dc.contributor.author | Ali, Ahmed | |
dc.contributor.author | Davidson, Shawn | |
dc.contributor.author | Fraenkel, Ernest | |
dc.contributor.author | Gilmore, Ian | |
dc.contributor.author | Hankemeier, Thomas | |
dc.contributor.author | Kirwan, Jennifer A. | |
dc.contributor.author | Lane, Andrew N. | |
dc.contributor.author | Lanekoff, Ingela | |
dc.contributor.author | Larion, Mioara | |
dc.contributor.author | McCall, Laura-Isobel | |
dc.contributor.author | Murphy, Michael | |
dc.contributor.author | Sweedler, Jonathan V. | |
dc.contributor.author | Zhu, Caigang | |
dc.date.accessioned | 2022-10-03T12:15:56Z | |
dc.date.available | 2022-10-03T12:15:56Z | |
dc.date.issued | 2022-10-01 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/145638 | |
dc.description.abstract | Abstract
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics. | en_US |
dc.publisher | Springer US | en_US |
dc.relation.isversionof | https://doi.org/10.1007/s11306-022-01934-3 | en_US |
dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
dc.source | Springer US | en_US |
dc.title | Single cell metabolism: current and future trends | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Metabolomics. 2022 Oct 01;18(10):77 | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | |
dc.contributor.department | Massachusetts Institute of Technology. Computational and Systems Biology Program | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2022-10-02T03:14:19Z | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature | |
dspace.embargo.terms | Y | |
dspace.date.submission | 2022-10-02T03:14:19Z | |
mit.license | PUBLISHER_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |