Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq

Synaptic Properties and Plasticity Mechanisms of Invertebrate Tonic and Phasic Neurons

Defining neuronal cell types and their associated biophysical and synaptic diversity has become an important goal in neuroscience as a mechanism to create comprehensive brain cell atlases in the post-genomic age. Beyond broad classification such as neurotransmitter expression, interneuron vs. pyramidal, sensory or motor, the field is still in the early stages of understanding closely related cell types. In both vertebrate and invertebrate nervous systems, one well-described distinction related to firing characteristics and synaptic release properties are tonic and phasic neuronal subtypes. In vertebrates, these classes were defined based on sustained firing responses during stimulation (tonic) vs. transient responses that rapidly adapt (phasic). In crustaceans, the distinction expanded to include synaptic release properties, with tonic motoneurons displaying sustained firing and weaker synapses that undergo short-term facilitation to maintain muscle contraction and posture. In contrast, phasic motoneurons with stronger synapses showed rapid depression and were recruited for short bursts during fast locomotion. Tonic and phasic motoneurons with similarities to those in crustaceans have been characterized in Drosophila, allowing the genetic toolkit associated with this model to be used for dissecting the unique properties and plasticity mechanisms for these neuronal subtypes. This review outlines general properties of invertebrate tonic and phasic motoneurons and highlights recent advances that characterize distinct synaptic and plasticity pathways associated with two closely related glutamatergic neuronal cell types that drive invertebrate locomotion.

How is flexible electronics advancing neuroscience research?

Innovative neurotechnology must be leveraged to experimentally answer the multitude of pressing questions in modern neuroscience. Driven by the desire to address the existing neuroscience problems with newly engineered tools, we discuss in this review the benefits of flexible electronics for neuroscience studies. We first introduce the concept and define the properties of flexible and stretchable electronics. We then categorize the four dimensions where flexible electronics meets the demands of modern neuroscience: chronic stability, interfacing multiple structures, multi-modal compatibility, and neuron-type-specific recording. Specifically, with the bending stiffness now approaching that of neural tissue, implanted flexible electronic devices produce little shear motion, minimizing chronic immune responses and enabling recording and stimulation for months, and even years. The unique mechanical properties of flexible electronics also allow for intimate conformation to the brain, the spinal cord, peripheral nerves, and the retina. Moreover, flexible electronics enables optogenetic stimulation, microfluidic drug delivery, and neural activity imaging during electrical stimulation and recording. Finally, flexible electronics can enable neuron-type identification through analysis of high-fidelity recorded action potentials facilitated by its seamless integration with the neural circuitry. We argue that flexible electronics will play an increasingly important role in neuroscience studies and neurological therapies via the fabrication of neuromorphic devices on flexible substrates and the development of enhanced methods of neuronal interpenetration.

Looking at neurodevelopment through a big data lens

The formation of the human brain, which contains nearly 100 billion neurons making an average of 1000 connections each, represents an astonishing feat of self-organization. Despite impressive progress, our understanding of how neurons form the nervous system and enable function is very fragmentary, especially for the human brain. New technologies that produce large volumes of high-resolution measurements-big data-are now being brought to bear on this problem. Single-cell molecular profiling methods allow the exploration of neural diversity with increasing spatial and temporal resolution. Advances in human genetics are shedding light on the genetic architecture of neurodevelopmental disorders, and new approaches are revealing plausible neurobiological mechanisms underlying these conditions. Here, we review the opportunities and challenges of integrating large-scale genomics and genetics for the study of brain development.

A community-based transcriptomics classification and nomenclature of neocortical cell types

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Upgrading the Physiological Relevance of Human Brain Organoids

The recent advent of human pluripotent stem cell (PSC)-derived 3D brain organoids has opened a window into aspects of human brain development that were not accessible before, allowing tractable monitoring and assessment of early developmental processes. However, their broad and effective use for modeling later stages of human brain development and disease is hampered by the lack of a stereotypic anatomical organization, which limits maturation processes dependent upon formation of unique cellular interactions and short- and long-range network connectivity. Emerging methods and technologies aimed at tighter regulatory control through bioengineering approaches, along with newer unbiased organoid analysis readouts, should resolve several of the current limitations. Here, we review recent advances in brain organoid generation and characterization with a focus on highlighting future directions utilizing interdisciplinary strategies that will be important for improving the physiological relevance of this model system.

Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells

Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.

Linking axon morphology to gene expression: a strategy for neuronal cell-type classification

To study how the brain drives cognition and behavior we need to understand its cellular composition. Advances in single-cell transcriptomics have revolutionized our ability to characterize neuronal diversity. To arrive at meaningful descriptions of cell types, however, gene expression must be linked to structural and functional properties. Axonal projection patterns are an appropriate measure, as they are diverse, change only gradually over time, and they influence and constrain circuit function. Here, we consider how efforts to map transcriptional and morphological diversity in the mouse brain could be linked to generate a modern taxonomy of the mouse brain.

