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Single-cell lipidomics: protocol development for reliable cellular profiling using capillary sampling.

Anastasia Kontiza1, Johanna von Gerichten1, Matt Spick2

  • 1School of Chemistry and Chemical Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, GU2 7XH, Guildford, UK. m.bailey@surrey.ac.uk.

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Summary
This summary is machine-generated.

Optimizing single-cell lipidomics involves controlling sampling variables like blank correction and aspiration volume for accurate pancreatic cancer cell analysis. These best practices enhance biomarker discovery and metabolic research.

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Cancer Research

Background:

  • Single-cell lipidomics offers detailed cellular lipidome analysis but faces challenges like small sample volumes and background interference.
  • Lack of standardized validation data hinders the reliability of single-cell lipidomics studies.

Purpose of the Study:

  • To investigate the impact of various sampling variables on lipid profiles of single pancreatic cancer cells.
  • To establish best practices for robust and adaptable single-cell lipidomics workflows.

Main Methods:

  • Utilized automated and manual capillary sampling for isolating living single cells.
  • Evaluated effects of sampling media, capillary tips, aspiration volume, and environmental controls (temperature, humidity).
  • Employed liquid chromatography-mass spectrometry (LC-MS) for lipid profile detection.

Main Results:

  • Automated and manual capillary sampling produced comparable lipid profiles under controlled conditions.
  • Blank correction, capillary tip selection, and aspiration volume control are critical for maintaining detection sensitivity.
  • The choice of sampling medium did not significantly impact lipidomics results.

Conclusions:

  • Methodological variables significantly influence single-cell lipidomics outcomes, particularly for pancreatic cancer cells.
  • Standardized protocols for sampling are essential for reliable biomarker discovery and metabolic research.
  • The study provides foundational best practices for improving the robustness of single-cell lipidomics workflows.