Phenotypic variation of transcriptomic cell types in mouse motor cortex

Neuronal subtype-specific transcriptomic changes in the cerebral neocortex associated with sleep pressure

Sleep is homeostatically regulated by sleep pressure, which increases during wakefulness and dissipates during sleep. Recent studies have suggested that the cerebral neocortex, a six-layered structure composed of various layer- and projection-specific neuronal subtypes, is involved in the representation of sleep pressure governed by transcriptional regulation. Here, we examined the transcriptomic changes in neuronal subtypes in the neocortex upon increased sleep pressure using single-nucleus RNA sequencing datasets and predicted the putative intracellular and intercellular molecules involved in transcriptome alterations. We revealed that sleep deprivation (SD) had the greatest effect on the transcriptome of layer 2 and 3 intratelencephalic (L2/3 IT) neurons among the neocortical glutamatergic neuronal subtypes. The expression of mutant SIK3 (SLP), which is known to increase sleep pressure, also induced profound changes in the transcriptome of L2/3 IT neurons. We identified Junb as a candidate transcription factor involved in the alteration of the L2/3 IT neuronal transcriptome by SD and SIK3 (SLP) expression. Finally, we inferred putative intercellular ligands, including BDNF, LSAMP, and PRNP, which may be involved in SD-induced alteration of the transcriptome of L2/3 IT neurons. We suggest that the transcriptome of L2/3 IT neurons is most impacted by increased sleep pressure among neocortical glutamatergic neuronal subtypes and identify putative molecules involved in such transcriptional alterations.

Areal specializations in the morpho-electric and transcriptomic properties of primate layer 5 extratelencephalic projection neurons

Large-scale analysis of single-cell gene expression has revealed transcriptomically defined cell subclasses present throughout the primate neocortex with gene expression profiles that differ depending upon neocortical region. Here, we test whether the interareal differences in gene expression translate to regional specializations in the physiology and morphology of infragranular glutamatergic neurons by performing Patch-seq experiments in brain slices from the temporal cortex (TCx) and motor cortex (MCx) of the macaque. We confirm that transcriptomically defined extratelencephalically projecting neurons of layer 5 (L5 ET neurons) include retrogradely labeled corticospinal neurons in the MCx and find multiple physiological properties and ion channel genes that distinguish L5 ET from non-ET neurons in both areas. Additionally, while infragranular ET and non-ET neurons retain distinct neuronal properties across multiple regions, there are regional morpho-electric and gene expression specializations in the L5 ET subclass, providing mechanistic insights into the specialized functional architecture of the primate neocortex.

Transcriptomic cell-type specificity of local cortical circuits

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types.

NEURD offers automated proofreading and feature extraction for connectomics

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Sub-threshold neuronal activity and the dynamical regime of cerebral cortex

Cortical neurons exhibit temporally irregular spiking patterns and heterogeneous firing rates. These features arise in model circuits operating in a 'fluctuation-driven regime', in which fluctuations in membrane potentials emerge from the network dynamics. However, it is still debated whether the cortex operates in such a regime. We evaluated the fluctuation-driven hypothesis by analyzing spiking and sub-threshold membrane potentials of neurons in the frontal cortex of mice performing a decision-making task. We showed that while standard fluctuation-driven models successfully account for spiking statistics, they fall short in capturing the heterogeneity in sub-threshold activity. This limitation is an inevitable outcome of bombarding single-compartment neurons with a large number of pre-synaptic inputs, thereby clamping the voltage of all neurons to more or less the same average voltage. To address this, we effectively incorporated dendritic morphology into the standard models. Inclusion of dendritic morphology in the neuronal models increased neuronal selectivity and reduced error trials, suggesting a functional role for dendrites during decision-making. Our work suggests that, during decision-making, cortical neurons in high-order cortical areas operate in a fluctuation-driven regime.

scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features

The abundance of unpaired multimodal single-cell data has motivated a growing body of research into the development of diagonal integration methods. However, the state-of-the-art suffers from the loss of biological information due to feature conversion and struggles with modality-specific populations. To overcome these crucial limitations, we here introduce scConfluence, a method for single-cell diagonal integration. scConfluence combines uncoupled autoencoders on the complete set of features with regularized Inverse Optimal Transport on weakly connected features. We extensively benchmark scConfluence in several single-cell integration scenarios proving that it outperforms the state-of-the-art. We then demonstrate the biological relevance of scConfluence in three applications. We predict spatial patterns for Scgn, Synpr and Olah in scRNA-smFISH integration. We improve the classification of B cells and Monocytes in highly heterogeneous scRNA-scATAC-CyTOF integration. Finally, we reveal the joint contribution of Fezf2 and apical dendrite morphology in Intra Telencephalic neurons, based on morphological images and scRNA.

2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons

Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the transcriptional properties to functionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible place and silent cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.

Linking brain activity across scales with simultaneous opto- and electrophysiology

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.

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

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

Learning meaningful representation of single-neuron morphology via large-scale pre-training

SUMMARY

Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph transformers for feature encoding and enforcing the consistency between multiple augmented views of neuron morphology, MorphRep achieves the state of the art performance on widely used benchmarking datasets. Meanwhile, MorphRep can accurately characterize the neuron morphology space across neuron morphometrics, fine-grained cell types, brain regions and ages. Furthermore, MorphRep can be applied to distinguish neurons under a wide range of conditions, including genetic perturbation, drug injection, environment change and disease. In summary, MorphRep provides an effective strategy to embed and represent neuron morphology and can be a valuable tool in integrating cell morphology into single-cell multiomics analysis.

AVAILABILITY AND IMPLEMENTATION

The codebase has been deposited in https://github.com/YaxuanLi-cn/MorphRep.

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.

Sleep need driven oscillation of glutamate synaptic phenotype

Sleep loss increases AMPA-synaptic strength and number in the neocortex. However, this is only part of the synaptic sleep loss response. We report increased AMPA/NMDA EPSC ratio in frontal-cortical pyramidal neurons of layers 2-3. Silent synapses are absent, decreasing the plastic potential to convert silent NMDA to active AMPA synapses. These sleep loss changes are recovered by sleep. Sleep genes are enriched for synaptic shaping cellular components controlling glutamate synapse phenotype, overlap with autism risk genes and are primarily observed in excitatory pyramidal neurons projecting intra-telencephalically. These genes are enriched with genes controlled by the transcription factor, MEF2c and its repressor, HDAC4. Sleep genes can thus provide a framework within which motor learning and training occurs mediated by sleep-dependent oscillation of glutamate-synaptic phenotypes.

A dorsal hippocampus-prodynorphinergic dorsolateral septum-to-lateral hypothalamus circuit mediates contextual gating of feeding

Adaptive regulation of feeding depends on linkage of internal states and food outcomes with contextual cues. Human brain imaging has identified dysregulation of a hippocampal-lateral hypothalamic area (LHA) network in binge eating, but mechanistic instantiation of underlying cell-types and circuitry is lacking. Here, we identify an evolutionary conserved and discrete Prodynorphin (Pdyn)-expressing subpopulation of Somatostatin (Sst)-expressing inhibitory neurons in the dorsolateral septum (DLS) that receives primarily dorsal, but not ventral, hippocampal inputs. DLS(Pdyn) neurons inhibit LHA GABAergic neurons and confer context- and internal state-dependent calibration of feeding. Viral deletion of Pdyn in the DLS mimicked effects seen with optogenetic silencing of DLS Pdyn INs, suggesting a potential role for DYNORPHIN-KAPPA OPIOID RECEPTOR signaling in contextual regulation of food-seeking. Together, our findings illustrate how the dorsal hippocampus has evolved to recruit an ancient LHA feeding circuit module through Pdyn DLS inhibitory neurons to link contextual information with regulation of food consumption.

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.

