Abstract
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.
Highlights
Cells rely on transmembrane signaling to interact with the outside world
To allow for the freehand design of ground-truth data while simulating realistic experimental output, we included a simulation module similar to FluoSim (Lagardère et al, 2020). This module allows the generation of user-defined clusters of molecules combined with a selected level of randomly placed background molecules. The results of this ground truth are modeled as images resulting from an Single-Molecule Localisation Microscopy (SMLM)-experiment emulated based on experimental statistics of dye blinking, camera noise, and localization accuracy
Our software significantly reduces processing time and allows the user to select different algorithms to identify and quantify cluster formation
Summary
Cells rely on transmembrane signaling to interact with the outside world. It is essential that cells can and decisively be put into action in response to signals in a noisy and complex environment (Pierce et al, 2002). Mechanisms have evolved that allow the triggering of an all-or-none, lasting response if required This often involves a threshold number of ligand-activated membrane molecules that recruit auxiliary molecules to form a larger assembly that, upon reaching threshold size, will switch the cell into a different state. These signaling assemblies appear as clusters of membrane proteins in the plasma membrane of cells. Several cluster algorithms have been adapted for the analysis of single-molecule fluorescence data of membrane proteins (Owen et al, 2010; Annibale et al, 2011a,b; Nicovich et al, 2017; Baumgart et al, 2019; Arnold et al, 2020; Pike et al, 2020). Our software will simplify and accelerate cluster analysis as a readout of membrane protein function
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