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Related Experiment Video

Updated: Mar 25, 2026

Autofluorescence Imaging to Evaluate Cellular Metabolism
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Mapping Cell Metabolic States by Image-Enabled Gating Metabolomic Cytometry.

Yuanyi He1,2,3,4, Zijun Ren5, Xiaojie Chen1,2,3

  • 1Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China.

Analytical Chemistry
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

CyMeta-ImaGating enables the creation of single-cell metabolic atlases by verifying cell profiles using image-enabled gating. This method accurately captures cellular metabolism in complex samples, advancing cell atlas research.

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Area of Science:

  • Single-cell biology
  • Metabolomics
  • Systems biology

Background:

  • Cell atlases reveal tissue heterogeneity, but generating single-cell metabolic atlases is challenging due to signal capture difficulties.
  • Accurate single-cell signal capture is crucial for understanding complex biological samples.

Purpose of the Study:

  • To develop a method for generating single-cell metabolic atlases with verified single-cell profiles from complex samples.
  • To introduce CyMeta-ImaGating, a metabolomic cytometry technique with image-enabled gating.

Main Methods:

  • CyMeta-ImaGating combines metabolomic cytometry with image-enabled gating.
  • Verified single-cell profiles are obtained by matching metabolomic data with brightfield single-cell images.
  • The method was tested on HeLa cell suspensions and spleen, liver, and cancer cells.

Main Results:

  • Image-enabled gating significantly increased the proportion of single-cell profiles from 28% to 91-100% in HeLa cell suspensions.
  • Generated spleen cell atlases distinguished immune cell types and revealed metabolic state transitions post-pathogen activation.
  • Characterized doxorubicin's effects on liver and cancer cells, showing drug specificity and metabolic robustness.

Conclusions:

  • CyMeta-ImaGating enables the generation of high-quality single-cell metabolic atlases from complex samples.
  • Single-cell metabolic atlases are critical tools for resolving cellular metabolic states and dynamics.
  • The developed method has implications for understanding immune responses and drug effects.