Denis Wirtz

This study enhances CAR-T cell migration through synthetic cytokine receptors, reducing metastasis in TNBC mouse models.

TiSAM improves whole-slide image segmentation by fusing spatial and histopathology features, achieving high accuracy across diverse datasets and scanners.

VITAL-3D quantifies single-cell drug responses in 2D and 3D cultures, revealing microenvironment-dependent efficacy differences in breast cancer.

CODAvision provides a user-friendly interface for rapid, customizable medical image segmentation using deep learning, enabling non-experts to train and evaluate models across diverse imaging modalities.

The paper identifies two distinct senescent β-cell subpopulations with different functional roles in islet aging and dysfunction using single-cell proteomics and transcriptomics.

The paper designs an AND-gated CAR T cell receptor called MANTIS that uses extracellular size differences to spatially regulate signaling for precise antigen recognition.

This paper introduces SMILE, a diffusion model for converting H&E stains to multiplex IHC images, showing superior performance in pancreatic islet analysis and breast cancer stain conversion.

The paper uses 3D imaging to analyze vascular and nervous systems in embryos, finding shared structural principles but distinct architectures shaped by biophysical constraints.

This paper introduces DeepBIT, a deep learning method for automated cell migration tracking, enabling high-throughput analysis of cancer cell motility under various conditions and identifying new regulators of migration.

The paper develops a 3D multi-omic pipeline using deep learning to analyze chemotherapy response in pancreatic cancer, identifying molecular signatures of resistance and fibroblast-cancer interactions.

This study uses 3D multi-omic imaging to identify biomarkers of the pre-metastatic niche in lung cancer, revealing myeloid and senescent cell signatures associated with metastasis risk.

This paper introduces a 3D in vitro assay to study immune cell infiltration and cytotoxicity in solid tumors, showing it captures key steps of immune cell interaction with the tumor microenvironment.

The paper identifies Tbx5 as a gene critical for forming a compartment boundary in the interventricular septum, and shows its disruption leads to congenital heart defects.

The paper identifies and maps senescent cells in postmenopausal ovaries using p16 expression and spatial transcriptomics, revealing a fibro-inflammatory signature associated with aging.

This study develops anti-9G4 CAR-T and TCR-T cells to selectively target autoreactive B cells in SLE, reducing immunosuppression and cytokine toxicity.

This study optimizes velocity receptor (VR) transduction in CAR T cells by evaluating vector modifications and transduction efficiency, finding that VR5IL8 and VR5TNF perform best.

NLTD 2.0 improves color deconvolution in histopathology by handling more than three stains without requiring optimal color vectors, enhancing reliability and accessibility through an ImageJ plugin.

This study shows hyper-migratory CAR T cells reduce ovarian cancer metastasis and improve survival in mice.

The paper presents a 3D multi-omic framework to detect and characterize ovarian cancer precursors in fallopian tubes, identifying TP53 mutations in microscopic lesions.

This paper develops a 3D multi-compartment assembloid that mimics human endometrial tissue and its menstrual cycle phases, enabling study of compartment-specific hormonal responses and paracrine signaling.

This study identifies functionally distinct senescent cell subpopulations based on cell cycle status, showing G2-arrested cells secrete more IL-6 and are more sensitive to senolytic treatment.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

PIVOT is an open-source tool for nonlinear registration of multi-omic spatial data, demonstrating improved throughput compared to existing workflows.

This study shows that 3D assessments are necessary to accurately determine tissue composition and neoplastic content, as 2D methods under-sample spatial heterogeneity.

The paper integrates 3D imaging with nuclear segmentation to analyze nuclear morphology in human tissue, revealing spatial patterns and interactions in disease progression.

The paper develops a machine learning approach to reconstruct complex biological systems from partial faceted datasets, showing successful reconstruction in both mechanical and biological examples.

The paper profiles the immune microenvironment of PanINs in 3D at single-cell resolution to understand inflammation's role in cancer progression and finds immune heterogeneity and correlations with PanIN grade.

The paper uses AI-based 3D tissue mapping to show PanIN lesion sizes follow a power-law distribution and identifies growth mechanisms driving pancreatic cancer precursor lesions.

This study identifies high-motility tumor-associated monocytes (TAMos) that drive macrophage infiltration and cancer cell proliferation via IL-6 and MAPK pathways, suggesting IL-6 as a therapeutic target.

This study introduces qMAP, a deep learning-based workflow for microanatomical mapping of human skin, identifying 10 core processes strongly associated with aging and achieving age prediction accuracy comparable to epigenetic clocks.