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Peroxisomes are specialized organelles present in fungi, plant, and animal cells. It can vary in number, size, morphology, and activity depending on the type of tissue and the nutritional state of the cell. For example, cells with active lipid metabolism, such as adipocytes, neurons, and hepatocytes, have more peroxisomes than other cells in the body. Besides their primary role in breaking down complex organic molecules, peroxisomes can also synthesize specific macromolecules and participate in...
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Automated, image-based quantification of peroxisome characteristics with perox-per-cell.

Maxwell L Neal1, Nandini Shukla2, Fred D Mast1

  • 1Seattle Children's Research Institute, Center for Global Infectious Disease Research, Seattle, WA USA.

Bioinformatics (Oxford, England)
|July 13, 2024
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Summary
This summary is machine-generated.

Perox-per-cell software automates the quantification of peroxisome features in yeast cells from microscopy images. This tool enables high-throughput analysis, improving efficiency in peroxisome research.

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Area of Science:

  • Cell Biology
  • Biophysics

Background:

  • Peroxisome research often involves time-consuming, image-based data collection.
  • Manual quantification of peroxisomal features can be a bottleneck in high-throughput studies.

Purpose of the Study:

  • To develop and validate automated software for quantifying peroxisome characteristics in yeast cells.
  • To streamline image-based data collection in peroxisome research.

Main Methods:

  • The software, coded in Python, utilizes off-the-shelf image processing tools.
  • It automatically segments yeast cells and peroxisomes from microscopy images.
  • Quantitative metrics such as peroxisome counts per cell and spatial areas are generated.

Main Results:

  • Perox-per-cell successfully automates image-based data collection for peroxisome research.
  • The software provides quantitative metrics for peroxisome features.
  • Validation tests show agreement with manual quantification of peroxisomal counts and cell instances.

Conclusions:

  • Perox-per-cell significantly enhances the efficiency of peroxisome research by automating data collection.
  • The software enables high-throughput quantification of peroxisomal characteristics.
  • It provides a reliable tool for researchers studying peroxisomes in yeast.