RNA Sequencing Describes the Expression Profile of Iron Regulatory Genes in Human Islets

Eukaryotic and prokaryotic RNA sequencing is used to discover expressed genes in cells, tissues, or individuals under different physiological or pathological conditions. A transcriptome bonds a genome’s genetic information and biological functions. Nowadays, RNA sequencing is widely applied to a wide variety of biological research as well as clinical diagnosis and drug development.

Epidemiological and clinical studies have shown that cellular damage resulting from iron overload has been linked with type 2 diabetes (T2D). Iron overload promotes the generation of reactive oxygen species (ROS). Pancreatic β-cells can counter oxidative stress through multiple anti-oxidant responses. However, the core molecular signature of the protective anti-oxidant response to iron overload is not clear. Therefore, RNA sequencing is utilized to describe the expression profile of iron regulatory genes in human islets with or without diabetes. Functional experiments including siRNA silencing, qPCR, western blotting, cell viability, ELISA, and RNA sequencing were performed as a means of identifying the genetic signature of the protective response following iron overload-induced stress in human islets and INS-1.

With Synbio Technologies’s high-throughput sequencing technology, almost all RNA information of a tissue or organ can be sequenced comprehensively. We have rich experience in library construction for RNA sequencing to reach optimal rRNA removal efficiency. It also has high coverage, strand-specific RNA-seq libraries, comprehensive analysis, integrative analysis of multiomics services, etc.


Medical Research: Disease markers, disease diagnosis and classification, disease recurrence diagnosis, disease mechanism, clinical efficacy evaluation, drug toxicology evaluation, and personalized therapy.

Life Science Research: Abiotic environmental relationships, plants and microorganisms, phenotypic identification, metabolic pathway and functional genomic studies, and medicinal plants.

Data Analysis

Reference: DOI : 10.1016/j.mce.2021.111462