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Their ability to neutralize cellular targets through Fc receptor\u2010mediated phagocytosis has relied upon immunotherapy that has become of particular interest for the treatment of cancer and autoimmune diseases. A detailed investigation of phagocytosis is the key to the improvement of the therapeutic efficiency of existing medications and the creation of new ones. A promising method for studying the process is imaging flow cytometry (IFC) that acquires thousands of cell images per second in up to 12 optical channels and allows multiparametric fluorescent and morphological analysis of samples in the flow. However, conventional IFC data analysis approaches are based on a highly subjective manual choice of masks and other processing parameters that can lead to the loss of valuable information embedded in the original image. Here, we show the application of a Faster region\u2010based convolutional neural network (CNN) for accurate quantitative analysis of phagocytosis using imaging flow cytometry data. Phagocytosis of erythrocytes by peritoneal macrophages was chosen as a model system. CNN performed automatic high\u2010throughput processing of datasets and demonstrated impressive results in the identification and classification of macrophages and erythrocytes, despite the variety of shapes, sizes, intensities, and textures of cells in images. The developed procedure allows determining the number of phagocytosed cells, disregarding cases with a low probability of correct classification. We believe that CNN\u2010based approaches will enable powerful in\u2010depth investigation of a wide range of biological processes and will reveal the intricate nature of heterogeneous objects in images, leading to completely new capabilities in diagnostics and therapy.<\/jats:p>","DOI":"10.1002\/bit.27986","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T04:14:51Z","timestamp":1636431291000},"page":"626-635","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis"],"prefix":"10.1002","volume":"119","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3157-7158","authenticated-orcid":false,"given":"Elizaveta N.","family":"Mochalova","sequence":"first","affiliation":[{"name":"Nanobiotechnology Laboratory Moscow Institute of Physics and Technology Moscow Russia"},{"name":"Biophotonics Laboratory Prokhorov General Physics Institute of the Russian Academy of Sciences Moscow Russia"},{"name":"Nanobiomedicine Division Sirius University of Science and Technology Sochi Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan A.","family":"Kotov","sequence":"additional","affiliation":[{"name":"Nanobiotechnology Laboratory Moscow Institute of Physics and Technology Moscow Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitry A.","family":"Lifanov","sequence":"additional","affiliation":[{"name":"Nanobiotechnology Laboratory Moscow Institute of Physics and Technology Moscow Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soumyananda","family":"Chakraborti","sequence":"additional","affiliation":[{"name":"Nanobiotechnology Laboratory Moscow Institute of Physics and Technology Moscow Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5682-5891","authenticated-orcid":false,"given":"Maxim P.","family":"Nikitin","sequence":"additional","affiliation":[{"name":"Nanobiotechnology Laboratory Moscow Institute of Physics and Technology Moscow Russia"},{"name":"Nanobiomedicine Division Sirius University of Science and Technology Sochi Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"9","article-title":"Of macrophages and red blood cells: A complex love story","volume":"5","author":"Back D. 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