Statistical approaches and software for clustering islet cell functional heterogeneity

Abstract

Worldwide efforts are underway to replace or repair lost or dysfunctional pancreatic b-cells to cure diabetes. However, it is unclear what the final product of these efforts should be, as b-cells are thought to be heterogeneous. To enable the analysis of b-cell heterogeneity in an unbiased and quantitative way, we developed model-free and model-based statistical clustering approaches, and created new software called TraceCluster. Using an example data set, we illustrate the utility of these approaches by clustering dynamic intracellular Ca2+ responses to high glucose in ~300 simultaneously imaged single islet cells. Using feature extraction from the Ca2+ traces on this reference data set, we identified 2 distinct populations of cells with b-like responses to glucose. To the best of our knowledge, this report represents the first unbiased cluster-based analysis of human b-cell functional heterogeneity of simultaneous recordings. We hope that the approaches and tools described here will be helpful for those studying heterogeneity in primary islet cells, as well as excitable cells derived from embryonic stem cells or induced pluripotent cells.