Search

Cell-cell interactions control the physiology and pathology of the central nervous system (CNS). To study astrocyte cell interactions in vivo, we developed rabies barcode interaction detection followed by sequencing (RABID-seq), which combines barcoded viral tracing and single-cell RNA sequencing (scRNA-seq). Using RABID-seq, we identified axon guidance molecules as candidate mediators of microglia-astrocyte interactions that promote CNS pathology in experimental autoimmune encephalomyelitis (EAE) and, potentially, multiple sclerosis (MS). In vivo cell-specific genetic perturbation EAE studies, in vitro systems, and the analysis of MS scRNA-seq datasets and CNS tissue established that Sema4D and Ephrin-B3 expressed in microglia control astrocyte responses via PlexinB2 and EphB3, respectively. Furthermore, a CNS-penetrant EphB3 inhibitor suppressed astrocyte and microglia proinflammatory responses and ameliorated EAE. In summary, RABID-seq identified microglia-astrocyte interactions and candidate therapeutic targets.

Individual variation in the addiction liability of amphetamines has a heritable genetic component. We previously identified Hnrnph1 (heterogeneous nuclear ribonucleoprotein H1) as a quantitative trait gene underlying decreased methamphetamine-induced locomotor activity in mice. Here, we showed that mice (both females and males) with a heterozygous mutation in the first coding exon of Hnrnph1 (H1+/-) showed reduced methamphetamine reinforcement and intake and dose-dependent changes in methamphetamine reward as measured via conditioned place preference. Furthermore, H1+/- mice showed a robust decrease in methamphetamine-induced dopamine release in the NAc with no change in baseline extracellular dopamine, striatal whole-tissue dopamine, dopamine transporter protein, dopamine uptake, or striatal methamphetamine and amphetamine metabolite levels. Immunohistochemical and immunoblot staining of midbrain dopaminergic neurons and their forebrain projections for TH did not reveal any major changes in staining intensity, cell number, or forebrain puncta counts. Surprisingly, there was a twofold increase in hnRNP H protein in the striatal synaptosome of H1+/- mice with no change in whole-tissue levels. To gain insight into the mechanisms linking increased synaptic hnRNP H with decreased methamphetamine-induced dopamine release and behaviors, synaptosomal proteomic analysis identified an increased baseline abundance of several mitochondrial complex I and V proteins that rapidly decreased at 30 min after methamphetamine administration in H1+/- mice. In contrast, the much lower level of basal synaptosomal mitochondrial proteins in WT mice showed a rapid increase. We conclude that H1+/- decreases methamphetamine-induced dopamine release, reward, and reinforcement and induces dynamic changes in basal and methamphetamine-induced synaptic mitochondrial function.SIGNIFICANCE STATEMENT Methamphetamine dependence is a significant public health concern with no FDA-approved treatment. We discovered a role for the RNA binding protein hnRNP H in methamphetamine reward and reinforcement. Hnrnph1 mutation also blunted methamphetamine-induced dopamine release in the NAc, a key neurochemical event contributing to methamphetamine addiction liability. Finally, Hnrnph1 mutants showed a marked increase in basal level of synaptosomal hnRNP H and mitochondrial proteins that decreased in response to methamphetamine, whereas WT mice showed a methamphetamine-induced increase in synaptosomal mitochondrial proteins. Thus, we identified a potential role for hnRNP H in basal and dynamic mitochondrial function that informs methamphetamine-induced cellular adaptations associated with reduced addiction liability.

The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan. [ABSTRACT FROM AUTHOR]