Comparison of EpCAMhighCD44+ cancer stem cells with EpCAMhighCD44- tumor cells in colon cancer by single-cell sequencing
Cancer stem cells (CSCs) are considered to be responsible for tumorigenesis and cancer relapse. EpCAMhighCD44+ tumor cells are putative colorectal CSCs that express high levels of stem cell genes, while the EpCAMhighCD44- population mostly contains differentiated tumor cells (DTCs). This study aims to determine whether single CSC (EpCAMhighCD44+) and DTC (EpCAMhighCD44-) can be distinguished in terms of somatic copy number alterations (SCNAs). We applied fluorescence-activated cell sorting to isolate the CD45-EpCAMhighCD44+ and CD45-EpCAMhighCD44- populations from two primary colon tumors, on which low-coverage single-cell whole-genome sequencing (WGS) was then performed ∼0.1x depth. We compared the SCNAs of the CSCs and DTCs at single-cell resolution. In total, 47 qualified single cells of the two populations underwent WGS. The single-cell SCNA profiles showed that there were obvious SCNAs in both the CSCs and DTCs of each patient, and each patient had a specific copy number alteration pattern. Hierarchical clustering and correlation analysis both showed that the SCNA profiles of CSCs and DTCs from the same patient had similar SCNA pattern, while there were regional differences in the CSCs and DTCs in certain patient. SCNAs of CSCs in the same patient were highly reproducible. Our data suggest that major SCNAs occurred at an early stage and were inherited steadily. The similarity of ubiquitous SCNAs between the CSCs and DTCs might have arisen from lineage differentiation. CSCs from the same patient had reproducible SCNA profiles, indicating that gain or loss in certain chromosome is required for colon cancer development.Keywords: Cancer stem cells; Colon cancer; Differentiated tumor cells; Punctuated evolution; Single-cell sequencing; Somatic copy number alterations; Tumorigenesis.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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