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dc.contributor.authorAli, Ahmed
dc.contributor.authorDavidson, Shawn
dc.contributor.authorFraenkel, Ernest
dc.contributor.authorGilmore, Ian
dc.contributor.authorHankemeier, Thomas
dc.contributor.authorKirwan, Jennifer A.
dc.contributor.authorLane, Andrew N.
dc.contributor.authorLanekoff, Ingela
dc.contributor.authorLarion, Mioara
dc.contributor.authorMcCall, Laura-Isobel
dc.contributor.authorMurphy, Michael
dc.contributor.authorSweedler, Jonathan V.
dc.contributor.authorZhu, Caigang
dc.date.accessioned2022-10-03T12:15:56Z
dc.date.available2022-10-03T12:15:56Z
dc.date.issued2022-10-01
dc.identifier.urihttps://hdl.handle.net/1721.1/145638
dc.description.abstractAbstract 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.publisherSpringer USen_US
dc.relation.isversionofhttps://doi.org/10.1007/s11306-022-01934-3en_US
dc.rightsArticle 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.sourceSpringer USen_US
dc.titleSingle cell metabolism: current and future trendsen_US
dc.typeArticleen_US
dc.identifier.citationMetabolomics. 2022 Oct 01;18(10):77en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineering
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Program
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-10-02T03:14:19Z
dc.language.rfc3066en
dc.rights.holderThe Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
dspace.embargo.termsY
dspace.date.submission2022-10-02T03:14:19Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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