Genetic approaches to elucidating cortical and hippocampal GABAergic interneuron diversity

GABAergic interneurons (INs) in the mammalian forebrain represent a diverse population of cells that provide specialized forms of local inhibition to regulate neural circuit activity. Over the last few decades, the development of a palette of genetic tools along with the generation of single-cell transcriptomic data has begun to reveal the molecular basis of IN diversity, thereby providing deep insights into how different IN subtypes function in the forebrain. In this review, we outline the emerging picture of cortical and hippocampal IN speciation as defined by transcriptomics and developmental origin and summarize the genetic strategies that have been utilized to target specific IN subtypes, along with the technical considerations inherent to each approach. Collectively, these methods have greatly facilitated our understanding of how IN subtypes regulate forebrain circuitry via cell type and compartment-specific inhibition and thus have illuminated a path toward potential therapeutic interventions for a variety of neurocognitive disorders.

Modulation of large rhythmic depolarizations in human large basket cells by norepinephrine and acetylcholine

Rhythmic brain activity is critical to many brain functions and is sensitive to neuromodulation, but so far very few studies have investigated this activity on the cellular level in vitro in human brain tissue samples. This study reveals and characterizes a novel rhythmic network activity in the human neocortex. Using intracellular patch-clamp recordings of human cortical neurons, we identify large rhythmic depolarizations (LRDs) driven by glutamate release but not by GABA. These LRDs are intricate events made up of multiple depolarizing phases, occurring at ~0.3 Hz, have large amplitudes and long decay times. Unlike human tissue, rat neocortex layers 2/3 exhibit no such activity under identical conditions. LRDs are mainly observed in a subset of L2/3 interneurons that receive substantial excitatory inputs and are likely large basket cells based on their morphology. LRDs are highly sensitive to norepinephrine (NE) and acetylcholine (ACh), two neuromodulators that affect network dynamics. NE increases LRD frequency through β-adrenergic receptor activity while ACh decreases it via M4 muscarinic receptor activation. Multi-electrode array recordings show that NE enhances and synchronizes oscillatory network activity, whereas ACh causes desynchronization. Thus, NE and ACh distinctly modulate LRDs, exerting specific control over human neocortical activity.

A brief history of somatostatin interneuron taxonomy or: how many somatostatin subtypes are there, really?

We provide a brief (and unabashedly biased) overview of the pre-transcriptomic history of somatostatin interneuron taxonomy, followed by a chronological summary of the large-scale, NIH-supported effort over the last ten years to generate a comprehensive, single-cell RNA-seq-based taxonomy of cortical neurons. Focusing on somatostatin interneurons, we present the perspective of experimental neuroscientists trying to incorporate the new classification schemes into their own research while struggling to keep up with the ever-increasing number of proposed cell types, which seems to double every two years. We suggest that for experimental analysis, the most useful taxonomic level is the subdivision of somatostatin interneurons into ten or so "supertypes," which closely agrees with their more traditional classification by morphological, electrophysiological and neurochemical features. We argue that finer subdivisions ("t-types" or "clusters"), based on slight variations in gene expression profiles but lacking clear phenotypic differences, are less useful to researchers and may actually defeat the purpose of classifying neurons to begin with. We end by stressing the need for generating novel tools (mouse lines, viral vectors) for genetically targeting distinct supertypes for expression of fluorescent reporters, calcium sensors and excitatory or inhibitory opsins, allowing neuroscientists to chart the input and output synaptic connections of each proposed subtype, reveal the position they occupy in the cortical network and examine experimentally their roles in sensorimotor behaviors and cognitive brain functions.

