High-throughput measurement of the content and properties of nano-sized bioparticles with single-particle profiler.

We introduce a method, single-particle profiler, that provides single-particle information on the content and biophysical properties of thousands of particles in the size range 5-200 nm. We use our single-particle profiler to measure the messenger RNA encapsulation efficiency of lipid nanoparticles, the viral binding efficiencies of different nanobodies, and the biophysical heterogeneity of liposomes, lipoproteins, exosomes and viruses.

Sensorimotor Integration Supporting Perception Requires Syngap1 Expression in Cortex.

Perception, a cognitive construct, emerges through sensorimotor integration (SMI). The molecular and cellular mechanisms that shape SMI within circuits that promote cognition are poorly understood. Here, we demonstrate that expression of the autism/intellectual disability gene, Syngap1, in mouse cortical excitatory neurons promotes touch sensitivity required to elicit perceptual behaviors. Cortical Syngap1 expression enabled touch-induced feedback signals within sensorimotor loops by assembling circuits that support tactile sensitivity. These circuits also encoded correlates of attention that promoted self-generated whisker movements underlying purposeful and sustained object exploration. As Syngap1 deficient animals explored objects with whiskers, relatively weak touch signals were integrated with relatively strong motor signals. This produced a signal-to-noise deficit consistent with impaired tactile sensitivity, reduced tactile exploration, and weak tactile learning. Thus, Syngap1 expression in cortex promotes tactile perception by assembling circuits that integrate touch and whisker motor signals. Deficient Syngap1 expression likely contributes to cognitive impairment through abnormal top-down SMI.

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.

Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses.

We present barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel insitu analyses (BOLORAMIS), a reverse transcription-free method for spatially-resolved, targeted, in situ RNA identification of single or multiple targets. BOLORAMIS was demonstrated on a range of cell types and human cerebral organoids. Singleplex experiments to detect coding and non-coding RNAs in human iPSCs showed a stem-cell signature pattern. Specificity of BOLORAMIS was found to be 92% as illustrated by a clear distinction between human and mouse housekeeping genes in a co-culture system, as well as by recapitulation of subcellular localization of lncRNA MALAT1. Sensitivity of BOLORAMIS was quantified by comparing with single molecule FISH experiments and found to be 11%, 12% and 35% for GAPDH, TFRC and POLR2A, respectively. To demonstrate BOLORAMIS for multiplexed gene analysis, we targeted 96 mRNAs within a co-culture of iNGN neurons and HMC3 human microglial cells. We used fluorescence in situ sequencing to detect error-robust 8-base barcodes associated with each of these genes. We then used this data to uncover the spatial relationship among cells and transcripts by performing single-cell clustering and gene-gene proximity analyses. We anticipate the BOLORAMIS technology for in situ RNA detection to find applications in basic and translational research.

Highlights from the Era of Open Source Web-Based Tools.

High digital connectivity and a focus on reproducibility are contributing to an open science revolution in neuroscience. Repositories and platforms have emerged across the whole spectrum of subdisciplines, paving the way for a paradigm shift in the way we share, analyze, and reuse vast amounts of data collected across many laboratories. Here, we describe how open access web-based tools are changing the landscape and culture of neuroscience, highlighting six free resources that span subdisciplines from behavior to whole-brain mapping, circuits, neurons, and gene variants.

A Comparison of Dopaminergic and Cholinergic Populations Reveals Unique Contributions of VTA Dopamine Neurons to Short-Term Memory.

We systematically compare the contributions of two dopaminergic and two cholinergic ascending populations to a spatial short-term memory task in rats. In ventral tegmental area dopamine (VTA-DA) and nucleus basalis cholinergic (NB-ChAT) populations, trial-by-trial fluctuations in activity during the delay period relate to performance with an inverted-U, despite the fact that both populations have low activity during that time. Transient manipulations reveal that only VTA-DA neurons, and not the other three populations we examine, contribute causally and selectively to short-term memory. This contribution is most significant during the delay period, when both increases and decreases in VTA-DA activity impair short-term memory. Our results reveal a surprising dissociation between when VTA-DA neurons are most active and when they have the biggest causal contribution to short-term memory, and they also provide support for classic ideas about an inverted-U relationship between neuromodulation and cognition.

BRICseq Bridges Brain-wide Interregional Connectivity to Neural Activity and Gene Expression in Single Animals.

Comprehensive analysis of neuronal networks requires brain-wide measurement of connectivity, activity, and gene expression. Although high-throughput methods are available for mapping brain-wide activity and transcriptomes, comparable methods for mapping region-to-region connectivity remain slow and expensive because they require averaging across hundreds of brains. Here we describe BRICseq (brain-wide individual animal connectome sequencing), which leverages DNA barcoding and sequencing to map connectivity from single individuals in a few weeks and at low cost. Applying BRICseq to the mouse neocortex, we find that region-to-region connectivity provides a simple bridge relating transcriptome to activity: the spatial expression patterns of a few genes predict region-to-region connectivity, and connectivity predicts activity correlations. We also exploited BRICseq to map the mutant BTBR mouse brain, which lacks a corpus callosum, and recapitulated its known connectopathies. BRICseq allows individual laboratories to compare how age, sex, environment, genetics, and species affect neuronal wiring and to integrate these with functional activity and gene expression.

A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart.

The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.

A hypothalamus-habenula circuit controls aversion.

