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

Two types of somatostatin-expressing GABAergic interneurons in the superficial layers of the mouse cingulate cortex

Somatostatin-expressing (SOM⁺), inhibitory interneurons represent a heterogeneous group of cells and given their remarkable diversity, classification of SOM⁺ interneurons remains a challenging task. Electrophysiological, morphological and neurochemical classes of SOM⁺ interneurons have been proposed in the past but it remains unclear as to what extent these classes are congruent. We performed whole-cell patch-clamp recordings from 127 GFP-labeled SOM⁺ interneurons ('GIN') of the superficial cingulate cortex with subsequent biocytin-filling and immunocytochemical labeling. Principal component analysis followed by k-means clustering predicted two putative subtypes of SOM⁺ interneurons, which we designated as group I and group II GIN. A key finding of our study is the fact that these electrophysiologically and morphologically distinct groups of SOM⁺ interneurons can be correlated with two neurochemical subtypes of SOM⁺ interneurons described recently in our laboratory. In particular, all SOM⁺ interneurons expressing calbindin but no calretinin could be classified as group I GIN, whereas all but one neuropeptide Y- and calretinin-positive interneurons were found in group II.

Membrane potential and dynamics in a ternary lipid mixture: insights from molecular dynamics simulations

Transmembrane potential (Vm) plays critical roles in cell signaling and other functions. However, the impact of Vm on the structure and dynamics of membrane lipids and proteins, which are critical for the regulation of signaling, is still an open question. All-atom molecular dynamics (MD) simulation is emerging as a useful technique to address this issue. Previous atomistic MD simulations of pure or binary model membranes indicated that both ion imbalance and electric field can be used to generate Vm, but both approaches failed to yield structural changes in lipids with statistical significance. We hypothesized that a possible reason for this could be oversimplified membrane composition or limited sampling. In this work, we tested if and how Vm modulates the structure and dynamics of lipids in a physiologically relevant model membrane. Using a detailed side-by-side comparison, we first show that while both ion imbalance and electric field generate Vm in our complex membranes, only the latter could produce physiologically relevant Vm. We further show that double bonds in lipid acyl chains have a relatively large sensitivity to Vm. A single-bilayer model with an electric field showed the highest sensitivity in simulations under the isothermal-isobaric (NPT) ensemble, reproducing expected responses of head-group dipoles to Vm and suggesting that this approach may be more suitable for studying the structural effects of Vm. Our findings also shed light on the relationship between the macroscopic Vm and its atomic-level underpinnings.

Quantitative single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast. Here, we review the basic principles underlying the different experimental protocols and how to benchmark them. We also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis. We hope that this helps to choose appropriate experimental and computational methods for the research question at hand.

Single Cell Multiplex Reverse Transcription Polymerase Chain Reaction After Patch-clamp

The cerebral cortex is composed of numerous cell types exhibiting various morphological, physiological, and molecular features. This diversity hampers easy identification and characterization of these cell types, prerequisites to study their specific functions. This article describes the multiplex single cell reverse transcription polymerase chain reaction (RT-PCR) protocol, which allows, after patch-clamp recording in slices, to detect simultaneously the expression of tens of genes in a single cell. This simple method can be implemented with morphological characterization and is widely applicable to determine the phenotypic traits of various cell types and their particular cellular environment, such as in the vicinity of blood vessels. The principle of this protocol is to record a cell with the patch-clamp technique, to harvest and reverse transcribe its cytoplasmic content, and to detect qualitatively the expression of a predefined set of genes by multiplex PCR. It requires a careful design of PCR primers and intracellular patch-clamp solution compatible with RT-PCR. To ensure a selective and reliable transcript detection, this technique also requires appropriate controls from cytoplasm harvesting to amplification steps. Although precautions discussed here must be strictly followed, virtually any electrophysiological laboratory can use the multiplex single cell RT-PCR technique.

Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics

Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.

Genetic approaches to access cell types in mammalian nervous systems

Understanding brain circuit organization and function requires systematic dissection of its cellular components. With vast cell number and diversity, mammalian nervous systems present a daunting challenge for achieving specific and comprehensive cell type access-prerequisite to circuit analysis. Genetic approaches in the mouse have relied on germline engineering to access marker-defined cell populations. Combinatorial strategies that engage marker intersection, anatomy and projection pattern (e.g. antero-grade and retro-grade viral vectors), and developmental lineage substantially increase the specificity of cell type targeting. While increasing number of mouse cell types are becoming experimentally accessible, comprehensive coverage requires larger coordinated efforts with strategic infrastructural and fiscal planning. CRISPR-based genome editing may enable cell type access in other species, but issues of time, cost and ethics remain, especially for primates. Novel approaches that bypass the germline, such as somatic cell engineering and cell surface-based gene delivery, may reduce the barrier of genetic access to mammalian cell types.

Single-Cell (Multi)omics Technologies

Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

Schaffer Collateral Inputs to CA1 Excitatory and Inhibitory Neurons Follow Different Connectivity Rules

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3 and CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks.SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies.