Core principles for the implementation of the neurodata without borders data standard

BACKGROUND

The Neurodata Without Borders data standard (NWB) unifies diverse modalities of neurophysiology data in a single format. Integrating NWB with a database unleashes its full potential to promote collaboration, standardize analyses, capitalize on historical data, and ensures data integrity by maintaining process transparency. NWB database technology is the bedrock of analytical systems used by academic leaders including the Allen Institute and the International Brain Laboratory. Here we present the benefits of incorporating NWB design principles in a big data analytics application.

NEW METHOD

Data standards and databases are the foundation of big data analytics. To demonstrate the benefits of using these systems together, we implemented NWB in Jupyter notebooks using DataJoint to streamline database operations.

RESULTS

We demonstrate the utility of combining the NWB with DataJoint in a Jupyter-based electronic lab journal. We convert open-field behavioral data (using X, Y coordinates) to NWB format and process it with a DataJoint pipeline. Additional notebooks demonstrate working NWB files, data sharing, combining data from diverse sources, and retrospective analyses with data query filtering techniques.

COMPARISON WITH EXISTING METHODS

NWB describes how to structure and store neurophysiology data and is streamlined for research settings. In contrast to other data standards, combining NWB with DataJoint's database interface can dramatically increase data analytical capabilities.

CONCLUSIONS

The joint use of NWB with DataJoint transforms traditional laboratory datasets and workflows. Our Jupyter notebooks showcase the analytical and collaborative advantages of adopting big data analytics and can be tailored to other modalities by researchers interested in evaluating NWB.

Identification of an inhibitory neuron subtype, the L-stellate cell of the cochlear nucleus

Auditory processing depends upon inhibitory signaling by interneurons, even at its earliest stages in the ventral cochlear nucleus (VCN). Remarkably, to date only a single subtype of inhibitory neuron has been documented in the VCN, a projection neuron termed the D-stellate cell. With the use of a transgenic mouse line, optical clearing, and imaging techniques, combined with electrophysiological tools, we revealed a population of glycinergic cells in the VCN distinct from the D-stellate cell. These multipolar glycinergic cells were smaller in soma size and dendritic area, but over ten-fold more numerous than D-stellate cells. They were activated by auditory nerve and T-stellate cells, and made local inhibitory synaptic contacts on principal cells of the VCN. Given their abundance, combined with their narrow dendritic fields and axonal projections, it is likely that these neurons, here termed L-stellate cells, play a significant role in frequency-specific processing of acoustic signals.

A 'Marginal' tale: the development of the neocortical layer 1

The development of neocortical layer 1 is a very dynamic process and the scene of multiple transient events, with Cajal-Retzius cell death being one of the most characteristic ones. Layer 1 is also the route of migration for a substantial number of GABAergic interneurons during embryogenesis and where some of which will ultimately remain in the adult. The two cell types, together with a diverse set of incoming axons and dendrites, create an early circuit that will dramatically change in structure and function in the adult cortex to give prominence to inhibition. Through the engagement of a diverse set of GABAergic inhibitory cells by bottom-up and top-down inputs, adult layer 1 becomes a powerful computational platform for the neocortex.

Molecular and functional profiling of human islets: from heterogeneity to human phenotypes

Efforts to phenotype pancreatic islets have contributed tremendously to our present understanding of endocrine function and diabetes. A continued evolution in approaches to study islet physiology is important given the need to establish reference points for mature islet functionality, understanding biological variation amongst individuals and cells, and the ongoing appreciation of the role for islets in diabetes susceptibility. Recent efforts in islet biology have focused on technological improvements in imaging, molecular profiling and data analysis, along with a push for enhanced transparency and reporting. The integration of these approaches within a classical islet physiology framework, and approaches to link these data with in vivo human phenotypes, will be critical as we move towards a better understanding of islet function in health and disease. Here we discuss what we feel are important issues and useful approaches to consider as we move forward as a field in islet and beta cell phenotyping. Graphical abstract.

Agonist-induced functional analysis and cell sorting associated with single-cell transcriptomics characterizes cell subtypes in normal and pathological brain

To gain better insight into the dynamic interaction between cells and their environment, we developed the agonist-induced functional analysis and cell sorting (aiFACS) technique, which allows the simultaneous recording and sorting of cells in real-time according to their immediate and individual response to a stimulus. By modulating the aiFACS selection parameters, testing different developmental times, using various stimuli, and multiplying the analysis of readouts, it is possible to analyze cell populations of any normal or pathological tissue. The association of aiFACS with single-cell transcriptomics allows the construction of functional tissue cartography based on specific pharmacological responses of cells. As a proof of concept, we used aiFACS on the dissociated mouse brain, a highly heterogeneous tissue, enriching it in interneurons by stimulation with KCl or with AMPA, an agonist of the glutamate receptors, followed by sorting based on calcium levels. After AMPA stimulus, single-cell transcriptomics of these aiFACS-selected interneurons resulted in a nine-cluster classification. Furthermore, we used aiFACS on interneurons derived from the brain of the Fmr1-KO mouse, a rodent model of fragile X syndrome. We showed that these interneurons manifest a generalized defective response to AMPA compared with wild-type cells, affecting all the analyzed cell clusters at one specific postnatal developmental time.

