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PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.

Anastasia Razdaibiedina1,2,3, Alexander Brechalov1,2, Helena Friesen2

  • 1Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.

Molecular Systems Biology
|March 13, 2024
PubMed
Summary
This summary is machine-generated.

We developed PIFiA, a self-supervised method for protein functional annotation using fluorescence microscopy images. PIFiA extracts robust features from cell images, enabling new insights into protein organization and function.

Keywords:
ImagingMachine LearningProteinSelf-supervisedSingle-cell

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Area of Science:

  • Cell biology
  • Proteomics
  • Bioimaging

Background:

  • Fluorescence microscopy provides single-cell resolution of protein localization.
  • Extracting functional information from cell images is challenging.
  • Existing methods often require supervised labels and struggle with noise.

Purpose of the Study:

  • To develop a self-supervised approach for protein functional annotation from microscopy data.
  • To generate robust and noise-invariant feature profiles for proteins.
  • To enable quantitative analysis of protein organization and function.

Main Methods:

  • Developed PIFiA (Protein Image-based Functional Annotation), a self-supervised learning method.
  • Applied PIFiA to the global yeast ORF-GFP collection.
  • Generated protein feature profiles from single-cell images.

Main Results:

  • PIFiA outperforms existing methods in molecular representation learning.
  • Generated hierarchical functional organization, studied cell heterogeneity, and identified functional modules.
  • Confirmed new PIFiA predictions, suggesting novel biological roles for proteins.

Conclusions:

  • PIFiA is an effective self-supervised tool for protein functional annotation.
  • The method facilitates the exploration of protein organization and cellular heterogeneity.
  • PIFiA provides a valuable resource for quantitative cell biology research.