Summary
Local Ca2+ transients such as puffs and sparks form the building blocks of cellular Ca2+ signaling in numerous cell types. They have traditionally been studied by line scan confocal microscopy, but advances in TIRF microscopy together with improved electron-multiplied CCD (EMCCD) camera snow enable rapid (>500 frames s−1) imaging of subcellular Ca2+ signals with high spatial resolution in two dimensions. This approach yields vastly more information (ca. 1GB per minute) than line scan imaging, rendering visual identification and analysis of local events imaged both laborious and subject to user bias. Here we describe a routine to rapidly automate identification and analysis of local Ca2+ events. This features an intuitive graphical user-interfaces and runs under Matlab and the open-source Python software. The underlying algorithm features spatial and temporal noise filtering to reliably detect even small events in the presence of noisy and fluctuating baselines; localizes sites of Ca2+ release with sub-pixel resolution; facilitates user review and editing of data; and outputs time-sequences of fluorescence ratio signals for identified event sites along with Excel-compatible tables listing amplitudes and kinetics of events.
Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape’s CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quickly and economically leveraging such apps in reproducible workflows that scale independently to large data sets and production runs.
Adjacency matrices are useful for storing pairwise interaction data, such as correlations between gene pairs in a pathway or similarities between genes and conditions. The
aMatReader app enables users to import one or multiple adjacency matrix files into Cytoscape, where each file represents an edge attribute in a network. Our goal was to import the diverse adjacency matrix formats produced by existing scripts and libraries written in R, MATLAB, and Python, and facilitate importing that data into Cytoscape. To accelerate the import process, aMatReader attempts to predict matrix import parameters by analyzing the first two lines of the file. We also exposed CyREST endpoints to allow researchers to import network matrix data directly into Cytoscape from their language of choice. Many analysis tools deal with networks in the form of an adjacency matrix, and exposing the aMatReader API to automation users enables scripts to transfer those networks directly into Cytoscape with little effort.
The copycatLayout app is a network-based visual differential analysis tool that improves upon the existing layoutSaver app and is delivered pre-installed with Cytoscape, beginning with v3.6.0. LayoutSaver cloned a network layout by mapping node locations from one network to another based on node attribute values, but failed to clone view scale and location, and provided no means of identifying which nodes were successfully mapped between networks. Copycat addresses these issues and provides additional layout options. With the advent of Cytoscape Automation (packaged in Cytoscape v3.6.0), researchers can utilize the Copycat layout and its output in workflows written in their language of choice by using only a few simple REST calls. Copycat enables researchers to visually compare groups of homologous genes, generate network comparison images for publications, and quickly identify differences between similar networks at a glance without leaving their script. With a few extra REST calls, scripts can discover nodes present in one network but not in the other, which can feed into more complex analyses (e.g., modifying mismatched nodes based on new data, then re-running the layout to highlight additional network changes).
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