Jens Rittscher

This paper introduces ARMS DNAseq, a scalable spatial genomic assay that maps copy number subclones in archival tissue, revealing tumor evolution and subclone associations with morphology and transcriptomic features.

SPC-Clean reduces speckle and isolated pixel noise in fluorescence microscopy images using a topology-aware method without altering fluorescence intensities.

This study uses spatial transcriptomic analysis and machine learning to redefine the topology of human bone marrow in health and myeloproliferative neoplasms, revealing new insights into cellular heterogeneity and fibrosis.

This paper introduces MAPS, a deep-learning approach for automated segmentation of mitochondrial ultrastructure in 3D pan-ExM images, enabling high-throughput quantification of morphology and protein localization.

The paper uses single-cell phenomics to study DVE cell migration in mouse embryos, revealing mechanical heterogeneities and how surrounding cells facilitate collective migration.

The paper introduces DeepCytometer, a deep learning pipeline for segmenting whole slides of mouse white adipose tissue, and uses it to analyze phenotypes in Klf14 mouse models.

TRAIT2D is a Python software for analyzing single particle diffusion data, offering tracking, analysis, and simulation with a graphical interface.

KDML predicts multi-scale gene functions from genetic perturbation screens, outperforming traditional methods and identifying clinically relevant gene function associations in colorectal cancer.

MOSES quantifies cellular motion phenotypes across different epithelial cell types and identifies boundary formation changes in response to external stimuli.