Michael L. Whitfield

Compartmental Profiling of PDE4B in Systemic Sclerosis

This study characterizes PDE4B expression in systemic sclerosis (SSc) tissues, showing dysregulation in immune cells and protein enrichment in activated fibroblasts, supporting PDE4B as a therapeutic target.

Anti-CD206 CAR T Cell Treatment Restores Fibrosis-Induced Loss of Dermal White Adipose Tissue

The paper demonstrates that anti-CD206 CAR T cells restore dermal white adipose tissue in systemic sclerosis by targeting CD206+ macrophages and reversing their effects on adipose-derived stem cells.

RUNX1 is Expressed in a Subpopulation of Dermal Fibroblasts and Higher RUNX1 Levels are Associated with the Severity of Systemic Sclerosis

This study shows RUNX1 is expressed in SSc fibroblasts and higher levels correlate with fibrosis severity, with hypomethylation and inhibitor effects supporting its role in fibrosis.

Single-cell epigenomic dysregulation of Systemic Sclerosis fibroblasts via CREB1/EGR1 axis in self-assembled human skin equivalents

The paper characterizes single-cell transcriptomic and epigenomic features of a 3D skin-like model of systemic sclerosis, identifying fibroblast subpopulations and their interactions with macrophages.

RNA-seq analyses of molecular abundance (RoMA) for detecting differential gene expression

RoMA improves differential gene expression detection in RNA-seq by integrating mRNA abundance and raw counts, showing high accuracy and robustness in simulated and real data.

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

This paper uses machine learning to classify heart disease subgroups in systemic sclerosis using echocardiographic and skin transcriptome data, identifying distinct phenotypes and associated molecular pathways.

A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

The paper performs a meta-analysis of gene expression data from SSc clinical trials to identify therapeutic targets and predict treatment response using machine learning.