Single-cell transcriptomic profiling unravels the adenoma-initiation role of protein tyrosine kinases during colorectal tumorigenesis
The adenoma-carcinoma sequence is a well-accepted roadmap for the development of sporadic colorectal cancer. However, cellular heterogeneity in aberrant epithelial cells limits our understanding of carcinogenesis in colorectal tissues. Here, we performed a single-cell RNA sequencing survey of 54,788 cells from patient-matched tissue samples, including blood, normal tissue, para-cancer, polyp, and colorectal cancer. At each stage of carcinogenesis, we characterized cell types, transcriptional signatures, and differentially expressed genes of distinct cell populations. The molecular signatures of epithelial cells at normal, benign, and malignant stages were defined at the single-cell scale. Adenoma and carcinoma precursor cell populations were identified and characterized followed by validation with large cohort biopsies. Protein tyrosine kinases (PTKs) BMX and HCK were identified as potential drivers of adenoma initiation. Specific BMX and HCK upregulations were observed in adenoma precursor cell populations from normal and adenoma biopsies. Overexpression of BMX and HCK significantly promoted colorectal epithelial cell proliferation. Importantly, in the organoid culture system, BMX and HCK upregulations resulted in the formation of multilayered polyp-like buds protruding towards the organoid lumen, mimicking the pathological polyp morphology often observed in colorectal cancer. Molecular mechanism analysis revealed that upregulation of BMX or HCK activated the JAK-STAT pathway. In conclusion, our work improved the current knowledge regarding colorectal epithelial evolution during carcinogenesis at the single-cell resolution. These findings may lead to improvements in colorectal cancer diagnosis and treatment.
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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
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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