Single cell transcriptomics in neuroscience: cell classification and beyond

Utilization of Microfluidic Droplet-Based Methods in Diagnosis and Treatment Methods of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is associated with high morbidity and mortality. One of the main challenges in the management of HCC is late clinical presentation and thus diagnosis of the disease, which results in poor survival. The pathogenesis of HCC is complex and involves chronic liver injury and genetic alterations. Diagnosis of HCC can be made either by biopsy or imaging; however, conventional tissue-based biopsy methods and serological biomarkers such as AFP have limited clinical applications. While hepatocellular carcinoma is associated with a range of molecular alterations, including the activation of oncogenic signaling pathways, such as Wnt-TGFβ, PI3K-AKT-mTOR, RAS-MAPK, MET, IGF, and Wnt-β-catenin and TP53 and TERT promoter mutations, microfluidic applications have been limited. Early diagnosis is crucial for advancing treatments that would address the heterogeneity of HCC. In this context, microfluidic droplet-based methods are crucial, as they enable comprehensive analysis of the genome and transcriptome of individual cells. Single-cell RNA sequencing (scRNA-seq) allows the examination of individual cell transcriptomes, identifying their heterogeneity and cellular evolutionary relationships. Other microfluidic methods, such as Drop-seq, InDrop, and ATAC-seq, are also employed for single-cell analysis. Here, we examine and compare these microfluidic droplet-based methods, exploring their advantages and limitations in liver cancer research. These technologies provide new opportunities to understand liver cancer biology, diagnosis, treatment, and prognosis, contributing to scientific efforts in combating this challenging disease.

Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches.

Decoding cellular plasticity and niche regulation of limbal stem cells during corneal wound healing

BACKGROUND

Dysfunction or deficiency of corneal epithelium results in vision impairment or blindness in severe cases. The rapid and effective regeneration of corneal epithelial cells relies on the limbal stem cells (LSCs). However, the molecular and functional responses of LSCs and their niche cells to injury remain elusive.

METHODS

Single-cell RNA sequencing was performed on corneal tissues from normal mice and corneal epithelium defect models. Bioinformatics analysis was performed to confirm the distinct characteristics and cell fates of LSCs. Knockdown of Creb5 and OSM treatment experiment were performed to determine their roles of in corneal epithelial wound healing.

RESULTS

Our data defined the molecular signatures of LSCs and reconstructed the pseudotime trajectory of corneal epithelial cells. Gene network analyses characterized transcriptional landmarks that potentially regulate LSC dynamics, and identified a transcription factor Creb5, that was expressed in LSCs and significantly upregulated after injury. Loss-of-function experiments revealed that silencing Creb5 delayed the corneal epithelial healing and LSC mobilization. Through cell-cell communication analysis, we identified 609 candidate regeneration-associated ligand-receptor interaction pairs between LSCs and distinct niche cells, and discovered a unique subset of Arg1+ macrophages infiltrated after injury, which were present as the source of Oncostatin M (OSM), an IL-6 family cytokine, that were demonstrated to effectively accelerate the corneal epithelial wound healing.

CONCLUSIONS

This research provides a valuable single-cell resource and reference for the discovery of mechanisms and potential clinical interventions aimed at ocular surface reconstruction.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Cellular-resolution gene expression mapping reveals organization in the head ganglia of the gastropod, Berghia stephanieae

Gastropod molluscs such as Aplysia, Lymnaea, and Tritonia have been important for determining fundamental rules of motor control, learning, and memory because of their large, individually identifiable neurons. Yet only a small number of gastropod neurons have known molecular markers, limiting the ability to establish brain-wide structure-function relations. Here we combine high-throughput, single-cell RNA sequencing with in situ hybridization chain reaction in the nudibranch Berghia stephanieae to identify and visualize the expression of markers for cell types. Broad neuronal classes were characterized by genes associated with neurotransmitters, like acetylcholine, glutamate, serotonin, and GABA, as well as neuropeptides. These classes were subdivided by other genes including transcriptional regulators and unannotated genes. Marker genes expressed by neurons and glia formed discrete, previously unrecognized regions within and between ganglia. This study provides the foundation for understanding the fundamental cellular organization of gastropod nervous systems.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

An introduction to representation learning for single-cell data analysis

Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Correlation Analysis of Molecularly-Defined Cortical Interneuron Populations with Morpho-Electric Properties in Layer V of Mouse Neocortex

Cortical interneurons can be categorized into distinct populations based on multiple modalities, including molecular signatures and morpho-electrical (M/E) properties. Recently, many transcriptomic signatures based on single-cell RNA-seq have been identified in cortical interneurons. However, whether different interneuron populations defined by transcriptomic signature expressions correspond to distinct M/E subtypes is still unknown. Here, we applied the Patch-PCR approach to simultaneously obtain the M/E properties and messenger RNA (mRNA) expression of >600 interneurons in layer V of the mouse somatosensory cortex (S1). Subsequently, we identified 11 M/E subtypes, 9 neurochemical cell populations (NCs), and 20 transcriptomic cell populations (TCs) in this cortical lamina. Further analysis revealed that cells in many NCs and TCs comprised several M/E types and were difficult to clearly distinguish morpho-electrically. A similar analysis of layer V interneurons of mouse primary visual cortex (V1) and motor cortex (M1) gave results largely comparable to S1. Comparison between S1, V1, and M1 suggested that, compared to V1, S1 interneurons were morpho-electrically more similar to M1. Our study reveals the presence of substantial M/E variations in cortical interneuron populations defined by molecular expression.