DNA Methylation-Dependent Dysregulation of GABAergic Interneuron Functionality in Neuropsychiatric Diseases

Neuropsychiatric diseases, such as mood disorders, schizophrenia, and autism, represent multifactorial disorders, differing in causes, disease onset, severity, and symptoms. A common feature of numerous neuropsychiatric conditions are defects in the cortical inhibitory GABAergic system. The balance of excitation and inhibition is fundamental for proper and efficient information processing in the cerebral cortex. Thus, altered inhibition is suggested to account for pathological symptoms like cognitive impairments and dysfunctional multisensory integration. While it became apparent that most of these diseases have a clear genetic component, environmental influences emerged as an impact of disease manifestation, onset, and severity. Epigenetic mechanisms of transcriptional control, such as DNA methylation, are known to be responsive to external stimuli, and are suspected to be implicated in the functional impairments of GABAergic interneurons, and hence, the pathophysiology of neuropsychiatric diseases. Here, we provide an overview about the multifaceted functional implications of DNA methylation and DNA methyltransferases in cortical interneuron development and function in health and disease. Apart from the regulation of gamma-aminobutyric acid-related genes and genes relevant for interneuron development, we discuss the role of DNA methylation-dependent regulation of synaptic transmission by the modulation of endocytosis-related genes as potential pathophysiological mechanisms underlying neuropsychiatric conditions. Deciphering the hierarchy and mechanisms of changes in epigenetic signatures is crucial to develop effective strategies for treatment and prevention.

Multiple Origins of Secretagogin Expressing Cortical GABAergic Neuron Precursors in the Early Human Fetal Telencephalon

Secretagogin (SCGN) which acts as a calcium signaling sensor, has previously been shown to be expressed by a substantial population of cortical GABAergic neurons at mid-gestation in humans but not in mice. The present study traced SCGN expression in cortical GABAergic neurons in human fetal forebrain from earlier stages than previously studied. Multiple potential origins of SCGN-expressing neurons were identified in the caudal ganglionic eminence (CGE) lateral ganglionic eminence (LGE) septum and preoptic area; these cells largely co-expressed SP8 but not the medial ganglionic eminence marker LHX6. They followed various migration routes to reach their target regions in the neocortex, insular and olfactory cortex (OC) and olfactory bulbs. A robust increase in the number of SCGN-expressing GABAergic cortical neurons was observed in the midgestational period; 58% of DLX2+ neurons expressed SCGN in the cortical wall at 19 post-conceptional weeks (PCW), a higher proportion than expressed calretinin, a marker for GABAergic neurons of LGE/CGE origin. Furthermore, although most SCGN+ neurons co-expressed calretinin in the cortical plate (CP) and deeper layers, in the marginal zone (MZ) SCGN+ and calretinin+ cells formed separate populations. In the adult mouse, it has previously been shown that in the rostral migratory stream (RMS), SCGN, annexin V (ANXA5), and matrix metalloprotease 2 (MMP2) are co-expressed forming a functioning complex that exocytoses MMP2 in response to calcium. In the present study, ANXA5 showed widespread expression throughout the cortical wall, although MMP2 expression was very largely limited to the CP. We found co-expression of these proteins in some SCGN+ neurons in the subventricular zones (SVZ) suggesting a limited role for these cells in remodeling the extracellular matrix, perhaps during cell migration.

Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception

The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a 'barrel' (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.

Heterogeneous somatostatin-expressing neuron population in mouse ventral tegmental area

The cellular architecture of the ventral tegmental area (VTA), the main hub of the brain reward system, remains only partially characterized. To extend the characterization to inhibitory neurons, we have identified three distinct subtypes of somatostatin (Sst)-expressing neurons in the mouse VTA. These neurons differ in their electrophysiological and morphological properties, anatomical localization, as well as mRNA expression profiles. Importantly, similar to cortical Sst-containing interneurons, most VTA Sst neurons express GABAergic inhibitory markers, but some of them also express glutamatergic excitatory markers and a subpopulation even express dopaminergic markers. Furthermore, only some of the proposed marker genes for cortical Sst neurons were expressed in the VTA Sst neurons. Physiologically, one of the VTA Sst neuron subtypes locally inhibited neighboring dopamine neurons. Overall, our results demonstrate the remarkable complexity and heterogeneity of VTA Sst neurons and suggest that these cells are multifunctional players in the midbrain reward circuitry.

Heterogeneous somatostatin-expressing neuron population in mouse ventral tegmental area

The cellular architecture of the ventral tegmental area (VTA), the main hub of the brain reward system, remains only partially characterized. To extend the characterization to inhibitory neurons, we have identified three distinct subtypes of somatostatin (Sst)-expressing neurons in the mouse VTA. These neurons differ in their electrophysiological and morphological properties, anatomical localization, as well as mRNA expression profiles. Importantly, similar to cortical Sst-containing interneurons, most VTA Sst neurons express GABAergic inhibitory markers, but some of them also express glutamatergic excitatory markers and a subpopulation even express dopaminergic markers. Furthermore, only some of the proposed marker genes for cortical Sst neurons were expressed in the VTA Sst neurons. Physiologically, one of the VTA Sst neuron subtypes locally inhibited neighboring dopamine neurons. Overall, our results demonstrate the remarkable complexity and heterogeneity of VTA Sst neurons and suggest that these cells are multifunctional players in the midbrain reward circuitry.

