BMC BiologyGenomicsPeer-reviewed journal article
Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model
Hanako OnoiD, Yasuhito AraiiD, Eisaku Furukawa, Daichi NarushimaiD, Tetsuya MatsuuraiD, Hiromi K. NakamuraiD, Daisuke ShiokawaiD, Momoko Nagai, Toshio ImaiiD, Koshi MimoriiD, Koji OkamotoiD, Yoshitaka HippoiD, Tatsuhiro ShibataiD, Mamoru KatoiD
Abstract
Intra-tumor heterogeneity (ITH) encompasses cellular differences in tumors and is related to clinical outcomes, such as drug resistance. However, little is known about the dynamics of ITH, owing to the lack of time-series analysis at the single-cell level. We performed single-cell exome and transcriptome sequencing of 200 cells and investigated how ITH is generated from one single cell in a mouse colorectal cancer model. The ITH of the transcriptome increased after transplantation from cultured organoids, while that of the exome decreased. Mutations generated in the culture did not greatly change at the transplantation at the bulk-cell level. The RNA ITH increase was due to the emergence of new transcriptional subpopulations. In contrast to the initial cells expressing mesenchymal-marker genes, new subpopulations repressed these genes at transplantation. Analyses of colorectal cancer data from The Cancer Genome Atlas revealed a high proportion of metastatic cases in human subjects with expression patterns similar to the new cell subpopulations in mouse. These results suggest that the birth of transcriptional subpopulations may be a key for adaptation to drastic micro-environmental changes when cancer cells have sufficient genetic alterations at later tumor stages. This study revealed an evolutionary dynamics of single-cell RNA and DNA changes in tumor progression, giving insights into the mesenchymal-epithelial transformation of tumor cells at metastasis in colorectal cancer.
- organism: mouse
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 34
- FWCI
- 2.88
- Field percentile
- top 7%
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