the explanation for this failure may be the genetic differences—some targets not being conserved between species—and, most importantly, the differences in regulation of gene expression. This prompts the use of human-derived models to study NDDS. The generation of human induced pluripotent stem cells (hIPSCs) added a new suitable alternative to overcome species limitations, allowing for the study of human neuronal development while maintaining the genetic background of the donor patient. Several hIPSC models of NDDs already proved their worth by mimicking several pathological phenotypes found in humans. In this review, we highlight the utility of hIPSCs to pave new paths for NDD research and development of new therapeutic tools, summarize the challenges and advances of hIPSC-culture and neuronal differentiation protocols and discuss the best way to take advantage of these models, illustrating this with examples of success for some NDDs.

1. Neurodevelopmental Disorders (NDDs)

Neurodevelopmental disorders (NDDs) are a group of disorders with childhood onset [1], typically associated with the disruption of the tightly controlled and essential neuronal development processes (proliferation, differentiation, migration, circuit integration, myelination, synapse formation and pruning), leading to an inability to achieve mature healthy cognitive, emotional and motor skills throughout childhood and adulthood [2]. Globally, it is estimated that the prevalence of NDDs in people under 18 years of age ranges from 15% to 20%, a fluctuation caused by the multitude of estimation procedures used in different countries and that is thought to be below their real value [3,4]. Our current knowledge of the molecular pathways mediating NDDs did not allow us to find curative approaches for NDDs yet, but only (in some specific cases) to attenuate the associated symptomatology and/or minimize functional limitations. An early (often molecular) diagnosis of NDDs is vital to provide adequate patient management and medical assistance, easing their day-to-day life, which often represents a challenge. Different types of mutations within a vast array of genes are the root of NDDs, ranging from chromosomal rearrangements, copy number variations and small indels to point mutations. However, phenotype–genotype correlation studies revealed that patients with overlapping genetic etiology may show different clinical profiles with varying degrees of severity [5,6], hampering the diagnostic process. Based on the currently available data, NDDs are mainly considered multifactorial and/or polygenic disorders, rather than purely monogenic, although less frequent forms are caused by sporadic de novo variants (e.g., Rett syndrome). Essentially, the phenotypic outcome observed in NDD patients can be explained through two general principles: gene vulnerability, which reflects the capacity of a gene to endure disruptive variants, and mutational load. Some genes show a lower tolerance to mutations (i.e., they are more vulnerable), as is the case of some NDD-associated haploinsufficient genes. Mutations in one of these genes are rare but are associated with high disease risk and penetrance, likely inducing a disease phenotype even in the absence of additional mutations and therefore leading to monogenic NDDs; often resulting from de novo mutational events, they are therefore associated with sporadic cases of NDDs, where no family history of a previous disease exists [7]. Events in nonvulnerable genes are more common and tend to be transmitted in families for generations [8]. These single genetic variants often do not cause the disease per se, they rather lead to a disease phenotype due to additive effects, contributing to polygenic NDDs [8,9,10], in which the resulting phenotype will depend on the sum of the effects of the single mutations (an increase in the number of mutations being positively correlated with the quantity and severity of symptoms [11]), but also on the interaction between the affected genes [12]. Additionally, some cases fall into a third scenario: the two-hit model [13]. Here, high mutational loads are caused by a combination of germline and somatic events, in which inherited mutations generate vulnerable backgrounds that, combined with a somatic event during development, will culminate in a disease phenotype. These two variants can occur in different gene loci, which increases the likelihood of developing a NDD phenotype [14], partly explaining the great phenotypic heterogeneity observed in NDDs.
NDDs comprise autism spectrum disorders (ASD), intellectual disability (ID), attention deficit hyperactivity disorder (ADHD), communication disorders (CD), neurodevelopmental motor disorders (Tic’s, Tourette’s, stereotypic disorders) and specific learning disorders (difficulties in writing, reading, mathematics) [1]. As there is a great overlap between them and childhood mental health disorders, their diagnosis is based on behavioral phenotypes specified in the statistical manual of mental disorders (DSM). In this review, we will focus mainly on ASD and ID. ASD is characterized by deficits in social communication and interaction accompanied by repetitive patterns of behavior, interests or activities [1]. In some cases, ASD patients are also diagnosed with ID, an umbrella term for NDDs featuring deficits in general mental abilities (reasoning, problem solving, abstract thinking) acquired during development (in contrast to dementia, in which cognitive deficits appear later), with an IQ below 70 and a significant impact on daily living. Overall, NDDs translate to patients’ daily lives, as they lack their personal independence and have impaired social interactions [1], and they may also negatively impact the wellbeing of families. Another condition that may co-exist (or not) with ASD is ADHD [15]. ADHD patients show impaired levels of attention, disorganization and/or hyperactivity–impulsivity that often persist into adulthood culminating in social and academic life impairments [1].
Despite their heterogeneous etiology, NDDs often present overlapping clinical manifestations such as impaired cognition, learning and intellectual disabilities and dysfunction of psychomotor skills [8,16]. Several studies suggest that different NDDs might share key molecular pathways, explaining the diffuse phenotypic boundaries among different NDDs [17,18] as well as their comorbidity. In addition, genetic epidemiology studies revealed an overlap between the genes responsible for increasing the risk of or effectively causing different NDDs [8]. Taken together, recent studies revealed that both rare and common variants driving NDDs affect genes that are linked to a few converging conserved pathways, causing a perturbation in brain function during development at the cellular, circuit and whole brain levels (Reviewed in [19]). Due to a multimodal approach combining the use of animal models with genetic, molecular, electrophysiological and neuroimaging tools, significant advances were made in the field of NDDs. The ever-increasing power of next-generation sequencing tools allowed for the identification of different genetic mutations in a very large number of genes in NDD patients [20]. The creation of genetically modified animal models in which the equivalent of these mutations are introduced allows for a better understanding of their role in the pathogenesis of NDDs and allows for analysis at different stages of neurodevelopment, thus contributing to the formulation of better hypotheses on the onset, progression and therapeutic targets of these disorders (reviewed in [21]). In order to be effective in addition to this concept validity, animal models should present face validity, resembling as much as possible the patients’ symptoms. This is often challenging since, although brain disorder-related genes are frequently conserved among species, their precise function and relevance may vary, introducing confounding information upon translation to humans. As a complement to rodents, the most extensively used animal models, an increasing number of alternative models such as Drosophila melanogaster, Caenorhabditis elegans (C. elegans), zebrafish (Danio rerio) and non-human primates may also be useful, as summarized in Table 1.
Overall, animal models have been highly relevant for increasing our knowledge about the mechanisms underlying NDDs through the study of behavioral phenotypes, neuronal morphology, gene expression and brain networks at different stages of development, allowing for the identification of novel biomarkers and the in vivo testing of innovative treatment strategies. The study of the genes identified to date as involved in NDD pathogenesis have provided essential clues on the pathways mediating these disorders, belonging to four main categories: regulation of protein synthesis, transcriptional and epigenetic regulation, synaptic signaling and embryonically expressed genes [34,35,36] (Figure 1).
Overall, these animal models highlight the relevance of a correct genetic dosage and function for a healthy neuronal development and give some insight on NDD-associated mechanisms. However, discrepancies between the observed phenotypes and what is seen in patients, along with the failure of many clinical trials in the neurology and neuropsychiatric fields based on animal model data, raise questions about the transability of animal model discoveries to clinics. Although some gross neuronal structure and function as well as neocortical developmental processes remained similar among mammals, allowing researchers to extrapolate the conclusions obtained from animal models to humans, at some point in evolution, humans and rodents diverged, with humans acquiring a significantly increased cortical expansion [90] and brain circuitry complexity. As so, the best “model” to tackle NDDs are human patients themselves, although this path is hindered by the difficulty to obtain post-mortem fetal and adult brains for analysis as well as by some ethical concerns. Important gene expression studies brought useful information about NDD pathology, using tissues [91,92] and blood samples from patients [93,94]. Recently, human pluripotent stem cells (hPSCs) appeared as a suitable alternative to model human disease and to develop new treatments, maintaining the patient genetic background and excluding the species limitation [95]. Due to their pluripotency, hPSCs maintain the capacity to differentiate into any human cell population [96], allowing for the generation of disease-relevant cell types, tissues or organoids, for further study of the cellular and molecular events underlying neurodevelopment and the screening of new therapeutical compounds. Thus, the use of hPSCs to model NDDs is highly valuable due to their capacity to recapitulate the genetic, morphological and molecular phenotypes found in patients early in development, such as neural progenitor proliferation and differentiation, but perhaps not so useful to assess neuronal function and plasticity, which is only achieved in more mature stages and/or in the context of functional neuronal circuits within living animals. In this review, we will tackle the different steps involved in hIPSC generation and differentiation while highlighting the advantages and setbacks of the different methodologies available. In addition, we will provide examples of recent contributions of hIPSC-based studies for the NDD field, through the use of gene editing techniques and a multiplicity of approaches to the analysis of the neurodevelopmental processes as well as drug screenings.

2. Modeling NDDs with Human Induced Pluripotent Stem Cells (hIPSCs)

2.1. Human Induced Pluripotent Stem Cells (hIPSCs)

Adult neural stem cells (NSCs) were the first multipotent cells to be successfully generated from hippocampal and subventricular zone (SVZ) biopsies, challenging the old model that suggested the adult brain was unable to regenerate and form new neurons [97]. However, they did not represent a promising cellular model due to the difficulty in obtaining donor tissue, loss of differentiation capacity after few passages, low graft survival and high heterogeneity, which hampers the interpretation of the results and reproducibility of experiments [69,98]. In the meantime, mesenchymal stem cells (MSCs) appeared as suitable substitutes for NSCs (for transplantation purposes) due to their self-renewal capacities and ability to differentiate into cells from three different lineages: osteocytes, adipocytes and chondrocytes. Although these cells revealed promising results for disease modulation [70] and cell therapy [71], their differentiation potential is dependent on the tissue of origin [72,73], they are sensitive to the age of the donor, and they show impaired neuroectodermal differentiation impairment [74]. Finally, a new promising category of cells named human-derived pluripotent stem cells (hPSCs) opened new venues to model human disorders, especially those related with neurodevelopment. hPSCs, which comprise embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), have extensive capacity of self-renewal and differentiation into any adult cell type derived from the three primary germ layers—ectoderm, mesoderm and endoderm [75,76]. These cells widened the tools available to study human disorders, especially NDDs, and increased the capacity of research teams to perform high-throughput screening (HTS) due their self-renewal attributes. In addition, they are powerful tools for cell therapy, achieved by transplanting the specific-tissue cells obtained from the differentiated cell type of interest into patients [77]. Before 2007, the only hPSCs available were ESCs, which are derived from the inner cell mass of pre-implantation embryos (morula- or blastocyte-stage) [78] and are characterized by the expression of a range of pluripotency markers (OCT4, SOX2, NANOG, SSEA-3 and SSEA4) and high levels of telomerase [79]; however, donor cells do not come from the recipient patient [99]. With the discovery of iPSCs, generated by reprogramming somatic cells using four well-defined pluripotency-associated factors [100] (OCT4, SOX2, cMYC, KLF4) [100], this drawback was surpassed, while maintaining the immortality and multi-lineage differentiation potential, the main advantages of hESCs. A big advantage of these cells over hESCs is the maintenance of the genetic background of the donor, allowing for the study of neuropsychiatric conditions that arise from a single mutation with high penetrance and, most importantly, disorders generated by a combination of multiple genetic insults. Additionally, when thinking of cell-based therapies, these cells can be produced in a patient-specific manner, granting their transplantation without immunological rejection by the host. As the donor cells used to reprogram iPSCs contain the donor’s DNA information, informed and voluntary consent from the patients is needed [101]. In addition, as happens with all cell culture-based approaches, the cells cultured in vitro are not in the same environment as in living organisms, and culture conditions still need to be improved to obtain fully optimized and enriched neuronal populations.
When starting an iPSC culture in a new lab, there are some points to consider: (i) somatic cell isolation, selecting the type of cell to collect and providing the documents necessary to cover all ethical concerns; (ii) optimization of iPSC generation steps, from the safety of the reprogramming strategy used, to ensure high quality iPSC clones, to the appearance of potential undesired genetic modifications; (iii) iPSC differentiation, selecting the most adequate controls and performing different quality and functionality evaluations; (iv) the end goal, applying the best re-programming and differentiation strategies to obtain quality cells with adjusted safety, according to the end procedure (transplantation to humans for cell therapy; to mice, to obtain chimeras; or for disease-mechanism or drug discovery basic research) in which they will be applied. All these relevant considerations will be tackled throughout this review with continuous highlight of the main pros and cons of the different alternative techniques used in each stage of the re-programming and iPSC differentiation process in different cell types, along with some possible solutions for the problems that may appear. In addition, we will present different experimental designs available to program research studies with high quality, along with different cell integrity and quality control techniques to ensure the excellence of the obtained cells.

2.2. Current Methodological Approaches to Differentiate Neuronal Cells from hIPSCs—An Overview

2.2.1. Source Cells—Selection

Modeling NDDs using iPSCs requires great expertise to achieve the discovery of cellular phenotypes. The first critical step to start working with iPSCs to decipher NDDs is verifying whether the disease of interest has a known and well-defined biomarker that will bring clinical validity to the model, minimizing the variation of the experimental system. Then, the human IPSCs (hIPSCs) may be obtained from ready-to-use pluripotent stem cell repositories (WiCell Research Institute, Human Induced Pluripotent Stem Cell Initiative (HipSci), European Bank for Induced Pluripotent Stem Cells (EBiSC); profoundly reviewed in [102]), which provide readily available hIPSCs mimicking several NDDs and their corresponding controls (reviewed in [103], Table 2), with the advantage of having some literature support, or patient somatic cells reprogramming. Following the second option, the next step is the selection of the initial study sample from which the hIPSCs will be obtained, involving the collection of the samples, which may be [104]: urine (>30 mL), which is easy to obtain in a non-invasive manner but prone to failure due to the low percentage of transformable cells; blood (5–10 mL in ethylenediaminetetraacetic (EDTA) tubes), which requires trained staff for the collection; skin, to obtain fibroblasts, which requires minor surgery/biopsy; hair and saliva, which can be used but should be avoided, as they are more expensive and unreliable for hIPSC generation. As discussed before, informed consent needs to be obtained and signed by the donors (and/or persons in charge of the donors, in the case of NDDs). Equally important is the choice of good case/control matched pairs, which should be defined from the beginning, to allow for a deep characterization of the cell lines and for the creation of multiple cell replicates as well as to establish and optimize the protocols. The case should be a patient with a representative phenotype of the disease of interest while the control should be a sex-matched healthy family member or a genetically engineered line in which the disease-causing genetic variant is connected to its wild-type (WT) form, creating an isogenic control. Sex and age should not be confounding factors triggering differences in cell phenotype, even if it is expected that the genetic effects overcome the impact of sex differences, and the age of the somatic cells is thought to be mostly irrelevant, considering that cells are going to be reprogrammed to a stem cell-like state [105,106]. During the reprogramming process, hIPSCs reset their telomeres length (known to shrink throughout life), which is determinant for their proliferation and self-renewal properties [107]. Good practices suggest the differentiation of 2–3 different hIPSCs lines for each individual per experiment to obtain robust results. Although the genetic heterogeneity may be advantageous for the study of NDDs, capturing the true genetic complexity of the patient population, it may present a challenge when it comes to comparing results, bringing the need to use larger sample sizes, which sometimes is difficult. To overcome these issues, it is advisable to select some material from patients carrying the same mutation and displaying similar phenotypes, avoiding genetic variants that are present in healthy subjects (reviewed in [103]).

2.2.2. Somatic Cell Reprogramming into hIPSCs

There are two major categories of somatic cell reprogramming: (i) direct reprogramming, where the direct conversion of the (differentiation) donor somatic cell to the desired cell population is achieved, without transition to a pluripotent state; (ii) reprogramming to pluripotency, in which differentiated somatic cells are first reprogrammed into hIPSCs and are then further differentiated into the cellular population of interest [108]. Cells obtained by direct reprogramming of somatic cells retain the molecular age of the original cell and are less abundantly differentiated, while the generation of iPSCs prior to cell differentiation erases the epigenetic memory of the donor cell and generates cells that can be differentiated into several lineages following a more “natural” developmental process. The process of reprogramming to pluripotency includes the expression of genes that were developmentally silenced (encoding the proteins known as the Yamanaka factors: OCT4, SOX2, KLF4 and C-MYC) [100], which are mostly found within heterochromatin regions, through the expression of transcription factors, miRNAs and other molecules delivered using different tools. However, over the years, new protocols using a reduced number of transcription factors have been tested and showed promising results, overcoming the limitations presented by some of the initial ones [109,110,111,112]. Several gene delivery strategies (reviewed in [113]) have been developed thus far for hIPSC generation such as viral integrating transfection (retro- and lentiviruses), non-integrative viruses (Adenovirus [114], Adeno-associated virus (AAV) [115], Sendai virus (SeV) [116]) and non-integrative non-viral DNA approaches (direct introduction of recombinant proteins combined with protein transduction domains (PTDs) [117,118], synthetic mRNA transfections [119], plasmid transfection, mini-circle DNA vectors [120], episomal vectors [121], transposons [122], and liposomal magnetofection [123]). Although the classical viral integrative methods are more efficient and robust, they induce permanent genomic modifications due to viral integration into the host genome, increasing the risk of mutagenesis and tumorigenesis in the resulting hIPSCs [124,125], of critical relevance particularly when considering transplantation applications. Another drawback of these methods is the possible reduction of the differentiation capacity, due to unpredictable silencing/activation of some transgenes [126,127]. With the development of new non-viral integration-free techniques, most of these issues were solved, but the efficiency of the reprogramming decreased considerably, not suiting efficient scientific investigations or clinical applications. As so, the next step in this field is to improve these systems to make them robust, highly efficient and easily reproducible, in order to generate clinical-grade hIPSCs [128]. The advantages and disadvantages of each of these strategies are summarized in Table 2.
Table 2. Summary of cell reprogramming methods used for iPSC generation. Green—high reprogramming efficiency; yellow—moderate reprogramming efficiency; red—low reprogramming efficiency.
Table 2. Summary of cell reprogramming methods used for iPSC generation. Green—high reprogramming efficiency; yellow—moderate reprogramming efficiency; red—low reprogramming efficiency.
Reprogramming MethodDeliveryTypeEfficiencyAdvantagesDisadvantages
Retro- and lentivirusIntegrative transfectionViral Stable transgene expression
[113]
Integration into the genome
Increased risk of mutagenesis and tumorigenesis
[113]
AdenovirusNon-integrative transfection No integration
Well-defined biologyNot all cell types respond equally
Genetic stabilityPossibility of genome integration
Easy large-scale productionTransient expression due to rapid clearance from dividing cells
[129,130,131,132,133]
Safe to use in clinical trials
[134]
AAV Absence of immune/toxic reactionsRequire a helper virus to replicate
Decreased titer production
Stable transgene expressionLimited packaging capacity
Safe to use in clinical trials
[135,136]
Possibility of genome integration
[134,136,137]
SeV No integration
Non-pathogenic to humansDifficult to remove from the generated hIPSCs
Cytoplasmic replicative cycle Cytotoxicity
Low propensity for genomic/epigenetic aberrationsDifficult to work with
High and fast protein expressionEnhanced fusogenicity and immunogenicity
High transduction efficiencySensitivity to transgenic sequences
[138,139]
Fast cellular uptakeLow capacity to cross the cell membrane
Lack of nuclear localization
Challenging production of pure proteins
Poor solubility and stability
Sequestration of the transduced reprogramming proteins
[117,118,140,141,142]
Ideal transgene expression
[116,138,143,144,145,146,147]
Recombinant proteinsProtein
Low capacity to cross the cell membrane
Lack of nuclear localization
No IntegrationChallenging production of pure proteins
Virus free
[118]
Poor solubility and stability
Sequestration of the transduced reprogramming proteins
[117,118,140,141,142]
mRNA transfectionsmRNA No integrationDifficult to work with
Virus free
Low reprogramming timeTriggers immune system response
Safe and high transduction
[119,148,149,150]
Need for feeder cells and animal-derived molecules
[151,152,153,154,155,156,157,158,159,160]
Plasmid transfectionDNA Transient expression of reprogramming factors
Virus freeVariation of transfection efficiency between cells
No vulnerability to exonucleases
[161]
Large size
Lack of self-replication requiring multiple transfections
[161,162]
Mini-circle vectors No integration
Virus free
High transgene expressionLack of self-replication capacity
Easy to synthetize and deliverDecreased expression time
Small sizeRequire multiple transfections
[113,163,164,165]
Less prone to transcriptional silencing
Controlled concentration and application time
[120,166,167]
Episomal vectors No integrationLow efficiency
[121,137]
Virus free
Single transfection
Long-term, stable transgene expression
Fast protein expression
Absence of genome manipulation
Lack of regulatory constraints in the target gene
[121,137]
Transposons Stable integrationPossible reintroduction in the genome
[168]
Virus free
Carry large cargoes
Single transfection with long-term, strong gene expression
Inexpensive
Easy to work with
Removal of transgene cassette without induction of genetic mutations
Low immunogenicity
[137,169,170]
Liposomal magnetofection No integrationLow efficiency
[171]
Virus free
Single transfection with low immunogenicity
[171,172]
Another important factor to carefully address is the culture media in which hIPSCs are maintained, which differ according to the final use of these cells (reviewed in [173]). hIPSCs used for research are usually generated and maintained on feeder cells, while hIPSCs destined for clinical use should be kept in feeder-free (Ff) and xeno-free (free of foreign species) (Xf) culture conditions [174]. In addition, supplementation of the reprogramming culture media with antioxidants may be useful to reduce the appearance of genomic aberrations in hIPSCs [175].
Before proceeding to differentiation of hIPSCs into the target cell population, the quality of the cells obtained should be ensured. To do so, immunohistochemistry and gene expression profiling should be performed to assess the expression of well-defined pluripotency markers (e.g., SOX2, OCT4, TRA1-60, TRA1-81, NANOG) and their ability to generate cells from the three germ lines (endoderm, mesoderm and ectoderm). High-quality hIPSCs clones are expected to present clearly defined margins, with several pluripotency markers on their surface. In addition, no miscellaneous differentiated cells should be growing in the culture. Then, to assess the potential of the resulting cells to differentiate into the three germ layers, two strategies can be used: (i) histological analysis of the tissue composition of the tumor (teratoma) that grows upon injection of the obtained hIPSCs into a mouse; (ii) using specific defined media, promote the differentiation of the cells into the different germ layers [176].

2.2.3. hIPSCs Differentiation into Neuronal Populations

Due to the temporal expression of specific transcription factors during development, different neurons are characterized by multiple population-specific features essential for their function, such as the expression of a variety of molecules that ensure their synaptic specificity, the development of specific morphologies, generation of electrical or chemical synapses to deliver and receive several neurotransmitters, which allow for communication with other neurons, and secretion of specific molecules. A big advantage of hIPSCs is their capacity to generate nearly every neuronal population that exists [177]. This is achieved by the supplementation of the hIPSC culture medium with the appropriate differentiation and growth factors, at specific timepoints of development and in specific combinations and concentrations, according to the desired neuronal population. hIPSCs are maintained in the appropriate neural maintenance medium throughout the quality tests and during experimentation. Due to the recent progress in the field, it is now possible to identify programming factors in a more effective way through the use of several techniques, such as single-cell RNA sequencing (sc-RNAseq), in multiple steps of development and trajectory inference. Currently, there are already several protocols available to differentiate hIPSCs into almost all neuronal cell types, such as motor [178,179,180,181,182], striatal [183], cortical [184,185,186] excitatory [187] and GABAergic [188], dopaminergic [189,190,191,192,193] and serotoninergic [194,195], and peripheral sensory neurons [196,197].
Finally, quality and functionality of the cells obtained should be evaluated. Primarily, analysis of biomarkers specific to neuronal differentiation such as NeuN, which is expressed in newborn neurons, along with neuronal cytoskeletal proteins such as Nestin, Tuj1, MAP2, Syn1 and specific subtype markers, should be performed using immunohistochemistry or Western blotting. Then, it is mandatory to verify the absence of human infectious agents and mycoplasma and to perform a molecular karyotype of the cells, to clarify whether some chromosome rearrangements happened during the reprogramming or differentiation steps. In addition, to clarify the functionality of the cells obtained, their ability to generate electrophysiological properties should be measured, using microelectrode arrays (MEAs) and/or patch-clamp electrophysiology. After all these quality measurements, the last step before starting to answer the scientific questions is to thoroughly authenticate and characterize the cell model established. One of the main drawbacks of the differentiation process is its low efficiency, with a percentage of cells displaying the desired identity ranging from low (<10%) [178] to moderate (60%) [179], which may be the result of ignoring the natural path of neuronal development by supplementing terminal transcription factors to hIPSCs. Additionally, a recent work highlighted the possibility that the number of passages may affect the differentiation of hIPSCs into the desired neuronal subtype, suggesting that lower passage numbers may be better for the differentiation into some populations [198]. In addition, neuronal differentiation is a highly complex process that usually results in a mixed neuronal population, even under tightly controlled experimental conditions, raising questions about the suitability of the available protocols to produce reliable disease phenotypes and, consequently, the correlation of the data obtained with the clinic [199]. This suggests that an exhaustive identity analysis should be performed to clearly pinpoint the neuronal populations present in culture as summarized in Figure 2.

2.2.4. Experimental Design

Several experimental designs were developed over the years to successfully use hIPSCs for disease modeling, such as: matched pairs, family and isogenic designs. Currently, the most powerful design used for hIPSCs [200] is the matched pairs design in which confounding variables (sex, age, genetic background) are resolved at the design stage instead of during the analysis by matching the cases and the controls based on one or more factors. This strategy is advantageous mainly in small sample size experiments, where matching is more efficient than controlling for each confounding factor, and it decreases the associated cost. As a subdivision of this strategy, is the family design in which the genetic background is the controlled factor. This design is preferentially used in genomic studies to evaluate genetic transmission through generations, allowing for the correlation between genetic background and phenotypes observed in hIPSCs [201]. It allows for great control of the genetic background, responsible for a big proportion of hIPSC heterogeneity, with special relevance to use in experiments with small sample size. Comparing hIPSCs originating from family members with different ages (parents and children) could represent a challenge if the age of the somatic cells persists as epigenetic memory. However, it was already shown that after reprogramming, the cell resets into a stem-cell state, mostly eliminating this confounding effect [105,106]. Finally, the isogenic design is a powerful strategy that requires the use of isogenic controls, where the disease-causing genetic variants present in the donor cells are corrected, which has increased value to identify functional effects of genetic and epigenetic variations in the same cell line. When investigating the role of single mutations, the comparison between isogenic cells overcomes, at least partially, the variability produced by the genetic background [202], highlighting their utility.

2.2.5. Gene-Editing Techniques Applicable to hIPSCs

Traditional methods such as homologous recombination (HR) and endogenous repair of double-stranded DNA breaks (DSB) have opened new venues to generate sophisticated gene-editing tools. In HR, the foreign genetic material is introduced in the host genome resulting in a deletion (to generate knock-out animals or cells), gene addition (to generate knock-in animals or cells) or a genetic mutation that will allow for the study of the function of the gene of interest or its role in disease [203,204]. HR-based techniques applicable to hIPSCs include electroporation, nucleofection or chemical-based transfection of linearized plasmid linear DNA constructs, forming homology regions of less than 2 kb, designed to disrupt or delete exons from the target gene. However, introduction of genetic changes by HR was proven to be inefficient in hPSCs [205] due to low cell-survival [206] and to human DNA repair processes [207]. Curiously, it was discovered that DBSs significantly improve the efficiency of HR in human cells [208]. In response to a DBS, the cell activates repair mechanisms through non-homologous end joining (NHEJ) or homology-directed repair (HDR), allowing for the manipulation of the genome. Through NHEJ, the cell repairs nucleotide mismatches or strand breaks in the absence of a homologous template, resulting in the insertion or deletion of nucleotides, originating in a functional knock-out. Contrarily, in HDR, a partially homologous DNA sequence is used as a template to proceed with the repair of the DNA, or an exogenous homologous repair template allows for the insertion of specific DNA sequences [208].
Further studies were conducted in an attempt to optimize the use of these techniques, using designer nucleases. This opened the door to the most customized and widely used gene-editing tools: the zinc-finger nuclease (ZFN), the transcription activator-like effector nucleases (TALENs) [209] and the clustered regularly interspaced short palindromic repeats (CRISPR)-based techniques [210]. Their combination with hIPSC cultures has been very powerfully exploited for the efficient generation of hIPSC lines differing only in the presence or not of the candidate disease-causing gene variant—isogenic lines. With these advances, the manipulation of single-nucleotide mutations that recapitulate common disease-associated variants was finally possible [211,212] (reviewed in [213]).
Briefly, ZFNs, fusion proteins with several tandem Zinc-finger DNA binding domains coupled to an endonuclease catalytic domain (recognizing specific nucleotide triplets), allowed for the first corrections of genetic mutations in patient-derived hIPSCs [214,215] and for insertion of disease-relevant genomic variations in control hIPSCs [216], but also for the production of cell type-specific reporter systems to investigate disease mechanisms [217]. However, they are difficult to engineer, and their design and application remain technically challenging. Further advances in gene-editing tools were accomplished with the generation of TALENs, which are composed of a TALE DNA binding domain fused to an unspecific FokI nuclease. They were also applied to induce disease-causing mutations in healthy hIPSCs [218,219] or to correct disease-causing mutations in patient-derived hIPSCs [218,219], but also to produce specific reporter system for stem cell-based research [220,221,222]. However, studies suggest that low binding affinity leads to reduced TALEN activity, while strong affinities diminish its specificity. In addition, although not initially expected, as the design strategy used for TALENs includes a pair of sequences spanning around 45–60 nucleotides, off-target activity was reported [223]. CRISPR/CRISPR-associated protein 9 (Cas9) is a nuclease-based system used to induce target mutations and gene insertions [210]. Specifically, Cas9 functions as a nuclease targeting a specific genomic locus through the action of a guide RNA (gRNA), causing DBS, which activates an endogenous DNA repair response (NHEJ) [224]. However, when using Cas9, especially in common alleles with small effect size, the possibility of off-target events should be considered. CRISPR/Cas9 modulation of gene expression happens in the promoter/enhancer, including the entire range of alternative splicing isoforms. In hIPSCs, CRISPR/Cas9 can be used to insert patient-specific mutations in control hIPSCs or to repair them in patient-derived hIPSCs, generating isogenic control lines [224]. The generation of these isogenic lines allows for the creation of well-controlled and -defined systems to robustly dissect the effect of genetic risk variants associated with the disease under study. Finally, CRISPR/Cas9 can also be used to target several genes in a single experiment, using CRISPR knock-out (CRISPR KO) and knock-in (CRISPR KI) strategies quicker than with other techniques [225]. However, although they represent valuable assets to understand pathological mechanisms underlying several disorders, few studies use these techniques, as protocols still need to be optimized, especially when focusing on hIPSCs. Finally, recent advances allowed for the combination of CRISPR-based tools with single-cell sequencing for detection of analytical readouts, to conduct large-scale genetic screens, such as Perturb-seq, CRISPR-seq, CRISPR-droplet sequencing (CROP-seq) and expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq) [226,227,228,229].
In addition, several techniques may be useful to detect the inserted or deleted nucleotides as a measure of gene-editing cleavage activity, such as the Surveyor assay, digital droplet PCR (ddPCR), Sanger and NGS sequencing or Tracking Indels by Decomposition (TIDE). After successful delivery of the nucleases to the hIPSCs, the final step is the selection of the gene-edited hIPSCs from the mixed-genotype culture that is generated. The use of fluorescent reporters along with fluorescent activated cell sorting (FACS) or electrophoresis of mRNA isolated from different clones, selecting positive clones, followed by Sanger sequencing for genome confirmation may be useful methods to conduct this selection [230]. Finally, to clarify disease mechanisms or to identify potential therapeutic targets, genomic variation analysis (WGS, DNA methylation, ATAC-Seq, GWAS + QTL) along with transcriptomics (scRNA-Seq, RNA-seq, TAP-Seq) and proteomics/cellular architecture analysis (Quantitative Mass Spectrometry (qMS), ELISA, High Throughput Imaging) are key techniques to evaluate functional genome variation.

