Lukas Hatscher
@loggas.bsky.social
69 followers 130 following 25 posts
MD, PhD candidate in Institute of Computational Biomedicine - AG Schapiro, Interested in quantitative tissue analysis 💻 www.github.com/LukasHats
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loggas.bsky.social
2 decades of self-injecting venom and hundreds of snake bites
loggas.bsky.social
And last but not least thanks a lot to @denisschapiro.bsky.social who established the collaboration and mentored me.
loggas.bsky.social
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)
loggas.bsky.social
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.
loggas.bsky.social
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.
loggas.bsky.social
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:
loggas.bsky.social
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
loggas.bsky.social
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.
loggas.bsky.social
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.
loggas.bsky.social
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)
loggas.bsky.social
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)
loggas.bsky.social
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
marcovarrone.bsky.social
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
marcovarrone.bsky.social
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
marcovarrone.bsky.social
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.
Reposted by Lukas Hatscher
schapirolab.bsky.social
Spotted: @loggas.bsky.social and @arojhada.bsky.social teaching phenotyping to our Advanced Systems Biology students
Reposted by Lukas Hatscher
slavovlab.bsky.social
An alternative to tSNE & UMAP for more accurate data visualization:

Tree representations for distortion-free visualization and exploratory analysis of single-cell omics data.

The trees are constructed to accurately represent true distances between the objects in the high-dimensional space.
Reposted by Lukas Hatscher
joshuabull.bsky.social
In addition to the two #SpatialBiology posts in the thread below, we're now also advertising for a Research Software Engineer #RSE to help us take #MuSpAn to the next level - come and join us!

my.corehr.com/pls/uoxrecru...

#academic #jobs #softwaredeveloper #compsci