The neuron identity problem: form meets function

A deep learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetics and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep learning classifier that predicts cell types with greater than 95% accuracy based on the waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously recorded cell types during behavior.

Targeting mitophagy in neurodegenerative diseases

Mitochondrial dysfunction is a hallmark of idiopathic neurodegenerative diseases, including Parkinson disease, amyotrophic lateral sclerosis, Alzheimer disease and Huntington disease. Familial forms of Parkinson disease and amyotrophic lateral sclerosis are often characterized by mutations in genes associated with mitophagy deficits. Therefore, enhancing the mitophagy pathway may represent a novel therapeutic approach to targeting an underlying pathogenic cause of neurodegenerative diseases, with the potential to deliver neuroprotection and disease modification, which is an important unmet need. Accumulating genetic, molecular and preclinical model-based evidence now supports targeting mitophagy in neurodegenerative diseases. Despite clinical development challenges, small-molecule-based approaches for selective mitophagy enhancement - namely, USP30 inhibitors and PINK1 activators - are entering phase I clinical trials for the first time.

Neural circuit mechanisms to transform cerebellar population dynamics for motor control in monkeys

We exploit identification of neuron types during extracellular recording to demonstrate how the cerebellar cortex's well-established architecture transforms inputs into outputs. During smooth pursuit eye movements, the floccular complex performs distinct input-output transformations of temporal dynamics and directional response properties. The responses of different interneuron types localize the circuit mechanisms of each transformation. Mossy fibers and unipolar brush cells emphasize eye position dynamics uniformly across the cardinal axes; Purkinje cells and molecular layer interneurons code eye velocity along directionally biased axes; Golgi cells show unmodulated firing. Differential directional response properties of different neuron types localize the directional input-output transformation to the last-order inputs to Purkinje cells. Differential temporal dynamics pinpoint the site of the temporal input-output transformation to granule cells. Specific granule cell population dynamics allow the temporal transformations required in the area we study and generalize to many temporal transformations, providing a complete framework to understand cerebellar circuit computation.

Impact statement

We dissect the circuit computations performed by the floccular complex of the cerebellum during an exemplar sensory-motor behavior, taking advantage of knowledge of the circuit architecture, existence of discrete neuron types, and a newfound ability to identify neuron types from extracellular recordings. Our results describe the contributions of the major neuron types to the cerebellar input-output computations, identify the population dynamics needed in granule cells to support those computations, and to create a basis set to enable temporally-specific motor behavior and motor learning.

Strategies to decipher neuron identity from extracellular recordings in the cerebellum of behaving non-human primates

Identification of neuron type is critical to understand computation in neural circuits through extracellular recordings in awake, behaving animal subjects. Yet, modern recording probes have limited power to resolve neuron type. Here, we leverage the well-characterized architecture of the cerebellar circuit to perform expert identification of neuron type from extracellular recordings in behaving non-human primates. Using deep-learning classifiers we evaluate the information contained in readily accessible extracellular features for neuron identification. Waveform, discharge statistics, anatomical layer, and functional interactions each can inform neuron labels for a sizable fraction of cerebellar units. Together, as inputs to a deep-learning classifier, the features perform even better. Our tools and methodologies, validated during smooth pursuit eye movements in the cerebellar floccular complex of awake behaving monkeys, can guide expert identification of neuron type during cerebellar-dependent tasks in behaving animals across species. They lay the groundwork for characterization of information processing in the cerebellar cortex.

Exposure to PFOA, PFOS, and PFHxS induces Alzheimer's disease-like neuropathology in cerebral organoids

Per- and polyfluoroalkyl substances (PFASs), a class of ubiquitous synthetic organic chemicals, are widely utilized across various industrial applications. However, the long-term neurological health effects of PFAS mixture exposure in humans remain poorly understood. To address this gap, we have designed a comprehensive study to predict and validate cell-type-specific neurotoxicity of PFASs using single-cell RNA sequencing (scRNA-seq) and cerebral organoids. Cerebral organoids were exposed to a PFAS mixture at concentrations of 1 × (10 ng/ml PFOS and PFOA, and 1 ng/ml PFHxS), 30 × , and 900 × over 35 days, with a follow-up analysis at day 70. Pathological alterations and lipidomic profiles were analyzed to identify disrupted molecular pathways and mechanisms. The scRNA-seq data revealed a significant impact of PFASs on neurons, suggesting a potential role in Alzheimer's Disease (AD) pathology, as well as intellectual and cognitive impairments. PFAS-treated cerebral organoids exhibited Aβ accumulation and tau phosphorylation. Lipidomic analyses further revealed lipid disturbances in response to PFAS mixture exposure, linking PFAS-induced AD-like neuropathology to sphingolipid metabolism disruption. Collectively, our findings provide novel insights into the PFAS-induced neurotoxicity, highlighting the significance of sphingolipid metabolism in the development of AD-like neuropathology. The use of cerebral organoids and scRNA-seq offers a powerful methodology for evaluating the health risks associated with environmental contaminants, particularly those with neurotoxic potential.

