A Novel ImageJ Macro for Automated Cell Death Quantitation in the Retina
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.
Insights
This study introduces an automated ImageJ macro for quantifying cell death using the TUNEL assay in retinal images. The macro offers a repeatable, unbiased, and accurate method for cell death quantitation, improving upon manual counting.
Area of Science:
- Neuroscience
- Cell Biology
- Ophthalmology
Background:
- The TUNEL assay is crucial for evaluating cell death in tissue sections.
- Manual quantification of TUNEL-positive cells is time-consuming, error-prone, and lacks reproducibility.
- Existing methods hinder objective comparisons across research groups.
Purpose of the Study:
- To develop an automated ImageJ macro for precise quantification of TUNEL-positive cells in retinal cross-sections.
- To address the limitations of manual cell death assessment.
- To provide a standardized tool for retina researchers.
Main Methods:
- An ImageJ macro was developed using IJ1 programming language for automated cell death quantification.
- Retinal cross-section images from TUNEL assays were used for validation.
- Manual cell counts and layer area measurements were performed by two observers for comparison.
Main Results:
- The macro successfully segmented the outer nuclear layer (ONL) and inner nuclear layer (INL).
- Automated cell counts fell between those of inexperienced and experienced observers.
- The macro demonstrated lower coefficients of variation (COV) compared to inter-observer variability, with COVs of 23.37% for ONL and 23.44% for INL.
Conclusions:
- An accurate and precise ImageJ macro for TUNEL assay quantification in retinal images was developed and validated.
- The macro provides repeatable, unbiased, fast, and accurate cell death quantitation.
- This open-source tool can facilitate standardized measurements and improve result comparability among researchers.