Degeneracy in the robust expression of spectral selectivity, subthreshold oscillations, and intrinsic excitability of entorhinal stellate cells

Biological heterogeneities are ubiquitous and play critical roles in the emergence of physiology at multiple scales. Although neurons in layer II (LII) of the medial entorhinal cortex (MEC) express heterogeneities in channel properties, the impact of such heterogeneities on the robustness of their cellular-scale physiology has not been assessed. Here, we performed a 55-parameter stochastic search spanning nine voltage- or calcium-activated channels to assess the impact of channel heterogeneities on the concomitant emergence of 10 in vitro electrophysiological characteristics of LII stellate cells (SCs). We generated 150,000 models and found a heterogeneous subpopulation of 449 valid models to robustly match all electrophysiological signatures. We employed this heterogeneous population to demonstrate the emergence of cellular-scale degeneracy in SCs, whereby disparate parametric combinations expressing weak pairwise correlations resulted in similar models. We then assessed the impact of virtually knocking out each channel from all valid models and demonstrate that the mapping between channels and measurements was many-to-many, a critical requirement for the expression of degeneracy. Finally, we quantitatively predict that the spike-triggered average of SCs should be endowed with theta-frequency spectral selectivity and coincidence detection capabilities in the fast gamma-band. We postulate this fast gamma-band coincidence detection as an instance of cellular-scale-efficient coding, whereby SC response characteristics match the dominant oscillatory signals in LII MEC. The heterogeneous population of valid SC models built here unveils the robust emergence of cellular-scale physiology despite significant channel heterogeneities, and forms an efficacious substrate for evaluating the impact of biological heterogeneities on entorhinal network function.

NEW & NOTEWORTHY We assessed the impact of heterogeneities in channel properties on the robustness of cellular-scale physiology of medial entorhinal cortical stellate neurons. We demonstrate that neuronal models with disparate channel combinations were endowed with similar physiological characteristics, as a consequence of the many-to-many mapping between channel properties and the physiological characteristics that they modulate. We predict that the spike-triggered average of stellate cells should be endowed with theta-frequency spectral selectivity and fast gamma-band coincidence detection capabilities.

Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons

KEY POINTS

Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of KV 1- and A-type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking-pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co-occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa.

ABSTRACT

Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low-threshold (KV 1-type) potassium conductance and an inactivating (A-type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. Tonic spiking likely arises from a separate distribution of potassium channel densities. These results argue in favour of two cell populations, one characterized by tonic spiking and the other by heterogeneous spiking patterns. We present algorithms to predict spiking pattern proportions based on ion channel density distributions and, conversely, to estimate ion channel density distributions based on spiking pattern proportions. The implications for classifying cells based on spiking pattern are discussed.

Diverse Long-Range Axonal Projections of Excitatory Layer 2/3 Neurons in Mouse Barrel Cortex

Excitatory projection neurons of the neocortex are thought to play important roles in perceptual and cognitive functions of the brain by directly connecting diverse cortical and subcortical areas. However, many aspects of the anatomical organization of these inter-areal connections are unknown. Here, we studied long-range axonal projections of excitatory layer 2/3 neurons with cell bodies located in mouse primary somatosensory barrel cortex (wS1). As a population, these neurons densely projected to secondary whisker somatosensory cortex (wS2) and primary/secondary whisker motor cortex (wM1/2), with additional axon in the dysgranular zone surrounding the barrel field, perirhinal temporal association cortex and striatum. In three-dimensional reconstructions of 6 individual wS2-projecting neurons and 9 individual wM1/2-projecting neurons, we found that both classes of neurons had extensive local axon in layers 2/3 and 5 of wS1. Neurons projecting to wS2 did not send axon to wM1/2, whereas a small subset of wM1/2-projecting neurons had relatively weak projections to wS2. A small fraction of projection neurons solely targeted wS2 or wM1/2. However, axon collaterals from wS2-projecting and wM1/2-projecting neurons were typically also found in subsets of various additional areas, including the dysgranular zone, perirhinal temporal association cortex and striatum. Our data suggest extensive diversity in the axonal targets selected by individual nearby cortical long-range projection neurons with somata located in layer 2/3 of wS1.

Single Cell Multi-Omics Technology: Methodology and Application

In the era of precision medicine, multi-omics approaches enable the integration of data from diverse omics platforms, providing multi-faceted insight into the interrelation of these omics layers on disease processes. Single cell sequencing technology can dissect the genotypic and phenotypic heterogeneity of bulk tissue and promises to deepen our understanding of the underlying mechanisms governing both health and disease. Through modification and combination of single cell assays available for transcriptome, genome, epigenome, and proteome profiling, single cell multi-omics approaches have been developed to simultaneously and comprehensively study not only the unique genotypic and phenotypic characteristics of single cells, but also the combined regulatory mechanisms evident only at single cell resolution. In this review, we summarize the state-of-the-art single cell multi-omics methods and discuss their applications, challenges, and future directions.

Placing RNA in context and space - methods for spatially resolved transcriptomics

Single-cell transcriptomics provides us with completely new insights into the molecular diversity of different cell types and the different states they can adopt. The technique generates inventories of cells that constitute the building blocks of multicellular organisms. However, since the method requires isolation of discrete cells, information about the original location within tissue is lost. Therefore, it is not possible to draw detailed cellular maps of tissue architecture and their positioning in relation to other cells. In order to better understand the cellular and tissue function of multicellular organisms, we need to map the cells within their physiological, morphological, and anatomical context and space. In this review, we will summarize and compare the different methods of in situ RNA analysis and the most recent developments leading to more comprehensive and highly multiplexed spatially resolved transcriptomic approaches. We will discuss their highlights and advantages as well as their limitations and challenges and give an outlook on promising future applications and directions both within basic research as well as clinical integration.