Can brain neurons change identity? Lessons from obesity

It has long been thought that the functional identity of mammalian brain neurons is programmed during development and remains stable throughout adult life; however, certain populations of neurons continue to express active regulators of neuronal identity into adulthood. Prolonged exposure to diet-induced metabolic stress induces features of neuronal identity modification in adult mice, and maladaptive changes in neuronal identity maintenance have been linked to cognitive impairment in humans suffering from neurodegenerative diseases often associated with obesity. Here we discuss how, by unraveling the neurological roots of obesity, we may solve the puzzle of whether mammalian brain neurons retain identity plasticity into adulthood, while advancing knowledge of the pathogenic mechanisms at the interface of metabolic and neurodegenerative disorders.

Tau load in select brainstem neurons predicts the severity and nature of balance deficits in the absence of cell death

Patients with tauopathies present with profoundly different clinical symptoms 1 , even within the same disorder 2 . A central hypothesis in the field, well-supported by biomarker studies 3,4 and post-mortem pathology 5-7 , is that clinical heterogeneity reflects differential degeneration of vulnerable neuronal populations responsible for specific neurological functions. Recent work has revealed mechanisms underlying susceptibility of particular cell types 8-10 , but relating tau load to disrupted behavior - es- pecially before cell death - requires a targeted circuit-level approach. Here we studied two distinct balance behaviors in larval zebrafish 11 expressing a human 0N/4R-tau allele 12 in select populations of evolutionarily-conserved and well-characterized brainstem vestibular circuits 13,14 . We observed that human tau load predicted the severity of circuit-specific deficits in posture and navigation in the ab- sence of cell death. Targeting expression to either mid- or hindbrain balance neurons recapitulated these particular deficits in posture and navigation. By parametrically linking tau load in specific neu- rons to early behavioral deficits, our work moves beyond cell type to close the gap between pathological and neurological conceptions of tauopathy.

Continuous cell type diversification throughout the embryonic and postnatal mouse visual cortex development

The mammalian cortex is composed of a highly diverse set of cell types and develops through a series of temporally regulated events that build out the cell type and circuit foundation for cortical function. The mechanisms underlying the development of different cell types remain elusive. Single-cell transcriptomics provides the capacity to systematically study cell types across the entire temporal range of cortical development. Here, we present a comprehensive and high-resolution transcriptomic and epigenomic cell type atlas of the developing mouse visual cortex. The atlas was built from a single-cell RNA-sequencing dataset of 568,674 high-quality single-cell transcriptomes and a single-nucleus Multiome dataset of 194,545 high-quality nuclei providing both transcriptomic and chromatin accessibility profiles, densely sampled throughout the embryonic and postnatal developmental stages from E11.5 to P56. We computationally reconstructed a transcriptomic developmental trajectory map of all excitatory, inhibitory, and non-neuronal cell types in the visual cortex, identifying branching points marking the emergence of new cell types at specific developmental ages and defining molecular signatures of cellular diversification. In addition to neurogenesis, gliogenesis and early postmitotic maturation in the embryonic stage which gives rise to all the cell classes and nearly all subclasses, we find that increasingly refined cell types emerge throughout the postnatal differentiation process, including the late emergence of many cell types during the eye-opening stage (P11-P14) and the onset of critical period (P21), suggesting continuous cell type diversification at different stages of cortical development. Throughout development, we find cooperative dynamic changes in gene expression and chromatin accessibility in specific cell types, identifying both chromatin peaks potentially regulating the expression of specific genes and transcription factors potentially regulating specific peaks. Furthermore, a single gene can be regulated by multiple peaks associated with different cell types and/or different developmental stages. Collectively, our study provides the most detailed dynamic molecular map directly associated with individual cell types and specific developmental events that reveals the molecular logic underlying the continuous refinement of cell type identities in the developing visual cortex.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here we propose an unsupervised method, Mixture Model Inference with Discrete-coupled AutoencoderS (MMIDAS), which combines a generalized mixture model with a multi-armed deep neural network to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both unimodal and multimodal datasets.