Single-cell transcriptome analysis reveals cell lineage specification in temporal-spatial patterns in human cortical development

Neurogenesis processes differ in different areas of the cortex in many species, including humans. Here, we performed single-cell transcriptome profiling of the four cortical lobes and pons during human embryonic and fetal development. We identified distinct subtypes of neural progenitor cells (NPCs) and their molecular signatures, including a group of previously unidentified transient NPCs. We specified the neurogenesis path and molecular regulations of the human deep-layer, upper-layer, and mature neurons. Neurons showed clear spatial and temporal distinctions, while glial cells of different origins showed development patterns similar to those of mice, and we captured the developmental trajectory of oligodendrocyte lineage cells until the human mid-fetal stage. Additionally, we verified region-specific characteristics of neurons in the cortex, including their distinct electrophysiological features. With systematic single-cell analysis, we decoded human neuronal development in temporal and spatial dimensions from GW7 to GW28, offering deeper insights into the molecular regulations underlying human neurogenesis and cortical development.

Distinct subnetworks of the thalamic reticular nucleus

The thalamic reticular nucleus (TRN), the major source of thalamic inhibition, is known to regulate thalamocortical interactions critical for sensory processing, attention and cognition^1-5. TRN dysfunction has been linked to sensory abnormality, attention deficit and sleep disturbance across multiple neurodevelopmental disorders^6-9. Currently, little is known about the organizational principles underlying its divergent functions. We performed an integrative study linking single-cell molecular and electrophysiological features of the mouse TRN to connectivity and systems-level function. We found that TRN cellular heterogeneity is characterized by a transcriptomic gradient of two negatively correlated gene expression profiles, each containing hundreds of genes. Neurons in the extremes of this transcriptomic gradient express mutually exclusive markers, exhibit core/shell-like anatomical structure and have distinct electrophysiological properties. The two TRN subpopulations make differential connections to the functionally distinct first-order and higher-order thalamic nuclei to form molecularly defined TRN-thalamus subnetworks. Selective perturbation of the two subnetworks in vivo revealed their differential role in regulating sleep. Taken together, our study provides a comprehensive atlas for TRN neurons at the single-cell resolution, and links molecularly defined subnetworks to the functional organization of the thalamo-cortical circuits.

GSK3β Modulates Timing-Dependent Long-Term Depression Through Direct Phosphorylation of Kv4.2 Channels

Spike timing-dependent plasticity (STDP) is a form of activity-dependent remodeling of synaptic strength that underlies memory formation. Despite its key role in dictating learning rules in the brain circuits, the molecular mechanisms mediating STDP are still poorly understood. Here, we show that spike timing-dependent long-term depression (tLTD) and A-type K+ currents are modulated by pharmacological agents affecting the levels of active glycogen-synthase kinase 3 (GSK3) and by GSK3β knockdown in layer 2/3 of the mouse somatosensory cortex. Moreover, the blockade of A-type K+ currents mimics the effects of GSK3 up-regulation on tLTD and occludes further changes in synaptic strength. Pharmacological, immunohistochemical and biochemical experiments revealed that GSK3β influence over tLTD induction is mediated by direct phosphorylation at Ser-616 of the Kv4.2 subunit, a molecular determinant of A-type K+ currents. Collectively, these results identify the functional interaction between GSK3β and Kv4.2 channel as a novel mechanism for tLTD modulation providing exciting insight into the understanding of GSK3β role in synaptic plasticity.

Lessons from single cell sequencing in CNS cell specification and function

Modern RNA sequencing methods have greatly increased our understanding of the molecular fingerprint of neurons, astrocytes and oligodendrocytes throughout the central nervous system (CNS). Technical approaches with greater sensitivity and throughput have uncovered new connections between gene expression, cell biology, and ultimately CNS function. In recent years, single cell RNA-sequencing (scRNA-seq) has made a large impact on the neurosciences by enhancing the resolution of types of cells that make up the CNS and shedding light on their developmental trajectories and how their diversity is modified across species. Here we will review the advantages, innovations, and challenges of the single cell genomics era and highlight how it has impacted our understanding of neurodevelopment and neurological function.

A Central Amygdala-Ventrolateral Periaqueductal Gray Matter Pathway for Pain in a Mouse Model of Depression-like Behavior

BACKGROUND

The mechanisms underlying depression-associated pain remain poorly understood. Using a mouse model of depression, the authors hypothesized that the central amygdala-periaqueductal gray circuitry is involved in pathologic nociception associated with depressive states.

METHODS

The authors used chronic restraint stress to create a mouse model of nociception with depressive-like behaviors. They then used retrograde tracing strategies to dissect the pathway from the central nucleus of the amygdala to the ventrolateral periaqueductal gray. The authors performed optogenetic and chemogenetic experiments to manipulate the activity of this pathway to explore its roles for nociception.

