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Quantification of golgi dispersal and classification using machine learning models.

Rutika Sansaria1, Krishanu Dey Das1, Alwin Poulose2

  • 1School of Biology, Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM), Vithura, Thiruvananthapuram 695551, Kerala, India.

Micron (Oxford, England : 1993)
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PubMed
Summary
This summary is machine-generated.

This study automates Golgi dispersion quantification using machine learning. The developed method accurately classifies dispersed Golgi images, aiding disease research.

Keywords:
Golgi BodyGolgi DispersionGolgi ImagesImage ClassificationsMachine Learning ModelsQuantification

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Area of Science:

  • Cell Biology: Investigates the structure and function of the Golgi apparatus in eukaryotic cells.
  • Bioinformatics: Applies machine learning for quantitative analysis of biological image data.
  • Genetics: Explores the impact of genetic modifications (CARP1 variants) on Golgi morphology.

Background:

  • The Golgi body is vital for protein and lipid modification in eukaryotic cells.
  • Alterations in Golgi structure, known as Golgi dispersion, are linked to stress, disease, and aging.
  • Current methods for quantifying Golgi dispersion are lacking, hindering disease research.

Purpose of the Study:

  • To automate the quantification of Golgi dispersion from microscopy images.
  • To develop machine learning models for classifying dispersed versus undispersed Golgi images.
  • To provide tools for analyzing Golgi structure changes relevant to disease identification.

Main Methods:

  • Collected confocal microscopy images of HeLa cells expressing Galactose-1-phosphate uridylyltransferase (GALT)-green fluorescent protein (GFP).
  • Implemented automated image processing including mean and Gaussian filters, Otsu thresholding, and watershed segmentation for Golgi particle analysis.
  • Extracted image features and employed machine learning classifiers (logistic regression, decision tree, random forest, Naive Bayes, KNN, gradient boosting) for classification tasks.

Main Results:

  • Achieved 65% classification accuracy for distinguishing empty vector (EV) from CARP1 wildtype (CARP1 WT) using a gradient boosting classifier.
  • Attained 65% classification accuracy for differentiating empty vector (EV) from CARP1 ring mutant (CARP1 RM) with a random forest classifier.
  • Demonstrated the feasibility of automated quantification and machine learning-based classification of Golgi dispersion.

Conclusions:

  • The developed automated quantification method effectively analyzes Golgi dispersion.
  • Machine learning models can accurately classify Golgi dispersion states, aiding in the study of associated diseases.
  • This approach offers a valuable tool for researchers investigating Golgi body alterations and their implications.