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity

Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.

The Drivers of Diversity: Integrated genetic and hormonal cues regulate neural diversity

Proper functioning of the nervous system relies not only on the generation of a vast repertoire of distinct neural cell types but also on the precise neural circuitry within them. How the generation of highly diverse neural populations is regulated during development remains a topic of interest. Landmark studies in Drosophila have identified the genetic and temporal cues regulating neural diversity and thus have provided valuable insights into our understanding of temporal patterning of the central nervous system. The development of the Drosophila central complex, which is mostly derived from type II neural stem cell (NSC) lineages, showcases how a small pool of NSCs can give rise to vast and distinct progeny. Similar to the human outer subventricular zone (OSVZ) neural progenitors, type II NSCs generate intermediate neural progenitors (INPs) to expand and diversify lineages that populate higher brain centers. Each type II NSC has a distinct spatial identity and timely regulated expression of many transcription factors and mRNA binding proteins. Additionally, INPs derived from them show differential expression of genes depending on their birth order. Together type II NSCs and INPs display a combinatorial temporal patterning that expands neural diversity of the central brain lineages. We cover advances in current understanding of type II NSC temporal patterning and discuss similarities and differences in temporal patterning mechanisms of various NSCs with a focus on how cell-intrinsic and extrinsic hormonal cues regulate temporal transitions in NSCs during larval development. Cell extrinsic ligands activate conserved signaling pathways and extrinsic hormonal cues act as a temporal switch that regulate temporal progression of the NSCs. We conclude by elaborating on how a progenitor's temporal code regulates the fate specification and identity of distinct neural types. At the end, we also discuss open questions in linking developmental cues to neural identity, circuits, and underlying behaviors in the adult fly.

Dense and Sparse Labelling of Mitral Cells by Oral and Intraperitoneal Routes of Tamoxifen Administration

Cell type-specific labelling and manipulation using Cre-driver lines have become integral to analyses of neuronal circuits in the brain. To study how mitral cells of the olfactory bulb process olfactory information and how they contribute to behavior, an inducible Cre-driver line, Lbhd2-CreERT2, can be used. In this chapter, we describe two methods for administering tamoxifen. The first method achieves a dense recombination pattern using tamoxifen-containing food, while the second method involving an intraperitoneal injection is suited for sparse labelling.

Learning cell identity in immunology, neuroscience, and cancer

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

Mapping brain gene coexpression in daytime transcriptomes unveils diurnal molecular networks and deciphers perturbation gene signatures

Brain tissue transcriptomes may be organized into gene coexpression networks, but their underlying biological drivers remain incompletely understood. Here, we undertook a large-scale transcriptomic study using 508 wild-type mouse striatal tissue samples dissected exclusively in the afternoons to define 38 highly reproducible gene coexpression modules. We found that 13 and 11 modules are enriched in cell-type and molecular complex markers, respectively. Importantly, 18 modules are highly enriched in daily rhythmically expressed genes that peak or trough with distinct temporal kinetics, revealing the underlying biology of striatal diurnal gene networks. Moreover, the diurnal coexpression networks are a dominant feature of daytime transcriptomes in the mouse cortex. We next employed the striatal coexpression modules to decipher the striatal transcriptomic signatures from Huntington's disease models and heterozygous null mice for 52 genes, uncovering novel functions for Prkcq and Kdm4b in oligodendrocyte differentiation and bipolar disorder-associated Trank1 in regulating anxiety-like behaviors and nocturnal locomotion.

What is a cell type and how to define it?

Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism's function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.

A Molecular Landscape of Mouse Hippocampal Neuromodulation

Adaptive neuronal circuit function requires a continual adjustment of synaptic network parameters known as “neuromodulation.” This process is now understood to be based primarily on the binding of myriad secreted “modulatory” ligands such as dopamine, serotonin and the neuropeptides to G protein-coupled receptors (GPCRs) that, in turn, regulate the function of the ion channels that establish synaptic weights and membrane excitability. Many of the basic molecular mechanisms of neuromodulation are now known, but the organization of neuromodulation at a network level is still an enigma. New single-cell RNA sequencing data and transcriptomic neurotaxonomies now offer bright new lights to shine on this critical “dark matter” of neuroscience. Here we leverage these advances to explore the cell-type-specific expression of genes encoding GPCRs, modulatory ligands, ion channels and intervening signal transduction molecules in mouse hippocampus area CA1, with the goal of revealing broad outlines of this well-studied brain structure’s neuromodulatory network architecture.

