Modeling Diabetic Endothelial Dysfunction with Patient-Specific Induced Pluripotent Stem Cells
Diabetes is a known risk factor for various cardiovascular complications, mediated by endothelial dysfunction. Despite the high prevalence of this metabolic disorder, there is a lack of in vitro models that recapitulate the complexity of genetic and environmental factors associated with diabetic endothelial dysfunction. Here, we utilized human induced pluripotent stem cell (iPSC)-derived endothelial cells (ECs) to develop in vitro models of diabetic endothelial dysfunction. We found that the diabetic phenotype was recapitulated in diabetic patient-derived iPSC-ECs, even in the absence of a diabetogenic environment. Subsequent exposure with culture conditions that mimic the diabetic clinical chemistry induced a diabetic phenotype in healthy iPSC-ECs but did not affect the already dysfunctional diabetic iPSC-ECs. RNA-seq analysis revealed extensive transcriptome-wide differences between cells derived from healthy individuals and diabetic patients. The in vitro disease models were used as a screening platform which identified angiotensin receptor blockers (ARBs) that improved endothelial function in vitro for each patient. In summary, we present in vitro models of diabetic endothelial dysfunction using iPSC technology, taking into account the complexity of genetic and environmental factors in the metabolic disorder. Our study provides novel insights into the pathophysiology of diabetic endothelial dysfunction and highlights the potential of iPSC-based models for drug discovery and personalized medicine. Overall design: Bulk paired end (61:8:8:61) RNA sequencing was done using MiRNeasy Micro Kit (Qiagen 217084) for extraction, NEBNext® Poly(A) mRNA Magnetic Isolation Module (E7490L), NEBNext® Ultra™ II RNA Library Prep Kit for Illumina® (E7770L), and NEBNext® Multiplex Oligos for Illumina® (Dual Index Primers Set 1) (E7600S), as described previously (1,2). Prior to library preparation, the samples were randomized to avoid experimental and human biases. We started our experiments with a total of 24 samples (iPSCs and differentiated iPSCs-ECs) from healthy and disease patients. During library preparation, 2 DiPSC and 2 HiPSC-EC patient samples were excluded from sequencing due to insufficient RNA isolation. RNA-seq reads were pseudo-aligned to the human transcriptome (GRCH38 cDNA) utilizing Kallisto v0.48.0 to generate estimated gene count matrices(3). Count matrices were normalized and differential gene expression analyses were done with DESeq2 v1.38.3 using R v4.2.3 for each subgrouping comparison (4). The count matrix was further filtered for low expression counts such that any gene less than a total of 10 counts across samples cutoff were removed from downstream analysis. To compare between samples, differential gene expression data was extracted. The top 50 most up and down regulated genes (total 100, unless the number of differentially expressed genes were <100) were filtered utilizing an ordered Log2FC gene list and then used to generate corresponding comparisons within heat maps. GO analysis was performed using clusterProfiler in R. Scripts for all extraction and downstream differential expression analysis are provided at the Goyal laboratory GitHub: https://github.com/GoyalLab/Bulk-RNA-Jiang-Analysis.git .