Single-Cell Genomics Moving Forward

A special issue of Genes (ISSN 2073-4425).

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6195

Special Issue Editors


E-Mail Website
Guest Editor
Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20052, USA
Interests: genomics; transcriptomics; cancer genomics; computational biology; bioinformatics; RNA seq; bioinformatic tools
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Associate Professor of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
Interests: bioinformatics machine learning; data mining; biological databases; computational genomics

Special Issue Information

Dear Colleagues,

The Genes 2023—Single-Cell Genomics Moving Forward Conference will be held on March 29–31, 2023 in Barcelona, Spain. The webpage of the event is https://genes2023.sciforum.net/.

The goal of this Conference is to facilitate innovative collaborative research on the interface of single-cell genomics, spatial transcriptomics and artificial intelligence, particularly focusing on identifying future trends and needs of these fields, and on proposing and discussing innovative interdisciplinary approaches to address them.

The current Special Issue invites submissions on unpublished original work describing recent advances in single-cell multi-omics technologies and analyses, artificial intelligence in single-cell genomics and spatial genomics and transcriptomics.

Prof. Dr. Anelia D. Horvath
Dr. Liangjiang Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • single cell multi-omics
  • artificial intelligence
  • single cell genomics
  • spatial genomics and transcriptomics

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 1305 KiB  
Article
Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model
by Dongyuan Wu, Jeremy T. Gaskins, Michael Sekula and Susmita Datta
Genes 2023, 14(7), 1368; https://doi.org/10.3390/genes14071368 - 28 Jun 2023
Cited by 1 | Viewed by 1535
Abstract
Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling [...] Read more.
Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell–cell communication and provide a robust and straightforward result. Full article
(This article belongs to the Special Issue Single-Cell Genomics Moving Forward)
Show Figures

Figure 1

25 pages, 44302 KiB  
Article
Characterization and Optimization of Multiomic Single-Cell Epigenomic Profiling
by Leticia Sandoval, Wazim Mohammed Ismail, Amelia Mazzone, Mihai Dumbrava, Jenna Fernandez, Amik Munankarmy, Terra Lasho, Moritz Binder, Vernadette Simon, Kwan Hyun Kim, Nicholas Chia, Jeong-Heon Lee, S. John Weroha, Mrinal Patnaik and Alexandre Gaspar-Maia
Genes 2023, 14(6), 1245; https://doi.org/10.3390/genes14061245 - 10 Jun 2023
Viewed by 3964
Abstract
The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling [...] Read more.
The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification. Full article
(This article belongs to the Special Issue Single-Cell Genomics Moving Forward)
Show Figures

Figure 1

Back to TopTop