The neuron classification problem

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.

Network architecture of intrinsic connectivity in a mammalian spinal cord (the central nervous system's caudal sector)

The vertebrate spinal cord (SP) is the long, thin extension of the brain forming the central nervous system's caudal sector. Functionally, the SP directly mediates motor and somatic sensory interactions with most parts of the body except the face, and it is the preferred model for analyzing relatively simple reflex behaviors. Here, we analyze the organization of axonal connections between the 50 gray matter regions forming the bilaterally symmetric rat SP. The assembled dataset suggests that there are about 385 of a possible 2,450 connections between the 50 regions for a connection density of 15.7%. Multiresolution consensus cluster analysis reveals a hierarchy of structure-function subsystems in this neural network, with 4 subsystems at the top level and 12 at the bottom-level. The top-level subsystems include a) a bilateral subsystem related most clearly to somatic and autonomic motor functions and centered in the ventral horn and intermediate zone; b) a bilateral subsystem associated with general somatosensory functions and centered in the base, neck, and head of the dorsal horn; and c) a pair of unilateral, bilaterally symmetric subsystems associated with nociceptive information processing and occupying the apex of the dorsal horn. The intrinsic SP network displayed no hubs, rich club, or small-world attributes, which are common measures of global functionality. Advantages and limitations of our methodology are discussed in some detail. The present work is part of a comprehensive project to assemble and analyze the neurome of a mammalian nervous system and its interactions with the body.

Hippocampal GABAergic interneurons and memory

One of the most captivating questions in neuroscience revolves around the brain's ability to efficiently and durably capture and store information. It must process continuous input from sensory organs while also encoding memories that can persist throughout a lifetime. What are the cellular-, subcellular-, and network-level mechanisms that underlie this remarkable capacity for long-term information storage? Furthermore, what contributions do distinct types of GABAergic interneurons make to this process? As the hippocampus plays a pivotal role in memory, our review focuses on three aspects: (1) delineation of hippocampal interneuron types and their connectivity, (2) interneuron plasticity, and (3) activity patterns of interneurons during memory-related rhythms, including the role of long-range interneurons and disinhibition. We explore how these three elements, together showcasing the remarkable diversity of inhibitory circuits, shape the processing of memories in the hippocampus.

A brain flatmap data visualization tool for mouse, rat, and human

Here we provide open-access brain data flatmap visualization and analysis tools for the mouse, rat, and human. The present work stems from a previous JCN Toolbox article that introduced a novel flatmap of the mouse brain and substantially enhanced flatmaps of the rat and human brain. These brain flatmap data visualization tools enable computer-generated graphical flatmap representation of tabulated user-entered data. For mouse and rat, they are designed to accommodate data resolved spatially up to the level of gray matter regions, supported by parcellation and nomenclature defined in current brain reference atlases. For human, Brodmann cerebral cortical parcellation is emphasized, and all other major brain divisions are represented. A comprehensive user guide is included along with several use examples. These brain data visualization tools enable the tabulation and automatic graphical flatmap representation of any type of mouse, rat, or human brain data that is spatially localized. The formalized presentation afforded by these graphical tools facilitates comparative analysis between data sets within or between the represented species.

The structure and diversity of retinal ganglion cells in the masked greenling Hexagrammos octogrammus Pallas, 1814 (Pisces: Scorpaeniformes: Hexagrammidae)

The authors studied the structure and diversity of retinal ganglion cells (GC) in the masked greenling Hexagrammos octogrammus. In vivo labelling with horseradish peroxidase revealed GCs of various structures in retinal wholemounts. A total of 154 cells were camera lucida drawn, and their digital models were generated. Each cell was characterized by 17 structural and topological parameters. Using nine clustering algorithms, a variety of clusterings were obtained. The optimum clustering was found using silhouette analysis. It was based on a set of three variables associated with dendritic field size and dendrite stratification depth in the retina. A total of nine cell types were discovered. A number of non-parametric tests showed significant pair-wise between-cluster differences in at least four parameters with medium and large effect sizes. Three large-field types differed mainly in dendritic field size, total dendrite length, level of dendrite stratification in the retina and position of somata. Six medium- to small-field types differed mainly in the structural complexity of dendritic arbors and level of dendrite arborization. Cells similar and obviously homologous to types 1-4 were identified in many fish species, including teleosts. Potential homologues of type 5 cells were identified in fewer teleost species. Cells similar to types 6-9 in relative dendritic field size and dendrite arborization pattern were also described in several teleostean species. Nonetheless, their homology is more questionable as their stratification patterns do not match so well as they do in large types. Potential functional matches of the GC types were identified in a number of teleostean species. Type 1 and 2 cells probably match spontaneously active units with the large receptive field centre, so-called dimming and lightening detectors; type 4 may be a counterpart of changing contrast detectors with medium receptive field centre size preferring fast-moving stimuli. Type 3 (biplexiform) cells have no obvious functional matches. Probable functional matches of types 6, 8 and 9 belong to ON-centre elements with small receptive fields such as ON-type direction-selective cells, ON-type spot detectors or ON-type spontaneously active units. Type 5 and 7 cells may match ON-OFF type units, in particular, changing contrast detectors or orientation-selective units. Potential functional matches of GC types presently described are involved in a wide spectrum of visual reactions related to adaptation to gradual change in illumination, predator escape, prey detection and capture, habitat selection and social behaviour.

Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors

Neurons and glia are the two main cell classes in the nervous systems of most animals. Although functionally distinct, neurons and glia are both characterized by multiple branching arbors stemming from the cell bodies. Glial processes are generally known to form smaller trees than neuronal dendrites. However, the full extent of morphological differences between neurons and glia in multiple species and brain regions has not yet been characterized, nor is it known whether these cells can be reliably distinguished based on geometric features alone. Here, we show that multiple supervised learning algorithms deployed on a large database of morphological reconstructions can systematically classify neuronal and glial arbors with nearly perfect accuracy and precision. Moreover, we report multiple morphometric properties, both size related and size independent, that differ substantially between these cell types. In particular, we newly identify an individual morphometric measurement, Average Branch Euclidean Length that can robustly separate neurons from glia across multiple animal models, a broad diversity of experimental conditions, and anatomical areas, with the notable exception of the cerebellum. We discuss the practical utility and physiological interpretation of this discovery.

Homeobox genes and the specification of neuronal identity

The enormous diversity of cell types that characterizes any animal nervous system is defined by neuron-type-specific gene batteries that endow cells with distinct anatomical and functional properties. To understand how such cellular diversity is genetically specified, one needs to understand the gene regulatory programmes that control the expression of cell-type-specific gene batteries. The small nervous system of the nematode Caenorhabditis elegans has been comprehensively mapped at the cellular and molecular levels, which has enabled extensive, nervous system-wide explorations into whether there are common underlying mechanisms that specify neuronal cell-type diversity. One principle that emerged from these studies is that transcription factors termed 'terminal selectors' coordinate the expression of individual members of neuron-type-specific gene batteries, thereby assigning unique identities to individual neuron types. Systematic mutant analyses and recent nervous system-wide expression analyses have revealed that one transcription factor family, the homeobox gene family, is broadly used throughout the entire C. elegans nervous system to specify neuronal identity as terminal selectors. I propose that the preponderance of homeobox genes in neuronal identity control is a reflection of an evolutionary trajectory in which an ancestral neuron type was specified by one or more ancestral homeobox genes, and that this functional linkage then duplicated and diversified to generate distinct cell types in an evolving nervous system.

In silico analysis of the transcriptional regulatory logic of neuronal identity specification throughout the C. elegans nervous system

The generation of the enormous diversity of neuronal cell types in a differentiating nervous system entails the activation of neuron type-specific gene batteries. To examine the regulatory logic that controls the expression of neuron type-specific gene batteries, we interrogate single cell expression profiles of all 118 neuron classes of the Caenorhabditis elegans nervous system for the presence of DNA binding motifs of 136 neuronally expressed C. elegans transcription factors. Using a phylogenetic footprinting pipeline, we identify cis-regulatory motif enrichments among neuron class-specific gene batteries and we identify cognate transcription factors for 117 of the 118 neuron classes. In addition to predicting novel regulators of neuronal identities, our nervous system-wide analysis at single cell resolution supports the hypothesis that many transcription factors directly co-regulate the cohort of effector genes that define a neuron type, thereby corroborating the concept of so-called terminal selectors of neuronal identity. Our analysis provides a blueprint for how individual components of an entire nervous system are genetically specified.

