RT Journal Article
SR Electronic
T1 AggreCount: An unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatial manner
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.25.221267
DO 10.1101/2020.07.25.221267
A1 Jacob Aaron Klickstein
A1 Sirisha Mukkavalli
A1 Malavika Raman
YR 2020
UL http://biorxiv.org/content/early/2020/07/26/2020.07.25.221267.abstract
AB Protein quality control is maintained by a number of integrated cellular pathways that monitor the folding and functionality of the cellular proteome. Defects in these pathways lead to the accumulation of misfolded or faulty proteins that may become insoluble and aggregate over time. Protein aggregates significantly contribute to the development of a number of human diseases such as Amyotrophic lateral sclerosis, Huntington’s and Alzheimer’s Disease. In vitro, imaging-based, cellular studies have defined key components that recognize and clear aggregates; however, no unifying method is available to quantify cellular aggregates. Here we describe an ImageJ macro called AggreCount to identify and measure protein aggregates in cells. AggreCount is designed to be intuitive, easy to use and customizable for different types of aggregates observed in cells. Minimal experience in coding is required to utilize the script. Based on a user defined image, AggreCount will report a number of metrics: (i) total number of cellular aggregates, (ii) percent cells with aggregates, (iii) aggregates per cell, (iv) area of aggregates and (v) localization of aggregates (cytosol, perinuclear or nuclear). A data table of aggregate information on a per cell basis as well as a summary table is provided for further data analysis. We demonstrate the versatility of AggreCount by analyzing a number of different cellular aggregates including aggresomes, stress granules and inclusion bodies caused by Huntingtin polyQ expansion.Competing Interest StatementThe authors have declared no competing interest.