Patch-seq: Past, Present, and Future

Unraveling the functional complexity of the locus coeruleus-norepinephrine system: insights from molecular anatomy to neurodynamic modeling

The locus coeruleus (LC), as the primary source of norepinephrine (NE) in the brain, is central to modulating cognitive and behavioral processes. This review synthesizes recent findings to provide a comprehensive understanding of the LC-NE system, highlighting its molecular diversity, neurophysiological properties, and role in various brain functions. We discuss the heterogeneity of LC neurons, their differential responses to sensory stimuli, and the impact of NE on cognitive processes such as attention and memory. Furthermore, we explore the system's involvement in stress responses and pain modulation, as well as its developmental changes and susceptibility to stressors. By integrating molecular, electrophysiological, and theoretical modeling approaches, we shed light on the LC-NE system's complex role in the brain's adaptability and its potential relevance to neurological and psychiatric disorders.

Integrating physiological and transcriptomic analyses at the single-neuron level

Neurons generate various spike patterns to execute different functions. Understanding how these physiological neuronal spike patterns are related to their molecular characteristics is a long-standing issue in neuroscience. Herein, we review the results of recent studies that have addressed this issue by integrating physiological and transcriptomic techniques. A sequence of experiments, including in vivo recording and/or labeling, brain tissue slicing, cell collection, and transcriptomic analysis, have identified the gene expression profiles of brain neurons at the single-cell level, with activity patterns recorded in living animals. Although these techniques are still in the early stages, this methodological idea is principally applicable to various brain regions and neuronal activity patterns. Accumulating evidence will contribute to a deeper understanding of neuronal characteristics by integrating insights from molecules to cells, circuits, and behaviors.

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

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

Connectivity of single neurons classifies cell subtypes in mouse brains

Classification of single neurons at a brain-wide scale is a way to characterize the structural and functional organization of brains. Here we acquired and standardized a large morphology database of 20,158 mouse neurons and generated a potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy-morphology-connectivity mapping, we defined neuron connectivity subtypes for neurons in 31 brain regions. We found that cell types defined by connectivity show distinct separation from each other. Within this context, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our findings underscore the importance of connectivity in characterizing the modularity of brain anatomy at the single-cell level. These results highlight that connectivity subtypes supplement conventionally recognized transcriptomic cell types, electrophysiological cell types and morphological cell types as factors to classify cell classes and their identities.

Nociceptor sodium channels shape subthreshold phase, upstroke, and shoulder of action potentials

Voltage-gated sodium channels (VGSCs) in the peripheral nervous system shape action potentials (APs) and thereby support the detection of sensory stimuli. Most of the nine mammalian VGSC subtypes are expressed in nociceptors, but predominantly, three are linked to several human pain syndromes: while Nav1.7 is suggested to be a (sub-)threshold channel, Nav1.8 is thought to support the fast AP upstroke. Nav1.9, as it produces large persistent currents, is attributed a role in determining the resting membrane potential. We characterized the gating of Nav1.1-Nav1.3 and Nav1.5-Nav1.9 in manual patch clamp with a focus on the AP subthreshold depolarization phase. Nav1.9 exhibited the most hyperpolarized activation, while its fast inactivation resembled the depolarized inactivation of Nav1.8. For some VGSCs (e.g., Nav1.1 and Nav1.2), a positive correlation between ramp current and window current was detected. Using a modified Hodgkin-Huxley model that accounts for the time needed for inactivation to occur, we used the acquired data to simulate two nociceptive nerve fiber types (an Aδ- and a mechano-insensitive C-nociceptor) containing VGSC conductances according to published human RNAseq data. Our simulations suggest that Nav1.9 is supporting both the AP upstroke and its shoulder. A reduced threshold for AP generation was induced by enhancing Nav1.7 conductivity or shifting its activation to more hyperpolarized potentials, as observed in Nav1.7-related pain disorders. Here, we provide a comprehensive, comparative functional characterization of VGSCs relevant in nociception and describe their gating with Hodgkin-Huxley-like models, which can serve as a tool to study their specific contributions to AP shape and sodium channel-related diseases.

Evolution of organoid genetics

Organoids have revolutionized in vitro research by offering three-dimensional, multicellular systems that recapitulate the structure, function, and genetics of human tissues. Initially developed from both pluripotent stem cells (PSCs) and adult stem cells (AdSCs), organoids have expanded to model nearly every major human organ, significantly advancing developmental biology, disease modeling, and therapeutic screening. This review highlights the progression of organoid technologies, emphasizing the integration of genetic tools, including CRISPR-Cas9, prime editing, and lineage tracing. These advancements have facilitated precise modeling of human-specific pathologies and drug responses, often surpassing traditional 2D cultures and animal models in accuracy. Emerging technologies, such as organoid fusion, xenografting, and optogenetics, are expected to further enhance our understanding of cellular interactions and microenvironmental dynamics. As organoid complexity and genetic engineering methods continue to evolve, they will become increasingly indispensable for personalized medicine and translational research, bridging gaps between in vitro and in vivo systems.

Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types

Neural cell types have classically been characterized by their anatomy and electrophysiology. More recently, single-cell transcriptomics has enabled an increasingly fine genetically defined taxonomy of cortical cell types, but the link between the gene expression of individual cell types and their physiological and anatomical properties remains poorly understood. Here, we develop a hybrid modeling approach to bridge this gap. Our approach combines statistical and mechanistic models to predict cells' electrophysiological activity from their gene expression pattern. To this end, we fit biophysical Hodgkin-Huxley-based models for a wide variety of cortical cell types using simulation-based inference, while overcoming the challenge posed by the mismatch between the mathematical model and the data. Using multimodal Patch-seq data, we link the estimated model parameters to gene expression using an interpretable sparse linear regression model. Our approach recovers specific ion channel gene expressions as predictive of biophysical model parameters including ion channel densities, directly implicating their mechanistic role in determining neural firing.

In Search of Transcriptomic Correlates of Neuronal Firing-Rate Adaptation across Subtypes, Regions and Species: A Patch-seq Analysis

Can the transcriptomic profile of a neuron predict its physiological properties? Using a Patch-seq dataset of the primary visual cortex, we addressed this question by focusing on spike rate adaptation (SRA), a well-known phenomenon that depends on small conductance calcium (Ca)-dependent potassium (SK) channels. We first show that in parvalbumin-expressing (PV) and somatostatin-expressing (SST) interneurons (INs), expression levels of genes encoding the ion channels underlying action potential generation are correlated with the half-width (HW) of spikes. Surprisingly, the SK encoding gene is not correlated with the degree of SRA (dAdap). Instead, genes that encode proteins upstream from the SK current are correlated with dAdap, a finding validated by a different dataset from the mouse's primary motor cortex that includes pyramidal cells and interneurons, as well as physiological datasets from multiple regions of macaque monkeys. Finally, we construct a minimal model to reproduce observed heterogeneity across cells, with testable predictions.

Comparative Physiology and Morphology of BLA-Projecting NBM/SI Cholinergic Neurons in Mouse and Macaque

Cholinergic projection neurons of the nucleus basalis and substantia innominata (NBM/SI) densely innervate the basolateral amygdala (BLA) and have been shown to contribute to the encoding of fundamental and life-threatening experiences. Given the vital importance of these circuits in the acquisition and retention of memories that are essential for survival in a changing environment, it is not surprising that the basic anatomical organization of the NBM/SI is well conserved across animal classes as diverse as teleost and mammal. What is not known is the extent to which the physiology and morphology of NBM/SI neurons have also been conserved. To address this issue, we made patch-clamp recordings from NBM/SI neurons in ex vivo slices of two widely divergent mammalian species, mouse and rhesus macaque, focusing our efforts on cholinergic neurons that project to the BLA. We then reconstructed most of these recorded neurons post hoc to characterize neuronal morphology. We found that rhesus macaque BLA-projecting cholinergic neurons were both more intrinsically excitable and less morphologically compact than their mouse homologs. Combining measurements of 18 physiological features and 13 morphological features, we illustrate the extent of the separation. Although macaque and mouse neurons both exhibited considerable within-group diversity and overlapped with each other on multiple individual metrics, a combined morphoelectric analysis demonstrates that they form two distinct neuronal classes. Given the shared purpose of the circuits in which these neurons participate, this finding raises questions about (and offers constraints on) how these distinct classes result in similar behavior.

NeuroTD: A Time-Frequency Based Multimodal Learning Approach to Analyze Time Delays in Neural Activities

Studying temporal features of neural activities is crucial for understanding the functions of neurons as well as underlying neural circuits. To this end, recent researches employ emerging techniques including calcium imaging, Neuropixels, depth electrodes, and Patch-seq to generate multimodal time-series data that depict the activities of single neurons, groups of neurons, and behaviors. However, challenges persist, including the analysis of noisy, high-sampling-rate neuronal data, and the modeling of temporal dynamics across various modalities. To address these challenges, we developed NeuroTD, a novel deep learning approach to align multimodal time-series datasets and infer cross-modality temporal relationships such as time delays or shifts. Particularly, NeuroTD integrates Siamese neural networks with frequency domain transformations and complex value optimization for inference. We applied NeuroTD to three multimodal datasets to (1) analyze electrophysiological (ephys) time series measured by depth electrodes, identifying time delays among neurons across various positions, (2) investigate neural activity and behavioral time series data derived from Neuropixels and 3D motion captures, establishing causal relationships between neural activities and corresponding behavioral activities, and (3) explore gene expression and ephys data of single neurons from Patch-seq, identifying gene expression signatures highly correlated with time shifts in ephys responses. Finally, NeuroTD is open-source at https://github.com/daifengwanglab/NeuroTD for general use.

