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.
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.
Authors: M Filippi; M A Horsfield; M Rovaris; T Yousry; M A Rocca; C Baratti; S Bressi; G Comi Journal: AJNR Am J Neuroradiol Date: 1998-02 Impact factor: 3.825
Authors: Nicolás Cuenca; Laura Fernández-Sánchez; Laura Campello; Victoria Maneu; Pedro De la Villa; Pedro Lax; Isabel Pinilla Journal: Prog Retin Eye Res Date: 2014-07-17 Impact factor: 21.198
Authors: Pavlina Tsoka; Paulo R Barbisan; Keiko Kataoka; Xiaohong Nancy Chen; Bo Tian; Peggy Bouzika; Joan W Miller; Eleftherios I Paschalis; Demetrios G Vavvas Journal: Exp Eye Res Date: 2019-01-29 Impact factor: 3.467
Authors: Christopher R Starr; Cyril N A Nyankerh; Xiaoping Qi; Yang Hu; Oleg S Gorbatyuk; Nahum Sonenberg; Michael E Boulton; Marina S Gorbatyuk Journal: Invest Ophthalmol Vis Sci Date: 2019-11-01 Impact factor: 4.799
Authors: Lei Zhang; Zhirui Yang; Yidan Zhao; Xinyu Yang; Xintong Meng; Juan Liu; Yi Liu; Can Yan; Dan Yan Journal: Sci Rep Date: 2020-02-06 Impact factor: 4.379