2026 NI Manuscript

Materials and code for analysis in 2026 Manuscript. Spatial analysis of PI16+ Fibroblasts, PRG4+ Fibroblasts, and VWF+ endothelium across whole, human, developmental manual digits with accompanying tissue annotations for compartment analysis.

@article{TBA,
  title={The embryonic origins of site-specific arthritis},
  author={Sarah Davidson*, Davide Simone*, Kathrin Jansen*, Max Cowan, Caio Machado, Ian Reekie, Ananya Bhalla, Rowie Borst, Cesar Prada Medina, Joshua Bull, Zhi Yi Wong, Sarah Hill, Micon Garvilles, Sam Pledger, Patricia Reis Nisa, Nora Schwingen, Dylan Windell, Moustafa Attar, Catherine Disney, Andrew J Bodey, Alissa Parmenter, Helen Byrne, Sharif Ahmed, Shashidhara Marathe, Peter D Lee, Chris Mahony, Adam P Croft, Stephen Sansom#, Mark C Coles# Christopher D Buckley#},
  year={2026}
}

* These authors contributed equally
# These authors jointly supervised this work

Please note, article in press

Environment Setup

Requirements

• CUDA 12.1 compatible GPU (≥8 GB VRAM recommended)
• conda or mamba

Option A: conda/mamba (recommended)

mamba env create -f environment.yml
conda activate ni_manuscript

Option B: Docker (GPU)

docker build -t ni_manuscript .
docker run --gpus all -it --rm -v $(pwd):/workspace ni_manuscript

Note on MuSpAn: This package is not on PyPI - See https://docs.muspan.co.uk for documentation.

Workflow

For full methods (including data collection and staining protocols / procedures) please refer to the original publication. Images acquired samples derived from 3 donors on the CellDIVE platform (20x magnification, 0.325µm/pixel) with 4 markers: DAPI (DAPI_INIT and DAPI_FINAL), PI16, PRG4, and VWF.

Ingest and Segmentation

• run_batch_analysis.py calling run_spatial_analysis.py.

Sample phenotyping

Cell types were phenotyped by mixed methods, including graph-based clustering per sample. The latter was used for downstream analysis.

Spatial Analysis

• Generate metadata file: generate_metadata_from_samples.py
• run_spatial_statistics_v3.py

Proximal/Distal Enrichment

python scripts/prox_dist_enrichment_analysis.py --data-dir /path/to/data/processed/prox_dist
# Optional: specify a separate output directory
python scripts/prox_dist_enrichment_analysis.py --data-dir ./data/processed/prox_dist --output-dir ./results/enrichment

References and acknowledgements

Please refer to the original manuscript for full acknowledgements and references.
Particularly grateful to the developers and maintainers of MuSpAn, SpatialData, and CellPose.

@article{bull2024muspan,
  title={MuSpAn: a toolbox for multiscale spatial analysis}, 
  author={Bull, Joshua A and Moore, Joshua W and Mulholland, Eoghan J and Leedham, Simon J and Byrne, Helen M},
  journal={bioRxiv},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}

@article{marconato2025spatialdata,
  title={SpatialData: an open and universal data framework for spatial omics},
  author={Marconato, Luca and Palla, Giovanni and Yamauchi, Kevin A and Virshup, Isaac and Heidari, Elyas and Treis, Tim and Vierdag, Wouter-Michiel and Toth, Marcella and Stockhaus, Sonja and Shrestha, Rahul B and others},
  journal={Nature methods},
  volume={22},
  number={1},
  pages={58--62},
  year={2025},
  publisher={Nature Publishing Group US New York}
}

@article{stringer2021cellpose,
  title={Cellpose: a generalist algorithm for cellular segmentation},
  author={Stringer, Carsen and Wang, Tim and Michaelos, Michalis and Pachitariu, Marius},
  journal={Nature methods},
  volume={18},
  number={1},
  pages={100--106},
  year={2021},
  publisher={Nature Publishing Group US New York}
}

Data Availability

Data Repository: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD3270