Transcriptomic Correlates of State Modulation in GABAergic Interneurons: A Cross-Species Analysis

GABAergic inhibitory interneurons comprise many subtypes that differ in their molecular, anatomical, and functional properties. In mouse visual cortex, they also differ in their modulation with an animal's behavioral state, and this state modulation can be predicted from the first principal component (PC) of the gene expression matrix. Here, we ask whether this link between transcriptome and state-dependent processing generalizes across species. To this end, we analysed seven single-cell and single-nucleus RNA sequencing datasets from mouse, human, songbird, and turtle forebrains. Despite homology at the level of cell types, we found clear differences between transcriptomic PCs, with greater dissimilarities between evolutionarily distant species. These dissimilarities arise from two factors: divergence in gene expression within homologous cell types and divergence in cell-type abundance. We also compare the expression of cholinergic receptors, which are thought to causally link transcriptome and state modulation. Several cholinergic receptors predictive of state modulation in mouse interneurons are differentially expressed between species. Circuit modelling and mathematical analyses suggest conditions under which these expression differences could translate into functional differences.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain.

Investigating Mechanically Activated Currents from Trigeminal Neurons of Non-Human Primates

Introduction

Pain sensation has predominantly mechanical modalities in many pain conditions. Mechanically activated (MA) ion channels on sensory neurons underly responsiveness to mechanical stimuli. The study aimed to address gaps in knowledge regarding MA current properties in higher order species such as non-human primates (NHP; common marmosets), and characterization of MA currents in trigeminal (TG) neuronal subtypes.

Methods

We employed patch clamp electrophysiology and immunohistochemistry (IHC) to associate MA current types to different marmoset TG neuronal groups. TG neurons were grouped according to presumed marker expression, action potential (AP) width, characteristic AP features, after-hyperpolarization parameters, presence/absence of AP trains and transient outward currents, and responses to mechanical stimuli.

Results

Marmoset TG were clustered into 5 C-fiber and 5 A-fiber neuronal groups. The C1 group likely represent non-peptidergic C-nociceptors, the C2-C4 groups resembles peptidergic C-nociceptors, while the C5 group could be either cold-nociceptors or C-low-threshold-mechanoreceptors (C-LTMR). Among C-fiber neurons only C4 were mechanically responsive. The A1 and A2 groups are likely A-nociceptors, while the A3-A5 groups probably denote different subtypes of A-low-threshold-mechanoreceptors (A-LTMRs). Among A-fiber neurons only A1 was mechanically unresponsive. IHC data was correlated with electrophysiology results and estimates that NHP TG has ∼25% peptidergic C-nociceptors, ∼20% non-peptidergic C-nociceptors, ∼30% A-nociceptors, ∼5% C-LTMR, and ∼20% A-LTMR.

Conclusion

Overall, marmoset TG neuronal subtypes and their associated MA currents have common and unique properties compared to previously reported data. Findings from this study could be the basis for investigation on MA current sensitizations and mechanical hypersensitivity during head and neck pain conditions.

Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective

Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.

Comparative physiology and morphology of BLA-projecting NBM/SI cholinergic neurons in mouse and macaque

Cholinergic projection neurons of the nucleus basalis and substantia innominata (NBM/SI) densely innervate the basolateral amygdala (BLA) and have been shown to contribute to the encoding of fundamental and life-threatening experiences. Given the vital importance of these circuits in the acquisition and retention of memories that are essential for survival in a changing environment, it is not surprising that the basic anatomical organization of the NBM/SI is well conserved across animal classes as diverse as teleost and mammal. What is not known is the extent to which the physiology and morphology of NBM/SI neurons have also been conserved. To address this issue, we made patch-clamp recordings from NBM/SI neurons in ex vivo slices of two widely divergent mammalian species, mouse and rhesus macaque, focusing our efforts on cholinergic neurons that project to the BLA. We then reconstructed most of these recorded neurons post hoc to characterize neuronal morphology. We found that rhesus macaque BLA-projecting cholinergic neurons were both more intrinsically excitable and less morphologically compact than their mouse homologs. Combining measurements of 18 physiological features and 13 morphological features, we illustrate the extent of the separation. Although macaque and mouse neurons both exhibited considerable within-group diversity and overlapped with each other on multiple individual metrics, a combined morpho-electric analysis demonstrates that they form two distinct neuronal classes. Given the shared purpose of the circuits in which these neurons participate, this finding raises questions about (and offers constraints on) how these distinct classes result in similar behavior.

High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy

Neuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations of cells can obscure such insights. Here, we report large-scale automation of neuronal morphology reconstruction and analysis on a dataset of 813 inhibitory neurons characterized using the Patch-seq method, which enables measurement of multiple properties from individual neurons, including local morphology and transcriptional signature. We demonstrate that these automated reconstructions can be used in the same manner as manual reconstructions to understand the relationship between some, but not all, cellular properties used to define cell types. We uncover gene expression correlates of laminar innervation on multiple transcriptomically defined neuronal subclasses and types. In particular, our results reveal correlates of the variability in Layer 1 (L1) axonal innervation in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex.

Compound models and Pearson residuals for single-cell RNA-seq data without UMIs

Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI data. To extend this approach to non-UMI data, we model the additional amplification step with a compound distribution: we assume that sequenced RNA molecules follow a negative binomial distribution, and are then replicated following an amplification distribution. We show how this model leads to compound Pearson residuals, which yield meaningful gene selection and embeddings of Smart-seq2 datasets. Further, we suggest that amplification distributions across several sequencing protocols can be described by a broken power law. The resulting compound model captures previously unexplained overdispersion and zero-inflation patterns in non-UMI data.