A Phase I, First-in-Human, Healthy Volunteer Study to Investigate the Safety, Tolerability, and Pharmacokinetics of CVN424, a Novel G Protein-Coupled Receptor 6 Inverse Agonist for Parkinson's Disease

CVN424 is a novel small molecule and first-in-class candidate therapeutic to selectively modulate GPR6, an orphan G-protein coupled receptor. Expression of GPR6 is largely confined to the subset of striatal projection neurons that give rise to the indirect (striatopallidal) pathway, important in the control of movement. CVN424 improves motor function in preclinical animal models of Parkinson's disease. Here, we report results of a phase 1, first-in-human study investigating the safety, tolerability, and pharmacokinetics of CVN424 in healthy volunteers. The study (NCT03657030) was randomized, double-blind, and placebo controlled. CVN424 was orally administered in ascending doses to successive cohorts as inpatients in a clinical research unit. Single doses ranged from 1 mg to 225 mg, and repeated (7 day) daily doses were 25, 75, or 150 mg. CVN424 peak plasma concentrations were reached within 2 h post-dose in the fasted state and increased with increasing dose. Dosing after a standardized high-fat meal reduced and delayed the peak plasma concentration, but total plasma exposure was similar. Mean terminal half-life ranged from 30 to 41 h. CVN424 was generally well tolerated: no serious or severe adverse effects were observed, and there were no clinically significant changes in vital signs or laboratory parameters. We conclude that CVN424, a nondopaminergic compound that modulates a novel therapeutic target, was safe and well tolerated. A phase 2 study in patients with Parkinson's disease is underway. SIGNIFICANCE STATEMENT: This is the first-in-human clinical study of a first-in-class candidate therapeutic. CVN424 modulates a novel drug target, GPR6, which is selectively expressed in a pathway in the brain that has been implicated in the motor dysfunction of patients with Parkinson's disease. This study paves the way for investigating this novel mechanism of action in patients with Parkinson's disease.

Open Frontiers in Neural Cell Type Investigations; Lessons From Caenorhabditis elegans and Beyond, Toward a Multimodal Integration

Nervous system cells, the building blocks of circuits, have been studied with ever-progressing resolution, yet neural circuits appear still resistant to schemes of reductionist classification. Due to their sheer numbers, complexity and diversity, their systematic study requires concrete classifications that can serve reduced dimensionality, reproducibility, and information integration. Conventional hierarchical schemes transformed through the history of neuroscience by prioritizing criteria of morphology, (electro)physiological activity, molecular content, and circuit function, influenced by prevailing methodologies of the time. Since the molecular biology revolution and the recent advents in transcriptomics, molecular profiling gains ground toward the classification of neurons and glial cell types. Yet, transcriptomics entails technical challenges and more importantly uncovers unforeseen spatiotemporal heterogeneity, in complex and simpler nervous systems. Cells change states dynamically in space and time, in response to stimuli or throughout their developmental trajectory. Mapping cell type and state heterogeneity uncovers uncharted terrains in neurons and especially in glial cell biology, that remains understudied in many aspects. Examining neurons and glial cells from the perspectives of molecular neuroscience, physiology, development and evolution highlights the advantage of multifaceted classification schemes. Among the amalgam of models contributing to neuroscience research, Caenorhabditis elegans combines nervous system anatomy, lineage, connectivity and molecular content, all mapped at single-cell resolution, and can provide valuable insights for the workflow and challenges of the multimodal integration of cell type features. This review reflects on concepts and practices of neuron and glial cells classification and how research, in C. elegans and beyond, guides nervous system experimentation through integrated multidimensional schemes. It highlights underlying principles, emerging themes, and open frontiers in the study of nervous system development, regulatory logic and evolution. It proposes unified platforms to allow integrated annotation of large-scale datasets, gene-function studies, published or unpublished findings and community feedback. Neuroscience is moving fast toward interdisciplinary, high-throughput approaches for combined mapping of the morphology, physiology, connectivity, molecular function, and the integration of information in multifaceted schemes. A closer look in mapped neural circuits and understudied terrains offers insights for the best implementation of these approaches.

