Abstract
Single-cell sequencing is useful for illustrating the cellular heterogeneities inherent in many intricate biological systems, particularly in human cancer. However, owing to the difficulties in acquiring, amplifying and analyzing single-cell genetic material, obstacles remain for single-cell diversity assessments such as single nucleotide polymorphism (SNP) analyses, rendering biological interpretations of single-cell omics data elusive. We used RNA-Seq data from single-cell and bulk colon cancer samples to analyze the SNP profiles for both structural and functional comparisons. Colon cancer-related pathways with single-cell level SNP enrichment, including the TGF-β and p53 signaling pathways, were also investigated based on both their SNP enrichment patterns and gene expression. We also detected a certain number of fusion transcripts, which may promote tumorigenesis, at the single-cell level. Based on these results, single-cell analyses not only recapitulated the SNP analysis results from the bulk samples but also detected cell-to-cell and cell-to-bulk variations, thereby aiding in early diagnosis and in identifying the precise mechanisms underlying cancers at the single-cell level.
Highlights
Genome amplification (WGA) of such a small amount of DNA in an individual cell remains difficult owing to unregulated artificial errors, an inconsistent amplification ratio and lower coverage
We obtained three sets of SNP calling results generated by The Genome Analysis Toolkit (GATK)15, SAMTools16 and Genotype Model Selection (GeMS)17, respectively
While SNPs from GATK set targeted 761 genes, those detected by SAMTools and GeMS only targeted less than 60 genes, most of which were the same
Summary
Genome amplification (WGA) of such a small amount of DNA in an individual cell remains difficult owing to unregulated artificial errors, an inconsistent amplification ratio and lower coverage. Many of these tools do not account for the intrinsic problems originated from current single-cell amplification Owing to these technical and analytical difficulties, only a few systematically generated single-cell genomic or transcriptomic data are available for routine omics interpretations. To examine the differences in the identified SNPs based on single-cell and bulk samples, we performed SNP analyses on RNA-Seq data using bulk cancer and normal samples By comparing these results at the single-cell and bulk levels, it was clear that single-cell analyses were capable of recapitulating the bulk analyses results such as SNP profiles, cancer-related genes and pathways, and specialized in detecting some variances and genetic features such as single-cell specific variations in BMP7, CYCS and some 14-3-3 protein-encoding genes in the subpopulations of single cells. These comparisons revealed the globally consistent but locally different cell-to-cell and cell-to-bulk SNP variations
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