2.2.6. Quality vs. Quantity, Cost-Associated, Time Needed

The differentiation capacity of hIPSCs into different neuronal cell types or tissues to model different NDDs, mimicking disease phenotypes and granting access to several neurodevelopmental mechanisms, is one of their major advantages [231]. However, the establishment and maintenance of hIPSC lines is time-consuming, taking several weeks to obtain the cells of interest, and has a high associated cost, due to the amount of reagents needed and the application of gene-editing techniques. As so, the refinement of the differentiation protocols is vital, to allow for more specific and reliable research work. However, thus far, most protocols face several problems leading to low and variable efficiency, such as the varying differentiation capacities of different hIPSC lines, incomplete reporting of the protocol steps and limitations and lack of standardized reagents. The latter weaknesses are easily solved by the use of high-quality control cells and suitable protocols and reagents, along with transparent reporting thoroughly describing all the reagents, steps, problems with respective solutions and tips to improve the culturing and differentiation process [232,233]. Thus, the problem with the varying differentiation capacity of different cell lines has been addressed by upgrading currently available protocols, increasing their efficiency and enhancing the availability of improved stem cells generated from enhanced somatic reprogramming and cell culture [234]. A major recent advance was the avoidance of generating embryoid bodies in order to directly and efficiently generate neural progenitor cells (NPCs) along with the expansion of nanospheres, further generating a variety of neuronal populations [235,236].
Overall, optimized and shorter protocols should be developed to decrease the cost and time associated with the use of hIPSCs for research as well as to improve the quality and reliability of the cells obtained.

2.2.7. Genetic/Epigenetic Instability

Although hIPSCs represent a sophisticated new approach to mimicking human disease, they should maintain their genome integrity for appropriate reprogramming and differentiation, which sometimes is challenging, considering the cellular processes altered upon initiation of these procedures, the prolonged time they spend in culture and the environmental stressors they are constantly exposed to. The appearance of genetic aberrations in hIPSCs is a major concern, as they may be implicated in tumorigenesis (if transplantation is the goal) and in the development of several defects, emphasizing the need to comprehend and control their underlying molecular processes. These genomic aberrations range from point mutations to subchromosomal and chromosomal defects. De novo single-nucleotide variations (SNVs) and copy-number variations (CNVs) were previously identified in patient-derived hIPSCs [237,238]. Another widely reported anomaly is aneuploidy, where there is a variation in the number of chromosomes, the most common being an additional copy of chromosome 8 [239,240]. These chromosomal aberrations appear as mosaicism in the cellular culture, and their proportion increases with passage number [241]. Another key factor is aging-related mutagenesis of the cells, as replication stress has been associated with the appearance of variations in chromosomal DNA [242]. The increased use of non-integrative vectors also reduced the possibility of this potential for instability. Along the years, several techniques have been developed to allow for the detection of such genomic alterations, such as comparative genomic hybridization (CGH) array, single nucleotide polymorphism (SNP) array and high-throughput sequencing.
Since their discovery, genetic instability in hIPSC cells has been well documented, but epigenetic aberrations have been much less explored. It has been reported that hIPSCs have different propensities for differentiation [243] due to aberrant DNA methylation, in which methyl groups are added to the fifth carbon of the cytosine residues. This modification is considered stable in somatic cells and is mostly linked to genetic repression, maintaining heterochromatic memory and gene silencing [244]. However, in hIPSCs, DNA methylation aberrations appear in both gene promoters and non-coding regions or as residual signatures from the source somatic cells as part of their epigenetic memory [245,246,247,248,249]. Although some DNA methylation changes may appear in culture over time, the reprogramming stage is considered their main cause, due to the several epigenetic alterations driven by the induction of expression of the pluripotency-inducing genes, leading to transcriptional changes. Thus, DNA methylation analysis to clarify its distribution, specificity and stability may be advantageous and can be performed through several techniques such as whole-genome bisulfite sequencing (WGBS), methylation arrays and bisulfite sequencing (reviewed in [244]). Nevertheless, this epigenetic aberration may constitute an important challenge for neurodevelopmental studies, as the possibility of occurrence of DNA methylation variations in neurodevelopment-related promoters may alter the differentiation capacity of hIPSCs into different neuronal lineages. Another widely described epigenomic alteration is loss of parental imprinting (LOI). Parental imprinting is an epigenetic process, where differentially methylated regions (DMRs) are induced at specific loci of parents’ gametocyte genomes, leading to the expression of only one copy of the target gene in a parent-specific way [250]. This is a highly stable process throughout life and across different tissues, and its dysregulation leads to the appearance of neurodevelopmental disorders such as Prader–Willi and Angelman syndromes [251,252,253]. Several studies revealed a high incidence of LOIs in hIPSCs, leading to the expression of imprinted genes from both alleles instead of one, accompanied with altered DNA methylation in imprinting DMRs [254,255,256,257], which persists after the differentiation stage, affecting hIPSCs growth and integrity. The most reliable method to identify LOIs is allelic expression quantification (high throughput RNA sequencing, SNP arrays (DNA and RNA) and Sanger sequencing), as RNA analysis is highly sensitive and is almost not affected by secondary events. For the study of human neurodevelopment, it is of major importance that parental imprinting remains intact in hIPSCs, as LOI in specific imprinted regions biases the differentiation process towards particular cell types. For example, in Prader–Willi syndrome the necdin (NDN) gene, responsible for the differentiation of forebrain GABAergic neurons [258], is not expressed, leading to irregular GABAergic signaling [259]. However, abnormal imprinting may affect several genes associated with the same locus [250,260], becoming unclear how individual genes contribute to the disease phenotype. Finally, several studies revealed that different hIPSC lines have different states of X chromosome inactivation (XCI) [261,262], an epigenetic process responsible for the compensation of the gene expression of both female XX chromosomes to match the dosage of a single male X chromosome [263]. This mechanism happens randomly in the female embryo, with silencing of either the male or female X chromosome, during embryogenesis. XCI is triggered by transcription factors followed by the removal of active histone marks (histone acetylation) and accumulation of repressive histone marks in the inactive X (Xi) chromosome by the action of the long non-coding RNA (lncRNA) X inactive specific transcript (XIST) [264,265]. In humans, the lack of XIST RNA is linked to variations in the transcriptional profile, such as increased upregulation of cancer-related genes, and reduced developmental potential, raising several concerns about the use of hIPSCs in translational medicine. Downregulation of XIST expression tends to happen in some hIPSC cultures [266,267]. Additionally, XCI “erosion” was observed in hIPSCs cultured for long periods, leading to the reactivation of some genes, a phenomenon that remains after differentiation and hampers the modeling of X-linked disorders [268]. Evaluation of the XCI state is possible but requires a combination of different measures to be accurate, such as RNA fluorescent in situ hybridization (RNA-FISH), high throughput RNA sequencing (RNAseq), sc-RNAseq and immunostainings [269,270]. Overall, along with the fact that hIPSCs XCI do not recapitulate the random XCI that happens in developmental stages, the above-mentioned information suggests the possibility of an inappropriate XCI, raising some concerns regarding the use of hIPSCs derived from females for the study of neurodevelopmental disorders.

2.2.8. hIPSC-Derived 3D Brain Organoids

Although traditional 2D hIPSC cultures represent a great model to mimic NDDs, allowing for the generation of several neuronal populations [271,272,273,274,275,276,277] and uncovering several developmental mechanisms underlying these disorders, they fail to reproduce what happens in mature neurons, lacking synaptic maturity and the interaction between different cell populations in surrounding areas [278]. For these reasons, and although they are clearly valuable to evaluate the therapeutic capacity of several compounds through drug screenings, the translation of the findings to clinics may remain inefficient. In an attempt to overcome these limitations and to further advance hIPSCs disease modeling, the development of hIPSCs-derived organoids emerged [279,280]. Organoids, 3D multicellular aggregates derived from stem cells, have the capacity to differentiate and self-organize into a variety of brain regions [278,281,282] or whole-brain-like structures [283]. This technique was inspired by the generation of embryoid bodies (EBs) from ESCs, which are aggregates of PSCs generated due to the detachment of ESCs from the culture plate in the presence of neural induction medium, generating neural tube-like rosettes [282]. For organoid generation, these neural tube-like rosettes are embedded in hydrogels mimicking the extracellular matrix and are placed in a spinning bioreactor, stimulating differentiation by enhancing oxygen and nutrient uptake and by decreasing apoptosis [282]. Cerebral organoids express several markers of cell types originating from multiple brain regions such as forebrain, midbrain and hindbrain neurons [282,284], which become more expressed as pluripotency marker expression decreases during organoid development, suggesting that these models impressively recapitulate human neurodevelopment [282]. In addition, immunohistochemical staining revealed the presence of glial entities as well as cells resembling astrocytic and oligodendrocytic morphologies [285] in addition to neurons, highlighting the complexity of these models. The combination of specified neuronal organoids is highly relevant for the study of fundamental features of brain development and disease, allowing for the comprehension of the interaction of neuronal populations from areas of interest in a dish. Although they may improve the possibilities for evaluation of neuronal electrophysiological properties in comparison with hIPSC experiments, by allowing for the analysis of complex 3D neuronal interactions [286,287]. They are still time-consuming models, as 2D neuron cultures require only few weeks to reach electrophysiological properties, while 3D organoids require several months to obtain similar features [288]. Another major limitation of brain organoids is the lack of a vascular system, the absence of which leads to impaired cell viability and architecture in older and larger organoids, as the culture in bioreactors is not enough to make the oxygen and nutrients reach the core. One way to overcome this issue is the culture of organoid slices, which may improve nutrient supply and consequently cell survival and maturation [289]. Another alternative is the transplantation of brain organoids to an adult mouse brain, where these have access to the host vascular system, nourishing the organoid with their blood flow and originating healthy mature neurons [286]. Yet, it would be interesting to develop new strategies in the future to find new approaches that better resemble the brain environment without use of invasive methodologies in mice, such as by preparing a co-culture with organoids and endothelial precursors or by taking advantage of organoids derived from other human organs that already possess a vascular system. Overall, the above-mentioned constraints should be addressed to generate safer and more reliable brain organoids for biomedical research. However, organoids are still a sophisticated model to address the biological basis of neuropsychiatric disorders, especially NDDs, due to their potential for resemblance with the human brain but also with their development process (reviewed in [290]).

2.3. hIPSC Models for the Study of NDDs

Using hIPSCs derived from NDD patients allows for the study of several disease-relevant phenotypes such as neuronal differentiation, morphology, electrophysiological properties and gene expression, examples of which will be discussed below.

2.3.1. hIPSC Models of Autism Spectrum Disorders (ASD)

ASD is a heterogenous group of genetic neurodevelopmental disorders characterized by impairments in communication, social interaction, and stereotypic repetitive behaviors.
As discussed above, dysregulation of the synaptic function is thought to be one of the main mechanisms underlying ASD-related phenotypes. NPCs derived from ASD patients revealed increased cell proliferation mediated by the dysregulation of the β-catenin/BRN2 transcriptional cascade, as well as abnormal neurogenesis and reduced synaptogenesis leading to functional abnormalities in neuronal networks [291]. Likewise, in hIPSCs-derived neurons from ASD patients, a decrease in synaptic PSD-95 clusters [292] was described, along with impaired neurite outgrowth [292]. Defects in synaptic connectivity were also described, comprising deficits in AMPA/NMDA current ratio [293], decreased glutamate release, decreased number of excitatory synapses, and a decreased expression of synaptic proteins [294]. In addition, a study using hIPSCs from a patient carrying a heterozygous deletion of SHANK2 and hIPSCs engineered using shRNA to silence SHANK3, both risk genes for ASD, revealed a reduced growth area and increased soma size in these neurons [295,296,297]. However, when control and labeled SHANK2 mutant hIPSCs were cocultured, increased dendritic length and complexity, synaptic number, and frequency of spontaneous excitatory postsynaptic currents (sEPSC) were observed. These anomalies were further confirmed in a gene-edited hIPSC cell line carrying a homozygous SHANK2 knockout, but were rescued by SHANK2 gene dosage correction [298]. Another gene whose deletion is commonly associated with NDDs, especially ASD, is NRXN1A. Differentiation of cortical neuron-like cells from hIPSCs originated from patients carrying a NRXN1A deletion revealed increased calcium signaling sensitive to voltage-gated sodium and calcium blockers [299], as well as larger sodium currents, higher AP amplitude and accelerated depolarization time in comparison with control cells [300]. In addition, pathogenic variants in NLGN4, another relevant gene, were associated with impaired ability to form synapses derived from patient hIPSCs [301]. Finally, the role of genetic variants in Down syndrome cell adhesion molecule (DSCAM), an ASD-related gene involved in the development of human nervous system, was investigated in neurons derived from hIPSCs carrying a heterozygous point mutation in this gene. This revealed that DSCAM mRNA levels and density in dendrites were decreased in ASD hIPSCs in comparison with controls. In addition, genes involved in synaptic function, such as those encoding NMDA receptor subunits, were downregulated in ASD neurons, which goes along with the decrease found in NMDA receptor-mediated currents. The same was observed in hIPSC-derived neurons after shRNA-mediated DSCAM knockdown but was rescued after shRNA-induced overexpression of DSCAM [302]. Overall, these results suggest that mutations in genes involved in synaptic function may underlie ASD phenotypes through NMDA function dysregulation.
Nevertheless, the excitatory circuitry is not the only one affected in ASD. Neurons derived from ASD hIPSCs revealed decreased GABA levels and GABA receptor expression [291]. ASD patient-derived forebrain organoids revealed an accelerated cell cycle accompanied by overproduction of the transcriptional repressor FOXG1, triggering increased production of GABAergic interneurons in comparison with glutamatergic neurons, suggesting an imbalance in the differentiation process that may be relevant in mediating ASD phenotypes [201]. To clarify the implications of penetrant and weak polygenic risk variants for ASD, glutamatergic neurons derived from hiPSCs from 25 individuals from 12 families carrying heterozygous de novo or rare-inherited damaging ASD variants were generated. Using MEAs and patch-clamp recordings, a spontaneous network hyperactivity was revealed in neurons lacking one copy of the contactin 5 (CNTN5) gene, a common ASD risk factor, bearing a rare missense variant in the euchromatin histone lysine methyltransferase 2 (EHMT2) gene, suggesting the involvement of both types of abnormalities in ASD-related phenotypes [303]. Neurons derived from hIPSCs from a patient with a functional deletion of TSC2, a gene involved in ASD, revealed neuronal hyperactivity linked with neuronal network dysfunction, with low synchronization of neuronal bursting and decreased spatial connectivity. The observed deficits were attributed to an elevation in the expression of genes associated with GABA and glutamate signaling and to imbalances in inhibitory/excitatory circuitry [304]. Additionally, Martin and colleagues explored the role of TSC1 in ASD-evoked neurodevelopmental alterations. They generated NPCs derived from a patient with a heterozygous pathogenic variant in exon 15 of TSC1 and generated isogenic controls (heterozygous, null and corrected wildtype (WT)) using CRISPR/Cas9. TSC1-mutated hIPSCs revealed an enlarged cell size, enhanced proliferation and altered neurite outgrowth [305]. Although less addressed, epigenetics may play a major role in the pathogenesis of ASD, especially through altered microRNA (miRNA) function. It was previously shown that miR-92a plays an important role in neuronal neurite outgrowth, neuronal differentiation and GABAergic neuronal maturation [306]. Considering this, studies focusing on miR-92a-2-5p, which shares the same seed sequence as miR-92a, using NPCs derived from ASD-hIPSCs, revealed increased levels of miR-92a-2-5p concomitant with decreased proliferation, with a delay in S phase progression, early differentiation deficits and reduced number of inhibitory neurons [307]. In addition, inhibition of this miRNA rescued the proliferation and differentiation deficits found in ASD NPCs, suggesting that miR-92a-2-5p may be a regulator of these processes, especially in inhibitory neurons. A summary of the phenotypes described in humans, rodents and hIPSCs can be found in Table 3.

2.3.2. hIPSC Models of Rett Syndrome (RTT)

RTT is a NDD that mainly affects females and is caused by mutations in the X-linked gene MECP2. This disease is characterized by breathing and sleep problems, movement disorders, language and learning impairments and repetitive stereotyped movements.
The study of glutamatergic and GABAergic neurons derived from RTT syndrome patient hIPSCs carrying a variety of pathogenic variants in MECP2, revealed decreased calcium signaling, along with electrophysiological impairments characterized by reduced sEPSC and spontaneous inhibitory postsynaptic currents (sIPSC) as well as morphological deficits, with establishment of fewer synaptic contacts and reduced cell soma size, dendritic branching, spine density and impaired neuronal maturation [382,383,384,385,386,387,388,389], as shown in post-mortem studies of patients brains. Another curious observation was the heterogenous profile of X chromosome inactivation, as some hIPSCs maintained X chromosome inactivation while others did not [382], suggesting the possibility of an epigenetic alteration in this hIPSC line.
Recent studies addressed the role of the severity of MECP2 mutation on the phenotypes observed in RTT hIPSCs. For that, excitatory neurons were differentiated from hIPSCs obtained from patients carrying mutations associated with phenotypes ranging from mild to severe RTT and patients carrying MECP2 null mutations, and the respective isogenic controls. Overall, mild RTT neurons displayed core phenotypes such as increased input resistance, impaired voltage-gated Na+ and K+ currents and reduced dendritic complexity, while MECP2-null neurons revealed all of the above plus depolarized resting membrane potential, reduced cell capacitance along with decreased soma area and dendritic length, and deficits in excitatory synapse transmission. This study suggests that MECP2 dosage and/or variant type impact the phenotypes observed in RTT [317,318].
Using RTT 3D brain organoids, deficits in neuronal migration and maturation, such as reduced neurite outgrowth and fewer synapses and reduced neuronal migration, were described [319]. In cerebral cortex–ganglionic eminence (GE)-combined organoids, in which excitatory and inhibitory neurons fully integrate, a neural network dysfunction was observed, caused by GE-derived interneurons malfunction, in brain organoids derived from RTT hIPSCs. These phenotypes were rescued by the treatment with the neuroregulatory drug Pifithrin-α [320]. In addition, RTT hIPSCs-derived organoids showed an increased number of vasoactive intestinal peptide (VIP)+ and calbindin 2 (CALB2)+ interneurons and decreased percentage of parvalbumin (PV)+/somatostatin (SST)+ and calbindin 1 (CALB1)+ in comparison with controls, suggesting an imbalance in interneuron subtypes in RTT [320]. The decrease in SST interneurons-mediated inhibition caused by an increased number of VIP interneurons may mediate the loss of low-frequency oscillations in RTT-combined organoids, while the decrease in the percentage of PV interneurons may contribute to the loss of gamma oscillations. Additional staining analysis revealed an increase in the density of excitatory puncta in MECP2-mutant organoids, without changes in inhibitory synapses [320]. Overall, these data highlight the imbalance in cortical excitatory/inhibitory circuitry present in RTT, which appears to have a major contribution in inhibitory interneurons. Another study using dorsal and ventral forebrain organoids obtained from RTT patient hIPSCs and controls revealed premature development of deep-cortical layer associated with decreased expression of progenitor and proliferative cells and defective function of RTT neurons in RTT dorsal forebrain organoids [321]. Fusion of both ventral and dorsal forebrain organoids demonstrated impairments in interneurons migration [321] in early stages of neuronal development. Levels of Nkx2.1, a marker of medial ganglionic eminence (MGE) neurons, which is the origin site of SST+ and PV+ interneurons, were highly decreased in ventral RTT organoids upon ventral patterning of hIPSCs, increasing the evidence for a disruption in interneurons development in RTT. In addition to MECP2, mutations in FOXG1 may also lead to RTT-like phenotypes. A study using neurons derived from FOXG1-mutant patient hIPSCs revealed an increase in orphan glutamate receptor δ-1 subunit (GLUD1), which is responsible for synaptic differentiation and the shift in the balance of excitatory toward inhibitory synapses and in inhibitory markers (GAD67, GABA AR-α1) and for a decrease in excitatory markers (VGLUT1, GluA1, GluN1, PSD95) [322]. A summary of the phenotypes described in humans, rodents and hIPSCs can be found in Table 4.

2.3.3. hIPSC Models of Down Syndrome

Down syndrome is caused by the presence of an extra copy of chromosome 21 and is mainly characterized by physical phenotypes (short stature, small, low-set ears, flat nasal bridge, small mouth), along with cognitive and learning deficits, ID, heart congenital defects, leukemia, gastrointestinal structure defects and hearing and vision problems. The mechanisms underlying these cognitive deficits can be addressed using hIPSCs-derived neurons and/or glial cells.
Morphometric analysis of DS hIPSC-derived cortical interneurons showed that GABAergic interneurons were smaller and had fewer neuronal processes than controls. In addition, the proportion of calretinin (CR)+ over CB+ neurons was reduced, which was associated with a decreased migration capacity [336,337,338]. DS progenitor cells generated fewer COUP-TFII+ progenitors, a marker of caudal ganglionic eminence (CGE) neurons (interneurons progenitor cells), and these showed a reduced proliferation [337,339]. Additionally, Wnt signaling was reduced in DS hIPSCs, and its activation restored the COUP-TFII+ progenitor population [337]. Along with migration impairment, DS hIPSCs also showed abnormal neuronal differentiation, which appeared to be dependent on DYRK1A expression [340]. DS hIPSCs also revealed decreased mitochondrial membrane potential along with an increased number and abnormal morphology of mitochondria [339]. Moreover, hIPSCs derived from DS patients showed an exacerbated production of OLIG2+ ventral forebrain NPCs. OLIG2 directly upregulates interneuron lineage-determining transcription factors favoring the production of GABAergic interneurons, overproducing them, as observed in brain organoids derived from DS hIPSCs [341]. Consistently, OLIG2 knockdown by shRNA restored its expression in DS NPCs and the normal production of GABAergic interneurons in brain organoids [341], suggesting a role for OLIG2 in DS neuropathology. A summary of the phenotypes described in humans, rodents and hIPSCs can be found in Table 5.

2.4. Tackling Molecular Pathways Affected in NDDs Using hIPSCs

2.4.1. High-Throughput Screenings Using hIPSCs (HTS)

The unlimited self-renewal capacity of hIPSCs makes them unique models for large-scale genetic screens. Genetic manipulation of hIPSCs including chemical and RNA interference (RNAi) screens, such as gain- and loss-of-function (GOF, LOF) studies, give valuable information about novel regulators of neurodevelopment and allow for testing of candidate therapeutic targets and agents. In chemical screenings, there is the identification of molecular targets of compounds being tested to promote hIPSC differentiation into different lineages. Additionally, the evaluation of multiple cellular phenotypes may be performed simultaneously and in an unbiased manner using high-content imaging-based assays, which adds relevant additional information. Although this is often challenging, it allows for the discovery of novel genes and signaling pathways involved in the differentiation process, as well as of compounds targeting these pathways, and their organization into chemical libraries in an arrayed form. The main limitation of this type of screening is the reduced number of genes that can be targeted. RNAi screenings appeared first and revolutionized the field of gene regulation, by allowing for gene silencing by double-stranded DNA, in a vast diversity of models, ranging from simple organisms such as D. melanogaster or C. elegans to more complex ones such as rodents. Two different approaches are possible with this technique: small-interfering RNA (siRNA)-based or short hairpin RNA (shRNA)-based stable gene knockdown. siRNA transfection can temporarily downregulate the target gene in cells of interest, which may be improved using retro- and lentiviruses to allow for shRNA integration into the genome and maintenance of prolonged expression. Typically, siRNA libraries are composed of chemically synthetized nucleotide siRNAs presented in an array format. HTS readouts may vary from protein expression to morphological alterations and are mainly based on fluorescence through the use of different techniques, among which include the use of fluorescent proteins as biosensors of multiple molecular mechanisms, immunofluorescence, protein redistribution assays, reporter genes, protein-fragmentation complementation assays (PCA), coupled fluorophores for Forster resonance energy transfer (FRET)-based assays, and reactive fluorophore-binding peptide tags to label intracellular proteins or HaloTag® technology-based assays.

2.4.2. Molecular Pathways Affected in NDDs—Insight from hIPSC-Based Studies

Autism Spectrum Disorders (ASD)

Transcriptomic analysis of hIPSC-derived neuronal lineages from ASD patients revealed dysregulation of genes involved in protein synthesis in ASD NPCs, while in neurons, there was dysregulation of synapse/neurotransmission and translation. Further proteomic analysis of NPCs revealed a potential link between the pathways altered in NPCs and neurons [342]. A study using ASD hIPSCs and their derived NPCs and neuronal cells suggests that the first pathological mechanisms triggering ASD are developed in early stages of neuronal development, as substantially more genes were differentially expressed in NPCs than in hIPSCs, whereas gene set variation analysis revealed that the activity of known ASD-related pathways in NPCs and neuronal cells is significantly different from hIPSCs [343].
SHANK3 is a PSD component of glutamatergic synapses that plays a key role in the adult brain, mediating excitatory transmission and leading to deficits in corticostriatal circuits [344], and it is one of the most studied ASD-associated risk genes. A study using hIPSCs used shRNA to silence SHANK3 and to evaluate its neurodevelopmental role in ASD phenotypes. SHANK3 deficiency led to impaired morphology early in development with reducing dendritic arborization, soma size and the growth cone area of glutamatergic, GABAergic and dopaminergic neurons [296]. Additionally, SHANK3 knockdown altered the transcriptomic profile throughout development, with particular impact on PI3K-associated pathways [296], increasing the evidence that links the mTOR/PI3K cascade to ASD phenotypes. Another gene commonly associated with ASD is SHANK2. A study using hIPSCs from a patient carrying a heterozygous deletion of SHANK2 identified ERK1/2 pathway dysregulation in both young and mature neurons, accompanied by reduced apoptosis and increased cell proliferation [297]. Similarly, a heterozygous single-nucleotide variant (SNV) in the DSCAM gene also caused ERK1/2 pathway dysregulation, with significantly low levels of p27hospho-ERK1/2 in neurons derived from DSCAM-mutated hIPSCs [302], while heterozygous mutation in exon 15 of TSC1 led to mTOR and ERK1/2 activation, along with the differential expression of genes known to be linked with ASD, epilepsy and ID [305]. Overall, these data suggest a role for the ERK1/2 and mTOR pathways in the development of ASD-related phenotypes. NRXN1 deletions have been linked to ASD phenotypes and other NDDs [345]. Transcriptomic analysis of cortical neuron-like cells derived from hIPSCs belonging to a patient carrying a NRXN1 isoform deletion revealed that these neurons displayed upregulation of glutamatergic synaptic and ion channels/transporter activity-related genes including GRIN1, GRIN3B, SLC17A6, CACNG3, CACNA1A and SHANK1 [300]. In addition, a study using CRISPR-Cas9-edited hIPSCs carrying a homozygous deletion of SHANK2 revealed transcriptomic changes in neurodevelopment gene sets, identifying increased GRIN2B, GRM1 and GRM5 transcripts as differentially expressed [298]. The increase in the levels of different GRIN genes is highly relevant for ASD neuropathology, as they encode NMDA receptors, and GRIN-related mutations themselves have been associated with several neuropsychiatric conditions, including NDDs [346].
Deficits in glutamatergic signaling were found using ASD patient hIPSC-derived organoids carrying a mutation on glutamate decarboxylase 1 (GAD1), which encodes an enzyme responsible for the conversion of glutamate into GABA [347]. This study revealed that, although both control and GAD1-mutant organoids showed high levels of methylation across the CpG sites of the GAD1 region of interest, GAD1-mutant hIPSCs showed specific DNA methylation patterns, suggesting that GAD1 suffers different methylation effects, which may indicate variable epigenetic regulation [348].
Other synapse-related molecules also seem to be altered in ASD hIPSCs. Although the most widely studied molecule is the X-linked NLGN4X, NLGN4Y is equally relevant, as it shares around 97% homology with NLGN4X. A functional comparison in this context between these two using neurons derived from hIPSCs revealed that the impact of ASD-associated mutations in NLGN4X is phenocopied when they are introduced in NLGN4Y, impairing its trafficking to the cell surface, but it also showed that NLGN4Y cannot compensate for the functional deficits caused by NLGN4X ASD-associated mutations [349].
Macrocephaly is one of the comorbidities described in ASD patients, suggesting that the mechanisms underlying excessive neuronal growth may be also underlying some ASD phenotypes. To address this question, a study using hIPSCs-derived NPCs from ASD patients with macrocephaly was performed. Altered DNA replication and increased DNA damage were found in ASD NPCs along with elevated ASD-related DNA DSBs in replication stress-susceptible genes, as shown by high-throughput genome-wide translocation sequencing (HTGTS) [350]. These results suggest that hyperproliferation of NPCs may be linked to ASD pathogenesis through the disruption of genes involved in cell adhesion and migration. Another study using patient-derived forebrain organoids was conducted to comprehend the impact of contactin-associated protein 2 (CNTNPAP2) gene variants on embryonic cortical development, as genetic abnormalities in this ASD-related gene are thought to be related with brain overgrowth. In agreement with this hypothesis, forebrain organoids carrying the CNTNPAP2 pathogenic variants revealed increased volume and total cell number caused by increased production of NPCs, which was reverted by CRISPR/Cas9 correction of the variant [351]. Sc-RNAseq showed that CNTNPAP2 is mainly expressed in excitatory neurons, while gene ontology analysis reveals that the DEGs associated with pathogenic variants in this gene are enriched for ASD-associated genes [351]. In addition, transcriptomic profiling of NPCs and neurons derived from hIPSCs engineered with CRISPR/Cas9 to carry a CHD8 deletion, as recurrently seen in ASD patients, revealed that genes associated with brain volume were differentially expressed in knockdown CHD8 NPCs and neurons [352], which adds valuable evidence to the previous studies since CHD8 is involved in calcium-dependent cell–cell adhesion and is an established ASD-risk gene. The same study also showed altered expression of genes involved in neuronal development, B-catenin/Wnt and PTEN signaling and extracellular matrix skeletal system development, such as TCF4, which overlap with genes associated with ASD or downstream transcriptional targets of those [352], such as NXRN1. The same study was then performed using brain organoids carrying the same mutation compared to isogenic controls. In accordance with the previous study, TCF4 expression was upregulated, as was DLX1 and DLX6-AS1, two genes known to be involved in GABAergic interneuron differentiation. Overall, enrichment of differentially expressed genes involved in neurogenesis, neuronal differentiation, forebrain development, B-catenin/Wnt signaling and axonal guidance was found in heterozygous knockout CHD8 brain organoids [353]. These studies highlight the convergence of several molecular mechanisms between NDDs, as most of the DEGs found to be altered in CHD8 mutants are also key players in other disorders.