Molecular classification of zebrafish retinal ganglion cells links genes to cell types to behavior

Retinal ganglion cells (RGCs) form an array of feature detectors, which convey visual information to central brain regions. Characterizing RGC diversity is required to understand the logic of the underlying functional segregation. Using single-cell transcriptomics, we systematically classified RGCs in adult and larval zebrafish, thereby identifying marker genes for >30 mature types and several developmental intermediates. We used this dataset to engineer transgenic driver lines, enabling specific experimental access to a subset of RGC types. Expression of one or few transcription factors often predicts dendrite morphologies and axonal projections to specific tectal layers and extratectal targets. In vivo calcium imaging revealed that molecularly defined RGCs exhibit specific functional tuning. Finally, chemogenetic ablation of eomesa+ RGCs, which comprise melanopsin-expressing types with projections to a small subset of central targets, selectively impaired phototaxis. Together, our study establishes a framework for systematically studying the functional architecture of the visual system.

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.

Deciphering cell-type specific signal transduction in the brain: Challenges and promises

Signal transduction designates the set of molecular events that take place within a cell upon extracellular stimulation to mediate a functional outcome. Decades after the discovery that dopamine triggers opposing signaling pathways in D1- and D2-expressing medium spiny neurons, it is now clear that there are as many different flavors of signaling pathways in the brain as there are neuron types. One of the biggest challenges in molecular neuroscience is to elucidate cell-type specific signaling, in order to understand neurological diseases with regional vulnerability, but also to identify targets for precision drugs devoid of off-target effects. Here, we make a case for the importance of the study of neuron-type specific molecular characteristics. We then review the technologies that exist to study neurons in their full diversity and highlight their disease-relevant idiosyncrasies.

Histamine: neural circuits and new medications

Histamine was first identified in the brain about 50 years ago, but only in the last few years have researchers gained an understanding of how it regulates sleep/wake behavior. We provide a translational overview of the histamine system, from basic research to new clinical trials demonstrating the usefulness of drugs that enhance histamine signaling. The tuberomammillary nucleus is the sole neuronal source of histamine in the brain, and like many of the arousal systems, histamine neurons diffusely innervate the cortex, thalamus, and other wake-promoting brain regions. Histamine has generally excitatory effects on target neurons, but paradoxically, histamine neurons may also release the inhibitory neurotransmitter GABA. New research demonstrates that activity in histamine neurons is essential for normal wakefulness, especially at specific circadian phases, and reducing activity in these neurons can produce sedation. The number of histamine neurons is increased in narcolepsy, but whether this affects brain levels of histamine is controversial. Of clinical importance, new compounds are becoming available that enhance histamine signaling, and clinical trials show that these medications reduce sleepiness and cataplexy in narcolepsy.

Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction

Coordinated activity-induced transcriptional changes across multiple neuron subtypes of the prefrontal cortex (PFC) play a pivotal role in encoding and regulating major cognitive behaviors. Yet, the specific transcriptional programs in each neuron subtype remain unknown. Using single-cell RNA sequencing (scRNA-seq), here we comprehensively classify all unique cell subtypes in the PFC. We analyze transcriptional dynamics of each cell subtype under a naturally adaptive and an induced condition. Adaptive changes during adolescence (between P21 and P60), a highly dynamic phase of postnatal neuroplasticity, profoundly impacted transcription in each neuron subtype, including cell type-specific regulation of genes implicated in major neuropsychiatric disorders. On the other hand, an induced plasticity evoked by chronic cocaine addiction resulted in progressive transcriptional changes in multiple neuron subtypes and became most pronounced upon prolonged drug withdrawal. Our findings lay a foundation for understanding cell type-specific postnatal transcriptional dynamics under normal PFC function and in neuropsychiatric disease states.

New Insights Into Cholinergic Neuron Diversity

Cholinergic neurons comprise a small population of cells in the striatum but have fundamental roles in fine tuning brain function, and in the etiology of neurological and psychiatric disorders such as Parkinson’s disease (PD) or schizophrenia. The process of developmental cell specification underlying neuronal identity and function is an area of great current interest. There has been significant progress in identifying the developmental origins, commonalities in molecular markers, and physiological properties of the cholinergic neurons. Currently, we are aware of a number of key factors that promote cholinergic fate during development. However, the extent of cholinergic cell diversity is still largely underestimated. New insights into the biological basis of their specification indicate that cholinergic neurons may be far more diverse than previously thought. This review article, highlights the physiological features and the synaptic properties that segregate cholinergic cell subtypes. It provides an accurate picture of cholinergic cell diversity underlying their organization and function in neuronal networks. This review article, also discusses current challenges in deciphering the logic of the cholinergic cell heterogeneity that plays a fundamental role in the control of neural processes in health and disease.

Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method

Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.

