MOTIVATION: Neutrophil extracellular traps (NETs) are believed to be essential in controlling several bacterial pathogens. Quantification of NETs in vitro is an important tool in studies aiming to clarify the biological and chemical factors contributing to NET production, stabilization and degradation. This estimation can be performed on the basis of fluorescent microscopy images using appropriate labelings. In this context, it is desirable to automate the analysis to eliminate both the tedious process of manual annotation and possible operator-specific biases. RESULTS: We propose a framework for the automated determination of NET content, based on visually annotated images which are used to train a supervised machine-learning method. We derive several methods in this framework. The best results are obtained by combining these into a single prediction. The overall Q(2) of the combined method is 93%. By having two experts label part of the image set, we were able to compare the performance of the algorithms to the human interoperator variability. We find that the two operators exhibited a very high correlation on their overall assessment of the NET coverage area in the images (R(2) is 97%), although there were consistent differences in labeling at pixel level (Q(2), which unlike R(2) does not correct for additive and multiplicative biases, was only 89%). AVAILABILITY AND IMPLEMENTATION: Open source software (under the MIT license) is available at https://github.com/luispedro/Coelho2015_NetsDetermination for both reproducibility and application to new data.
MOTIVATION: Neutrophil extracellular traps (NETs) are believed to be essential in controlling several bacterial pathogens. Quantification of NETs in vitro is an important tool in studies aiming to clarify the biological and chemical factors contributing to NET production, stabilization and degradation. This estimation can be performed on the basis of fluorescent microscopy images using appropriate labelings. In this context, it is desirable to automate the analysis to eliminate both the tedious process of manual annotation and possible operator-specific biases. RESULTS: We propose a framework for the automated determination of NET content, based on visually annotated images which are used to train a supervised machine-learning method. We derive several methods in this framework. The best results are obtained by combining these into a single prediction. The overall Q(2) of the combined method is 93%. By having two experts label part of the image set, we were able to compare the performance of the algorithms to the human interoperator variability. We find that the two operators exhibited a very high correlation on their overall assessment of the NET coverage area in the images (R(2) is 97%), although there were consistent differences in labeling at pixel level (Q(2), which unlike R(2) does not correct for additive and multiplicative biases, was only 89%). AVAILABILITY AND IMPLEMENTATION: Open source software (under the MIT license) is available at https://github.com/luispedro/Coelho2015_NetsDetermination for both reproducibility and application to new data.
Authors: Sebastian Boeltz; Poorya Amini; Hans-Joachim Anders; Felipe Andrade; Rostyslav Bilyy; Simon Chatfield; Iwona Cichon; Danielle M Clancy; Jyaysi Desai; Tetiana Dumych; Nishant Dwivedi; Rachael Ann Gordon; Jonas Hahn; Andrés Hidalgo; Markus H Hoffmann; Mariana J Kaplan; Jason S Knight; Elzbieta Kolaczkowska; Paul Kubes; Moritz Leppkes; Angelo A Manfredi; Seamus J Martin; Christian Maueröder; Norma Maugeri; Ioannis Mitroulis; Luis E Munoz; Daigo Nakazawa; Indira Neeli; Victor Nizet; Elmar Pieterse; Marko Z Radic; Christiane Reinwald; Konstantinos Ritis; Patrizia Rovere-Querini; Michal Santocki; Christine Schauer; Georg Schett; Mark Jay Shlomchik; Hans-Uwe Simon; Panagiotis Skendros; Darko Stojkov; Peter Vandenabeele; Tom Vanden Berghe; Johan van der Vlag; Ljubomir Vitkov; Maren von Köckritz-Blickwede; Shida Yousefi; Alexander Zarbock; Martin Herrmann Journal: Cell Death Differ Date: 2019-01-08 Impact factor: 15.828
Authors: Shane V van Breda; Lenka Vokalova; Claire Neugebauer; Simona W Rossi; Sinuhe Hahn; Paul Hasler Journal: Front Immunol Date: 2019-07-10 Impact factor: 7.561
Authors: Laila Elsherif; Noah Sciaky; Carrington A Metts; Md Modasshir; Ioannis Rekleitis; Christine A Burris; Joshua A Walker; Nadeem Ramadan; Tina M Leisner; Stephen P Holly; Martis W Cowles; Kenneth I Ataga; Joshua N Cooper; Leslie V Parise Journal: Sci Rep Date: 2019-11-15 Impact factor: 4.379