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Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

Author

Listed:
  • Jiajun Du

    (California Institute of Technology)

  • Yapeng Su

    (California Institute of Technology
    Institute for Systems Biology)

  • Chenxi Qian

    (California Institute of Technology)

  • Dan Yuan

    (Institute for Systems Biology)

  • Kun Miao

    (California Institute of Technology)

  • Dongkwan Lee

    (California Institute of Technology)

  • Alphonsus H. C. Ng

    (Institute for Systems Biology)

  • Reto S. Wijker

    (California Institute of Technology)

  • Antoni Ribas

    (University of California Los Angeles)

  • Raphael D. Levine

    (University of California Los Angeles)

  • James R. Heath

    (Institute for Systems Biology)

  • Lu Wei

    (California Institute of Technology)

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.

Suggested Citation

  • Jiajun Du & Yapeng Su & Chenxi Qian & Dan Yuan & Kun Miao & Dongkwan Lee & Alphonsus H. C. Ng & Reto S. Wijker & Antoni Ribas & Raphael D. Levine & James R. Heath & Lu Wei, 2020. "Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18376-x
    DOI: 10.1038/s41467-020-18376-x
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    Cited by:

    1. Manuel Sigle & Anne-Katrin Rohlfing & Martin Kenny & Sophia Scheuermann & Na Sun & Ulla Graeßner & Verena Haug & Jessica Sudmann & Christian M. Seitz & David Heinzmann & Katja Schenke-Layland & Patric, 2023. "Translating genomic tools to Raman spectroscopy analysis enables high-dimensional tissue characterization on molecular resolution," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Yuying Tan & Junjie Li & Guangyuan Zhao & Kai-Chih Huang & Horacio Cardenas & Yinu Wang & Daniela Matei & Ji-Xin Cheng, 2022. "Metabolic reprogramming from glycolysis to fatty acid uptake and beta-oxidation in platinum-resistant cancer cells," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

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