Deciphering the Transcriptomic Landscape of Type 2 Diabetes: Insights from Bulk RNA Sequencing and Single-Cell Analysis

Abstract Type 2 diabetes (T2D) is a chronic metabolic disorder marked by insulin resistance and relative insulin deficiency, affecting over 422 million people globally and projected to increase, making it a major public health concern. This condition is associated with severe complications such as retinopathy, nephropathy, cardiovascular diseases, and neuropathy, highlighting the need for a deeper understanding of its mechanisms to develop more effective prevention and treatment strategies. Transcriptomic analysis, particularly through RNA-seq, has provided valuable insights into the gene expression patterns in T2D, highlighting pathways involved in insulin signaling, metabolic regulation, and inflammation. This approach, including single-cell RNA sequencing, helps overcome the challenges of immune cell population heterogeneity, enabling the identification of distinct cell types and their specific roles in T2D progression. In this paper, we present a dataset of single-cell and bulk RNA sequencing results comparing expression patterns in blood cells between diabetes and healthy samples and describe some preliminary analysis of the dataset, highlighting differences in both gene expression and cell type composition and putting them in the context of existing research of T2D.