Moritz Gerstung
Scientist developing AI for oncology. Division head at the German Cancer Research Centre DKFZ. Prof at the University of Heidelberg, Germany. Previously at EMBL-EBI and Wellcome Sanger Institute. Alumnus of ETH Zurich.
- Reposted by Moritz Gerstung🚨 new glioblastoma preprint alert! we present the first spatially resolved single cell atlas comparing radionecrotic changes (RN) and recurrent IDH-wildtype glioblastoma (GB) –– shedding light on a long-standing diagnostic challenge. 🧵 1/
- Join us: With @abigailsuwala.bsky.social we are looking for a postdoc to drive our spatial transcriptomics analysis efforts of brain tumours. If you enjoy multidisciplinary biomedical research, big data and coding, you are in the right place. karriere.klinikum.uni-heidelberg.de/index.php?ac...
- A spatial transcriptomics analysis led by my student Zaira reveals the distinct nature (gene expression, tumour cell states and local microenvironments) of radionecrosis and recurrence in glioblastoma. 👏
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- Reposted by Moritz GerstungWhat if you could get a glimpse of the future of your health, today? Our scientists have developed a new generative AI model, trained using large-scale health records, that can estimate how human health may change over time. Watch to find out more. 🖥️🧬
- This is a great set of methods for studying combinatorial effects of cancer mutations on spatial phenotypes. Clever experimental design by @breinigmarco.bsky.social hijacking Visium and elegant analysis by @lomakinai.bsky.social and @elihei.bsky.social www.nature.com/articles/s41...
- Reposted by Moritz GerstungHow does tumour heterogeneity arise? How can we predict cancer cell plasticity? In 2 new studies, we trace #glioblastoma heterogeneity to a spatial cancer cell trajectory w. multimodal cell atlassing bit.ly/4mkrWgs & predict plasticity w. snRNA/ATAC+deep learning bit.ly/3FbI6Ic 🧵
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- Some classical statistics today. ebmstate, an R package for multistate models with empirical Bayes covariate effect estimation. Developed by Rui Costa during his postdoc in my group. doi.org/10.32614/RJ-...
- Reposted by Moritz GerstungFrom great collaborations come great things. Excited to share Segger, the solution to segmentation of spatial transcriptomics (ST) data, with the @steglelab.bsky.social and @moritzgerstung.bsky.social labs, spearheaded by the great Andrew Moorman and Elyas Heidari www.biorxiv.org/content/10.1...
- Reposted by Moritz Gerstung1/ New preprint! 🍳 @elihei.bsky.social and our team at @embl.org , @dkfz.bsky.social, and @mskcancercenter.bsky.social built #segger - a fast, accurate cell segmentation tool for spatial transcriptomics that assigns transcripts to their cell origins! doi.org/10.1101/2025...
- Spatial transcriptomics holds great promise to understand biological tissue function, but the assignment of transcripts to cells has been a substantial bottleneck. For this reason, Elyas Heidari, a student in my lab and in @steglelab.bsky.social built segger. www.biorxiv.org/content/10.1...
- Reposted by Moritz GerstungNew preprint! We worked with @msftresearch.bsky.social and @broadinstitute.org to see whether large language models (LLMs) can be useful to variant scientists in deciding whether genetic variants seen in a patient are responsible for their disease. tl;dr yes they can: www.biorxiv.org/content/10.1...
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- Reposted by Moritz GerstungResharing here a recent X post. In this preprint, we introduce an improved version of NanoSeq, a duplex sequencing protocol with <5 errors per billion bp in single DNA molecules, and use it to study the somatic mutation landscape of oral epithelium in >1000 people. 1/ www.medrxiv.org/content/10.1...
- Reposted by Moritz GerstungWe strongly suggest that academic publishers and other platforms that host research rapidly implement a Share to Bluesky button for their articles. Here's how: docs.bsky.app/docs/advance... #AcademicSky #HigherEd #Altmetrics
- 5:20 start this morning for a virtual talk at the WEHI in Melbourne, covering: * Cancer risk models * Delphi multi-disease genAI * Paion, a new brain tumor digital pathology algorithm Summary below:
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