Lost in Translation: Traversing the Complex Path from Genomics to Therapeutics in Autism Spectrum Disorder

Recent progress in the genomics of non-syndromic autism spectrum disorder (nsASD) highlights rare, large-effect, germline, heterozygous de novo coding mutations. This distinguishes nsASD from later-onset psychiatric disorders where gene discovery efforts have predominantly yielded common alleles of small effect. These differences point to distinctive opportunities for clarifying the neurobiology of nsASD and developing novel treatments. We argue that the path ahead also presents key challenges, including distinguishing human pathophysiology from the potentially pleiotropic neurobiology mediated by established risk genes. We present our view of some of the conceptual limitations of traditional studies of model organisms, suggest a strategy focused on investigating the convergence of multiple nsASD genes, and propose that the detailed characterization of the molecular and cellular landscapes of developing human brain is essential to illuminate disease mechanisms. Finally, we address how recent advances are leading to novel strategies for therapeutics that target various points along the path from genes to behavior.

Ultrasonically-guided flow focusing generates precise emulsion droplets for high-throughput single cell analyses

Emulsion-based techniques have dramatically advanced our understanding of single-cell biology and complex single-cell features over the past two decades. Most approaches for precise single cell isolation rely on microfluidics, which has proven highly effective but requires substantial investment in equipment and expertise that can be difficult to access for researchers that specialize in other areas of bioengineering and molecular biotechnology. Inspired by the robust droplet generation technologies in modern flow cytometry instrumentation, here we established a new platform for high-throughput isolation of single cells within droplets of tunable sizes by combining flow focusing with ultrasonic vibration for rapid and effective droplet formation. Application of ultrasonic pressure waves to the flowing jet provided enhanced control of emulsion droplet size, permitting capture of 25,000 to 50,000 single cells per minute. As an example application, we applied this new droplet generation platform to sequence the antibody variable region heavy and light chain pairings (VH:VL) from large repertoires of single B cells. We demonstrated the recovery of > 40,000 paired CDRH3:CDRL3 antibody clusters from a single individual, validating that these droplet systems can enable the genetic analysis of very large single-cell populations. These accessible new technologies will allow rapid, large-scale, and precise single-cell analyses for a broad range of bioengineering and molecular biotechnology applications.

Disentangling chromatin architecture to gain insights into the etiology of brain disorders

Chromatin organization, together with DNA and histone modifications, is directly linked to the spatiotemporal control of gene expression that specifies and maintains cell type-specific functions. This is particularly important in the brain where hundreds of cell types with distinct functions reside. Recent advances in molecular and computational technologies have enabled the query of chromatin architecture at unprecedented resolution and detail. Here, we review recent studies on the emerging importance of chromatin architecture in the pathogenesis of brain disorders, with emphasis on schizophrenia, autism spectrum disorders (ASD), and unstable repeat expansion disorders. These studies provide molecular insights into how these brain disorders arise at the level of chromatin architecture and implicate new therapeutic directions.

Perspectives on defining cell types in the brain

The diversity of brain cell types was one of the earliest observations in modern neuroscience and continues to be one of the central concerns of current neuroscience research. Despite impressive recent progress, including single cell transcriptome and epigenome profiling as well as anatomical methods, we still lack a complete census or taxonomy of brain cell types. We argue this is due partly to the conceptual difficulty in defining a cell type. By considering the biological drivers of cell identity, such as networks of genes and gene regulatory elements, we propose a definition of cell type that emphasizes self-stabilizing regulation. We explore the predictions and hypotheses that arise from this definition. Integration of data from multiple modalities, including molecular profiling of genes and gene products, epigenetic landscape, cellular morphology, connectivity, and physiology, will be essential for a meaningful and broadly useful definition of brain cell types.

A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.

Important advances in Alzheimer's disease from the use of induced pluripotent stem cells

Among the various types of dementia, Alzheimer’s disease (AD) is the most prevalent and is clinically defined as the appearance of progressive deficits in cognition and memory. Considering that AD is a central nervous system disease, getting tissue from the patient to study the disease before death is challenging. The discovery of the technique called induced pluripotent stem cells (iPSCs) allows to reprogram the patient’s somatic cells to a pluripotent state by the forced expression of a defined set of transcription factors. Many studies have shown promising results and made important conclusions beyond AD using iPSCs approach. Due to the accumulating knowledge related to this topic and the important advances obtained until now, we review, using PubMed, and present an update of all publications related to AD from the use of iPSCs. The first iPSCs generated for AD were carried out in 2011 by Yahata et al. (PLoS One 6:e25788, 2011) and Yaqi et al. (Hum Mol Genet 20:4530–9, 2011). Like other authors, both authors used iPSCs as a pre-clinical tool for screening therapeutic compounds. This approach is also essential to model AD, testing early toxicity and efficacy, and developing a platform for drug development. Considering that the iPSCs technique is relatively recent, we can consider that the AD field received valuable contributions from iPSCs models, contributing to our understanding and the treatment of this devastating disorder.

