A Novel ImageJ Macro for Automated Cell Death Quantitation in the Retina

Abstract

Purpose: TUNEL assay is widely used to evaluate cell death. Quantification of TUNEL-positive (TUNEL+) cells in tissue sections is usually performed manually, ideally by two masked observers. This process is time consuming, prone to measurement errors, and not entirely reproducible. In this paper, we describe an automated quantification approach to address these difficulties.

Methods: We developed an ImageJ macro to quantitate cell death by TUNEL assay in retinal cross-section images. The script was coded using IJ1 programming language. To validate this tool, we selected a dataset of TUNEL assay digital images, calculated layer area and cell count manually (done by two observers), and compared measurements between observers and macro results.

Results: The automated macro segmented outer nuclear layer (ONL) and inner nuclear layer (INL) successfully. Automated TUNEL+ cell counts were in-between counts of inexperienced and experienced observers. The intraobserver coefficient of variation (COV) ranged from 13.09% to 25.20%. The COV between both observers was 51.11 ± 25.83% for the ONL and 56.07 ± 24.03% for the INL. Comparing observers' results with macro results, COV was 23.37 ± 15.97% for the ONL and 23.44 ± 18.56% for the INL.

Conclusions: We developed and validated an ImageJ macro that can be used as an accurate and precise quantitative tool for retina researchers to achieve repeatable, unbiased, fast, and accurate cell death quantitation. We believe that this standardized measurement tool could be advantageous to compare results across different research groups, as it is freely available as open source.

Figure 1

Segmentation of ONL, INL, and quantitation of TUNEL-positive cells by ImageJ macro. From the native RGB image (A), an 8-bit blue channel was extracted (B), and a Gaussian blur filter was applied (C). Using the Tsai moment-preserving thresholding method, we segmented ONL and INL layers (D) and, by determining the local maxima corresponding to cell nuclei, we identified individual cells at these layers (E). The 8-bit green channel was extracted, and background noise was subtracted (F). Using the Tsai moment-preserving thresholding method and binary watershed segmentation, we counted TUNEL⁺ cells (G). A JPEG image overlay was automatically created and exported for visual assessment of the quantitation (H). Results from the macro were reported (I) as area (mm²), total cells (count), TUNEL⁺ cells (count), TUNEL⁺ cells-to-area (count/mm²) ratio, and percentage of TUNEL⁺ cells over total cells (%), for both ONL and INL.

Figure 2

TUNEL-positive cell counts for the macro and mean values of inexperienced and experienced observers. Cell counts for the ImageJ macro and first and second measurements from observers plotted for every image, for ONL (A) and INL (B) datasets. A colored trendline represents mean variations for each observer. Macro TUNEL⁺ counts are located between observer's measurements.

Figure 3

Outer nuclear layer inter-observer agreement and correlation. Bland-Altman plots for ONL measurement agreement between observers (A) and between observers and ImageJ macro (B) are shown. Red lines represent mean differences or bias. Red dashed lines represent 95% limit of agreement (±1.96 SD). Correlation analysis between observers (C) and between observers and ImageJ macro (D). Red lines represent the fitted linear regression trendline. Green lines represent the 95% confidence interval area.

Figure 4

Inner nuclear layer interobserver agreement and correlation. Bland-Altman plots for INL measurement agreement between observers (A) and between observers and ImageJ macro (B). Red lines represent mean differences or bias. Red dashed lines represent the 95% limit of agreement (±1.96 SD). Correlation analysis between observers (C) and between observers and ImageJ macro (D). Red lines represent the fitted linear regression trendline. Green lines represent the 95% confidence interval area.

Figure 5

Experimental testing of ImageJ macro. From the ONL dataset, 30 images from a WT control group and 30 from an experimental (MST2 knock-out) group were selected (A). From the INL dataset, 30 images from a 10 nM NMDA group and 30 from a 100 nM NMDA group were selected (B). Columns represent mean and standard errors of the mean values for the TUNEL⁺ cells-to-area per observer ratio. Wilcoxon signed rank test between groups within the same observer. One-way ANOVA with Tukey post hoc correction for comparison between all control groups or 10 nM against the macro. One-way ANOVA with Tukey post hoc correction for comparison between all experimental groups or 100 nM against the macro.