Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas

Multiple single-cell RNA sequencing (scRNA-seq) datasets have been generated to study pancreatic islet cells during development, homeostasis, and diabetes progression. However, despite the time and resources invested into the past scRNA-seq studies, there is still no consensus on islet cell states and associated pathways in health and dysfunction as well as the value of frequently used preclinical mouse diabetes models. Since these challenges can be only resolved with a joint analysis of multiple datasets, we present a scRNA-seq cross-condition mouse islet atlas (MIA). We integrated over 300,000 cells from nine datasets with 56 samples, varying in age, sex, and diabetes models, including autoimmune type 1 diabetes (T1D) model (NOD), gluco-/lipotoxicity T2D model (db/db), and chemical streptozotocin (STZ) β-cell ablation model. MIA is a curated resource that enables interactive exploration of gene expression and transfer of cell types and states. We use MIA to obtain new insights into islet cells in health and disease that cannot be reached from individual datasets. Based on the MIA β-cell landscape we report cross-publication differences between previously suggested marker genes of individual phenotypes. We further show that in the STZ model β-cells transcriptionally correlate to human T2D and mouse db/db model β-cells, but are less similar to human T1D and mouse NOD model β-cells. We define new cell states involved in disease progression across diabetes models. We also observe different pathways shared between immature, aged, and diabetes model β-cell states. In conclusion, our work presents the first comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation, and demise.