Macrophages play an important role in the adaptive immune system. Their ability to neutralize cellular targets through Fc receptor-mediated 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-based 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-throughput 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-based approaches will enable powerful in-depth 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.

Introduction. Since the outbreak of the COVID-19 pandemic caused by SARS-CoV-2 novel coronavirus, the international community has been concerned about the emergence of mutations altering some biological properties of the pathogen like increasing its infectivity or virulence. Particularly, since the end of 2020, several variants of concern have been identified around the world, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2). However, the existing mechanism of detecting important mutations are not always effective enough, since only a relatively small part of all pathogen samples can be examined by whole genome sequencing due to its high cost.Material and methods. In this study, we have designed special primer panel and used it for targeted highthroughput sequencing of several significant S-gene (spike) regions of SARS-CoV-2. The Illumina platform averaged approximately 50,000 paired-end reads with a length of ≥150 bp per sample. This method was used to examine 579 random samples obtained from COVID-19 patients in Moscow and the Moscow region from February to June 2021.Results. This study demonstrated the dynamics of distribution of several SARS-CoV-2 strains and its some single mutations. It was found that the Delta strain appeared in the region in May 2021, and became prevalent in June, partially displacing other strains.Discussion. The obtained results provide an opportunity to assign the viral samples to one of the strains, including the previously mentioned in time- and cost-effective manner. The approach can be used for standardization of the procedure of searching for mutations in individual regions of the SARS-CoV-2 genome. It allows to get a more detailed data about the epidemiological situation in a region.