RESULTS

The authors found that γ-aminobutyric acid-mediated (GABAergic) neurons from the central amygdala project onto GABAergic neurons of the ventrolateral periaqueductal gray, which, in turn, locally innervate their adjacent glutamatergic neurons. After chronic restraint stress, male mice displayed reliable nociception (control, mean ± SD: 0.34 ± 0.11 g, n = 7 mice; chronic restraint stress, 0.18 ± 0.11 g, n = 9 mice, P = 0.011). Comparable nociception phenotypes were observed in female mice. After chronic restraint stress, increased circuit activity was generated by disinhibition of glutamatergic neurons of the ventrolateral periaqueductal gray by local GABAergic interneurons via receiving enhanced central amygdala GABAergic inputs. Inhibition of this circuit increased nociception in chronic restraint stress mice (median [25th, 75th percentiles]: 0.16 [0.16, 0.16] g to 0.07 [0.04, 0.16] g, n = 7 mice per group, P < 0.001). In contrast, activation of this pathway reduced nociception (mean ± SD: 0.16 ± 0.08 g to 0.34 ± 0.13 g, n = 7 mice per group, P < 0.001).

CONCLUSIONS

These findings indicate that the central amygdala-ventrolateral periaqueductal gray pathway may mediate some aspects of pain symptoms under depression conditions.

A Single-Neuron: Current Trends and Future Prospects

The brain is an intricate network with complex organizational principles facilitating a concerted communication between single-neurons, distinct neuron populations, and remote brain areas. The communication, technically referred to as connectivity, between single-neurons, is the center of many investigations aimed at elucidating pathophysiology, anatomical differences, and structural and functional features. In comparison with bulk analysis, single-neuron analysis can provide precise information about neurons or even sub-neuron level electrophysiology, anatomical differences, pathophysiology, structural and functional features, in addition to their communications with other neurons, and can promote essential information to understand the brain and its activity. This review highlights various single-neuron models and their behaviors, followed by different analysis methods. Again, to elucidate cellular dynamics in terms of electrophysiology at the single-neuron level, we emphasize in detail the role of single-neuron mapping and electrophysiological recording. We also elaborate on the recent development of single-neuron isolation, manipulation, and therapeutic progress using advanced micro/nanofluidic devices, as well as microinjection, electroporation, microelectrode array, optical transfection, optogenetic techniques. Further, the development in the field of artificial intelligence in relation to single-neurons is highlighted. The review concludes with between limitations and future prospects of single-neuron analyses.

Precise coupling of the thalamic head-direction system to hippocampal ripples

The anterior thalamus is a key relay of neuronal signals within the limbic system. During sleep, the occurrence of hippocampal sharp wave-ripples (SWRs), believed to mediate consolidation of explicit memories, is modulated by thalamocortical network activity, yet how information is routed around SWRs and how this communication depends on neuronal dynamics remains unclear. Here, by simultaneously recording ensembles of neurons in the anterior thalamus and local field potentials in the CA1 area of the hippocampus, we show that the head-direction (HD) cells of the anterodorsal nucleus are set in stable directions immediately before SWRs. This response contrasts with other thalamic cells that exhibit diverse couplings to the hippocampus related to their intrinsic dynamics but independent of their anatomical location. Thus, our data suggest a specific and homogeneous contribution of the HD signal to hippocampal activity and a diverse and cell-specific coupling of non-HD neurons.

Single-cell sequencing of miRNAs: A modified technology

The recent development of next-generation sequencing technologies has offered valuable insights into individual cells. This technology is centered on the characterization of single cells for epigenomics, genomics, and transcriptomics. Ever since the first report appeared in 2009, the single-cell RNA-sequencing saga started to explore deeper into the mechanics intrigued within a single cell. microRNA (miRNA) has been increasingly recognized as an essential molecule triggering an additional layer for gene regulation. Therefore, single-cell sequencing of miRNAs is crucial to explore the logical riddles surrounding the epigenomics, genomics, and transcriptomics of an individual cell. Scientists from the Vienna Biocenter Campus have lately performed single-cell sequencing of miRNAs in the fly, Drosophila, and nematode, Caenorhabditis elegans. In this review, we present the latest scientific explorations supported by all-inclusive data on this novel subject matter of next-generation sequencing.

Improved mouse models and advanced genetic and genomic technologies for the study of neutrophils

Mice have been excellent surrogates for studying neutrophil biology and, furthermore, murine models of human disease have provided fundamental insights into the roles of human neutrophils in innate immunity. The emergence of novel humanized mice and high-diversity mouse populations offers the research community innovative and powerful platforms for better understanding, respectively, the mechanisms by which human neutrophils drive pathogenicity, and how genetic differences underpin the variation in neutrophil biology observed among humans. Here, we review key examples of these new resources. Additionally, we provide an overview of advanced genetic engineering tools available to further improve such murine model systems, of sophisticated neutrophil-profiling technologies, and of multifunctional nanoparticle (NP)-based neutrophil-targeting strategies.

Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes

Impaired function of pancreatic islet cells is a major cause of metabolic dysregulation and disease in humans. Despite this, it remains challenging to directly link physiological dysfunction in islet cells to precise changes in gene expression. Here we show that single-cell RNA sequencing combined with electrophysiological measurements of exocytosis and channel activity (patch-seq) can be used to link endocrine physiology and transcriptomes at the single-cell level. We collected 1,369 patch-seq cells from the pancreata of 34 human donors with and without diabetes. An analysis of function and gene expression networks identified a gene set associated with functional heterogeneity in β cells that can be used to predict electrophysiology. We also report transcriptional programs underlying dysfunction in type 2 diabetes and extend this approach to cryopreserved cells from donors with type 1 diabetes, generating a valuable resource for understanding islet cell heterogeneity in health and disease.

From the Transcriptome to Electrophysiology: Searching for the Underlying Cause of Diabetes

Cells within the islet of Langerhans are heterogeneous. Camunas-Soler et al. (2020) implement a patch-seq technique to collect both transcriptomic and electrophysiological data from the same cell. By doing so, they discover new genes that correlate with functional heterogeneity and find that shifts in these correlations indicate β cell compensation in type 2 diabetes.

Integrative analysis of in vivo recording with single-cell RNA-seq data reveals molecular properties of light-sensitive neurons in mouse V1

Vision formation is classically based on projections from retinal ganglion cells (RGC) to the lateral geniculate nucleus (LGN) and the primary visual cortex (V1). Neurons in the mouse V1 are tuned to light stimuli. Although the cellular information of the retina and the LGN has been widely studied, the transcriptome profiles of single light-stimulated neuron in V1 remain unknown. In our study, in vivo calcium imaging and whole-cell electrophysiological patch-clamp recording were utilized to identify 53 individual cells from layer 2/3 of V1 as light-sensitive (LS) or non-light-sensitive (NS) by single-cell light-evoked calcium evaluation and action potential spiking. The contents of each cell after functional tests were aspirated in vivo through a patch-clamp pipette for mRNA sequencing. Moreover, the three-dimensional (3-D) morphological characterizations of the neurons were reconstructed in a live mouse after the whole-cell recordings. Our sequencing results indicated that V1 neurons with a high expression of genes related to transmission regulation, such as Rtn4r and Rgs7, and genes involved in membrane transport, such as Na⁺/K⁺ ATPase and NMDA-type glutamatergic receptors, preferentially responded to light stimulation. Furthermore, an antagonist that blocks Rtn4r signals could inactivate the neuronal responses to light stimulation in live mice. In conclusion, our findings of the vivo-seq analysis indicate the key role of the strength of synaptic transmission possesses neurons in V1 of light sensory.

Genetic predispositions of Parkinson's disease revealed in patient-derived brain cells

Parkinson's disease (PD) is the second most prevalent neurological disorder and has been the focus of intense investigations to understand its etiology and progression, but it still lacks a cure. Modeling diseases of the central nervous system in vitro with human induced pluripotent stem cells (hiPSC) is still in its infancy but has the potential to expedite the discovery and validation of new treatments. Here, we discuss the interplay between genetic predispositions and midbrain neuronal impairments in people living with PD. We first summarize the prevalence of causal Parkinson's genes and risk factors reported in 74 epidemiological and genomic studies. We then present a meta-analysis of 385 hiPSC-derived neuronal lines from 67 recent independent original research articles, which point towards specific impairments in neurons from Parkinson's patients, within the context of genetic predispositions. Despite the heterogeneous nature of the disease, current iPSC models reveal converging molecular pathways underlying neurodegeneration in a range of familial and sporadic forms of Parkinson's disease. Altogether, consolidating our understanding of robust cellular phenotypes across genetic cohorts of Parkinson's patients may guide future personalized drug screens in preclinical research.

Gene regulatory networks during the development of the Drosophila visual system

The Drosophila visual system integrates input from 800 ommatidia and extracts different features in stereotypically connected optic ganglia. The development of the Drosophila visual system is controlled by gene regulatory networks that control the number of precursor cells, generate neuronal diversity by integrating spatial and temporal information, coordinate the timing of retinal and optic lobe cell differentiation, and determine distinct synaptic targets of each cell type. In this chapter, we describe the known gene regulatory networks involved in the development of the different parts of the visual system and explore general components in these gene networks. Finally, we discuss the advantages of the fly visual system as a model for gene regulatory network discovery in the era of single-cell transcriptomics.

A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation

The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.

A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation

The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.

Cell type composition and circuit organization of clonally related excitatory neurons in the juvenile mouse neocortex

Clones of excitatory neurons derived from a common progenitor have been proposed to serve as elementary information processing modules in the neocortex. To characterize the cell types and circuit diagram of clonally related excitatory neurons, we performed multi-cell patch clamp recordings and Patch-seq on neurons derived from Nestin-positive progenitors labeled by tamoxifen induction at embryonic day 10.5. The resulting clones are derived from two radial glia on average, span cortical layers 2-6, and are composed of a random sampling of transcriptomic cell types. We find an interaction between shared lineage and connection type: related neurons are more likely to be connected vertically across cortical layers, but not laterally within the same layer. These findings challenge the view that related neurons show uniformly increased connectivity and suggest that integration of vertical intra-clonal input with lateral inter-clonal input may represent a developmentally programmed connectivity motif supporting the emergence of functional circuits.

Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identifies a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results suggest that VENs are a regionally distinctive type of ET neuron. Additionally, we describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

Robust RNA-Seq of aRNA-amplified single cell material collected by patch clamp

Most single cell RNA sequencing protocols start with single cells dispersed from intact tissue. High-throughput processing of the separated cells is enabled using microfluidics platforms. However, dissociation of tissue results in loss of information about cell location and morphology and potentially alters the transcriptome. An alternative approach for collecting RNA from single cells is to re-purpose the electrophysiological technique of patch clamp recording. A hollow patch pipette is attached to individual cells, enabling the recording of electrical activity, after which the cytoplasm may be extracted for single cell RNA-Seq ("Patch-Seq"). Since the tissue is not disaggregated, the location of cells is readily determined, and the morphology of the cells is maintained, making possible the correlation of single cell transcriptomes with cell location, morphology and electrophysiology. Recent Patch-Seq studies utilizes PCR amplification to increase amount of nucleic acid material to the level required for current sequencing technologies. PCR is prone to create biased libraries - especially with the extremely high degrees of exponential amplification required for single cell amounts of RNA. We compared a PCR-based approach with linear amplifications and demonstrate that aRNA amplification (in vitro transcription, IVT) is more sensitive and robust for single cell RNA collected by a patch clamp pipette.

All-Optical Electrophysiology Reveals the Role of Lateral Inhibition in Sensory Processing in Cortical Layer 1

Cortical layer 1 (L1) interneurons have been proposed as a hub for attentional modulation of underlying cortex, but the transformations that this circuit implements are not known. We combined genetically targeted voltage imaging with optogenetic activation and silencing to study the mechanisms underlying sensory processing in mouse barrel cortex L1. Whisker stimuli evoked precisely timed single spikes in L1 interneurons, followed by strong lateral inhibition. A mild aversive stimulus activated cholinergic inputs and evoked a bimodal distribution of spiking responses in L1. A simple conductance-based model that only contained lateral inhibition within L1 recapitulated the sensory responses and the winner-takes-all cholinergic responses, and the model correctly predicted that the network would function as a spatial and temporal high-pass filter for excitatory inputs. Our results demonstrate that all-optical electrophysiology can reveal basic principles of neural circuit function in vivo and suggest an intuitive picture for how L1 transforms sensory and modulatory inputs. VIDEO ABSTRACT.

Heterogeneities in intrinsic excitability and frequency-dependent response properties of granule cells across the blades of the rat dentate gyrus

The dentate gyrus (DG), the input gate to the hippocampus proper, is anatomically segregated into three different sectors, namely, the suprapyramidal blade, the crest region, and the infrapyramidal blade. Although there are well-established differences between these sectors in terms of neuronal morphology, connectivity patterns, and activity levels, differences in electrophysiological properties of granule cells within these sectors have remained unexplored. Here, employing somatic whole cell patch-clamp recordings from the rat DG, we demonstrate that granule cells in these sectors manifest considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, these neurons showed positive temporal summation of their responses to inputs mimicking excitatory postsynaptic currents and showed little to no sag in their voltage responses to pulse currents. Consistently, the impedance amplitude profile manifested low-pass characteristics and the impedance phase profile lacked positive phase values at all measured frequencies and voltages and for all sectors. Granule cells in all sectors exhibited class I excitability, with broadly linear firing rate profiles, and granule cells in the crest region fired significantly fewer action potentials compared with those in the infrapyramidal blade. Finally, we found weak pairwise correlations across the 18 different measurements obtained individually from each of the three sectors, providing evidence that these measurements are indeed reporting distinct aspects of neuronal physiology. Together, our analyses show that granule cells act as integrators of afferent information and emphasize the need to account for the considerable physiological heterogeneities in assessing their roles in information encoding and processing.NEW & NOTEWORTHY We employed whole cell patch-clamp recordings from granule cells in the three subregions of the rat dentate gyrus to demonstrate considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, granule cells did not express membrane potential resonance, and their impedance profiles lacked inductive phase leads at all measured frequencies. Our analyses also show that granule cells manifest class I excitability characteristics, categorizing them as integrators of afferent information.

Single-cell alternative splicing analysis reveals dominance of single transcript variant

Alternative splicing contributes to the diversity of gene products by producing multiple transcript variants from one gene. Previous studies have revealed highly variable splicing patterns in single cells, but there is still a controversy in the understanding of the simultaneous expression of multiple transcript variants. Here we show that the dominance of a single transcript variant is a common phenomenon in single cells. We analyzed several single-cell RNA sequencing datasets and observed consistent results. Our results demonstrate that single cells tend to express one major transcript variant of a gene, and the diversity of transcript variants in cell populations mainly results from the heterogeneity of splicing pattern in single cells.

