Organization of basal ganglia circuits

Anatomical regions and how we define boundaries between and within structures in the brain put a constraint on our understanding of the brain. More than a century of research on the brain has revealed its fundamental organization to be based on single nerve cells communicating through electrical signals over chemical synapses. The fundamental organization on higher levels than circuits has been harder to unravel and often left to intuitive definitions based on immediate visual appearances like cell density or other cytoarchitectural criteria. In this thesis, I've developed an anatomical method where the entire mouse brain is reconstructed from tissue sections. This method combined with a genetic strategy for retrograde transsynaptic labeling of genetically-defined populations lets us tease apart the connectivity in an interconnected set of subcortical nuclei known as the basal ganglia. The method can further be extended across a large number of cell types to find generic patterns of connectivity across the entire brain. Lastly, the method enables us to utilize recent advances in mRNA sequencing to resolve the entire transcriptome of a single brain. The method, therefore, enables investigators to map out the fundamental organization of the brain both in terms of gene expression and connectivity, thereby providing a novel way to redefine neuroanatomy

In Situ Transcriptome Accessibility Sequencing (INSTA-seq)

Subcellular RNA localization regulates spatially polarized cellular processes, but unbiased investigation of its control in vivo remains challenging. Current hybridization-based methods cannot differentiate small regulatory variants, while in situ sequencing is limited by short reads. We solved these problems using a bidirectional sequencing chemistry to efficiently image transcript-specific barcode in situ , which are then extracted and assembled into longer reads using NGS. In the Drosophila retina, genes regulating eye development and cytoskeletal organization were enriched compared to methods using extracted RNA. We therefore named our method In Situ Transcriptome Accessibility sequencing (INSTA-seq). Sequencing reads terminated near 3’ UTR cis -motifs (e.g. Zip48C, stau ), revealing RNA-protein interactions. Additionally, Act5C polyadenylation isoforms retaining zipcode motifs were selectively localized to the optical stalk, consistent with their biology. Our platform provides a powerful way to visualize any RNA variants or protein interactions in situ to study their regulation in animal development

A Whole-Brain Atlas of Inputs to Serotonergic Neurons of the Dorsal and Median Raphe Nuclei

SummaryThe 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

Replicability of spatial gene expression atlas data from the adult mouse brain

Background Spatial gene expression is particularly interesting in the mammalian brain, with the potential to serve as a link between many data types. 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 sub-divisions, between the Allen Institute’s in situ hybridization data from the adult mouse brain (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. Results We use 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, but that performance is higher in the ABA than ST. Further, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bi-directionally. Finally, while an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. Conclusions 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

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

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

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