See 👇 our new manuscript introducing SpaCEy, an explainable method for predicting clinical outcomes from spatial omics data 🧬 📄 Paper: www.biorxiv.org/content/10.6... 💻 Code repo: github.com/saezlab/SpaCEy

🧭 Colorectal cancer doesn’t follow a single path. Using spatial proteomics on ~500 tumors, we found distinct trajectories from early to late stage, involving the whole tumor microenvironment and its metabolic state. 📄 Preprint: arxiv.org/abs/2510.05083 #SpatialBiology #CRC #ImageBasedProfiling 1/5

Robust multicellular programs dissect the complex tumor microenvironment and track disease progression in colorectal adenocarcinomas

Colorectal cancer (CRC) is highly heterogeneous, with five-year survival rates dropping from $\sim$90% in localized disease to $\sim$15% with distant metastases. Disease progression is shaped not only...

arxiv.org

Introducing ParTIpy, a python package for Pareto Task Inference that scales to large-scale datasets, including single-cell and spatial transcriptomics. 🔗 Manuscript: www.biorxiv.org/content/10.1... 💻 Code: partipy.readthedocs.io

🎉 Such a great work by everyone involved in this major push forward in spatial multiplexing and next-generation pathology. I‘m glad to have been able to contribute to this effort and shed a light on the discovery of sub-cellular to tissue level organization patterns by xAI based on this technology.

Malte Kuehl@maltekuehl.com · last yr.

🚨Scaling multiplexed imaging 📈 We are excited to share Pathology-oriented multiPlexing (PathoPlex). Now out in @nature.com: www.nature.com/articles/s41... 🧵Walk-through thread below ⬇️

The latest version of the Kasumi manuscript is now published in Nature Comms www.nature.com/articles/s41... Kasumi identifies patterns in tissue patches, enabling analysis of disease progression and treatment response while providing insights into spatial coordination at cell-type or marker level

Learning tissue representation by identification of persistent local patterns in spatial omics data - Nature Communications

Spatial omics reveal tissue structures and can aid patient stratification. The authors present a method to identify patterns in tissue patches, enabling analysis of disease progression and treatment r...

nature.com

Brilliant session focused entirely on spatial multiomics @theaacr.bsky.social #AUA25 Well said @tanevski.bsky.social "Cancer is a spatial disease-spatialomics is the future of cancer science"! Wonderful composite spatial data from Linghua Wang @mdanderson.bsky.social www.nature.com/articles/s41...

🚨 New preprint: Topography Aware Optimal Transport for Alignment of Spatial Omics Data We present our new alignment framework TOAST www.biorxiv.org/content/10.1...

@chiaraschiller.bsky.social did an amazing job describing the landscape of methods for pairwise-association analysis in immediate spatial neighborhoods. Addressing limitations she proposes COZI and shows its ability to consistently recover directional cell-type associations and generate new insights

Chiara Schiller@chiaraschiller.bsky.social · last yr.

1/ Ever wondered how to best quantify cell-cell neighbor preferences in tissues? We compared 9+ neighbor preference (NEP) methods for analysing spatial omics data and propose a novel approach that combines the most relevant analysis features which we call COZI 🔬✨ Read more: doi.org/10.1101/2025...