利用单细胞转录组分析技术对肿瘤内部异质性的系统研究

Intra-tumoral cell populations display remarkable heterogeneity, which is both cause and consequence of tumor initiation and progression. Therefore a comprehensive view on intra-tumoral heterogeneity should provide valuable information for understanding important cancer-related processes such as tumor development, metastasis, drug sensitivities, etc. Traditional high-throughput profiling techniques provide snapshots of molecular species' abundance averaged on thousands to millions of cells, therefore falling short of capturing the heterogeneous nature of tumors. The recently developed single-cell techniques now allow comprehensive transcriptome profiling for a large number of single-cells. In the proposed research, we plan to take advantages of the most advanced single-cell transcriptome profiling technique, and systematically study the intra-tumoral heterogeneity of a hepatocellular carcinoma (HCC) case as an example. The project includes the following major steps: 1) data collection. We will collect tumor single-cells from a HCC model, and perform transcriptome profiling to obtain mRNA expression profiles of single-cells. 2) data analysis. We will perform statistical analyses such as 2-dimensional hierarchical clustering and principle components analysis. This will cluster the single-cells into transcriptionally distinct sub-populations, and identify their signature genes. We will also apply systems biology methodologies to reconstruct the gene transcriptional regulation network, using the single-cell gene expression data. 3) network and transcriptome profile analysis. We will further use the gene transcriptional regulatory network to dissect the transcriptome profiles of the single-cell sub-populations and infer their master regulators that transcriptionally drive the phenotypic differences between cell sub-populations. 4) experimental validations. We will isolate the single-cell sub-populations, and assess their phenotypic differences such as proliferation, migration, invasion, and in vivo tumorigenesis. We will also validate the involvements of master regulators in driving the differences between the cell sub-populations. The proposed research will yield very valuable results: 1) the first large-scale HCC single-cell transcriptome profiles. 2) a novel intra-tumor cell map composed of single-cells clustered based on their gene expression profiles. 3) the first "personalized" regulatory network to capture the gene transcriptional regulatory machineries that are derived from one case of cancer. 4) key transcription factors that drive the phenotypic differences between cell sub-populations. In summary, by systematically interrogating intra-tumoral heterogeneity, the proposed research will provide, for the first time, a valuable basis of data, methodology, and preliminary mechanistic information for further research on patient-specific cancer mechanisms and potentially for the development of personalized medicine.

肿瘤内部细胞之间具有高度的异质性。对该异质性的深入研究有望为理解肿瘤的发生发展、转移、药物敏感性等疾病过程提供重要信息。本项目将以肝细胞癌为例，使用最新的单细胞分析技术，对肿瘤内部单细胞进行转录组分析。以此为基础，我们将确定该组细胞中的细胞亚群，及各细胞亚群的标志性基因，从而首次提出肿瘤异质性在单细胞转录组层次的详细图谱。其次，我们将采用肿瘤系统生物学的方法，使用单细胞转录组数据构建该肝细胞癌的基因转录调控网络。对该转录调控网络及单细胞转录组数据的进一步深入分析将提出各肿瘤细胞亚群区别于正常及其它肿瘤细胞的核心转录调控因子。最后，我们将通过实验分析手段，研究各细胞亚群的功能表型差异，并验证其核心转录调控因子对细胞功能表型的调控作用。总之，本项目首次从单细胞转录组角度系统研究肿瘤内部异质性，为该肝细胞癌中细胞的起源与发展提供机理性信息，并为其它单细胞层次的肿瘤机理研究提供数据及方法上的支持。

肿瘤内部细胞之间具有高度的异质性，对该异质性的深入研究有望为理解肿瘤的发生发展、转移、药物敏感性等疾病过程提供重要信息。本项目以肝细胞癌为例，使用高通量单细胞分析技术，对肿瘤内部单细胞进行了转录组分析。以此为基础，我们确定了该组细胞中的细胞亚群，及各细胞亚群的标志性基因，从而首次提出肿瘤异质性在单细胞转录组层次的详细图谱。其次，我们采用肿瘤系统生物学的方法，使用单细胞转录组数据构建了该肝细胞癌的基因转录调控网络。对该转录调控网络及单细胞转录组数据的进一步深入分析提出了各肿瘤细胞亚群区别于正常及其它肿瘤细胞的核心转录调控因子。最后，我们通过实验分析手段，研究了各细胞亚群的功能表型差异，并验证了其核心转录调控因子对细胞功能表型的调控作用。总之，本项目首次从单细胞转录组角度系统研究肿瘤内部异质性，为该肝细胞癌中细胞的起源与发展提供机理性信息，并为其它单细胞层次的肿瘤机理研究提供数据及方法上的支持。

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