PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets.
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Vu Ha T H, Sun Han, Kudtarkar Parul, Sharp Seth A, Brusman Liza, Feng Fan, Bate Thomas, Jurgens Julie A, Wang Yiqun, Huang Yuanhao, Mao Runbo, Corban Sierra, Huber Amanda K, Shilin Alex, Sun Ying, Narayanaswamy Sara, Jang Dongkeun, Robertson Catherine C, Shrestha Shristi, Nguyen Trang, Smadbeck Patrick, Zhang Lu, Brandes Mackenzie, PanKbase Consortium, Flannick Jason, Burtt Noel, Chen Shuibing, Liu Jie, Cartailler Jean-Philippe, Voight Benjamin F, Stitzel Michael L, Brissova Marcela, Gloyn Anna L, Gaulton Kyle J, Parker Stephen C J
Аннотация
Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications.