Sascha Dietrich

This study identifies spatially distinct fibroblast subpopulations and niches in human myocardial fibrosis across different disease states and their associations with fibrotic remodeling and cardiac dysfunction.

This paper introduces a dual-photoreactive adapter to enable rapid, one-step DNA barcoding of antibody panels for multiplexed imaging.

The paper introduces spatialproteomics, a Python toolbox for analyzing highly multiplexed fluorescence images, demonstrating its use on lymph node and lymphoma data.

The paper shows CMV replication increases IFNγ, which makes AML cells more vulnerable to NK cell killing via the NKG2C-HLA-E pathway.

This study uses blood vessel organoids to model systemic sclerosis vasculopathy and fibrosis, showing that SSc serum induces epigenetic and molecular changes linked to endothelial dysfunction and potential therapeutic targets.

This paper introduces scPCA, a dimensionality reduction framework that integrates cellular heterogeneity and condition effects in scRNA-seq data, revealing transcriptional changes across conditions and cell types.

The paper uses spatial transcriptomic methods to identify novel fibroblast subpopulations in systemic sclerosis skin and their pathogenic roles.

This paper introduces SDR-seq, a multiomic scDNA-scRNA-seq method to link genotypes with gene expression, showing variants affect expression in iPSCs and B-cell lymphoma.

This study reveals that cellular differentiation drives intratumor heterogeneity in B-cell lymphomas, with distinct maturation states associated with different survival outcomes.

This paper introduces scNOVA, a method to analyze somatic structural variants in single-cell multiomics data, revealing their functional impacts on gene regulation and cancer pathways.

This study identifies subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia using RNA-sequencing and genomic data.

diffTF quantifies differential transcription factor activity and classifies TFs as activators or repressors using multi-omics data, identifying known and novel TFs in two case studies.

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.