Neurogliaform cortical interneurons derive from cells in the preoptic area

Delineating the basic cellular components of cortical inhibitory circuits remains a fundamental issue in order to understand their specific contributions to microcircuit function. It is still unclear how current classifications of cortical interneuron subtypes relate to biological processes such as their developmental specification. Here we identified the developmental trajectory of neurogliaform cells (NGCs), the main effectors of a powerful inhibitory motif recruited by long-range connections. Using in vivo genetic lineage-tracing in mice, we report that NGCs originate from a specific pool of 5-HT_3AR-expressing Hmx3+ cells located in the preoptic area (POA). Hmx3-derived 5-HT_3AR+ cortical interneurons (INs) expressed the transcription factors PROX1, NR2F2, the marker reelin but not VIP and exhibited the molecular, morphological and electrophysiological profile of NGCs. Overall, these results indicate that NGCs are a distinct class of INs with a unique developmental trajectory and open the possibility to study their specific functional contribution to cortical inhibitory microcircuit motifs.

Our brain contains over a 100 billion nerve cells or neurons, and each of them is thought to connect to over 1,000 other neurons. Together, these cells form a complex network to convey information from our surroundings or transmit messages to designated destinations. This circuitry forms the basis of our unique cognitive abilities.

In the cerebral cortex – the largest region of the brain – two main types of neurons can be found: projection neurons, which transfer information to other regions in the brain, and interneurons, which connect locally to different neurons and harmonize this information by inhibiting specific messages. The over 20 different types of known interneurons come in different shapes and properties and are thought to play a key role in powerful computations such as learning and memory.

Since interneurons are hard to track, it is still unclear when and how they start to form and mature as the brain of an embryo develops. For example, one type of interneurons called the neurogliaform cells, have a very distinct shape and properties. But, until now, the origin of this cell type had been unknown.

To find out how neurogliaform cells develop, Niquille, Limoni, Markopoulos et al. used a specific gene called Hmx3 to track these cells over time. With this strategy, the shapes and properties of the cells could be analyzed. The results showed that neurogliaform cells originate from a region outside of the cerebral cortex called the preoptic area, and later travel over long distances to reach their final location. The cells reach the cortex a few days after their birth and take several weeks to mature.

These results suggest that the traits of a specific type of neuron is determined very early in life. By labeling this unique subset of interneurons, researchers will now be able to identify the specific molecular mechanisms that help the neurogliaform cells to develop. Furthermore, it will provide a new strategy to fully understand what role these cells play in processing information and guiding behavior.

Exploring the Complexity of Cortical Development Using Single-Cell Transcriptomics

The developing neocortex in the mammalian brain is composed of multiple cell types including apical progenitors (AP), basal progenitors (BP), and neurons that populate three different layers, the ventricular zone (VZ), the subventricular zone (SVZ), and the cortical plate (CP). Despite recent advances, the diversity of the existing cell populations including those which are differentiating and mature, their biogenesis and the underlying gene regulatory mechanisms remain poorly known. Recent studies have taken advantage of the rapidly emerging single-cell technologies to decode the heterogeneity of cell populations at the transcriptome level during cortical development and their molecular details. Here we review these studies and provide an overview of the steps in single-cell transcriptomics including both experimental and computational analysis. We also discuss how single-cell genomics holds a big potential in future for brain research and discuss its possible applications and biological insights that can be achieved from these approaches. We conclude this review by discussing the current challenges in the implementation of single-cell techniques toward a comprehensive understanding of the genetic and epigenetic mechanisms underlying neocortex development.

The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing

The stereotyped spatial architecture of the brain is both beautiful and fundamentally related to its function, extending from gross morphology to individual neuron types, where soma position, dendritic architecture, and axonal projections determine their roles in functional circuitry. Our understanding of the cell types that make up the brain is rapidly accelerating, driven in particular by recent advances in single-cell transcriptomics. However, understanding brain function, development, and disease will require linking molecular cell types to morphological, physiological, and behavioral correlates. Emerging spatially resolved transcriptomic methods promise to fill this gap by localizing molecularly defined cell types in tissues, with simultaneous detection of morphology, activity, or connectivity. Here, we review the requirements for spatial transcriptomic methods toward these goals, consider the challenges ahead, and describe promising applications.

Origin and Segmental Diversity of Spinal Inhibitory Interneurons

Motor output varies along the rostro-caudal axis of the tetrapod spinal cord. At limb levels, ∼60 motor pools control the alternation of flexor and extensor muscles about each joint, whereas at thoracic levels as few as 10 motor pools supply muscle groups that support posture, inspiration, and expiration. Whether such differences in motor neuron identity and muscle number are associated with segmental distinctions in interneuron diversity has not been resolved. We show that select combinations of nineteen transcription factors that specify lumbar V1 inhibitory interneurons generate subpopulations enriched at limb and thoracic levels. Specification of limb and thoracic V1 interneurons involves the Hox gene Hoxc9 independently of motor neurons. Thus, early Hox patterning of the spinal cord determines the identity of V1 interneurons and motor neurons. These studies reveal a developmental program of V1 interneuron diversity, providing insight into the organization of inhibitory interneurons associated with differential motor output.

