![]() An optional third channel may be used for cell body identification. Each image must consist of one channel for nuclei and one for aggregate quantification. TIFF, JPEG, etc.) and proprietary file formats commonly used in microscopy that are recognized by ImageJ. AggreCount is compatible with both common ( e.g. Images that will be analyzed as part of one experiment must be gathered into a single folder (the macro code and detailed instructions to run AggreCount are provided in Figs. The general workflow for analysis with the AggreCount macro involves three phases: assembling images, macro setup, and batch analysis. Results Defining settings in AggreCount for the multiparametric quantification of aggregates AggreCount will permit researchers to carry out unbiased quantification of diverse protein aggregates and is particularly suited to high-content image-based screening approaches. We have used AggreCount to analyze and quantify a variety of cellular aggregates including aggresomes, stress granules, aggresome-like induced structures (ALIS), and Htt polyQ inclusions. Furthermore, this tool is written in the ImageJ macro language, which is more accessible to those with limited programming knowledge. All processing steps use native ImageJ plugins and functions such that additional downloads or plugins are not necessary. In addition to delivering an unbiased analysis of images, this level of detail provides increased analytical depth and better evaluation of subtle phenotypes that may otherwise be overlooked. ![]() This automated tool uses common stains for nuclei, cell bodies, and aggregates as input and quantifies aggregate number, area, and cellular localization on a cell-by-cell basis. ![]() To improve ease, precision, speed, and reproducibility of aggregate quantification, we developed AggreCount, a novel image-processing macro, using ImageJ as a platform, to process, analyze, and quantify images. protein misfolding p97/valosin-containing protein.amyotrophic lateral sclerosis (ALS) (Lou Gehrig disease).We demonstrate the versatility of AggreCount by analyzing a number of different cellular aggregates including aggresomes, stress granules, and inclusion bodies caused by huntingtin polyglutamine expansion. A data table of aggregate information on a per cell basis, as well as a summary table, is provided for further data analysis. Based on a user-defined image, AggreCount will report a number of metrics: (i) total number of cellular aggregates, (ii) percentage of cells with aggregates, (iii) aggregates per cell, (iv) area of aggregates, and (v) localization of aggregates (cytosol, perinuclear, or nuclear). Minimal experience in coding is required to utilize the script. AggreCount is designed to be intuitive, easy to use, and customizable for different types of aggregates observed in cells. Here we describe an ImageJ macro called AggreCount to identify and measure protein aggregates in cells. In vitro, imaging-based, cellular studies have defined key biomolecular components that recognize and clear aggregates however, no unifying method is available to quantify cellular aggregates, limiting our ability to reproducibly and accurately quantify these structures. Protein aggregates significantly contribute to the development of a number of human diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Alzheimer's disease. Defects in these pathways lead to the accumulation of misfolded or faulty proteins that may become insoluble and aggregate over time. Protein quality control is maintained by a number of integrated cellular pathways that monitor the folding and functionality of the cellular proteome. Glycobiology and Extracellular Matrices.
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