BrainWiki-A Wiki-Style, User Driven, Comparative Brain Anatomy Tool

The mouse is the most important animal model within neuroscientific research, a position strengthened by the wide-spread use of transgenic mouse models. Discoveries in animals are followed by corroboration in humans, and the interchange between these fields of research is essential to our understanding of the human brain. With the advent of advanced technologies such as single-cell transcriptomics, epigenetic profiling and diffusion MRI, many prominent research institutes and collaborations have emerged, aiming to construct complete human or mouse brain atlases with data on gene expression, connectivity and cell types. These initiatives are indispensable resources, but frequently require extensive, time-consuming development, and rely on updates by the provider. They often come in the shape of applications which require practice or prior technical know-how. Importantly, none of them place the human and the mouse brain next to each other to allow for immediate comparison. We present BrainWiki, a user-friendly, web-based atlas that links the human and the mouse brain together, side-by-side. The platform gives the user a simple overview of brain anatomy along with published articles relating to each brain region that allows the user to delve deeper into the current state of research concerning circuitry, brain functions and pathology. The website relies on interactivity and supports user contributions resulting in a dynamic website that evolves at the pace of neuroscience. It is designed to allow for constant updates and new features in the future which will contain data such as gene expression and neuronal cell types.

Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the "mass" of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.

Operations Research Methods for Estimating the Population Size of Neuron Types

Understanding brain computation requires assembling a complete catalog of its architectural components. Although the brain is organized into several anatomical and functional regions, it is ultimately the neurons in every region that are responsible for cognition and behavior. Thus, classifying neuron types throughout the brain and quantifying the population sizes of distinct classes in different regions is a key subject of research in the neuroscience community. The total number of neurons in the brain has been estimated for multiple species, but the definition and population size of each neuron type are still open questions even in common model organisms: the so called "cell census" problem. We propose a methodology that uses operations research principles to estimate the number of neurons in each type based on available information on their distinguishing properties. Thus, assuming a set of neuron type definitions, we provide a solution to the issue of assessing their relative proportions. Specifically, we present a three-step approach that includes literature search, equation generation, and numerical optimization. Solving computationally the set of equations generated by literature mining yields best estimates or most likely ranges for the number of neurons in each type. While this strategy can be applied towards any neural system, we illustrate its usage on the rodent hippocampus.

Distributions of hypothalamic neuron populations coexpressing tyrosine hydroxylase and the vesicular GABA transporter in the mouse

The hypothalamus contains catecholaminergic neurons marked by the expression of tyrosine hydroxylase (TH). As multiple chemical messengers coexist in each neuron, we determined if hypothalamic TH-immunoreactive (ir) neurons express vesicular glutamate or GABA transporters. We used Cre/loxP recombination to express enhanced GFP (EGFP) in neurons expressing the vesicular glutamate (vGLUT2) or GABA transporter (vGAT), then determined whether TH-ir neurons colocalized with native EGFP^Vglut2 - or EGFP^Vgat -fluorescence, respectively. EGFP^Vglut2 neurons were not TH-ir. However, discrete TH-ir signals colocalized with EGFP^Vgat neurons, which we validated by in situ hybridization for Vgat mRNA. To contextualize the observed pattern of colocalization between TH-ir and EGFP^Vgat , we first performed Nissl-based parcellation and plane-of-section analysis, and then mapped the distribution of TH-ir EGFP^Vgat neurons onto atlas templates from the Allen Reference Atlas (ARA) for the mouse brain. TH-ir EGFP^Vgat neurons were distributed throughout the rostrocaudal extent of the hypothalamus. Within the ARA ontology of gray matter regions, TH-ir neurons localized primarily to the periventricular hypothalamic zone, periventricular hypothalamic region, and lateral hypothalamic zone. There was a strong presence of EGFP^Vgat fluorescence in TH-ir neurons across all brain regions, but the most striking colocalization was found in a circumscribed portion of the zona incerta (ZI)-a region assigned to the hypothalamus in the ARA-where every TH-ir neuron expressed EGFP^Vgat . Neurochemical characterization of these ZI neurons revealed that they display immunoreactivity for dopamine but not dopamine β-hydroxylase. Collectively, these findings indicate the existence of a novel mouse hypothalamic population that may signal through the release of GABA and/or dopamine.