Single-cell RNA sequencing in cornea research: Insights into limbal stem cells and their niche regulation

The corneal epithelium is composed of stratified squamous epithelial cells on the outer surface of the eye, which acts as a protective barrier and is critical for clear and stable vision. Its continuous renewal or wound healing depends on the proliferation and differentiation of limbal stem cells (LSCs), a cell population that resides at the limbus in a highly regulated niche. Dysfunction of LSCs or their niche can cause limbal stem cell deficiency, a disease that is manifested by failed epithelial wound healing or even blindness. Nevertheless, compared to stem cells in other tissues, little is known about the LSCs and their niche. With the advent of single-cell RNA sequencing, our understanding of LSC characteristics and their microenvironment has grown considerably. In this review, we summarized the current findings from single-cell studies in the field of cornea research and focused on important advancements driven by this technology, including the heterogeneity of the LSC population, novel LSC markers and regulation of the LSC niche, which will provide a reference for clinical issues such as corneal epithelial wound healing, ocular surface reconstruction and interventions for related diseases.

Evolution of homology: From archetype towards a holistic concept of cell type

The concept of homology lies in the heart of comparative biological science. The distinction between homology as structure and analogy as function has shaped the evolutionary paradigm for a century and formed the axis of comparative anatomy and embryology, which accept the identity of structure as a ground measure of relatedness. The advent of single-cell genomics overturned the classical view of cell homology by establishing a backbone regulatory identity of cell types, the basic biological units bridging the molecular and phenotypic dimensions, to reveal that the cell is the most flexible unit of living matter and that many approaches of classical biology need to be revised to understand evolution and diversity at the cellular level. The emerging theory of cell types explicitly decouples cell identity from phenotype, essentially allowing for the divergence of evolutionarily related morphotypes beyond recognition, as well as it decouples ontogenetic cell lineage from cell-type phylogeny, whereby explicating that cell types can share common descent regardless of their structure, function or developmental origin. The article succinctly summarizes current progress and opinion in this field and formulates a more generalistic view of biological cell types as avatars, transient or terminal cell states deployed in a continuum of states by the developmental programme of one and the same omnipotent cell, capable of changing or combining identities with distinct evolutionary histories or inventing ad hoc identities that never existed in evolution or development. It highlights how the new logic grounded in the regulatory nature of cell identity transforms the concepts of cell homology and phenotypic stability, suggesting that cellular evolution is inherently and massively network-like, with one-to-one homologies being rather uncommon and restricted to shallower levels of the animal tree of life.

Gene panel selection for targeted spatial transcriptomics

Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

Defining Anorectal Transition Zone Heterogeneity Using Single-Cell RNA Sequencing

Special regions called transition zones (TZs) are found at numerous places in the body. TZs represent the junction between two different types of epithelia and are located between the esophagus and the stomach, in the cervix, in the eye, and between the anal canal and the rectum. TZ is a heterogeneous population, and the detailed characterization of its populations requires an analysis at the single-cell level. In this chapter, we provide a protocol to do single-cell RNA sequencing primary analysis of anal canal, TZ, and rectum epithelia.

In Vivo Model for Isolating Epithelial Cells of the Anorectal Transition Zone

Different epithelia line the body and organs and form a continuous lining of cells. The junction of two different types of epithelia represents a special region called transition zone (TZ). TZ are small areas found in numerous places in the body such as between the esophagus and the stomach, in the cervix, in the eye, and between the anal canal and the rectum. These zones are associated with diverse pathologies such as cancers; however, the cellular and molecular mechanisms involved in tumor progression are poorly investigated. We recently characterized the role of anorectal TZ cells during homeostasis and after injury using an in vivo (lineage tracing) approach. To follow TZ cells, we previously developed a mouse model of lineage tracing using cytokeratin 17 (Krt17) as a promoter and GFP as a reporter. Krt17 is expressed by TZ but also by anal glands located below the TZ in the stroma that can interfere with TZ cell population isolation and analysis afterward. In this chapter, we provide a new dissection method to remove specifically anal glands without affecting anorectal TZ cells. This protocol allows the specific dissection and isolation of anal canal, TZ, and rectum epithelia.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

Well-Paired-Seq: A Size-Exclusion and Locally Quasi-Static Hydrodynamic Microwell Chip for Single-Cell RNA-Seq