Diversity and Connectivity of Layer 5 Somatostatin-Expressing Interneurons in the Mouse Barrel Cortex

Inhibitory interneurons represent 10-15% of the neurons in the somatosensory cortex, and their activity powerfully shapes sensory processing. Three major groups of GABAergic interneurons have been defined according to developmental, molecular, morphological, electrophysiological, and synaptic features. Dendritic-targeting somatostatin-expressing interneurons (SST-INs) have been shown to display diverse morphological, electrophysiological, and molecular properties and activity patterns in vivo However, the correlation between these properties and SST-IN subtype is unclear. In this study, we aimed to correlate the morphological diversity of layer 5 (L5) SST-INs with their electrophysiological and molecular diversity in mice of either sex. Our morphological analysis demonstrated the existence of three subtypes of L5 SST-INs with distinct electrophysiological properties: T-shaped Martinotti cells innervate L1, and are low-threshold spiking; fanning-out Martinotti cells innervate L2/3 and the lower half of L1, and show adapting firing patterns; non-Martinotti cells innervate L4, and show a quasi-fast spiking firing pattern. We estimated the proportion of each subtype in L5 and found that T-shaped Martinotti, fanning-out Martinotti, and Non-Martinotti cells represent ∼10, ∼50, and ∼40% of L5 SST-INs, respectively. Last, we examined the connectivity between the three SST-IN subtypes and L5 pyramidal cells (PCs). We found that L5 T-shaped Martinotti cells inhibit the L1 apical tuft of nearby PCs; L5 fanning-out Martinotti cells also inhibit nearby PCs but they target the dendrite mainly in L2/3. On the other hand, non-Martinotti cells inhibit the dendrites of L4 neurons while avoiding L5 PCs. Our data suggest that morphologically distinct SST-INs gate different excitatory inputs in the barrel cortex.SIGNIFICANCE STATEMENT Morphologically diverse layer 5 SST-INs show different patterns of activity in behaving animals. However, little is known about the abundance and connectivity of each morphological type and the correlation between morphological subtype and spiking properties. We demonstrate a correlation between the morphological and electrophysiological diversity of layer 5 SST-INs. Based on these findings we built a classifier to infer the abundance of each morphological subtype. Last, using paired recordings combined with morphological analysis, we investigated the connectivity of each morphological subtype. Our data suggest that, by targeting different cell types and cellular compartments, morphologically diverse SST-INs might gate different excitatory inputs in the mouse barrel cortex.

Progress in automating patch clamp cellular physiology

Patch clamp electrophysiology has transformed research in the life sciences over the last few decades. Since their inception, automatic patch clamp platforms have evolved considerably, demonstrating the capability to address both voltage- and ligand-gated channels, and showing the potential to play a pivotal role in drug discovery and biomedical research. Unfortunately, the cell suspension assays to which early systems were limited cannot recreate biologically relevant cellular environments, or capture higher order aspects of synaptic physiology and network dynamics. In vivo patch clamp electrophysiology has the potential to yield more biologically complex information and be especially useful in reverse engineering the molecular and cellular mechanisms of single-cell and network neuronal computation, while capturing important aspects of human disease mechanisms and possible therapeutic strategies. Unfortunately, it is a difficult procedure with a steep learning curve, which has restricted dissemination of the technique. Luckily, in vivo patch clamp electrophysiology seems particularly amenable to robotic automation. In this review, we document the development of automated patch clamp technology, from early systems based on multi-well plates through to automated planar-array platforms, and modern robotic platforms capable of performing two-photon targeted whole-cell electrophysiological recordings in vivo.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq

Neurons exhibit a rich diversity of morphological phenotypes, electrophysiological properties, and gene-expression patterns. Understanding how these different characteristics are interrelated at the single-cell level has been difficult because of the lack of techniques for multimodal profiling of individual cells. We recently developed Patch-seq, a technique that combines whole-cell patch-clamp recording, immunohistochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single neurons from mouse brain slices. Here, we present a detailed step-by-step protocol, including modifications to the patching mechanics and recording procedure, reagents and recipes, procedures for immunohistochemistry, and other tips to assist researchers in obtaining high-quality morphological, electrophysiological, and transcriptomic data from single neurons. Successful implementation of Patch-seq allows researchers to explore the multidimensional phenotypic variability among neurons and to correlate gene expression with phenotype at the level of single cells. The entire procedure can be completed in ∼2 weeks through the combined efforts of a skilled electrophysiologist, molecular biologist, and biostatistician.

Correlating Anatomy and Function with Gene Expression in Individual Neurons by Combining in Vivo Labeling, Patch Clamp, and Single Cell RNA-seq

The classification of neurons into distinct types is an ongoing effort aimed at revealing and understanding the diversity of the components of the nervous system. Recently available methods allow us to determine the gene expression pattern of individual neurons in the mammalian cerebral cortex to generate powerful categorization schemes. For a thorough understanding of neuronal diversity such genetic categorization schemes need to be combined with traditional classification parameters like position, axonal projection or response properties to sensory stimulation. Here we describe a method to link the gene expression of individual neurons with their position, axonal projection, or sensory response properties. Neurons are labeled in vivo based on their anatomical or functional properties and, using patch clamp pipettes, their RNA individually harvested in vitro for RNAseq. We validate the methodology using multiple established molecularly and anatomically distinct cell populations and explore molecular differences between uncharacterized neurons in mouse visual cortex. Gene expression patterns between L5 neurons projecting to frontal or contralateral cortex are distinct while L2 neurons differing in position, projection, or function are molecularly similar. With this method we can determine the genetic expression pattern of functionally and anatomically identified individual neurons.