The diversity of GABAergic neurons and neural communication elements

The phenotypic diversity of cortical GABAergic neurons is probably necessary for their functional versatility in shaping the spatiotemporal dynamics of neural circuit operations underlying cognition. Deciphering the logic of this diversity requires comprehensive analysis of multi-modal cell features and a framework of neuronal identity that reflects biological mechanisms and principles. Recent high-throughput single-cell analyses have generated unprecedented data sets characterizing the transcriptomes, morphology and electrophysiology of interneurons. We posit that cardinal interneuron types can be defined by their synaptic communication properties, which are encoded in key transcriptional signatures. This conceptual framework integrates multi-modal cell features, captures neuronal input-output properties fundamental to circuit operation and may advance understanding of the appropriate granularity of neuron types, towards a biologically grounded and operationally useful interneuron taxonomy.

Morphological Neuron Classification Based on Dendritic Tree Hierarchy

The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.

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.

Precise segmentation of densely interweaving neuron clusters using G-Cut

Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects.

Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons

Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.

Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons

Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.

Astrocyte-Neuron Networks: A Multilane Highway of Signaling for Homeostatic Brain Function

Research on glial cells over the past 30 years has confirmed the critical role of astrocytes in pathophysiological brain states. However, most of our knowledge about astrocyte physiology and of the interactions between astrocytes and neurons is based on the premises that astrocytes constitute a homogeneous cell type, without considering the particular properties of the circuits or brain nuclei in which the astrocytes are located. Therefore, we argue that more-sophisticated experiments are required to elucidate the specific features of astrocytes in different brain regions, and even within different layers of a particular circuit. Thus, in addition to considering the diverse mechanisms used by astrocytes to communicate with neurons and synaptic partners, it is necessary to take into account the cellular heterogeneity that likely contributes to the outcomes of astrocyte-neuron signaling. In this review article, we briefly summarize the current data regarding the anatomical, molecular and functional properties of astrocyte-neuron communication, as well as the heterogeneity within this communication.

Brain maps 4.0-Structure of the rat brain: An open access atlas with global nervous system nomenclature ontology and flatmaps

The fourth edition (following editions in 1992, 1998, 2004) of Brain maps: structure of the rat brain is presented here as an open access internet resource for the neuroscience community. One new feature is a set of 10 hierarchical nomenclature tables that define and describe all parts of the rat nervous system within the framework of a strictly topographic system devised previously for the human nervous system. These tables constitute a global ontology for knowledge management systems dealing with neural circuitry. A second new feature is an aligned atlas of bilateral flatmaps illustrating rat nervous system development from the neural plate stage to the adult stage, where most gray matter regions, white matter tracts, ganglia, and nerves listed in the nomenclature tables are illustrated schematically. These flatmaps are convenient for future development of online applications analogous to "Google Maps" for systems neuroscience. The third new feature is a completely revised Atlas of the rat brain in spatially aligned transverse sections that can serve as a framework for 3-D modeling. Atlas parcellation is little changed from the preceding edition, but the nomenclature for rat is now aligned with an emerging panmammalian neuroanatomical nomenclature. All figures are presented in Adobe Illustrator vector graphics format that can be manipulated, modified, and resized as desired, and freely used with a Creative Commons license.

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.

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.

Single-cell RNA-sequencing of the brain

Single-cell RNA-sequencing (scRNA-seq) is revolutionizing our understanding of the genomic, transcriptomic and epigenomic landscapes of cells within organs. The mammalian brain is composed of a complex network of millions to billions of diverse cells with either highly specialized functions or support functions. With scRNA-seq it is possible to comprehensively dissect the cellular heterogeneity of brain cells, and elucidate their specific functions and state. In this review, we describe the current experimental methods used for scRNA-seq. We also review bioinformatic tools and algorithms for data analyses and discuss critical challenges. Additionally, we summarized recent mouse brain scRNA-seq studies and systematically compared their main experimental approaches, computational tools implemented, and important findings. scRNA-seq has allowed researchers to identify diverse cell subpopulations within many brain regions, pinpointing gene signatures and novel cell markers, as well as addressing functional differences. Due to the complexity of the brain, a great deal of work remains to be accomplished. Defining specific brain cell types and functions is critical for understanding brain function as a whole in development, health, and diseases.

Morphological Neuron Classification Using Machine Learning

Classification and quantitative characterization of neuronal morphologies from histological neuronal reconstruction is challenging since it is still unclear how to delineate a neuronal cell class and which are the best features to define them by. The morphological neuron characterization represents a primary source to address anatomical comparisons, morphometric analysis of cells, or brain modeling. The objectives of this paper are (i) to develop and integrate a pipeline that goes from morphological feature extraction to classification and (ii) to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population of the somatosensory cortex in rats. For supervised algorithms, linear discriminant analysis provided better classification results in comparison with others. For unsupervised algorithms, the affinity propagation and the Ward algorithms provided slightly better results.

