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Mann Lab

@mannlab.bsky.social

The Mann Lab is a pioneer in mass spectrometry-based proteomics. Posts represent personal views from lab members and Matthias Mann

680 Followers  |  1 Following  |  106 Posts  |  Joined: 17.12.2024  |  2.1956

Latest posts by mannlab.bsky.social on Bluesky

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We congratulate @georgwa.bsky.social from the @mannlab.bsky.social on his sucessful #PhD defense!

27.11.2025 15:17 β€” πŸ‘ 0    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0

Honored to be included in this reflection on 20 excellent years of MolSystBiol! From our first phosphotyrosine interactome in 2005 to proteome-wide networks - scale has grown exponentially. MS proteomics + AI is transforming systems-level understanding. Exciting times!
embopress.org/doi/pdf/10.1...

25.11.2025 12:44 β€” πŸ‘ 10    πŸ” 4    πŸ’¬ 0    πŸ“Œ 0

Elegant chemistry and cryo-EM explain the selective Lys8 ubiquitylation. A clear example of how metabolite sensing and the ubiquitin system intersect.

17.11.2025 10:27 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Great to see Brenda Schulman’s team post their new bioRxiv paper. MS-based proteomics helped uncover a beautifully simple mechanism: cysteine levels tune metabolism by flipping the inverse stability of LRRC58 and its substrate CDO1.
www.biorxiv.org/content/bior...

17.11.2025 10:27 β€” πŸ‘ 5    πŸ” 0    πŸ’¬ 1    πŸ“Œ 1
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Fantastic time at #HUPO2025 in Toronto! Congratulations to our talented Early Career researchers! Proud winners: Oeller Marc OellerπŸ₯‡ 1st Place Poster, Kathrin Korff πŸ₯ˆ Runner-up Poster, and @carolineweiss.bsky.social 🎀 3-Minute Thesis award! The future of #proteomics research is bright!

13.11.2025 12:01 β€” πŸ‘ 16    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
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If you are at HUPO 2025, catch up with our team @mannlab.bsky.social to hear about our exciting work in single-cell proteomics, immunopeptidomics, spatial proteomics, and the latest advances in mass spectrometry technology!

07.11.2025 13:20 β€” πŸ‘ 8    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
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The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis - Nature Communications Researchers incorporate mass spectrometry-based proteomics and machine learning to assess the potential of a less invasive diagnostic approach for Lyme neuroborreliosis, a common nervous system infect...

In Nature Communications: MS-based proteomics + machine learning to diagnose Lyme neuroborreliosis with 92% accuracy (CSF) and 80% (blood) - paving the way for earlier, less invasive testing.
www.nature.com/articles/s41...

31.10.2025 13:06 β€” πŸ‘ 4    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
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Awesome talk by @erictopol.bsky.social at the Bavarian Academy of Sciences and Humanities (BAdW). A positive and hopeful take on #AI in medicine. Thanks for highlighting #DVP, too 😊

29.10.2025 08:58 β€” πŸ‘ 5    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

AlphaDIA is open-source and free for academic and commercial users. We are currently preparing version 2.0 - stay tuned!
#AlphaDIA #Bioinformatics #DeepLearning #MassSpectrometry

22.10.2025 13:36 β€” πŸ‘ 2    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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AlphaDIA enables DIA transfer learning for feature-free proteomics - Nature Biotechnology An open-source platform for data-independent acquisition proteomics adapts predicted libraries to experimental settings.

Excited to share AlphaDIA's publication in Nature Biotechnology!
Our open-source DIA framework brings deep learning directly to raw MS data with feature-free processing, transfer learning for any PTM, and performance matching top tools.
www.nature.com/articles/s41...

22.10.2025 13:36 β€” πŸ‘ 27    πŸ” 7    πŸ’¬ 2    πŸ“Œ 0

How much hands-on lab expertise gets lost? We developed a multimodal AI agent that turns videos into protocols and detects procedural errors. Making science accessible. Great collaboration with #Google.
Preprint: www.biorxiv.org/content/10.1...
@patiskowronek.bsky.social explainsπŸ‘‡

09.10.2025 10:54 β€” πŸ‘ 9    πŸ” 2    πŸ’¬ 1    πŸ“Œ 1

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If this interests you:
πŸ” Retweet the first post:
bsky.app/profile/mann...
⭐️ Give our github repo a star github.com/MannLabs/scP...
❓ tell us what you are going to do with #scPortrait

24.09.2025 13:39 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

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This work was a fantastic collaboration:
@mannlab.bsky.social
@fabiantheis.bsky.social
@v-hornung.bsky.social
A big shoutout to all of our co-authors: Alessandro Palma,
Altana Namsaraeva, Ali Oğuz Can, Varvara Varlamova, Mahima Arunkumar, @lukasheumos.bsky.social, @georgwa.bsky.social

24.09.2025 13:34 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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scPortrait integrates single-cell images into multimodal modeling Machine learning increasingly uncovers rules of biology directly from data, enabled by large, standardized datasets. Microscopy images provide rich information on cellular architecture and are accessi...

