bioimage-archive>projects> spora

AI-ready Spatial Proteomics Datasets
and Platform

Download and explore standardized multiplexed imaging data to understand tissue architecture across cancer types and organs with spora.

MX Images
13,546
Patients
5,477
Cohorts
35
Technologies
5

A project by:

AIMM Logo EPFL Logo

Dataset Browser

Browse, filter, and download all 35 cohorts by cancer type, technology, and more.

Explore Tissue Data

Explore every cohort in spora through an interactive multiplex visualizer with multi-channel overlays, right in your browser.


Diverse Data Collection

spora spans 35 cohorts across CRC, TNBC, NSCLC, Lymphoma, Melanoma, PDAC, and more, captured across IMC, CODEX, Orion, and MIBI platforms.

Explore included cohorts ›
Fluorescent tissueLung tissue microarray coreOrganoid tissue

Standardized File Types and Annotations for Each Sample

Multiplex Images
Paired H&E Images
Tissue Masks
Cell Masks
Cell Types
Patient Data
Meta Data

Dataset Browser

Browse and filter all 35 cohorts by cancer type, technology, and more. Download any cohort directly from the command line or through the spora-io Python library.

View all cohorts ›

spora [io]

Load any single or combination of datasets in two lines of Python. The spora-io library provides a unified interface to browse, tile, and load spatial proteomics data with matched H&E and diverse intensity standardizations.

Read the docs ›

spora [bench]

Benchmark multiplex foundation models across cell-level and patient-level tasks with standardized evaluation protocols.

View benchmarks ›

Explore Tissue Data

Explore every cohort in spora through an interactive multiplex visualizer with multi-channel overlays, right in your browser.

Launch Tissue Visualizer ›

Getting Started

Download the full collection or explore a small cohort as a quick demo.

Download via Command Line

Browse all cohorts and download spora or specific datasets using rclone from your terminal.

Open Dataset Browser ›

Download a Small Cohort Directly

Try spora immediately by downloading allam2022spatially, a small lung cancer IMC cohort (~500MB).

Download command


Citation

If you use spora in your research, please cite:

@inproceedings{querfurth2026spora,
  title = {{spora: A Unified Multimodal Dataset and Platform for Spatial Proteomics}},
  author = {von Querfurth, Benedikt and Wenckstern, Johann and Jain, Eeshaan and Klein, Lukas and Cheng, Phil F. and Cheng, Yexiang and Vincent-Cuaz, Cédric and Frossard, Pascal and Haberecker, Martina and Wicki, Andreas and Michielin, Olivier and Bunne, Charlotte},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS) Evaluations & Datasets (E&D) Track},
  year = {2026}
}

Contribute Data to Spora

Do you want to contribute to spora? Get in touch! spora@epfl.ch