Rett Syndrome

Using publicly available RNA-sequencing databases of MECP2 mutant hIPSCs, a study evaluated the role of MECP2 deficiency in the transcriptomic modifications found in RTT patients using Weighted Gene Correlation Network Analysis (WGCNA) [354]. Overall, the results suggested that translational dysregulation and proteasome ubiquitin dysfunction begin in NPCs even before lineage commitment, as perturbation of translation, ribosomal function and ubiquitination were found in MECP2-mutant hIPSCs along with altered gene expression in ubiquitination pathways and neurotransmission in NPCs and hIPSC-derived neurons [354]. Curiously, one of the genes found to be upregulated in both MECP2-mutant hIPSC-derived neurons and hIPSCs was PDZ-binding kinase (PBK) [354]. The protein encoded by this gene is responsible for the activation of the MAPK and PI3K/PTEN/AKT pathways, widely associated with NDDs (as discussed above), suggesting the involvement of some specific and some shared pathways as observed in RTT. In line with the translation dysregulation observed in this study, another work aimed at finding shifts in mRNA ribosomal engagement during human neurodevelopment. To accomplish that, they used parallel translating ribosome affinity purification sequencing (TRAP-seq) and RNAseq in RTT hIPSCs, hIPSC-derived NPCs and hIPSC-derived cortical neurons vs. their matched controls. The study suggested that around 30% of key gene sets involved in neurodevelopment, transcription, translation and glycolysis are translationally regulated, and that translation is globally impaired, along with mTOR signaling, in RTT cortical neurons [355]. In addition, CREB signaling, which was already shown to regulate MECP2 mRNA levels and to be simultaneously regulated by MeCP2 in mice [356,357], was recently involved in RTT pathology. Neurons derived from MECP2-mutant hIPSCs showed a significant reduction in CREB and phospho-CREB levels [389]. Moreover, overexpression of CREB through treatment with a PDE-4 inhibitor ameliorated the synaptic morphology deficits observed.
Neuron-specific K(+)-Cl(−) cotransporter 2 (KCC2) is a known downstream target of MECP2. Differentiation of neurons from RTT patient-derived hIPSCs revealed decreased expression of KCC2, which was responsible for a delayed GABAergic functional switch from excitation to inhibition that was reverted by KCC2 overexpression [358]. Considering that KCC2 reaches its maximum expression relatively late in nervous system development, this may explain in part the delayed onset of RTT symptoms and suggests the possible involvement of GABAergic neuronal dysregulation in this disorder. A proteomic analysis of NPCs derived from RTT patient hIPSCs showed that neuronal proteomic alterations appear long before the appearance of the first phenotypes [359]. GO enrichment analysis revealed changes among the differentially expressed proteins in several pathways such as cell adhesion, cytoskeletal organization and synaptic function [359]. Recently, a study addressed the role of the c-Jun N-terminal kinase (JNK) stress pathway in RTT using MECP2-mutated hIPSCs-derived neurons. It was discovered that the JNK pathway was activated in mutant neurons, which exhibit c-Jun hyperphosphorylation and cell death, which was reverted by the treatment with a JNK1 inhibitor [360]. This presents the first evidence suggesting JNK as a possible therapeutic target for RTT. P53, which is mostly known due to its function as a tumor suppressor gene, also mediates cell death, which was found to be activated in MECP2-mutant neurons. Curiously, neurons derived from patient hIPSCs lacking MECP2 revealed signs of replicative stress concomitant with P53 induction and senescence, which were reverted by P53 inhibition along with the deficits in dendritic complexity [362]. Activation of P53 targets was also detected in brains from RTT patients [362], validating the results obtained in vitro. However, these results raise an important question that should be clarified: why there are aging-related genes and pathways activated in NDDs, especially when the neuropathology is present in early stages of development, even before the clinical onset of the disease.
Among the several mechanisms known to be altered in RTT, synaptic function is one of the most affected. Transcriptomic analysis of neurons derived from RTT patient hIPSCs revealed disruption of the GABAergic functional circuitry along with cytoskeletal dynamics [363]. Curiously, this study found increased levels of NRG1 and NRG3, which are mainly expressed in interneurons, as well as NRXN1 and NRXN3, which modulate GABAergic transmission [363]. In addition, it was found that abnormal increases in the chromatin binding of bromodomain containing 4 (BRD4), which is an epigenetic and transcriptional regulator, lead to abnormal transcription in interneuron-related genes in MECP2-mutant hIPSCs [364]. Curiously, these deficits were rescued by treatment with JQ1, a known inhibitor of BRD4, suggesting that dysregulation of BRD4 may be involved in the altered function of interneurons in RTT, underlying the disease phenotypes. Finally, a study using a combined organoid system comprising cerebral cortex and GE-like tissue derived from RTT hIPSCs, using GO enrichment analysis, revealed that genes upregulated in MECP2-mutant organoids were associated with neuronal projection, morphogenesis and synaptic assembly, while genes found to be downregulated were mainly linked to mRNA metabolism, ER targeting and protein translation. Neuronatin (NNAT), a gene involved in synaptic function, was the most upregulated gene identified in this study, being enriched in inhibitory neuron groups. However, other genes, such as NRXN1, were also upregulated [320], suggesting a perturbation of synaptic function regulators in RTT pathology.

Down Syndrome

Transcriptomic analysis of human tissue from DS patients revealed that this syndrome is associated with genomic-wide transcriptional disruption due to overexpression of genes regulated by chromosome 21. In addition, dysregulation of neuronal development has been known to be a key feature of this NDD. As so, comprehending which mechanisms may be underlying this malfunction is mandatory, starting with NPCs, which are responsible for the generation of the different neuronal population. NPCs derived from DS hIPSCs exhibited genome-wide increased intra-chromosome interactions, disruption of lamina-domains and changes in chromatin accessibility along with transcriptional and nuclear architecture changes characteristic of senescent cells [365]. Consistently, treatment with senolytic drugs, which promote the elimination of senescent cells (aging cells), reversed all the trisomy 21-driven observed phenotypes [365], suggesting a role for cell senescence in the development of DS neuropathology. Evaluation of cellular stress responses, which may accelerate cell senescence, in DS hIPSCs revealed increase probability of apoptotic cell death, dysregulation of protein homeostasis and increased expression of ER stress pathway. Surprisingly, these deficits were also ameliorated by treatment with the small-molecule 4-phenylbutyrate (4-PBA), which prevents protein misfolding and aggregation [366]. Overall, these studies raise important questions about the role that protein aggregation and accumulation may play in DS neuropathology.
Transcriptomic and proteomic analysis of DS hIPSCs at different stages of development revealed disturbed DNA replication and synaptic maturation early in development, with differentiation, oxidative phosphorylation and glycolysis being the most compromised clusters [367]. Additionally, upregulation of the amyloid precursor protein (APP), known to be involved in Alzheimer’s disease, and of the OLIG1 and OLIG2 transcription factors was found, supporting the idea of deficits in neurogenesis and neuronal differentiation [367]. However, analyses conducted in chromosome 21 trisomic and disomic hIPSCs cells and their derived cortical neurons indicated that trisomy of chromosome 21 may not limit neuronal differentiation, but instead may interfere with the maintenance of pluripotency, as trisomic hIPSCs revealed higher levels of neural transcripts in comparison with disomic hIPSCs, and differentiated faster into cortical neurons [368]. DS hIPSCs gene profiling also revealed altered expression of genes related to cell migratory pathways, which was corrected by inhibition of the overexpressed p21-activated kinase (PAK1) pathway, restoring neuronal migration [336].
Curiously, the DSCAM/PAK1 pathway, which is known to be involved in cortical development, also seems to be relevant in DS pathogenesis. Sc-RNAseq in brain organoids derived from DS patients hIPSCs revealed an over-activation of DSCAM/PAK1, impaired neurogenesis, decreased cell proliferation and diminished expression of cortical layer II and IV markers, which may explain the reduced size of the DS-derived organoids. Further CRISPR/Cas9-, CRISPRi- or small molecule-induced inhibition of DSCAM/PAK1 pathway reversed the observed phenotypes [412]. Another vital cellular mechanism that seems to be impaired in DS models, contributing to pathology, is mitochondrial function. Gene set enrichment analysis of DS hIPSCs showed that most differentially expressed lncRNAs were associated with neuronal development, acting through cis-acting target genes, and with mitochondrial functions [413]. These results were further confirmed by qRT-PCR analysis of mitochondrial function-related genes, which were shown to be downregulated in DS hIPSCs [413]. Intriguingly, the mitochondrial dysfunction found in neurons derived from DS hIPSCs was already present early in development, in NPCs [414]. To unravel the mechanisms underlying this dysfunction, several studies were performed using DS patient-derived hIPSCs. GABAergic neurons and MGE organoids derived from DS patients showed abnormally perinuclearly clustered mitochondria exhibiting abnormal mitochondrial function, which was reversed by inhibition of the DSCAM/PAK1 pathway using gene-editing or treatment with small molecules [415].
The DNA methylation pattern of known CpG regions and promoters was assessed in neurons derived from DS hIPSCs corresponding to early- and middle-gestation. This analysis revealed that some differential methylation profiles appear early in development in DS and are mostly found in neurodevelopment-related genes, specifically involved in DNA binding and chromatin remodeling [416]. Neurons derived from DS hIPSCs showed overexpression of cytoplasmic polyadenylation element binding protein 1 (CPEB1) in neuronal dendrites, which is responsible for dendritic mRNA transport, suggesting a disruption of this process [417]. Proteomic evaluation of these cells also revealed impaired axonal trafficking and enhanced synaptic vesicle release [418].
Although challenging, chromosomal dosage compensation is not impossible and can be achieved in a near future. Intriguingly, induction of the XIST RNA in differentiated neuronal cells was already demonstrated to trigger chromosome 21 silencing in DS hIPSCs. Neuronal differentiation disruption in these cells was also corrected by XIST induction at different neurodevelopmental stages [419].

2.4.3. Other NDDs

Besides ASD, RTT and DS, other NDDs have been tackled using hIPSCs. Timothy syndrome, a rare and lethal disorder, is caused by a de novo missense gain-of-function mutation in the calcium voltage-gated channel subunit alpha 1C (CACNA1C) gene. Carriers of this mutation exhibit developmental language delays along with impaired motor skills and cognitive dysfunction. The average life expectancy is 2.5 years of age, with the most common cause of death being arrhythmia. Neurons derived from Timothy syndrome patient hIPSCs revealed abnormal expression of tyrosine hydroxylase and increased levels of the neurotransmitters dopamine and norepinephrine; these deficits were rescued using roscovitine, a cyclin-dependent kinase inhibitor [100,420]. In addition, abnormal neurogenesis and reduced synaptogenesis were identified in these cells, using MEAs [291]. Additionally, CNVs consisting of duplication or deletion of chromosomal regions have already been linked to some NDDs (reviewed in [421]). Among these, disruption of the chromosome 15q11-q13 region may lead to three different conditions: Prader–Willi syndrome (PWS), Angelman syndrome (AS) or duplication syndrome (Dup15q). Deletion of the paternal 15q11-q13 chromosome region leads to PWS, with patients carrying this CNV exhibiting excessive eating behaviors leading to obesity, hypogonadism, hypothalamus atrophy and ID. Neurons derived from PWS hIPSCs revealed impaired neuronal differentiation and abnormal DNA methylation [422,423]. Angelman syndrome is mostly associated with a large maternal deletion of 15q11-q13, leading to loss of function of the UBE3A gene. Patients carrying this CNV show developmental delay and language and motor impairments. In AS patient hIPSC-derived forebrain neurons, altered excitatory synaptic activity is decreased along with capacity for activity-dependent synaptic plasticity reduction [424]. 15q11-q13 duplication (Dup15q) syndrome is caused by maternal duplication of this chromosomal region. Dup15q patients exhibit the same phenotypes observed in AS, with additional ASD behaviors and high prevalence of seizures. Neurons derived from Dup15q hIPSCs revealed increased excitatory synaptic transmission frequency and amplitude, increased action potential firing, along with increased density of dendritic protrusions and decreased inhibitory transmission [425]. Finally, another recurrent CNV described in NDD patients is the chromosome 16p11.2 deletion, characterized by developmental delay, language impairment, epilepsy and ASD along with microcephaly. Curiously, duplication of this chromosome leads to an increased susceptibility to schizophrenia and bipolar disorder. Neurons derived from hIPSCs carrying the 16p11.2 deletion exhibited increased soma size and dendritic length along with reduced synaptic density [426].
Overall, the data obtained from neurons derived from hIPSCs of NDD patients confirmed the convergence of neuronal phenotypes and molecular pathways between different NDDs and highlighted some potential therapeutical targets of relevance for their treatment as shown in Figure 3.

2.4.4. Translation to Clinics

High-throughput screenings may be valuable in different steps of drug discovery, starting with identification and validation of suitable therapeutic targets to determine the mechanism of action (MOA) of compounds and their safety. LOF studies are the most commonly used for target identification through coupling of siRNA-induced knockdown of the gene of interest with HTS readout techniques. However, the high probability of off-target effects and low stability of siRNA along with non-optimized transfection conditions may lead to unreliable results, limiting the success rate of such strategies. Fortunately, the appearance of CRISPR/Cas9 allowed for the validation of the results obtained with siRNA, as had happened with the DSCAM/PAK1 pathway in DS hIPSCs [412]. Due to effective hIPSCs neuronal differentiation protocols and the resemblance of this process with human brain development, hIPSCs constitute a great model to identify novel therapeutic targets for NDDs. Chemical screenings are usually avoided in this first stage due to the amount of compounds required, substantially increasing the associated costs. However, with the recent advances in technology and the appearance of artificial intelligence (AI), it is already possible to predict which compounds will more likely be disease-modifying using computational models. This alternative is undoubtedly timesaving and allows for an enhanced efficiency of the entire process [427]. Next, after identifying a promising candidate and finding biological relevance to further pursue it, it will be studied in cellular models to confirm their MOA and whether the phenotypes observed using siRNA are similar to those triggered by the pharmacological treatment. Again, combination of hIPSCs with CRISPR-based tools and imaging (immunofluorescence), molecular (Western blotting, qRT-PCR), electrophysiological (MEAs, patch-clamp) and transcriptomic techniques (sc-RNAseq) to identify different MOAs of the candidate drug as well as associated phenotypes, predicting patients’ response, is a valuable methodological approach to address these issues. The great promise of hIPSCs for drug screening relies on the predictability of patients’ responses to the treatment based on hIPSCs genotype or specific phenotypes, in the stability associated with drug responsiveness during lifetime and in the fact that drug responsiveness depends only on the engagement with the therapeutic target, not being modulated by liver metabolism and side effects, which cannot be monitored in hIPSCs. In this context, both 2D and 3D hIPSC models have been widely used for toxicity assessments [428,429]; however, due to their increased complexity, 3D organoids may be more efficient to identify novel therapeutic drugs. Allied to these methodologies, better stratification of the patients, achieved by genomic identification of risk factors, followed by matching according to overlapping risk factors into genetically defined groups, would significantly improve the outcomes of the clinical target studies by decreasing their heterogeneity. However, before reaching the clinic, drug candidates validated in hIPSCs for toxicity and efficacy need to see their biological relevance verified in vivo, using suitable animal models.

2.5. Drug Screenings for NDDs Using hIPSCs

A key aspect in which hIPSCs are advantageous in comparison with ESCs is their suitability for drug screenings. Using hIPSCs to mimic different disease phenotypes allows for the study of developmental mechanisms that cannot be analyzed using other models. This opens a window of opportunity to explore the therapeutic capacity of multiple compounds in a cost-saving and fast way. Overall, hIPSCs have been used to evaluate some therapeutic targets for a variety of NDDs, leading to the identification of clinical-grade drugs. Although valuable, these screenings rely on protocols that require several weeks to differentiate hIPSCs into cellular populations relevant for the disease, increasing the chances of jeopardizing their quality. This highlights the need to develop shorter and more stable differentiation protocols that result in globally more mature neuronal populations. Despite these limitations, multiple molecular and cellular phenotypes observed in hIPSCs were ameliorated by pharmacological treatment in several NDDs.
In ASD, neurons derived from hIPSCs were used to screen compounds by their ability to increase SHANK3 mRNA expression. From these, lithium, valproate acid (VPA) and fluoxetine increased SHANK3 levels and its colocalization with PSD95 in the synaptic compartment. However, only lithium and VPA increased spontaneous calcium oscillations, an effect that was blocked by SHANK3 shRNA, showing that the observed effect was SHANK3-dependent. Finally, these drugs were also tested in hIPSCs carrying ASD-associated genetic variants, to clarify whether they were capable of rescuing the observed phenotypes, which happened, as both drugs increased SHANK3-containing synapses as well as spontaneous calcium oscillations [430]. Lithium further progressed for a clinical trial with an ASD patient and efficiently improved her mood deficits, revealing promising results [431]. Currently, it is being tested in a phase III clinical trial for ASD (NCT04623398).
For RTT syndrome, several drugs have been tested using hIPSCs. Insulin-like growth factor 1 (IGF-1), a drug that entered the clinical trial phase, showing safety, tolerability and mild efficacy in RTT girls in phase I (NCT01253317) but failing to show significant improvements in phase II (NCT01777542) [432], was previously applied to hIPSC-derived RTT neurons and rescued the reduction found in excitatory synapses [383]. In addition, a female patient carrying a MECP2 missense mutation revealed decreased symptomatology after 6 months of combined treatment with IGF1, melatonin and blackcurrant extracts [433]. Neurons derived from hIPSCs from patients with idiopathic ASD and matched controls were used to uncover the transcriptional changes induced by the treatment with IGF-1, both acutely and chronically. The results indicate that IGF1 treatment differentially impacts control and ASD neurons, and within ASD neurons, it has a heterogeneous effect that is correlated with the expression of the IGF1 receptor [434]. This study suggests that we should be cautious when evaluating the effect of IGF1 in ASD. Of note, a new and more powerful analog of IGF1 was developed and revealed safety, tolerability and efficacy in RTT females in a phase II clinical trial (NCT02715115), ameliorating the breathing problems, the motor dysfunction, the mood abnormalities and disruptive behavior as well as the frequency of seizures [435]. Currently, it is in a phase III clinical trial (NCT04279314) and has already showed some positive results [436]. Another study using RTT hIPSCs and FOXG1-mutated hIPSC-derived neurons revealed that AAV-mediated gene expression corrected FOXG1 mutation [437]. Preclinical studies in animal models were also conducted using an AAV-transduced MECP2 gene expression cassette with endogenous regulatory elements, allowing for a safe expression of the transgene in brain cells [438]. A clinical trial is expected to be initiated soon, to test the efficacy of this system in RTT patients. Although these are the most promising therapies tested in hIPSCs, several other molecules can be chosen in the future to follow the same path, such as nefiracetam and PHA, gentamicin, JQ1, choline, rolipram and HDAC inhibitors [363,364,383,386,389,439].
For DS, there are no hIPSC-validated drugs being tested in clinical trials. However, several compounds already showed promising results in attenuating some of the trisomy 21-related phenotypes observed in neurons derived from DS patient hIPSCs. Treatment of DS hIPSCs-derived neurons with a chemical chaperone, sodium 4-phenylbutyrate, which already showed promising results in a clinical trial for amyotrophic lateral sclerosis (ALS) [440], reduced the presence of misfolded protein aggregates and prevented the progression of apoptotic processes in DS neurons [366]. In addition, treatment of GABAergic progenitors derived from DS hIPSCs with FRAX486, an inhibitor of the PAK1 protein (known to be overexpressed in these cells) rescued the interneuron migration defects previously observed [336]. Overall, this highlights the increased appearance of future possible therapeutic targets to develop drugs that may attenuate DS phenotypes.

3. Conclusions and Future Perspectives

By now, the vital role that hIPSCs play in the neurosciences field and, especially, for the study of NDDs is unquestionable. Their ability to mimic neurodevelopmental stages and clinically relevant cell types and phenotypes, easy access and genetic manipulation, and human origin are the major virtues of these cells. Thus, they are valuable assets to help us clarify the developmental neuropathology triggered by several NDD-related genetic variants, but also to find better therapeutic targets and drug treatments. This article started by focusing on traditional models used to study NDDs, highlighting what was found and their main pros/cons. Then, throughout the review, we tried to address pertinent questions surrounding hIPSC generation, differentiation and manipulation, giving tips to select the best conditions and techniques in order to obtain high-quality neuronal populations. Every advantage and pitfall of the different methods was discussed, and plausible solutions were presented to the most frequent problems. At the same time, a literature research update was performed, reporting what was discovered thus far in the NDDs field using hIPSCs as well as the currently on-going clinical trials based on drug screenings performed in these cells.
Now that the hIPSCs field is starting to mature, it is time to start addressing key issues for effective translation to clinics: their high cost and-time consuming protocols, the genetical instability often observed in hIPSCs and the presence of genetic/epigenetic mutations that leads to heterogenous and unreliable results, high mutational risk associated with the reprogramming process—that may jeopardize the entire experiment—and lack of reproducibility of existing protocols due to poor reporting of the experimental conditions. This highlights the need to optimize protocols and combine the use of hIPSCs with animal models to obtain more robust results. However, the use of neurons derived from hIPSCs obtained from patients carrying NND causing mutations highlighted several disease phenotypes that correlated to those observed in humans and animal models. Additionally, several pathways known to be impaired in humans and animals were also impaired in neurons derived from NDD hIPSCs, and convergence between them was also observed, supporting the potential for translatability of the results obtained. After a thorough literature search, one can conclude that different protocols of differentiation for the same neuronal population revealed similar disease phenotypes, revealing that there is more than one possible combination of factors and conditions to differentiate the same neuronal population, raising the question of how alike they are and whether the obtained results are comparable between differentially generated populations of the same neuronal subtype. However, the reduced time in vitro leads to the generation of immature synapses and reduced levels of synaptic proteins, which hamper the study of the synaptic component of NDDs. Although this limitation may be partially solved by the use of brain organoids or by transplantation into a rodent model, the results obtained thus far are inconsistent and highly controversial, highlighting the need for better protocols to obtain robust synaptic properties in vitro in an easy, reliable way. Overall, the need for improved techniques and experimental designs to better tackle NDD-associated mechanisms is evident; however, hIPSCs are without a doubt a precious model to improve basic research and drug screening for high-quality and clinically translatable scientific findings in the future.

Author Contributions

The authors contributed equally to the planning of the review. S.G. performed the literature search and wrote the first draft. P.M. provided critical revisions and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

