Machine Learning to Quantitate Neutrophil NETosis.

We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in differentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for NETosis detection. Furthermore, by using CNNs and tools to determine object dispersion, we uncovered differences in NETotic nuclei clustering between major NETosis pathways that is useful in understanding NETosis signaling events. Our study also shows that neutrophils from patients with sickle cell disease were unresponsive to one of two major NETosis pathways. Thus, we demonstrate the design, performance, and implementation of ML tools for rapid quantitative and qualitative cell analysis in basic science.

How Müller glial cells in macaque fovea coat and isolate the synaptic terminals of cone photoreceptors.

A cone synaptic terminal in macaque fovea releases quanta of glutamate from approximately 20 active zones at a high rate in the dark. The transmitter reaches approximately 500 receptor clusters on bipolar and horizontal cell processes by diffusion laterally along the terminal's 50 microm(2) secretory face and approximately 2 microm inward. To understand what shapes transmitter flow, we investigated from electron photomicrographs of serial sections the relationship between Müller glial processes and cone terminals. We find that each Müller cell has one substantial trunk that ascends in the outer plexiform layer below the space between the "footprints" of the terminals. We find exactly equal numbers of Müller cell trunks and foveal cone terminals, which may make the fovea particularly vulnerable to Müller cell dysfunction. The processes that emerge from the single trunk do not ensheathe a single terminal. Instead, each Müller cell partially coats two to three terminals; in turn, each terminal is completely coated by two to three Müller cells. Therefore, the Müller cells that coat one terminal also partially coat the surrounding ( approximately six) terminals, creating a common environment for the cones supplying the center/surround receptive field of foveal midget bipolar and ganglion cells. Upon reaching the terminals, the trunk divides into processes that coat the terminals' sides but not their secretory faces. This glial framework minimizes glutamate transporter (EAAT1) beneath a terminal's secretory face but maximizes EAAT1 between adjacent terminals, thus permitting glutamate to diffuse locally along the secretory face and inward toward inner receptor clusters but reducing its effective spillover to neighboring terminals.