This repository contains supporting scripts and codes used in the analysis for the paper: "High-content morphological profiling by Cell Painting in 3D spheroids".
Cell Painting is a popular assay for morphological profiling of 2D monolayer cell cultures.
In the paper, we propose a scalable method to apply Cell Painting in 3D.
The workflow is largely based on existing analysis strategies,
with some adaptations to enable single-cell morphological profiling of 3D spheroids.
This repository contains a collection of notebooks for processing CellProfiler features of 3D spheroids and creating figures that accompany the manuscript.
A one-click reproducible Code Ocean capsule is available at: <DOI — to be added upon publication>. The capsule bundles the environment, a data subset, and the notebooks so the analysis can be re-run without local setup.
To run locally instead, follow Installation and Test dataset below.
Requires Python 3.10
python3.10 -m venv venv source venv/bin/activate pip install -r requirements.txt
The data underlying this analysis are deposited in the BioImage Archive under accession S-BIAD2254 (https://www.ebi.ac.uk/biostudies/studies/S-BIAD2254).
The archive contains:
- Raw images — 16-bit OME-TIFF, organised across per-plate result folders under Files/results/.
- Single-cell feature tables — per-compartment CellProfiler features (featICF_cells.parquet, featICF_cytoplasm.parquet, featICF_nuclei.parquet) within each acquisition's results folder (e.g. Files/results/PB000137/).
- Segmentation masks — Cellpose masks in the segmentation/ subfolder of each results folder.
- Detection demonstrator dataset — an example dataset for benchmarking spheroid detection, at Files/detection_example_dataset/.
- CellProfiler pipeline + cellpose models - the pipeline is provided at Files/feature_extraction/.
- Image acquisition files - JOBS, OCs, and GA3 pipelines are provided at Files/image_acquisition/.