This work is supported by DevINDS ERA-NET Neuron project (https://www.neuron-eranet.eu/projects/DevInDS/) (accessed on 1 February 2023) and by Fundação para Ciência e Tecnologia (FCT) through the project (NEURON/0001/2021) and a PhD fellowship to S.G. (2022.11724.BD).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Arlington, VA, USA, 2013. [Google Scholar]
  2. Mullin, A.P.; Gokhale, A.; Moreno-De-Luca, A.; Sanyal, S.; Waddington, J.L.; Faundez, V. Neurodevelopmental disorders: Mechanisms and boundary definitions from genomes, interactomes and proteomes. Transl. Psychiatry 2013, 3, e329. [Google Scholar] [CrossRef] [PubMed]
  3. Francés, L.; Quintero, J.; Fernández, A.; Ruiz, A.; Caules, J.; Fillon, G.; Hervás, A.; Soler, C.V. Current state of knowledge on the prevalence of neurodevelopmental disorders in childhood according to the DSM-5: A systematic review in accordance with the PRISMA criteria. Child Adolesc. Psychiatry Ment. Health 2022, 16, 1–15. [Google Scholar] [CrossRef]
  4. Zablotsky, B.; Black, L.I.; Maenner, M.J.; Schieve, L.A.; Danielson, M.L.; Bitsko, R.H.; Blumberg, S.J.; Kogan, M.D.; Boyle, C.A. Prevalence and trends of developmental disabilities among children in the United States: 2009–2017. Pediatrics 2019, 144, e20190811. [Google Scholar] [CrossRef] [PubMed]
  5. Li, Y.; Jia, X.; Wu, H.; Xun, G.; Ou, J.; Zhang, Q.; Li, H.; Bai, T.; Hu, Z.; Zou, X.; et al. Genotype and phenotype correlations for SHANK3 de novo mutations in neurodevelopmental disorders. Am. J. Med. Genet. Part A 2018, 176, 2668–2676. [Google Scholar] [CrossRef]
  6. Casanova, E.L.; Gerstner, Z.; Sharp, J.L.; Casanova, M.F.; Feltus, F.A. Widespread Genotype-Phenotype Correlations in Intellectual Disability. Front. Psychiatry 2018, 9, 535. [Google Scholar] [CrossRef] [PubMed]
  7. Iossifov, I.; Levy, D.; Allen, J.; Ye, K.; Ronemus, M.; Lee, Y.H.; Yamrom, B.; Wigler, M. Low load for disruptive mutations in autism genes and their biased transmission. Proc. Natl. Acad. Sci. USA 2015, 112, E5600–E5607. [Google Scholar] [CrossRef] [PubMed]
  8. Niemi, M.E.K.; Martin, H.C.; Rice, D.L.; Gallone, G.; Gordon, S.; Kelemen, M.; McAloney, K.; McRae, J.; Radford, E.J.; Yu, S.; et al. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 2018, 562, 268–271. [Google Scholar] [CrossRef]
  9. Kurki, M.I.; Saarentaus, E.; Pietiläinen, O.; Gormley, P.; Lal, D.; Kerminen, S.; Torniainen-Holm, M.; Hämäläinen, E.; Rahikkala, E.; Keski-Filppula, R.; et al. Contribution of rare and common variants to intellectual disability in a sub-isolate of Northern Finland. Nat. Commun. 2019, 10, 410. [Google Scholar] [CrossRef]
  10. Pizzo, L.; Jensen, M.; Polyak, A.; Rosenfeld, J.A.; Mannik, K.; Krishnan, A.; McCready, E.; Pichon, O.; Le Caignec, C.; Van Dijck, A.; et al. Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants. Genet. Med. 2019, 21, 816–825. [Google Scholar] [CrossRef]
  11. Posey, J.E.; Harel, T.; Liu, P.; Rosenfeld, J.A.; James, R.A.; Coban Akdemir, Z.H.; Walkiewicz, M.; Bi, W.; Xiao, R.; Ding, Y.; et al. Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation. N. Engl. J. Med. 2017, 376, 21–31. [Google Scholar] [CrossRef]
  12. Mitra, I.; Lavillaureix, A.; Yeh, E.; Traglia, M.; Tsang, K.; Bearden, C.E.; Rauen, K.A.; Weiss, L.A. Reverse Pathway Genetic Approach Identifies Epistasis in Autism Spectrum Disorders. PLoS Genet. 2017, 13, e1006516. [Google Scholar] [CrossRef] [PubMed]
  13. Deb, B.K.; Bateup, H.S. Modeling Somatic Mutations Associated With Neurodevelopmental Disorders in Human Brain Organoids. Front. Mol. Neurosci. 2022, 14, 341. [Google Scholar] [CrossRef] [PubMed]
  14. Guo, H.; Wang, T.; Wu, H.; Long, M.; Coe, B.P.; Li, H.; Xun, G.; Ou, J.; Chen, B.; Duan, G.; et al. Inherited and multiple de novo mutations in autism/developmental delay risk genes suggest a multifactorial model. Mol. Autism 2018, 9, 64. [Google Scholar] [CrossRef]
  15. Avni, E.; Ben-Itzchak, E.; Zachor, D.A. The Presence of Comorbid ADHD and Anxiety Symptoms in Autism Spectrum Disorder: Clinical Presentation and Predictors. Front. Psychiatry 2018, 9, 717. [Google Scholar] [CrossRef] [PubMed]
  16. Tărlungeanu, D.C.; Novarino, G. Genomics in neurodevelopmental disorders: An avenue to personalized medicine. Exp. Mol. Med. 2018, 50, 1–7. [Google Scholar] [CrossRef]
  17. Cristino, A.S.; Williams, S.M.; Hawi, Z.; An, J.Y.; Bellgrove, M.A.; Schwartz, C.E.; Costa, L.D.F.; Claudianos, C. Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system. Mol. Psychiatry 2013, 19, 294–301. [Google Scholar] [CrossRef]
  18. Hormozdiari, F.; Penn, O.; Borenstein, E.; Eichler, E.E. The discovery of integrated gene networks for autism and related disorders. Genome Res. 2015, 25, 142–154. [Google Scholar] [CrossRef]
  19. Mullins, C.; Fishell, G.; Tsien, R.W. Unifying Views of Autism Spectrum Disorders: A Consideration of Autoregulatory Feedback Loops. Neuron 2016, 89, 1131–1156. [Google Scholar] [CrossRef]
  20. Vissers, L.E.L.M.; Gilissen, C.; Veltman, J.A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 2016, 17, 9–18. [Google Scholar] [CrossRef]
  21. Megagiannis, P.; Suresh, R.; Rouleau, G.A.; Zhou, Y. Reversibility and therapeutic development for neurodevelopmental disorders, insights from genetic animal models. Adv. Drug Deliv. Rev. 2022, 191, 114562. [Google Scholar] [CrossRef]
  22. Ugur, B.; Chen, K.; Bellen, H.J. Drosophila tools and assays for the study of human diseases. DMM Dis. Model. Mech. 2016, 9, 235–244. [Google Scholar] [CrossRef] [PubMed]
  23. Mariano, V.; Achsel, T.; Bagni, C.; Kanellopoulos, A.K. Modelling Learning and Memory in Drosophila to Understand Intellectual Disabilities. Neuroscience 2020, 445, 12–30. [Google Scholar] [CrossRef]
  24. Vaz, R.; Hofmeister, W.; Lindstrand, A. Zebrafish models of neurodevelopmental disorders: Limitations and benefits of current tools and techniques. Int. J. Mol. Sci. 2019, 20, 1296. [Google Scholar] [CrossRef]
  25. Kim, Y.; Park, Y.; Hwang, J.; Kwack, K. Comparative genomic analysis of the human and nematode caenorhabditis elegans uncovers potential reproductive genes and disease associations in humans. Physiol. Genom. 2018, 50, 1002–1014. [Google Scholar] [CrossRef] [PubMed]
  26. Bessa, C.; Maciel, P.; Rodrigues, A.J. Using C. Elegans to decipher the cellular and molecular mechanisms underlying neurodevelopmental disorders. Mol. Neurobiol. 2013, 48, 465–489. [Google Scholar] [CrossRef] [PubMed]
  27. Tavares, B.; Santos Lopes, S. The Importance of Zebrafish in Biomedical Research. Acta Med. Port. 2013, 26, 583–592. [Google Scholar] [CrossRef]
  28. de Abreu, M.S.; Genario, R.; Giacomini, A.C.V.V.; Demin, K.A.; Lakstygal, A.M.; Amstislavskaya, T.G.; Fontana, B.D.; Parker, M.O.; Kalueff, A.V. Zebrafish as a Model of Neurodevelopmental Disorders. Neuroscience 2020, 445, 3–11. [Google Scholar] [CrossRef] [PubMed]
  29. Silverman, J.L.; Yang, M.; Lord, C.; Crawley, J.N. Behavioural phenotyping assays for mouse models of autism. Nat. Rev. Neurosci. 2010, 11, 490–502. [Google Scholar] [CrossRef]
  30. Homberg, J.R.; Kyzar, E.J.; Nguyen, M.; Norton, W.H.; Pittman, J.; Poudel, M.K.; Gaikwad, S.; Nakamura, S.; Koshiba, M.; Yamanouchi, H.; et al. Understanding autism and other neurodevelopmental disorders through experimental translational neurobehavioral models. Neurosci. Biobehav. Rev. 2016, 65, 292–312. [Google Scholar] [CrossRef]
  31. Silverman, J.L.; Ellegood, J. Behavioral and neuroanatomical approaches in models of neurodevelopmental disorders: Opportunities for translation. Curr. Opin. Neurol. 2018, 31, 126–133. [Google Scholar] [CrossRef]
  32. Aida, T.; Feng, G. The dawn of non-human primate models for neurodevelopmental disorders. Curr. Opin. Genet. Dev. 2020, 65, 160–168. [Google Scholar] [CrossRef]
  33. Jennings, C.G.; Landman, R.; Zhou, Y.; Sharma, J.; Hyman, J.; Movshon, J.A.; Qiu, Z.; Roberts, A.C.; Roe, A.W.; Wang, X.; et al. Opportunities and challenges in modeling human brain disorders in transgenic primates. Nat. Neurosci. 2016, 19, 1123–1130. [Google Scholar] [CrossRef] [PubMed]
  34. Ebrahimi-Fakhari, D.; Sahin, M. Autism and the synapse: Emerging mechanisms and mechanism-based therapies. Curr. Opin. Neurol. 2015, 28, 91–102. [Google Scholar] [CrossRef]
  35. Zoghbi, H.Y.; Bear, M.F. Synaptic dysfunction in neurodevelopmental disorders associated with autism and intellectual disabilities. Cold Spring Harb. Perspect. Biol. 2012, 4, a009886. [Google Scholar] [CrossRef] [PubMed]
  36. Kelleher, R.J.; Bear, M.F. The Autistic Neuron: Troubled Translation? Cell 2008, 135, 401–406. [Google Scholar] [CrossRef] [PubMed]
  37. Sahin, M.; Sur, M. Genes, circuits, and precision therapies for autism and related neurodevelopmental disorders. Science 2015, 350, aab3897. [Google Scholar] [CrossRef] [PubMed]
  38. Crino, P.B. mTOR signaling in epilepsy: Insights from malformations of cortical development. Cold Spring Harb. Perspect. Med. 2015, 5, 1–18. [Google Scholar] [CrossRef]
  39. Borrie, S.C.; Brems, H.; Legius, E.; Bagni, C. Cognitive Dysfunctions in Intellectual Disabilities: The Contributions of the Ras-MAPK and PI3K-AKT-mTOR Pathways. Annu. Rev. Genom. Hum. Genet. 2017, 18, 115–142. [Google Scholar] [CrossRef]
  40. Winden, K.D.; Ebrahimi-Fakhari, D.; Sahin, M. Abnormal mTOR Activation in Autism. Annu. Rev. Neurosci. 2018, 41, 1–23. [Google Scholar] [CrossRef]
  41. Curatolo, P.; Moavero, R.; de Vries, P.J. Neurological and neuropsychiatric aspects of tuberous sclerosis complex. Lancet Neurol. 2015, 14, 733–745. [Google Scholar] [CrossRef]
  42. Iffland, P.H.; Carson, V.; Bordey, A.; Crino, P.B. GATORopathies: The role of amino acid regulatory gene mutations in epilepsy and cortical malformations. Epilepsia 2019, 60, 2163–2173. [Google Scholar] [CrossRef] [PubMed]
  43. Ronan, J.L.; Wu, W.; Crabtree, G.R. From neural development to cognition: Unexpected roles for chromatin. Nat. Rev. Genet. 2013, 14, 347–359. [Google Scholar] [CrossRef] [PubMed]
  44. Pierson, T.M.; Otero, M.G.; Grand, K.; Choi, A.; Graham, J.M.; Young, J.I.; Mackay, J.P. The NuRD complex and macrocephaly associated neurodevelopmental disorders. Am. J. Med. Genet. Part C Semin. Med. Genet. 2019, 181, 548–556. [Google Scholar] [CrossRef] [PubMed]
  45. Bernier, R.; Golzio, C.; Xiong, B.; Stessman, H.A.; Coe, B.P.; Penn, O.; Witherspoon, K.; Gerdts, J.; Baker, C.; Vulto-Van Silfhout, A.T.; et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 2014, 158, 263–276. [Google Scholar] [CrossRef]
  46. Sim, J.C.H.; White, S.M.; Lockhart, P.J. ARID1B-mediated disorders: Mutations and possible mechanisms. Intractable Rare Dis. Res. 2015, 4, 17–23. [Google Scholar] [CrossRef] [PubMed]
  47. Fernandes, I.R.; Cruz, A.C.P.; Ferrasa, A.; Phan, D.; Herai, R.H.; Muotri, A.R. Genetic variations on SETD5 underlying autistic conditions. Dev. Neurobiol. 2018, 78, 500–518. [Google Scholar] [CrossRef]
  48. Hevner, R.F.; Shi, L.; Justice, N.; Hsueh, Y.P.; Sheng, M.; Smiga, S.; Bulfone, A.; Goffinet, A.M.; Campagnoni, A.T.; Rubenstein, J.L.R. Tbr1 regulates differentiation of the preplate and layer 6. Neuron 2001, 29, 353–366. [Google Scholar] [CrossRef]
  49. Notwell, J.H.; Heavner, W.E.; Darbandi, S.F.; Katzman, S.; McKenna, W.L.; Ortiz-Londono, C.F.; Tastad, D.; Eckler, M.J.; Rubenstei, J.L.R.; McConnell, S.K.; et al. TBR1 regulates autism risk genes in the developing neocortex. Genome Res. 2016, 26, 1013–1022. [Google Scholar] [CrossRef]
  50. Traylor, R.N.; Dobyns, W.B.; Rosenfeld, J.A.; Wheeler, P.; Spence, J.E.; Bandholz, A.M.; Bawle, E.V.; Carmany, E.P.; Powell, C.M.; Hudson, B.; et al. Investigation of TBR1 hemizygosity: Four individuals with 2q24 microdeletions. Mol. Syndromol. 2012, 3, 102–112. [Google Scholar] [CrossRef]
  51. Huang, T.N.; Chuang, H.C.; Chou, W.H.; Chen, C.Y.; Wang, H.F.; Chou, S.J.; Hsueh, Y.P. Tbr1 haploinsufficiency impairs amygdalar axonal projections and results in cognitive abnormality. Nat. Neurosci. 2014, 17, 240–247. [Google Scholar] [CrossRef]
  52. Huang, T.N.; Hsueh, Y.P. Brain-specific transcriptional regulator T-brain-1 controls brain wiring and neuronal activity in autism spectrum disorders. Front. Neurosci. 2015, 9, 406. [Google Scholar] [CrossRef] [PubMed]
  53. Kentrup, H.; Becker, W.; Heukelbach, J.; Wilmes, A.; Schürmann, A.; Huppertz, C.; Kainulainen, H.; Joost, H.G. Dyrk, a dual specificity protein kinase with unique structural features whose activity is dependent on tyrosine residues between subdomains VII and VIII. J. Biol. Chem. 1996, 271, 3488–3495. [Google Scholar] [CrossRef]
  54. O’Roak, B.J.; Vives, L.; Fu, W.; Egertson, J.D.; Stanaway, I.B.; Phelps, I.G.; Carvill, G.; Kumar, A.; Lee, C.; Ankenman, K.; et al. Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 2012, 338, 1619–1622. [Google Scholar] [CrossRef] [PubMed]
  55. Evers, J.M.G.; Laskowski, R.A.; Bertolli, M.; Clayton-Smith, J.; Deshpande, C.; Eason, J.; Elmslie, F.; Flinter, F.; Gardiner, C.; Hurst, J.A.; et al. Structural analysis of pathogenic mutations in the DYRK1A gene in patients with developmental disorders. Hum. Mol. Genet. 2017, 26, 519–526. [Google Scholar] [CrossRef]
  56. Guimera, J.; Casas, C.; Estivill, X.; Pritchard, M. Human minibrain homologue (MNBH/DYRK1): Characterization, alternative splicing, differential tissue expression, and overexpression in Down syndrome. Genomics 1999, 57, 407–418. [Google Scholar] [CrossRef] [PubMed]
  57. Reeves, R.H.; Irving, N.G.; Moran, T.H.; Wohn, A.; Kitt, C.; Sisodia, S.S.; Schmidt, C.; Bronson, R.T.; Davisson, M.T. A mouse model for Down syndrome exhibits learning and behaviour deficits. Nat. Genet. 1995, 11, 177–184. [Google Scholar] [CrossRef]
  58. Altafaj, X.; Dierssen, M.; Baamonde, C.; Martí, E.; Visa, J.; Guimerà, J.; Oset, M.; González, J.R.; Flórez, J.; Fillat, C.; et al. Neurodevelopmental delay, motor abnormalities and cognitive deficits in transgenic mice overexpressing Dyrk1A (minibrain), a murine model of Down’s syndrome. Hum. Mol. Genet. 2001, 10, 1915–1923. [Google Scholar] [CrossRef] [PubMed]
  59. Kim, O.H.; Cho, H.J.; Han, E.; Hong, T.I.; Ariyasiri, K.; Choi, J.H.; Hwang, K.S.; Jeong, Y.M.; Yang, S.Y.; Yu, K.; et al. Zebrafish knockout of Down syndrome gene, DYRK1A, shows social impairments relevant to autism. Mol. Autism 2017, 8, 50. [Google Scholar] [CrossRef]
  60. Raveau, M.; Shimohata, A.; Amano, K.; Miyamoto, H.; Yamakawa, K. DYRK1A-haploinsufficiency in mice causes autistic-like features and febrile seizures. Neurobiol. Dis. 2018, 110, 180–191. [Google Scholar] [CrossRef]
  61. Arranz, J.; Balducci, E.; Arató, K.; Sánchez-Elexpuru, G.; Najas, S.; Parras, A.; Rebollo, E.; Pijuan, I.; Erb, I.; Verde, G.; et al. Impaired development of neocortical circuits contributes to the neurological alterations in DYRK1A haploinsufficiency syndrome. Neurobiol. Dis. 2019, 127, 210–222. [Google Scholar] [CrossRef]
  62. Leslie, N.R.; Longy, M. Inherited PTEN mutations and the prediction of phenotype. Semin. Cell Dev. Biol. 2016, 52, 30–38. [Google Scholar] [CrossRef]
  63. Mingo, J.; Rodríguez-Escudero, I.; Luna, S.; Fernández-Acero, T.; Amo, L.; Jonasson, A.R.; Zori, R.T.; López, J.I.; Molina, M.; Cid, V.J.; et al. A pathogenic role for germline PTEN variants which accumulate into the nucleus. Eur. J. Hum. Genet. 2018, 26, 1180–1187. [Google Scholar] [CrossRef]
  64. Rademacher, S.; Eickholt, B.J. PTEN in autism and neurodevelopmental disorders. Cold Spring Harb. Perspect. Med. 2019, 9, a036780. [Google Scholar] [CrossRef]
  65. Shin, S.; Santi, A.; Huang, S. Conditional Pten knockout in parvalbumin- or somatostatin-positive neurons sufficiently leads to autism-related behavioral phenotypes. Mol. Brain 2021, 14, 24. [Google Scholar] [CrossRef] [PubMed]
  66. Clipperton-Allen, A.E.; Page, D.T. Pten haploinsufficient mice show broad brain overgrowth but selective impairments in autism-relevant behavioral tests. Hum. Mol. Genet. 2014, 23, 3490–3505. [Google Scholar] [CrossRef]
  67. Jamain, S.; Quach, H.; Betancur, C.; Råstam, M.; Colineaux, C.; Gillberg, C.; Soderstrom, H.; Giros, B.; Leboyer, M.; Gillberg, C.; et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat. Genet. 2003, 34, 27–29. [Google Scholar] [CrossRef]
  68. Laumonnier, F.; Bonnet-Brilhault, F.; Gomot, M.; Blanc, R.; David, A.; Moizard, M.P.; Raynaud, M.; Ronce, N.; Lemonnier, E.; Calvas, P.; et al. X-Linked Mental Retardation and Autism Are Associated with a Mutation in the NLGN4 Gene, a Member of the Neuroligin Family. Am. J. Hum. Genet. 2004, 74, 552–557. [Google Scholar] [CrossRef] [PubMed]
  69. Anderson, L.; Burnstein, R.M.; He, X.; Luce, R.; Furlong, R.; Foltynie, T.; Sykacek, P.; Menon, D.K.; Caldwell, M.A. Gene expression changes in long term expanded human neural progenitor cells passaged by chopping lead to loss of neurogenic potential in vivo. Exp. Neurol. 2007, 204, 512–524. [Google Scholar] [CrossRef]
  70. Trzaska, K.A.; Kuzhikandathil, E.V.; Rameshwar, P. Specification of a Dopaminergic Phenotype from Adult Human Mesenchymal Stem Cells. Stem Cells 2007, 25, 2797–2808. [Google Scholar] [CrossRef] [PubMed]
  71. Hayashi, T.; Wakao, S.; Kitada, M.; Ose, T.; Watabe, H.; Kuroda, Y.; Mitsunaga, K.; Matsuse, D.; Shigemoto, T.; Ito, A.; et al. Autologous mesenchymal stem cell-derived dopaminergic neurons function in parkinsonian macaques. J. Clin. Investig. 2013, 123, 272–284. [Google Scholar] [CrossRef] [Green Version]
  72. Urrutia, D.N.; Caviedes, P.; Mardones, R.; Minguell, J.J.; Vega-Letter, A.M.; Jofre, C.M. Comparative study of the neural differentiation capacity of mesenchymal stromal cells from different tissue sources: An approach for their use in neural regeneration therapies. PLoS ONE 2019, 14, e0213032. [Google Scholar] [CrossRef] [PubMed]
  73. Wegmeyer, H.; Bröske, A.M.; Leddin, M.; Kuentzer, K.; Nisslbeck, A.K.; Hupfeld, J.; Wiechmann, K.; Kuhlen, J.; Von Schwerin, C.; Stein, C.; et al. Mesenchymal stromal cell characteristics vary depending on their origin. Stem Cells Dev. 2013, 22, 2606–2618. [Google Scholar] [CrossRef]
  74. Hermann, A.; List, C.; Habisch, H.J.; Vukicevic, V.; Ehrhart-Bornstein, M.; Brenner, R.; Bernstein, P.; Fickert, S.; Storch, A. Age-dependent neuroectodermal differentiation capacity of human mesenchymal stromal cells: Limitations for autologous cell replacement strategies. Cytotherapy 2010, 12, 17–30. [Google Scholar] [CrossRef]
  75. Thomson, J.A. Embryonic stem cell lines derived from human blastocysts. Science 1998, 282, 1145–1147. [Google Scholar] [CrossRef] [PubMed]
  76. Takahashi, K.; Yamanaka, S. Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors. Cell 2006, 126, 663–676. [Google Scholar] [CrossRef]
  77. Moradi, S.; Mahdizadeh, H.; Šarić, T.; Kim, J.; Harati, J.; Shahsavarani, H.; Greber, B.; Moore, J.B. Research and therapy with induced pluripotent stem cells (iPSCs): Social, legal, and ethical considerations. Stem Cell Res. Ther. 2019, 10, 1–13. [Google Scholar] [CrossRef]
  78. Hasegawa, K.; Pomeroy, J.E.; Pera, M.F. Current technology for the derivation of pluripotent stem cell lines from human embryos. Cell Stem Cell 2010, 6, 521–531. [Google Scholar] [CrossRef] [PubMed]
  79. Corti, S.; Faravelli, I.; Cardano, M.; Conti, L. Human pluripotent stem cells as tools for neurodegenerative and neurodevelopmental disease modeling and drug discovery. Expert Opin. Drug Discov. 2015, 10, 615–629. [Google Scholar] [CrossRef]
  80. Quartier, A.; Courraud, J.; Thi Ha, T.; McGillivray, G.; Isidor, B.; Rose, K.; Drouot, N.; Savidan, M.A.; Feger, C.; Jagline, H.; et al. Novel mutations in NLGN3 causing autism spectrum disorder and cognitive impairment. Hum. Mutat. 2019, 40, 2021–2032. [Google Scholar] [CrossRef]
  81. Nguyen, T.A.; Lehr, A.W.; Roche, K.W. Neuroligins and Neurodevelopmental Disorders: X-Linked Genetics. Front. Synaptic Neurosci. 2020, 12, 33. [Google Scholar] [CrossRef] [PubMed]
  82. Yan, J.; Oliveira, G.; Coutinho, A.; Yang, C.; Feng, J.; Katz, C.; Sram, J.; Bockholt, A.; Jones, I.R.; Craddock, N.; et al. Analysis of the neuroligin 3 and 4 genes in autism and other neuropsychiatric patients [1]. Mol. Psychiatry 2005, 10, 329–332. [Google Scholar] [CrossRef]
  83. Tabuchi, K.; Blundell, J.; Etherton, M.R.; Hammer, R.E.; Liu, X.; Powell, C.M.; Südhof, T.C. A neuroligin-3 mutation implicated in autism increases inhibitory synaptic transmission in mice. Science 2007, 318, 71–76. [Google Scholar] [CrossRef]
  84. Speed, H.E.; Masiulis, I.; Gibson, J.R.; Powell, C.M. Increased cortical inhibition in Autism-Linked neuroligin-3R451C mice is due in part to loss of endocannabinoid signaling. PLoS ONE 2015, 10, e0140638. [Google Scholar] [CrossRef] [PubMed]
  85. Etherton, M.; Földy, C.; Sharma, M.; Tabuchi, K.; Liu, X.; Shamloo, M.; Malenka, R.C.; Südhof, T.C. Autism-linked neuroligin-3 R451C mutation differentially alters hippocampal and cortical synaptic function. Proc. Natl. Acad. Sci. USA 2011, 108, 13764–13769. [Google Scholar] [CrossRef]
  86. Monteiro, P.; Feng, G. SHANK proteins: Roles at the synapse and in autism spectrum disorder. Nat. Rev. Neurosci. 2017, 18, 147–157. [Google Scholar] [CrossRef] [PubMed]
  87. Berkel, S.; Marshall, C.R.; Weiss, B.; Howe, J.; Roeth, R.; Moog, U.; Endris, V.; Roberts, W.; Szatmari, P.; Pinto, D.; et al. Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nat. Genet. 2010, 42, 489–491. [Google Scholar] [CrossRef] [PubMed]
  88. Durand, C.M.; Betancur, C.; Boeckers, T.M.; Bockmann, J.; Chaste, P.; Fauchereau, F.; Nygren, G.; Rastam, M.; Gillberg, I.C.; Anckarsäter, H.; et al. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat. Genet. 2007, 39, 25–27. [Google Scholar] [CrossRef]
  89. Garrido, D.; Beretta, S.; Grabrucker, S.; Bauer, H.F.; Bayer, D.; Sala, C.; Verpelli, C.; Roselli, F.; Bockmann, J.; Proepper, C.; et al. Shank2/3 double knockout-based screening of cortical subregions links the retrosplenial area to the loss of social memory in autism spectrum disorders. Mol. Psychiatry 2022, 27, 4994–5006. [Google Scholar] [CrossRef]
  90. Bystron, I.; Blakemore, C.; Rakic, P. Development of the human cerebral cortex: Boulder Committee revisited. Nat. Rev. Neurosci. 2008, 9, 110–122. [Google Scholar] [CrossRef]
  91. Miller, J.A.; Ding, S.L.; Sunkin, S.M.; Smith, K.A.; Ng, L.; Szafer, A.; Ebbert, A.; Riley, Z.L.; Royall, J.J.; Aiona, K.; et al. Transcriptional landscape of the prenatal human brain. Nature 2014, 508, 199–206. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Konopka, G.; Friedrich, T.; Davis-Turak, J.; Winden, K.; Oldham, M.C.; Gao, F.; Chen, L.; Wang, G.Z.; Luo, R.; Preuss, T.M.; et al. Human-Specific Transcriptional Networks in the Brain. Neuron 2012, 75, 601–617. [Google Scholar] [CrossRef] [PubMed]
  93. Iossifov, I.; O’Roak, B.J.; Sanders, S.J.; Ronemus, M.; Krumm, N.; Levy, D.; Stessman, H.A.; Witherspoon, K.T.; Vives, L.; Patterson, K.E.; et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 2014, 515, 216–221. [Google Scholar] [CrossRef]
  94. Yuen, R.K.C.; Thiruvahindrapuram, B.; Merico, D.; Walker, S.; Tammimies, K.; Hoang, N.; Chrysler, C.; Nalpathamkalam, T.; Pellecchia, G.; Liu, Y.; et al. Whole-genome sequencing of quartet families with autism spectrum disorder. Nat. Med. 2015, 21, 185–191. [Google Scholar] [CrossRef] [PubMed]
  95. Imaizumi, K.; Okano, H. Modeling neurodevelopment in a dish with pluripotent stem cells. Dev. Growth Differ. 2021, 63, 18–25. [Google Scholar] [CrossRef] [PubMed]
  96. Zhang, S.C. Neural subtype specification from embryonic stem cells. Brain Pathol. 2006, 16, 132–142. [Google Scholar] [CrossRef]
  97. Kumar, A.; Pareek, V.; Faiq, M.A.; Ghosh, S.K.; Kumari, C. Adult neurogenesis in humans: A review of basic concepts, history, current research, and clinical implications. Innov. Clin. Neurosci. 2019, 16, 30–37. [Google Scholar]
  98. Yu, Y.; Gu, S.; Huang, H.; Wen, T. Combination of bFGF, heparin and laminin induce the generation of dopaminergic neurons from rat neural stem cells both in vitro and in vivo. J. Neurol. Sci. 2007, 255, 81–86. [Google Scholar] [CrossRef]
  99. Taylor, C.J.; Bolton, E.M.; Bradley, J.A. Immunological considerations for embryonic and induced pluripotent stem cell banking. Philos. Trans. R. Soc. B Biol. Sci. 2011, 366, 2312–2322. [Google Scholar] [CrossRef]
  100. Takahashi, K.; Tanabe, K.; Ohnuki, M.; Narita, M.; Ichisaka, T.; Tomoda, K.; Yamanaka, S. Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors. Cell 2007, 131, 861–872. [Google Scholar] [CrossRef]
  101. Jefford, M.; Moore, R. Improvement of informed consent and the quality of consent documents. Lancet Oncol. 2008, 9, 485–493. [Google Scholar] [CrossRef]
  102. Huang, C.Y.; Liu, C.L.; Ting, C.Y.; Chiu, Y.T.; Cheng, Y.C.; Nicholson, M.W.; Hsieh, P.C.H. Human iPSC banking: Barriers and opportunities. J. Biomed. Sci. 2019, 26, 87. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Engle, S.J.; Blaha, L.; Kleiman, R.J. Best Practices for Translational Disease Modeling Using Human iPSC-Derived Neurons. Neuron 2018, 100, 783–797. [Google Scholar] [CrossRef] [PubMed]
  104. Raab, S.; Klingenstein, M.; Liebau, S.; Linta, L. A Comparative View on Human Somatic Cell Sources for iPSC Generation. Stem Cells Int. 2014, 2014I, 768391. [Google Scholar] [CrossRef] [PubMed]
  105. Lapasset, L.; Milhavet, O.; Prieur, A.; Besnard, E.; Babled, A.; Ät-Hamou, N.; Leschik, J.; Pellestor, F.; Ramirez, J.M.; De Vos, J.; et al. Rejuvenating senescent and centenarian human cells by reprogramming through the pluripotent state. Genes Dev. 2011, 25, 2248–2253. [Google Scholar] [CrossRef] [PubMed]
  106. Rohani, L.; Johnson, A.A.; Arnold, A.; Stolzing, A. The aging signature: A hallmark of induced pluripotent stem cells? Aging Cell 2014, 13, 2–7. [Google Scholar] [CrossRef] [PubMed]
  107. Boyle, J.M.; Hennick, K.M.; Regalado, S.G.; Vogan, J.M.; Zhang, X.; Collins, K.; Hockemeyer, D. Telomere length set point regulation in human pluripotent stem cells critically depends on the shelterin protein TPP1. Mol. Biol. Cell 2020, 31, 2583–2596. [Google Scholar] [CrossRef]
  108. Rivetti di Val Cervo, P.; Besusso, D.; Conforti, P.; Cattaneo, E. hiPSCs for predictive modelling of neurodegenerative diseases: Dreaming the possible. Nat. Rev. Neurol. 2021, 17, 381–392. [Google Scholar] [CrossRef]
  109. Huangfu, D.; Osafune, K.; Maehr, R.; Guo, W.; Eijkelenboom, A.; Chen, S.; Muhlestein, W.; Melton, D.A. Induction of pluripotent stem cells from primary human fibroblasts with only Oct4 and Sox2. Nat. Biotechnol. 2008, 26, 1269–1275. [Google Scholar] [CrossRef]
  110. Kim, J.B.; Zaehres, H.; Wu, G.; Gentile, L.; Ko, K.; Sebastiano, V.; Araúzo-Bravo, M.J.; Ruau, D.; Han, D.W.; Zenke, M.; et al. Pluripotent stem cells induced from adult neural stem cells by reprogramming with two factors. Nature 2008, 454, 646–650. [Google Scholar] [CrossRef]
  111. Kim, J.B.; Greber, B.; Arazo-Bravo, M.J.; Meyer, J.; Park, K.I.; Zaehres, H.; Schöler, H.R. Direct reprogramming of human neural stem cells by OCT4. Nature 2009, 461, 649–653. [Google Scholar] [CrossRef]
  112. Kim, J.B.; Sebastiano, V.; Wu, G.; Araúzo-Bravo, M.J.; Sasse, P.; Gentile, L.; Ko, K.; Ruau, D.; Ehrich, M.; van den Boom, D.; et al. Oct4-Induced Pluripotency in Adult Neural Stem Cells. Cell 2009, 136, 411–419. [Google Scholar] [CrossRef] [PubMed]
