Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells.
Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells.
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DOI:
10.1038/s41467-020-18376-x
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发表时间:
2020-09-24
影响因子:
16.6
通讯作者:
Wei L
中科院分区:
文献类型:
--
作者:
Du J;Su Y;Qian C;Yuan D;Miao K;Lee D;Ng AHC;Wijker RS;Ribas A;Levine RD;Heath JR;Wei L
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. Single-cell metabolomics can offer deep insights into the metabolic reprogramming that accompanies disease states. Here, the authors use Raman spectro-microscopy for non-invasive metabolite analysis and identification of druggable metabolic susceptibilities in single live melanoma cells.
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影响因子:
64.5
作者:
Hugo W;Shi H;Sun L;Piva M;Song C;Kong X;Moriceau G;Hong A;Dahlman KB;Johnson DB;Sosman JA;Ribas A;Lo RS
通讯作者:
Lo RS
影响因子:
64.5
作者:
Hauser AS;Chavali S;Masuho I;Jahn LJ;Martemyanov KA;Gloriam DE;Babu MM
通讯作者:
Babu MM
影响因子:
12.3
作者:
Budnik B;Levy E;Harmange G;Slavov N
通讯作者:
Slavov N
影响因子:
8.8
作者:
Delgado-Goni, Teresa;Galobart, Teresa Casals;Beloueche-Babari, Mounia
通讯作者:
Beloueche-Babari, Mounia
影响因子:
3.3
作者:
Fu, Dan;Holtom, Gary;Freudiger, Christian;Zhang, Xu;Xie, Xiaoliang Sunney
通讯作者:
Xie, Xiaoliang Sunney