Abstract

Background: Diabetes prevalence in Saudi Arabia is rising rapidly, yet population-specific genetic predispositions remain poorly characterized due to limitations in current bulk-genomic and single-cell profiling techniques. Conventional methods often fail to capture cellular heterogeneity and lack scalability for extensive cohort studies. This study aims to develop a high-throughput microfluidic single-cell genomic profiling platform specifically designed to investigate diabetes-related genetic predisposition in the Saudi Arabian population.
Methods: We developed a high-throughput microfluidic platform for single-cell genomic profiling tailored to diabetes research in Saudi Arabian populations. The system combines magnetic ionic liquid-based cell lysis, centrifugal microfluidics for parallel single-cell partitioning, and on-chip next-generation sequencing (NGS). Optimized hydrodynamic trapping ensures >95% single-cell occupancy. Targeted amplification of diabetes-associated genes is performed on-chip directly, while integrated graphene oxide-based sensors provide real-time amplicon detection. Genetic data are integrated with electronic health records (EHRs) using an attention-based fusion model, allowing combined analysis of clinical and genomic features. Graph-based clustering of the resulting single-cell profiles identifies genetic subpopulations with distinct diabetes pathways.
Results: The platform successfully identified rare genetic subtypes, including those with isolated β-cell dysfunction and insulin resistance phenotypes. Real-time detection reduced sequencing turnaround time by 40%, and the integrated fusion model enabled accurate stratification of patient risk scores when combined with EHR-derived clinical data. The system demonstrated seamless interoperability with national health databases and showed high reproducibility across multiple sample batches.
Conclusion: This microfluidic platform enables scalable, automated, and high-resolution single-cell genomic analysis of diabetes-related variants in underrepresented populations. By capturing population-specific genetic heterogeneity, the framework supports personalized risk assessment and informs precision medicine strategies in Saudi Arabia and beyond.

Key words: High-throughput microfluidics, Single-cell genomics, Diabetes predisposition, Population-specific genetics, Saudi Arabia, Magnetic ionic liquid lysis, Centrifugal microfluidics, On-chip NGS, Graphene oxide sensors, Genetic heterogeneity, β-cell.