Jovan Tanevski
@tanevski.bsky.social
Group leader - computational biomedical discovery. Heidelberg University & Heidelberg University Hospital. https://www.tanevskilab.org
computational scientific discovery, biomedicine, spatial omics
computational scientific discovery, biomedicine, spatial omics
Reposted by Jovan Tanevski
🧭 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
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
October 8, 2025 at 5:01 PM
🧭 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
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
Reposted by Jovan Tanevski
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
🔗 Manuscript: www.biorxiv.org/content/10.1...
💻 Code: partipy.readthedocs.io
September 15, 2025 at 8:40 AM
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
🔗 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.
🚨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 ⬇️
🧵Walk-through thread below ⬇️
July 22, 2025 at 7:30 AM
🎉 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.
Reposted by Jovan Tanevski
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...
www.nature.com
May 7, 2025 at 1:11 PM
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
Reposted by Jovan Tanevski
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...
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...
April 29, 2025 at 8:25 PM
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...
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...
We present our new alignment framework TOAST www.biorxiv.org/content/10.1...
April 22, 2025 at 9:37 AM
🚨 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...
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
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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...
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...
April 15, 2025 at 4:12 PM
@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