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Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

Du, Jiajun and Su, Yapeng and Qian, Chenxi and Yuan, Dan and Miao, Kun and Lee, Dongkwan and Ng, Alphonsus H. C. and Wijker, Reto S. and Ribas, Antoni and Levine, Raphael D. and Heath, James R. and Wei, Lu (2020) Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells. Nature Communications, 11 . Art. No. 4830. ISSN 2041-1723. https://resolver.caltech.edu/CaltechAUTHORS:20200924-122708002

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Abstract

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41467-020-18376-xDOIArticle
https://github.com/mesako/Melanoma-PublicationRelated ItemCode
ORCID:
AuthorORCID
Su, Yapeng0000-0002-6305-8467
Qian, Chenxi0000-0003-4815-5565
Miao, Kun0000-0001-6567-3650
Ng, Alphonsus H. C.0000-0003-0074-4598
Wijker, Reto S.0000-0001-5104-9849
Ribas, Antoni0000-0003-3669-8458
Heath, James R.0000-0001-5356-4385
Wei, Lu0000-0001-9170-2283
Additional Information:© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 21 January 2020; Accepted 14 August 2020; Published 24 September 2020. L.W. acknowledges start-up fund support from California Institute of Technology. We acknowledge the following agencies and foundations for support: NIH Grant U01 CA217655 (to J.R.H.); the Parker Institute for Cancer Immunotherapy (J.R.H. and A.R.), the WA State Andy Hill CARE Foundation (J.R.H.), and an ISB Innovator Award (Y.S.). Data availability: All the data supporting the findings of this study are available within the article and its Supplementary Information files and from the corresponding author upon reasonable request. The following databases are used: Human Reference Genome (UCSC hg 19), Gene Expression Omnibus database (GEO), Molecular Signatures Database (MSigDB). RNA-seq data have been deposited to array express with accession number of E-MTAB-8842. Source data are provided with this paper. Code availability: Custom code for the surprisal analysis of Raman spectra has previously been published and deposited on GitHub (https://github.com/mesako/Melanoma-Publication) 36. Source data are provided with this paper. These authors contributed equally: Jiajun Du, Yapeng Su. Author Contributions: L.W., J.R.H., J.D., and Y.S. conceived the study and designed the experiments. J.D., Y.S., C.Q., D.Y., K.M., D.L., A.N., and R.W. performed the experiments. J.D. and Y.S. analyzed and interpreted the data. L.W., J.R.H., A.R., and R.L. provided conceptual advice on data analysis and interpretation. L.W., J.D., J.R.H., and Y.S. wrote the manuscript. L.W. and J.R.H. supervised this study. The authors declare no competing interests. Peer review information: Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
Funders:
Funding AgencyGrant Number
CaltechUNSPECIFIED
NIHU01 CA217655
Parker Institute for Cancer ImmunotherapyUNSPECIFIED
Washington State Andy Hill CARE FoundationUNSPECIFIED
Institute for Systems BiologyUNSPECIFIED
Subject Keywords:Cancer metabolism; Cellular imaging; Lipids; Raman spectroscopy; Target identification
Record Number:CaltechAUTHORS:20200924-122708002
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200924-122708002
Official Citation:Du, J., Su, Y., Qian, C. et al. Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells. Nat Commun 11, 4830 (2020). https://doi.org/10.1038/s41467-020-18376-x
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105516
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Sep 2020 20:12
Last Modified:24 Sep 2020 20:12

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