In search of a periodic table of the neurons: Axonal-dendritic circuitry as the organizing principle: Patterns of axons and dendrites within distinct anatomical parcels provide the blueprint for circuit-based neuronal classification

No one knows yet how to organize, in a simple yet predictive form, the knowledge concerning the anatomical, biophysical, and molecular properties of neurons that are accumulating in thousands of publications every year. The situation is not dissimilar to the state of Chemistry prior to Mendeleev's tabulation of the elements. We propose that the patterns of presence or absence of axons and dendrites within known anatomical parcels may serve as the key principle to define neuron types. Just as the positions of the elements in the periodic table indicate their potential to combine into molecules, axonal and dendritic distributions provide the blueprint for network connectivity. Furthermore, among the features commonly employed to describe neurons, morphology is considerably robust to experimental conditions. At the same time, this core classification scheme is suitable for aggregating biochemical, physiological, and synaptic information.

Hubs and spokes of the lateral hypothalamus: cell types, circuits and behaviour

The hypothalamus is among the most phylogenetically conserved regions in the vertebrate brain, reflecting its critical role in maintaining physiological and behavioural homeostasis. By integrating signals arising from both the brain and periphery, it governs a litany of behaviourally important functions essential for survival. In particular, the lateral hypothalamic area (LHA) is central to the orchestration of sleep-wake states, feeding, energy balance and motivated behaviour. Underlying these diverse functions is a heterogeneous assembly of cell populations typically defined by neurochemical markers, such as the well-described neuropeptides hypocretin/orexin and melanin-concentrating hormone. However, anatomical and functional evidence suggests a rich diversity of other cell populations with complex neurochemical profiles that include neuropeptides, receptors and components of fast neurotransmission. Collectively, the LHA acts as a hub for the integration of diverse central and peripheral signals and, through complex local and long-range output circuits, coordinates adaptive behavioural responses to the environment. Despite tremendous progress in our understanding of the LHA, defining the identity of functionally discrete LHA cell types, and their roles in driving complex behaviour, remain significant challenges in the field. In this review, we discuss advances in our understanding of the neurochemical and cellular heterogeneity of LHA neurons and the recent application of powerful new techniques, such as opto- and chemogenetics, in defining the role of LHA circuits in feeding, reward, arousal and stress. From pioneering work to recent developments, we review how the interrogation of LHA cells and circuits is contributing to a mechanistic understanding of how the LHA coordinates complex behaviour.

NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases

Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish.

Video Abstract

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NBLAST is a fast and sensitive algorithm to measure pairwise neuronal similarity

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NBLAST can distinguish neuronal types at the finest level without training

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Automated clustering of 16,129 Drosophila neurons identifies 1,052 classes

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Online search tool for databases of single neurons or genetic driver lines

Molecular heterogeneity of midbrain dopaminergic neurons--Moving toward single cell resolution

Since their discovery, midbrain dopamine (DA) neurons have been researched extensively, in part because of their diverse functions and involvement in various neuropsychiatric disorders. Over the last few decades, reports have emerged that midbrain DA neurons were not a homogeneous group, but that DA neurons located in distinct anatomical locations within the midbrain had distinctive properties in terms of physiology, function, and vulnerability. Accordingly, several studies focused on identifying heterogeneous gene expression across DA neuron clusters. Here we review the importance of understanding DA neuron heterogeneity at the molecular level, previous studies detailing heterogeneous gene expression in DA neurons, and finally recent work which brings together previous heterogeneous gene expression profiles in a coordinated manner, at single cell resolution.

A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks

BACKGROUND

A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings.

NEW METHOD

In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model.

RESULTS

As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression.

COMPARISON WITH EXISTING METHODS

To our best understanding, there are no similar analyses to compare with.

CONCLUSIONS

A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity.

Binary matrix shuffling filter for feature selection in neuronal morphology classification

A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained.

