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

Abstract

Correlative imaging is a powerful analytical approach in bioimaging, as it offers complementary information on the samples measured by different modalities. Particularly, correlative transmission electron microscopy (EM) and nanoscale secondary ion mass spectrometry (NanoSIMS) imaging enable high-resolution morphological and chemical analysis at the subcellular level. However, manual segmentation and correlation of regions of interest (ROIs) in large EM and NanoSIMS data sets are time-consuming, prone to user bias, and limited in throughput. To address this, we developed a computer vision-assisted image analysis pipeline for automatic classification and segmentation of subcellular organelles in EM images, enabling rapid and reproducible correlation with NanoSIMS ion data. Using human neuronal progenitor cells (hNPCs) and differentiated postmitotic neurons, we trained a YOLOv8 deep learning model to recognize six major organelle types. The pipeline included EM image preprocessing, segmentation via YOLOv8, morphological filtering, and image registration with NanoSIMS ion maps. Performance evaluation demonstrated a robust model accuracy. We applied the pipeline to measure 15N-leucine abundance to study protein turnover in single organelles across different cell states. Results showed distinct turnover dynamics among organelles, with slower turnover observed in differentiated neurons compared to hNPCs. The automated pipeline significantly reduced the analysis time (from hours to minutes) while maintaining consistency with manual segmentation. Our approach demonstrates how computer vision can streamline correlative imaging workflows, improve data quality, and enable deeper insights into subcellular processes such as protein turnover, making it especially valuable for SIMS users and broader bioimaging applications.