Ricardo Melo Ferreira

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.

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.

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.

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.

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.

The paper creates a high-resolution cellular atlas of healthy and injured kidney cell states and niches using single-cell and spatial technologies, identifying molecular signatures and pathways related to kidney injury.

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 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.