A Prelimbic Cortex-Thalamus Circuit Bidirectionally Regulates Innate and Stress-Induced Anxiety-Like Behavior

Anxiety-related disorders respond to cognitive behavioral therapies, which involved the medial prefrontal cortex (mPFC). Previous studies have suggested that subregions of the mPFC have different and even opposite roles in regulating innate anxiety. However, the specific causal targets of their descending projections in modulating innate anxiety and stress-induced anxiety have yet to be fully elucidated. Here, we found that among the various downstream pathways of the prelimbic cortex (PL), a subregion of the mPFC, PL-mediodorsal thalamic nucleus (MD) projection, and PL-ventral tegmental area (VTA) projection exhibited antagonistic effects on anxiety-like behavior, while the PL-MD projection but not PL-VTA projection was necessary for the animal to guide anxiety-related behavior. In addition, MD-projecting PL neurons bidirectionally regulated remote but not recent fear memory retrieval. Notably, restraint stress induced high-anxiety state accompanied by strengthening the excitatory inputs onto MD-projecting PL neurons, and inhibiting PL-MD pathway rescued the stress-induced anxiety. Our findings reveal that the activity of PL-MD pathway may be an essential factor to maintain certain level of anxiety, and stress increased the excitability of this pathway, leading to inappropriate emotional expression, and suggests that targeting specific PL circuits may aid the development of therapies for the treatment of stress-related disorders.

Control of innate olfactory valence by segregated cortical amygdala circuits

Animals perform innate behaviors that are stereotyped responses to specific evolutionarily relevant stimuli in the absence of prior learning or experience. These behaviors can be reduced to an axis of valence, whereby specific odors evoke approach or avoidance. The cortical amygdala (plCoA) mediates innate attraction and aversion to odor. However, little is known about how this brain area gives rise to behaviors of opposing motivational valence. Here, we sought to define the circuit features of plCoA that give rise to innate olfactory behaviors of valence. We characterized the physiology, gene expression, and projections of this structure, identifying a divergent, topographic organization that selectively controls innate attraction and avoidance to odor. First, we examined odor-evoked responses in these areas and found sparse encoding of odor identity, but not valence. We next considered a topographic organization and found that optogenetic stimulation of the anterior and posterior domains of plCoA elicits attraction and avoidance, respectively, suggesting a functional axis for valence. Using single cell and spatial RNA sequencing, we identified the molecular cell types in plCoA, revealing an anteroposterior gradient in cell types, whereby anterior glutamatergic neurons preferentially express Slc17a6 and posterior neurons express Slc17a7. Activation of these respective cell types recapitulates appetitive and aversive valence behaviors, and chemogenetic inhibition reveals partial necessity for valence responses to innate appetitive or aversive odors. Finally, we identified topographically organized circuits defined by projections, whereby anterior neurons preferentially project to medial amygdala, and posterior neurons preferentially project to nucleus accumbens, which are respectively sufficient and necessary for innate negative and positive olfactory valence. Together, these data advance our understanding of how the olfactory system generates stereotypic, hardwired attraction and avoidance, and supports a model whereby distinct, topographically distributed plCoA populations direct innate olfactory valence responses by signaling to divergent valence-specific targets, linking upstream olfactory identity to downstream valence behaviors, through a population code. This represents a novel circuit motif in which valence encoding is represented not by the firing properties of individual neurons, but by population level identity encoding that is routed through divergent targets to mediate distinct valence.

Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex

Since the beautiful images of Santiago Ramón y Cajal provided a first glimpse into the immense diversity and complexity of cell types found in the cerebral cortex, neuroscience has been challenged and inspired to understand how these diverse cells are generated and how they interact with each other to orchestrate the development of this remarkable tissue. Some fundamental questions drive the field's quest to understand cortical development: what are the mechanistic principles that govern the emergence of neuronal diversity? How do extrinsic and intrinsic signals integrate with physical forces and activity to shape cell identity? How do the diverse populations of neurons and glia influence each other during development to guarantee proper integration and function? The advent of powerful new technologies to profile and perturb cortical development at unprecedented resolution and across a variety of modalities has offered a new opportunity to integrate past knowledge with brand new data. Here, we review some of this progress using cortical excitatory projection neurons as a system to draw out general principles of cell diversification and the role of cell-cell interactions during cortical development.

