The mouse accessory olfactory system (AOS) supports social and reproductive behavior through the sensation of environmental chemosignals. A growing number of excreted steroids have been shown to be potent AOS cues, including bile acids (BAs) found in feces. As is still the case with most AOS ligands, the specific receptors used by vomeronasal sensory neurons (VSNs) to detect BAs remain unknown. To identify VSN BA receptors, we first performed a deep analysis of VSN BA tuning using volumetric GCaMP6f/s Ca2+ imaging. These experiments revealed multiple populations of BA-receptive VSNs with submicromolar sensitivities. We then developed a new physiology-forward approach for identifying AOS ligand-receptor interactions, which we call Fluorescence Live Imaging for Cell Capture and RNA sequencing, or FLICCR-seq. FLICCR-seq analysis revealed five specific V1R family receptors enriched in BA-sensitive VSNs. These studies introduce a powerful new approach for ligand-receptor matching and reveal biological mechanisms underlying mammalian BA chemosensation.

The accessory olfactory system (AOS) guides behaviours that are important for survival and reproduction, but understanding of AOS function is limited by a lack of identified natural ligands. Here we report that mouse faeces are a robust source of AOS chemosignals and identify bile acids as a class of natural AOS ligands. Single-unit electrophysiological recordings from accessory olfactory bulb neurons in ex vivo preparations show that AOS neurons are strongly and selectively activated by peripheral stimulation with mouse faecal extracts. Faecal extracts contain several unconjugated bile acids that cause concentration-dependent neuronal activity in the AOS. Many AOS neurons respond selectively to bile acids that are variably excreted in male and female mouse faeces, and others respond to bile acids absent in mouse faeces. These results identify faeces as a natural source of AOS information, and suggest that bile acids may be mammalian pheromones and kairomones.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Single-cell technologies measure unique cellular signatures but are typically limited to a single modality. Computational approaches allow the fusion of diverse single-cell data types, but their efficacy is difficult to validate in the absence of authentic multi-omic measurements. To comprehensively assess the molecular phenotypes of single cells, we devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types and assessed the effectiveness of computational data fusion methods. Correlation analysis in individual cells revealed distinct relations between methylation and gene expression. Our integrative approach enabled joint analyses of the methylome, transcriptome, chromatin accessibility, and conformation for 63 human cortical cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling the prediction of cell types that are associated with diseases.