  113. Brouwer, M.; Zhou, H.; Nadif Kasri, N. Choices for Induction of Pluripotency: Recent Developments in Human Induced Pluripotent Stem Cell Reprogramming Strategies. Stem Cell Rev. Rep. 2016, 12, 54–72. [Google Scholar] [CrossRef]
  114. BORDET, P. The adenoviruses. Brux. Med. 1960, 40, 701–708. [Google Scholar] [CrossRef]
  115. Siegl, G.; Bates, R.C.; Berns, K.I.; Carter, B.J.; Kelly, D.C.; Kurstak, E.; Tattersall, P. Characteristics and taxonomy of Parvoviridae. Intervirology 1985, 23, 61–73. [Google Scholar] [CrossRef] [PubMed]
  116. Fusaki, N.; Ban, H.; Nishiyama, A.; Saeki, K.; Hasegawa, M. Efficient induction of transgene-free human pluripotent stem cells using a vector based on Sendai virus, an RNA virus that does not integrate into the host genome. Proc. Jpn. Acad. Ser. B Phys. Biol. Sci. 2009, 85, 348–362. [Google Scholar] [CrossRef]
  117. Kim, D.; Kim, C.H.; Moon, J.; Chung, Y.G.; Chang, M.Y.; Han, B.S.; Ko, S.; Yang, E.; Cha, K.Y.; Lanza, R.; et al. Generation of Human Induced Pluripotent Stem Cells by Direct Delivery of Reprogramming Proteins. Cell Stem Cell 2009, 4, 472–476. [Google Scholar] [CrossRef]
  118. Zhou, H.; Wu, S.; Joo, J.Y.; Zhu, S.; Han, D.W.; Lin, T.; Trauger, S.; Bien, G.; Yao, S.; Zhu, Y.; et al. Generation of Induced Pluripotent Stem Cells Using Recombinant Proteins. Cell Stem Cell 2009, 4, 381–384. [Google Scholar] [CrossRef]
  119. Warren, L.; Manos, P.D.; Ahfeldt, T.; Loh, Y.H.; Li, H.; Lau, F.; Ebina, W.; Mandal, P.K.; Smith, Z.D.; Meissner, A.; et al. Highly efficient reprogramming to pluripotency and directed differentiation of human cells with synthetic modified mRNA. Cell Stem Cell 2010, 7, 618–630. [Google Scholar] [CrossRef] [PubMed]
  120. Jia, F.; Wilson, K.D.; Sun, N.; Gupta, D.M.; Huang, M.; Li, Z.; Panetta, N.J.; Chen, Z.Y.; Robbins, R.C.; Kay, M.A.; et al. A nonviral minicircle vector for deriving human iPS cells. Nat. Methods 2010, 7, 197–199. [Google Scholar] [CrossRef]
  121. Okita, K.; Matsumura, Y.; Sato, Y.; Okada, A.; Morizane, A.; Okamoto, S.; Hong, H.; Nakagawa, M.; Tanabe, K.; Tezuka, K.I.; et al. A more efficient method to generate integration-free human iPS cells. Nat. Methods 2011, 8, 409–412. [Google Scholar] [CrossRef] [PubMed]
  122. Woltjen, K.; Michael, I.P.; Mohseni, P.; Desai, R.; Mileikovsky, M.; Hämäläinen, R.; Cowling, R.; Wang, W.; Liu, P.; Gertsenstein, M.; et al. PiggyBac transposition reprograms fibroblasts to induced pluripotent stem cells. Nature 2009, 458, 766–770. [Google Scholar] [CrossRef] [Green Version]
  123. Plank, C.; Scherer, F.; Schillinger, U.; Bergemann, C.; Anton, M. Magnetofection: Enhancing and targeting gene delivery with superparamagnetic nanoparticles and magnetic fields. J. Liposome Res. 2003, 13, 29–32. [Google Scholar] [CrossRef] [PubMed]
  124. Ben-David, U.; Benvenisty, N. The tumorigenicity of human embryonic and induced pluripotent stem cells. Nat. Rev. Cancer 2011, 11, 268–277. [Google Scholar] [CrossRef]
  125. Okita, K.; Ichisaka, T.; Yamanaka, S. Generation of germline-competent induced pluripotent stem cells. Nature 2007, 448, 313–317. [Google Scholar] [CrossRef] [PubMed]
  126. Sommer, C.A.; Christodoulou, C.; Gianotti-Sommer, A.; Shen, S.S.; Sailaja, B.S.; Hezroni, H.; Spira, A.; Meshorer, E.; Kotton, D.N.; Mostoslavsky, G. Residual Expression of Reprogramming Factors Affects the Transcriptional Program and Epigenetic Signatures of Induced Pluripotent Stem Cells. PLoS ONE 2012, 7, e51711. [Google Scholar] [CrossRef] [PubMed]
  127. Kadari, A.; Lu, M.; Li, M.; Sekaran, T.; Thummer, R.P.; Guyette, N.; Chu, V.; Edenhofer, F. Excision of viral reprogramming cassettes by Cre protein transduction enables rapid, robust and efficient derivation of transgene-free human induced pluripotent stem cells. Stem Cell Res. Ther. 2014, 5, 47. [Google Scholar] [CrossRef]
  128. Hu, K. All roads lead to induced pluripotent stem cells: The technologies of iPSC generation. Stem Cells Dev. 2014, 23, 1285–1300. [Google Scholar] [CrossRef]
  129. Stadtfeld, M.; Nagaya, M.; Utikal, J.; Weir, G.; Hochedlinger, K. Induced pluripotent stem cells generated without viral integration. Science 2008, 322, 945–949. [Google Scholar] [CrossRef]
  130. Zhou, W.; Freed, C.R. Adenoviral gene delivery can reprogram human fibroblasts to induced pluripotent stem cells. Stem Cells 2009, 27, 2667–2674. [Google Scholar] [CrossRef]
  131. Shao, L.; Wu, W.S. Gene-delivery systems for iPS cell generation. Expert Opin. Biol. Ther. 2010, 10, 231–242. [Google Scholar] [CrossRef]
  132. Wang, H.; Shayakhmetov, D.M.; Leege, T.; Harkey, M.; Li, Q.; Papayannopoulou, T.; Stamatoyannopolous, G.; Lieber, A. A Capsid-Modified Helper-Dependent Adenovirus Vector Containing the β-Globin Locus Control Region Displays a Nonrandom Integration Pattern and Allows Stable, Erythroid-Specific Gene Expression. J. Virol. 2005, 79, 10999–11013. [Google Scholar] [CrossRef] [Green Version]
  133. Zheng, C.; Baum, B.J.; Iadarola, M.J.; O’Connell, B.C. Genomic integration and gene expression by a modified adenoviral vector. Nat. Biotechnol. 2000, 18, 176–180. [Google Scholar] [CrossRef]
  134. Lee, C.S.; Bishop, E.S.; Zhang, R.; Yu, X.; Farina, E.M.; Yan, S.; Zhao, C.; Zeng, Z.; Shu, Y.; Wu, X.; et al. Adenovirus-mediated gene delivery: Potential applications for gene and cell-based therapies in the new era of personalized medicine. Genes Dis. 2017, 4, 43–63. [Google Scholar] [CrossRef] [PubMed]
  135. Zaiss, A.; Muruve, D. Immune Responses to Adeno-Associated Virus Vectors. Curr. Gene Ther. 2005, 5, 323–331. [Google Scholar] [CrossRef]
  136. Hirsch, M.L.; Wolf, S.J.; Samulski, R.J. Delivering transgenic DNA exceeding the carrying capacity of AAV vectors. Methods Mol. Biol. 2016, 1382, 21–39. [Google Scholar] [PubMed]
  137. Haridhasapavalan, K.K.; Borgohain, M.P.; Dey, C.; Saha, B.; Narayan, G.; Kumar, S.; Thummer, R.P. An insight into non-integrative gene delivery approaches to generate transgene-free induced pluripotent stem cells. Gene 2019, 686, 146–159. [Google Scholar] [CrossRef] [PubMed]
  138. Bayart, E.; Cohen-Haguenauer, O. Technological Overview of iPS Induction from Human Adult Somatic Cells. Curr. Gene Ther. 2013, 13, 73–92. [Google Scholar] [CrossRef]
  139. Beers, J.; Linask, K.L.; Chen, J.A.; Siniscalchi, L.I.; Lin, Y.; Zheng, W.; Rao, M.; Chen, G. A cost-effective and efficient reprogramming platform for large-scale production of integration-free human induced pluripotent stem cells in chemically defined culture. Sci. Rep. 2015, 5, 11319. [Google Scholar] [CrossRef]
  140. Dey, C.; Narayan, G.; Krishna Kumar, H.; Borgohain, M.; Lenka, N. Cell-Penetrating Peptides as a Tool to Deliver Biologically Active Recombinant Proteins to Generate Transgene-Free Induced Pluripotent Stem Cells. Stud. Stem Cells Res. Ther. 2017, 3, 006–015. [Google Scholar] [CrossRef]
  141. Thier, M.; Münst, B.; Mielke, S.; Edenhofer, F. Cellular reprogramming employing recombinant Sox2 protein. Stem Cells Int. 2012, 2012, 549846. [Google Scholar] [CrossRef]
  142. Thier, M.; Münst, B.; Edenhofer, F. Exploring refined conditions for reprogramming cells by recombinant Oct4 protein. Int. J. Dev. Biol. 2011, 54, 1713–1721. [Google Scholar] [CrossRef] [PubMed]
  143. Bitzer, M.; Armeanu, S.; Lauer, U.M.; Neubert, W.J. Sendai virus vectors as an emerging negative-strand RNA viral vector system. J. Gene Med. 2003, 5, 543–553. [Google Scholar] [CrossRef]
  144. Hosoya, N.; Miura, T.; Kawana-Tachikawa, A.; Koibuchi, T.; Shioda, T.; Odawara, T.; Nakamura, T.; Kitamura, Y.; Kano, M.; Kato, A.; et al. Comparison between Sendai virus and adenovirus vectors to transduce HIV-1 genes into human dendritic cells. J. Med. Virol. 2008, 80, 373–382. [Google Scholar] [CrossRef]
  145. Nishimura, K.; Sano, M.; Ohtaka, M.; Furuta, B.; Umemura, Y.; Nakajima, Y.; Ikehara, Y.; Kobayashi, T.; Segawa, H.; Takayasu, S.; et al. Development of defective and persistent Sendai virus vector: A unique gene delivery/expression system ideal for cell reprogramming. J. Biol. Chem. 2011, 286, 4760–4771. [Google Scholar] [CrossRef]
  146. Nishimura, K.; Ohtaka, M.; Takada, H.; Kurisaki, A.; Tran, N.V.K.; Tran, Y.T.H.; Hisatake, K.; Sano, M.; Nakanishi, M. Simple and effective generation of transgene-free induced pluripotent stem cells using an auto-erasable Sendai virus vector responding to microRNA-302. Stem Cell Res. 2017, 23, 13–19. [Google Scholar] [CrossRef]
  147. Ban, H.; Nishishita, N.; Fusaki, N.; Tabata, T.; Saeki, K.; Shikamura, M.; Takada, N.; Inoue, M.; Hasegawa, M.; Kawamata, S.; et al. Efficient generation of transgene-free human induced pluripotent stem cells (iPSCs) by temperature-sensitive Sendai virus vectors. Proc. Natl. Acad. Sci. USA 2011, 108, 14234–14239. [Google Scholar] [CrossRef] [PubMed]
  148. Yakubov, E.; Rechavi, G.; Rozenblatt, S.; Givol, D. Reprogramming of human fibroblasts to pluripotent stem cells using mRNA of four transcription factors. Biochem. Biophys. Res. Commun. 2010, 394, 189–193. [Google Scholar] [CrossRef] [PubMed]
  149. Plews, J.R.; Li, J.L.; Jones, M.; Moore, H.D.; Mason, C.; Andrews, P.W.; Na, J. Activation of pluripotency genes in human fibroblast cells by a novel mRNA based approach. PLoS ONE 2010, 5, e14397. [Google Scholar] [CrossRef]
  150. Steichen, C.; Luce, E.; Maluenda, J.; Tosca, L.; Moreno-Gimeno, I.; Desterke, C.; Dianat, N.; Goulinet-Mainot, S.; Awan-Toor, S.; Burks, D.; et al. Messenger RNA- Versus Retrovirus-Based Induced Pluripotent Stem Cell Reprogramming Strategies: Analysis of Genomic Integrity. Stem Cells Transl. Med. 2014, 3, 686–691. [Google Scholar] [CrossRef]
  151. Drews, K.; Tavernier, G.; Demeester, J.; Lehrach, H.; De Smedt, S.C.; Rejman, J.; Adjaye, J. The cytotoxic and immunogenic hurdles associated with non-viral mRNA-mediated reprogramming of human fibroblasts. Biomaterials 2012, 33, 4059–4068. [Google Scholar] [CrossRef]
  152. Angel, M.; Yanik, M.F. Innate immune suppression enables frequent transfection with RNA encoding reprogramming proteins. PLoS ONE 2010, 5, e11756. [Google Scholar] [CrossRef] [Green Version]
  153. Preskey, D.; Allison, T.F.; Jones, M.; Mamchaoui, K.; Unger, C. Synthetically modified mRNA for efficient and fast human iPS cell generation and direct transdifferentiation to myoblasts. Biochem. Biophys. Res. Commun. 2016, 473, 743–751. [Google Scholar] [CrossRef] [PubMed]
  154. Rohani, L.; Fabian, C.; Holland, H.; Naaldijk, Y.; Dressel, R.; Löffler-Wirth, H.; Binder, H.; Arnold, A.; Stolzing, A. Generation of human induced pluripotent stem cells using non-synthetic mRNA. Stem Cell Res. 2016, 16, 662–672. [Google Scholar] [CrossRef]
  155. Choi, H.Y.; Lee, T.J.; Yang, G.M.; Oh, J.; Won, J.; Han, J.; Jeong, G.J.; Kim, J.; Kim, J.H.; Kim, B.S.; et al. Efficient mRNA delivery with graphene oxide-polyethylenimine for generation of footprint-free human induced pluripotent stem cells. J. Control. Release 2016, 235, 222–235. [Google Scholar] [CrossRef] [PubMed]
  156. Poleganov, M.A.; Eminli, S.; Beissert, T.; Herz, S.; Moon, J.; Goldmann, J.; Beyer, A.; Heck, R.; Burkhart, I.; Barea Roldan, D.; et al. Efficient Reprogramming of Human Fibroblasts and Blood-Derived Endothelial Progenitor Cells Using Nonmodified RNA for Reprogramming and Immune Evasion. Hum. Gene Ther. 2015, 26, 751–766. [Google Scholar] [CrossRef]
  157. Kogut, I.; McCarthy, S.M.; Pavlova, M.; Astling, D.P.; Chen, X.; Jakimenko, A.; Jones, K.L.; Getahun, A.; Cambier, J.C.; Pasmooij, A.M.G.; et al. High-efficiency RNA-based reprogramming of human primary fibroblasts. Nat. Commun. 2018, 9, 745. [Google Scholar] [CrossRef]
  158. Heng, B.C.; Heinimann, K.; Miny, P.; Iezzi, G.; Glatz, K.; Scherberich, A.; Zulewski, H.; Fussenegger, M. MRNA transfection-based, feeder-free, induced pluripotent stem cells derived from adipose tissue of a 50-year-old patient. Metab. Eng. 2013, 18, 9–24. [Google Scholar] [CrossRef] [PubMed]
  159. Warren, L.; Ni, Y.; Wang, J.; Guo, X. Feeder-free derivation of human induced pluripotent stem cells with messenger RNA. Sci. Rep. 2012, 2, 1–7. [Google Scholar] [CrossRef] [PubMed]
  160. Durruthy, J.D.; Sebastiano, V. Derivation of GMP-compliant integration-free hiPSCs using modified mRNAs. Methods Mol. Biol. 2015, 1283, 31–42. [Google Scholar] [CrossRef]
  161. Maurisse, R.; De Semir, D.; Emamekhoo, H.; Bedayat, B.; Abdolmohammadi, A.; Parsi, H.; Gruenert, D.C. Comparative transfection of DNA into primary and transformed mammalian cells from different lineages. BMC Biotechnol. 2010, 10, 1–9. [Google Scholar] [CrossRef]
  162. Montserrat, N.; Garreta, E.; González, F.; Gutiérrez, J.; Eguizábal, C.; Ramos, V.; Borrós, S.; Belmonte, J.C.I. Simple generation of human induced pluripotent stem cells using poly-β-amino esters as the non-viral gene delivery system. J. Biol. Chem. 2011, 286, 12417–12428. [Google Scholar] [CrossRef] [Green Version]
  163. Chabot, S.; Orio, J.; Schmeer, M.; Schleef, M.; Golzio, M.; Teissié, J. Minicircle DNA electrotransfer for efficient tissue-targeted gene delivery. Gene Ther. 2013, 20, 62–68. [Google Scholar] [CrossRef] [PubMed]
  164. Ma, X.; Kong, L.; Zhu, S. Reprogramming cell fates by small molecules. Protein Cell 2017, 8, 328–348. [Google Scholar] [CrossRef]
  165. Yoshida, Y.; Takahashi, K.; Okita, K.; Ichisaka, T.; Yamanaka, S. Hypoxia Enhances the Generation of Induced Pluripotent Stem Cells. Cell Stem Cell 2009, 5, 237–241. [Google Scholar] [CrossRef]
  166. Maucksch, C.; Bohla, A.; Hoffmann, F.; Schleef, M.; Aneja, M.K.; Elfinger, M.; Hartl, D.; Rudolph, C. Transgene expression of transfected supercoiled plasmid DNA concatemers in mammalian cells. J. Gene Med. 2009, 11, 444–453. [Google Scholar] [CrossRef] [PubMed]
  167. Chen, Z.Y.; He, C.Y.; Ehrhardt, A.; Kay, M.A. Minicircle DNA vectors devoid of bacterial DNA result in persistent and high-level transgene expression in vivo. Mol. Ther. 2003, 8, 495–500. [Google Scholar] [CrossRef] [PubMed]
  168. Galla, M.; Schambach, A.; Falk, C.S.; Maetzig, T.; Kuehle, J.; Lange, K.; Zychlinski, D.; Heinz, N.; Brugman, M.H.; Göhring, G.; et al. Avoiding cytotoxicity of transposases by dose-controlled mRNA delivery. Nucleic Acids Res. 2011, 39, 7147–7160. [Google Scholar] [CrossRef]
  169. Tipanee, J.; VandenDriessche, T.; Chuah, M.K. Transposons: Moving Forward from Preclinical Studies to Clinical Trials. Hum. Gene Ther. 2017, 28, 1087–1104. [Google Scholar] [CrossRef]
  170. Kumar, D.; Talluri, T.R.; Anand, T.; Kues, W.A. Transposon-based reprogramming to induced pluripotency. Histol. Histopathol. 2015, 30, 1397–1409. [Google Scholar] [CrossRef]
  171. Scherer, F.; Anton, M.; Schillinger, U.; Henke, J.; Bergemann, C.; Krüger, A.; Gänsbacher, B.; Plank, C. Magnetofection: Enhancing and targeting gene delivery by magnetic force in vitro and in vivo. Gene Ther. 2002, 9, 102–109. [Google Scholar] [CrossRef]
  172. Park, H.Y.; Noh, E.H.; Chung, H.M.; Kang, M.J.; Kim, E.Y.; Park, S.P. Efficient Generation of Virus-Free iPS Cells Using Liposomal Magnetofection. PLoS ONE 2012, 7, e45812. [Google Scholar] [CrossRef] [PubMed]
  173. Liu, G.; David, B.T.; Trawczynski, M.; Fessler, R.G. Advances in Pluripotent Stem Cells: History, Mechanisms, Technologies, and Applications. Stem Cell Rev. Rep. 2020, 16, 3–32. [Google Scholar] [CrossRef] [PubMed]
  174. Nakagawa, M.; Taniguchi, Y.; Senda, S.; Takizawa, N.; Ichisaka, T.; Asano, K.; Morizane, A.; Doi, D.; Takahashi, J.; Nishizawa, M.; et al. A novel efficient feeder-Free culture system for the derivation of human induced pluripotent stem cells. Sci. Rep. 2014, 4, 3594. [Google Scholar] [CrossRef]
  175. Ji, J.; Sharma, V.; Qi, S.; Guarch, M.E.; Zhao, P.; Luo, Z.; Fan, W.; Wang, Y.; Mbabaali, F.; Neculai, D.; et al. Antioxidant supplementation reduces genomic aberrations in human induced pluripotent stem cells. Stem Cell Rep. 2014, 2, 44–51. [Google Scholar] [CrossRef]
  176. Karagiannis, P.; Takahashi, K.; Saito, M.; Yoshida, Y.; Okita, K.; Watanabe, A.; Inoue, H.; Yamashita, J.K.; Todani, M.; Nakagawa, M.; et al. Induced pluripotent stem cells and their use in human models of disease and development. Physiol. Rev. 2019, 99, 79–114. [Google Scholar] [CrossRef] [PubMed]
  177. Begum, A.N.; Guoynes, C.; Cho, J.; Hao, J.; Lutfy, K.; Hong, Y. Rapid generation of sub-type, region-specific neurons and neural networks from human pluripotent stem cell-derived neurospheres. Stem Cell Res. 2015, 15, 731–741. [Google Scholar] [CrossRef] [PubMed]
  178. Son, E.Y.; Ichida, J.K.; Wainger, B.J.; Toma, J.S.; Rafuse, V.F.; Woolf, C.J.; Eggan, K. Conversion of mouse and human fibroblasts into functional spinal motor neurons. Cell Stem Cell 2011, 9, 205–218. [Google Scholar] [CrossRef]
  179. Hester, M.E.; Murtha, M.J.; Song, S.; Rao, M.; Miranda, C.J.; Meyer, K.; Tian, J.; Boulting, G.; Schaffer, D.V.; Zhu, M.X.; et al. Rapid and efficient generation of functional motor neurons from human pluripotent stem cells using gene delivered transcription factor codes. Mol. Ther. 2011, 19, 1905–1912. [Google Scholar] [CrossRef]
  180. Santos, D.P.; Kiskinis, E. Generation of spinal motor neurons from human pluripotent stem cells. Methods Mol. Biol. 2017, 1538, 53–66. [Google Scholar] [PubMed]
  181. Solomon, E.; Davis-Anderson, K.; Hovde, B.; Micheva-Viteva, S.; Harris, J.F.; Twary, S.; Iyer, R. Global transcriptome profile of the developmental principles of in vitro iPSC-to-motor neuron differentiation. BMC Mol. Cell Biol. 2021, 22, 13. [Google Scholar] [CrossRef]
  182. Imamura, K.; Kawaguchi, J.; Shu, T.; Inoue, H. Generation of Motor Neurons from Human ESCs/iPSCs Using Sendai Virus Vectors. Methods Mol. Biol. 2021, 2352, 127–132. [Google Scholar]
  183. Amimoto, N.; Nishimura, K.; Shimohama, S.; Takata, K. Generation of striatal neurons from human induced pluripotent stem cells by controlling extrinsic signals with small molecules. Stem Cell Res. 2021, 55, 102486. [Google Scholar] [CrossRef]
  184. Dhingra, A.; Täger, J.; Bressan, E.; Rodriguez-Nieto, S.; Bedi, M.S.; Bröer, S.; Sadikoglou, E.; Fernandes, N.; Castillo-Lizardo, M.; Rizzu, P.; et al. Automated production of human induced pluripotent stem cell-derived cortical and dopaminergic neurons with integrated live-cell monitoring. J. Vis. Exp. 2020, 2020, 1–29. [Google Scholar] [CrossRef]
  185. Silva, M.C.; Nandi, G.; Haggarty, S.J. Differentiation of Human Induced Pluripotent Stem Cells into Cortical Neurons to Advance Precision Medicine. Methods Mol. Biol. 2022, 2429, 143–174. [Google Scholar]
  186. Lopez-Lengowski, K.; Kathuria, A.; Gerlovin, K.; Karmacharya, R. Co-culturing microglia and cortical neurons differentiated from human induced pluripotent stem cells. J. Vis. Exp. 2021, 2021, e62480. [Google Scholar] [CrossRef]
  187. Ho, S.M.; Hartley, B.J.; TCW, J.; Beaumont, M.; Stafford, K.; Slesinger, P.A.; Brennand, K.J. Rapid Ngn2-induction of excitatory neurons from hiPSC-derived neural progenitor cells. Methods 2016, 101, 113–124. [Google Scholar] [CrossRef]
  188. Liu, Y.; Liu, H.; Sauvey, C.; Yao, L.; Zarnowska, E.D.; Zhang, S.C. Directed differentiation of forebrain GABA interneurons from human pluripotent stem cells. Nat. Protoc. 2013, 8, 1670–1679. [Google Scholar] [CrossRef] [PubMed]
  189. Caiazzo, M.; Dell’Anno, M.T.; Dvoretskova, E.; Lazarevic, D.; Taverna, S.; Leo, D.; Sotnikova, T.D.; Menegon, A.; Roncaglia, P.; Colciago, G.; et al. Direct generation of functional dopaminergic neurons from mouse and human fibroblasts. Nature 2011, 476, 224–227. [Google Scholar] [CrossRef] [PubMed]
  190. Tofoli, F.A.; Semeano, A.T.S.; Oliveira-Giacomelli, Á.; Gonçalves, M.C.B.; Ferrari, M.F.R.; Veiga Pereira, L.; Ulrich, H. Midbrain dopaminergic neurons differentiated from human-induced pluripotent stem cells. Methods Mol. Biol. 2019, 1919, 97–118. [Google Scholar]
  191. Corti, S.; Bonjean, R.; Legier, T.; Rattier, D.; Melon, C.; Salin, P.; Toso, E.A.; Kyba, M.; Kerkerian-Le Goff, L.; Maina, F.; et al. Enhanced differentiation of human induced pluripotent stem cells toward the midbrain dopaminergic neuron lineage through GLYPICAN-4 downregulation. Stem Cells Transl. Med. 2021, 10, 725–742. [Google Scholar] [CrossRef] [PubMed]
  192. Mahajani, S.; Raina, A.; Fokken, C.; Kügler, S.; Bähr, M. Homogenous generation of dopaminergic neurons from multiple hiPSC lines by transient expression of transcription factors. Cell Death Dis. 2019, 10, 898. [Google Scholar] [CrossRef]
  193. Xue, Y.; Zhan, X.; Sun, S.; Karuppagounder, S.S.; Xia, S.; Dawson, V.L.; Dawson, T.M.; Laterra, J.; Zhang, J.; Ying, M. Synthetic mRNAs Drive Highly Efficient iPS Cell Differentiation to Dopaminergic Neurons. Stem Cells Transl. Med. 2019, 8, 112–123. [Google Scholar] [CrossRef]
  194. Xu, Z.; Jiang, H.; Zhong, P.; Yan, Z.; Chen, S.; Feng, J. Direct conversion of human fibroblasts to induced serotonergic neurons. Mol. Psychiatry 2016, 21, 62–70. [Google Scholar] [CrossRef]
  195. Jansch, C.; Ziegler, G.C.; Forero, A.; Gredy, S.; Wäldchen, S.; Vitale, M.R.; Svirin, E.; Zöller, J.E.M.; Waider, J.; Günther, K.; et al. Serotonin-specific neurons differentiated from human iPSCs form distinct subtypes with synaptic protein assembly. J. Neural Transm. 2021, 128, 225–241. [Google Scholar] [CrossRef]
  196. Neureiter, A.; Eberhardt, E.; Lampert, A. Differentiation of iPS-Cells into Peripheral Sensory Neurons. Methods Mol. Biol. 2022, 2429, 175–188. [Google Scholar] [PubMed]
  197. Gupta, S.; Sivalingam, D.; Hain, S.; Makkar, C.; Sosa, E.; Clark, A.; Butler, S.J. Deriving Dorsal Spinal Sensory Interneurons from Human Pluripotent Stem Cells. Stem Cell Rep. 2018, 10, 390–405. [Google Scholar] [CrossRef]
  198. Cantor, E.L.; Shen, F.; Jiang, G.; Tan, Z.; Cunningham, G.M.; Wu, X.; Philips, S.; Schneider, B.P. Passage number affects differentiation of sensory neurons from human induced pluripotent stem cells. Sci. Rep. 2022, 12, 15869. [Google Scholar] [CrossRef] [PubMed]
  199. Hanna, J.H.; Saha, K.; Jaenisch, R. Pluripotency and cellular reprogramming: Facts, hypotheses, unresolved issues. Cell 2010, 143, 508–525. [Google Scholar] [CrossRef] [PubMed]
  200. Topol, A.; English, J.A.; Flaherty, E.; Rajarajan, P.; Hartley, B.J.; Gupta, S.; Desland, F.; Zhu, S.; Goff, T.; Friedman, L.; et al. Increased abundance of translation machinery in stem cell–derived neural progenitor cells from four schizophrenia patients. Transl. Psychiatry 2015, 5, e662. [Google Scholar] [CrossRef]
  201. Mariani, J.; Coppola, G.; Zhang, P.; Abyzov, A.; Provini, L.; Tomasini, L.; Amenduni, M.; Szekely, A.; Palejev, D.; Wilson, M.; et al. FOXG1-Dependent Dysregulation of GABA/Glutamate Neuron Differentiation in Autism Spectrum Disorders. Cell 2015, 162, 375–390. [Google Scholar] [CrossRef]
  202. Choi, J.; Lee, S.; Mallard, W.; Clement, K.; Tagliazucchi, G.M.; Lim, H.; Choi, I.Y.; Ferrari, F.; Tsankov, A.M.; Pop, R.; et al. A comparison of genetically matched cell lines reveals the equivalence of human iPSCs and ESCs. Nat. Biotechnol. 2015, 33, 1173–1181. [Google Scholar] [CrossRef] [PubMed]
  203. Hall, B.; Limaye, A.; Kulkarni, A.B. Overview: Generation of Gene Knockout Mice. Curr. Protoc. Cell Biol. 2009, 44, 19.12.1–19.12.17. [Google Scholar] [CrossRef]
  204. Tong, C.; Huang, G.; Ashton, C.; Li, P.; Ying, Q.L. Generating gene knockout rats by homologous recombination in embryonic stem cells. Nat. Protoc. 2011, 6, 827–844. [Google Scholar] [CrossRef]
  205. Zwaka, T.P.; Thomson, J.A. Homologous recombination in human embryonic stem cells. Nat. Biotechnol. 2003, 21, 319–321. [Google Scholar] [CrossRef]
  206. Watanabe, K.; Ueno, M.; Kamiya, D.; Nishiyama, A.; Matsumura, M.; Wataya, T.; Takahashi, J.B.; Nishikawa, S.; Nishikawa, S.I.; Muguruma, K.; et al. A ROCK inhibitor permits survival of dissociated human embryonic stem cells. Nat. Biotechnol. 2007, 25, 681–686. [Google Scholar] [CrossRef] [PubMed]
  207. Bañuelos, C.A.; Banáth, J.P.; MacPhail, S.H.; Zhao, J.; Eaves, C.A.; O’Connor, M.D.; Lansdorp, P.M.; Olive, P.L. Mouse but not human embryonic stem cells are deficient in rejoining of ionizing radiation-induced DNA double-strand breaks. DNA Repair 2008, 7, 1471–1483. [Google Scholar] [CrossRef] [PubMed]
  208. Jasin, M.; Rothstein, R. Repair of strand breaks by homologous recombination. Cold Spring Harb. Perspect. Biol. 2013, 5, a012740. [Google Scholar] [CrossRef]
  209. Tesson, L.; Usal, C.; Ménoret, S.; Leung, E.; Niles, B.J.; Remy, S.; Santiago, Y.; Vincent, A.I.; Meng, X.; Zhang, L.; et al. Knockout rats generated by embryo microinjection of TALENs. Nat. Biotechnol. 2011, 29, 695–696. [Google Scholar] [CrossRef]
  210. Gasiunas, G.; Barrangou, R.; Horvath, P.; Siksnys, V. Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc. Natl. Acad. Sci. USA 2012, 109, E2579–E2586. [Google Scholar] [CrossRef]
  211. Soldner, F.; Laganière, J.; Cheng, A.W.; Hockemeyer, D.; Gao, Q.; Alagappan, R.; Khurana, V.; Golbe, L.I.; Myers, R.H.; Lindquist, S.; et al. Generation of isogenic pluripotent stem cells differing exclusively at two early onset parkinson point mutations. Cell 2011, 146, 318–331. [Google Scholar] [CrossRef]
  212. Ding, Q.; Lee, Y.K.; Schaefer, E.A.K.; Peters, D.T.; Veres, A.; Kim, K.; Kuperwasser, N.; Motola, D.L.; Meissner, T.B.; Hendriks, W.T.; et al. A TALEN genome-editing system for generating human stem cell-based disease models. Cell Stem Cell 2013, 12, 238–251. [Google Scholar] [CrossRef] [PubMed]
  213. Czerwińska, P.; Mazurek, S.; Kołodziejczak, I.; Wiznerowicz, M. Gene delivery methods and genome editing of human pluripotent stem cells. Rep. Pract. Oncol. Radiother. 2019, 24, 180–187. [Google Scholar] [CrossRef] [PubMed]
  214. Korecka, J.A.; Talbot, S.; Osborn, T.M.; de Leeuw, S.M.; Levy, S.A.; Ferrari, E.J.; Moskites, A.; Atkinson, E.; Jodelka, F.M.; Hinrich, A.J.; et al. Neurite Collapse and Altered ER Ca 2+ Control in Human Parkinson Disease Patient iPSC-Derived Neurons with LRRK2 G2019S Mutation. Stem Cell Rep. 2019, 12, 29–41. [Google Scholar] [CrossRef] [PubMed]
  215. Wang, C.; Najm, R.; Xu, Q.; Jeong, D.E.; Walker, D.; Balestra, M.E.; Yoon, S.Y.; Yuan, H.; Li, G.; Miller, Z.A.; et al. Gain of toxic apolipoprotein E4 effects in human iPSC-derived neurons is ameliorated by a small-molecule structure corrector article. Nat. Med. 2018, 24, 647–657. [Google Scholar] [CrossRef] [PubMed]
  216. Verheyen, A.; Diels, A.; Reumers, J.; Van Hoorde, K.; Van den Wyngaert, I.; van Outryve d’Ydewalle, C.; De Bondt, A.; Kuijlaars, J.; De Muynck, L.; De Hoogt, R.; et al. Genetically Engineered iPSC-Derived FTDP-17 MAPT Neurons Display Mutation-Specific Neurodegenerative and Neurodevelopmental Phenotypes. Stem Cell Rep. 2018, 11, 363–379. [Google Scholar] [CrossRef] [PubMed]
  217. Zhang, P.W.; Haidet-Phillips, A.M.; Pham, J.T.; Lee, Y.; Huo, Y.; Tienari, P.J.; Maragakis, N.J.; Sattler, R.; Rothstein, J.D. Generation of GFAP::GFP astrocyte reporter lines from human adult fibroblast-derived iPS cells using zinc-finger nuclease technology. Glia 2016, 64, 63–75. [Google Scholar] [CrossRef]
  218. Wen, Z.; Nguyen, H.N.; Guo, Z.; Lalli, M.A.; Wang, X.; Su, Y.; Kim, N.S.; Yoon, K.J.; Shin, J.; Zhang, C.; et al. Synaptic dysregulation in a human iPS cell model of mental disorders. Nature 2014, 515, 414–418. [Google Scholar] [CrossRef]
  219. Akiyama, T.; Suzuki, N.; Ishikawa, M.; Fujimori, K.; Sone, T.; Kawada, J.; Funayama, R.; Fujishima, F.; Mitsuzawa, S.; Ikeda, K.; et al. Aberrant axon branching via Fos-B dysregulation in FUS-ALS motor neurons. eBioMedicine 2019, 45, 362–378. [Google Scholar] [CrossRef]