Sample collection protocol for single-cell RNA sequencing of functionally identified neuronal populations in vivo

Here, we present a sample collection protocol for single-cell RNA sequencing of functionally identified neuronal populations in vivo with a virally delivered activity-dependent labeling tool (CaMPARI2). We describe steps for photoconversion in mice during the onset of computationally relevant events in a virtual reality environment, followed by removal and dissociation of the photo-labeled tissue, and separation of differentially labeled groups with fluorescence-activated cell sorting (FACS). We then detail procedures for characterizing and examining functionally relevant groups using standard bioinformatic techniques. For complete details on the use and execution of this protocol, please refer to O'Toole et al.1.

A phosphate transporter in VIPergic neurons of the suprachiasmatic nucleus gates locomotor activity during the light/dark transition in mice

The suprachiasmatic nucleus (SCN) encodes time of day through changes in daily firing; however, the molecular mechanisms by which the SCN times behavior are not fully understood. To identify factors that could encode day/night differences in activity, we combine patch-clamp recordings and single-cell sequencing of individual SCN neurons in mice. We identify PiT2, a phosphate transporter, as being upregulated in a population of Vip+Nms+ SCN neurons at night. Although nocturnal and typically showing a peak of activity at lights off, mice lacking PiT2 (PiT2-/-) do not reach the activity level seen in wild-type mice during the light/dark transition. PiT2 loss leads to increased SCN neuronal firing and broad changes in SCN protein phosphorylation. PiT2-/- mice display a deficit in seasonal entrainment when moving from a simulated short summer to longer winter nights. This suggests that PiT2 is responsible for timing activity and is a driver of SCN plasticity allowing seasonal entrainment.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons, however, the electrophysiological properties endogenous to tumor cells remain obscure. To address this, we employed Patch-sequencing on human glioma specimens and found that one third of patched cells in IDH mutant (IDH mut ) tumors demonstrate properties of both neurons and glia by firing single, short action potentials. To define these hybrid cells (HCs) and discern if they are tumor in origin, we developed a computational tool, Single Cell Rule Association Mining (SCRAM), to annotate each cell individually. SCRAM revealed that HCs represent tumor and non-tumor cells that feature GABAergic neuron and oligodendrocyte precursor cell signatures. These studies are the first to characterize the combined electrophysiological and molecular properties of human glioma cells and describe a new cell type in human glioma with unique electrophysiological and transcriptomic properties that are likely also present in the non-tumor mammalian brain.

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.

Molecular and cellular mechanisms of selective vulnerability in neurodegenerative diseases

The selective vulnerability of specific neuronal subtypes is a hallmark of neurodegenerative diseases. In this Review, I summarize our current understanding of the brain regions and cell types that are selectively vulnerable in different neurodegenerative diseases and describe the proposed underlying cell-autonomous and non-cell-autonomous mechanisms. I highlight how recent methodological innovations - including single-cell transcriptomics, CRISPR-based screens and human cell-based models of disease - are enabling new breakthroughs in our understanding of selective vulnerability. An understanding of the molecular mechanisms that determine selective vulnerability and resilience would shed light on the key processes that drive neurodegeneration and point to potential therapeutic strategies to protect vulnerable cell populations.

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

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

A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices

Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.

Tuning neural circuits and behaviors by microglia in the adult brain

Microglia are the primary immune cells of the CNS, contributing to both inflammatory damage and tissue repair in neurological disorder. In addition, emerging evidence highlights the role of homeostatic microglia in regulating neuronal activity, interacting with synapses, tuning neural circuits, and modulating behaviors. Herein, we review how microglia sense and regulate neuronal activity through synaptic interactions, thereby directly engaging with neural networks and behaviors. We discuss current studies utilizing microglial optogenetic and chemogenetic approaches to modulate adult neural circuits. These manipulations of microglia across different CNS regions lead to diverse behavioral consequences. We propose that spatial heterogeneity of microglia-neuron interaction lays the groundwork for understanding diverse functions of microglia in neural circuits and behaviors.

Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca2+ imaging and electrophysiology

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.

Neuronal Spike Shapes (NSS): A straightforward approach to investigate heterogeneity in neuronal excitability states

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Patch-seq: Advances and Biological Applications

Multimodal analysis of gene-expression patterns, electrophysiological properties, and morphological phenotypes at the single-cell/single-nucleus level has been arduous because of the diversity and complexity of neurons. The emergence of Patch-sequencing (Patch-seq) directly links transcriptomics, morphology, and electrophysiology, taking neuroscience research to a multimodal era. In this review, we summarized the development of Patch-seq and recent applications in the cortex, hippocampus, and other nervous systems. Through generating multimodal cell type atlases, targeting specific cell populations, and correlating transcriptomic data with phenotypic information, Patch-seq has provided new insight into outstanding questions in neuroscience. We highlight the challenges and opportunities of Patch-seq in neuroscience and hope to shed new light on future neuroscience research.