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

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

Microglial function interacts with the environment to affect sex-specific depression risk

There is a two-fold higher incidence of depression in females compared to men with recent studies suggesting a role for microglia in conferring this sex-dependent depression risk. In this study we investigated the nature of this relation. Using GWAS enrichment, gene-set enrichment analysis and Mendelian randomization, we found minimal evidence for a direct relation between genes functionally related to microglia and sex-dependent genetic risk for depression. We then used expression quantitative trait loci and single nucleus RNA-sequencing resources to generate polygenic scores (PGS) representative of individual variation in microglial function in the adult (UK Biobank; N = 54753-72682) and fetal (ALSPAC; N = 1452) periods. The adult microglial PGS moderated the association between BMI (UK Biobank; beta = 0.001, 95 %CI 0.0009 to 0.003, P = 7.74E-6) and financial insecurity (UK Biobank; beta = 0.001, 95 %CI 0.005 to 0.015, P = 2E-4) with depressive symptoms in females. The fetal microglia PGS moderated the association between maternal prenatal depressive symptoms and offspring depressive symptoms at 24 years in females (ALSPAC; beta = 0.04, 95 %CI 0.004 to 0.07, P = 0.03). We found no evidence for an interaction between the microglial PGS and depression risk factors in males. Our results illustrate a role for microglial function in the conferral of sex-dependent depression risk following exposure to a depression risk factor.

CCK+ Interneurons Contribute to Thalamus-Evoked Feed-Forward Inhibition in the Prelimbic Prefrontal Cortex

Interneurons in the medial prefrontal cortex (PFC) regulate local neural activity to influence cognitive, motivated, and emotional behaviors. Parvalbumin-expressing (PV+) interneurons are the primary mediators of thalamus-evoked feed-forward inhibition across the mouse cortex, including the anterior cingulate cortex, where they are engaged by inputs from the mediodorsal (MD) thalamus. In contrast, in the adjacent prelimbic (PL) cortex, we find that PV+ interneurons are scarce in the principal thalamorecipient layer 3 (L3), suggesting distinct mechanisms of inhibition. To identify the interneurons that mediate MD-evoked inhibition in PL, we combine slice physiology, optogenetics, and intersectional genetic tools in mice of both sexes. We find interneurons expressing cholecystokinin (CCK+) are abundant in L3 of PL, with cells exhibiting fast-spiking (fs) or non-fast-spiking (nfs) properties. MD inputs make stronger connections onto fs-CCK+ interneurons, driving them to fire more readily than nearby L3 pyramidal cells and other interneurons. CCK+ interneurons in turn make inhibitory, perisomatic connections onto L3 pyramidal cells, where they exhibit cannabinoid 1 receptor (CB1R) mediated modulation. Moreover, MD-evoked feed-forward inhibition, but not direct excitation, is also sensitive to CB1R modulation. Our findings indicate that CCK+ interneurons contribute to MD-evoked inhibition in PL, revealing a mechanism by which cannabinoids can modulate MD-PFC communication.

The vertebrate retina: a window into the evolution of computation in the brain

Animal brains are probably the most complex computational machines on our planet, and like everything in biology, they are the product of evolution. Advances in developmental and palaeobiology have been expanding our general understanding of how nervous systems can change at a molecular and structural level. However, how these changes translate into altered function - that is, into 'computation' - remains comparatively sparsely explored. What, concretely, does it mean for neuronal computation when neurons change their morphology and connectivity, when new neurons appear or old ones disappear, or when transmitter systems are slowly modified over many generations? And how does evolution use these many possible knobs and dials to constantly tune computation to give rise to the amazing diversity in animal behaviours we see today? Addressing these major gaps of understanding benefits from choosing a suitable model system. Here, I present the vertebrate retina as one perhaps unusually promising candidate. The retina is ancient and displays highly conserved core organisational principles across the entire vertebrate lineage, alongside a myriad of adjustments across extant species that were shaped by the history of their visual ecology. Moreover, the computational logic of the retina is readily interrogated experimentally, and our existing understanding of retinal circuits in a handful of species can serve as an anchor when exploring the visual circuit adaptations across the entire vertebrate tree of life, from fish deep in the aphotic zone of the oceans to eagles soaring high up in the sky.

