Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis

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.

Zebrafish models of diabetes-related CNS pathogenesis

Diabetes mellitus (DM) is a common metabolic disorder that affects multiple organ systems. DM also affects brain processes, contributing to various CNS disorders, including depression, anxiety and Alzheimer's disease. Despite active research in humans, rodent models and in-vitro systems, the pathogenetic link between DM and brain disorders remains poorly understood. Novel translational models and new model organisms are therefore essential to more fully study the impact of DM on CNS. The zebrafish (Danio rerio) is a powerful novel model species to study metabolic and CNS disorders. Here, we discuss how DM alters brain functions and behavior in zebrafish, and summarize their translational relevance to studying DM-related CNS pathogenesis in humans. We recognize the growing utility of zebrafish models in translational DM research, as they continue to improve our understanding of different brain pathologies associated with DM, and may foster the discovery of drugs that prevent or treat these diseases.