Markus Eckstein

The paper uses spatial proteomic profiling to analyze cardiac stromal populations and microenvironments in systemic sclerosis-associated myocarditis, identifying distinct fibroblast subtypes and their spatial interactions.

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 study identifies a neuroimmune IL-13 axis involved in human enteric nervous system development through transcriptomic and functional analyses of neonatal ileum tissue.

This study identifies NGFR as a marker of a basal BLCA subpopulation associated with poor prognosis and reduced immunotherapy response.

The paper identifies non-covalent inhibitors of PRDX5, a protein upregulated in MPM, through a machine learning guided pipeline and biochemical validation, suggesting it as a therapeutic target.

Phoenix predicts spatially resolved single-cell gene expression from histology, identifying new biomarkers and validating them across cancers and treatment regimens.

This paper benchmarks the Ultima UG100 and Illumina NovaSeq platforms on FFPE tissues for various sequencing modalities, finding they produce comparable results with subtle discrepancies in non-coding transcripts.

This study identifies polyploid cardiomyocytes as disease-specific transcriptional states in the mammalian heart and suggests targeting them for heart failure treatment.

This study predicts spatial gene expression from histopathology images using contrastive learning, improving correlation metrics for gene prediction across multiple cancer datasets.

The paper identifies E2F transcription factors as potential druggable targets in uveal melanoma by analyzing SOX10's role and its downstream effects.

The paper identifies a prognostic infiltration zone in glioblastoma using histology and spatial transcriptomic integration, revealing specific cell composition associated with long-term survival.

This study uses deep learning to predict YAP1/TEAD activity from histology slides, linking histological patterns to disease aggressiveness in cancer patients.