Esr1+ hypothalamic-habenula neurons shape aversive states.

Excitatory projections from the lateral hypothalamic area (LHA) to the lateral habenula (LHb) drive aversive responses. We used patch-sequencing (Patch-seq) guided multimodal classification to define the structural and functional heterogeneity of the LHA-LHb pathway. Our classification identified six glutamatergic neuron types with unique electrophysiological properties, molecular profiles and projection patterns. We found that genetically defined LHA-LHb neurons signal distinct aspects of emotional or naturalistic behaviors, such as estrogen receptor 1-expressing (Esr1+) LHA-LHb neurons induce aversion, whereas neuropeptide Y-expressing (Npy+) LHA-LHb neurons control rearing behavior. Repeated optogenetic drive of Esr1+ LHA-LHb neurons induces a behaviorally persistent aversive state, and large-scale recordings showed a region-specific neural representation of the aversive signals in the prelimbic region of the prefrontal cortex. We further found that exposure to unpredictable mild shocks induced a sex-specific sensitivity to develop a stress state in female mice, which was associated with a specific shift in the intrinsic properties of bursting-type Esr1+ LHA-LHb neurons. In summary, we describe the diversity of LHA-LHb neuron types and provide evidence for the role of Esr1+ neurons in aversion and sexually dimorphic stress sensitivity.

Inhibitory control of synaptic signals preceding locomotion in mouse frontal cortex.

The frontal cortex is essential for organizing voluntary movement. The secondary motor cortex (MOs) is a frontal subregion thought to integrate internal and external inputs before motor action. However, how excitatory and inhibitory synaptic inputs to MOs neurons are integrated preceding movement remains unclear. Here, we address this question by performing in vivo whole-cell recordings from MOs neurons of head-fixed mice moving on a treadmill. We find that principal neurons produce slowly increasing membrane potential and spike ramps preceding spontaneous running. After goal-directed training, ramps show larger amplitudes and accelerated kinetics. Chemogenetic suppression of interneurons combined with modeling suggests that the interplay between parvalbumin-positive (PV+) and somatostatin-positive (SOM+) interneurons, along with principal neuron recurrent connectivity, shape ramping signals. Plasticity of excitatory synapses on SOM+ interneurons can explain the ramp acceleration after training. Altogether, our data reveal that local interneurons differentially control task-dependent ramping signals when MOs neurons integrate inputs preceding movement.

Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.

High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute's in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute's whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain.

Spatial Transcriptomics: Molecular Maps of the Mammalian Brain.

Maps of the nervous system inspire experiments and theories in neuroscience. Advances in molecular biology over the past decades have revolutionized the definition of cell and tissue identity. Spatial transcriptomics has opened up a new era in neuroanatomy, where the unsupervised and unbiased exploration of the molecular signatures of tissue organization will give rise to a new generation of brain maps. We propose that the molecular classification of brain regions on the basis of their gene expression profile can circumvent subjective neuroanatomical definitions and produce common reference frameworks that can incorporate cell types, connectivity, activity, and other modalities. Here we review the technological and conceptual advances made possible by spatial transcriptomics in the context of advancing neuroanatomy and discuss how molecular neuroanatomy can redefine mapping of the nervous system.

Molecular atlas of the adult mouse brain.

Brain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain based purely on the unbiased identification of spatially defining features by employing whole-brain spatial transcriptomics. We found that the molecular information was sufficient to deduce the complex and detailed neuroanatomical organization of the brain. The unsupervised (non-expert, data-driven) classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and new subdivisions of striatum. The molecular atlas further supports the characterization of the spatial identity of neurons from their single-cell RNA profile, and provides a resource for annotating the brain using a minimal gene set-a brain palette. In summary, we have established a molecular atlas to formally define the spatial organization of brain regions, including the molecular code for mapping and targeting of discrete neuroanatomical domains.

Publisher Correction: A whole-brain atlas of monosynaptic input targeting four different cell types in the medial prefrontal cortex of the mouse.

The Supplementary Information is available in the online version of this Publisher Correction.

A whole-brain atlas of monosynaptic input targeting four different cell types in the medial prefrontal cortex of the mouse.

The local and long-range connectivity of cortical neurons are considered instrumental to the functional repertoire of the cortical region in which they reside. In cortical networks, distinct cell types build local circuit structures enabling computational operations. Computations in the medial prefrontal cortex (mPFC) are thought to be central to cognitive operation, including decision-making and memory. We used a retrograde trans-synaptic rabies virus system to generate brain-wide maps of the input to excitatory neurons as well as three inhibitory interneuron subtypes in the mPFC. On the global scale the input patterns were found to be mainly cell type independent, with quantitative differences in key brain regions, including the basal forebrain. Mapping of the local mPFC network revealed high connectivity between the different subtypes of interneurons. The connectivity mapping gives insight into the information that the mPFC processes and the structural architecture underlying the mPFC's unique functions.