title: Recent advances in mathematical modeling and statistical analysis of exocytosis in endocrine cells.
abstract: Most endocrine cells secrete hormones as a result of Ca2+-regulated exocytosis, i.e., fusion of the membranes of hormone-containing secretory granules with the cell membrane, which allows the hormone molecules to escape to the extracellular space. As in neurons, electrical activity and cell depolarization open voltage-sensitive Ca2+channels, and the resulting Ca2+influx elevate the intracellular Ca2+concentration, which in turn causes exocytosis. Whereas the main molecular components involved in exocytosis are increasingly well understood, quantitative understanding of the dynamical aspects of exocytosis is still lacking. Due to the nontrivial spatiotemporal Ca2+dynamics, which depends on the particular pattern of electrical activity as well as Ca2+channel kinetics, exocytosis is dependent on the spatial arrangement of Ca2+channels and secretory granules. For example, the creation of local Ca2+microdomains, where the Ca2+concentration reaches tens of µM, are believed to be important for triggering exocytosis. Spatiotemporal simulations of buffered Ca2+diffusion have provided important insight into the interplay between electrical activity, Ca2+channel kinetics, and the location of granules and Ca2+channels. By confronting simulations with statistical time-to-event (or survival) regression analysis of single granule exocytosis monitored with TIRF microscopy, a direct connection between location and rate of exocytosis can be obtained at the local, single-granule level. To get insight into whole-cell secretion, simplifications of the full spatiotemporal dynamics have shown to be highly helpful. Here, we provide an overview of recent approaches and results for quantitative analysis of Ca2+regulated exocytosis of hormone-containing granules.
authors.affiliation: Department of Information Engineering, University of Padua, Via Gradenigo 6/B, 35131 Padova, Italy. Electronic address: pedersen@dei.unipd.it.
authors.forename: Morten Gram
authors.initials: MG
authors.lastname: Pedersen
authors.affiliation: Department of Information Engineering, University of Padua, Via Gradenigo 6/B, 35131 Padova, Italy.
authors.forename: Alessia
authors.initials: A
authors.lastname: Tagliavini
authors.affiliation: Department of Statistical Sciences, University of Padua, Via Cesare Battisti 141, 35121 Padova, Italy.
authors.forename: Giuliana
authors.initials: G
authors.lastname: Cortese
authors.affiliation: Department of Information Engineering, University of Padua, Via Gradenigo 6/B, 35131 Padova, Italy; Sanofi, Industriepark Höchst, 65926 Frankfurt am Main, Germany.
authors.forename: Michela
authors.initials: M
authors.lastname: Riz
authors.affiliation: Department of Information Engineering, University of Padua, Via Gradenigo 6/B, 35131 Padova, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Montefusco
mesh_terms: D000818:Animals; D003627:Data Interpretation, Statistical; D055098:Endocrine Cells / Q000502:physiology*; D005089:Exocytosis / Q000502:physiology*; D006801:Humans; D008962:Models, Theoretical
keywords: Ca microdomains; Hormone secretion; Large dense-core vesicles; Mixed-effects modeling; Pituitary cells; Secretory granules; Spatiotemporal Ca dynamics; Survival analysis; α-cells; β-cells