Activation of Kv7 channels normalizes hyperactivity of the VTA-NAcLat circuit and attenuates methamphetamine-induced conditioned place preference and sensitization in mice

The brain circuit projecting from the ventral tegmental area (VTA) to the nucleus accumbens lateral shell (NAcLat) has a key role in methamphetamine (MA) addiction. As different dopamine (DA) neuron subpopulations in the VTA participate in different neuronal circuits, it is a challenge to isolate these DA neuron subtypes. Using retrograde tracing and Patch-seq, we isolated DA neurons in the VTA-NAcLat circuit in MA-treated mice and performed gene expression profiling. Among the differentially expressed genes, KCNQ genes were dramatically downregulated. KCNQ genes encode Kv7 channel proteins, which modulate neuronal excitability. Injection of both the Kv7.2/3 agonist ICA069673 and the Kv7.4 agonist fasudil into the VTA attenuated MA-induced conditioned place preference and locomotor sensitization and decreased neuronal excitability. Increasing Kv7.2/3 activity decreased neural oscillations, synaptic plasticity and DA release in the VTA-NacLat circuit in MA-treated mice. Furthermore, overexpression of only Kv7.3 channels in the VTA-NacLat circuit was sufficient to attenuate MA-induced reward behavior and decrease VTA neuron excitability. Activation of Kv7 channels in the VTA may become a novel treatment strategy for MA abuse.

MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology

Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multimodal cell clusters. The use of those methods can be challenging without computational expertise or suitable computing infrastructure for computationally expensive methods. To address this, we developed a cloud-based web application, MANGEM (multimodal analysis of neuronal gene expression, electrophysiology, and morphology). MANGEM provides a step-by-step accessible and user-friendly interface to machine learning alignment methods of neuronal multimodal data. It can run asynchronously for large-scale data alignment, provide users with various downstream analyses of aligned cells, and visualize the analytic results. We demonstrated the usage of MANGEM by aligning multimodal data of neuronal cells in the mouse visual cortex.

Heralding a new era of oxytocinergic research: New tools, new problems?

According to classic neuroendocrinology, hypothalamic oxytocin cells can be categorized into parvo- and magnocellular neurons. However, research in the last decade provided ample evidence that this black-and-white model of oxytocin neurons is most likely oversimplified. Novel genetic, functional and morphological studies indicate that oxytocin neurons might be organized in functional modules and suggest the existence of five or more distinct oxytocinergic subpopulations. However, many of these novel, automated high-throughput techniques might be inherently biased and interpretation of acquired data needs to be approached with caution to enable drawing sound and reliable conclusions. In addition, the recent finding that astrocytes in various brain regions express functional oxytocin receptors represents a paradigm shift and challenges the view that oxytocin primarily acts as a direct peptidergic neurotransmitter. This review highlights the latest technical advances in oxytocinergic research, puts recent studies on the oxytocin system into context and formulates various provocative ideas based on novel findings that challenges various prevailing hypotheses and dogmas about oxytocinergic modulation.

The organization of spinal neurons: Insights from single cell sequencing

To understand how the spinal cord enacts complex sensorimotor functions, researchers have studied, classified, and functionally probed it's many neuronal populations for over a century. Recent developments in single-cell RNA-sequencing can characterize the gene expression signatures of the entire set of spinal neuron types and can simultaneously provide an unbiased view of their relationships to each other. This approach has revealed that the location of neurons predicts transcriptomic variability, as dorsal spinal neurons become highly distinct over development as ventral spinal neurons become less so. Temporal specification is also a major source of gene expression variation, subdividing many of the canonical embryonic lineage domains. Together, birthdate and cell body location are fundamental organizing features of spinal neuron diversity.

Retinal ganglion cell repopulation for vision restoration in optic neuropathy: a roadmap from the RReSTORe Consortium

Retinal ganglion cell (RGC) death in glaucoma and other optic neuropathies results in irreversible vision loss due to the mammalian central nervous system's limited regenerative capacity. RGC repopulation is a promising therapeutic approach to reverse vision loss from optic neuropathies if the newly introduced neurons can reestablish functional retinal and thalamic circuits. In theory, RGCs might be repopulated through the transplantation of stem cell-derived neurons or via the induction of endogenous transdifferentiation. The RGC Repopulation, Stem Cell Transplantation, and Optic Nerve Regeneration (RReSTORe) Consortium was established to address the challenges associated with the therapeutic repair of the visual pathway in optic neuropathy. In 2022, the RReSTORe Consortium initiated ongoing international collaborative discussions to advance the RGC repopulation field and has identified five critical areas of focus: (1) RGC development and differentiation, (2) Transplantation methods and models, (3) RGC survival, maturation, and host interactions, (4) Inner retinal wiring, and (5) Eye-to-brain connectivity. Here, we discuss the most pertinent questions and challenges that exist on the path to clinical translation and suggest experimental directions to propel this work going forward. Using these five subtopic discussion groups (SDGs) as a framework, we suggest multidisciplinary approaches to restore the diseased visual pathway by leveraging groundbreaking insights from developmental neuroscience, stem cell biology, molecular biology, optical imaging, animal models of optic neuropathy, immunology & immunotolerance, neuropathology & neuroprotection, materials science & biomedical engineering, and regenerative neuroscience. While significant hurdles remain, the RReSTORe Consortium's efforts provide a comprehensive roadmap for advancing the RGC repopulation field and hold potential for transformative progress in restoring vision in patients suffering from optic neuropathies.

Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.

Investigating microglia-neuron crosstalk by characterizing microglial contamination in human and mouse patch-seq datasets

Microglia are cells with diverse roles, including the regulation of neuronal excitability. We leveraged Patch-seq to assess the presence and effects of microglia in the local microenvironment of recorded neurons. We first quantified the amounts of microglial transcripts in three Patch-seq datasets of human and mouse neocortical neurons, observing extensive contamination. Variation in microglial contamination was explained foremost by donor identity, particularly in human samples, and additionally by neuronal cell type identity in mice. Gene set enrichment analysis suggests that microglial contamination is reflective of activated microglia, and that these transcriptional signatures are distinct from those captured via single-nucleus RNA-seq. Finally, neurons with greater microglial contamination differed markedly in their electrophysiological characteristics, including lowered input resistances and more depolarized action potential thresholds. Our results generalize beyond Patch-seq to suggest that activated microglia may be widely present across brain slice preparations and contribute to neuron- and donor-related electrophysiological variability in vitro.

Toward the Next Generation of Neural Iontronic Interfaces

Neural interfaces are evolving at a rapid pace owing to advances in material science and fabrication, reduced cost of scalable complementary metal oxide semiconductor (CMOS) technologies, and highly interdisciplinary teams of researchers and engineers that span a large range from basic to applied and clinical sciences. This study outlines currently established technologies, defined as instruments and biological study systems that are routinely used in neuroscientific research. After identifying the shortcomings of current technologies, such as a lack of biocompatibility, topological optimization, low bandwidth, and lack of transparency, it maps out promising directions along which progress should be made to achieve the next generation of symbiotic and intelligent neural interfaces. Lastly, it proposes novel applications that can be achieved by these developments, ranging from the understanding and reproduction of synaptic learning to live-long multimodal measurements to monitor and treat various neuronal disorders.

Differential expression of GABAA receptor subunits δ and α6 mediates tonic inhibition in parvalbumin and somatostatin interneurons in the mouse hippocampus

Inhibitory γ-aminobutyric acid (GABA)-ergic interneurons mediate inhibition in neuronal circuitry and support normal brain function. Consequently, dysregulation of inhibition is implicated in various brain disorders. Parvalbumin (PV) and somatostatin (SST) interneurons, the two major types of GABAergic inhibitory interneurons in the hippocampus, exhibit distinct morpho-physiological properties and coordinate information processing and memory formation. However, the molecular mechanisms underlying the specialized properties of PV and SST interneurons remain unclear. This study aimed to compare the transcriptomic differences between these two classes of interneurons in the hippocampus using the ribosome tagging approach. The results revealed distinct expressions of genes such as voltage-gated ion channels and GABAA receptor subunits between PV and SST interneurons. Gabrd and Gabra6 were identified as contributors to the contrasting tonic GABAergic inhibition observed in PV and SST interneurons. Moreover, some of the differentially expressed genes were associated with schizophrenia and epilepsy. In conclusion, our results provide molecular insights into the distinct roles of PV and SST interneurons in health and disease.

CAJAL enables analysis and integration of single-cell morphological data using metric geometry

High-resolution imaging has revolutionized the study of single cells in their spatial context. However, summarizing the great diversity of complex cell shapes found in tissues and inferring associations with other single-cell data remains a challenge. Here, we present CAJAL, a general computational framework for the analysis and integration of single-cell morphological data. By building upon metric geometry, CAJAL infers cell morphology latent spaces where distances between points indicate the amount of physical deformation required to change the morphology of one cell into that of another. We show that cell morphology spaces facilitate the integration of single-cell morphological data across technologies and the inference of relations with other data, such as single-cell transcriptomic data. We demonstrate the utility of CAJAL with several morphological datasets of neurons and glia and identify genes associated with neuronal plasticity in C. elegans. Our approach provides an effective strategy for integrating cell morphology data into single-cell omics analyses.

Neuronal Connectivity as a Determinant of Cell Types and Subtypes

Classifications of single neurons at brain-wide scale is a powerful way to characterize the structural and functional organization of a brain. We acquired and standardized a large morphology database of 20,158 mouse neurons, and generated a whole-brain scale potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy-morphology-connectivity mapping, we defined neuron connectivity types and subtypes (both called "c-types" for simplicity) for neurons in 31 brain regions. We found that neuronal subtypes defined by connectivity in the same regions may share statistically higher correlation in their dendritic and axonal features than neurons having contrary connectivity patterns. Subtypes defined by connectivity show distinct separation with each other, which cannot be recapitulated by morphology features, population projections, transcriptomic, and electrophysiological data produced to date. Within this paradigm, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our finding underscores the importance of connectivity in characterizing the modularity of brain anatomy, as well as the cell types and their subtypes. These results highlight that c-types supplement conventionally recognized transcriptional cell types (t-types), electrophysiological cell types (e-types), and morphological cell types (m-types) as an important determinant of cell classes and their identities.