Single-cell analysis of diversity in human stem cell-derived neurons

Neural stem and progenitor cells produce one of the most remarkable organs in nature, the human brain. Among neural stem cell progeny, post-mitotic neurons are likewise remarkably diverse. Single-cell transcriptomic approaches are now cataloging a long-sought-after molecular taxonomy of neuronal diversity in the brain. Contemporary single-cell omic classifications of neuronal diversity build from electrophysiological approaches that for decades have measured and cataloged diverse biophysical properties of single neurons. With the widespread application of human pluripotent stem cell-based models of neurogenesis to investigate disease pathology and to develop new drugs, a high-resolution understanding of neuronal diversity in vivo is essential to benchmark the state of in vitro models of human neurological disease.

Linking neuronal structure to function in rodent hippocampus: a methodological prospective

Since the discovery of place cells, hippocampus-dependent spatial navigation has proven to be an ideal model system for resolving the relationship between neural coding and behavior. Electrical recordings from the hippocampal formation in freely moving animals have revealed a rich repertoire of spatial firing patterns and have enormously advanced our understanding of the neural principles of spatial representation. However, limited progress has been achieved in resolving the underlying cellular mechanisms. This is partially attributable to the inability of standard recording techniques to link neuronal structure to function directly. In this review, we summarize recent efforts aimed at filling this gap. We also highlight the development of methodologies that allow functional measurements from identified neuronal elements in behaving rodents. Recent progress in the dentate gyrus serves as a showcase to reveal the potential of such methodologies and the necessity of resolving structure-function relationships in order to access the cellular mechanisms of hippocampal circuit computations.

The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas

A comprehensive characterization of neuronal cell types, their distributions, and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium, ten pilot projects funded by the U.S. BRAIN Initiative, in developing, validating, and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans.

Transcriptomic correlates of neuron electrophysiological diversity

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.

Human iPSC-Derived Cerebellar Neurons from a Patient with Ataxia-Telangiectasia Reveal Disrupted Gene Regulatory Networks

Ataxia-telangiectasia (A-T) is a rare genetic disorder caused by loss of function of the ataxia-telangiectasia-mutated kinase and is characterized by a predisposition to cancer, pulmonary disease, immune deficiency and progressive degeneration of the cerebellum. As animal models do not faithfully recapitulate the neurological aspects, it remains unclear whether cerebellar degeneration is a neurodevelopmental or neurodegenerative phenotype. To address the necessity for a human model, we first assessed a previously published protocol for the ability to generate cerebellar neuronal cells, finding it gave rise to a population of precursors highly enriched for markers of the early hindbrain such as EN1 and GBX2, and later more mature cerebellar markers including PTF1α, MATH1, HOXB4, ZIC3, PAX6, and TUJ1. RNA sequencing was used to classify differentiated cerebellar neurons generated from integration-free A-T and control induced pluripotent stem cells. Comparison of RNA sequencing data with datasets from the Allen Brain Atlas reveals in vitro-derived cerebellar neurons are transcriptionally similar to discrete regions of the human cerebellum, and most closely resemble the cerebellum at 22 weeks post-conception. We show that patient-derived cerebellar neurons exhibit disrupted gene regulatory networks associated with synaptic vesicle dynamics and oxidative stress, offering the first molecular insights into early cerebellar pathogenesis of ataxia-telangiectasia.

Concise Review: Induced Pluripotent Stem Cell Models for Neuropsychiatric Diseases

The major neuropsychiatric conditions of schizophrenia, affective disorders, and infantile autism are characterized by chronic symptoms of episodic, stable, or progressive nature that result in significant morbidity. Symptomatic treatments are the mainstay but do not resolve the underlying disease processes, which are themselves poorly understood. The prototype psychotropic drugs are of variable efficacy, with therapeutic mechanisms of action that are still uncertain. Thus, neuropsychiatric disorders are ripe for new technologies and approaches with the potential to revolutionize mechanistic understanding and drive the development of novel targeted treatments. The advent of methods to produce patient‐derived stem cell models and three‐dimensional organoids with the capacity to differentiate into neurons and the various neuronal cellular lineages mark such an advance. We discuss numerous techniques involved, their applications, and areas that require further optimization. Stem Cells Translational Medicine2017;6:2062–2070

Whole-Cell Recording of Neuronal Membrane Potential during Behavior

Neuronal membrane potential is of fundamental importance for the mechanistic understanding of brain function. This review discusses progress in whole-cell patch-clamp recordings for low-noise measurement of neuronal membrane potential in awake behaving animals. Whole-cell recordings can be combined with two-photon microscopy to target fluorescently labeled neurons, revealing cell-type-specific membrane potential dynamics of retrogradely or genetically labeled neurons. Dual whole-cell recordings reveal behavioral modulation of membrane potential synchrony and properties of synaptic transmission in vivo. Optogenetic manipulations are also readily integrated with whole-cell recordings, providing detailed information about the effect of specific perturbations on the membrane potential of diverse types of neurons. Exciting developments for future behavioral experiments include dendritic whole-cell recordings and imaging, and use of the whole-cell recording pipette for single-cell delivery of drugs and DNA, as well as RNA expression profiling. Whole-cell recordings therefore offer unique opportunities for investigating the neuronal circuits and synaptic mechanisms driving membrane potential dynamics during behavior.

