Single-cell virtual knockout network perturbation prioritizes candidate regulators of beta-cell functional failure in type 2 diabetes.

BACKGROUND

Progressive pancreatic beta-cell dysfunction is a central feature of type 2 diabetes (T2D), but disease-associated transcriptional changes alone cannot distinguish likely regulatory drivers from secondary stress markers. Single-cell transcriptomics resolves beta-cell heterogeneity across disease states, while virtual-knockout network analysis estimates transcriptional effects after perturbing selected genes. We combined human islet single-cell RNA sequencing, beta-cell functional-state modeling, candidate scoring, scTenifoldKnk virtual knockout, and external validation to prioritize regulators associated with T2D-related beta-cell functional failure.

METHODS

We analyzed the official processed GSE221156 beta-cell matrix at two levels. Cell-level analyses described beta-cell states, whereas donor-level pseudobulk models covered 48 donors and adjusted for sex, age, BMI, sequencing chemistry, and ethnicity. We then connected a prior-free genome-wide screen to an independent scTenifoldKnk network-evidence analysis. The fixed 14-gene panel was tested against random panels matched for expression, detection, and network in- and out-strength. For the scale experiment, each network contained 200, 500, or 1,000 cells, and each setting used 3, 5, or 10 independently inferred networks across three seeds. Expression evidence came from GSE153855, GSE81608, GSE101207, and GSE50244; orthogonal perturbation evidence came from GSE52258, GSE230728, primary-human-pseudoislet PCSK1 CRISPR data, and quantitative PCSK2 and SLC2A2 studies. A 2,000-cell point was additionally evaluated at the primary 10-network setting.

RESULTS

GSE221156 captured substantial beta-cell heterogeneity and a redistribution of cell states. Of 26,164 expressed genes, 17,989 passed pseudobulk filtering, and 674 (3.75%) differed significantly between T2D and ND after adjustment. Larger GRNs ranked ATF4, DKK3, SLC30A8, PCSK1, and SLC2A2 highest within the prespecified 14-gene panel. The fixed 14-gene panel also exceeded 200,000 expression-, detection-, and network-strength-matched random panels (observed mean 0.619; null mean 0.578; empirical P = 0.0111). Evidence varied across individual candidates, and none survived correction across the 14 genes. Scale mattered: median cross-seed whole-profile Spearman correlation rose from 0.265 with 200 cells and 3 networks to 0.884 with 1,000 cells and 10 networks and reached 0.913 with 2,000 cells and 10 networks. Across three external beta-cell cohorts, DKK3, PCSK2, SLC2A2, and ASCL2 retained concordant directions.

CONCLUSIONS

By anchoring network perturbation to donor-level disease signals, our framework maps regulatory vulnerability in T2D beta cells. The matched-null analysis identifies collective network information in the fixed 14-gene panel beyond expression and connectivity. Within this collectively enriched panel, the expanded GRNs place ATF4, DKK3, SLC30A8, PCSK1, and SLC2A2 at the leading ranks, defining focused candidates for experimental testing.