AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization

TL;DR

This paper introduces DeepOrganelle, a deep learning tool that enables large-scale, high-resolution analysis of cellular organelles in tissue samples, revealing new insights into organelle dynamics during spermatogenesis.

Contribution

The study presents a novel deep learning framework for comprehensive 2D/3D electron microscopy analysis of tissues, enabling detailed subcellular mapping at scale.

Findings

Uncovered stage-dependent mitochondria-ER contact dynamics during meiosis

Revealed organelle redistribution in Sertoli cells related to blood-testis barrier formation

Demonstrated the ability to digitize tissue remodeling at subcellular resolution

Abstract

The distribution and interactions of cellular organelles play a critical role in mediating cellular physiology and pathology. Large-scale electron microscopy enables visualization of organelle distribution and interactions at the tissue level with nanometer resolution, but robust and efficient computational analysis tools are lacking. Here, we present a deep learning tool for universal large-scale 2D/3D electron microscopy analysis, DeepOrganelle. This new tool enables high-throughput, cell-resolved spatiotemporal mapping and digitization of organelle distribution and interactions. When applied to spermatogenesis across 12 stages and 22 differentiation status of the germ cells, DeepOrganelle uncovered previously unrecognized, stage-dependent dynamics of mitochondria-endoplasmic reticulum contact sites within one subphase of prophase I during meiosis. It also revealed coordinated organelle redistribution in Sertoli cells towards the blood-testis barrier, digitizing the remodeling dynamics of the tissue. This study demonstrates that DeepOrganelle provides a powerful framework that captures subcellular dynamics at the whole-tissue level.