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Lukas Hatscher

@loggas.bsky.social

69 followers 130 following 30 posts

MD, PhD candidate in Institute of Computational Biomedicine - AG Schapiro, Interested in quantitative tissue analysis 💻 www.github.com/LukasHats

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Lukas Hatscher @loggas.bsky.social · 1d

Yes that’s exactly what I meant, I think it’s unique ;)

Lukas Hatscher @loggas.bsky.social · 1d

Entrance of this helix? Or should we meet at your poster?

Lukas Hatscher @loggas.bsky.social · 1d

Sounds good, how about tomorrows coffee break at 10:00?

Lukas Hatscher @loggas.bsky.social · 1d

Would love to hear and talk about muspan and its integration into existing single cell data formats. When are you presenting your poster?

Reposted by Lukas Hatscher

Gregor Sturm @grst.bsky.social · Aug 13

Our benchmark + guidelines for atlas-level differential gene expression of single cells is online:

academic.oup.com/bib/article/...

Bottom line: Use pseudobulk + DESeq2 in simple and pseudobulk + DREAM in more complex settings.

Collab w/ @leonhafner.bsky.social @itisalist.bsky.social

Lukas Hatscher @loggas.bsky.social · 3d

Well this also depends on how we define the term hallucinations and the output an LLM is generating. I think it’s more terminology problem here. Which does not change the fact that a lot of people do not understand what an LLM is doing and how to proper interpret the outputs.

Lukas Hatscher @loggas.bsky.social · 27d

2 decades of self-injecting venom and hundreds of snake bites

Lukas Hatscher @loggas.bsky.social · 27d

This guy really dedicated his life to science:

www.sciencedirect.com/science/arti...

www.science.org/content/arti...

Snake venom protection by a cocktail of varespladib and broadly neutralizing human antibodies

Snake envenomation is a neglected tropical disease, with 600 species causing over 100,000 deaths and 300,000 permanent disabilities in humans annually…

www.sciencedirect.com

Lukas Hatscher @loggas.bsky.social · Sep 13

Thanks Jim!

Lukas Hatscher @loggas.bsky.social · Sep 13

And last but not least thanks a lot to @denisschapiro.bsky.social who established the collaboration and mentored me.

Lukas Hatscher @loggas.bsky.social · Sep 13

I am happy to have worked with my collaborators from NTNU (Ingrid and Therese) on this amazing project, as well as @chiaraschiller.bsky.social who developed COZi (www.biorxiv.org/content/10.1...). shout out to CellCharter developer @marcovarrone.bsky.social for his amazing method(and collaboration)

Lukas Hatscher @loggas.bsky.social · Sep 13

The spatial analysis highlight is that we uncover this signal using 2 different spatial resolutions (cell and neighborhood level) and 2 independent methods (COZI and CellCharter). We hope that this will open up new research paths in Myeloma focusing on these cell interactions.

Lukas Hatscher @loggas.bsky.social · Sep 13

To our very surprise we find that increased “interaction” of PCs and a variety of immune cells, especially CD4+Tcells, is associated to increased risk of progression, which is contrary to many findings in other tumors where tumor immune interaction seem to generally be beneficial for patients.

Lukas Hatscher @loggas.bsky.social · Sep 13

Lastly we apply cell neighbor preference analysis with COZI (developed by @chiaraschiller.bsky.social ) and CellCharter’s neighborhood enrichment method and connect these findings to associated clinical metadata:

Lukas Hatscher @loggas.bsky.social · Sep 13

This questions the common belief that malignant PCs solely rely on glycolytic metabolism for cancer progression and niche establishment.

We further show that the aggregate size of the PC_OXPHOS neighborhood negatively correlates with immune infiltration

Lukas Hatscher @loggas.bsky.social · Sep 13

This led to the finding of 2 different malignant PC neighborhoods: 1) PC_OXPHOS characterized by huge vascularized aggregates of PCs with increased oxidative phosphorylation and 2)PC_MYELOID, where PCs show glycolytic metabolism and are loosely scattered around including myeloid cells.

Lukas Hatscher @loggas.bsky.social · Sep 13

As our antibody panel focused on functional markers, we used a novel neighborhood algorithm CellCharter ( @marcovarrone.bsky.social ) to structure the tissue into neighborhoods driven by not only cell types but also functional state.

Lukas Hatscher @loggas.bsky.social · Sep 13

We show that:

MM patients with bone disease (a frequent comorbidity) show an increased abundance of malignant Plasma Cells (PCs) in the vicinity of Osteoclasts and that PCs display a bone distance dependent expression of factors involved in bone degradation (IL32, HIF1A)

Lukas Hatscher @loggas.bsky.social · Sep 13

We apply IMC to biopsies from 65 MM patients, 6 SMM and 5 MGUS patients with an antibody panel focusing on immune, bone cells and metabolism. The dataset consists of roughly 1 million labeled cells including distance to the next bone surface for every image (soon on zenodo 10.5281/zenodo.17093203)

Lukas Hatscher @loggas.bsky.social · Sep 13

I am excited to share my first work in @schapirolab.bsky.social on Multiple Myeloma (MM) in collaboration with the Standal Lab from NTNU Norway. We use Imaging Mass Cytometry (IMC) on bone marrow biopsies from MM and precursor patients (details below): www.biorxiv.org/content/10.1...

Highly multiplexed imaging recovers immune and metabolic niches in multiple myeloma associated with disease progression and bone involvement

Multiple myeloma (MM) is a cancer arising from genetically aberrant plasma cells (PCs) that remain dependent on the bone marrow (BM) microenvironment for disease establishment and progression. Here, w...

www.biorxiv.org

Reposted by Lukas Hatscher

Ming Tommy Tang @tommytang.bsky.social · Aug 3

Nature Method: Temporal and spatial omics technologies for 4D profiling
www.nature.com/articles/s4...

Temporal and spatial omics technologies for 4D profiling

Nature Methods - This Perspective explores advances and future directions in spatiotemporal omics methods for four-dimensional profiling.

Lukas Hatscher @loggas.bsky.social · Jul 16

Thanks @marcovarrone.bsky.social it was a pleasure, learnt a lot from the way you built your codebase. We have a paper coming up with a lot of cellcharter in there! Amazing method :)

Reposted by Lukas Hatscher

Marco Varrone @marcovarrone.bsky.social · Jul 15

And for anyone who has considered contributing to an open source package: don't be scared to propose changes.

Even if it's not a complete and perfect solution, whoever is maintaining the package will help you in get to the right solution and they will be incredibly grateful.

Reposted by Lukas Hatscher

Marco Varrone @marcovarrone.bsky.social · Jul 15

For people like me who don't have a team behind a package like CellCharter, contributions like these mean a lot. So thank you Lukas :)

And congratulations, it's not always easy to jump into an existing codebase and propose changes.

Reposted by Lukas Hatscher

Marco Varrone @marcovarrone.bsky.social · Jul 15

RCS measures how large a cell niche (aka spatial domain aka spatial cluster) is compared to what would be normally expected.

This pushed me to completely rewrite the system for generating and plotting boundaries for cell niches.
The new system is now more efficient, consistent, and visually clear.