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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics.

Renmeng Liu1, Jiannong Li2, Yunpeng Lan1

  • 1Chemistry and Biochemistry Department, University of Oklahoma, Norman, Oklahoma 73072, United States.

Analytical Chemistry
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

Single cell mass spectrometry (MS) combined with new bioinformatics software reveals distinct metabolic subpopulations in melanoma cells. This approach quantifies cellular heterogeneity and identifies key metabolite biomarkers for understanding cancer and drug response.

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Area of Science:

  • Metabolomics and Cancer Research
  • Single-cell analysis techniques
  • Bioinformatics and computational biology

Background:

  • Mass spectrometry (MS) is crucial for metabolomics, but single-cell analysis faces challenges due to limitations in experimental platforms, algorithms, and quantitative analysis of cell heterogeneity.
  • Understanding cell heterogeneity and subpopulations is vital for cancer research and predicting response to therapy.

Purpose of the Study:

  • To develop and validate a method for global metabolomics profiling at the single-cell level.
  • To characterize tumor heterogeneity, quantify cell subpopulations, and prioritize metabolite biomarkers using a novel approach.
  • To investigate changes in melanoma cell lines upon anticancer drug treatment.

Main Methods:

  • Integration of the Single-probe single cell MS (SCMS) experimental technique with the SinCHet-MS bioinformatics software package.
  • Label-free quantitative analysis of metabolite profiles in two melanoma cell lines (WM115 and WM266-4) before and after vemurafenib treatment.
  • Characterization of emergent and altered cell subpopulations based on metabolic profiles.

Main Results:

  • A new subpopulation emerged in WM115 (primary melanoma) cells after vemurafenib treatment.
  • The proportion of existing subpopulations shifted in WM266-4 (metastatic melanoma) cells following drug treatment.
  • Metabolite biomarkers specific to each subpopulation were successfully prioritized.

Conclusions:

  • The combined SCMS and SinCHet-MS approach enables quantitative analysis of single-cell metabolic heterogeneity.
  • This label-free method aids in prioritizing potential biomarkers for further investigation.
  • The findings enhance the understanding of cell metabolism in human diseases and therapeutic responses.