Michael T. Eadon

The paper compares miRNA-mRNA profiles across human and murine AKI models to identify shared and specific molecular programs associated with different injury etiologies and biological heterogeneity.

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.

The paper develops an AI-based method to generate spatial Banff t- and g-scores using spatial transcriptomics and histology, showing full concordance in severe rejection cases.

This paper constructs a transcriptional patient map of SLE using single-cell and bulk RNA-seq data, identifying conserved immune programs in blood and kidney tissue.

The paper builds a cross-species single-cell multi-omics kidney atlas to identify epithelial cells as a driver of epigenetic aging in diseased kidneys.

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 study shows cannabidiol increases T effector memory cells and has mixed inflammatory effects in humans.

This study shows that NAD+ supplementation protects against CKD in Alport syndrome by restoring FAO in proximal tubule cells and reveals sex differences in transcriptional responses.

This study investigates the combined effect of rs699 and rs5051 on angiotensinogen expression and hypertension, finding no significant difference in liver AGT expression or hypertension phenotypes when the variants occur independently.

This study identifies AZIN1 A-to-I editing as a marker of kidney recovery potential following inflammation.

This paper introduces REGNN, a graph deep learning framework for analyzing spatially resolved transcriptomics data, showing improved performance in identifying mosaic-like tissue architectures in kidney diseases.

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.

The paper develops regSNPs-intron, a random forest classifier to prioritize intronic SNVs for disease, validated using ASSET-seq functional assays.