  220. Cerbini, T.; Funahashi, R.; Luo, Y.; Liu, C.; Park, K.; Rao, M.; Malik, N.; Zou, J. Transcription activator-like effector nuclease (TALEN)-mediated CLYBL targeting enables enhanced transgene expression and one-step generation of dual reporter human induced pluripotent stem cell (iPSC) and neural stem cell (NSC) lines. PLoS ONE 2015, 10, e0116032. [Google Scholar] [CrossRef]
  221. Pei, Y.; Sierra, G.; Sivapatham, R.; Swistowski, A.; Rao, M.S.; Zeng, X. A platform for rapid generation of single and multiplexed reporters in human iPSC lines. Sci. Rep. 2015, 5, 1–10. [Google Scholar] [CrossRef]
  222. Luo, Y.; Rao, M.; Zou, J. Generation of GFP reporter human induced pluripotent stem cells using AAVS1 safe harbor transcription activator-like effector nuclease. Curr. Protoc. Stem Cell Biol. 2014, 2014, 5a–7. [Google Scholar] [CrossRef] [PubMed]
  223. Guilinger, J.P.; Pattanayak, V.; Reyon, D.; Tsai, S.Q.; Sander, J.D.; Joung, J.K.; Liu, D.R. Broad specificity profiling of TALENs results in engineered nucleases with improved DNA-cleavage specificity. Nat. Methods 2014, 11, 429–435. [Google Scholar] [CrossRef]
  224. Fischer, J.; Heide, M.; Huttner, W.B. Genetic Modification of Brain Organoids. Front. Cell. Neurosci. 2019, 13, 558. [Google Scholar] [CrossRef]
  225. Cong, L.; Ran, F.A.; Cox, D.; Lin, S.; Barretto, R.; Habib, N.; Hsu, P.D.; Wu, X.; Jiang, W.; Marraffini, L.A.; et al. Multiplex genome engineering using CRISPR/Cas systems. Science 2013, 339, 819–823. [Google Scholar] [CrossRef] [Green Version]
  226. Mimitou, E.P.; Cheng, A.; Montalbano, A.; Hao, S.; Stoeckius, M.; Legut, M.; Roush, T.; Herrera, A.; Papalexi, E.; Ouyang, Z.; et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 2019, 16, 409–412. [Google Scholar] [CrossRef] [PubMed]
  227. Datlinger, P.; Rendeiro, A.F.; Schmidl, C.; Krausgruber, T.; Traxler, P.; Klughammer, J.; Schuster, L.C.; Kuchler, A.; Alpar, D.; Bock, C. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 2017, 14, 297–301. [Google Scholar] [CrossRef] [PubMed]
  228. Adamson, B.; Norman, T.M.; Jost, M.; Cho, M.Y.; Nuñez, J.K.; Chen, Y.; Villalta, J.E.; Gilbert, L.A.; Horlbeck, M.A.; Hein, M.Y.; et al. A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell 2016, 167, 1867–1882.e21. [Google Scholar] [CrossRef]
  229. Dixit, A.; Parnas, O.; Li, B.; Chen, J.; Fulco, C.P.; Jerby-Arnon, L.; Marjanovic, N.D.; Dionne, D.; Burks, T.; Raychowdhury, R.; et al. Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 2016, 167, 1853–1866.e17. [Google Scholar] [CrossRef]
  230. Hendriks, W.T.; Warren, C.R.; Cowan, C.A. Genome Editing in Human Pluripotent Stem Cells: Approaches, Pitfalls, and Solutions. Cell Stem Cell 2016, 18, 53–65. [Google Scholar] [CrossRef]
  231. Kim, D.S.; Joel Ross, P.; Zaslavsky, K.; Ellis, J. Optimizing neuronal differentiation from induced pluripotent stem cells to model ASD. Front. Cell. Neurosci. 2014, 8, 109. [Google Scholar] [CrossRef]
  232. Plant, A.L.; Locascio, L.E.; May, W.E.; Gallagher, P.D. Improved reproducibility by assuring confidence in measurements in biomedical research. Nat. Methods 2014, 11, 895–898. [Google Scholar] [CrossRef]
  233. Crook, J.M.; Hei, D.; Stacey, G. The international stem cell banking initiative (ISCBI): Raising standards to bank on. Vitr. Cell. Dev. Biol.-Anim. 2010, 46, 169–172. [Google Scholar] [CrossRef]
  234. Li, J.; Song, W.; Pan, G.; Zhou, J. Advances in understanding the cell types and approaches used for generating induced pluripotent stem cells. J. Hematol. Oncol. 2014, 7, 1–18. [Google Scholar] [CrossRef]
  235. Shofuda, T.; Fukusumi, H.; Kanematsu, D.; Yamamoto, A.; Yamasaki, M.; Arita, N.; Kanemura, Y. A method for efficiently generating neurospheres from human-induced pluripotent stem cells using microsphere arrays. Neuroreport 2013, 24, 84–90. [Google Scholar] [CrossRef] [PubMed]
  236. Stover, A.E.; Brick, D.J.; Nethercott, H.E.; Banuelos, M.G.; Sun, L.; O’Dowd, D.K.; Schwartz, P.H. Process-based expansion and neural differentiation of human pluripotent stem cells for transplantation and disease modeling. J. Neurosci. Res. 2013, 91, 1247–1262. [Google Scholar] [CrossRef] [PubMed]
  237. Garber, K. RIKEN suspends first clinical trial involving induced pluripotent stem cells. Nat. Biotechnol. 2015, 33, 890–891. [Google Scholar] [CrossRef]
  238. Streeter, I.; Harrison, P.W.; Faulconbridge, A.; Consortium, T.H.S.; Flicek, P.; Parkinson, H.; Clarke, L. The human-induced pluripotent stem cell initiative-Data resources for cellular genetics. Nucleic Acids Res. 2017, 45, D691–D697. [Google Scholar] [CrossRef] [PubMed]
  239. Taapken, S.M.; Nisler, B.S.; Newton, M.A.; Sampsell-Barron, T.L.; Leonhard, K.A.; McIntire, E.M.; Montgomery, K.D. Karotypic abnormalities in human induced pluripotent stem cells and embryonic stem cells. Nat. Biotechnol. 2011, 29, 313–314. [Google Scholar] [CrossRef]
  240. Kilpinen, H.; Goncalves, A.; Leha, A.; Afzal, V.; Alasoo, K.; Ashford, S.; Bala, S.; Bensaddek, D.; Casale, F.P.; Culley, O.J.; et al. Common genetic variation drives molecular heterogeneity in human iPSCs. Nature 2017, 546, 370–375. [Google Scholar] [CrossRef]
  241. D’Antonio, M.; Benaglio, P.; Jakubosky, D.; Greenwald, W.W.; Matsui, H.; Donovan, M.K.R.; Li, H.; Smith, E.N.; D’Antonio-Chronowska, A.; Frazer, K.A. Insights into the Mutational Burden of Human Induced Pluripotent Stem Cells from an Integrative Multi-Omics Approach. Cell Rep. 2018, 24, 883–894. [Google Scholar] [CrossRef]
  242. Kang, E.; Wang, X.; Tippner-Hedges, R.; Ma, H.; Folmes, C.D.L.; Gutierrez, N.M.; Lee, Y.; Van Dyken, C.; Ahmed, R.; Li, Y.; et al. Age-related accumulation of somatic mitochondrial DNA mutations in adult-derived human ipscs. Cell Stem Cell 2016, 18, 625–636. [Google Scholar] [CrossRef]
  243. Miura, K.; Okada, Y.; Aoi, T.; Okada, A.; Takahashi, K.; Okita, K.; Nakagawa, M.; Koyanagi, M.; Tanabe, K.; Ohnuki, M.; et al. Variation in the safety of induced pluripotent stem cell lines. Nat. Biotechnol. 2009, 27, 743–745. [Google Scholar] [CrossRef] [PubMed]
  244. Bar, S.; Benvenisty, N. Epigenetic aberrations in human pluripotent stem cells. EMBO J. 2019, 38, 1–18. [Google Scholar] [CrossRef] [PubMed]
  245. Kim, K.; Doi, A.; Wen, B.; Ng, K.; Zhao, R.; Cahan, P.; Kim, J.; Aryee, M.J.; Ji, H.; Ehrlich, L.I.R.; et al. Epigenetic memory in induced pluripotent stem cells. Nature 2010, 467, 285–290. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  246. Kim, K.; Zhao, R.; Doi, A.; Ng, K.; Unternaehrer, J.; Cahan, P.; Hongguang, H.; Loh, Y.H.; Aryee, M.J.; Lensch, M.W.; et al. Donor cell type can influence the epigenome and differentiation potential of human induced pluripotent stem cells. Nat. Biotechnol. 2011, 29, 1117–1119. [Google Scholar] [CrossRef]
  247. Bar-Nur, O.; Russ, H.A.; Efrat, S.; Benvenisty, N. Epigenetic memory and preferential lineage-specific differentiation in induced pluripotent stem cells derived from human pancreatic islet beta cells. Cell Stem Cell 2011, 9, 17–23. [Google Scholar] [CrossRef]
  248. Lister, R.; Pelizzola, M.; Kida, Y.S.; Hawkins, R.D.; Nery, J.R.; Hon, G.; Antosiewicz-Bourget, J.; Ogmalley, R.; Castanon, R.; Klugman, S.; et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 2011, 471, 68–73. [Google Scholar] [CrossRef]
  249. Roost, M.S.; Slieker, R.C.; Bialecka, M.; Van Iperen, L.; Gomes Fernandes, M.M.; He, N.; Suchiman, H.E.D.; Szuhai, K.; Carlotti, F.; De Koning, E.J.P.; et al. DNA methylation and transcriptional trajectories during human development and reprogramming of isogenic pluripotent stem cells. Nat. Commun. 2017, 8, 908. [Google Scholar] [CrossRef]
  250. Bartolomei, M.S.; Ferguson-Smith, A.C. Mammalian genomic imprinting. Cold Spring Harb. Perspect. Biol. 2011, 3, 1–17. [Google Scholar] [CrossRef]
  251. Chen, H.; Kaitlyn Victor, A.; Klein, J.; Tacer, K.F.; Tai, D.J.C.; de Esch, C.; Nuttle, A.; Temirov, J.; Burnett, L.C.; Rosenbaum, M.; et al. Loss of MAGEL2 in prader-willi syndrome leads to decreased secretory granule and neuropeptide production. JCI Insight. 2020, 5, e138576. [Google Scholar] [CrossRef]
  252. Yang, L.; Shu, X.; Mao, S.; Wang, Y.; Du, X.; Zou, C. Genotype–phenotype correlations in angelman syndrome. Genes 2021, 12, 987. [Google Scholar] [CrossRef]
  253. Pólvora-Brandão, D.; Joaquim, M.; Godinho, I.; Aprile, D.; Álvaro, A.R.; Onofre, I.; Raposo, A.C.; Pereira de Almeida, L.; Duarte, S.T.; da Rocha, S.T. Loss of hierarchical imprinting regulation at the Prader-Willi/Angelman syndrome locus in human iPSCs. Hum. Mol. Genet. 2018, 27, 3999–4011. [Google Scholar] [CrossRef]
  254. Pick, M.; Stelzer, Y.; Bar-Nur, O.; Mayshar, Y.; Eden, A.; Benvenisty, N. Clone- and gene-specific aberrations of parental imprinting in human induced pluripotent stem cells. Stem Cells 2009, 27, 2686–2690. [Google Scholar] [CrossRef]
  255. Nazor, K.L.; Altun, G.; Lynch, C.; Tran, H.; Harness, J.V.; Slavin, I.; Garitaonandia, I.; Müller, F.J.; Wang, Y.C.; Boscolo, F.S.; et al. Recurrent variations in DNA methylation in human pluripotent stem cells and their differentiated derivatives. Cell Stem Cell 2012, 10, 620–634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  256. Johannesson, B.; Sagi, I.; Gore, A.; Paull, D.; Yamada, M.; Golan-Lev, T.; Li, Z.; LeDuc, C.; Shen, Y.; Stern, S.; et al. Comparable frequencies of coding mutations and loss of imprinting in human pluripotent cells derived by nuclear transfer and defined factors. Cell Stem Cell 2014, 15, 634–642. [Google Scholar] [CrossRef] [PubMed]
  257. Bar, S.; Schachter, M.; Eldar-Geva, T.; Benvenisty, N. Large-Scale Analysis of Loss of Imprinting in Human Pluripotent Stem Cells. Cell Rep. 2017, 19, 957–968. [Google Scholar] [CrossRef]
  258. Kuwajima, T.; Nishimura, I.; Yoshikawa, K. Cellular/Molecular Necdin Promotes GABAergic Neuron Differentiation in Cooperation with Dlx Homeodomain Proteins. J. Neurosci. 2006, 26, 5383–5392. [Google Scholar] [CrossRef]
  259. Dimitropoulos, A.; Fearer, I.D.; Roof, E.; Stone, W.; Butler, M.G.; Sutcliffe, J.; Thompson, T. Appetitive behavior, compulsivity, and neurochemistry in Prader-Willi syndrome. Ment. Retard. Dev. Disabil. Res. Rev. 2000, 6, 125–130. [Google Scholar] [CrossRef] [PubMed]
  260. Ishida, M.; Moore, G.E. The role of imprinted genes in humans. Mol. Aspects Med. 2013, 34, 826–840. [Google Scholar] [CrossRef]
  261. Lessing, D.; Anguera, M.C.; Lee, J.T. X chromosome inactivation and epigenetic responses to cellular reprogramming. Annu. Rev. Genom. Hum. Genet. 2013, 14, 85–110. [Google Scholar] [CrossRef]
  262. Bruck, T.; Benvenisty, N. Meta-analysis of the heterogeneity of X chromosome inactivation in human pluripotent stem cells. Stem Cell Res. 2011, 6, 187–193. [Google Scholar] [CrossRef] [PubMed]
  263. Lyon, M.F. Gene action in the X-chromosome of the mouse (Mus musculus L.). Nature 1961, 190, 372–373. [Google Scholar] [CrossRef] [PubMed]
  264. Plath, K.; Fang, J.; Mlynarczyk-Evans, S.K.; Cao, R.; Worringer, K.A.; Wang, H.; De la Cruz, C.C.; Otte, A.P.; Panning, B.; Zhang, Y. Role of histone H3 lysine 27 methylation in X inactivation. Science 2003, 300, 131–135. [Google Scholar] [CrossRef]
  265. Marks, H.; Chow, J.C.; Denissov, S.; Françoijs, K.J.; Brockdorff, N.; Heard, E.; Stunnenberg, H.G. High-resolution analysis of epigenetic changes associated with X inactivation. Genome Res. 2009, 19, 1361–1373. [Google Scholar] [CrossRef] [Green Version]
  266. Tchieu, J.; Kuoy, E.; Chin, M.H.; Trinh, H.; Patterson, M.; Sherman, S.P.; Aimiuwu, O.; Lindgren, A.; Hakimian, S.; Zack, J.A.; et al. Female human iPSCs retain an inactive X chromosome. Cell Stem Cell 2010, 7, 329–342. [Google Scholar] [CrossRef] [PubMed]
  267. Pomp, O.; Dreesen, O.; Leong, D.F.M.; Meller-Pomp, O.; Tan, T.T.; Zhou, F.; Colman, A. Unexpected X chromosome skewing during culture and reprogramming of human somatic cells can be alleviated by exogenous telomerase. Cell Stem Cell 2011, 9, 156–165. [Google Scholar] [CrossRef]
  268. Mekhoubad, S.; Bock, C.; De Boer, A.S.; Kiskinis, E.; Meissner, A.; Eggan, K. Erosion of dosage compensation impacts human iPSC disease modeling. Cell Stem Cell 2012, 10, 595–609. [Google Scholar] [CrossRef]
  269. Yue, M.; Charles Richard, J.L.; Yamada, N.; Ogawa, A.; Ogawa, Y. Quick fluorescent in situ hybridization protocol for Xist RNA combined with immunofluorescence of histone modification in X-chromosome inactivation. J. Vis. Exp. 2014, 93, e52053. [Google Scholar] [CrossRef]
  270. Deng, Q.; Ramsköld, D.; Reinius, B.; Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 2014, 343, 193–196. [Google Scholar] [CrossRef]
  271. Zhang, Y.; Pak, C.H.; Han, Y.; Ahlenius, H.; Zhang, Z.; Chanda, S.; Marro, S.; Patzke, C.; Acuna, C.; Covy, J.; et al. Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron 2013, 78, 785–798. [Google Scholar] [CrossRef]
  272. Yang, N.; Chanda, S.; Marro, S.; Ng, Y.H.; Janas, J.A.; Haag, D.; Ang, C.E.; Tang, Y.; Flores, Q.; Mall, M.; et al. Generation of pure GABAergic neurons by transcription factor programming. Nat. Methods 2017, 14, 621–628. [Google Scholar] [CrossRef] [PubMed]
  273. Theka, I.; Caiazzo, M.; Dvoretskova, E.; Leo, D.; Ungaro, F.; Curreli, S.; Managò, F.; Dell’Anno, M.T.; Pezzoli, G.; Gainetdinov, R.R.; et al. Rapid Generation of Functional Dopaminergic Neurons From Human Induced Pluripotent Stem Cells Through a Single-Step Procedure Using Cell Lineage Transcription Factors. Stem Cells Transl. Med. 2013, 2, 473–479. [Google Scholar] [CrossRef] [PubMed]
  274. Vadodaria, K.C.; Mertens, J.; Paquola, A.; Bardy, C.; Li, X.; Jappelli, R.; Fung, L.; Marchetto, M.C.; Hamm, M.; Gorris, M.; et al. Generation of functional human serotonergic neurons from fibroblasts. Mol. Psychiatry 2016, 21, 49–61. [Google Scholar] [CrossRef]
  275. Goto, K.; Imamura, K.; Komatsu, K.; Mitani, K.; Aiba, K.; Nakatsuji, N.; Inoue, M.; Kawata, A.; Yamashita, H.; Takahashi, R.; et al. Simple Derivation of Spinal Motor Neurons from ESCs/iPSCs Using Sendai Virus Vectors. Mol. Ther. Methods Clin. Dev. 2017, 4, 115–125. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  276. Canals, I.; Ginisty, A.; Quist, E.; Timmerman, R.; Fritze, J.; Miskinyte, G.; Monni, E.; Hansen, M.G.; Hidalgo, I.; Bryder, D.; et al. Rapid and efficient induction of functional astrocytes from human pluripotent stem cells. Nat. Methods 2018, 15, 693–696. [Google Scholar] [CrossRef]
  277. Ehrlich, M.; Mozafari, S.; Glatza, M.; Starost, L.; Velychko, S.; Hallmann, A.L.; Cui, Q.L.; Schambach, A.; Kim, K.P.; Bachelin, C.; et al. Rapid and efficient generation of oligodendrocytes from human induced pluripotent stem cells using transcription factors. Proc. Natl. Acad. Sci. USA 2017, 114, E2243–E2252. [Google Scholar] [CrossRef]
  278. Quadrato, G.; Brown, J.; Arlotta, P. The promises and challenges of human brain organoids as models of neuropsychiatric disease. Nat. Med. 2016, 22, 1220–1228. [Google Scholar] [CrossRef]
  279. Liu, C.; Oikonomopoulos, A.; Sayed, N.; Wu, J.C. Modeling human diseases with induced pluripotent stem cells: From 2D to 3D and beyond. Development 2018, 145, dev156166. [Google Scholar] [CrossRef]
  280. McCauley, H.A.; Wells, J.M. Pluripotent stem cell-derived organoids: Using principles of developmental biology to grow human tissues in a dish. Development 2017, 144, 958–962. [Google Scholar] [CrossRef]
  281. Iefremova, V.; Manikakis, G.; Krefft, O.; Jabali, A.; Weynans, K.; Wilkens, R.; Marsoner, F.; Brändl, B.; Müller, F.J.; Koch, P.; et al. An Organoid-Based Model of Cortical Development Identifies Non-Cell-Autonomous Defects in Wnt Signaling Contributing to Miller-Dieker Syndrome. Cell Rep. 2017, 19, 50–59. [Google Scholar] [CrossRef]
  282. Lancaster, M.A.; Renner, M.; Martin, C.A.; Wenzel, D.; Bicknell, L.S.; Hurles, M.E.; Homfray, T.; Penninger, J.M.; Jackson, A.P.; Knoblich, J.A. Cerebral organoids model human brain development and microcephaly. Nature 2013, 501, 373–379. [Google Scholar] [CrossRef]
  283. Xiang, Y.; Cakir, B.; Park, I.H. Deconstructing and reconstructing the human brain with regionally specified brain organoids. Semin. Cell Dev. Biol. 2021, 111, 40–51. [Google Scholar] [CrossRef]
  284. Tanaka, Y.; Cakir, B.; Xiang, Y.; Sullivan, G.J.; Park, I.H. Synthetic Analyses of Single-Cell Transcriptomes from Multiple Brain Organoids and Fetal Brain. Cell Rep. 2020, 30, 1682–1689.e3. [Google Scholar] [CrossRef] [PubMed]
  285. Renner, M.; Lancaster, M.A.; Bian, S.; Choi, H.; Ku, T.; Peer, A.; Chung, K.; Knoblich, J.A. Self-organized developmental patterning and differentiation in cerebral organoids. EMBO J. 2017, 36, 1316–1329. [Google Scholar] [CrossRef] [PubMed]
  286. Mansour, A.A.; Gonçalves, J.T.; Bloyd, C.W.; Li, H.; Fernandes, S.; Quang, D.; Johnston, S.; Parylak, S.L.; Jin, X.; Gage, F.H. An in vivo model of functional and vascularized human brain organoids. Nat. Biotechnol. 2018, 36, 432–441. [Google Scholar] [CrossRef] [PubMed]
  287. Cakir, B.; Xiang, Y.; Tanaka, Y.; Kural, M.H.; Parent, M.; Kang, Y.J.; Chapeton, K.; Patterson, B.; Yuan, Y.; He, C.S.; et al. Engineering of human brain organoids with a functional vascular-like system. Nat. Methods 2019, 16, 1169–1175. [Google Scholar] [CrossRef]
  288. Pasca, A.M.; Sloan, S.A.; Clarke, L.E.; Tian, Y.; Makinson, C.D.; Huber, N.; Kim, C.H.; Park, J.Y.; O’Rourke, N.A.; Nguyen, K.D.; et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat. Methods 2015, 12, 671–678. [Google Scholar] [CrossRef] [PubMed]
  289. Giandomenico, S.L.; Mierau, S.B.; Gibbons, G.M.; Wenger, L.M.D.; Masullo, L.; Sit, T.; Sutcliffe, M.; Boulanger, J.; Tripodi, M.; Derivery, E.; et al. Cerebral organoids at the air–liquid interface generate diverse nerve tracts with functional output. Nat. Neurosci. 2019, 22, 669–679. [Google Scholar] [CrossRef]
  290. Chen, H.; Jin, X.; Li, T.; Ye, Z. Brain organoids: Establishment and application. Front. Cell Dev. Biol. 2022, 10, 1029873. [Google Scholar] [CrossRef]
  291. Marchetto, M.C.; Belinson, H.; Tian, Y.; Freitas, B.C.; Fu, C.; Vadodaria, K.C.; Beltrao-Braga, P.C.; Trujillo, C.A.; Mendes, A.P.D.; Padmanabhan, K.; et al. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Mol. Psychiatry 2017, 22, 820–835. [Google Scholar] [CrossRef]
  292. Grunwald, L.M.; Stock, R.; Haag, K.; Buckenmaier, S.; Eberle, M.C.; Wildgruber, D.; Storchak, H.; Kriebel, M.; Weißgraeber, S.; Mathew, L.; et al. Comparative characterization of human induced pluripotent stem cells (hiPSC) derived from patients with schizophrenia and autism. Transl. Psychiatry 2019, 9, 179. [Google Scholar] [CrossRef]
  293. Shcheglovitov, A.; Shcheglovitova, O.; Yazawa, M.; Portmann, T.; Shu, R.; Sebastiano, V.; Krawisz, A.; Froehlich, W.; Bernstein, J.A.; Hallmayer, J.F.; et al. SHANK3 and IGF1 restore synaptic deficits in neurons from 22q13 deletion syndrome patients. Nature 2013, 503, 267–271. [Google Scholar] [CrossRef]
  294. Russo, F.B.; Freitas, B.C.; Pignatari, G.C.; Fernandes, I.R.; Sebat, J.; Muotri, A.R.; Beltrão-Braga, P.C.B. Modeling the Interplay Between Neurons and Astrocytes in Autism Using Human Induced Pluripotent Stem Cells. Biol. Psychiatry 2018, 83, 569–578. [Google Scholar] [CrossRef]
  295. Kathuria, A.; Nowosiad, P.; Jagasia, R.; Aigner, S.; Taylor, R.D.; Andreae, L.C.; Gatford, N.J.F.; Lucchesi, W.; Srivastava, D.P.; Price, J. Stem cell-derived neurons from autistic individuals with SHANK3 mutation show morphogenetic abnormalities during early development. Mol. Psychiatry 2018, 23, 735–746. [Google Scholar] [CrossRef] [Green Version]
  296. Huang, G.; Chen, S.; Chen, X.; Zheng, J.; Xu, Z.; Doostparast Torshizi, A.; Gong, S.; Chen, Q.; Ma, X.; Yu, J.; et al. Uncovering the functional link between SHANK3 deletions and deficiency in neurodevelopment using iPSC-derived human neurons. Front. Neuroanat. 2019, 13, 23. [Google Scholar] [CrossRef]
  297. Lutz, A.K.; Pérez Arévalo, A.; Ioannidis, V.; Stirmlinger, N.; Demestre, M.; Delorme, R.; Bourgeron, T.; Boeckers, T.M. SHANK2 Mutations Result in Dysregulation of the ERK1/2 Pathway in Human Induced Pluripotent Stem Cells-Derived Neurons and Shank2(−/−) Mice. Front. Mol. Neurosci. 2021, 14, 1–15. [Google Scholar] [CrossRef] [PubMed]
  298. Zaslavsky, K.; Zhang, W.B.; McCready, F.P.; Rodrigues, D.C.; Deneault, E.; Loo, C.; Zhao, M.; Ross, P.J.; El Hajjar, J.; Romm, A.; et al. SHANK2 mutations associated with autism spectrum disorder cause hyperconnectivity of human neurons. Nat. Neurosci. 2019, 22, 556–564. [Google Scholar] [CrossRef] [PubMed]
  299. Avazzadeh, S.; McDonagh, K.; Reilly, J.; Wang, Y.; Boomkamp, S.D.; McInerney, V.; Krawczyk, J.; Fitzgerald, J.; Feerick, N.; O’Sullivan, M.; et al. Increased Ca2+ signaling in NRXN1α +/- neurons derived from ASD induced pluripotent stem cells. Mol. Autism 2019, 10, 52. [Google Scholar] [CrossRef] [PubMed]
  300. Avazzadeh, S.; Quinlan, L.R.; Reilly, J.; McDonagh, K.; Jalali, A.; Wang, Y.; McInerney, V.; Krawczyk, J.; Ding, Y.; Fitzgerald, J.; et al. NRXN1α+/- is associated with increased excitability in ASD iPSC-derived neurons. BMC Neurosci. 2021, 22, 1–14. [Google Scholar] [CrossRef] [PubMed]
  301. Kim, J.E.; O’Sullivan, M.L.; Sanchez, C.A.; Hwang, M.; Israel, M.A.; Brennand, K.; Deerinck, T.J.; Goldstein, L.S.B.; Gage, F.H.; Ellisman, M.H.; et al. Investigating synapse formation and function using human pluripotent stem cell-derived neurons. Proc. Natl. Acad. Sci. USA 2011, 108, 3005–3010. [Google Scholar] [CrossRef]
  302. Lim, C.S.; Kim, M.J.; Choi, J.E.; Islam, M.A.; Lee, Y.K.; Xiong, Y.; Shim, K.W.; Yang, J.; Lee, R.U.; Lee, J.; et al. Dysfunction of NMDA receptors in neuronal models of an autism spectrum disorder patient with a DSCAM mutation and in Dscam-knockout mice. Mol. Psychiatry 2021, 26, 7538–7549. [Google Scholar] [CrossRef]
  303. Deneault, E.; Faheem, M.; White, S.H.; Rodrigues, D.C.; Sun, S.; Wei, W.; Piekna, A.; Thompson, T.; Howe, J.L.; Chalil, L.; et al. CNTN5 -/+ or EHMT2 -/+ human iPSC-derived neurons from individuals with autism develop hyperactive neuronal networks. eLife 2019, 8, e40092. [Google Scholar] [CrossRef] [PubMed]
  304. Alsaqati, M.; Heine, V.M.; Harwood, A.J. Pharmacological intervention to restore connectivity deficits of neuronal networks derived from ASD patient iPSC with a TSC2 mutation. Mol. Autism 2020, 11, 80. [Google Scholar] [CrossRef] [PubMed]
  305. Martin, P.; Wagh, V.; Reis, S.A.; Erdin, S.; Beauchamp, R.L.; Shaikh, G.; Talkowski, M.; Thiele, E.; Sheridan, S.D.; Haggarty, S.J.; et al. TSC patient-derived isogenic neural progenitor cells reveal altered early neurodevelopmental phenotypes and rapamycin-induced MNK-eIF4E signaling. Mol. Autism 2020, 11, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  306. Li, X.; Wang, Z.; Tan, L.; Wang, Y.; Lu, C.; Chen, R.; Zhang, S.; Gao, Y.; Liu, Y.; Yin, Y.; et al. Correcting miR92a-vGAT-Mediated GABAergic Dysfunctions Rescues Human Tau-Induced Anxiety in Mice. Mol. Ther. 2017, 25, 140–152. [Google Scholar] [CrossRef]
  307. Zhuang, W.; Liu, H.; He, Z.; Ju, J.; Gao, Q.; Shan, Z.; Lei, L. miR-92a-2-5p Regulates the Proliferation and Differentiation of ASD-Derived Neural Progenitor Cells. Curr. Issues Mol. Biol. 2022, 44, 2431–2442. [Google Scholar] [CrossRef]
  308. Jung, E.-M.; Moffat, J.J.; Liu, J.; Dravid, S.M.; Gurumurthy, C.B.; Kim, W.-Y. Arid1b haploinsufficiency disrupts cortical interneuron development and mouse behavior. Nat. Neurosci. 2017, 20, 1694–1707. [Google Scholar] [CrossRef]
  309. Smith, A.L.; Jung, E.M.; Jeon, B.T.; Kim, W.Y. Arid1b haploinsufficiency in parvalbumin- or somatostatin-expressing interneurons leads to distinct ASD-like and ID-like behavior. Sci. Rep. 2020, 10, 7834. [Google Scholar] [CrossRef]
  310. Celen, C.; Chuang, J.C.; Luo, X.; Nijem, N.; Walker, A.K.; Chen, F.; Zhang, S.; Chung, A.S.; Nguyen, L.H.; Nassour, I.; et al. Arid1b haploinsufficient mice reveal neuropsychiatric phenotypes and reversible causes of growth impairment. eLife 2017, 6, e25730. [Google Scholar] [CrossRef]
  311. Ellegood, J.; Petkova, S.P.; Kinman, A.; Qiu, L.R.; Adhikari, A.; Wade, A.A.; Fernandes, D.; Lindenmaier, Z.; Creighton, A.; Nutter, L.M.J.; et al. Neuroanatomy and behavior in mice with a haploinsufficiency of AT-rich interactive domain 1B (ARID1B) throughout development. Mol. Autism 2021, 12, 25. [Google Scholar] [CrossRef]
  312. Shibutani, M.; Horii, T.; Shoji, H.; Morita, S.; Kimura, M.; Terawaki, N.; Miyakawa, T.; Hatada, I. Arid1b haploinsufficiency causes abnormal brain gene expression and autism-related behaviors in mice. Int. J. Mol. Sci. 2017, 18, 1872. [Google Scholar] [CrossRef] [PubMed]
  313. Kim, H.; Kim, D.; Cho, Y.; Kim, K.; Roh, J.D.; Kim, Y.; Yang, E.; Kim, S.S.; Ahn, S.; Kim, H.; et al. Early postnatal serotonin modulation prevents adult-stage deficits in Arid1b-deficient mice through synaptic transcriptional reprogramming. Nat. Commun. 2022, 13, 5051. [Google Scholar] [CrossRef] [PubMed]
  314. Ka, M.; Chopra, D.A.; Dravid, S.M.; Kim, W.Y. Essential Roles for ARID1B in Dendritic Arborization and Spine Morphology of Developing Pyramidal Neurons. J. Neurosci. 2016, 36, 2723–2742. [Google Scholar] [CrossRef]
  315. Weiss, K.; Lazar, H.P.; Kurolap, A.; Martinez, A.F.; Paperna, T.; Cohen, L.; Smeland, M.F.; Whalen, S.; Heide, S.; Keren, B.; et al. The CHD4-related syndrome: A comprehensive investigation of the clinical spectrum, genotype-phenotype correlations, and molecular basis. Genet. Med. 2020, 22, 389–397. [Google Scholar] [CrossRef] [PubMed]
  316. Weiss, K.; Terhal, P.A.; Cohen, L.; Bruccoleri, M.; Irving, M.; Martinez, A.F.; Rosenfeld, J.A.; Machol, K.; Yang, Y.; Liu, P.; et al. De Novo Mutations in CHD4, an ATP-Dependent Chromatin Remodeler Gene, Cause an Intellectual Disability Syndrome with Distinctive Dysmorphisms. Am. J. Hum. Genet. 2016, 99, 934–941. [Google Scholar] [CrossRef] [PubMed]
  317. Mok, R.S.F.; Zhang, W.; Sheikh, T.I.; Pradeepan, K.; Fernandes, I.R.; DeJong, L.C.; Benigno, G.; Hildebrandt, M.R.; Mufteev, M.; Rodrigues, D.C.; et al. Wide spectrum of neuronal and network phenotypes in human stem cell-derived excitatory neurons with Rett syndrome-associated MECP2 mutations. Transl. Psychiatry 2022, 12, 450. [Google Scholar] [CrossRef]
  318. Perego, S.; Alari, V.; Pietra, G.; Lamperti, A.; Vimercati, A.; Camporeale, N.; Garzo, M.; Cogliati, F.; Milani, D.; Vignoli, A.; et al. Modeling RTT Syndrome by iPSC-Derived Neurons from Male and Female Patients with Heterogeneously Severe Hot-Spot MECP2 Variants. Int. J. Mol. Sci. 2022, 23, 4491. [Google Scholar] [CrossRef]
  319. Mellios, N.; Feldman, D.A.; Sheridan, S.D.; Ip, J.P.K.; Kwok, S.; Amoah, S.K.; Rosen, B.; Rodriguez, B.A.; Crawford, B.; Swaminathan, R.; et al. MeCP2-regulated miRNAs control early human neurogenesis through differential effects on ERK and AKT signaling. Mol. Psychiatry 2018, 23, 1051–1065. [Google Scholar] [CrossRef]
  320. Samarasinghe, R.A.; Miranda, O.A.; Buth, J.E.; Mitchell, S.; Ferando, I.; Watanabe, M.; Allison, T.F.; Kurdian, A.; Fotion, N.N.; Gandal, M.J.; et al. Identification of neural oscillations and epileptiform changes in human brain organoids. Nat. Neurosci. 2021, 24, 1488–1500. [Google Scholar] [CrossRef]
