title: Statistical Frailty Modeling for Quantitative Analysis of Exocytotic Events Recorded by Live Cell Imaging: Rapid Release of Insulin-Containing Granules Is Impaired in Human Diabetic β-cells.
abstract: Hormones and neurotransmitters are released when secretory granules or synaptic vesicles fuse with the cell membrane, a process denoted exocytosis. Modern imaging techniques, in particular total internal reflection fluorescence (TIRF) microscopy, allow the investigator to monitor secretory granules at the plasma membrane before and when they undergo exocytosis. However, rigorous statistical approaches for temporal analysis of such exocytosis data are still lacking. We propose here that statistical methods from time-to-event (also known as survival) analysis are well suited for the problem. These methods are typically used in clinical settings when individuals are followed over time to the occurrence of an event such as death, remission or conception. We model the rate of exocytosis in response to pulses of stimuli in insulin-secreting pancreatic β-cell from healthy and diabetic human donors using piecewise-constant hazard modeling. To study heterogeneity in the granule population we exploit frailty modeling, which describe unobserved differences in the propensity to exocytosis. In particular, we insert a discrete frailty in our statistical model to account for the higher rate of exocytosis in an immediately releasable pool (IRP) of insulin-containing granules. Estimates of parameters are obtained from maximum-likelihood methods. Since granules within the same cell are correlated, i.e., the data are clustered, a modified likelihood function is used for log-likelihood ratio tests in order to perform valid inference. Our approach allows us for example to estimate the size of the IRP in the cells, and we find that the IRP is deficient in diabetic cells. This novel application of time-to-event analysis and frailty modeling should be useful also for the study of other well-defined temporal events at the cellular level.
authors.affiliation: Department of Statistical Sciences, University of Padua, Padua, Italy.
authors.forename: Giuliana
authors.initials: G
authors.lastname: Cortese
authors.affiliation: Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden.
authors.forename: Nikhil R
authors.initials: NR
authors.lastname: Gandasi
authors.affiliation: Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Barg
authors.affiliation: Department of Information Engineering, University of Padua, Padua, Italy.
authors.forename: Morten Gram
authors.initials: MG
authors.lastname: Pedersen
mesh_terms: D000465:Algorithms; D001692:Biological Transport; D003920:Diabetes Mellitus / Q000378:metabolism*; D005089:Exocytosis; D006801:Humans; D007328:Insulin / Q000378:metabolism*; D000078790:Insulin Secretion; D050417:Insulin-Secreting Cells / Q000378:metabolism*; D008856:Microscopy, Fluorescence; D015233:Models, Statistical; D057054:Molecular Imaging / Q000379:methods; D022142:Secretory Vesicles / Q000378:metabolism*
chemical_list: D007328:Insulin