Single-cell RNA sequencing (scRNA-seq) is a powerful technology for revealing the heterogeneity of cellular states. However, existing scRNA-seq platforms that utilize bead-based technologies suffer from a large number of empty microreactors and a low cell/bead capture efficiency. Here, Well-paired-seq is presented, which consists of thousands of size exclusion and quasi-static hydrodynamic dual wells to address these limitations. The size-exclusion principle allows one cell and one bead to be trapped in the bottom well (cell-capture-well) and the top well (bead-capture-well), respectively, while the quasi-static hydrodynamic principle ensures that the trapped cells are difficult to escape from cell-capture-wells, achieving cumulative capture of cells and effective buffer exchange. By the integration of quasi-static hydrodynamic and size-exclusion principles, the dual wells ensure single cells/beads pairing with high density, achieving excellent efficiency of cell capture (≈91%), cell/bead pairing (≈82%), and cell-free RNA removal. The high utilization of microreactors and single cells/beads enable to achieve a high throughput (≈10⁵ cells) with low collision rates. The technical performance of Well-paired-seq is demonstrated by collecting transcriptome data from around 200 000 cells across 21 samples, successfully revealing the heterogeneity of single cells and showing the wide applicability of Well-paired-seq for basic and clinical research.

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.

Parvalbumin-Positive Interneurons Regulate Cortical Sensory Plasticity in Adulthood and Development Through Shared Mechanisms

Parvalbumin-positive neurons are the largest class of GABAergic, inhibitory neurons in the central nervous system. In the cortex, these fast-spiking cells provide feedforward and feedback synaptic inhibition onto a diverse set of cell types, including pyramidal cells, other inhibitory interneurons, and themselves. Cortical inhibitory networks broadly, and cortical parvalbumin-expressing interneurons (cPVins) specifically, are crucial for regulating sensory plasticity during both development and adulthood. Here we review the functional properties of cPVins that enable plasticity in the cortex of adult mammals and the influence of cPVins on sensory activity at four spatiotemporal scales. First, cPVins regulate developmental critical periods and adult plasticity through molecular and structural interactions with the extracellular matrix. Second, they activate in precise sequence following feedforward excitation to enforce strict temporal limits in response to the presentation of sensory stimuli. Third, they implement gain control to normalize sensory inputs and compress the dynamic range of output. Fourth, they synchronize broad network activity patterns in response to behavioral events and state changes. Much of the evidence for the contribution of cPVins to plasticity comes from classic models that rely on sensory deprivation methods to probe experience-dependent changes in the brain. We support investigating naturally occurring, adaptive cortical plasticity to study cPVin circuits in an ethologically relevant framework, and discuss recent insights from our work on maternal experience-induced auditory cortical plasticity.

Highly unstable heterogeneous representations in VIP interneurons of the anterior cingulate cortex

A hallmark of the anterior cingulate cortex (ACC) is its functional heterogeneity. Functional and imaging studies revealed its importance in the encoding of anxiety-related and social stimuli, but it is unknown how microcircuits within the ACC encode these distinct stimuli. One type of inhibitory interneuron, which is positive for vasoactive intestinal peptide (VIP), is known to modulate the activity of pyramidal cells in local microcircuits, but it is unknown whether VIP cells in the ACC (VIPACC) are engaged by particular contexts or stimuli. Additionally, recent studies demonstrated that neuronal representations in other cortical areas can change over time at the level of the individual neuron. However, it is not known whether stimulus representations in the ACC remain stable over time. Using in vivo Ca2+ imaging and miniscopes in freely behaving mice to monitor neuronal activity with cellular resolution, we identified individual VIPACC that preferentially activated to distinct stimuli across diverse tasks. Importantly, although the population-level activity of the VIPACC remained stable across trials, the stimulus-selectivity of individual interneurons changed rapidly. These findings demonstrate marked functional heterogeneity and instability within interneuron populations in the ACC. This work contributes to our understanding of how the cortex encodes information across diverse contexts and provides insight into the complexity of neural processes involved in anxiety and social behavior.

Brain circuits for promoting homeostatic and non-homeostatic appetites

As the principal means of acquiring nutrients, feeding behavior is indispensable to the survival and well-being of animals. In response to energy or nutrient deficits, animals seek and consume food to maintain energy homeostasis. On the other hand, even when animals are calorically replete, non-homeostatic factors, such as the sight, smell, and taste of palatable food, or environmental cues that predict food, can stimulate feeding behavior. These homeostatic and non-homeostatic factors have traditionally been investigated separately, but a growing body of literature highlights that these factors work synergistically to promote feeding behavior. Furthermore, recent breakthroughs in cell type-specific and circuit-specific labeling, recording, and manipulation techniques have markedly accelerated the discovery of well-defined neural populations underlying homeostatic and non-homeostatic appetite control, as well as overlapping circuits that contribute to both types of appetite. This review aims to provide an update on our understanding of the neural circuit mechanisms for promoting homeostatic and non-homeostatic appetites, focusing on the function of recently identified, genetically defined cell types.

Research on the neural circuit mechanisms underlying feeding behaviors is critical to identifying therapeutic targets for food-related disorders like obesity and anorexia. Sung-Yon Kim and colleagues at Seoul National University, South Korea, reviewed the current understanding of neural circuits promoting feeding behavior, which is regulated by homeostatic and non-homeostatic appetites. In response to deficits in energy (caloric) or nutrients, specific populations of neurons sensitive to hormones leptin and ghrelin generate homeostatic appetite and promote feeding. In addition, diverse neural populations stimulate non-homeostatic appetite in the absence of immediate internal needs and are thought to drive overconsumption in the modern obesogenic environment. These appetites extensively interact through overlapping neural circuits to jointly promote feeding behaviors.