Chromatin Changes Associated with Neuronal Maintenance and Their Pharmacological Application

Background:

The transcriptional control of neuronal specification and early development has been intensively stud-ied over the past few decades. However, relatively little is known about transcriptional programs associated with the mainte-nance of terminally differentiated neuronal cells with respect to their functions, structures, and cell type-specific identity features.

Methods:

Notably, largely because of the recent advances in related techniques such as next generation sequencing and chromatin immunoprecipitation sequencing, the physiological implications of system-wide regulation of gene expression through changes in chromatin states have begun to be extensively studied in various contexts and systems, including the nervous system.

Results:

Here, we attempt to review our current understanding of the link between chromatin changes and neuronal mainte-nance in the period of life after the completion of neuronal development. Perturbations involving chromatin changes in the system-wide transcriptional control are believed to be closely associated with diverse aspects of neuronal aging and neuro-degenerative conditions.

Conclusion:

In this review, we focused on heterochromatin and epigenetic dysregulation in neurodegenerative conditions as well as neuronal aging, the most important risk factor leading to neuronal degeneration, in order to highlight the close association between chromatin changes and neuronal maintenance. Lastly, we reviewed the cur-rently available and potential future applications of pharmacological control of the chromatin states associated with neuronal maintenance.

Classification of GABAergic neuron subtypes from the globus pallidus using wild-type and transgenic mice

KEY POINTS

Classifying different subtypes of neurons in deep brain structures is a challenge and is crucial to better understand brain function. Understanding the diversity of neurons in the globus pallidus (GP), a brain region positioned to influence afferent and efferent information processing within basal ganglia, could help to explain a variety of brain functions. We present a classification of neurons from the GP using electrophysiological data from wild-type mice and confirmation using transgenic mice. This work will help researchers to identify specific neuronal subsets in the GP of wild-type mice when transgenic mice with labelled neurons are lacking.

ABSTRACT

Classification of the extensive neuronal diversity in the brain is fundamental for neuroscience. The globus pallidus external segment (GPe), also referred to as the globus pallidus in rodents, is a large nucleus located in the core of the basal ganglia whose circuitry is implicated in action control, decision-making and reward. Although considerable progress has been made in characterizing different GPe neuronal subtypes, no work has directly attempted to characterize these neurons in non-transgenic mice. Here, we provide data showing the degree of overlap in expression of neuronal PAS domain protein (Npas1), LIM homeobox 6 (Lhx6), parvalbumin (PV) and transcription factor FoxP2 biomarkers in mouse GPe neurons. We used an unbiased statistical method to classify neurons based on electrophysiological properties from nearly 200 neurons from C57BL/6J mice. In addition, we examined the subregion distribution of the neuronal subtypes. Cluster analysis using firing rate and hyperpolarization-induced membrane potential sag variables revealed three distinct neuronal clusters: type 1, characterized by low firing rate and small sag potential; type 2, with low firing rate and larger sag potential; and type 3, with high firing rate and small sag potential. We used other electrophysiological variables and data from marker-expressing neurons to evaluate the clusters. We propose that the GPe GABAergic neurons should be classified into three subgroups: arkypallidal, low-firing prototypical and high-firing prototypical neurons. This work will help researchers identify GPe neuron subtypes when transgenic mice with labelled neurons cannot be used.

Cell type-specific CLIP reveals that NOVA regulates cytoskeleton interactions in motoneurons

Background

Alternative RNA processing plays an essential role in shaping cell identity and connectivity in the central nervous system. This is believed to involve differential regulation of RNA processing in various cell types. However, in vivo study of cell type-specific post-transcriptional regulation has been a challenge. Here, we describe a sensitive and stringent method combining genetics and CLIP (crosslinking and immunoprecipitation) to globally identify regulatory interactions between NOVA and RNA in the mouse spinal cord motoneurons.

Results

We developed a means of undertaking motoneuron-specific CLIP to explore motoneuron-specific protein–RNA interactions relative to studies of the whole spinal cord in mouse. This allowed us to pinpoint differential RNA regulation specific to motoneurons, revealing a major role for NOVA in regulating cytoskeleton interactions in motoneurons. In particular, NOVA specifically promotes the palmitoylated isoform of the cytoskeleton protein Septin 8 in motoneurons, which enhances dendritic arborization.

