Project description:<p>Single-cell metabolomics reveals cell heterogeneity and elucidates intracellular molecular mechanisms. However, general concentration measurement of metabolites can only provide a static delineation of metabolomics, lacking the metabolic activity information of biological pathways. Herein, we developed a universal system for dynamic metabolomics by stable isotope tracing at the single-cell level. This system comprises an organic mass cytometry-based single-cell metabolomic platform and an automated metabolite quantification and untargeted metabolic flux data processing platform, providing an integrated workflow for dynamic metabolomics of living single cells. As a proof-of-concept, a total of 40 labeled metabolites were tracked in single breast cancer cells, enabling the global profiling and correlation analysis of metabolic activities across various metabolic pathways, as well as flow analysis of interlaced metabolic networks. The significance of metabolic activity profiling was underscored by a 2-deoxyglucose inhibition model, demonstrating delicate metabolic alteration within single cells which cannot reflected by concentration analysis. Significantly, the system combined with a neural network model successfully enabled the metabolomic profiling of direct co-cultured tumor cells and macrophages. This revealed intricate cell-cell interaction mechanisms within the tumor microenvironment and firstly identified versatile polarization subtypes of tumor-associated macrophages based on their metabolic signatures. Importantly, these findings are in line with the renewed diversity atlas of macrophages from genome features obtained through single-cell RNA-sequencing. The developed system facilitates a comprehensive understanding and opens a new avenue of single-cell metabolomics from both static and dynamic perspectives.</p>
Project description:This research clinical trial studies high definition single cell analysis in blood and tissue samples from patients with colorectal cancer which has spread to the liver. High definition single cell analysis allows doctors to study the properties of cancer cells that are sometimes found in the blood of patients and to determine how the genes and proteins in them may change over time. Studying samples from patients with colorectal cancer in the laboratory may help doctors learn more about how cancer spreads, as well as how to predict the disease outcomes in patients with cancer.
Project description:Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell SPAtially resolved METabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet pipeline to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil and endometrium tissues, respectively. ScSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet’s ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.