Nellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy

TL;DR

Nellie is an automated, adaptable pipeline for segmentation, tracking, and feature extraction of intracellular structures in live-cell microscopy, enabling detailed analysis without user input.

Contribution

It introduces a fully automated, hierarchical segmentation and tracking pipeline with a user-friendly GUI, adaptable to various imaging conditions and capable of extensive feature extraction.

Findings

Successfully unmixes multiple organelles from single-channel images.

Quantifies mitochondrial changes using graph autoencoders after treatment.

Characterizes ER networks across cell types and time points.

Abstract

The analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased user-friendly pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie adapts to image metadata, eliminating user input. Nellie's preprocessing pipeline enhances structural contrast on multiple intracellular scales allowing for robust hierarchical segmentation of sub-organellar regions. Internal motion capture markers are generated and tracked via a radius-adaptive pattern matching scheme, and used as guides for sub-voxel flow interpolation. Nellie extracts a plethora of features at multiple hierarchical levels for deep and customizable analysis. Nellie features a point-and-click Napari-based GUI that allows for code-free operation and visualization, while its modular open-source codebase invites extension by experienced users. We demonstrate Nellie's wide variety of use cases with three examples: unmixing multiple organelles from a single channel using feature-based classification, training an unsupervised graph autoencoder on mitochondrial multi-mesh graphs to quantify latent space embedding changes following ionomycin treatment, and performing in-depth characterization and comparison of endoplasmic reticulum networks across different cell types and temporal frames.