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Genetic Variation and Splicing from Single Cell RNA-Sequencing
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Genetic Variation and Splicing from Single Cell RNA-Sequencing

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 34623

Special Issue Editor

Prof. Dr. Anelia D. Horvath

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

Special Issue Information

Dear colleagues,

Single cell RNA-sequencing (scRNA-seq) provides a unique opportunity to study inter-molecular relationships. In recent years, hundreds of studies have employed scRNA-seq to depict the within-cell dynamics of gene expression. The single cell transcriptome constitutes a unique composite of molecular integrity, which, in addition to gene expression, amalgamates a variety of features, including expressed genetic variation, splicing, and post-transcriptional modifications. These features are functionally and structurally linked with each other and with gene expression, and the underlying links constitute essential building blocks of the cellular interactome. ScRNA-seq provides a unique opportunity to study inter-molecular relationships. In contrast to bulk RNA-sequencing, single cell level assessment preserves the mutual correlations between the different transcriptome features, thus enabling the retrieval of regulatory and structural molecular relationships. For example, the co-expression of a genetic variant and a transcript might indicate a positive regulatory role of the variation on the transcript expression; such co-expression can be easily missed in the bulk RNA-seq analysis due to averaging of the measurements across multiple cells. Furthermore, scRNA-seq provides the opportunity to study allele-specific expression at an unprecedented resolution, and, accordingly, to reveal functional insights into the preferentially expressed alleles.

We invite submissions of both methodological and original research papers assessing genetic variation, splicing and post-transcriptional modifications from scRNA-seq, and, where possible, their integration with gene and transcript expression. Topics may include, but are not limited to, studies of expressed genetic variation, including single nucleotide variants (SNVs), splicing and posttranscriptional modifications such as RNA-editing. The overarching aim of this issue is to stimulate the emerging and promising research on single cell transcriptomics, pursuing at the same time new exploratory and collaborative venues to address its challenges.

Prof. Dr. Anelia D. Horvath
Guest Editor

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Keywords

  • Single cell RNA-sequencing
  • Genetic variation
  • Splicing
  • Post-transcriptional modifications
  • RNA-editing
  • Gene expression
  • Single cell transcriptome
  • Allele-specific expression
  • Single nucleotide variants (SNVs).

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Published Papers (4 papers)

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14 pages, 2086 KiB  
Article
Single-Cell RNA Sequencing of Hematopoietic Stem and Progenitor Cells Treated with Gemcitabine and Carboplatin
by Niclas Björn, Ingrid Jakobsen, Kourosh Lotfi and Henrik Gréen
Genes 2020, 11(5), 549; https://doi.org/10.3390/genes11050549 - 14 May 2020
Cited by 5 | Viewed by 4749
Abstract
Treatments that include gemcitabine and carboplatin induce dose-limiting myelosuppression. The understanding of how human bone marrow is affected on a transcriptional level leading to the development of myelosuppression is required for the implementation of personalized treatments in the future. In this study, we [...] Read more.
Treatments that include gemcitabine and carboplatin induce dose-limiting myelosuppression. The understanding of how human bone marrow is affected on a transcriptional level leading to the development of myelosuppression is required for the implementation of personalized treatments in the future. In this study, we treated human hematopoietic stem and progenitor cells (HSPCs) harvested from a patient with chronic myelogenous leukemia (CML) with gemcitabine/carboplatin. Thereafter, scRNA-seq was performed to distinguish transcriptional effects induced by gemcitabine/carboplatin. Gene expression was calculated and evaluated among cells within and between samples compared to untreated cells. Cell cycle analysis showed that the treatments effectively decrease cell proliferation, indicated by the proportion of cells in the G2M-phase dropping from 35% in untreated cells to 14.3% in treated cells. Clustering and t-SNE showed that cells within samples and between treated and untreated samples were affected differently. Enrichment analysis of differentially expressed genes showed that the treatments influence KEGG pathways and Gene Ontologies related to myeloid cell proliferation/differentiation, immune response, cancer, and the cell cycle. The present study shows the feasibility of using scRNA-seq and chemotherapy-treated HSPCs to find genes, pathways, and biological processes affected among and between treated and untreated cells. This indicates the possible gains of using single-cell toxicity studies for personalized medicine. Full article
(This article belongs to the Special Issue Genetic Variation and Splicing from Single Cell RNA-Sequencing)
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17 pages, 4155 KiB  
Article
Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-seq Data
by Weilai Chi and Minghua Deng
Genes 2020, 11(5), 532; https://doi.org/10.3390/genes11050532 - 11 May 2020
Cited by 11 | Viewed by 4917
Abstract
Single-cell RNA-seq (scRNA-seq) is quite prevalent in studying transcriptomes, but it suffers from excessive zeros, some of which are true, but others are false. False zeros, which can be seen as missing data, obstruct the downstream analysis of single-cell RNA-seq data. How to [...] Read more.
Single-cell RNA-seq (scRNA-seq) is quite prevalent in studying transcriptomes, but it suffers from excessive zeros, some of which are true, but others are false. False zeros, which can be seen as missing data, obstruct the downstream analysis of single-cell RNA-seq data. How to distinguish true zeros from false ones is the key point of this problem. Here, we propose sparsity-penalized stacked denoising autoencoders (scSDAEs) to impute scRNA-seq data. scSDAEs adopt stacked denoising autoencoders with a sparsity penalty, as well as a layer-wise pretraining procedure to improve model fitting. scSDAEs can capture nonlinear relationships among the data and incorporate information about the observed zeros. We tested the imputation efficiency of scSDAEs on recovering the true values of gene expression and helping downstream analysis. First, we show that scSDAE can recover the true values and the sample–sample correlations of bulk sequencing data with simulated noise. Next, we demonstrate that scSDAEs accurately impute RNA mixture dataset with different dilutions, spike-in RNA concentrations affected by technical zeros, and improves the consistency of RNA and protein levels in CITE-seq data. Finally, we show that scSDAEs can help downstream clustering analysis. In this study, we develop a deep learning-based method, scSDAE, to impute single-cell RNA-seq affected by technical zeros. Furthermore, we show that scSDAEs can recover the true values, to some extent, and help downstream analysis. Full article
(This article belongs to the Special Issue Genetic Variation and Splicing from Single Cell RNA-Sequencing)
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15 pages, 5029 KiB  
Article
Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data
by Prashant N. M., Hongyu Liu, Pavlos Bousounis, Liam Spurr, Nawaf Alomran, Helen Ibeawuchi, Justin Sein, Dacian Reece-Stremtan and Anelia Horvath
Genes 2020, 11(3), 240; https://doi.org/10.3390/genes11030240 - 25 Feb 2020
Cited by 17 | Viewed by 7852
Abstract
With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele [...] Read more.
With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics. Full article
(This article belongs to the Special Issue Genetic Variation and Splicing from Single Cell RNA-Sequencing)
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17 pages, 3334 KiB  
Article
SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples
by Ze Zhang, Danni Luo, Xue Zhong, Jin Huk Choi, Yuanqing Ma, Stacy Wang, Elena Mahrt, Wei Guo, Eric W Stawiski, Zora Modrusan, Somasekar Seshagiri, Payal Kapur, Gary C. Hon, James Brugarolas and Tao Wang
Genes 2019, 10(7), 531; https://doi.org/10.3390/genes10070531 - 12 Jul 2019
Cited by 149 | Viewed by 16105
Abstract
Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, [...] Read more.
Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation–maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods. Full article
(This article belongs to the Special Issue Genetic Variation and Splicing from Single Cell RNA-Sequencing)
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