Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets

TL;DR

This paper presents a practical, user-friendly pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets, suitable for researchers with limited computational expertise.

Contribution

It provides detailed guidelines and compatible open-source tools for efficient segmentation, spatial analysis, and visualization of cellular organelles in EM datasets, accessible on standard workstations.

Findings

Efficient pipeline for organelle segmentation with minimal manual effort

Tools for analyzing spatial interactions between organelles

Methods for high-quality 3D visualization of EM data

Abstract

Volume electron microscopy is the method of choice for the in-situ interrogation of cellular ultrastructure at the nanometer scale. Recent technical advances have led to a rapid increase in large raw image datasets that require computational strategies for segmentation and spatial analysis. In this protocol, we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. We specifically target researchers in the life sciences with limited computational expertise, who face the following tasks within their volume electron microscopy projects: i) How to generate 3D segmentation labels for different types of cell organelles while minimizing manual annotation efforts, ii) how to analyze the spatial interactions between organelle instances, and iii) how to best visualize the 3D segmentation results. To meet these demands we give detailed guidelines for choosing the most efficient segmentation tools for the specific cell organelle. We furthermore provide easily executable components for spatial analysis and 3D rendering and bridge compatibility issues between freely available open-source tools, such that others can replicate our full pipeline starting from a raw dataset up to the final plots and rendered images. We believe that our detailed description can serve as a valuable reference for similar projects requiring special strategies for single- or multiple organelle analysis which can be achieved with computational resources commonly available to single-user setups.