VIP-Expressing GABAergic Neurons: Disinhibitory vs. Inhibitory Motif and Its Role in Communication Across Neocortical Areas

GABAergic neurons play a crucial role in shaping cortical activity. Even though GABAergic neurons constitute a small fraction of cortical neurons, their peculiar morphology and functional properties make them an intriguing and challenging task to study. Here, we review the basic anatomical features, the circuit properties, and the possible role in the relevant behavioral task of a subclass of GABAergic neurons that express vasoactive intestinal polypeptide (VIP). These studies were performed using transgenic mice in which the VIP-expressing neurons can be recognized using fluorescent proteins and optogenetic manipulation to control (or regulate) their electrical activity. Cortical VIP-expressing neurons are more abundant in superficial cortical layers than other cortical layers, where they are mainly studied. Optogenetic and paired recordings performed in ex vivo cortical preparations show that VIP-expressing neurons mainly exert their inhibitory effect onto somatostatin-expressing (SOM) inhibitory neurons, leading to a disinhibitory effect onto excitatory pyramidal neurons. However, this subclass of GABAergic neurons also releases neurotransmitters onto other GABAergic and non-GABAergic neurons, suggesting other possible circuit roles than a disinhibitory effect. The heterogeneity of VIP-expressing neurons also suggests their involvement and recruitment during different functions via the inhibition/disinhibition of GABAergic and non-GABAergic neurons locally and distally, depending on the specific local circuit in which they are embedded, with potential effects on the behavioral states of the animal. Although VIP-expressing neurons represent only a tiny fraction of GABAergic inhibitory neurons in the cortex, these neurons’ selective activation/inactivation could produce a relevant behavioral effect in the animal. Regardless of the increasing finding and discoveries on this subclass of GABAergic neurons, there is still a lot of missing information, and more studies should be done to unveil their role at the circuit and behavior level in different cortical layers and across different neocortical areas.

Transcriptomic evidence for dense peptidergic networks within forebrains of four widely divergent tetrapods

The primary function common to every neuron is communication with other neurons. Such cell-cell signaling can take numerous forms, including fast synaptic transmission and slower neuromodulation via secreted messengers, such as neuropeptides, dopamine, and many other diffusible small molecules. Individual neurons are quite diverse, however, in all particulars of both synaptic and neuromodulatory communication. Neuron classification schemes have therefore proven very useful in exploring the emergence of network function, behavior, and cognition from the communication functions of individual neurons. Recently published single-cell mRNA sequencing data and corresponding transcriptomic neuron classifications from turtle, songbird, mouse, and human provide evidence for a long evolutionary history and adaptive significance of localized peptidergic signaling. Across all four species, sets of at least twenty orthologous cognate pairs of neuropeptide precursor protein and receptor genes are expressed in individually sparse but heavily overlapping patterns suggesting that all forebrain neuron types are densely interconnected by local peptidergic signals.

Diverse midbrain dopaminergic neuron subtypes and implications for complex clinical symptoms of Parkinson's disease

Parkinson’s disease (PD), the most common degenerative movement disorder, is clinically manifested with various motor and non-motor symptoms. Degeneration of midbrain substantia nigra pas compacta (SNc) dopaminergic neurons (DANs) is generally attributed to the motor syndrome. The underlying neuronal mechanisms of non-motor syndrome are largely unexplored. Besides SNc, midbrain ventral tegmental area (VTA) DANs also produce and release dopamine and modulate movement, reward, motivation, and memory. Degeneration of VTA DANs also occurs in postmortem brains of PD patients, implying an involvement of VTA DANs in PD-associated non-motor symptoms. However, it remains to be established that there is a distinct segregation of different SNc and VTA DAN subtypes in regulating different motor and non-motor functions, and that different DAN subpopulations are differentially affected by normal ageing or PD. Traditionally, the distinction among different DAN subtypes was mainly based on the location of cell bodies and axon terminals. With the recent advance of single cell RNA sequencing technology, DANs can be readily classified based on unique gene expression profiles. A combination of specific anatomic and molecular markers shows great promise to facilitate the identification of DAN subpopulations corresponding to different behavior modules under normal and disease conditions. In this review, we first summarize the recent progress in characterizing genetically, anatomically, and functionally diverse midbrain DAN subtypes. Then, we provide perspectives on how the preclinical research on the connectivity and functionality of DAN subpopulations improves our current understanding of cell-type and circuit specific mechanisms of the disease, which could be critically informative for designing new mechanistic treatments.