Architecture of the cerebral cortical association connectome underlying cognition

Cognition presumably emerges from neural activity in the network of association connections between cortical regions that is modulated by inputs from sensory and state systems and directs voluntary behavior by outputs to the motor system. To reveal global architectural features of the cortical association connectome, network analysis was performed on >16,000 reports of histologically defined axonal connections between cortical regions in rat. The network analysis reveals an organization into four asymmetrically interconnected modules involving the entire cortex in a topographic and topologic core-shell arrangement. There is also a topographically continuous U-shaped band of cortical areas that are highly connected with each other as well as with the rest of the cortex extending through all four modules, with the temporal pole of this band (entorhinal area) having the most cortical association connections of all. These results provide a starting point for compiling a mammalian nervous system connectome that could ultimately reveal novel correlations between genome-wide association studies and connectome-wide association studies, leading to new insights into the cellular architecture supporting cognition.

Towards the automatic classification of neurons

The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and the availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-laboratory integration.

3D in vitro modeling of the central nervous system

There are currently more than 600 diseases characterized as affecting the central nervous system (CNS) which inflict neural damage. Unfortunately, few of these conditions have effective treatments available. Although significant efforts have been put into developing new therapeutics, drugs which were promising in the developmental phase have high attrition rates in late stage clinical trials. These failures could be circumvented if current 2D in vitro and in vivo models were improved. 3D, tissue-engineered in vitro systems can address this need and enhance clinical translation through two approaches: (1) bottom-up, and (2) top-down (developmental/regenerative) strategies to reproduce the structure and function of human tissues. Critical challenges remain including biomaterials capable of matching the mechanical properties and extracellular matrix (ECM) composition of neural tissues, compartmentalized scaffolds that support heterogeneous tissue architectures reflective of brain organization and structure, and robust functional assays for in vitro tissue validation. The unique design parameters defined by the complex physiology of the CNS for construction and validation of 3D in vitro neural systems are reviewed here.

The connectome of the basal ganglia

The basal ganglia of the laboratory rat consist of a few core regions that are specifically interconnected by efferents and afferents of the central nervous system. In nearly 800 reports of tract-tracing investigations the connectivity of the basal ganglia is documented. The readout of connectivity data and the collation of all the connections of these reports in a database allows to generate a connectome. The collation, curation and analysis of such a huge amount of connectivity data is a great challenge and has not been performed before (Bohland et al. PloS One 4:e7200, 2009) in large connectomics projects based on meta-analysis of tract-tracing studies. Here, the basal ganglia connectome of the rat has been generated and analyzed using the consistent cross-platform and generic framework neuroVIISAS. Several advances of this connectome meta-study have been made: the collation of laterality data, the network-analysis of connectivity strengths and the assignment of regions to a hierarchically organized terminology. The basal ganglia connectome offers differences in contralateral connectivity of motoric regions in contrast to other regions. A modularity analysis of the weighted and directed connectome produced a specific grouping of regions. This result indicates a correlation of structural and functional subsystems. As a new finding, significant reciprocal connections of specific network motifs in this connectome were detected. All three principal basal ganglia pathways (direct, indirect, hyperdirect) could be determined in the connectome. By identifying these pathways it was found that there exist many further equivalent pathways possessing the same length and mean connectivity weight as the principal pathways. Based on the connectome data it is unknown why an excitation pattern may prefer principal rather than other equivalent pathways. In addition to these new findings the local graph-theoretical features of regions of the connectome have been determined. By performing graph theoretical analyses it turns out that beside the caudate putamen further regions like the mesencephalic reticular formation, amygdaloid complex and ventral tegmental area are important nodes in the basal ganglia connectome. The connectome data of this meta-study of tract-tracing reports of the basal ganglia are available for further network studies, the integration into neocortical connectomes and further extensive investigations of the basal ganglia dynamics in population simulations.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Defining midbrain dopaminergic neuron diversity by single-cell gene expression profiling

Effective approaches to neuropsychiatric disorders require detailed understanding of the cellular composition and circuitry of the complex mammalian brain. Here, we present a paradigm for deconstructing the diversity of neurons defined by a specific neurotransmitter using a microfluidic dynamic array to simultaneously evaluate the expression of 96 genes in single neurons. With this approach, we successfully identified multiple molecularly distinct dopamine neuron subtypes and localized them in the adult mouse brain. To validate the anatomical and functional correlates of molecular diversity, we provide evidence that one Vip+ subtype, located in the periaqueductal region, has a discrete projection field within the extended amygdala. Another Aldh1a1+ subtype, located in the substantia nigra, is especially vulnerable in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of Parkinson's disease. Overall, this rapid, cost-effective approach enables the identification and classification of multiple dopamine neuron subtypes, with distinct molecular, anatomical, and functional properties.