  321. Gomes, A.R.; Fernandes, T.G.; Vaz, S.H.; Silva, T.P.; Bekman, E.P.; Xapelli, S.; Duarte, S.; Ghazvini, M.; Gribnau, J.; Muotri, A.R.; et al. Modeling Rett Syndrome With Human Patient-Specific Forebrain Organoids. Front. Cell Dev. Biol. 2020, 8, 610427. [Google Scholar] [CrossRef] [PubMed]
  322. Patriarchi, T.; Amabile, S.; Frullanti, E.; Landucci, E.; Lo Rizzo, C.; Ariani, F.; Costa, M.; Olimpico, F.; Hell, J.W.; Vaccarino, F.M.; et al. Imbalance of excitatory/inhibitory synaptic protein expression in iPSC-derived neurons from FOXG1 patients and in foxg1 mice. Eur. J. Hum. Genet. 2016, 24, 871–880. [Google Scholar] [CrossRef] [PubMed]
  323. Schaevitz, L.R.; Gómez, N.B.; Zhen, D.P.; Berger-Sweeney, J.E. MeCP2 R168X male and female mutant mice exhibit Rett-like behavioral deficits. Genes Brain Behav. 2013, 12, 732–740. [Google Scholar] [CrossRef]
  324. Tai, D.J.C.; Liu, Y.C.; Hsu, W.L.; Ma, Y.L.; Cheng, S.J.; Liu, S.Y.; Lee, E.H.Y. MeCP2 SUMOylation rescues Mecp2-mutant-induced behavioural deficits in a mouse model of Rett syndrome. Nat. Commun. 2016, 7, 10552. [Google Scholar] [CrossRef] [PubMed]
  325. Chapleau, C.A.; Boggio, E.M.; Calfa, G.; Percy, A.K.; Giustetto, M.; Pozzo-Miller, L. Hippocampal CA1 pyramidal neurons of Mecp2 mutant mice show a dendritic spine phenotype only in the presymptomatic stage. Neural Plast. 2012, 2012, 976164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  326. Ma, L.Y.; Wu, C.; Jin, Y.; Gao, M.; Li, G.H.; Turner, D.; Shen, J.X.; Zhang, S.J.; Narayanan, V.; Jentarra, G.; et al. Electrophysiological phenotypes of MeCP2 A140V mutant mouse model. CNS Neurosci. Ther. 2014, 20, 420–428. [Google Scholar] [CrossRef]
  327. Stearns, N.A.; Schaevitz, L.R.; Bowling, H.; Nag, N.; Berger, U.V.; Berger-Sweeney, J. Behavioral and anatomical abnormalities in Mecp2 mutant mice: A model for Rett syndrome. Neuroscience 2007, 146, 907–921. [Google Scholar] [CrossRef] [PubMed]
  328. Moretti, P.; Levenson, J.M.; Battaglia, F.; Atkinson, R.; Teague, R.; Antalffy, B.; Armstrong, D.; Arancio, O.; Sweatt, J.D.; Zoghbi, H.Y. Learning and memory and synaptic plasticity are impaired in a mouse model of Rett syndrome. J. Neurosci. 2006, 26, 319–327. [Google Scholar] [CrossRef]
  329. Chen, P.; Liu, Z.; Zhang, Q.; Lin, D.; Song, L.; Liu, J.; Jiao, H.F.; Lai, X.; Zou, S.; Wang, S.; et al. DSCAM Deficiency Leads to Premature Spine Maturation and Autism-like Behaviors. J. Neurosci. 2022, 42, 532–551. [Google Scholar] [CrossRef]
  330. Won, H.; Lee, H.R.; Gee, H.Y.; Mah, W.; Kim, J.I.; Lee, J.; Ha, S.; Chung, C.; Jung, E.S.; Cho, Y.S.; et al. Autistic-like social behaviour in Shank2-mutant mice improved by restoring NMDA receptor function. Nature 2012, 486, 261–265. [Google Scholar] [CrossRef]
  331. Morello, N.; Schina, R.; Pilotto, F.; Phillips, M.; Melani, R.; Plicato, O.; Pizzorusso, T.; Pozzo-Miller, L.; Giustetto, M. Loss of Mecp2 causes atypical synaptic and molecular plasticity of parvalbumin-expressing interneurons reflecting rett syndrome–like sensorimotor defects. eNeuro 2018, 5, ENEURO.0086-18.2018. [Google Scholar] [CrossRef]
  332. Medrihan, L.; Tantalaki, E.; Aramuni, G.; Sargsyan, V.; Dudanova, I.; Missler, M.; Zhang, W. Early defects of GABAergic synapses in the brain stem of a MeCP2 mouse model of Rett syndrome. J. Neurophysiol. 2008, 99, 112–121. [Google Scholar] [CrossRef] [PubMed]
  333. Asaka, Y.; Jugloff, D.G.M.; Zhang, L.; Eubanks, J.H.; Fitzsimonds, R.M. Hippocampal synaptic plasticity is impaired in the Mecp2-null mouse model of Rett syndrome. Neurobiol. Dis. 2006, 21, 217–227. [Google Scholar] [CrossRef] [PubMed]
  334. Wood, L.; Shepherd, G.M.G. Synaptic circuit abnormalities of motor-frontal layer 2/3 pyramidal neurons in a mutant mouse model of Rett syndrome. Neurobiol. Dis. 2010, 38, 281–287. [Google Scholar] [CrossRef] [PubMed]
  335. Dani, V.S.; Chang, Q.; Maffei, A.; Turrigiano, G.G.; Jaenisch, R.; Nelson, S.B. Reduced cortical activity due to a shift in the balance between excitation and inhibition in a mouse model of Rett syndrome. Proc. Natl. Acad. Sci. USA 2005, 102, 12560–12565. [Google Scholar] [CrossRef] [Green Version]
  336. Huo, H.Q.; Qu, Z.Y.; Yuan, F.; Ma, L.; Yao, L.; Xu, M.; Hu, Y.; Ji, J.; Bhattacharyya, A.; Zhang, S.C.; et al. Modeling Down Syndrome with Patient iPSCs Reveals Cellular and Migration Deficits of GABAergic Neurons. Stem Cell Rep. 2018, 10, 1251–1266. [Google Scholar] [CrossRef] [PubMed]
  337. Giffin-Rao, Y.; Sheng, J.; Strand, B.; Xu, K.; Huang, L.; Medo, M.; Risgaard, K.A.; Dantinne, S.; Mohan, S.; Keshan, A.; et al. Altered patterning of trisomy 21 interneuron progenitors. Stem Cell Rep. 2022, 17, 1366–1379. [Google Scholar] [CrossRef]
  338. Liu, H.; Huang, S.; Wang, W.; Wang, H.; Huang, W.; Zhai, Z.; Wang, D.; Fan, Y.; Sun, J.; Li, D.; et al. Migration deficits of the neural crest caused by CXADR triplication in a human Down syndrome stem cell model. Cell Death Dis. 2022, 13, 1018. [Google Scholar] [CrossRef]
  339. Murray, A.; Letourneau, A.; Canzonetta, C.; Stathaki, E.; Gimelli, S.; Sloan-Bena, F.; Abrehart, R.; Goh, P.; Lim, S.; Baldo, C.; et al. Brief report: Isogenic induced pluripotent stem cell lines from an adult with mosaic down syndrome model accelerated neuronal ageing and neurodegeneration. Stem Cells 2015, 33, 2077–2084. [Google Scholar] [CrossRef]
  340. Hibaoui, Y.; Grad, I.; Letourneau, A.; Sailani, M.R.; Dahoun, S.; Santoni, F.A.; Gimelli, S.; Guipponi, M.; Pelte, M.F.; Béna, F.; et al. Modelling and rescuing neurodevelopmental defect of Down syndrome using induced pluripotent stem cells from monozygotic twins discordant for trisomy 21. EMBO Mol. Med. 2014, 6, 259–277. [Google Scholar] [CrossRef]
  341. Xu, R.; Brawner, A.T.; Li, S.; Liu, J.J.; Kim, H.; Xue, H.; Pang, Z.P.; Kim, W.Y.; Hart, R.P.; Liu, Y.; et al. OLIG2 Drives Abnormal Neurodevelopmental Phenotypes in Human iPSC-Based Organoid and Chimeric Mouse Models of Down Syndrome. Cell Stem Cell 2019, 24, 908–926.e8. [Google Scholar] [CrossRef]
  342. Griesi-Oliveira, K.; Fogo, M.S.; Pinto, B.G.G.; Alves, A.Y.; Suzuki, A.M.; Morales, A.G.; Ezquina, S.; Sosa, O.J.; Sutton, G.J.; Sunaga-Franze, D.Y.; et al. Transcriptome of iPSC-derived neuronal cells reveals a module of co-expressed genes consistently associated with autism spectrum disorder. Mol. Psychiatry 2021, 26, 1589–1605. [Google Scholar] [CrossRef] [PubMed]
  343. Li, D.; Xu, J.; Yang, M.Q. Gene regulation analysis reveals perturbations of autism spectrum disorder during neural system development. Genes 2021, 12, 1901. [Google Scholar] [CrossRef] [PubMed]
  344. Zhou, Y.; Kaiser, T.; Monteiro, P.; Zhang, X.; Van der Goes, M.S.; Wang, D.; Barak, B.; Zeng, M.; Li, C.; Lu, C.; et al. Mice with Shank3 Mutations Associated with ASD and Schizophrenia Display Both Shared and Distinct Defects. Neuron 2016, 89, 147–162. [Google Scholar] [CrossRef] [PubMed]
  345. Kasem, E.; Kurihara, T.; Tabuchi, K. Neurexins and neuropsychiatric disorders. Neurosci. Res. 2018, 127, 53–60. [Google Scholar] [CrossRef]
  346. Benke, T.A.; Park, K.; Krey, I.; Camp, C.R.; Song, R.; Ramsey, A.J.; Yuan, H.; Traynelis, S.F.; Lemke, J. Clinical and therapeutic significance of genetic variation in the GRIN gene family encoding NMDARs. Neuropharmacology 2021, 199, 108805. [Google Scholar] [CrossRef] [PubMed]
  347. Bouché, N.; Fait, A.; Zik, M.; Fromm, H. The root-specific glutamate decarboxylase (GAD1) is essential for sustaining GABA levels in Arabidopsis. Plant Mol. Biol. 2004, 55, 315–325. [Google Scholar] [CrossRef]
  348. Pearson, G.; Song, C.; Hohmann, S.; Prokhorova, T.; Sheldrick-Michel, T.M.; Knöpfel, T. DNA Methylation Profiles of GAD1 in Human Cerebral Organoids of Autism Indicate Disrupted Epigenetic Regulation during Early Development. Int. J. Mol. Sci. 2022, 23, 9188. [Google Scholar] [CrossRef]
  349. Nguyen, T.A.; Wu, K.; Pandey, S.; Lehr, A.W.; Li, Y.; Bemben, M.A.; Badger, J.D.; Lauzon, J.L.; Wang, T.; Zaghloul, K.A.; et al. A Cluster of Autism-Associated Variants on X-Linked NLGN4X Functionally Resemble NLGN4Y. Neuron 2020, 106, 759–768.e7. [Google Scholar] [CrossRef]
  350. Wang, M.; Wei, P.C.; Lim, C.K.; Gallina, I.S.; Marshall, S.; Marchetto, M.C.; Alt, F.W.; Gage, F.H. Increased Neural Progenitor Proliferation in a hiPSC Model of Autism Induces Replication Stress-Associated Genome Instability. Cell Stem Cell 2020, 26, 221–233.e6. [Google Scholar] [CrossRef]
  351. de Jong, J.O.; Llapashtica, C.; Genestine, M.; Strauss, K.; Provenzano, F.; Sun, Y.; Zhu, H.; Cortese, G.P.; Brundu, F.; Brigatti, K.W.; et al. Cortical overgrowth in a preclinical forebrain organoid model of CNTNAP2-associated autism spectrum disorder. Nat. Commun. 2021, 12, 4087. [Google Scholar] [CrossRef]
  352. Wang, P.; Lin, M.; Pedrosa, E.; Hrabovsky, A.; Zhang, Z.; Guo, W.; Lachman, H.M.; Zheng, D. CRISPR/Cas9-mediated heterozygous knockout of the autism gene CHD8 and characterization of its transcriptional networks in neurodevelopment. Mol. Autism 2015, 6i, 55. [Google Scholar] [CrossRef]
  353. Wang, P.; Mokhtari, R.; Pedrosa, E.; Kirschenbaum, M.; Bayrak, C.; Zheng, D.; Lachman, H.M. CRISPR/Cas9-mediated heterozygous knockout of the autism gene CHD8 and characterization of its transcriptional networks in cerebral organoids derived from iPS cells. Mol. Autism 2017, 8, 11. [Google Scholar] [CrossRef]
  354. Haase, F.; Gloss, B.S.; Tam, P.P.L.; Gold, W.A. Wgcna identifies translational and proteasome-ubiquitin dysfunction in rett syndrome. Int. J. Mol. Sci. 2021, 22, 9954. [Google Scholar] [CrossRef]
  355. Rodrigues, D.C.; Mufteev, M.; Weatheritt, R.J.; Djuric, U.; Ha, K.C.H.; Ross, P.J.; Wei, W.; Piekna, A.; Sartori, M.A.; Byres, L.; et al. Shifts in Ribosome Engagement Impact Key Gene Sets in Neurodevelopment and Ubiquitination in Rett Syndrome. Cell Rep. 2020, 30, 4179–4196.e11. [Google Scholar] [CrossRef] [PubMed]
  356. Cheng, T.L.; Wang, Z.; Liao, Q.; Zhu, Y.; Zhou, W.H.; Xu, W.; Qiu, Z. MeCP2 Suppresses Nuclear MicroRNA Processing and Dendritic Growth by Regulating the DGCR8/Drosha Complex. Dev. Cell 2014, 28, 547–560. [Google Scholar] [CrossRef]
  357. Klein, M.E.; Lioy, D.T.; Ma, L.; Impey, S.; Mandel, G.; Goodman, R.H. Homeostatic regulation of MeCP2 expression by a CREB-induced microRNA. Nat. Neurosci. 2007, 10, 1513–1514. [Google Scholar] [CrossRef] [PubMed]
  358. Tang, X.; Kim, J.; Zhou, L.; Wengert, E.; Zhang, L.; Wu, Z.; Carromeu, C.; Muotri, A.R.; Marchetto, M.C.N.; Gage, F.H.; et al. KCC2 rescues functional deficits in human neurons derived from patients with Rett syndrome. Proc. Natl. Acad. Sci. USA 2016, 113, 751–756. [Google Scholar] [CrossRef] [PubMed]
  359. Varderidou-Minasian, S.; Hinz, L.; Hagemans, D.; Posthuma, D.; Altelaar, M.; Heine, V.M. Quantitative proteomic analysis of Rett iPSC-derived neuronal progenitors. Mol. Autism 2020, 11, 38. [Google Scholar] [CrossRef]
  360. Musi, C.A.; Castaldo, A.M.; Valsecchi, A.E.; Cimini, S.; Morello, N.; Pizzo, R.; Renieri, A.; Meloni, I.; Bonati, M.; Giustetto, M.; et al. JNK signaling provides a novel therapeutic target for Rett syndrome. BMC Biol. 2021, 19, 256. [Google Scholar] [CrossRef]
  361. Bassetti, D.; Lombardi, A.; Kirischuk, S.; Luhmann, H.J. Haploinsufficiency of Tsc2 leads to hyperexcitability of medial prefrontal cortex via weakening of tonic gabab receptor-mediated inhibition. Cereb. Cortex 2020, 30, 6313–6324. [Google Scholar] [CrossRef]
  362. Ohashi, M.; Korsakova, E.; Allen, D.; Lee, P.; Fu, K.; Vargas, B.S.; Cinkornpumin, J.; Salas, C.; Park, J.C.; Germanguz, I.; et al. Loss of MECP2 Leads to Activation of P53 and Neuronal Senescence. Stem Cell Rep. 2018, 10, 1453–1463. [Google Scholar] [CrossRef] [PubMed]
  363. Landucci, E.; Brindisi, M.; Bianciardi, L.; Catania, L.M.; Daga, S.; Croci, S.; Frullanti, E.; Fallerini, C.; Butini, S.; Brogi, S.; et al. iPSC-derived neurons profiling reveals GABAergic circuit disruption and acetylated α-tubulin defect which improves after iHDAC6 treatment in Rett syndrome. Exp. Cell Res. 2018, 368, 225–235. [Google Scholar] [CrossRef] [PubMed]
  364. Xiang, Y.; Tanaka, Y.; Patterson, B.; Hwang, S.M.; Hysolli, E.; Cakir, B.; Kim, K.Y.; Wang, W.; Kang, Y.J.; Clement, E.M.; et al. Dysregulation of BRD4 Function Underlies the Functional Abnormalities of MeCP2 Mutant Neurons. Mol. Cell 2020, 79, 84–98.e9. [Google Scholar] [CrossRef]
  365. Meharena, H.S.; Marco, A.; Dileep, V.; Lockshin, E.R.; Akatsu, G.Y.; Mullahoo, J.; Watson, L.A.; Ko, T.; Guerin, L.N.; Abdurrob, F.; et al. Down-syndrome-induced senescence disrupts the nuclear architecture of neural progenitors. Cell Stem Cell 2022, 29, 116–130.e7. [Google Scholar] [CrossRef]
  366. Hirata, K.; Nambara, T.; Kawatani, K.; Nawa, N.; Yoshimatsu, H.; Kusakabe, H.; Banno, K.; Nishimura, K.; Ohtaka, M.; Nakanishi, M.; et al. 4-Phenylbutyrate ameliorates apoptotic neural cell death in Down syndrome by reducing protein aggregates. Sci. Rep. 2020, 10, 14047. [Google Scholar] [CrossRef] [PubMed]
  367. Sobol, M.; Klar, J.; Laan, L.; Shahsavani, M.; Schuster, J.; Annerén, G.; Konzer, A.; Mi, J.; Bergquist, J.; Nordlund, J.; et al. Transcriptome and Proteome Profiling of Neural Induced Pluripotent Stem Cells from Individuals with Down Syndrome Disclose Dynamic Dysregulations of Key Pathways and Cellular Functions. Mol. Neurobiol. 2019, 56, 7113–7127. [Google Scholar] [CrossRef] [PubMed]
  368. Gonzales, P.K.; Roberts, C.M.; Fonte, V.; Jacobsen, C.; Stein, G.H.; Link, C.D. Transcriptome analysis of genetically matched human induced pluripotent stem cells disomic or trisomic for chromosome 21. PLoS ONE 2018, 13, e0194581. [Google Scholar] [CrossRef]
  369. El-Kordi, A.; Winkler, D.; Hammerschmidt, K.; Kästner, A.; Krueger, D.; Ronnenberg, A.; Ritter, C.; Jatho, J.; Radyushkin, K.; Bourgeron, T.; et al. Development of an autism severity score for mice using Nlgn4 null mutants as a construct-valid model of heritable monogenic autism. Behav. Brain Res. 2013, 251, 41–49. [Google Scholar] [CrossRef]
  370. Delattre, V.; La Mendola, D.; Meystre, J.; Markram, H.; Markram, K. Nlgn4 knockout induces network hypo-excitability in juvenile mouse somatosensory cortex in vitro. Sci. Rep. 2013, 3, 2897. [Google Scholar] [CrossRef]
  371. Unichenko, P.; Yang, J.W.; Kirischuk, S.; Kolbaev, S.; Kilb, W.; Hammer, M.; Krueger-Burg, D.; Brose, N.; Luhmann, H.J. Autism related neuroligin-4 knockout impairs intracortical processing but not sensory inputs in mouse barrel cortex. Cereb. Cortex 2018, 28, 2873–2886. [Google Scholar] [CrossRef]
  372. Ju, A.; Hammerschmidt, K.; Tantra, M.; Krueger, D.; Brose, N.; Ehrenreich, H. Juvenile manifestation of ultrasound communication deficits in the neuroligin-4 null mutant mouse model of autism. Behav. Brain Res. 2014, 270, 159–164. [Google Scholar] [CrossRef]
  373. Hammer, M.; Krueger-Burg, D.; Tuffy, L.P.; Cooper, B.H.; Taschenberger, H.; Goswami, S.P.; Ehrenreich, H.; Jonas, P.; Varoqueaux, F.; Rhee, J.S.; et al. Perturbed Hippocampal Synaptic Inhibition and γ-Oscillations in a Neuroligin-4 Knockout Mouse Model of Autism. Cell Rep. 2015, 13, 516–523. [Google Scholar] [CrossRef]
  374. Krueger, D.A.; Northrup, H.; Krueger, D.A.; Roberds, S.; Smith, K.; Sampson, J.; Korf, B.; Kwiatkowski, D.J.; Mowat, D.; Nellist, M.; et al. Tuberous sclerosis complex surveillance and management: Recommendations of the 2012 international tuberous sclerosis complex consensus conference. Pediatr. Neurol. 2013, 49, 255–265. [Google Scholar] [CrossRef] [PubMed]
  375. Waltereit, R.; Japs, B.; Schneider, M.; De Vries, P.J.; Bartsch, D. Epilepsy and Tsc2 haploinsufficiency lead to autistic-like social deficit behaviors in rats. Behav. Genet. 2011, 41, 364–372. [Google Scholar] [CrossRef]
  376. Neklyudova, A.; Smirnov, K.; Rebreikina, A.; Martynova, O.; Sysoeva, O. Electrophysiological and Behavioral Evidence for Hyper-and Hyposensitivity in Rare Genetic Syndromes Associated with Autism. Genes 2022, 13, 671. [Google Scholar] [CrossRef]
  377. Sundberg, M.; Tochitsky, I.; Buchholz, D.E.; Winden, K.; Kujala, V.; Kapur, K.; Cataltepe, D.; Turner, D.; Han, M.J.; Woolf, C.J.; et al. Purkinje cells derived from TSC patients display hypoexcitability and synaptic deficits associated with reduced FMRP levels and reversed by rapamycin. Mol. Psychiatry 2018, 23, 2167–2183. [Google Scholar] [CrossRef]
  378. Ehninger, D.; Han, S.; Shilyansky, C.; Zhou, Y.; Li, W.; Kwiatkowski, D.J.; Ramesh, V.; Silva, A.J. Reversal of learning deficits in a Tsc2+/- mouse model of tuberous sclerosis. Nat. Med. 2008, 14, 843–848. [Google Scholar] [CrossRef] [PubMed]
  379. Wundrach, D.; Martinetti, L.E.; Stafford, A.M.; Bilinovich, S.M.; Angara, K.; Prokop, J.W.; Crandall, S.R.; Vogt, D. A Human TSC1 Variant Screening Platform in Gabaergic Cortical Interneurons for Genotype to Phenotype Assessments. Front. Mol. Neurosci. 2020, 13, 1–10. [Google Scholar] [CrossRef]
  380. Moore, S.M.; Seidman, J.S.; Ellegood, J.; Gao, R.; Savchenko, A.; Troutman, T.D.; Abe, Y.; Stender, J.; Lee, D.; Wang, S.; et al. Setd5 haploinsufficiency alters neuronal network connectivity and leads to autistic-like behaviors in mice. Transl. Psychiatry 2019, 9, 24. [Google Scholar] [CrossRef]
  381. Deliu, E.; Arecco, N.; Morandell, J.; Dotter, C.P.; Contreras, X.; Girardot, C.; Käsper, E.L.; Kozlova, A.; Kishi, K.; Chiaradia, I.; et al. Haploinsufficiency of the intellectual disability gene SETD5 disturbs developmental gene expression and cognition. Nat. Neurosci. 2018, 21, 1717–1727. [Google Scholar] [CrossRef] [PubMed]
  382. Kim, K.Y.; Hysolli, E.; Park, I.H. Neuronal maturation defect in induced pluripotent stem cells from patients with Rett syndrome. Proc. Natl. Acad. Sci. USA 2011, 108, 14169–14174. [Google Scholar] [CrossRef]
  383. Marchetto, M.C.N.; Carromeu, C.; Acab, A.; Yu, D.; Yeo, G.W.; Mu, Y.; Chen, G.; Gage, F.H.; Muotri, A.R. A model for neural development and treatment of rett syndrome using human induced pluripotent stem cells. Cell 2010, 143, 527–539. [Google Scholar] [CrossRef]
  384. Cheung, A.Y.L.; Horvath, L.M.; Grafodatskaya, D.; Pasceri, P.; Weksberg, R.; Hotta, A.; Carrel, L.; Ellis, J. Isolation of MECP2-null Rett Syndrome patient hiPS cells and isogenic controls through X-chromosome inactivation. Hum. Mol. Genet. 2011, 20, 2103–2115. [Google Scholar] [CrossRef]
  385. Chen, X.; Han, X.; Blanchi, B.; Guan, W.; Ge, W.; Yu, Y.C.; Sun, Y.E. Graded and pan-neural disease phenotypes of Rett Syndrome linked with dosage of functional MeCP2. Protein Cell 2021, 12, 639–652. [Google Scholar] [CrossRef] [PubMed]
  386. Chin, E.W.M.; Marcy, G.; Yoon, S.I.; Ma, D.; Rosales, F.J.; Augustine, G.J.; Goh, E.L.K. Choline Ameliorates Disease Phenotypes in Human iPSC Models of Rett Syndrome. NeuroMolecular Med. 2016, 18, 364–377. [Google Scholar] [CrossRef] [PubMed]
  387. Ananiev, G.; Williams, E.C.; Li, H.; Chang, Q. Isogenic pairs of wild type and mutant induced pluripotent stem cell (iPSC) lines from rett syndrome patients as In Vitro disease model. PLoS ONE 2011, 6, e25255. [Google Scholar] [CrossRef]
  388. Djuric, U.; Cheung, A.Y.L.; Zhang, W.; Mok, R.S.; Lai, W.; Piekna, A.; Hendry, J.A.; Ross, P.J.; Pasceri, P.; Kim, D.S.; et al. MECP2e1 isoform mutation affects the form and function of neurons derived from Rett syndrome patient iPS cells. Neurobiol. Dis. 2015, 76, 37–45. [Google Scholar] [CrossRef]
  389. Bu, Q.; Wang, A.; Hamzah, H.; Waldman, A.; Jiang, K.; Dong, Q.; Li, R.; Kim, J.; Turner, D.; Chang, Q. CREB signaling is involved in rett syndrome pathogenesis. J. Neurosci. 2017, 37, 3671–3685. [Google Scholar] [CrossRef] [PubMed]
  390. Santos, M.; Silva-Fernandes, A.; Oliveira, P.; Sousa, N.; Maciel, P. Evidence for abnormal early development in a mouse model of Rett syndrome. Genes, Brain Behav. 2007, 6, 277–286. [Google Scholar] [CrossRef]
  391. Rietveld, L.; Stuss, D.P.; McPhee, D.; Delaney, K.R. Genotype-specific effects of Mecp2 loss-of-function on morphology of layer V pyramidal neurons in heterozygous female Rett syndrome model mice. Front. Cell. Neurosci. 2015, 9, 145. [Google Scholar] [CrossRef] [PubMed]
  392. Fukuda, T.; Itoh, M.; Ichikawa, T.; Washiyama, K.; Goto, Y.I. Delayed maturation of neuronal architecture and synaptogenesis in cerebral cortex of Mecp2-deficient mice. J. Neuropathol. Exp. Neurol. 2005, 64, 537–544. [Google Scholar] [CrossRef] [PubMed]
  393. Torres-Pérez, J.V.; Martínez-Rodríguez, E.; Forte, A.; Blanco-Gómez, C.; Stork, O.; Lanuza, E.; Santos, M.; Agustín-Pavón, C. Early life stress exacerbates behavioural and neuronal alterations in adolescent male mice lacking methyl-CpG binding protein 2 (Mecp2). Front. Behav. Neurosci. 2022, 16, 974692. [Google Scholar] [CrossRef]
  394. Fidler, D.J.; Prince, M.A.; Van Deusen, K.; Esbensen, A.J.; Thurman, A.J.; Abbeduto, L.; Patel, L.; Mervis, C.; Schworer, E.K.; Lee, N.R.; et al. Latent profiles of autism symptoms in children and adolescents with Down syndrome. J. Intellect. Disabil. Res. 2022, 66, 265–281. [Google Scholar] [CrossRef] [PubMed]
  395. Carter, J.C.; Capone, G.T.; Kaufmann, W.E. Neuroanatomic correlates of autism and stereotypy in children with Down syndrome. Neuroreport 2008, 19, 653–656. [Google Scholar] [CrossRef]
  396. Larsen, K.B.; Laursen, H.; Græm, N.; Samuelsen, G.B.; Bogdanovic, N.; Pakkenberg, B. Reduced cell number in the neocortical part of the human fetal brain in Down syndrome. Ann. Anat. 2008, 190, 421–427. [Google Scholar] [CrossRef] [PubMed]
  397. Utagawa, E.C.; Moreno, D.G.; Schafernak, K.T.; Arva, N.C.; Malek-Ahmadi, M.H.; Mufson, E.J.; Perez, S.E. Neurogenesis and neuronal differentiation in the postnatal frontal cortex in Down syndrome. Acta Neuropathol. Commun. 2022, 10, 86. [Google Scholar] [CrossRef] [PubMed]
  398. Kesslak, J.P.; Nagata, S.F.; Lott, I.; Nalcioglu, O. Magnetic resonance imaging analysis of age–related changes in the brains of individuals with down’s syndrome. Neurology 1994, 44, 1039–1045. [Google Scholar] [CrossRef]
  399. Kobayashi, S.; Tanaka, T.; Soeda, Y.; Almeida, O.F.X.; Takashima, A. Local Somatodendritic Translation and Hyperphosphorylation of Tau Protein Triggered by AMPA and NMDA Receptor Stimulation. EBioMedicine 2017, 20, 120–126. [Google Scholar] [CrossRef]
  400. Bayona-Bafaluy, M.P.; Garrido-Pérez, N.; Meade, P.; Iglesias, E.; Jiménez-Salvador, I.; Montoya, J.; Martínez-Cué, C.; Ruiz-Pesini, E. Down syndrome is an oxidative phosphorylation disorder. Redox Biol. 2021, 41, 101871. [Google Scholar] [CrossRef]
  401. Uguagliati, B.; Stagni, F.; Emili, M.; Giacomini, A.; Russo, C.; Guidi, S.; Bartesaghi, R. Early Appearance of Dendritic Alterations in Neocortical Pyramidal Neurons of the Ts65Dn Model of Down Syndrome. Dev. Neurosci. 2022, 44, 23–38. [Google Scholar] [CrossRef]
  402. Alldred, M.J.; Lee, S.H.; Petkova, E.; Ginsberg, S.D. Expression profile analysis of vulnerable CA1 pyramidal neurons in young-middle-aged Ts65Dn mice. J. Comp. Neurol. 2015, 523, 61–74. [Google Scholar] [CrossRef] [PubMed]
  403. Chakrabarti, L.; Best, T.K.; Cramer, N.P.; Carney, R.S.E.; Isaac, J.T.R.; Galdzicki, Z.; Haydar, T.F. Olig1 and Olig2 triplication causes developmental brain defects in Down syndrome. Nat. Neurosci. 2010, 13, 927–934. [Google Scholar] [CrossRef] [PubMed]
  404. Belichenko, P.V.; Kleschevnikov, A.M.; Masliah, E.; Wu, C.; Takimoto-Kimura, R.; Salehi, A.; Mobley, W.C. Excitatory-inhibitory relationship in the fascia dentata in the Ts65Dn mouse model of down syndrome. J. Comp. Neurol. 2009, 512, 453–466. [Google Scholar] [CrossRef]
  405. Pérez-Cremades, D.; Hernández, S.; Blasco-Ibáñez, J.M.; Crespo, C.; Nacher, J.; Varea, E. Alteration of inhibitory circuits in the somatosensory cortex of Ts65Dn mice, a model for Down’s syndrome. J. Neural Transm. 2010, 117, 445–455. [Google Scholar] [CrossRef] [Green Version]
  406. Weick, J.P.; Held, D.L.; Bonadurer, G.F.; Doers, M.E.; Liu, Y.; Maguire, C.; Clark, A.; Knackert, J.A.; Molinarolo, K.; Musser, M.; et al. Deficits in human trisomy 21 iPSCs and neurons. Proc. Natl. Acad. Sci. USA 2013, 110, 9962–9967. [Google Scholar] [CrossRef]
  407. Mizuno, G.O.; Wang, Y.; Shi, G.; Wang, Y.; Sun, J.; Papadopoulos, S.; Broussard, G.J.; Unger, E.K.; Deng, W.; Weick, J.; et al. Aberrant Calcium Signaling in Astrocytes Inhibits Neuronal Excitability in a Human Down Syndrome Stem Cell Model. Cell Rep. 2018, 24, 355–365. [Google Scholar] [CrossRef]
  408. Bhattacharyya, A.; McMillan, E.; Chen, S.I.; Wallace, K.; Svendsen, C.N. A critical period in cortical interneuron neurogenesis in down syndrome revealed by human neural progenitor cells. Dev. Neurosci. 2009, 31, 497–510. [Google Scholar] [CrossRef]
  409. Perluigi, M.; Di Domenico, F.; Buttterfield, D.A. Unraveling the complexity of neurodegeneration in brains of subjects with Down syndrome: Insights from proteomics. Proteom.-Clin. Appl. 2014, 8, 73–85. [Google Scholar] [CrossRef]
  410. Perluigi, M.; di Domenico, F.; Fiorini, A.; Cocciolo, A.; Giorgi, A.; Foppoli, C.; Butterfield, D.A.; Giorlandino, M.; Giorlandino, C.; Eugenia Schininà, M.; et al. Oxidative stress occurs early in Down syndrome pregnancy: A redox proteomics analysis of amniotic fluid. Proteom.-Clin. Appl. 2011, 5, 167–178. [Google Scholar] [CrossRef] [PubMed]
  411. Corrales, A.; Parisotto, E.B.; Vidal, V.; García-Cerro, S.; Lantigua, S.; Diego, M.; Wilhem Filho, D.; Sanchez-Barceló, E.J.; Martínez-Cué, C.; Rueda, N. Pre- and post-natal melatonin administration partially regulates brain oxidative stress but does not improve cognitive or histological alterations in the Ts65Dn mouse model of Down syndrome. Behav. Brain Res. 2017, 334, 142–154. [Google Scholar] [CrossRef]
  412. Tang, X.Y.; Xu, L.; Wang, J.; Hong, Y.; Wang, Y.; Zhu, Q.; Wang, D.; Zhang, X.Y.; Liu, C.Y.; Fang, K.H.; et al. DSCAM/PAK1 pathway suppression reverses neurogenesis deficits in iPSC-derived cerebral organoids from patients with down syndrome. J. Clin. Investig. 2021, 131, e135763. [Google Scholar] [CrossRef]
  413. Qiu, J.J.; Liu, Y.N.; Ren, Z.R.; Yan, J.B. Dysfunctions of mitochondria in close association with strong perturbation of long noncoding RNAs expression in down syndrome. Int. J. Biochem. Cell Biol. 2017, 92, 115–120. [Google Scholar] [CrossRef] [PubMed]
  414. Mollo, N.; Esposito, M.; Aurilia, M.; Scognamiglio, R.; Accarino, R.; Bonfiglio, F.; Cicatiello, R.; Charalambous, M.; Procaccini, C.; Micillo, T.; et al. Human trisomic ipscs from down syndrome fibroblasts manifest mitochondrial alterations early during neuronal differentiation. Biology 2021, 10, 609. [Google Scholar] [CrossRef]
  415. Xu, L.; Huo, H.Q.; Lu, K.Q.; Tang, X.Y.; Hong, Y.; Han, X.; Fu, Z.X.; Fang, K.H.; Xu, M.; Guo, X.; et al. Abnormal mitochondria in Down syndrome iPSC-derived GABAergic interneurons and organoids. Biochim. Biophys. Acta-Mol. Basis Dis. 2022, 1868, 166388. [Google Scholar] [CrossRef] [PubMed]
  416. Laan, L.; Klar, J.; Sobol, M.; Hoeber, J.; Shahsavani, M.; Kele, M.; Fatima, A.; Zakaria, M.; Annerén, G.; Falk, A.; et al. DNA methylation changes in down syndrome derived neural iPSCs uncover co-dysregulation of ZNF and HOX3 families of transcription factors. Clin. Epigenet. 2020, 12, 9. [Google Scholar] [CrossRef] [PubMed]