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Bottom line:
scPortrait turns microscopy images into a first-class modality for machine learning and multimodal foundation models, alongside RNA and proteomics πŸ”¬πŸ’»
πŸ”— Preprint: doi.org/10.1101/2025...
πŸ”— GitHub: github.com/MannLabs/scP...

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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Our extensive documentation and tutorials make scPortrait easy to use and accessible.
And as part of @scverse.bsky.social it’s compatible with existing tools like scanpy, squidpy, bento-tools or Moscot πŸš€πŸ
mannlabs.github.io/scPortrait/i... #OpenSourceTools #Tutorial #CodeDocumentation

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypes Forward genetic screening associates phenotypes with genotypes by randomly inducing mutations and then identifying those that result in phenotypic changes of interest. Here we present spatially resolv...

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scPortrait already scales:
1️⃣ 120M+ single-cell images from image-based genetic screens
2️⃣ applied on patient-derived datasets to perform AI-driven morphology analysis
Refs:
1️⃣ BioRxiv2023 ➑️ www.biorxiv.org/content/10.1...
2️⃣ Nature 2025➑️ www.nature.com/articles/s41...

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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We also ship a benchmark dataset of Golgi morphologies and use it to compare image featurization tools: #ConvNeXt, #SubCell, #CellProfiler

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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✨ Embedding images into transcriptome atlases ✨
We use scPortrait to embed single-cell images from a @10xgenomics.bsky.social Xenium ovarian cancer dataset into the #SCimilarity transcriptome atlas (R2 = 0.65), recovering meaningful cell types

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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✨ Morphology defined cell states ✨
Image embeddings generated with scPortrait resolve intra- vs extratumoral macrophages with distinct morphologies, linked to anti-inflammatory vs fibroblast-like programs

24.09.2025 11:01 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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✨ Transcriptomes from images ✨
Using optimal transport + flow matching, scPortrait generates gene expression directly from CODEX images, capturing canonical marker expression like TCL1A in germinal centers in the tonsil
#CODEX #flowmatching #OT

24.09.2025 11:01 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

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With standardized single-cell image datasets in place, the key question is: what new biology can we unlock?
We highlight three use-cases for scPortrait

24.09.2025 11:01 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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The new .h5sc format provides fast random access to single-cell images for ML training.
It follows #FAIR data principles (findable, accessible, interoperable, reusable) and integrates with @scverse.bsky.social tools via AnnData.

24.09.2025 11:01 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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The scPortrait pipeline transforms raw input images step by step:
β€’ stitch FOVs
β€’ segment & extract cells
β€’ output standardized .h5sc single-cell image datasets

From messy pixels β†’ inputs ready for training πŸ–₯️

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

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Problem: microscopy images are messy, fragmented, and hard to use for ML
Solution: scPortrait standardizes them into a new .h5sc single-cell image format turning πŸ”¬microscopy images into a reusable resource for integrative cell modeling

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

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Microscopy images are:
πŸ“ˆ easy to acquire across scales (organism β†’ subcellular)
πŸ–₯️ information-rich (cellular architecture, tissue structure, perturbation responses)
= πŸš€ ideal fuel for foundation models of cell behavior

24.09.2025 11:01 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

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AI has had major breakthroughs (#alphafold #chatgpt) & computational models can now detect patterns in complex datasets without external guidance 🧠πŸ–₯️

🧬 biological datasets often contain entangled information making them complex to interpret β†’πŸ§ πŸ–₯️ + 🧬 = unlock new biology

24.09.2025 11:01 β€” πŸ‘ 2    πŸ” 1    πŸ’¬ 1    πŸ“Œ 0
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Tomorrow’s large scale cross-modality models will further unlock biology, but they need standardized inputs. With @sophia-maedler.bsky.social & @nik-as.bsky.social, we built #scPortrait, @scverse.bsky.social package to turn microscopy images into single-cell image datasets for multimodal modeling

24.09.2025 11:01 β€” πŸ‘ 2    πŸ” 0    πŸ’¬ 1    πŸ“Œ 3
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scPortrait integrates single-cell images into multimodal modeling Machine learning increasingly uncovers rules of biology directly from data, enabled by large, standardized datasets. Microscopy images provide rich information on cellular architecture and are accessi...

Our preprint on scPortrait is out! We built a framework + format to turn microscopy into standardized single-cell image datasets. #scPortrait scales >100M cells, integrates with @scverse.bsky.social, & enables cross-modality modeling from morphology to transcriptomics
doi.org/10.1101/2025...

24.09.2025 11:01 β€” πŸ‘ 14    πŸ” 4    πŸ’¬ 1    πŸ“Œ 1
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Congratulations on the inaugural of #AITHYRA, the new Biomedical AI institute in Vienna. Thank you for the invitation to speak at the symposium and the fascinating discussions on AI Γ— proteomics. #AIforLifeScience

15.09.2025 08:10 β€” πŸ‘ 4    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Thanks CPR, reNEW, @ucph.bsky.social
Full story:
www.sciencedirect.com/science/arti...

10.09.2025 15:15 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

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