Editors' ChoiceCancer

Singled out: Profiling metabolic and proteomic heterogeneity

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Science Translational Medicine  15 Apr 2015:
Vol. 7, Issue 283, pp. 283ec64
DOI: 10.1126/scitranslmed.aab2767

Tumor cell populations vary widely in their aberrant activation of metabolic, signaling, and transcriptional networks. This cell-to-cell variability is associated with the emergence of drug resistance and tumor relapse, confounding therapies targeted against specific pathways. However, classical biochemical assays based on population averages obscure heterogeneity at the single-cell level. Moreover, it has been challenging to compare molecular information across analytical techniques with incompatible formats.

In a new study, Xue et al. developed a microfluidic assay to simultaneously measure four metabolites and seven related proteins from single cells. Approximately 100 glioblastoma cells (GBM39 cell line) were individually partitioned into microchambers with an adjacent “barcode” array encoding a panel of antibodies. Following lysis, four metabolites (glucose, glutathione, c-AMP, and c-GMP) were quantified using enzymatic amplification or Förster resonance energy transfer (FRET). Simultaneously, another seven metabolism-related proteins were quantified using a sandwich immunofluorescence assay. Altogether, the statistical distributions of these analytes classified two subpopulations of glioblastoma cells with distinct metabolic phenotypes: 80% of cells displayed high glucose uptake, low cAMP, and cGMP; the other 20% displayed low glucose uptake, high cAMP, and cGMP. Remarkably, after treatment with the epidermal growth factor receptor (EGFR) inhibitor erlotinib, the population was still comprised of these two metabolic phenotypes at a similar ratio. This exquisite single-cell sensitivity with multiple metabolites and proteins provides new insights that are not possible at the population level.

This single-cell technology could also interrogate interconnected metabolic and proteomic signaling networks in the context of aging, diabetes, obesity, regenerative medicine, or toxicity. An exciting translational application of this technology would be for analyzing clinical samples to predict patient response to therapy and survival and to guide therapy design, particularly when limited numbers of cells are available. Nevertheless, further work will be needed to integrate this device with upstream sample processing as well as to ensure that the capture chemistry and device operation are sufficiently robust for a clinical environment.

M. Xue et al., Chemical methods for the simultaneous quantitation of metabolites and proteins from single cells. J. Am. Chem. Soc. 137, 4066–4069 (2015). [Abstract]

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