Automatic Counting of Microglial Cells in Healthy and Glaucomatous Mouse Retinas.

Pablo De Gracia,Alberto Triviño,Blanca Rojas,Juan J Salazar,Ana I Ramírez,José M Ramírez,Rosa De Hoz,Beatriz I Gallego,Alexandre Hiroaki Kihara

doi:10.1371/journal.pone.0143278

Abstract

Proliferation of microglial cells has been considered a sign of glial activation and a hallmark of ongoing neurodegenerative diseases. Microglia activation is analyzed in animal models of different eye diseases. Numerous retinal samples are required for each of these studies to obtain relevant data of statistical significance. Because manual quantification of microglial cells is time consuming, the aim of this study was develop an algorithm for automatic identification of retinal microglia. Two groups of adult male Swiss mice were used: age-matched controls (naïve, n = 6) and mice subjected to unilateral laser-induced ocular hypertension (lasered; n = 9). In the latter group, both hypertensive eyes and contralateral untreated retinas were analyzed. Retinal whole mounts were immunostained with anti Iba-1 for detecting microglial cell populations. A new algorithm was developed in MATLAB for microglial quantification; it enabled the quantification of microglial cells in the inner and outer plexiform layers and evaluates the area of the retina occupied by Iba-1+ microglia in the nerve fiber-ganglion cell layer. The automatic method was applied to a set of 6,000 images. To validate the algorithm, mouse retinas were evaluated both manually and computationally; the program correctly assessed the number of cells (Pearson correlation R = 0.94 and R = 0.98 for the inner and outer plexiform layers respectively). Statistically significant differences in glial cell number were found between naïve, lasered eyes and contralateral eyes (P<0.05, naïve versus contralateral eyes; P<0.001, naïve versus lasered eyes and contralateral versus lasered eyes). The algorithm developed is a reliable and fast tool that can evaluate the number of microglial cells in naïve mouse retinas and in retinas exhibiting proliferation. The implementation of this new automatic method can enable faster quantification of microglial cells in retinal pathologies.

Highlights

Microglial cells are the primary immune-responsive cells in the central nervous system
To validate the new algorithm, the results of the automatic counting of cells without any further human interaction for the inner plexiform layer (IPL) and the outer plexiform layer (OPL) were compared with a direct manual quantification of microglial cells by an investigator
We have described a new, powerful, automated, image analysis method developed with MATLAB that is capable of detecting individual immunolabeled microglial cells and immunolabeled regions in whole-mount mouse retinas using a threshold strategy

Summary

Introduction

Microglial cells are the primary immune-responsive cells in the central nervous system. They serve in the surveillance, maintenance, protection, and restoration of nervous system homeostasis. Microglial cells are involved in vital tasks for the survival of neurons [2], microglia have been implicated as a causative factor in a range of neurodegenerative disorders [3,4,5,6]. Under stress conditions that might put neuronal survival at risk, microglial cells are reactivated and become capable of undergoing proliferative processes and interactions with damaged cells[7,8]. It has been reported that microglial cells play an important role in development of glaucoma [12]. The quantification of microglial cells provides information about ongoing stress situations in the nervous system, including the retina

Objectives

Methods

Results

Discussion

Conclusion