Neurochemical Heterogeneity Among Lateral Hypothalamic Hypocretin/Orexin and Melanin-Concentrating Hormone Neurons Identified Through Single-Cell Gene Expression Analysis

The lateral hypothalamic area (LHA) lies at the intersection of multiple neural and humoral systems and orchestrates fundamental aspects of behavior. Two neuronal cell types found in the LHA are defined by their expression of hypocretin/orexin (Hcrt/Ox) and melanin-concentrating hormone (MCH) and are both important regulators of arousal, feeding, and metabolism. Conflicting evidence suggests that these cell populations have a more complex signaling repertoire than previously appreciated, particularly in regard to their coexpression of other neuropeptides and the machinery for the synthesis and release of GABA and glutamate. Here, we undertook a single-cell expression profiling approach to decipher the neurochemical phenotype, and heterogeneity therein, of Hcrt/Ox and MCH neurons. In transgenic mouse lines, we used single-cell quantitative polymerase chain reaction (qPCR) to quantify the expression of 48 key genes, which include neuropeptides, fast neurotransmitter components, and other key markers, which revealed unexpected neurochemical diversity. We found that single MCH and Hcrt/Ox neurons express transcripts for multiple neuropeptides and markers of both excitatory and inhibitory fast neurotransmission. Virtually all MCH and approximately half of the Hcrt/Ox neurons sampled express both the machinery for glutamate release and GABA synthesis in the absence of a vesicular GABA release pathway. Furthermore, we found that this profile is characteristic of a subpopulation of LHA glutamatergic neurons but contrasts with a broad population of LHA GABAergic neurons. Identifying the neurochemical diversity of Hcrt/Ox and MCH neurons will further our understanding of how these populations modulate postsynaptic excitability through multiple signaling mechanisms and coordinate diverse behavioral outputs.

GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits

Cortical networks are composed of glutamatergic excitatory projection neurons and local GABAergic inhibitory interneurons that gate signal flow and sculpt network dynamics. Although they represent a minority of the total neocortical neuronal population, GABAergic interneurons are highly heterogeneous, forming functional classes based on their morphological, electrophysiological, and molecular features, as well as connectivity and in vivo patterns of activity. Here we review our current understanding of neocortical interneuron diversity and the properties that distinguish cell types. We then discuss how the involvement of multiple cell types, each with a specific set of cellular properties, plays a crucial role in diversifying and increasing the computational power of a relatively small number of simple circuit motifs forming cortical networks. We illustrate how recent advances in the field have shed light onto the mechanisms by which GABAergic inhibition contributes to network operations.

Neuronal cell-type classification: challenges, opportunities and the path forward

Neurons have diverse molecular, morphological, connectional and functional properties. We believe that the only realistic way to manage this complexity - and thereby pave the way for understanding the structure, function and development of brain circuits - is to group neurons into types, which can then be analysed systematically and reproducibly. However, neuronal classification has been challenging both technically and conceptually. New high-throughput methods have created opportunities to address the technical challenges associated with neuronal classification by collecting comprehensive information about individual cells. Nonetheless, conceptual difficulties persist. Borrowing from the field of species taxonomy, we propose principles to be followed in the cell-type classification effort, including the incorporation of multiple, quantitative features as criteria, the use of discontinuous variation to define types and the creation of a hierarchical system to represent relationships between cells. We review the progress of classifying cell types in the retina and cerebral cortex and propose a staged approach for moving forward with a systematic cell-type classification in the nervous system.

Single-cell RNA sequencing: Technical advancements and biological applications

Cells are the basic building blocks of organisms and each cell is unique. Single-cell RNA sequencing has emerged as an indispensable tool to dissect the cellular heterogeneity and decompose tissues into cell types and/or cell states, which offers enormous potential for de novo discovery. Single-cell transcriptomic atlases provide unprecedented resolution to reveal complex cellular events and deepen our understanding of biological systems. In this review, we summarize and compare single-cell RNA sequencing technologies, that were developed since 2009, to facilitate a well-informed choice of method. The applications of these methods in different biological contexts are also discussed. We anticipate an ever-increasing role of single-cell RNA sequencing in biology with further improvement in providing spatial information and coupling to other cellular modalities. In the future, such biological findings will greatly benefit medical research.

Fast voltage-sensitive dye imaging of excitatory and inhibitory synaptic transmission in the rat granular retrosplenial cortex

Rodent granular retrosplenial cortex (GRS) has dense connections between the anterior thalamic nuclei (ATN) and hippocampal formation. GRS superficial pyramidal neurons exhibit distinctive late spiking (LS) firing property and form patchy clusters with prominent apical dendritic bundles. The aim of this study was to investigate spatiotemporal dynamics of signal transduction in the GRS induced by ATN afferent stimulation by using fast voltage-sensitive dye imaging in rat brain slices. In coronal slices, layer 1a stimulation, which presumably activated thalamic fibers, evoked propagation of excitatory synaptic signals from layers 2-4 to layers 5-6 in a direction perpendicular to the layer axis, followed by transverse signal propagation within each layer. In the presence of ionotropic glutamate receptor antagonists, inhibitory responses were observed in superficial layers, induced by direct activation of inhibitory interneurons in layer 1. In horizontal slices, excitatory signals in deep layers propagated transversely mainly from posterior to anterior via superficial layers. Cortical inhibitory responses upon layer 1a stimulation in horizontal slices were weaker than those in the coronal slices. Observed differences between coronal and horizontal planes suggest anisotropy of the intracortical circuitry. In conclusion, ATN inputs are processed differently in coronal and horizontal planes of the GRS and then conveyed to other cortical areas. In both planes, GRS superficial layers play an important role in signal propagation, which suggests that superficial neuronal cascade is crucial in the integration of multiple information sources.NEW & NOTEWORTHY Superficial neurons in the rat granular retrosplenial cortex (GRS) show distinctive late-spiking (LS) firing property. However, little is known about spatiotemporal dynamics of signal transduction in the GRS. We demonstrated LS neuron network relaying thalamic inputs to deep layers and anisotropic distribution of inhibition between coronal and horizontal planes. Since deep layers of the GRS receive inputs from the subiculum, GRS circuits may work as an integrator of multiple sources such as sensory and memory information.