Heterogeneous organization of Locus coeruleus: An intrinsic mechanism for functional complexity

Locus coeruleus (LC) is a small nucleus located deep in the brainstem that contains the majority of central noradrenergic neurons, which provide the primary source of noradrenaline (NA) throughout the entire central nervous system (CNS).The release of neurotransmitter NA is considered to modulate arousal, sensory processing, attention, aversive and adaptive stress responses as well as high-order cognitive function and memory, with the highly ramified axonal arborizations of LC-NA neurons sending wide projections to the targeted brain areas. For over 30 years, LC was thought to be a homogeneous nucleus in structure and function due to the widespread uniform release of NA by LC-NA neurons and simultaneous action in several CNS regions, such as the prefrontal cortex, hippocampus, cerebellum, and spinal cord. However, recent advances in neuroscience tools have revealed that LC is probably not so homogeneous as we previous thought and exhibits heterogeneity in various aspects. Accumulating studies have shown that the functional complexity of LC may be attributed to its heterogeneity in developmental origin, projection patterns, topography distribution, morphology and molecular organization, electrophysiological properties and sex differences. This review will highlight the heterogeneity of LC and its critical role in modulating diverse behavioral outcomes.

Current Best Practices for Analysis of Dendritic Spine Morphology and Number in Neurodevelopmental Disorder Research

Quantitative methods for assessing neural anatomy have rapidly evolved in neuroscience and provide important insights into brain health and function. However, as new techniques develop, it is not always clear when and how each may be used to answer specific scientific questions posed. Dendritic spines, which are often indicative of synapse formation and neural plasticity, have been implicated across many brain regions in neurodevelopmental disorders as a marker for neural changes reflecting neural dysfunction or alterations. In this Perspective we highlight several techniques for staining, imaging, and quantifying dendritic spines as well as provide a framework for avoiding potential issues related to pseudoreplication. This framework illustrates how others may apply the most rigorous approaches. We consider the cost-benefit analysis of the varied techniques, recognizing that the most sophisticated equipment may not always be necessary for answering some research questions. Together, we hope this piece will help researchers determine the best strategy toward using the ever-growing number of techniques available to determine neural changes underlying dendritic spine morphology in health and neurodevelopmental disorders.

Photoselective sequencing: microscopically guided genomic measurements with subcellular resolution

In biological systems, spatial organization and function are interconnected. Here we present photoselective sequencing, a new method for genomic and epigenomic profiling within morphologically distinct regions. Starting with an intact biological specimen, photoselective sequencing uses targeted illumination to selectively unblock a photocaged fragment library, restricting the sequencing-based readout to microscopically identified spatial regions. We validate photoselective sequencing by measuring the chromatin accessibility profiles of fluorescently labeled cell types within the mouse brain and comparing with published data. Furthermore, by combining photoselective sequencing with a computational strategy for decomposing bulk accessibility profiles, we find that the oligodendrocyte-lineage-cell population is relatively enriched for oligodendrocyte-progenitor cells in the cortex versus the corpus callosum. Finally, we leverage photoselective sequencing at the subcellular scale to identify features of chromatin that are correlated with positioning at the nuclear periphery. These results collectively demonstrate that photoselective sequencing is a flexible and generalizable platform for exploring the interplay of spatial structures with genomic and epigenomic properties.

Biosensors integrated 3D organoid/organ-on-a-chip system: A real-time biomechanical, biophysical, and biochemical monitoring and characterization

As a full-fidelity simulation of human cells, tissues, organs, and even systems at the microscopic scale, Organ-on-a-Chip (OOC) has significant ethical advantages and development potential compared to animal experiments. The need for the design of new drug high-throughput screening platforms and the mechanistic study of human tissues/organs under pathological conditions, the evolving advances in 3D cell biology and engineering, etc., have promoted the updating of technologies in this field, such as the iteration of chip materials and 3D printing, which in turn facilitate the connection of complex multi-organs-on-chips for simulation and the further development of technology-composite new drug high-throughput screening platforms. As the most critical part of organ-on-a-chip design and practical application, verifying the success of organ model modeling, i.e., evaluating various biochemical and physical parameters in OOC devices, is crucial. Therefore, this paper provides a logical and comprehensive review and discussion of the advances in organ-on-a-chip detection and evaluation technologies from a broad perspective, covering the directions of tissue engineering scaffolds, microenvironment, single/multi-organ function, and stimulus-based evaluation, and provides a more comprehensive review of the progress in the significant organ-on-a-chip research areas in the physiological state.

