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Metabolic imaging in multiple time scales.

V Krishnan Ramanujan1

  • 1Metabolic Photonics Laboratory, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Suite D6067, Los Angeles, CA 90048, USA; Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA 90048, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA 90048, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA 90048, USA.

Methods (San Diego, Calif.)
|September 10, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces new time-resolved imaging to analyze single-cell mitochondrial metabolism. Breast cancer cells show altered metabolic responses compared to normal cells, suggesting broader diagnostic applications.

Keywords:
Breast cancerMetabolic imagingMicroscopyMitochondriaNDUFS3Scaling behavior

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Area of Science:

  • Cellular and Molecular Metabolism
  • Biophysics
  • Medical Imaging

Background:

  • Mitochondrial metabolism is crucial for cellular function and disease.
  • Understanding real-time metabolic dynamics at the single-cell level is challenging.
  • Current methods often lack the temporal resolution to capture rapid metabolic changes.

Purpose of the Study:

  • To develop and apply novel time-resolved imaging techniques for probing mitochondrial metabolism.
  • To investigate single-cell metabolic dynamics across various time scales (microseconds to minutes).
  • To compare metabolic responses in normal versus cancer cells and in cells with mitochondrial dysfunction.

Main Methods:

  • Utilized a mitochondrial membrane potential reporter fluorescence.
  • Employed single-cell kinetics, fluorescence recovery after photobleaching (FRAP), and time-series analysis of mitochondrial fluorescence fluctuations.
  • Applied scaling analysis to time-series data for metabolic characterization.

Main Results:

  • Breast cancer cells exhibit significant alterations in metabolic responses compared to normal human mammary epithelial cells across all measured time scales.
  • Distinct metabolic differences were observed in cells with mitochondrial dysfunction.
  • The proposed methods revealed differences in single-cell metabolic kinetics and dynamics.

Conclusions:

  • The developed time-resolved imaging strategy effectively differentiates metabolic profiles of normal, cancerous, and dysfunctional cells.
  • The findings suggest broad applicability for diagnosing metabolic disorders, including cancer and mitochondrial myopathies.
  • This approach paves the way for portable, real-time metabolic measurement systems for diagnostics.