Central nervous system (CNS) immune activation is an important driver of neuronal injury during several neurodegenerative and neuroinflammatory diseases. During HIV infection, CNS immune activation is associated with high rates of neurocognitive impairment, even during sustained long-term suppressive antiretroviral therapy (ART). However, the cellular subsets that drive immune activation and neuronal damage in the CNS during HIV infection and other neurological conditions remain unknown, in part because CNS cells are difficult to access in living humans. Using single-cell RNA sequencing (scRNA-seq) on cerebrospinal fluid (CSF) and blood from adults with and without HIV, we identified a rare (<5% of cells) subset of myeloid cells that are found only in CSF and that present a gene expression signature that overlaps significantly with neurodegenerative disease-associated microglia. This highlights the power of scRNA-seq of CSF to identify rare CNS immune cell subsets that may perpetuate neuronal injury during HIV infection and other conditions.

T cells provide critical immune surveillance to the central nervous system (CNS), and the cerebrospinal fluid (CSF) is thought to be a main route for their entry. Further characterization of the state of T cells in the CSF in healthy individuals is important for understanding how T cells provide protective immune surveillance without damaging the delicate environment of the CNS and providing tissue-specific context for understanding immune dysfunction in neuroinflammatory disease. Here, we have profiled T cells in the CSF of healthy human donors and have identified signatures related to cytotoxic capacity and tissue adaptation that are further exemplified in clonally expanded CSF T cells. By comparing profiles of clonally expanded T cells obtained from the CSF of patients with multiple sclerosis (MS) and healthy donors, we report that clonally expanded T cells from the CSF of patients with MS have heightened expression of genes related to T cell activation and cytotoxicity.

Macrophages are innate immune cells that contribute to fighting infections, tissue repair, and maintaining tissue homeostasis. To enable such functional diversity, macrophages resolve potentially conflicting cues in the microenvironment via mechanisms that are unclear. Here, we use single-cell RNA sequencing to explore how individual macrophages respond when co-stimulated with inflammatory stimuli LPS and IFN-γ and the resolving cytokine IL-4. These co-stimulated macrophages display a distinct global transcriptional program. However, variable negative cross-regulation between some LPS + IFN-γ-specific and IL-4-specific genes results in cell-to-cell heterogeneity in transcription. Interestingly, negative cross-regulation leads to mutually exclusive expression of the T-cell-polarizing cytokine genes Il6 and Il12b versus the IL-4-associated factors Arg1 and Chil3 in single co-stimulated macrophages, and single-cell secretion measurements show that these specialized functions are maintained for at least 48 h. This study suggests that increasing functional diversity in the population is one strategy macrophages use to respond to conflicting environmental cues.

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used extensively to provide insight into both healthy systems and diseases, it has not been used for disease prediction or diagnostics. Graph Attention Networks (GAT) have proven to be versatile for a wide range of tasks by learning from both original features and graph structures.Here we present a graph attention model for predicting disease state from single-cell data on a large dataset of Multiple Sclerosis (MS) patients. MS is a disease of the central nervous system that can be difficult to diagnose. We train our model on single-cell data obtained from blood and cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy adults (HA), resulting in 66,667 individual cells. We achieve 92 % accuracy in predicting MS, outperforming other state-of-the-art methods such as a graph convolutional network and a random forest classifier. Further, we use the learned graph attention model to get insight into the features (cell types and genes) that are important for this prediction. The graph attention model also allow us to infer a new feature space for the cells that emphasizes the differences between the two conditions. Finally we use the attention weights to learn a new low-dimensional embedding that can be visualized. To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data. We envision applying this method to single-cell data for other diseases. * Both authors contributed equally to this research.

14Macrophages are innate immune cells that contribute to fighting infections, tissue repair, and 15 maintaining tissue homeostasis. To enable such functional diversity, macrophages resolve 16 potentially conflicting cues in the microenvironment via mechanisms that remain unclear. Here, 17we used single-cell RNA sequencing to explore how individual macrophages respond when co-18 stimulated with the inflammatory stimuli, LPS+IFN-γ, and the resolving cytokine, IL-4. We found 19 that co-stimulated macrophages displayed a distinct global transcriptional program. However, 20 variable negative cross-regulation between some LPS+IFN-γ-and IL-4-specific genes resulted in 21 significant cell-to-cell heterogeneity in transcription. Interestingly, negative cross-regulation led 22to mutually exclusive expression of the T-cell-polarizing cytokines Il6 and Il12b versus the IL-4-23 associated factors Arg1 and Chil3 in single co-stimulated macrophages, and single-cell secretion 24 measurements showed that these specialized functions were maintained for at least 48 hours. 25Overall, our study suggests that increasing functional diversity in the population is one strategy 26 macrophages use to respond to conflicting environmental cues. 27 LPS+IFN-γ or the IL-4 transcriptional program in response to co-stimulation. 131 132 Co-stimulation with LPS+IFN-γ and IL-4 induces transcriptional cross-regulation that 133 varies substantially across individual cells 134We next focused on how co-stimulation with LPS+IFN-γ and IL-4 affected the expression 135 of genes uniquely upregulated by either LPS+IFN-γ or IL-4 alone ( Fig. 2a), which we refer to as 136 the core gene programs. For both LPS+IFN-γ-and IL-4-induced genes, co-stimulation with the 137 other cue caused both transcriptional upregulation and inhibition in a subset of core genes 138 belonging to each program, consistent with our own and previously reported observations 14 . 139Among the LPS+IFN-γ-induced core genes, 87 were inhibited by co-stimulation and 97 were 140 augmented by co-stimulation, while among the IL-4-induced core genes, 196 were inhibited and 141 16 were augmented by co-stimulation (Fig. 2b). 142We compared observations from our single-cell dataset to population-level RT-qPCR data 514