Kutz, S. | Search bioRxiv

bioRxiv2023

An integrative and multi-indicator approach for wildlife health applied to an endangered caribou herd

Assessing wildlife health in remote regions requires a multi-faceted approach that may include convenience samplings and the involvement of local communities. Combining data from hunted and captured caribou, we assessed the value of multiple indicators for understanding the health of the endangered Dolphin and Union caribou herd in Arctic Canada. We documented health determinants (infectious diseases and trace elements), processes (cortisol, pathology), and health outcomes (pregnancy and body condition). During a recent period of steep population decline our results suggested relatively good body condition and pregnancy rates and decreasing levels of stress, along with low adult cow survival. We identified multiple determinants of health as potential contributors to reduced survival, including Brucella suis biovar 4, Erysipelothrix rhusiopathiae and lower hair trace minerals. This integrative approach that drew on multiple data sources has provided unprecedented knowledge on the health in this herd and highlights the value of documenting individual animal health to understand causes of wildlife declines.

microbiology↗

bioRxiv2021

An efficient GUI-based clustering software for simulation and Bayesian cluster analysis of single-molecule localization microscopy data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster's constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays.

bioinformatics↗