Encoding and predicting aversive events are critical functions of circuits that support survival and emotional well-being. Maladaptive circuit changes in emotional valence processing can underlie the pathophysiology of affective disorders. The lateral habenula (LHb) has been linked to aversion and mood regulation through modulation of the dopamine and serotonin systems. We have defined the identity and function of glutamatergic (Vglut2) control of the LHb, comparing the role of inputs originating in the globus pallidus internal segment (GPi), and lateral hypothalamic area (LHA), respectively. We found that LHb-projecting LHA neurons, and not the proposed GABA/glutamate co-releasing GPi neurons, are responsible for encoding negative value. Monosynaptic rabies tracing of the presynaptic organization revealed a predominantly limbic input onto LHA Vglut2 neurons, while sensorimotor inputs were more prominent onto GABA/glutamate co-releasing GPi neurons. We further recorded the activity of LHA Vglut2 neurons, by imaging calcium dynamics in response to appetitive versus aversive events in conditioning paradigms. LHA Vglut2 neurons formed activity clusters representing distinct reward or aversion signals, including a population that responded to mild foot shocks and predicted aversive events. We found that the LHb-projecting LHA Vglut2 neurons encode negative valence and rapidly develop a prediction signal for negative events. These findings establish the glutamatergic LHA-LHb circuit as a critical node in value processing.

An interactive framework for whole-brain maps at cellular resolution.

To deconstruct the architecture and function of brain circuits, it is necessary to generate maps of neuronal connectivity and activity on a whole-brain scale. New methods now enable large-scale mapping of the mouse brain at cellular and subcellular resolution. We developed a framework to automatically annotate, analyze, visualize and easily share whole-brain data at cellular resolution, based on a scale-invariant, interactive mouse brain atlas. This framework enables connectivity and mapping projects in individual laboratories and across imaging platforms, as well as multiplexed quantitative information on the molecular identity of single neurons. As a proof of concept, we generated a comparative connectivity map of five major neuron types in the corticostriatal circuit, as well as an activity-based map to identify hubs mediating the behavioral effects of cocaine. Thus, this computational framework provides the necessary tools to generate brain maps that integrate data from connectivity, neuron identity and function.

Publisher Correction: An interactive framework for whole-brain maps at cellular resolution.

In the version of this article initially published online, Daniel Fürth was not listed as a corresponding author. The error has been corrected in the print, PDF and HTML versions of this article.

A whole-brain atlas of inputs to serotonergic neurons of the dorsal and median raphe nuclei.

The serotonin system is proposed to regulate physiology and behavior and to underlie mood disorders; nevertheless, the circuitry controlling serotonergic neurons remains uncharacterized. We therefore generated a comprehensive whole-brain atlas defining the monosynaptic inputs onto forebrain-projecting serotonergic neurons of dorsal versus median raphe based on a genetically restricted transsynaptic retrograde tracing strategy. We identified discrete inputs onto serotonergic neurons from forebrain and brainstem neurons, with specific inputs from hypothalamus, cortex, basal ganglia, and midbrain, displaying a greater than anticipated complexity and diversity in cell-type-specific connectivity. We identified and functionally confirmed monosynaptic glutamatergic inputs from prefrontal cortex and lateral habenula onto serotonergic neurons as well as a direct GABAergic input from striatal projection neurons. In summary, our findings emphasize the role of hyperdirect inputs to serotonergic neurons. Cell-type-specific classification of connectivity patterns will allow for further functional analysis of the diverse but specific inputs that control serotonergic neurons during behavior.

Preliminary evidence that allelic variation in the LMX1A gene influences training-related working memory improvement.

LMX1A is a transcription factor involved in the development of dopamine (DA)-producing neurons in midbrain. Previous research has shown that allelic variations in three LMX1A single nucleotide polymorphisms (SNPs) were related to risk of Parkinson's disease (PD), suggesting that these SNPs may influence the number of mesencephalic DA neurons. Prompted by the established link between striatal DA functions and working memory (WM) performance, we examined two of these SNPs in relation to the ability to benefit from 4 weeks of WM training. One SNP (rs4657412) was strongly associated with the magnitude of training-related gains in verbal WM. The allele linked to larger gains has previously been suggested to be associated with higher dopaminergic nerve cell density. No differential gains of either SNP were observed for spatial WM, and the genotype groups were also indistinguishable in tests of attention, interference control, episodic memory, perceptual speed, and reasoning for both SNPs. This pattern of data is in agreement with previous findings from our group, suggesting that cognitive effects of DA-related genes may be more easily detected in a training context than for single-assessment performance scores.

Working memory plasticity modulated by dopamine transporter genotype.

Dopamine (DA) is implicated in working memory (WM) functioning. Variations in the DA transporter (DAT1) gene (SLC6A3) regulate DA availability in striatum. Compared to DAT1 9/10-repeat carriers, homozygosity of the DAT1 10-repeat allele has been related to less active dopaminergic pathways. A group of younger adults received 4 weeks of computerized adaptive training on several WM tasks. All participants improved their performance as a function of training. However, DAT1 9/10-repeat carriers showed larger training-related gains than DAT1 10-repeat carriers in visuospatial WM. By contrast, the two groups were indistinguishable in baseline WM performance as well as in a variety of tasks assessing different cognitive abilities. This pattern of results provides novel evidence that WM plasticity is a more sensitive indicator of DAT1 gene-related cognitive differences than single-assessment performance scores.