Progress of Induced Pluripotent Stem Cell Technologies to Understand Genetic Epilepsy

The study of the pathomechanisms by which gene mutations lead to neurological diseases has benefit from several cellular and animal models. Recently, induced Pluripotent Stem Cell (iPSC) technologies have made possible the access to human neurons to study nervous system disease-related mechanisms, and are at the forefront of the research into neurological diseases. In this review, we will focalize upon genetic epilepsy, and summarize the most recent studies in which iPSC-based technologies were used to gain insight on the molecular bases of epilepsies. Moreover, we discuss the latest advancements in epilepsy cell modeling. At the two dimensional (2D) level, single-cell models of iPSC-derived neurons lead to a mature neuronal phenotype, and now allow a reliable investigation of synaptic transmission and plasticity. In addition, functional characterization of cerebral organoids enlightens neuronal network dynamics in a three-dimensional (3D) structure. Finally, we discuss the use of iPSCs as the cutting-edge technology for cell therapy in epilepsy.

Sensory coding mechanisms revealed by optical tagging of physiologically defined neuronal types

Neural circuit analysis relies on having molecular markers for specific cell types. However, for a cell type identified only by its circuit function, the process of identifying markers remains laborious. We developed physiological optical tagging sequencing (PhOTseq), a technique for tagging and expression profiling of cells on the basis of their functional properties. PhOTseq was capable of selecting rare cell types and enriching them by nearly 100-fold. We applied PhOTseq to the challenge of mapping receptor-ligand pairings among pheromone-sensing neurons in mice. Together with in vivo ectopic expression of vomeronasal chemoreceptors, PhOTseq identified the complete combinatorial receptor code for a specific set of ligands.

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis

Single-cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell-type classification studies are unconstrained by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically and functionally identified neurons in 2 small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology.

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.

Informing disease modelling with brain-relevant functional genomic annotations

The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.

The art of using t-SNE for single-cell transcriptomics

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE.

Embryonic progenitor pools generate diversity in fine-scale excitatory cortical subnetworks

The mammalian neocortex is characterized by a variety of neuronal cell types and precise arrangements of synaptic connections, but the processes that generate this diversity are poorly understood. Here we examine how a pool of embryonic progenitor cells consisting of apical intermediate progenitors (aIPs) contribute to diversity within the upper layers of mouse cortex. In utero labeling combined with single-cell RNA-sequencing reveals that aIPs can generate transcriptionally defined glutamatergic cell types, when compared to neighboring neurons born from other embryonic progenitor pools. Whilst sharing layer-associated morphological and functional properties, simultaneous patch clamp recordings and optogenetic studies reveal that aIP-derived neurons exhibit systematic biases in both their intralaminar monosynaptic connectivity and the post-synaptic partners that they target within deeper layers of cortex. Multiple cortical progenitor pools therefore represent an important factor in establishing diversity amongst local and long-range fine-scale glutamatergic connectivity, which generates subnetworks for routing excitatory synaptic information.

Molecular signatures of retinal ganglion cells revealed through single cell profiling

Retinal ganglion cells can be classified into more than 40 distinct subtypes, whether by functional classification or transcriptomics. The examination of these subtypes in relation to their physiology, projection patterns, and circuitry would be greatly facilitated through the identification of specific molecular identifiers for the generation of transgenic mice. Advances in single cell transcriptomic profiling have enabled the identification of molecular signatures for cellular subtypes that are only rarely found. Therefore, we used single cell profiling combined with hierarchical clustering and correlate analyses to identify genes expressed in distinct populations of Parvalbumin-expressing cells and functionally classified RGCs. RGCs were manually isolated based either upon fluorescence or physiological distinction through cell-attached recordings. Microarray hybridization and RNA-Sequencing were employed for the characterization of transcriptomes and in situ hybridization was utilized to further characterize gene candidate expression. Gene candidates were identified based upon cluster correlation, as well as expression specificity within physiologically distinct classes of RGCs. Further, we identified Prph, Ctxn3, and Prkcq as potential candidates for ipRGC classification in the murine retina. The use of these genes, or one of the other newly identified subset markers, for the generation of a transgenic mouse would enable future studies of RGC-subtype specific function, wiring, and projection.

High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing

Understanding neural circuits requires deciphering interactions among myriad cell types defined by spatial organization, connectivity, gene expression, and other properties. Resolving these cell types requires both single-neuron resolution and high throughput, a challenging combination with conventional methods. Here, we introduce barcoded anatomy resolved by sequencing (BARseq), a multiplexed method based on RNA barcoding for mapping projections of thousands of spatially resolved neurons in a single brain and relating those projections to other properties such as gene or Cre expression. Mapping the projections to 11 areas of 3,579 neurons in mouse auditory cortex using BARseq confirmed the laminar organization of the three top classes (intratelencephalic [IT], pyramidal tract-like [PT-like], and corticothalamic [CT]) of projection neurons. In depth analysis uncovered a projection type restricted almost exclusively to transcriptionally defined subtypes of IT neurons. By bridging anatomical and transcriptomic approaches at cellular resolution with high throughput, BARseq can potentially uncover the organizing principles underlying the structure and formation of neural circuits.