Q&A: using Patch-seq to profile single cells

Individual neurons vary widely in terms of their gene expression, morphology, and electrophysiological properties. While many techniques exist to study single-cell variability along one or two of these dimensions, very few techniques can assess all three features for a single cell. We recently developed Patch-seq, which combines whole-cell patch clamp recording with single-cell RNA-sequencing and immunohistochemistry to comprehensively profile the transcriptomic, morphologic, and physiologic features of individual neurons. Patch-seq can be broadly applied to characterize cell types in complex tissues such as the nervous system, and to study the transcriptional signatures underlying the multidimensional phenotypes of single cells.

Disentangling neural cell diversity using single-cell transcriptomics

Cellular specialization is particularly prominent in mammalian nervous systems, which are composed of millions to billions of neurons that appear in thousands of different 'flavors' and contribute to a variety of functions. Even in a single brain region, individual neurons differ greatly in their morphology, connectivity and electrophysiological properties. Systematic classification of all mammalian neurons is a key goal towards deconstructing the nervous system into its basic components. With the recent advances in single-cell gene expression profiling technologies, it is now possible to undertake the enormous task of disentangling neuronal heterogeneity. High-throughput single-cell RNA sequencing and multiplexed quantitative RT-PCR have become more accessible, and these technologies enable systematic categorization of individual neurons into groups with similar molecular properties. Here we provide a conceptual and practical guide to classification of neural cell types using single-cell gene expression profiling technologies.

Transcriptomic Perspectives on Neocortical Structure, Development, Evolution, and Disease

The cerebral cortex is the source of our most complex cognitive capabilities and a vulnerable target of many neurological and neuropsychiatric disorders. Transcriptomics offers a new approach to understanding the cortex at the level of its underlying genetic code, and rapid technological advances have propelled this field to the high-throughput study of the complete set of transcribed genes at increasingly fine resolution to the level of individual cells. These tools have revealed features of the genetic architecture of adult cortical areas, layers, and cell types, as well as spatiotemporal patterning during development. This has allowed a fresh look at comparative anatomy as well, illustrating surprisingly large differences between mammals while at the same time revealing conservation of some features from avians to mammals. Finally, transcriptomics is fueling progress in understanding the causes of neurodevelopmental diseases such as autism, linking genetic association studies to specific molecular pathways and affected brain regions.

Reconstructing cell cycle pseudo time-series via single-cell transcriptome data

Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.In single-cell RNA sequencing data of heterogeneous cell populations, cell cycle stage of individual cells would often be informative. Here, the authors introduce a computational model to reconstruct a pseudo-time series from single cell transcriptome data, identify the cell cycle stages, identify candidate cell cycle-regulated genes and recover the methylome changes during the cell cycle.

Reconstructing cell cycle pseudo time-series via single-cell transcriptome data

Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.

In single-cell RNA sequencing data of heterogeneous cell populations, cell cycle stage of individual cells would often be informative. Here, the authors introduce a computational model to reconstruct a pseudo-time series from single cell transcriptome data, identify the cell cycle stages, identify candidate cell cycle-regulated genes and recover the methylome changes during the cell cycle.

mRNA transport & local translation in neurons

Neurons are amongst the most structurally complex cells and exhibit a high degree of spatial compartmentalization. Also, neurons exhibit rapid and dynamic signaling by processing information in a precise and, sometimes, spatially-restricted manner. The signaling that occurs in axons and dendrites necessitates the maintenance and modification of their local proteomes. Local translation of mRNAs into protein is one solution that neurons use to meet synaptic demand and activity. Here we review some of the key findings and recent discoveries that have shaped our understanding of local translation in neuronal function and highlight important new techniques that might pave the way for new insights.

Robotic navigation to subcortical neural tissue for intracellular electrophysiology in vivo

In vivo studies of neurophysiology using the whole cell patch-clamp technique enable exquisite access to both intracellular dynamics and cytosol of cells in the living brain but are underrepresented in deep subcortical nuclei because of fouling of the sensitive electrode tip. We have developed an autonomous method to navigate electrodes around obstacles such as blood vessels after identifying them as a source of contamination during regional pipette localization (RPL) in vivo. In mice, robotic navigation prevented fouling of the electrode tip, increasing RPL success probability 3 mm below the pial surface to 82% (n = 72/88) over traditional, linear localization (25%, n = 24/95), and resulted in high-quality thalamic whole cell recordings with average access resistance (32.0 MΩ) and resting membrane potential (-62.9 mV) similar to cortical recordings in isoflurane-anesthetized mice. Whole cell yield improved from 1% (n = 1/95) to 10% (n = 9/88) when robotic navigation was used during RPL. This method opens the door to whole cell studies in deep subcortical nuclei, including multimodal cell typing and studies of long-range circuits.NEW & NOTEWORTHY This work represents an automated method for accessing subcortical neural tissue for intracellular electrophysiology in vivo. We have implemented a novel algorithm to detect obstructions during regional pipette localization and move around them while minimizing lateral displacement within brain tissue. This approach leverages computer control of pressure, manipulator position, and impedance measurements to create a closed-loop platform for pipette navigation in vivo. This technique enables whole cell patching studies to be performed throughout the living brain.

