Rafael Kramann

This study establishes a mouse model of cyclic uremia without kidney injury, showing it drives cardiovascular disease and immunosenescence independently of traditional risk factors.

The paper introduces scVizComm, a web tool for visualizing cell-cell communication in single-cell transcriptomics data with pathway-centric prioritization.

This study identifies a microbiome-AhR-IL-17A axis that drives CKD-associated cardiac fibrosis and shows that AhR inhibition prevents remodeling.

The paper identifies three epithelial cyst types in ADPKD using single-nucleus RNA sequencing and machine learning, showing their association with therapeutic response.

This study shows that aging podocytes lose nuclear-cytoplasmic compartmentalization, leading to metabolic and transcriptional dysregulation.

This study identifies a novel mechanism in myelofibrosis where a single mutation drives both bone formation and resorption, revealing THBS1 as a therapeutic target.

This study identifies Spp1⁺ macrophages as key hubs coordinating fibrotic remodeling in myeloproliferative neoplasms using NicheSphere, a novel computational method for cell communication analysis.

SwarmMAP uses swarm learning to automate cell-type annotation in single-cell data without sharing raw data, achieving high accuracy across human tissues.

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

The paper shows that Wnt-dependent reprogramming of bone marrow niches drives fibrosis by activating osteolineage cells and altering hematopoietic support.

Single-cell and spatial maps of human coronary arteries identify FAP as a marker of SMC-derived modulated cells; an anti-FAP BiTE reduced plaque burden in mouse models.

This study uses multimodal single-cell profiling to identify immune cell crosstalk mechanisms in poor prognostic cholangiocarcinoma, revealing key ligand pairs and pathways contributing to immune exhaustion.

This study identifies RUNX1 as a key regulator of cardiac recovery by reprogramming macrophages, showing that its inhibition promotes heart function recovery in a chronic heart failure model.

This study identifies FNDC1 as a key regulator of vascular calcification through the FNDC1-NAMPT-NAD axis, showing its role in both microvascular and macrovascular diseases and its potential as a therapeutic target.

This paper uses time-resolved multi-omics analysis of kidney fibrosis to identify novel mechanisms and validate transcription factors involved in collagen deposition.

mcBERT generates patient-level representations from scRNA-seq data using a transformer-based model, enabling disease cohort identification and phenotype classification across diverse tissues.

PHLOWER infers complex differentiation trees from multi-modal single-cell data using Hodge Laplacian decomposition, showing effectiveness in kidney organoid data.

PICASO integrates single-cell gene expression data with biomedical databases to create networks that compare disease states and identify druggable targets.

scDecorr provides a self-supervised framework for integrating single-cell RNA-seq data by decorrelating features and adapting to domain differences, improving clustering and label transfer.

PILOT uses optimal transport to analyze single-cell and pathomics data, detecting disease-associated samples and non-linear changes in cell populations and tissue structures.

The paper integrates single-cell RNA-seq and spatial transcriptomics data to reveal cellular interactions and changes in human atherosclerosis.

scMEGA infers gene regulatory networks from single-cell multi-omics data, demonstrating its application in studying myofibroblast activation in human myocardial infarction.

The paper defines the cellular and transcriptional features of cardiac recovery using snRNA-seq, identifying RUNX1 downregulation as a key predictor and potential therapeutic target.

This study generates a high-resolution spatial multi-omic map of human myocardial infarction by integrating single-cell gene expression, chromatin accessibility, and spatial transcriptomic data to reveal cellular zones of injury, repair, and remodeling.

This study uses human pluripotent stem cell-derived kidney organoids to model nephrotic syndrome, showing they can replicate podocyte injury and respond to genetic correction.

MASI provides a fast, model-free method for standardizing cell-type annotation and integrating single-cell transcriptomics datasets, achieving comparable or superior performance to existing methods.

SciViewer is an interactive tool for visualizing and analyzing single-cell datasets, offering target-centric views and integration with pharmacogenomics databases.

scOpen improves downstream analysis of sparse scATAC-seq data by imputing open chromatin regions, revealing regulatory changes in kidney fibrosis.

COSMOS integrates multi-omics data with prior knowledge to generate mechanistic hypotheses about biological processes in renal cell carcinoma.

genesorteR ranks features in single cell data to identify marker genes more accurately and efficiently than differential expression analysis.