We discovered immune signatures associated with heart attack time course, outcome or chronic coronary syndrome. Read more: or the article

9/9 A huge thank you to our fantastic collaborators @Jessica_Pauli_ , @NadjaSachs, @LarsMaeg from @TU_Muenchen and @_MatthiasMunz, Ehsan Vafadarnejad, Tania Carrillo-Roa, Peter Kastner from @Roche! @CompHealthMuc.

8/9 Dive into the data and explore how our Integrated Single-Cell Atlas of Human Atherosclerotic Plaques can help push the boundaries of cardiovascular science. We can’t wait to see how this atlas drives new discoveries!.

7/9 Explore our atlas via CellxGene and GitHub for access to data and tools. This is a significant step forward in cardiovascular research. GitHub: Atlas: ArchMap:

6/9 Cross-organ insights: Our atlas reveals plaque-specific cell types like fibromyocytes are vascular-specific. It also validates our model’s ability to detect non-plaque-specific cells across different tissues.

5/9 Our atlas facilitates deconvolution of bulk RNA-seq data, revealing cell type proportions in plaques. We highlight the crucial role of macrophages and uncover new insights into how endothelial cells contribute to plaque progression.

4/9 Use our atlas for power analysis and experiment planning with the scPower tool! Design cost-effective experiments tailored to specific cell types involved in atherosclerosis, boosting the efficiency of future research.

3/9 Our atlas helps researchers accurately annotate and compare new plaque scRNA-seq datasets. We offer a Python script, Docker container, and integration with ArchMap for seamless annotation. Atlas: ArchMap:

2/9 We used scPoli, demonstrating top performance for data integration, to achieve robust and accurate cell type annotations, validated with expert labels and surface protein measurements. Plus, we provide a detailed list of plaque-specific marker genes for each cell type.

1/9 Excited to share our new preprint! Led by @KorbinianT, we present the Integrated single-cell Atlas of Human Atherosclerotic Plaques—a resource with 261,747 annotated cells from carotid, coronary, and femoral arteries.

RT @KPekayvaz: 1/Thrilled to share our most recent efforts @NatureMedicine on joint multi-omic analyses unravelling the comprehensive immun….

RT @gagneurlab: Our study on genomic and transcriptomic aberrations over 3,700 leukemia samples is now published! AbSplice on cancer, driv….

This was a great collaboration between @KPekayvaz @nicolai_leo and Konstantin Stark @LMU_Uniklinikum and @Corinna_Losert @CompHealthMuc @HelmholtzMunich.

7/7 Super happy about this great work by @FlorinRatajczak and a great team effort with @interactome @HmguInet @HelmholtzMunich @CompHealthMuc.

6/7 Interpretation of the model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. This demonstrates the potential of graph representation learning for studying core gene properties and future drug development.

5/7 Our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development

4/7 Moreover, all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance

3/7 we demonstrate that candidate genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes

2 The omnigenic model says that many small genetic changes propagate in molecular networks and accumulate in few causal core genes. But core genes are unknown. We built a graph neural network ensemble to identify core genes for 5 complex traits using Mendelian genes for training

1/7 How do 1000s of small genetic changes cause complex diseases? The core gene model helps to explain and we developed a novel method base on graph neural networks to identify core genes for 5 groups of complex diseases.