Genomics at cellular resolution: insights into cognitive disorders and their evolution

High-throughput genomic sequencing approaches have held the promise of understanding and ultimately leading to treatments for cognitive disorders such as autism spectrum disorders, schizophrenia and Alzheimer's disease. Although significant progress has been made into identifying genetic variants associated with these diseases, these studies have also uncovered that these disorders are mostly genetically complex and thus challenging to model in non-human systems. Improvements in such models might benefit from understanding the evolution of the human genome and how such modifications have affected brain development and function. The intersection of genome-wide variant information with cell-type-specific expression and epigenetic information will further assist in resolving the contribution of particular cell types in evolution or disease. For example, the role of non-neuronal cells in brain evolution and cognitive disorders has gone mostly underappreciated until the recent availability of single-cell transcriptomic approaches. In this review, we discuss recent studies that carry out cell-type-specific assessments of gene expression in brain tissue across primates and between healthy and disease populations. The emerging results from these studies are beginning to elucidate how specific cell types in the evolved human brain are contributing to cognitive disorders.

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.

Progress towards a cellularly resolved mouse mesoconnectome is empowered by data fusion and new neuroanatomy techniques

Over the past decade there has been a rapid improvement in techniques for obtaining large-scale cellular level data related to the mouse brain connectome. However, a detailed mapping of cell-type-specific projection patterns is lacking, which would, for instance, allow us to study the role of circuit motifs in cognitive processes. In this work, we review advanced neuroanatomical and data fusion techniques within the context of a proposed Multimodal Connectomic Integration Framework for augmenting the cellularly resolved mouse mesoconnectome. First, we emphasize the importance of registering data modalities to a common reference atlas. We then review a number of novel experimental techniques that can provide data for characterizing cell-types in the mouse brain. Furthermore, we examine a number of data integration strategies, which involve fine-grained cell-type classification, spatial inference of cell densities, latent variable models for the mesoconnectome and multi-modal factorisation. Finally, we discuss a number of use cases which depend on connectome augmentation techniques, such as model simulations of functional connectivity and generating mechanistic hypotheses for animal disease models.

Function and Regulation of ALDH1A1-Positive Nigrostriatal Dopaminergic Neurons in Motor Control and Parkinson's Disease

Dopamine is an important chemical messenger in the brain, which modulates movement, reward, motivation, and memory. Different populations of neurons can produce and release dopamine in the brain and regulate different behaviors. Here we focus our discussion on a small but distinct group of dopamine-producing neurons, which display the most profound loss in the ventral substantia nigra pas compacta of patients with Parkinson's disease. This group of dopaminergic neurons can be readily identified by a selective expression of aldehyde dehydrogenase 1A1 (ALDH1A1) and accounts for 70% of total nigrostriatal dopaminergic neurons in both human and mouse brains. Recently, we presented the first whole-brain circuit map of these ALDH1A1-positive dopaminergic neurons and reveal an essential physiological function of these neurons in regulating the vigor of movement during the acquisition of motor skills. In this review, we first summarize previous findings of ALDH1A1-positive nigrostriatal dopaminergic neurons and their connectivity and functionality, and then provide perspectives on how the activity of ALDH1A1-positive nigrostriatal dopaminergic neurons is regulated through integrating diverse presynaptic inputs and its implications for potential Parkinson's disease treatment.

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

Functional Dissection of Glutamatergic and GABAergic Neurons in the Bed Nucleus of the Stria Terminalis

The bed nucleus of the stria terminalis (BNST)-a key part of the extended amygdala-has been implicated in the regulation of diverse behavioral states, ranging from anxiety and reward processing to feeding behavior. Among the host of distinct types of neurons within the BNST, recent investigations employing cell type- and projection-specific circuit dissection techniques (such as optogenetics, chemogenetics, deep-brain calcium imaging, and the genetic and viral methods for targeting specific types of cells) have highlighted the key roles of glutamatergic and GABAergic neurons and their axonal projections. As anticipated from their primary roles in excitatory and inhibitory neurotransmission, these studies established that the glutamatergic and GABAergic subpopulations of the BNST oppositely regulate diverse behavioral states. At the same time, these studies have also revealed unexpected functional specificity and heterogeneity within each subpopulation. In this Minireview, we introduce the body of studies that investigated the function of glutamatergic and GABAergic BNST neurons and their circuits. We also discuss unresolved questions and future directions for a more complete understanding of the cellular diversity and functional heterogeneity within the BNST.

Input-output signal processing plasticity of vagal motor neurons in response to cardiac ischemic injury

Vagal stimulation is emerging as the next frontier in bioelectronic medicine to modulate peripheral organ health and treat disease. The neuronal molecular phenotypes in the dorsal motor nucleus of the vagus (DMV) remain largely unexplored, limiting the potential for harnessing the DMV plasticity for therapeutic interventions. We developed a mesoscale single-cell transcriptomics data from hundreds of DMV neurons under homeostasis and following physiological perturbations. Our results revealed that homeostatic DMV neuronal states can be organized into distinguishable input-output signal processing units. Remote ischemic preconditioning induced a distinctive shift in the neuronal states toward diminishing the role of inhibitory inputs, with concomitant changes in regulatory microRNAs miR-218a and miR-495. Chronic cardiac ischemic injury resulted in a dramatic shift in DMV neuronal states suggestive of enhanced neurosecretory function. We propose a DMV molecular network mechanism that integrates combinatorial neurotransmitter inputs from multiple brain regions and humoral signals to modulate cardiac health.

