The mammalian brain consists of an intricate tapestry of cell types, with diversity crucial for function that arises from both differential gene expression and circuit-specific anatomy. Yet, retrieving high-content gene-expression information while retaining 3D positional anatomy at cellular resolution has been difficult, limiting integrative understanding of brain structure and function. Here we introduce and apply a technology for 3D intact-tissue RNA sequencing, termed STARmap (Spatially-resolved Transcript Amplicon Readout Mapping), which integrates highly-specific signal amplification, novel hydrogel-tissue chemistry, and an error-reduction sequencing process. The capabilities of STARmap were tested by mapping from 160 to 1,020 distinct genes simultaneously in sections of mouse brain at single-cell resolution with unprecedented efficiency, accuracy and reproducibility. These experiments led to the discovery of multiple new neocortical cell types, with gene markers and spatial patterns of organization not previously described, by comparison of the molecularly-defined architectures of sensory versus cognitive neocortex, and by quantification of expression of activity-regulated genes as a function of stimulation condition, spatial position, and cell typology. By adapting STARmap to thick tissue blocks, we observed and confirmed a novel molecularly-defined gradient distribution of excitatory neuron subtypes across cubic millimeter-scale volumes (>30,000 cells), and discovered a short-range 3D pattern of self-clustering shared by many inhibitory neuron subtypes that was accurately identifiable with a 3D STARmap approach.
more »
« less
High-Complexity Barcoded Rabies Virus for Scalable Circuit Mapping Using Single-Cell and Single-Nucleus Sequencing
SUMMARY Single cell genomics has revolutionized our understanding of neuronal cell types. However, scalable technologies for probing single-cell connectivity are lacking, and we are just beginning to understand how molecularly defined cell types are organized into functional circuits. Here, we describe a protocol to generate high-complexity barcoded rabies virus (RV) for scalable circuit mapping from tens of thousands of individual starter cells in parallel. In addition, we introduce a strategy for targeting RV-encoded barcode transcripts to the nucleus so that they can be read out using single-nucleus RNA sequencing (snRNA-seq). We apply this tool in organotypic slice cultures of the developing human cerebral cortex, which reveals the emergence of cell type– specific circuit motifs in midgestation. By leveraging the power and throughput of single cell genomics for mapping synaptic connectivity, we chart a path forward for scalable circuit mapping of molecularly-defined cell types in healthy and disease states.
more »
« less
- Award ID(s):
- 2134955
- PAR ID:
- 10569007
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.more » « less
-
Abstract Spinal motor neurons (MNs) integrate sensory stimuli and brain commands to generate movements. In vertebrates, the molecular identities of the cardinal MN types such as those innervating limb versus trunk muscles are well elucidated. Yet the identities of finer subtypes within these cell populations that innervate individual muscle groups remain enigmatic. Here we investigate heterogeneity in mouse MNs using single-cell transcriptomics. Among limb-innervating MNs, we reveal a diverse neuropeptide code for delineating putative motor pool identities. Additionally, we uncover that axial MNs are subdivided into three molecularly distinct subtypes, defined by mediolaterally-biased Satb2, Nr2f2 or Bcl11b expression patterns with different axon guidance signatures. These three subtypes are present in chicken and human embryos, suggesting a conserved axial MN expression pattern across higher vertebrates. Overall, our study provides a molecular resource of spinal MN types and paves the way towards deciphering how neuronal subtypes evolved to accommodate vertebrate motor behaviors.more » « less
-
Abstract Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology2, 3, electrophysiology4, 5, transcriptomic expression6–8, connectivity9–13, or even a combination of such modalities14–16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells17–20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type22, 23known to be Somatostatin positive (Sst+)24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.more » « less
-
ABSTRACT Single-cell transcriptomics has uncovered the enormous heterogeneity of cell types that compose each region of the mammalian brain, but describing how such diverse types connect to form functional circuits has remained challenging. Current methods for measuring the probability and strength of cell-type specific connectivity motifs principally rely on low-throughput whole-cell recording approaches. The recent development of optical tools for perturbing and observing neural circuit activity, now notably including genetically encoded voltage indicators, presents an exciting opportunity to employ optical methods to greatly increase the throughput with which circuit connectivity can be mapped physiologically. At the same time, advances in spatial transcriptomics now enable the identification of cell typesin situbased on their unique gene expression signatures. Here, we demonstrate how long-range synaptic connectivity can be assayed optically with high sensitivity, high throughput, and cell-type specificity. We apply this approach in the motor cortex to examine cell-type-specific synaptic innervation patterns of long-range thalamic and contralateral input onto more than 1000 motor cortical neurons. We find that even cell types occupying the same cortical lamina receive vastly different levels of synaptic input, a finding which was previously not possible to uncover using lower-throughput approaches that can only describe the connectivity of broad cell types.more » « less
An official website of the United States government