Conclusions

Our study demonstrates that cell type-specific RNA regulation is important for fine tuning motoneuron physiology and highlights the value of defining RNA processing regulation at single cell type resolution.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1493-2) contains supplementary material, which is available to authorized users.

The sacral autonomic outflow is parasympathetic: Langley got it right

A recent developmental study of gene expression by Espinosa-Medina, Brunet and colleagues sparked controversy by asserting a revised nomenclature for divisions of the autonomic motor system. Should we re-classify the sacral autonomic outflow as sympathetic, as now suggested, or does it rightly belong to the parasympathetic system, as defined by Langley nearly 100 years ago? Arguments for rejecting Espinosa-Medina, Brunet et al.’s scheme subsequently appeared in e-letters and brief reviews. A more recent commentary in this journal by Brunet and colleagues responded to these criticisms by labeling Langley’s scheme as a historical myth perpetuated by ignorance. In reaction to this heated exchange, I now examine both sides to the controversy, together with purported errors by the pioneers in the field. I then explain, once more, why the sacral outflow should remain known as parasympathetic, and outline suggestions for future experimentation to advance the understanding of cellular identity in the autonomic motor system.

Revisiting Neuronal Cell Type Classification in Caenorhabditis elegans

We revisit the classification of neuronal cell types in the nervous system of the nematode Caenorhabditis elegans. Based on anatomy and synaptic connectivity patterns, the 302 neurons of the nervous system of the hermaphrodite were categorized into 118 neuron classes more than 30 years ago. Analysis of all presently available neuronal gene expression patterns reveals a remarkable congruence of anatomical and molecular classification and further suggests subclassification schemes. Transcription factor expression profiles alone are sufficient to uniquely classify more than 90% of all neuron classes in the C. elegans nervous system. Neuron classification in C. elegans may be paradigmatic for neuron classification schemes in vertebrate nervous systems.

MiRNA Influences in Neuroblast Modulation: An Introspective Analysis

Neuroblastoma (NB) is the most common occurring solid paediatric cancer in children under the age of five years. Whether of familial or sporadic origin, chromosome abnormalities contribute to the development of NB and cause dysregulation of microRNAs (miRNAs). MiRNAs are small non-coding, single stranded RNAs that target messenger RNAs at the post-transcriptional levels by repressing translation within all facets of human physiology. Such gene 'silencing' activities by miRNAs allows the development of regulatory feedback loops affecting multiple functions within the cell, including the possible differentiation of neural stem cell (NSC) lineage selection. Neurogenesis includes stages of self-renewal and fate specification of NSCs, migration and maturation of young neurones, and functional integration of new neurones into the neural circuitry, all of which are regulated by miRNAs. The role of miRNAs and their interaction in cellular processes are recognised aspects of cancer genetics, and miRNAs are currently employed as biomarkers for prognosis and tumour characterisation in multiple cancer models. Consequently, thorough understanding of the mechanisms of how these miRNAs interplay at the transcriptomic level will definitely lead to the development of novel, bespoke and efficient therapeutic measures, with this review focusing on the influences of miRNAs on neuroblast modulations leading to neuroblastoma.

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.

Biotin tagging of MeCP2 in mice reveals contextual insights into the Rett syndrome transcriptome

Mutations in MECP2 cause Rett syndrome (RTT), an X-linked neurological disorder characterized by regressive loss of neurodevelopmental milestones and acquired psychomotor deficits. However, the cellular heterogeneity of the brain impedes an understanding of how MECP2 mutations contribute to RTT. Here we developed a Cre-inducible method for cell-type-specific biotin tagging of MeCP2 in mice. Combining this approach with an allelic series of knock-in mice carrying frequent RTT-associated mutations (encoding T158M and R106W) enabled the selective profiling of RTT-associated nuclear transcriptomes in excitatory and inhibitory cortical neurons. We found that most gene-expression changes were largely specific to each RTT-associated mutation and cell type. Lowly expressed cell-type-enriched genes were preferentially disrupted by MeCP2 mutations, with upregulated and downregulated genes reflecting distinct functional categories. Subcellular RNA analysis in MeCP2-mutant neurons further revealed reductions in the nascent transcription of long genes and uncovered widespread post-transcriptional compensation at the cellular level. Finally, we overcame X-linked cellular mosaicism in female RTT models and identified distinct gene-expression changes between neighboring wild-type and mutant neurons, providing contextual insights into RTT etiology that support personalized therapeutic interventions.

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.

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.

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.