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An improved IHC image post-processing method for the semi-automatic quantification of astrocyte number and activation
1, 2 , 1, 2 , 1, 2 , 3, 4 , * 1, 2
1  UCIBIO – Applied Molecular Biosciences Unit, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
2  i4HB – Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
3  Unit of Anatomy, Department of Biomedicine, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni, 4200-319 Porto, Portugal
4  [email protected], Faculty of Medicine of the University of Porto, Porto, Portugal
Academic Editor: Maria Emília Sousa

Published: 01 November 2022 by MDPI in 8th International Electronic Conference on Medicinal Chemistry session General (registering DOI)

Immunohistochemical staining of cell and molecular targets in brain samples is a powerful tool that can provide valuable information on neurological mechanisms. However, post-processing of microphotographs acquired after 3, 3’-Diaminobenzidine (DAB) staining can be particularly challenging due to the complexity, size and number of the samples, the targets being analyzed, image quality, and even the subjectivity related with image analysis and morphological appreciation by different users. Conventional analysis of these data usually relies on the manual quantification of distinct parameters in a large set of images like, for example, the number and size of cells or, in more complex analysis, the number and size of cell branching (as in Sholl analysis, e.g.). These prove to be extremely time-consuming and complex tasks, inappropriate for the processing of high amounts of information.

Here we describe an improved semi-automatic method to quantify glial fibrillary acidic protein (GFAP)-labelled astrocytes in immunohistochemistry images of rat brain, at a magnification as low as 20x. This method is a straightforward adaptation of the Young & Morrison method, using the ImageJ plugin Skeletonize, coupled to an intuitive data processing in datasheet-based software.

This method allows a faster and more efficient post-processing of brain tissue samples for the quantification of astrocytes size and number, area occupied, as well as astrocyte branching and branch length (indicative of astrocyte activation). Thus, contributing to better understand the possible inflammatory response developed by astrocytes.

Keywords: astrocytes; skeletonize; GFAP; quantification; ImageJ; semi-automatic method.