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Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract. 

Microglia are the macrophages resident in the central nervous system. Brain injuries, such as traumatic brain injury, hypoxia, and stroke, can induce inflammatory responses accompanying microglial activation. The morphology of microglia is notably diverse and a prominent manifestation of activation. In this study, we propose to classify activated microglia using a convolutional neural network (CNN). Iba1 images were acquired from a control and cardiac arrest Long-Evans rat brain with a bright-field microscopy. The training data of 54,333 single-cell images were collected from the cortex and midbrain areas and curated by experienced neuroscientists. Results were compared between CNNs with different architectures, including Resnet18, Resnet50, Resnet101, and support vector machine classifiers. The highest model performance was found by Resnet18, trained after 120 epochs with a classification accuracy of 95.5-98.8 percent. The findings indicate a potential application for using CNN in the quantitative analysis of microglial morphology over regional differences in a large brain section.