Special Issue "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: 1 December 2021.

Special Issue Editor

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 and Collections in MDPI journals

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.

Dr. Anelia D. Horvath
Guest Editor

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

Published Papers (1 paper)

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Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments
Genes 2021, 12(10), 1558; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12101558 - 30 Sep 2021
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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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