Transcriptional and Genetic Tumor Heterogeneity through ScRNA-seq

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: closed (1 December 2021) | Viewed by 11201

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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
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Special Issue Information

Dear Colleagues,

By enabling cell-level analyses, scRNA-seq brings major advantages over the bulk RNA-seq approach, including the ability to distinguish cell populations and to assess cell-type specific phenotypes. Connecting these phenotypes to cell-level transcriptional and genetic variation is acknowledged as a critical challenge for phenotype interpretation. In cancer, studies on cell-level heterogeneity have been instrumental in tracing cell lineages and resolving subclonal tumor architecture. Genetically distinct tumor cell populations are shown to differ with respect to clinical features, including growth rate, disease aggressiveness, and sensitivity to drugs. Furthermore, linking genetic to transcriptional heterogeneity has demonstrated the advantages of the integrative analyses to characterize cancer programs and to outline drug-resistance cell populations.

With the quick progress of scRNA-seq technologies, including approaches to assess cell-level genetic heterogeneity, the anticipation is that scRNA-seq will soon be incorporated in the clinics. This process will greatly benefit from improved knowledge on tumor heterogeneity, the ability to interpret cell-level genetic and transcriptional variation, and, consequently, to distinguish and characterize sensitive and resistant clones. In the near future, this new knowledge is expected to be translated into better diagnosis and treatment of cancer patients.

We invite submissions of both methodological and original research papers assessing tumor heterogeneity through single-cell RNA sequencing. Special focus will be placed on research integrating genetic and transcriptional heterogeneity and identifying cell-level genetic determinants of phenotype. The overarching aim of this issue is to stimulate emerging and promising single-cell research, 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

  • scRNA-seq
  • heterogeneity
  • genetic variation
  • mutation
  • cancer
  • genetic heterogeneity
  • transcriptional heterogeneity
  • single-cell RNA sequencing

Published Papers (2 papers)

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29 pages, 2307 KiB  
Article
A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies
by Samarendra Das, Anil Rai, Michael L. Merchant, Matthew C. Cave and Shesh N. Rai
Genes 2021, 12(12), 1947; https://doi.org/10.3390/genes12121947 - 2 Dec 2021
Cited by 15 | Viewed by 6232
Abstract
Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge [...] Read more.
Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq. Full article
(This article belongs to the Special Issue Transcriptional and Genetic Tumor Heterogeneity through ScRNA-seq)
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15 pages, 17567 KiB  
Article
Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments
by Prashant N. M., Hongyu Liu, Christian Dillard, Helen Ibeawuchi, Turkey Alsaeedy, Hang Chan and Anelia Dafinova Horvath
Genes 2021, 12(10), 1558; https://doi.org/10.3390/genes12101558 - 30 Sep 2021
Cited by 8 | Viewed by 4179
Abstract
Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual [...] Read more.
Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality. Full article
(This article belongs to the Special Issue Transcriptional and Genetic Tumor Heterogeneity through ScRNA-seq)
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