Temporal evolution of single-cell transcriptomes of Drosophila olfactory projection neurons

Neurons undergo substantial morphological and functional changes during development to form precise synaptic connections and acquire specific physiological properties. What are the underlying transcriptomic bases? Here, we obtained the single-cell transcriptomes of Drosophila olfactory projection neurons (PNs) at four developmental stages. We decoded the identity of 21 transcriptomic clusters corresponding to 20 PN types and developed methods to match transcriptomic clusters representing the same PN type across development. We discovered that PN transcriptomes reflect unique biological processes unfolding at each stage-neurite growth and pruning during metamorphosis at an early pupal stage; peaked transcriptomic diversity during olfactory circuit assembly at mid-pupal stages; and neuronal signaling in adults. At early developmental stages, PN types with adjacent birth order share similar transcriptomes. Together, our work reveals principles of cellular diversity during brain development and provides a resource for future studies of neural development in PNs and other neuronal types.

Cell Surface Protein mRNAs Show Differential Transcription in Pyramidal and Fast-Spiking Cells as Revealed by Single-Cell Sequencing

The prefrontal cortex (PFC) plays a key role in higher order cognitive functions and psychiatric disorders such as autism, schizophrenia, and depression. In the PFC, the two major classes of neurons are the glutamatergic pyramidal (Pyr) cells and the GABAergic interneurons such as fast-spiking (FS) cells. Despite extensive electrophysiological, morphological, and pharmacological studies of the PFC, the therapeutically utilized drug targets are restricted to dopaminergic, glutamatergic, and GABAergic receptors. To expand the pharmacological possibilities as well as to better understand the cellular and network effects of clinically used drugs, it is important to identify cell-type-selective, druggable cell surface proteins and to link developed drug candidates to Pyr or FS cell targets. To identify the mRNAs of such cell-specific/enriched proteins, we performed ultra-deep single-cell mRNA sequencing (19 685 transcripts in total) on electrophysiologically characterized intact PFC neurons harvested from acute brain slices of mice. Several selectively expressed transcripts were identified with some of the genes that have already been associated with cellular mechanisms of psychiatric diseases, which we can now assign to Pyr (e.g., Kcnn2, Gria3) or FS (e.g., Kcnk2, Kcnmb1) cells. The earlier classification of PFC neurons was also confirmed at mRNA level, and additional markers have been provided.

Single-cell transcriptomics identifies limbal stem cell population and cell types mapping its differentiation trajectory in limbal basal epithelium of human cornea

PURPOSE

This study aimed to uncover novel cell types in heterogenous basal limbus of human cornea for identifying LSC at single cell resolution.

METHODS

Single cells of human limbal basal epithelium were isolated from young donor corneas. Single-cell RNA-Sequencing was performed using 10x Genomics platform, followed by clustering cell types through the graph-based visualization method UMAP and unbiased computational informatic analysis. Tissue RNA in situ hybridization with RNAscope, immunofluorescent staining and multiple functional assays were performed using human corneas and limbal epithelial culture models.

RESULTS

Single-cell transcriptomics of 16,360 limbal basal cells revealed 12 cell clusters belonging to three lineages. A smallest cluster (0.4% of total cells) was identified as LSCs based on their quiescent and undifferentiated states with enriched marker genes for putative epithelial stem cells. TSPAN7 and SOX17 are discovered and validated as new LSC markers based on their exclusive expression pattern and spatial localization in limbal basal epithelium by RNAscope and immunostaining, and functional role in cell growth and tissue regeneration models with RNA interference in cultures. Interestingly, five cell types/states mapping a developmental trajectory of LSC from quiescence to proliferation and differentiation are uncovered by Monocle3 and CytoTRACE pseudotime analysis. The transcription factor networks linking novel signaling pathways are revealed to maintain LSC stemness.

CONCLUSIONS

This human corneal scRNA-Seq identifies the LSC population and uncovers novel cell types mapping the differentiation trajectory in heterogenous limbal basal epithelium. The findings provide insight into LSC concept and lay the foundation for understanding the corneal homeostasis and diseases.

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r² score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r² score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

Cell Type-Specific Oxidative Stress Genomic Signatures in the Globus Pallidus of Dopamine-Depleted Mice

