Microscopic modulation and analysis of islets of Langerhans in living zebrafish larvae.

Microscopic analysis of molecules and physiology in living cells and systems is a powerful tool in life sciences. While in vivo subcellular microscopic analysis of healthy and diseased human organs remains impossible, zebrafish larvae allow studying pathophysiology of many organs using in vivo microscopy. Here, we review the potential of the larval zebrafish pancreas in the context of islets of Langerhans and Type 1 diabetes. We highlight the match of zebrafish larvae with the expanding toolbox of fluorescent probes that monitor cell identity, fate and/or physiology in real time. Moreover, fast and efficient modulation and localization of fluorescence at a subcellular level, through fluorescence microscopy, including confocal and light sheet (single plane illumination) microscopes tailored to in vivo larval research, is addressed. These developments make the zebrafish larvae an extremely powerful research tool for translational research. We foresee that living larval zebrafish models will replace many cell line-based studies in understanding the contribution of molecules, organelles and cells to organ pathophysiology in whole organisms.

Persistence Atlas for Critical Point Variability in Ensembles.

This paper presents a new approach for the visualization and analysis of the spatial variability of features of interest represented by critical points in ensemble data. Our framework, called Persistence Atlas, enables the visualization of the dominant spatial patterns of critical points, along with statistics regarding their occurrence in the ensemble. The persistence atlas represents in the geometrical domain each dominant pattern in the form of a confidence map for the appearance of critical points. As a by-product, our method also provides 2-dimensional layouts of the entire ensemble, highlighting the main trends at a global level. Our approach is based on the new notion of Persistence Map, a measure of the geometrical density in critical points which leverages the robustness to noise of topological persistence to better emphasize salient features. We show how to leverage spectral embedding to represent the ensemble members as points in a low-dimensional Euclidean space, where distances between points measure the dissimilarities between critical point layouts and where statistical tasks, such as clustering, can be easily carried out. Further, we show how the notion of mandatory critical point can be leveraged to evaluate for each cluster confidence regions for the appearance of critical points. Most of the steps of this framework can be trivially parallelized and we show how to efficiently implement them. Extensive experiments demonstrate the relevance of our approach. The accuracy of the confidence regions provided by the persistence atlas is quantitatively evaluated and compared to a baseline strategy using an off-the-shelf clustering approach. We illustrate the importance of the persistence atlas in a variety of real-life datasets, where clear trends in feature layouts are identified and analyzed. We provide a lightweight VTK-based C++ implementation of our approach that can be used for reproduction purposes.