Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology.
Thomas HuMayar AllamShuangyi CaiWalter HendersonBrian YuehAybuke GaripcanAnton V IevlevMaryam AfkarianSemir BeyazAhmet F CoskunPublished in: Nature communications (2023)
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 to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The 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.
Keyphrases
- single cell
- rna seq
- endothelial cells
- induced apoptosis
- high throughput
- gene expression
- mass spectrometry
- protein protein
- high resolution
- deep learning
- induced pluripotent stem cells
- cell cycle arrest
- machine learning
- amino acid
- squamous cell carcinoma
- binding protein
- oxidative stress
- endoplasmic reticulum stress
- signaling pathway
- cell death
- cancer therapy
- drug delivery
- bone marrow
- young adults
- liquid chromatography