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

A robot for high yield electrophysiology and morphology of single neurons in vivo

Single-cell characterization and perturbation of neurons provides knowledge critical to addressing fundamental neuroscience questions including the structure–function relationship and neuronal cell-type classification. Here we report a robot for efficiently performing in vivo single-cell experiments in deep brain tissues optically difficult to access. This robot automates blind (non-visually guided) single-cell electroporation (SCE) and extracellular electrophysiology, and can be used to characterize neuronal morphological and physiological properties of, and/or manipulate genetic/chemical contents via delivering extraneous materials (for example, genes) into single neurons in vivo. Tested in the mouse brain, our robot successfully reveals the full morphology of single-infragranular neurons recorded in multiple neocortical regions, as well as deep brain structures such as hippocampal CA3, with high efficiency. Our robot thus can greatly facilitate the study of in vivo full morphology and electrophysiology of single neurons in the brain.

Single-cell characterization and perturbation of neurons is critical for revealing the structure-function relationship of brain cells. Here the authors develop a robot that performs single-cell electroporation and extracellular electrophysiology and can be used for performing in vivo single-cell experiments in deep brain tissues optically difficult to access.

Revealing allele-specific gene expression by single-cell transcriptomics

Single-cell sequencing has emerged as a revolutionary method that reveals biological processes with unprecedented resolution and scale, and has already greatly impacted biology and medicine. To investigate processes such as alternative splicing, novel exon detection and allele-specific expression (ASE), full-length based single-cell RNA-seq methods are required for broad sequence coverage and single nucleotide polymorphism (SNP) identification. In this review, we revisit recent achievements from studies that used single-cell RNA-seq to advance our understanding of ASE in the context of both autosomal and X-chromosome genes. We also recapitulate useful bioinformatic tools developed to identify haplotype phase.

Inhibitory interneurons and their circuit motifs in the many layers of the barrel cortex

Recent years have seen substantial progress in studying the structural and functional properties of GABAergic interneurons and their roles in the neuronal networks of barrel cortex. Although GABAergic interneurons represent only about 12% of the total number of neocortical neurons, they are extremely diverse with respect to their structural and functional properties. It has become clear that barrel cortex interneurons not only serve the maintenance of an appropriate excitation/inhibition balance but also are directly involved in sensory processing. In this review we present different interneuron types and their axonal projection pattern framework in the context of the laminar and columnar organization of the barrel cortex. The main focus is here on the most prominent interneuron types, i.e. basket cells, chandelier cells, Martinotti cells, bipolar/bitufted cells and neurogliaform cells, but interneurons with more unusual axonal domains will also be mentioned. We describe their developmental origin, their classification with respect to molecular, morphological and intrinsic membrane and synaptic properties. Most importantly, we will highlight the most prominent circuit motifs these interneurons are involved in and in which way they serve feed-forward inhibition, feedback inhibition and disinhibition. Finally, this will be put into context to their functional roles in sensory signal perception and processing in the whisker system and beyond.

Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach

Neurons are often classified by their morphological and molecular properties. The online knowledge base Hippocampome.org primarily defines neuron types from the rodent hippocampal formation based on their main neurotransmitter (glutamate or GABA) and the spatial distributions of their axons and dendrites. For each neuron type, this open-access resource reports any and all published information regarding the presence or absence of known molecular markers, including calcium-binding proteins, neuropeptides, receptors, channels, transcription factors, and other molecules of biomedical relevance. The resulting chemical profile is relatively sparse: even for the best studied neuron types, the expression or lack thereof of fewer than 70 molecules has been firmly established to date. The mouse genome-wide in situ hybridization mapping of the Allen Brain Atlas provides a wealth of data that, when appropriately analyzed, can substantially augment the molecular marker knowledge in Hippocampome.org. Here we focus on the principal cell layers of dentate gyrus (DG), CA3, CA2, and CA1, which together contain approximately 90% of hippocampal neurons. These four anatomical parcels are densely packed with somata of mostly excitatory projection neurons. Thus, gene expression data for those layers can be justifiably linked to the respective principal neuron types: granule cells in DG and pyramidal cells in CA3, CA2, and CA1. In order to enable consistent interpretation across genes and regions, we screened the whole-genome dataset against known molecular markers of those neuron types. The resulting threshold values allow over 6000 very-high confidence (>99.5%) expressed/not-expressed assignments, expanding the biochemical information content of Hippocampome.org more than five-fold. Many of these newly identified molecular markers are potential pharmacological targets for major neurological and psychiatric conditions. Furthermore, our approach yields reasonable expression/non-expression estimates for every single gene in each of these four neuron types with >90% average confidence, providing a considerably complete genetic characterization of hippocampal principal neurons.