This project aims to investigate the maturation of human stem cell-derived islet cells compared to naive primary human islet cells. By employing high-resolution single-cell RNA-sequencing (scRNA-seq), we seek to identify key transcriptomic markers and developmental pathways that govern functional maturation, a crucial step for advancing cell-replacement therapies for diabetes. Data Composition and Integration: To ensure a robust and comprehensive comparison, this study utilizes a multi-source dataset (n=33 libraries) consisting of: Original scRNA-seq data generated in our laboratory and sequenced by the National Genomics Infrastructure (NGI Sweden); Collaborator's published data on related stem cell lines; Publicly available datasets of primary human islet cells sourced from international repositories (e.g., GEO) to serve as biological benchmarks. Computational Strategy: The computational workflow involves processing approximately 400 GB of raw FASTQ data. To minimize batch effects and ensure cross-dataset comparability, we will perform a unified re-processing of all raw files from scratch using the 10x Genomics Cell Ranger pipeline. Subsequent downstream analysis, including data integration (e.g., Harmony/Seurat), clustering, and trajectory inference, will be conducted using R-based and Python-based tools. Reason for Resource (Bianca): The combined dataset includes sensitive human genomic information from both clinical donors and stem cell lines. In strict accordance with GDPR and Swedish ethical regulations, these data are classified as sensitive personal data. The isolated and secure environment of the Bianca cluster is essential to protect the genetic privacy of donors and comply with the Data Access Agreements (DAA) associated with the published datasets. This research is conducted under Ethical Permit: 7619-2025.