A neuroinformatics of brain modeling and its implementation in the Brain Operation Database BODB

We present principles for an integrated neuroinformatics framework which makes explicit how models are grounded on empirical evidence, explain (or not) existing empirical results and make testable predictions. The new ontological framework makes explicit how models bring together structural, functional, and related empirical observations. We emphasize schematics of the model’s operation linked to summaries of empirical data (SEDs) used in both the design and testing of the model, with tests comparing SEDs to summaries of simulation results (SSRs) from the model. We stress the importance of protocols for models as well as experiments. We complement the structural ontology of nested brain structures with a functional ontology of Brain Operating Principles (BOPs) for observed neural function and an ontological framework for grounding models in empirical data. We present an implementation of this ontological framework in the Brain Operation Database (BODB), an environment in which modelers and experimentalists can work together by making use of their shared empirical data, models and expertise.

The neocortex of cetartiodactyls: I. A comparative Golgi analysis of neuronal morphology in the bottlenose dolphin (Tursiops truncatus), the minke whale (Balaenoptera acutorostrata), and the humpback whale (Megaptera novaeangliae)

The present study documents the morphology of neurons in several regions of the neocortex from the bottlenose dolphin (Tursiops truncatus), the North Atlantic minke whale (Balaenoptera acutorostrata), and the humpback whale (Megaptera novaeangliae). Golgi-stained neurons (n = 210) were analyzed in the frontal and temporal neocortex as well as in the primary visual and primary motor areas. Qualitatively, all three species exhibited a diversity of neuronal morphologies, with spiny neurons including typical pyramidal types, similar to those observed in primates and rodents, as well as other spiny neuron types that had more variable morphology and/or orientation. Five neuron types, with a vertical apical dendrite, approximated the general pyramidal neuron morphology (i.e., typical pyramidal, extraverted, magnopyramidal, multiapical, and bitufted neurons), with a predominance of typical and extraverted pyramidal neurons. In what may represent a cetacean morphological apomorphy, both typical pyramidal and magnopyramidal neurons frequently exhibited a tri-tufted variant. In the humpback whale, there were also large, star-like neurons with no discernable apical dendrite. Aspiny bipolar and multipolar interneurons were morphologically consistent with those reported previously in other mammals. Quantitative analyses showed that neuronal size and dendritic extent increased in association with body size and brain mass (bottlenose dolphin < minke whale < humpback whale). The present data thus suggest that certain spiny neuron morphologies may be apomorphies in the neocortex of cetaceans as compared to other mammals and that neuronal dendritic extent covaries with brain and body size.

The neocortex of cetartiodactyls. II. Neuronal morphology of the visual and motor cortices in the giraffe (Giraffa camelopardalis)

The present quantitative study extends our investigation of cetartiodactyls by exploring the neuronal morphology in the giraffe (Giraffa camelopardalis) neocortex. Here, we investigate giraffe primary visual and motor cortices from perfusion-fixed brains of three subadults stained with a modified rapid Golgi technique. Neurons (n = 244) were quantified on a computer-assisted microscopy system. Qualitatively, the giraffe neocortex contained an array of complex spiny neurons that included both "typical" pyramidal neuron morphology and "atypical" spiny neurons in terms of morphology and/or orientation. In general, the neocortex exhibited a vertical columnar organization of apical dendrites. Although there was no significant quantitative difference in dendritic complexity for pyramidal neurons between primary visual (n = 78) and motor cortices (n = 65), there was a significant difference in dendritic spine density (motor cortex > visual cortex). The morphology of aspiny neurons in giraffes appeared to be similar to that of other eutherian mammals. For cross-species comparison of neuron morphology, giraffe pyramidal neurons were compared to those quantified with the same methodology in African elephants and some cetaceans (e.g., bottlenose dolphin, minke whale, humpback whale). Across species, the giraffe (and cetaceans) exhibited less widely bifurcating apical dendrites compared to elephants. Quantitative dendritic measures revealed that the elephant and humpback whale had more extensive dendrites than giraffes, whereas the minke whale and bottlenose dolphin had less extensive dendritic arbors. Spine measures were highest in the giraffe, perhaps due to the high quality, perfusion fixation. The neuronal morphology in giraffe neocortex is thus generally consistent with what is known about other cetartiodactyls.