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Related Experiment Video

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Computer Vision-Assisted Data Analysis for Correlative Electron Microscopy and Secondary Ion Mass Spectrometry

André du Toit1, Alicia A Lork1, Carl Ernst2

  • 1Department of Chemistry & Molecular Biology, University of Gothenburg, Medicinaregatan 7B, 413 90 Göteborg, Sweden.

Analytical Chemistry
|October 12, 2025
PubMed
Summary

This study introduces a computer vision pipeline for automated organelle segmentation in electron microscopy (EM) images, enabling faster correlative analysis with nanoscale secondary ion mass spectrometry (NanoSIMS). The method accurately quantifies protein turnover in subcellular organelles, revealing differences between cell types.

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Area of Science:

  • Bioimaging
  • Cell Biology
  • Computer Vision

Background:

  • Correlative imaging combines complementary data from different modalities for comprehensive sample analysis.
  • Transmission electron microscopy (EM) and nanoscale secondary ion mass spectrometry (NanoSIMS) offer high-resolution morphological and chemical insights.
  • Manual analysis of large EM and NanoSIMS datasets is slow, biased, and limits throughput.

Purpose of the Study:

  • To develop an automated image analysis pipeline for segmenting subcellular organelles in EM images.
  • To enable rapid and reproducible correlation of EM data with NanoSIMS ion maps.
  • To facilitate the study of subcellular processes like protein turnover using correlative imaging.

Main Methods:

  • Developed a computer vision pipeline using YOLOv8 deep learning for organelle classification and segmentation in EM images.
  • Included EM image preprocessing, segmentation, morphological filtering, and registration with NanoSIMS data.
  • Trained the model on human neuronal progenitor cells (hNPCs) and differentiated neurons to recognize six organelle types.

Main Results:

  • The automated pipeline achieved robust accuracy in organelle segmentation and correlation with NanoSIMS data.
  • Successfully measured 15N-leucine abundance to study protein turnover in single organelles.
  • Observed distinct organelle protein turnover dynamics, with slower turnover in differentiated neurons compared to hNPCs.
  • Significantly reduced analysis time from hours to minutes while maintaining consistency with manual segmentation.

Conclusions:

  • Computer vision streamlines correlative imaging workflows, enhancing data quality and throughput.
  • The automated pipeline provides deeper insights into subcellular protein turnover and cellular processes.
  • This approach is valuable for nanoscale secondary ion mass spectrometry (NanoSIMS) users and broader bioimaging applications.