Neuron subtype dysfunction is a key contributor to neurologic disease circuits, but identifying associated gene regulatory pathways is complicated by the molecular complexity of the brain. For example, parvalbumin-expressing (PV+) neurons in the external globus pallidus (GPe) are critically involved in the motor deficits of dopamine-depleted mouse models of Parkinson's disease, where cell type-specific optogenetic stimulation of PV+ neurons over other neuron populations rescues locomotion. Despite the distinct roles these cell types play in the neural circuit, the molecular correlates remain unknown because of the difficulty of isolating rare neuron subtypes. To address this issue, we developed a new viral affinity purification strategy, Cre-Specific Nuclear Anchored Independent Labeling, to isolate Cre recombinase-expressing (Cre+) nuclei from the adult mouse brain. Applying this technology, we performed targeted assessments of the cell type-specific transcriptomic and epigenetic effects of dopamine depletion on PV+ and PV- cells within three brain regions of male and female mice: GPe, striatum, and cortex. We found GPe PV+ neuron-specific gene expression changes that suggested increased hypoxia-inducible factor 2α signaling. Consistent with transcriptomic data, regions of open chromatin affected by dopamine depletion within GPe PV+ neurons were enriched for hypoxia-inducible factor family binding motifs. The gene expression and epigenomic experiments performed on PV+ neurons isolated by Cre-Specific Nuclear Anchored Independent Labeling identified a transcriptional regulatory network mediated by the neuroprotective factor Hif2a as underlying neural circuit differences in response to dopamine depletion.SIGNIFICANCE STATEMENT Cre-Specific Nuclear Anchored Independent Labeling is an enhanced, virus-based approach to isolate nuclei of a specific cell type for transcriptome and epigenome interrogation that decreases dependency on transgenic animals. Applying this technology to GPe parvalbumin-expressing neurons in a mouse model of Parkinson's disease, we discovered evidence for an upregulation of the oxygen homeostasis maintaining pathway involving Hypoxia-inducible factor 2α. These results provide new insight into how neuron subtypes outside the substantia nigra pars compacta may be compensating at a molecular level for differences in the motor production neural circuit during the progression of Parkinson's disease. Furthermore, they emphasize the utility of cell type-specific technologies, such as Cre-Specific Nuclear Anchored Independent Labeling, for isolated assessment of specific neuron subtypes in complex systems.

Lessons from single cell sequencing in CNS cell specification and function

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

The sensitive detection of single-cell secreted lactic acid for glycolytic inhibitor screening with a microdroplet biosensor

Lactic acid (LA) plays an important role in the tumor metabolism and malignant progression of various cancers. Herein, we have developed a one-step, wash-free microfluidic approach with droplet biosensors for the sensitive detection of LA secreted by a single tumor cell. Our assay integrates the enzyme-assisted chemical conversion of LA in small-volume (4.2 nL) droplets for fluorescence signal readout. The microdroplet assay achieved a limit of detection of 1.02 μM and was more sensitive than the commercial ELISA kit by nearly two orders of magnitude. A good specificity has been demonstrated for this assay by testing various ions and biomolecules from the culture medium. This droplet assay allows us to acquire the profiles of the lactic acid secretion of tumor cells under the influence of glycolytic inhibitors at the single-cell level. It offers a useful research tool to study the cell-to-cell differences of LA secretion and glycolytic inhibitor screening for cancer research.

A single-cell transcriptomic and anatomic atlas of mouse dorsal raphe Pet1 neurons

Among the brainstem raphe nuclei, the dorsal raphe nucleus (DR) contains the greatest number of Pet1-lineage neurons, a predominantly serotonergic group distributed throughout DR subdomains. These neurons collectively regulate diverse physiology and behavior and are often therapeutically targeted to treat affective disorders. Characterizing Pet1 neuron molecular heterogeneity and relating it to anatomy is vital for understanding DR functional organization, with potential to inform therapeutic separability. Here we use high-throughput and DR subdomain-targeted single-cell transcriptomics and intersectional genetic tools to map molecular and anatomical diversity of DR-Pet1 neurons. We describe up to fourteen neuron subtypes, many showing biased cell body distributions across the DR. We further show that P2ry1-Pet1 DR neurons - the most molecularly distinct subtype - possess unique efferent projections and electrophysiological properties. These data complement and extend previous DR characterizations, combining intersectional genetics with multiple transcriptomic modalities to achieve fine-scale molecular and anatomic identification of Pet1 neuron subtypes.

Functional specification of CCK+ interneurons by alternative isoforms of Kv4.3 auxiliary subunits

CCK-expressing interneurons (CCK+INs) are crucial for controlling hippocampal activity. We found two firing phenotypes of CCK+INs in rat hippocampal CA3 area; either possessing a previously undetected membrane potential-dependent firing or regular firing phenotype, due to different low-voltage-activated potassium currents. These different excitability properties destine the two types for distinct functions, because the former is essentially silenced during realistic 8–15 Hz oscillations. By contrast, the general intrinsic excitability, morphology and gene-profiles of the two types were surprisingly similar. Even the expression of Kv4.3 channels were comparable, despite evidences showing that Kv4.3-mediated currents underlie the distinct firing properties. Instead, the firing phenotypes were correlated with the presence of distinct isoforms of Kv4 auxiliary subunits (KChIP1 vs. KChIP4e and DPP6S). Our results reveal the underlying mechanisms of two previously unknown types of CCK+INs and demonstrate that alternative splicing of few genes, which may be viewed as a minor change in the cells’ whole transcriptome, can determine cell-type identity.

Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Revealing fine-scale cellular heterogeneity among spatial context and the functional and structural foundations of tissue architecture is fundamental within biological research and pharmacology. Unlike traditional approaches involving single molecules or bulk omics, cutting-edge, spatially resolved transcriptomics techniques offer near-single-cell or even subcellular resolution within tissues. Massive information across higher dimensions along with position-coordinating labels can better map the whole 3D transcriptional landscape of tissues. In this review, we focus on developments and strategies in spatially resolved transcriptomics, compare the cell and gene throughput and spatial resolution in detail for existing methods, and highlight the enormous potential in biomedical research.

Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior

The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms-SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)-and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity.

Extraction of Distinct Neuronal Cell Types from within a Genetically Continuous Population

Single-cell transcriptomics of neocortical neurons have revealed more than 100 clusters corresponding to putative cell types. For inhibitory and subcortical projection neurons (SCPNs), there is a strong concordance between clusters and anatomical descriptions of cell types. In contrast, cortico-cortical projection neurons (CCPNs) separate into surprisingly few transcriptomic clusters, despite their diverse anatomical projection types. We used projection-dependent single-cell transcriptomic analyses and monosynaptic rabies tracing to compare mouse primary visual cortex CCPNs projecting to different higher visual areas. We find that layer 2/3 CCPNs with different anatomical projections differ systematically in their gene expressions, despite forming only a single genetic cluster. Furthermore, these neurons receive feedback selectively from the same areas to which they project. These findings demonstrate that gene-expression analysis in isolation is insufficient to identify neuron types and have important implications for understanding the functional role of cortical feedback circuits.

Classification of Midbrain Dopamine Neurons Using Single-Cell Gene Expression Profiling Approaches

Dysfunctional dopamine (DA) signaling has been associated with a broad spectrum of neuropsychiatric disorders, prompting investigations into how midbrain DA neuron heterogeneity may underpin this variety of behavioral symptoms. Emerging literature indeed points to functional heterogeneity even within anatomically defined DA clusters. Recognizing the need for a systematic classification scheme, several groups have used single-cell profiling to catalog DA neurons based on their gene expression profiles. We aim here not only to synthesize points of congruence but also to highlight key differences between the molecular classification schemes derived from these studies. In doing so, we hope to provide a common framework that will facilitate investigations into the functions of DA neuron subtypes in the healthy and diseased brain.

Molecular evolution of gland cell types and chemical interactions in animals

Across the Metazoa, the emergence of new ecological interactions has been enabled by the repeated evolution of exocrine glands. Specialized glands have arisen recurrently and with great frequency, even in single genera or species, transforming how animals interact with their environment through trophic resource exploitation, pheromonal communication, chemical defense and parental care. The widespread convergent evolution of animal glands implies that exocrine secretory cells are a hotspot of metazoan cell type innovation. Each evolutionary origin of a novel gland involves a process of 'gland cell type assembly': the stitching together of unique biosynthesis pathways; coordinated changes in secretory systems to enable efficient chemical release; and transcriptional deployment of these machineries into cells constituting the gland. This molecular evolutionary process influences what types of compound a given species is capable of secreting, and, consequently, the kinds of ecological interactions that species can display. Here, we discuss what is known about the evolutionary assembly of gland cell types and propose a framework for how it may happen. We posit the existence of 'terminal selector' transcription factors that program gland function via regulatory recruitment of biosynthetic enzymes and secretory proteins. We suggest ancestral enzymes are initially co-opted into the novel gland, fostering pleiotropic conflict that drives enzyme duplication. This process has yielded the observed pattern of modular, gland-specific biosynthesis pathways optimized for manufacturing specific secretions. We anticipate that single-cell technologies and gene editing methods applicable in diverse species will transform the study of animal chemical interactions, revealing how gland cell types are assembled and functionally configured at a molecular level.

Continuous and Discrete Neuron Types of the Adult Murine Striatum

The mammalian striatum is involved in many complex behaviors and yet is composed largely of a single neuron class: the spiny projection neuron (SPN). It is unclear to what extent the functional specialization of the striatum is due to the molecular specialization of SPN subtypes. We sought to define the molecular and anatomical diversity of adult SPNs using single-cell RNA sequencing (scRNA-seq) and quantitative RNA in situ hybridization (ISH). We computationally distinguished discrete versus continuous heterogeneity in scRNA-seq data and found that SPNs in the striatum can be classified into four major discrete types with no implied spatial relationship between them. Within these discrete types, we find continuous heterogeneity encoding spatial gradients of gene expression and defining anatomical location in a combinatorial mechanism. Our results suggest that neuronal circuitry has a substructure at far higher resolution than is typically interrogated, which is defined by the precise identity and location of a neuron.

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

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

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

Single-cell transcriptomic evidence for dense intracortical neuropeptide networks

Seeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study of 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens of molecularly distinct neuropeptidergic modulatory networks that directly interconnect all cortical neurons. This evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly abundant in all, or very nearly all, cortical neurons. Individual neurons express diverse subsets of NP signaling genes from palettes encoding 18 NPPs and 29 NP-GPCRs. These 47 genes comprise 37 cognate NPP/NP-GPCR pairs, implying the likelihood of local neuropeptide signaling. Here, we use neuron-type-specific patterns of NP gene expression to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.

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.