Spatial distribution of heterogeneity as a modulator of collective dynamics in pancreatic beta-cell networks and beyond

We study the impact of spatial distribution of heterogeneity on collective dynamics in gap-junction coupled beta-cell networks comprised on cells from two populations that differ in their intrinsic excitability. Initially, these populations are uniformly and randomly distributed throughout the networks. We develop and apply an iterative algorithm for perturbing the arrangement of the network such that cells from the same population are increasingly likely to be adjacent to one another. We find that the global input strength, or network drive, necessary to transition the network from a state of quiescence to a state of synchronised and oscillatory activity decreases as network sortedness increases. Moreover, for weak coupling, we find that regimes of partial synchronisation and wave propagation arise, which depend both on network drive and network sortedness. We then demonstrate the utility of this algorithm for studying the distribution of heterogeneity in general networks, for which we use Watts-Strogatz networks as a case study. This work highlights the importance of heterogeneity in node dynamics in establishing collective rhythms in complex, excitable networks and has implications for a wide range of real-world systems that exhibit such heterogeneity.

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types ¹ . Neural cell types have previously been defined by morphology ^{2, 3} , electrophysiology ^{4, 5} , transcriptomic expression ^6-8 , connectivity ^9-13 , or even a combination of such modalities ^14-16 . More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells ^17-20 . Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex ²¹ . It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type ^{22, 23} known to be Somatostatin positive (Sst+) ^{24, 25} , were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

Pimozide Increases a Delayed Rectifier K+ Conductance in Chicken Embryo Vestibular Hair Cells

Pimozide is a conventional antipsychotic drug largely used in the therapy for schizophrenia and Tourette's syndrome. Pimozide is assumed to inhibit synaptic transmission at the CNS by acting as a dopaminergic D₂ receptor antagonist. Moreover, pimozide has been shown to block voltage-gated Ca²⁺ and K⁺ channels in different cells. Despite its widespread clinical use, pimozide can cause several adverse effects, including extrapyramidal symptoms and cardiac arrhythmias. Dizziness and loss of balance are among the most common side effects of pimozide. By using the patch-clamp whole-cell technique, we investigated the effect of pimozide [3 μM] on K⁺ channels expressed by chicken embryo vestibular type-II hair cells. We found that pimozide slightly blocks a transient outward rectifying A-type K⁺ current but substantially increases a delayed outward rectifying K⁺ current. The net result was a significant hyperpolarization of type-II hair cells at rest and a strong reduction of their response to depolarizing stimuli. Our findings are consistent with an inhibitory effect of pimozide on the afferent synaptic transmission by type-II hair cells. Moreover, they provide an additional key to understanding the beneficial/collateral pharmacological effects of pimozide. The finding that pimozide can act as a K⁺ channel opener provides a new perspective for the use of this drug.

Isolation and transfection of myenteric neurons from mice for patch-clamp applications

The enteric nervous system (ENS) is a complex neuronal network organized in ganglionated plexuses that extend along the entire length of the gastrointestinal tract. Largely independent of the central nervous system, the ENS coordinates motility and peristalsis of the digestive tract, regulates secretion and absorption, and is involved in immunological processes. Electrophysiological methods such as the patch-clamp technique are particularly suitable to study the function of neurons as well as the biophysical parameters of the underlying ion channels under both physiological and pathophysiological conditions. However, application of the patch-clamp method to ENS neurons remained difficult because they are embedded in substantial tissue layers that limit access to and targeted manipulation of these cells. Here, we present a robust step-by-step protocol that involves isolation of ENS neurons from adult mice, culturing of the cells, their transfection with plasmid DNA, and subsequent electrophysiological characterization of individual neurons in current-clamp and voltage-clamp recordings. With this protocol, ENS neurons can be prepared, transfected, and electrophysiologically characterized within 72 h. Using isolated ENS neurons, we demonstrate the feasibility of the approach by functional overexpression of recombinant voltage-gated Na_V1.9 mutant channels associated with hereditary sensory and autonomic neuropathy type 7 (HSAN-7), a disorder characterized by congenital analgesia and severe constipation that can require parenteral nutrition. Although our focus is on the electrophysiological evaluation of isolated ENS neurons, the presented methodology is also useful to analyze molecules other than sodium channels or to apply alternative downstream assays including calcium imaging, proteomic and nucleic acid approaches, or immunochemistry.

Purkinje Cell Patterning-Insights from Single-Cell Sequencing

Despite their homogeneous appearance, Purkinje cells are remarkably diverse with respect to their molecular phenotypes, physiological properties, afferent and efferent connectivity, as well as their vulnerability to insults. Heterogeneity in Purkinje cells arises early in development, with molecularly distinct embryonic cell clusters present soon after Purkinje cell specification. Traditional methods have characterized cerebellar development and cell types, including Purkinje cell subtypes, based on knowledge of selected markers. However, recent single-cell RNA sequencing studies provide vastly increased resolution of the whole cerebellar transcriptome. Here we draw together the results of multiple single-cell transcriptomic studies in developing and adult cerebellum in both mouse and human. We describe how this detailed transcriptomic data has increased our understanding of the intricate development and function of Purkinje cells and provides first clues into features specific to human cerebellar development.