Single-cell SNP analyses and interpretations based on RNA-Seq data

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.

Researchers at the Huazhong University of Science and Technology 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. The researchers also detected a certain number of fusion transcripts, which may promote tumorigenesis, at the single-cell level.

Heatmap of SNP enrichment on human chromosomes
based on SNP calling results by the GATK


(A) Heatmap of the SNP-Freqc on each chromosome for the 83 single-cell samples obtained by the GATK. (B) Heatmap of the SNP-Freqc on each chromosome for the bulk (both cancer and normal) samples obtained by the GATK. The SNP enrichment on chromosomes for each single-cell and bulk (both cancer and normal) sample was shown in the heatmaps. In each heatmap, the columns represented the samples, and the rows corresponded to different chromosomes. The SNP-Freqc on a logarithmic scale (e-based) was encoded by a colored bar. The largest SNP-Freqc values were displayed in red, and the smallest values were displayed in blue. Chromosomes that fell into one cluster (horizontal axis) had a similar SNP-Freqc between the samples. The samples were also clustered (vertical axis) based on the SNP-Freqc value of each sample.

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.

Chen J, Zhou Q, Wang Y, Ning K. (2016) Single-cell SNP analyses and interpretations based on RNA-Seq data for colon cancer research. Sci Rep 6:34420. [article]

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