Tarek M. El‐Achkar

This study identifies altered thick ascending limb cells with UMOD retention as drivers of CKD using spatial proteomics and shows reducing UMOD retention attenuates CKD development.

This paper introduces STORM, a statistical framework for analyzing spatial patterns in multi-omics data, demonstrating reliable performance in detecting spatial structures and supporting study design decisions.

DigitAb predicts cell types from H&E-stained slides using deep learning, achieving high accuracy without specialized assays or immunostains.

This study identifies a MEF2C transcription factor network that regulates endothelial cell proliferation in diabetic kidney disease and shows that SGLT2i treatment can reverse its dysregulation.

This study investigates how Uromodulin affects immune zonation and inflammasome activity in kidney injury, showing it reduces inflammation and immune cell interactions in specific kidney regions.

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

FUSION is a web-based tool that integrates spatial -omics data with histology to analyze tissue states and identify spatial linkages between molecular and histopathologic features.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

The paper defines the chromatin landscape of healthy and injured kidney cells using multi-omics data, identifying regulatory elements and transcription factor networks involved in injury response.

This study maps the spatial transcriptomic landscape of the human kidney papilla in stone disease, identifying immune injury and matrix remodeling as key features.

The paper constructs a reference tissue atlas of the human kidney using multi-omics data to identify cell types, pathways, and genes across nephron segments and interstitium.

The paper identifies altered protein translation and carboxylic acid catabolism in diabetic kidney disease using human and mouse models.

The paper integrates spatial transcriptomics and single cell sequencing to identify immune and epithelial cell cross-talk patterns in acute kidney injury models.

This study uses single-cell RNA sequencing to define the temporal cellular and molecular changes in the kidney during sepsis, identifying key gene expression shifts and communication failures that mark critical points in the sepsis timeline.