( E) Metric matrix for the median TOS (linear) value for all cells in the sample ( n = 20). Green dash lines indicate thresholds selected by Costes’ method. Pixels with the highest intensity signal for each reporter channel have the lowest intensity signals for the other reporter, which indicates anticolocalization (blue circles). ( D) Scatterplot showing relationship between the signal intensity for two reporter channels for a random cell in the sample. ( C) Heat maps with cellular normalization showing localization regions of signal intensity for the cell shown in panel B. Red outline of the neuron indicates it has been identified as an object ( i.e. Large yellow box in left panel is a zoomed in view of the smaller yellow box. ( B) Cell identification using the F-actin reporter and filters to remove small non-cell objects (yellow arrow) based on their size ( i.e. Images are rat hippocampal neurons labelled with an F-actin probe and anti-tubulin antibody visualized by fluorescence microscopy (see main text). Bin Width is the width of each bin within the histogram.Īpplication 1: Cell selection using reporter images and physical parameters. Min and Max are the minimum and maximum values of the lowest and highest bin respectively (which are shown immediately under the histogram). The height of each bin is the relative frequency. ( E) Histogram generated from the results in Fig. 4C. ( D) Summary report (“Log”) of the results in Fig. 4C. Label = the image and unique cell number to identify individual cells Area = area of each cell in pixels and X = the average x-value of all pixels in a cell. ( C) Example of a data table showing metric values for Pearson correlation coefficient (PCC) and some of the parameter values for some of the cells in the analysis. The example code provided is for measuring colocalization by Pearson correlation coefficient. ( B) Analysis tab in the GUI for users to code custom metrics. ![]() Note: this example is for two reporter channels (see Fig. 8G for 3 reporter channels). ( A) Analysis tab in the GUI for selecting default metrics. ![]() These features make EzColocalization well-suited for experiments with low reporter signal, complex patterns of localization, and heterogeneous populations of cells and organisms.Īnalysis tab. Features of EzColocalization include: (i) tools to select individual cells and organisms from images (ii) filters to select specific types of cells and organisms based on physical parameters and signal intensity (iii) heat maps and scatterplots to visualize the localization patterns of reporters (iv) multiple metrics to measure colocalization for two or three reporters (v) metric matrices to systematically measure colocalization at multiple combinations of signal intensity thresholds and (vi) data tables that provide detailed information on each cell in a sample. EzColocalization is designed to be easy to use and customize for researchers with minimal experience in quantitative microscopy and computer programming. Here we describe an open source plugin for ImageJ called EzColocalization to visualize and measure colocalization in microscopy images. Insight into the function and regulation of biological molecules can often be obtained by determining which cell structures and other molecules they localize with (i.e.
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