  417. Casañas, J.J.; González-Corrales, M.; Urbano-Gámez, J.D.; Alves-Sampaio, A.; Troca-Marín, J.A.; Montesinos, M.L. CPEB1 is overexpressed in neurons derived from Down syndrome IPSCs and in the hippocampus of the mouse model Ts1Cje. Mol. Cell. Neurosci. 2019, 95, 79–85. [Google Scholar] [CrossRef]
  418. Wu, C.I.; Vinton, E.A.; Pearse, R.V.; Heo, K.; Aylward, A.J.; Hsieh, Y.C.; Bi, Y.; Adeleye, S.; Fancher, S.; Duong, D.M.; et al. APP and DYRK1A regulate axonal and synaptic vesicle protein networks and mediate Alzheimer’s pathology in trisomy 21 neurons. Mol. Psychiatry 2022, 27, 1970–1989. [Google Scholar] [CrossRef]
  419. Czermiński, J.T.; Lawrence, J.B. Silencing Trisomy 21 with XIST in Neural Stem Cells Promotes Neuronal Differentiation. Dev. Cell 2020, 52, 294–308.e3. [Google Scholar] [CrossRef]
  420. Paşca, S.P.; Portmann, T.; Voineagu, I.; Yazawa, M.; Shcheglovitov, A.; Paşca, A.M.; Cord, B.; Palmer, T.D.; Chikahisa, S.; Nishino, S.; et al. Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome. Nat. Med. 2011, 17, 1657–1662. [Google Scholar] [CrossRef] [PubMed]
  421. Lee, C.-T.; Freed, W.J.; Mash, D.C. CNVs in neurodevelopmental disorders. Oncotarget 2015, 6, 18238–18239. [Google Scholar] [CrossRef]
  422. Soeda, S.; Saito, R.; Fujita, N.; Fukuta, K.; Taniura, H. Neuronal differentiation defects in induced pluripotent stem cells derived from a Prader-Willi syndrome patient. Neurosci. Lett. 2019, 703, 162–167. [Google Scholar] [CrossRef]
  423. Soeda, S.; Saito, R.; Fujii, A.; Tojo, S.; Tokumura, Y.; Taniura, H. Abnormal DNA methylation in pluripotent stem cells from a patient with Prader-Willi syndrome results in neuronal differentiation defects. Stem Cell Res. 2021, 53, 102351. [Google Scholar] [CrossRef]
  424. Fink, J.J.; Robinson, T.M.; Germain, N.D.; Sirois, C.L.; Bolduc, K.A.; Ward, A.J.; Rigo, F.; Chamberlain, S.J.; Levine, E.S. Disrupted neuronal maturation in Angelman syndrome-derived induced pluripotent stem cells. Nat. Commun. 2017, 8, 15038. [Google Scholar] [CrossRef] [PubMed]
  425. Fink, J.J.; Schreiner, J.D.; Bloom, J.E.; James, J.; Baker, D.S.; Robinson, T.M.; Lieberman, R.; Loew, L.M.; Chamberlain, S.J.; Levine, E.S. Hyperexcitable Phenotypes in Induced Pluripotent Stem Cell–Derived Neurons From Patients With 15q11-q13 Duplication Syndrome, a Genetic Form of Autism. Biol. Psychiatry 2021, 90, 756–765. [Google Scholar] [CrossRef] [PubMed]
  426. Deshpande, A.; Yadav, S.; Dao, D.Q.; Wu, Z.Y.; Hokanson, K.C.; Cahill, M.K.; Wiita, A.P.; Jan, Y.N.; Ullian, E.M.; Weiss, L.A. Cellular Phenotypes in Human iPSC-Derived Neurons from a Genetic Model of Autism Spectrum Disorder. Cell Rep. 2017, 21, 2678–2687. [Google Scholar] [CrossRef] [PubMed]
  427. Klingler, F.M.; Gastreich, M.; Grygorenko, O.O.; Savych, O.; Borysko, P.; Griniukova, A.; Gubina, K.E.; Lemmen, C.; Moroz, Y.S. SAR by space: Enriching hit sets from the chemical space. Molecules 2019, 24, 3096. [Google Scholar] [CrossRef]
  428. Odawara, A.; Matsuda, N.; Ishibashi, Y.; Yokoi, R.; Suzuki, I. Toxicological evaluation of convulsant and anticonvulsant drugs in human induced pluripotent stem cell-derived cortical neuronal networks using an MEA system. Sci. Rep. 2018, 8, 10416. [Google Scholar] [CrossRef] [PubMed]
  429. Sirenko, O.; Parham, F.; Dea, S.; Sodhi, N.; Biesmans, S.; Mora-Castilla, S.; Ryan, K.; Behl, M.; Chandy, G.; Crittenden, C.; et al. Functional and mechanistic neurotoxicity profiling using human iPSC-Derived neural 3D cultures. Toxicol. Sci. 2019, 167, 249–257. [Google Scholar] [CrossRef]
  430. Darville, H.; Poulet, A.; Rodet-Amsellem, F.; Chatrousse, L.; Pernelle, J.; Boissart, C.; Héron, D.; Nava, C.; Perrier, A.; Jarrige, M.; et al. Human Pluripotent Stem Cell-derived Cortical Neurons for High Throughput Medication Screening in Autism: A Proof of Concept Study in SHANK3 Haploinsufficiency Syndrome. eBioMedicine 2016, 9, 293–305. [Google Scholar] [CrossRef]
  431. Dickstein, D.P.; Towbin, K.E.; Van Der Veen, J.W.; Rich, B.A.; Brotman, M.A.; Knopf, L.; Onelio, L.; Pine, D.S.; Leibenluft, E. Randomized double-blind placebo-controlled trial of lithium in youths with severe mood dysregulation. J. Child Adolesc. Psychopharmacol. 2009, 19, 61–73. [Google Scholar] [CrossRef] [PubMed]
  432. O’Leary, H.M.; Kaufmann, W.E.; Barnes, K.V.; Rakesh, K.; Kapur, K.; Tarquinio, D.C.; Cantwell, N.G.; Roche, K.J.; Rose, S.A.; Walco, A.C.; et al. Placebo-controlled crossover assessment of mecasermin for the treatment of Rett syndrome. Ann. Clin. Transl. Neurol. 2018, 5, 323–332. [Google Scholar] [CrossRef] [PubMed]
  433. Devesa, J.; Devesa, O.; Carrillo, M.; Casteleiro, N.; Devesa, A.; Llorente, D.; González, C. Rett Syndrome: Treatment with IGF-I, Melatonin, Blackcurrant Extracts, and Rehabilitation. Reports 2018, 1, 14. [Google Scholar] [CrossRef]
  434. Linker, S.B.; Mendes, A.P.D.; Marchetto, M.C.; Marchetto, M.C. IGF-1 treatment causes unique transcriptional response in neurons from individuals with idiopathic autism. Mol. Autism 2020, 11, 55. [Google Scholar] [CrossRef]
  435. Glaze, D.G.; Neul, J.L.; Kaufmann, W.E.; Berry-Kravis, E.; Condon, S.; Stoms, G.; Oosterholt, S.; Della Pasqua, O.; Glass, L.; Jones, N.E.; et al. Double-blind, randomized, placebo-controlled study of trofinetide in pediatric Rett syndrome. Neurology 2019, 92, E1912–E1925. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  436. Neul, J.L.; Percy, A.K.; Benke, T.A.; Berry-Kravis, E.M.; Glaze, D.G.; Peters, S.U.; Jones, N.E.; Youakim, J.M. Design and outcome measures of LAVENDER, a phase 3 study of trofinetide for Rett syndrome. Contemp. Clin. Trials 2022, 114, 106704. [Google Scholar] [CrossRef]
  437. Croci, S.; Carriero, M.L.; Capitani, K.; Daga, S.; Donati, F.; Papa, F.T.; Frullanti, E.; Lopergolo, D.; Lamacchia, V.; Tita, R.; et al. AAV-mediated FOXG1 gene editing in human Rett primary cells. Eur. J. Hum. Genet. 2020, 28, 1446–1458. [Google Scholar] [CrossRef] [PubMed]
  438. Gadalla, K.K.E.; Vudhironarit, T.; Hector, R.D.; Sinnett, S.; Bahey, N.G.; Bailey, M.E.S.; Gray, S.J.; Cobb, S.R. Development of a Novel AAV Gene Therapy Cassette with Improved Safety Features and Efficacy in a Mouse Model of Rett Syndrome. Mol. Ther. Methods Clin. Dev. 2017, 5, 180–190. [Google Scholar] [CrossRef]
  439. Trujillo, C.A.; Adams, J.W.; Negraes, P.D.; Carromeu, C.; Tejwani, L.; Acab, A.; Tsuda, B.; Thomas, C.A.; Sodhi, N.; Fichter, K.M.; et al. Pharmacological reversal of synaptic and network pathology in human MECP2 -KO neurons and cortical organoids. EMBO Mol. Med. 2021, 13, e12523. [Google Scholar] [CrossRef]
  440. Paganoni, S.; Macklin, E.A.; Hendrix, S.; Berry, J.D.; Elliott, M.A.; Maiser, S.; Karam, C.; Caress, J.B.; Owegi, M.A.; Quick, A.; et al. Trial of Sodium Phenylbutyrate–Taurursodiol for Amyotrophic Lateral Sclerosis. N. Engl. J. Med. 2020, 383, 919–930. [Google Scholar] [CrossRef]
Figure 1. NDD-causing gene variants are often responsible for homeostatic imbalances in protein synthesis early in development, in many cases specifically through impairment of phosphatidylinositol 3-kinase (PI3K)-mammalian target of rapamycin (mTOR) pathway [37,38,39,40]. mTOR is a serine/threonine kinase composed by two complexes (mTORC1 and mTORC2) responsible for cellular metabolism. Hyperactivation of mTOR due to variants affecting negative regulators of the PI3K-mTOR growth factor pathway (such as hamartin (TSC1), tuberin (TSC2) and PTEN) has already been reported in NDD-dominant patients with ASD, ID and epilepsy [41,42]. Other NDD-causing genes belong to the transcriptional regulators and chromatin remodelers category [43], which is the case of methyl CpG binding protein 2 (MECP2), chromodomain helicase DNA binding (CHD) [44,45], AT-rich interactive domain-containing protein 1B (ARID1B) [46] and SET-domain containing 5 (SETD5) [47]. Due to the multiplicity of targets, dysregulation of these epigenetic effectors might mediate several disease phenotypes [44,45,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. Among the embryonically expressed genes shown to be altered in NDDs, T-box brain transcription factor 1 (TBR1) [48,49,50,51,52], dual specificity tyrosine phosphorylation regulated kinase 1A (DYRK1A) [53,54,55,56,57,58,59,60,61] and phosphate and tensin homolog (PTEN) [62,63,64,65,66] are the ones in which gene variants are most commonly reported. Another key contributor for brain function and homeostasis is synaptic signaling, responsible for the communication between cells forming neuronal circuits. Loss-of-function mutations in neurexin 1 (NRXN1) [69,70,71,72,73,74,75,76,77,78,79], in neuroligins (NLGN) such as NLGN3 and NLGN4 genes [67,68,80,81,82,83,84,85] and in SH3 and multiple ankyrin repeat domain (SHANK) proteins encoding genes SHANK1, SHANK2 and SHANK3 have been widely associated with ASD and ID [86,87,88,89].
Figure 1. NDD-causing gene variants are often responsible for homeostatic imbalances in protein synthesis early in development, in many cases specifically through impairment of phosphatidylinositol 3-kinase (PI3K)-mammalian target of rapamycin (mTOR) pathway [37,38,39,40]. mTOR is a serine/threonine kinase composed by two complexes (mTORC1 and mTORC2) responsible for cellular metabolism. Hyperactivation of mTOR due to variants affecting negative regulators of the PI3K-mTOR growth factor pathway (such as hamartin (TSC1), tuberin (TSC2) and PTEN) has already been reported in NDD-dominant patients with ASD, ID and epilepsy [41,42]. Other NDD-causing genes belong to the transcriptional regulators and chromatin remodelers category [43], which is the case of methyl CpG binding protein 2 (MECP2), chromodomain helicase DNA binding (CHD) [44,45], AT-rich interactive domain-containing protein 1B (ARID1B) [46] and SET-domain containing 5 (SETD5) [47]. Due to the multiplicity of targets, dysregulation of these epigenetic effectors might mediate several disease phenotypes [44,45,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. Among the embryonically expressed genes shown to be altered in NDDs, T-box brain transcription factor 1 (TBR1) [48,49,50,51,52], dual specificity tyrosine phosphorylation regulated kinase 1A (DYRK1A) [53,54,55,56,57,58,59,60,61] and phosphate and tensin homolog (PTEN) [62,63,64,65,66] are the ones in which gene variants are most commonly reported. Another key contributor for brain function and homeostasis is synaptic signaling, responsible for the communication between cells forming neuronal circuits. Loss-of-function mutations in neurexin 1 (NRXN1) [69,70,71,72,73,74,75,76,77,78,79], in neuroligins (NLGN) such as NLGN3 and NLGN4 genes [67,68,80,81,82,83,84,85] and in SH3 and multiple ankyrin repeat domain (SHANK) proteins encoding genes SHANK1, SHANK2 and SHANK3 have been widely associated with ASD and ID [86,87,88,89].
Cells 12 00538 g001
Figure 2. hIPSC generation and applications. Sample collection of somatic cells can only be performed after the patient signs the informed consent. Reprogramming of healthy/disease cells into hIPSCs happens through the delivery of pluripotency-associated transcription factors. After hIPSC generation, these may be genetically modified using CRISPR/Cas9 to generate healthy/disease isogenic cells. Before differentiation into neuronal cultures, quality control techniques should be applied to ensure the pluripotency and genomic integrity of the obtained cells. Next, different neuronal populations may be obtained through the addition of differentiation and growth factors to the culture medium. Again, quality of the cells and their functional properties should be assessed throughout this step. Finally, hIPSCs may be used for mechanistic studies, target identification and drug screenings, for transplantation into rodent models or, ultimately, for cell-based therapies.
Figure 2. hIPSC generation and applications. Sample collection of somatic cells can only be performed after the patient signs the informed consent. Reprogramming of healthy/disease cells into hIPSCs happens through the delivery of pluripotency-associated transcription factors. After hIPSC generation, these may be genetically modified using CRISPR/Cas9 to generate healthy/disease isogenic cells. Before differentiation into neuronal cultures, quality control techniques should be applied to ensure the pluripotency and genomic integrity of the obtained cells. Next, different neuronal populations may be obtained through the addition of differentiation and growth factors to the culture medium. Again, quality of the cells and their functional properties should be assessed throughout this step. Finally, hIPSCs may be used for mechanistic studies, target identification and drug screenings, for transplantation into rodent models or, ultimately, for cell-based therapies.
Cells 12 00538 g002
Figure 3. NDD convergent molecular pathways. Simplified schematics of the major components known to be altered in NDDs. Mutations in some of the genes encoding these proteins may trigger altered protein translation and synaptic imbalances that culminate in the neuronal deficits already described in several NDDs. In the cytoplasm, PI3K/mTOR and MEK/ERK pathways, which regulate protein translation, are hyperactivated in response to mutations in PTEN, TSC1/2, NRXNs, NLGNs, FMRP, SYNGAP1 and DYRK1A, leading to abnormal protein production and synaptic malfunction; CHD8 mutations also contribute to this dysfunction through dysregulation of the cytoplasmic Wnt signaling. Additionally, mutations in transcriptional regulators and chromatin remodelers (CHD8, ARID1B, MECP2) favor/diminish the expression of genes that encode key proteins (Wnt signaling-associated genes, BDNF, GABAR genes) or alter their expression (MECP2), triggering excitation/inhibition (E/I) imbalances.
Figure 3. NDD convergent molecular pathways. Simplified schematics of the major components known to be altered in NDDs. Mutations in some of the genes encoding these proteins may trigger altered protein translation and synaptic imbalances that culminate in the neuronal deficits already described in several NDDs. In the cytoplasm, PI3K/mTOR and MEK/ERK pathways, which regulate protein translation, are hyperactivated in response to mutations in PTEN, TSC1/2, NRXNs, NLGNs, FMRP, SYNGAP1 and DYRK1A, leading to abnormal protein production and synaptic malfunction; CHD8 mutations also contribute to this dysfunction through dysregulation of the cytoplasmic Wnt signaling. Additionally, mutations in transcriptional regulators and chromatin remodelers (CHD8, ARID1B, MECP2) favor/diminish the expression of genes that encode key proteins (Wnt signaling-associated genes, BDNF, GABAR genes) or alter their expression (MECP2), triggering excitation/inhibition (E/I) imbalances.
Cells 12 00538 g003
Table 1. Comparison between the different animal models available to study NDDs and their degree of similarity to humans.
Table 1. Comparison between the different animal models available to study NDDs and their degree of similarity to humans.
Animal ModelAnimalAdvantagesDisadvantagesReferences
Drosophila melanogaster
Cells 12 00538 i001
-Easy to measure behavioral phenotypes [22,23,24]
Reduced size
Easy to manipulate genetically
Absence of genetic redundancyLack of neuronal complexity
Cost-effectiveOrgan and physiological disparity from humans
Easy maintenanceLower translatability to clinics
Short-life cycle
Suitable for large-scale drug and genetic screenings
Caenorhabditis elegans
Cells 12 00538 i002
-Well-characterized nervous system [25,26]
Transparent body
Reduced size
Easy to manipulate geneticallyLack of neuronal complexity
Cost-efficientOrgan and physiological disparity from humans
Easy maintenanceLower translatability to clinics
Short-life cycle
Suitable for large-scale drug and genetic screenings
Zebrafish (Danio rerio)
Cells 12 00538 i003
-Short reproductive cycle [27,28]
Presence of a brain
Transparent embryos and larvaeLack of neuronal complexity
Cost-efficientLower translatability to clinics
Useful for drug and genetic screenings
Presents well-defined circuits with conserved synaptic systems
Rodent models
Cells 12 00538 i004
Mice
(Mus musculus)
Rats
(Rattus norvegicus)
High face validity [29,30,31]
High resemblance to humans
Similar molecular pathways affected in diseaseInsufficient cortical and circuitry complexity
Some cognitive and social behaviors resembling humans’
Amenable to genetic manipulation
Suitable for pre-clinical drug testing
Non-human primates
Cells 12 00538 i005
CynomolgusHighest resemblance to human genetics, brain structure and cognitive functionDemanding genomic editing strategies[32,33]
Rhesus macaque Higher cognitive and social behaviors resemblance to humansHigh cost
MarmosetAmenable to genetic manipulationLong developmental time
Suitable for pre-clinical drug testing
Cells 12 00538 i006
Table 3. Described ASD human, rodent and hIPSCs phenotypes.
Table 3. Described ASD human, rodent and hIPSCs phenotypes.
NDDVariantHuman
Cells 12 00538 i007
Rodents
Cells 12 00538 i008
hIPSCs
Cells 12 00538 i009
ASD Small stature-
Smaller cerebellum
Larger hippocampus
Altered vocalizations
Corpus callosum underdevelopment/absenceSocial, learning and memory impairments
Characteristic facial featuresAnxiety-like behavior
ARID1B deletion
[308,309,310,311,312,313,314]
MicrocephalyCortical E/I imbalance
Cognitive deficitsDecreased excitatory synaptic density and transmission
IDDecreased number of cortical GABAergic interneurons
Reduced proliferation of interneuron NPCs
PV+ and SST+ interneurons impairments
CHD4 mutant
(r.Arg11276Gln, p.Trp1148Leu, p.Arg1173Leu, p.Gly1003Asp) [315,316]
Developmental delay, facial dysmorphisms, macrocephaly, ID--
Macrocephaly
Craniofacial abnormalities
Increased anxiety
Repetitive behavior
ASD
Reduced E/I balance
Fast early postnatal growthCHD8 predominantly expressed in MAP2+ and PV+ neurons
MacrocephalyReduced axon and dendritic growthAltered expression of genes related with brain volume
CHD8 deletion
[317,318,319,320,321,322,323,324,325,326,327,328]
Abnormal facial featuresAxonal projections disruption
CHD8 is predominantly expressed in MAP2+ and PV+ neuronsDelayed neuronal migration
Altered synaptic physiology in medium spiny neurons
Increased synchronized activity in cortico-hippocampal and auditory–parietal networks
Increased neuronal proliferation
Impaired social interaction and memory
Stereotypic behaviorsDecreased NMDAR-mediated currents
DSCAM deletion
[302,329]
ASDPremature spine maturationDownregulation of NMDAR subunits
Excessive glutamatergic transmission
Reduced NMDAR currents
ASD Decreased growth cone size
Increased soma size
Developmental delayReduced social interaction and vocalizationsIncreased neuronal proliferation
SHANK2 deletion
[297,298,330]
ASDStereotypic behaviorDecreased apoptosis
IDAltered spine volumeIncreased dendritic length, dendrite complexity, synapse number and frequency of sEPSCs
Decreased NMDAR function
Juvenile impaired social interaction
Enhanced self-grooming
Anxiety-like behavior
Social dominance behavior
Motor abnormalities
SHANK3 mutant
(R11117X, Q321R, c.1527G > A, c.2497delG, C.5008A > T, ctMUT, RS9616915SNP, exon (e)4-9, e14-16, S685) [331,332,333,334,335,336,337,338,339,340,341,342,343,344,345]
Abnormal development of sleep and arousal mechanismsAltered spinogenesis of pyramidal cortical neurons
Reduced cell soma size
Striatal synaptic transmission defects, before winning
PFC synaptic defects
Decreased neuronal excitability
Deficient LTP
Developmental delayReduced complexity of dendritic tree, spines and excitatory synapses
Language delayRepetitive grooming
Social deficits
ASDAnxiety-like behavior
Motor deficits
Altered SHANK3 methylation patternAltered light sensitivity
Reduced corpus callosum volumeImpaired neuronal development
NPCs early differentiationImpaired mature neuronal function
SHANK3 deletion
[346,347,348,349,350,351,352,353,354,355,356,357,358,359,360]
Increased cortical pyramidal neurons firingReduced neuronal soma size, growth cone area, neurite length and branch numbers
Abnormal striatal circuitry developmentDefects in E/I synaptic transmission
Reduced social memory—CA1 neurons
Glutamatergic but not GABAergic activity altered in CA3 at birth
Decreased cortical interneurons activity
Reduced cortical PV+ mRNA and protein levels
Decreased PV+ basket cells in the somatosensory cortex
ASD Immature neurons
Over-groomingAltered differentiation
Developmental delayAltered social responseReduced neuronal activity
Facial abnormalitiesCognitive deficitsReduced number of APs
NRXN1 deletion
[75,77,304,361,362,363,364,365,366,367,368]
ASDAggressive behavior in malesDecreased neurite number
Severe breathing problemsSex-dependent altered novelty responseDecreased neuronal length
Decrease synaptic strength in spiny projection neurons pathway Increased cortical calcium signaling, sodium currents, AP amplitude
Reduction in neurotransmitter release in spiny neuronsIncreased depolarization time
ASDSociability deficitsDecreased E/I ration
Reduced ultrasonic vocalizationDecreased E/I network response
NLGN4 deletion
[67,369,370,371,372,373]
Increased stereotypiesDecreased miniature EPSCs and IPSCs
Cognitive dysfunctionDecreased number of GABAergic and glutamatergic vesicles
Altered GABAergic hippocampal functionImpaired synaptic formation in NPCs
TSC1/TSC2 loss of function
[374]
--
MicrocephalyImpaired memory and UV vocalization
Short stature
Abnormal facial features
Social deficitsIncreased cell size, NPC proliferation and neurite outgrowth
IDSeizuresImpaired neuronal differentiation
SeizuresIncreased cell sizeE/I ratio imbalance
TSC2 deletion
[304,361,375,376,377,378]
Memory deficitsIncreased PV levelsNeuronal hyperactivity
Hypoconnected neural networksAt P7, Pax2+ cells increased and delayed maturation into PV+ interneurons Neuronal network dysfunction
Decreased AP after hyperpolarizationReduced synchronization of
neuronal bursting and spatial connectivity
Abnormal LTPDecreased expression of synaptic markers
ASD Hyperactivity, social and cognitive deficits
TSC1 mutant
(R336W, T360N, T393I, S403L and H732Y) [379]
Altered brain anatomy-
Reduced cortical thickness
Reduced cortical synaptic density and neurite outgrowth-
SETD5 deletion
[380,381]
ASDDecreased network activity and synchrony
Enhanced LTP-
Abnormal expression of postsynaptic density proteins
Table 4. Described MECP2 mutations in human, rodent and hIPSCs phenotypes.
Table 4. Described MECP2 mutations in human, rodent and hIPSCs phenotypes.
NDDVariantHuman
Cells 12 00538 i007
Rodents
Cells 12 00538 i008
hIPSCs
Cells 12 00538 i009
Rett Syndrome Decreased Ca2+ signaling
Decreased sEPSCs and sIPSCs
Impaired spatial, contextual fear and social memoryDecreased synaptic contacts
AnxietyDecreased cell soma size, dendritic branching, spine density
Increased vocalizationsImpaired neuronal maturation
MicrocephalyHypoactivityIncreased VIP+ and CB2+
interneurons
Impaired developmentStereotypiesDecreased PV+, SST+ and CB1+ interneurons
Mild IDReduced brain volumeLoss of low frequency and gamma oscillations
MECP2 mutant
(R168X, p24hospho(Thr308-Ser421), tm1.1Jae, tm1.1Bird, A140V, 1lox, 308) [323,324,325,326,327,328,390]
SeizuresSeizuresDecreased density of excitatory
puncta
Stereotypic behaviorMotor abnormalitiesPremature development of deep-cortical layer
AnxietyBreathing abnormalitiesExpression of progenitor
and proliferative cells
Breathing problemsImpaired growth maturationDefective forebrain neuronal function
Motor abnormalitiesReduced cortical spontaneous activity of pyramidal neuronsInterneurons migration deficits
Reduced miniature EPSCs amplitude, without changes in miniature IPSCsDecreased Nkx2.1 levels
Lower dendritic spine density in CA1 neurons, at P7, abolished at P15Increased input resistance
Deficits in LTP and LTDImpaired voltage-gated Na+/K+ currents
Decreased dendritic complexity
Gait and posture abnormalities
Breathing difficulties
Microcephaly
SeizuresDecreased soma area, dendritic length
Stereotypic behaviorsDecreased neurite outgrowth
IDHypoactivityReduced depolarized resting membrane potential
ASDE/I imbalanceReduced cell capacitance
MECP2 deletion
[331,332,333,334,335,391,392,393]
EpilepsyReduced excitatory networksDeficits in excitatory synapse transmission
Motor abnormalitiesReduced cortical basal dendritic lengthDecreased excitatory markers
Delayed cortical neuronal maturationDecreased neuronal migration
Reduced cortical neurons soma and nuclei sizeNetwork dysfunction caused by GE-derived interneurons malfunction
Premature synaptogenesisIncreased inhibitory synapses and markers
Elevated cortical PV expression
Abnormal excitatory inputs converging onto PV+ interneurons
FOXG1 mutant
[322]
Altered craniofacial structureAltered craniofacial structure-
Table 5. Described DS human, rodent and hIPSCs phenotypes.
Table 5. Described DS human, rodent and hIPSCs phenotypes.
NDDVariantHuman
Cells 12 00538 i007
Rodents
Cells 12 00538 i008
hIPSCs
Cells 12 00538 i009
Down Syndrome Short stature
Decreased brain size Reduced brain organoid size
Decreased white matter volume Reduced GABAergic interneurons
Hippocampal and neocortical sizeCognitive deficits (learning, memory and executive function) Reduced CR+/CB+ interneurons
ratio
Characteristic face phenotype
Alzheimer’s disease-like histopathology
Increased number of inhibitory neurons in the forebrainReduced COUP-TFII+ progenitors
and proliferation
IDDendritic spine defectsIncreased OLIG2+ ventral forebrain progenitors
Cognitive deficits (learning, memory and executive function)Decreased GABA-mediated inhibitionReduced interneuron lineage-determining transcription factors
Chromosome 21 trisomy
[336,341,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411]
Decreased excitatory and inhibitory neuronsDecreased GABA synthesis enzymes in hippocampal and cortical inhibitory synapses Increased GABAergic interneurons production
Decreased number of forebrain neuronsAltered dendritic lengthDecreased GABAergic interneuron neurogenesis and proliferation
Decreased CB+ and PV+ neurons Altered CA1 pyramidal neuron functionDecreased synaptic activity
Decreased CB+ and PV+ size neuronsDecreased LTPDecreased migration capacity
Dendritic spine defectsDecreased neurogenesis and proliferationAbnormal neuronal differentiation dependent on DYRK1A
Altered neuronal migration and differentiationOxidative stressDecreased mitochondrial membrane potential
Oxidative stress Increased mitochondria number
Decreased neurogenesis and proliferation Abnormal mitochondria morphology
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guerreiro, S.; Maciel, P. Transition from Animal-Based to Human Induced Pluripotent Stem Cells (iPSCs)-Based Models of Neurodevelopmental Disorders: Opportunities and Challenges. Cells 2023, 12, 538. https://doi.org/10.3390/cells12040538

AMA Style

Guerreiro S, Maciel P. Transition from Animal-Based to Human Induced Pluripotent Stem Cells (iPSCs)-Based Models of Neurodevelopmental Disorders: Opportunities and Challenges. Cells. 2023; 12(4):538. https://doi.org/10.3390/cells12040538

Chicago/Turabian Style

Guerreiro, Sara, and Patrícia Maciel. 2023. "Transition from Animal-Based to Human Induced Pluripotent Stem Cells (iPSCs)-Based Models of Neurodevelopmental Disorders: Opportunities and Challenges" Cells 12, no. 4: 538. https://doi.org/10.3390/cells12040538

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop