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Lipid droplet quantification based on iterative image processing.

Tarik Exner1, Carlo A Beretta2, Qi Gao2

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This study introduces a new ImageJ method for quantifying lipid droplets (LDs) in cells. The approach accurately measures LD size and number, even in challenging clustered conditions.

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ImageJ/Fijiendoplasmic reticulumfatty acid/metabolismfluorescence microscopyimage quantificationtriglycerides

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Area of Science:

  • Cell Biology
  • Biochemistry
  • Microscopy

Background:

  • Lipid droplets (LDs) are essential for neutral lipid storage and cellular metabolism.
  • Accurate quantification of LD number and size is crucial for understanding metabolic regulation and lipotoxicity.
  • Existing LD quantification methods have limitations in speed, accuracy, and applicability to clustered LDs.

Purpose of the Study:

  • To develop and validate a robust ImageJ-based method for detecting and quantifying lipid droplets.
  • To provide a versatile tool for analyzing LD parameters in various cell types and experimental conditions.
  • To address limitations of current manual and automated LD quantification techniques.

Main Methods:

  • An ImageJ-based image analysis workflow was developed for lipid droplet detection.
  • The method utilizes adjustable preprocessing to resolve clustered LDs.
  • LD identification relies on circular edge detection and central fluorescence intensity maxima.
  • The approach is adaptable to different cell types via interactive parameters.

Main Results:

  • The ImageJ method successfully detects and quantifies lipid droplets in fluorescence microscopy images.
  • Validation across three cell lines demonstrated accuracy in LD number and volume measurements.
  • The method effectively resolves and quantifies lipid droplets even when they are closely clustered.
  • Application showed that FATP4/ACSVL5 overexpression increased LD size but not number in oleate-treated COS7 cells.

Conclusions:

  • The presented ImageJ-based method offers a reliable and versatile tool for lipid droplet quantification.
  • This approach overcomes limitations of previous methods, particularly for high-density or clustered lipid droplets.
  • The tool facilitates deeper insights into cellular lipid metabolism and the regulation of lipid droplet dynamics.