Heterogeneity and metabolic plasticity of tumor cells based on single-cell metabolomics techniques
Project background:
Tumor cells are a highly heterogeneous and plastic cell type, which can adjust their metabolic state according to changes in the microenvironment to adapt to the needs of growth, proliferation, invasion and metastasis. The abnormal regulation of tumor cell metabolism is closely related to cancer occurrence, development and treatment response, so revealing the molecular mechanism and regulatory network of tumor cell metabolism is of great significance for understanding cancer biology and developing new therapeutic strategies. However, current studies of tumor cell metabolism are mainly based on population-level metabolomic analysis, which cannot reflect the metabolic heterogeneity and dynamics within and between tumor cells, nor can it reveal the correlation and interaction between metabolic states and other cellular characteristics (such as gene expression, epigenetic inheritance, signaling pathways, etc.).
Brief introduction of the project:
The purpose of this study is to systematically analyze the metabolic status, heterogeneity and plasticity of tumor cells of different types, stages and sources by using single-cell metabolomics technology and other single-cell metabolomics technologies, and to explore the relationship and mechanism between the metabolic status and the biological characteristics of tumor cells. In this study, high-throughput single-cell metabolomics platforms such as SpaceM will be used to classify, quantify and compare metabolic phenotypes of tumor cells from multiple levels (such as space, time, function, etc.), and combine single-cell transcriptomics, epigenomics, proteomics and other data to build tumor cell metabolic networks and identify key metabolic markers and regulatory factors. This topic will cover the following aspects:
Project content:
1. Data acquisition and preprocessing: tumor samples of different types, stages and sources were obtained from public databases or cooperative institutions, and pre-processing steps such as quality control, normalization and dimensionality reduction were carried out to facilitate subsequent analysis.
2. Single cell metabolomics analysis: SpaceM and other high-throughput single-cell metabolomics platforms are used to detect and image metabolites at the single-cell level in tumor samples, and statistical learning or deep learning methods are used to extract effective features and patterns from single-cell metabolic data for clustering, dimensionality reduction, pseudo-time sequencing and other analysis to reveal the metabolic state of tumor cells and its heterogeneity and plasticity.
3. Single cell multi-omics integration analysis: Combined with other single-cell omics data (such as transcriptome, epigenome, proteome, etc.), multimodal data integration methods (such as Seurat, MOFA+, etc.) were used to explore other molecular characteristics of tumor cells under different metabolic states, and to construct tumor cell metabolic networks to reveal the correlation and interaction between metabolic states and other cell characteristics.
4. Identification of metabolic markers and regulatory factors: Based on the results of single-cell multi-omics integration analysis, the metabolic markers and regulatory factors of tumor cells under different metabolic states were identified using differential analysis, enrichment analysis, network analysis and other methods, and experimental verification and functional analysis were conducted to explore their roles and mechanisms in the occurrence, development and treatment of cancer.
Topic innovation:
1. This study will use single-cell metabolomics technology, combined with other single-cell omics technologies, to systematically analyze the metabolic state, heterogeneity and plasticity of tumor cells of different types, stages and sources, so as to provide new perspectives and methods for revealing cancer biology and developing new therapeutic strategies.
2. This project will use SpaceM and other high-throughput single-cell metabolomics platforms to classify, quantify and compare metabolic phenotypes of tumor cells from multiple levels (such as space, time, function, etc.), and use advanced data mining and machine learning methods to extract effective features and patterns from single-cell metabolic data. To reveal the metabolic status, heterogeneity and plasticity of tumor cells.
3. This project will combine other single-cell omics data (such as transcriptome, epigenome, proteome, etc.), and use multi-modal data integration methods (such as Seurat, MOFA+, etc.) to explore other molecular characteristics of tumor cells under different metabolic states, and construct tumor cell metabolic networks. To reveal correlations and interactions between metabolic states and other cellular characteristics.
4. Based on the results of single-cell multi-omics integrated analysis, this study will identify metabolic markers and regulatory factors of tumor cells under different metabolic states, conduct experimental verification and functional analysis, and explore their roles and mechanisms in the occurrence, development and treatment of cancer.