Establishing a conceptual framework for holistic cell states and state transitions

Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.

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.

Multimodal cortical neuronal cell type classification

Since more than a century, neuroscientists have distinguished excitatory (glutamatergic) neurons with long-distance projections from inhibitory (GABAergic) neurons with local projections and established layer-dependent schemes for the ~ 80% excitatory (principal) cells as well as the ~ 20% inhibitory neurons. Whereas, in the early days, mainly morphological criteria were used to define cell types, later supplemented by electrophysiological and neurochemical properties, nowadays. single-cell transcriptomics is the method of choice for cell type classification. Bringing recent insight together, we conclude that despite all established layer- and area-dependent differences, there is a set of reliably identifiable cortical cell types that were named (among others) intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) for the excitatory cells, which altogether comprise ~ 56 transcriptomic cell types (t-types). By the same means, inhibitory neurons were subdivided into parvalbumin (PV), somatostatin (SST), vasoactive intestinal polypeptide (VIP), and "other (i.e. Lamp5/Sncg)" subpopulations, which altogether comprise ~ 60 t-types. The coming years will show which t-types actually translate into "real" cell types that show a common set of multimodal features, including not only transcriptome but also physiology and morphology as well as connectivity and ultimately function. Only with the better knowledge of clear-cut cell types and experimental access to them, we will be able to reveal their specific functions, a task which turned out to be difficult in a part of the brain being so much specialized for cognition as the cerebral cortex.

Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons

Single-cell multiomic techniques have sparked immense interest in developing a comprehensive multi-modal map of diverse neuronal cell types and their brain wide projections. However, investigating the spatial organization, transcriptional and epigenetic landscapes of brain wide projection neurons is hampered by the lack of efficient and easily adoptable tools. Here we introduce Projection-TAGs, a retrograde AAV platform that allows multiplex tagging of projection neurons using RNA barcodes. By using Projection-TAGs, we performed multiplex projection tracing of the mouse cortex and high-throughput single-cell profiling of the transcriptional and epigenetic landscapes of the cortical projection neurons. Projection-TAGs can be leveraged to obtain a snapshot of activity-dependent recruitment of distinct projection neurons and their molecular features in the context of a specific stimulus. Given its flexibility, usability, and compatibility, we envision that Projection-TAGs can be readily applied to build a comprehensive multi-modal map of brain neuronal cell types and their projections.

Functional specialization of hippocampal somatostatin-expressing interneurons

Hippocampal somatostatin-expressing (Sst) GABAergic interneurons (INs) exhibit considerable anatomical and functional heterogeneity. Recent single-cell transcriptome analyses have provided a comprehensive Sst-IN subpopulations census, a plausible molecular ground truth of neuronal identity whose links to specific functionality remain incomplete. Here, we designed an approach to identify and access subpopulations of Sst-INs based on transcriptomic features. Four mouse models based on single or combinatorial Cre- and Flp- expression differentiated functionally distinct subpopulations of CA1 hippocampal Sst-INs that largely tiled the morpho-functional parameter space of the Sst-INs superfamily. Notably, the Sst;;Tac1 intersection revealed a population of bistratified INs that preferentially synapsed onto fast-spiking interneurons (FS-INs) and were sufficient to interrupt their firing. In contrast, the Ndnf;;Nkx2-1 intersection identified a population of oriens lacunosum-moleculare INs that predominantly targeted CA1 pyramidal neurons, avoiding FS-INs. Overall, our results provide a framework to translate neuronal transcriptomic identity into discrete functional subtypes that capture the diverse specializations of hippocampal Sst-INs.

