Lukas Hatscher

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

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

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

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

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

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.

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

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…

This guy really dedicated his life to science:

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

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

Thanks Jim!

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

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)

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.

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.

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:

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

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.

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.

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)

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)

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...

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...

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.

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

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 :)

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.

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.

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.

GitHub - CSOgroup/cellcharter: A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data. A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data. - CSOgroup/cellcharter

A few weeks ago, we released CellCharter v0.3.5.

Among some bug fixes, we received our first contribution from an external contributor: @loggas.bsky.social !

He designed a new metric called Relative Component Size (RCS).

Spotted: @loggas.bsky.social and @arojhada.bsky.social teaching phenotyping to our Advanced Systems Biology students

Differential expression analysis with inmoose, the integrated multi-omic open-source environment in Python - BMC Bioinformatics Background Differential gene expression analysis is a prominent technique for the analysis of biomolecular data to identify genetic features associated with phenotypes. Limma—for microarray data –, an...

🖥️🧬 I used to have to tell my students not to bother with differential expression analysis in a language outside of R. Recently, with pydeseq2 and now InMoose, this is changing!

bmcbioinformatics.biomedcentral.com/articles/10....

Bonsai: Tree representations for distortion-free visualization and exploratory analysis of single-cell omics data Single-cell omics methods promise to revolutionize our understanding of gene regulatory processes during cell differentiation, but analysis of such data continues to pose a major challenge. Apart from...

Here it is! Bonsai. Now there is really no more excuse for using t-SNE/UMAP. Bonsai not only makes cool pictures of your data. It actually rigorously preserves its structure. No tunable parameters. Incredible work by @dhdegroot.bsky.social.
I'm so excited about this!
www.biorxiv.org/content/10.1...

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.