Special Issue "Diversity of Induced Pluripotent Stem Cells"
Deadline for manuscript submissions: 30 January 2024 | Viewed by 5328
Interests: pluripotency asqusition and maintenance; reprogramming effectors; mathematical modelling in hiPSCs biology; best clone selection; diversity of hiPSCs
It has been demonstrated that each embryonic stem cell (ESC) line has its own clonal differences and numerous induced pluripotent stem cell (iPSC) lines have shown greater diversity than ESCs. The cause of the clonal diversity can be explained by the retained epigenetic memory, genetic background, features obtained during reprogramming and many others. Several studies dissecting the reprogramming process revealed that the cells in transitional phase are dramatically distinct from both original and fully reprogrammed cells. Many of the reported ‘incomplete’ human iPSC lines are similar to ESCs in their morphology, core pluripotent marker expression and basic pluripotency signified by the teratoma formation, while they exhibit particular defects such as poor quality of differentiation, low growth rate, aberrant transcription, DNA methylation or chromatin regulation.
Efficient use of hiPSCs in personalized medicine and most recently in development of allogeneic cell therapies requires large quantities of high quality hiPSCs, obtainable via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition by microscopic observation. However, despite the urgency of this requirement, there are currently only a few automatic, image-processing-based solutions for routine qualitative hiPSCs control. Introduction of the interdisciplinary approaches, such as machine learning methods and deep learning approaches based on the use of convolutional neural networks (CNNs) for hiPSC data analysis, will enable enormous advances in computer vision and become an invaluable tool in automating the analysis of various types of cell images. These techniques have already been applied to numerous processes in stem cell research, including the automated inference of differentiation and prediction of function in iPSC derived cell types. Recently CNNs have been employed in automatically identifying clonality—one of the important requirements for the safe hiPSCs application in clinic. Artificial intelligence-based methods are becoming promising for evaluation of iPSCs in various practical contexts.
Dissecting the molecular and biological differences among the various iPSC lines by introducing mathematical modeling and machine learning approaches can greatly help in gaining an in-depth understanding of the mechanisms that are central to complete pluripotency. A deeper understanding of the ground state of human pluripotency will end the controversial comparisons and shed light on the goal of reprogramming.
The special issue aims to present a wide range of interdisciplinary studies in the field of stem cell biology, involving a combination of experimental and theoretical work and, particularly but not exclusively, describing various image processing methods, approaches to modeling various factors that control iPSC colony growth, as well as machine learning and deep learning approaches to iPSC data analysis.
Dr. Irina Neganova
Dr. Vitaly V. Gursky
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- clonal variability
- image processing in iPSC control
- mathematical modeling in iPSC biology
- machine learning and deep learning approaches in stem cell biology