Dataset Information

SpaceM reveals metabolic states of single cells

ABSTRACT:

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

All MALDI-imaging MS data as well as metabolite and lipid annotations and images are publicly available through METASPACE (https://metaspace2020.eu/project/Rappez_2021_SpaceM)

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, MS Imaging -, Liquid Chromatography MS - positive - reverse phase, MS Imaging - positive - direct infusion

SUBMITTER: Luca Rappez

PROVIDER: MTBLS78 | MetaboLights | 2021-05-11

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	MTBLS78	Other
	FILES	Other
	a_MTBLS78_LC-MS_cell_lipid_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS78_LC-MS_cell_lipid_positive_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS78_LC-MS_cell_negative_reverse-phase_metabolite_profiling.txt	Txt

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Publications

SpaceM reveals metabolic states of single cells.

Rappez Luca L Stadler Mira M Triana Sergio S Gathungu Rose Muthoni RM Ovchinnikova Katja K Phapale Prasad P Heikenwalder Mathias M Alexandrov Theodore T

Nature methods 20210705 7

PMID: 34226721

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Project description:This initiative is part of the Single Cell Analysis Program (SCAP) and is funded through the NIH Common Fund (See <a href="http://nihroadmap.nih.gov/" target="_blank">http://nihroadmap.nih.gov/</a>), which supports cross-cutting programs that are expected to have exceptionally high impact. Common Fund initiatives address key roadblocks in biomedical research that impede basic scientific discovery and its translation into improved human health. In addition, these programs capitalize on emerging opportunities to catalyze progress across multiple biomedical fields. Single cell analysis has recently emerged as an important field of research because technologies have improved in sensitivity and throughput sufficiently to begin measuring and understanding heterogeneity in complex biological systems and correlating it with changes in biological function and disease processes. By profiling individual cells it is possible to resolve rare cells, transient cell states, and the influence of organization and environment on such cells and states, which cannot be described by ensemble measurements. The long-term goal of the SCAP is to accelerate this move towards personalizing health to the cellular level by understanding the link between cell heterogeneity, tissue function and emergence of disease through the discovery, development and translation of innovative approaches which will dramatically change the way cells are characterized. The SCAP will focus on research, which will systematically measure, analyze and model cell-to-cell variation, and identify crucial differences and rare biological states, which may have important functional consequences. Under SCAP, there are three studies to evaluate the cellular heterogeneity using transcriptional profiling of single cells (U01): <ol> <li><a href="./study.cgi?study_id=phs000835">University of Pennsylvania</a>: Role of single cell mRNA variation in systems associated electrically excitable cells</li> <li><a href="./study.cgi?study_id=phs000836">University of Southern California</a>: Evaluation of cellular heterogeneity using patchclamp and RNA-seq of single cells</li> <li><a href="./study.cgi?study_id=phs000834">University of California at San Diego</a>: Single cell sequencing and in situ mapping of RNA transcripts in human brains</li> </ol> The SCAP has been designed as a five-year program with several components: (1) the collection, analysis and sharing of comprehensive expression datasets to understand the role of heterogeneity in tissues and systemically and identify critical parameters and states; (2) the discovery of new, innovative tools for spatiotemporal imaging, manipulation, analysis and modeling of a biologically relevant population of cells with minimal perturbation; (3) milestone-driven validation and translation of technologies for characterizing single cells in situ meeting the needs of end-users; and (4) development and coordination of a multidisciplinary research community through workshops and other collective endeavors. Further details about the NIH SCAP-T program can be found here <a href="http://commonfund.nih.gov/singlecell/)" target="_blank">http://commonfund.nih.gov/singlecell/</a>. The SCAP-T data sets include the detailed phenotype information, experimental protocols, QC information, RNA-sequencing data and NGS results from human heart and human brain cells. The first data release includes 697 single cells from human brain and heart. The second data release includes an additional 978 single cells from human brain and heart. The third data release includes an additional 1631 single cells from human brain and heart. The fourth data release is a correction to this study, but adds no new cells. The fifth data release includes an additional 2984 single cells from human brain and heart. The sixth data release includes an additional 944 single cells from human brain and heart. The seventh data release includes an additional 1977 single cells from human brain and heart. The <a href="https://www.scap-t.org/content/data-portal" target="_blank">SCAP-T data portal</a> provides a customized interface for users to quickly identify and retrieve files by phenotypes, and data properties such as sequencing facility or coverage for all of these 9231 single cells. For more information about the SCAP-T study and the data portal, please visit <a href="http://www.scap-t.org" target="_blank">http://www.scap-t.org</a>.