Tumor cells are a highly heterogeneous and plastic cell type, which can adjust their metabolic state according to changes in the microenvironment to adapt to the needs of growth, proliferation, invasion and metastasis. The abnormal regulation of tumor cell metabolism is closely related to cancer occurrence, development and treatment response, so revealing the molecular mechanism and regulatory network of tumor cell metabolism is of great significance for understanding cancer biology and developing new therapeutic strategies. However, current studies of tumor cell metabolism are mainly based on population-level metabolomic analysis, which cannot reflect the metabolic heterogeneity and dynamics within and between tumor cells, nor can it reveal the correlation and interaction between metabolic states and other cellular characteristics (such as gene expression, epigenetic inheritance, signaling pathways, etc.).
Brief introduction of the project:
The purpose of this study is to systematically analyze the metabolic status, heterogeneity and plasticity of tumor cells of different types, stages and sources by using single-cell metabolomics technology and other single-cell metabolomics technologies, and to explore the relationship and mechanism between the metabolic status and the biological characteristics of tumor cells. In this study, high-throughput single-cell metabolomics platforms such as SpaceM will be used to classify, quantify and compare metabolic phenotypes of tumor cells from multiple levels (such as space, time, function, etc.), and combine single-cell transcriptomics, epigenomics, proteomics and other data to build tumor cell metabolic networks and identify key metabolic markers and regulatory factors. This topic will cover the following aspects:
Project content:
1. Data acquisition and preprocessing: tumor samples of different types, stages and sources were obtained from public databases or cooperative institutions, and pre-processing steps such as quality control, normalization and dimensionality reduction were carried out to facilitate subsequent analysis.
2. Single cell metabolomics analysis: SpaceM and other high-throughput single-cell metabolomics platforms are used to detect and image metabolites at the single-cell level in tumor samples, and statistical learning or deep learning methods are used to extract effective features and patterns from single-cell metabolic data for clustering, dimensionality reduction, pseudo-time sequencing and other analysis to reveal the metabolic state of tumor cells and its heterogeneity and plasticity.
3. Single cell multi-omics integration analysis: Combined with other single-cell omics data (such as transcriptome, epigenome, proteome, etc.), multimodal data integration methods (such as Seurat, MOFA+, etc.) were used to explore other molecular characteristics of tumor cells under different metabolic states, and to construct tumor cell metabolic networks to reveal the correlation and interaction between metabolic states and other cell characteristics.
4. Identification of metabolic markers and regulatory factors: Based on the results of single-cell multi-omics integration analysis, the metabolic markers and regulatory factors of tumor cells under different metabolic states were identified using differential analysis, enrichment analysis, network analysis and other methods, and experimental verification and functional analysis were conducted to explore their roles and mechanisms in the occurrence, development and treatment of cancer.
Topic innovation:
1. This study will use single-cell metabolomics technology, combined with other single-cell omics technologies, to systematically analyze the metabolic state, heterogeneity and plasticity of tumor cells of different types, stages and sources, so as to provide new perspectives and methods for revealing cancer biology and developing new therapeutic strategies.
2. This project will use SpaceM and other high-throughput single-cell metabolomics platforms to classify, quantify and compare metabolic phenotypes of tumor cells from multiple levels (such as space, time, function, etc.), and use advanced data mining and machine learning methods to extract effective features and patterns from single-cell metabolic data. To reveal the metabolic status, heterogeneity and plasticity of tumor cells.
3. This project will combine other single-cell omics data (such as transcriptome, epigenome, proteome, etc.), and use multi-modal data integration methods (such as Seurat, MOFA+, etc.) to explore other molecular characteristics of tumor cells under different metabolic states, and construct tumor cell metabolic networks. To reveal correlations and interactions between metabolic states and other cellular characteristics.
4. Based on the results of single-cell multi-omics integrated analysis, this study will identify metabolic markers and regulatory factors of tumor cells under different metabolic states, conduct experimental verification and functional analysis, and explore their roles and mechanisms in the occurrence, development and treatment of cancer.
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