{"id":378689,"date":"2023-12-12T19:00:00","date_gmt":"2023-12-13T00:00:00","guid":{"rendered":"https:\/\/platohealth.ai\/single-cell-spatial-metabolomics-with-cell-type-specific-protein-profiling-for-tissue-systems-biology-nature-communications\/"},"modified":"2023-12-13T04:43:21","modified_gmt":"2023-12-13T09:43:21","slug":"single-cell-spatial-metabolomics-with-cell-type-specific-protein-profiling-for-tissue-systems-biology-nature-communications","status":"publish","type":"post","link":"https:\/\/platohealth.ai\/single-cell-spatial-metabolomics-with-cell-type-specific-protein-profiling-for-tissue-systems-biology-nature-communications\/","title":{"rendered":"Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology – Nature Communications","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"
With the advent of the current immunotherapy approaches, it is becoming critical to develop a comprehensive understanding of immune metabolism. The multi-omics scSpaMet approach has the great potential to link the multi-layer information of the proteomics data with the metabolism data on the same biological tissue. The scSpaMet starts with staining the tissues with the metal-isotope conjugated antibodies, performing the metabolic profiling using the ToF-SIMS imaging, and finally performing the proteomic profiling using IMC. 3D-SMF18<\/a><\/sup> \u00a0was developed to profile hundreds of metabolic fragments\u2019 mass spectrum peaks in tonsils using ToF-SIMS at the tissue level and the protein expression profile at the single cell level of immune cells in tonsil and lung tissues using IMC35<\/a>,36<\/a><\/sup>. Every multiplexed imaging region in the SIMS data has a resolution of better than 1 \u03bcm per pixel for over 200 m\/z peaks. Further, IMC provides targeted multiplex protein imaging data for deciphering distinct cell types (for instance, cancer\/epithelial, stroma, and\u00a0immune\u00a0cells) at 1 \u03bcm per pixel resolution for up to 40 markers. Compared to existing metabolomic profiling methods, scSpaMet allows correlation of\u00a0multiplex cell types to metabolic profile at the single-cell level. Compared to 3D-SMF, the scSpaMet imaging pipeline incorporated in situ sequential detection of metabolomic and proteomic within the same tissue (Supplementary Fig.\u00a01a<\/a> and Supplementary Table\u00a01<\/a>), providing correlative proteomics\/metabolomics analysis at the single-cell level by cross-modality spatial registration (Supplementary Fig.\u00a01b<\/a>). Accurate single-cell segmentation from the scSpaMet pipeline allowed single-cell level joint metabolite and protein downstream analysis, whereas 3D-SMF only allowed metabolite channel-level correlation, channel embedding, and pixel clustering from tissue regions (Supplementary Fig.\u00a01c<\/a>).<\/p>\n In the scSpaMet pipeline, the sequential ToF-SIMS and IMC datasets were combined and matched to the single cell level to integrate the information and perform comparative analysis (Fig.\u00a01a<\/a>). The\u00a0scSpaMet was used to dissect the metabolism in lung tumors (Fig.\u00a01b<\/a>) and tonsil tissues (Fig.\u00a01c<\/a>). First, a consecutive tissue slide is stained separately using Hematoxylin and Eosin (H&E) to identify the imaging region of interest before scSpaMet profiling and downstream analysis (Supplementary Figs.\u00a02a<\/a> and 3<\/a> and 4<\/a>). Next, sequential ToF-SIMS and IMC imaging procedures\u00a0are performed to extract spatial maps of metabolites and proteins. Pixel clustering of SIMS data reveals unique metabolic variation in the spatial context (Supplementary Figs.\u00a05<\/a>\u20137<\/a> and Methods). To quantify cell-type specific metabolic profiles, a cross-modality single-cell registration pipeline was developed utilizing Histone 3 and Intercalator markers in the IMC dataset, and Phosphate 79\u2009m\/z channels in TOF-SIMS dataset, allowing the\u00a0joint analysis of\u00a0protein-metabolite modalities in\u00a0single cells (Fig.\u00a02<\/a>, Supplementary Figs.\u00a08<\/a>\u201310<\/a> and Methods). Using affine transformation, the cross-modality pipeline yields higher structural similarity\u00a0(SSIM) and normalized root mean square error (NRMSE)\u00a0compared to rotation only and random shift. The registration quality was quantified using the same metrics of\u00a0SSIM and\u00a0NRMSE. Single-cell segmentation was used to extract the protein and metabolite expression levels and their spatial locations.<\/p>\n