Neuronal "parts list" and wiring diagram for a visual system

A catalog of neuronal cell types has often been called a "parts list" of the brain, and regarded as a prerequisite for understanding brain function. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven essential for understanding fly vision. Here we analyze the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. We more than double the list of known types. Most new cell types contain between 10 and 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi) at least double in number of types. We introduce a new Sm interneuron family, which contains more types than any other, and three new families of cross-neuropil types. Self-consistency of cell types is demonstrated through automatic assignment of cells to types by distance in high-dimensional feature space, and further validation is provided by algorithms that select small subsets of discriminative features. Cell types with similar connectivity patterns divide into clusters that are interpretable in terms of motion, object, and color vision. Our work showcases the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity, and reduction of the connectome to a drastically simpler wiring diagram of cell types, with immediate relevance for brain function and development.

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.

Most discriminative stimuli for functional cell type clustering

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.

Drug targeting in psychiatric disorders - how to overcome the loss in translation?

In spite of major efforts and investment in development of psychiatric drugs, many clinical trials have failed in recent decades, and clinicians still prescribe drugs that were discovered many years ago. Although multiple reasons have been discussed for the drug development deadlock, we focus here on one of the major possible biological reasons: differences between the characteristics of drug targets in preclinical models and the corresponding targets in patients. Importantly, based on technological advances in single-cell analysis, we propose here a framework for the use of available and newly emerging knowledge from single-cell and spatial omics studies to evaluate and potentially improve the translational predictivity of preclinical models before commencing preclinical and, in particular, clinical studies. We believe that these recommendations will improve preclinical models and the ability to assess drugs in clinical trials, reducing failure rates in expensive late-stage trials and ultimately benefitting psychiatric drug discovery and development.

Clonally heritable gene expression imparts a layer of diversity within cell types

Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.

A bioorthogonal cell sorting strategy for isolation of desired cell phenotypes

Here we describe a cost-effective and simplified cell sorting method using tetrazine bioorthogonal chemistry. We successfully isolated SKOV3 cells from complex mixtures, demonstrating efficacy in separating mouse lymphocytes expressing interferon and HeLa cells expressing virally transduced green fluorescent protein post-infection.

Identifying neuron types and circuit mechanisms in the auditory midbrain

The inferior colliculus (IC) is a critical computational hub in the central auditory pathway. From its position in the midbrain, the IC receives nearly all the ascending output from the lower auditory brainstem and provides the main source of auditory information to the thalamocortical system. In addition to being a crossroads for auditory circuits, the IC is rich with local circuits and contains more than five times as many neurons as the nuclei of the lower auditory brainstem combined. These results hint at the enormous computational power of the IC, and indeed, systems-level studies have identified numerous important transformations in sound coding that occur in the IC. However, despite decades of effort, the cellular mechanisms underlying IC computations and how these computations change following hearing loss have remained largely impenetrable. In this review, we argue that this challenge persists due to the surprisingly difficult problem of identifying the neuron types and circuit motifs that comprise the IC. After summarizing the extensive evidence pointing to a diversity of neuron types in the IC, we highlight the successes of recent efforts to parse this complexity using molecular markers to define neuron types. We conclude by arguing that the discovery of molecularly identifiable neuron types ushers in a new era for IC research marked by molecularly targeted recordings and manipulations. We propose that the ability to reproducibly investigate IC circuits at the neuronal level will lead to rapid advances in understanding the fundamental mechanisms driving IC computations and how these mechanisms shift following hearing loss.

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.

Neural heterogeneity controls computations in spiking neural networks

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

Diversity-induced trivialization and resilience of neural dynamics

Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.

Patch-seq: Multimodal Profiling of Single-Cell Morphology, Electrophysiology, and Gene Expression

Cells exhibit diverse morphologic phenotypes, biophysical and functional properties, and gene expression patterns. Understanding how these features are interrelated at the level of single cells has been challenging due to the lack of techniques for multimodal profiling of individual cells. We recently developed Patch-seq, a technique that combines whole-cell patch clamp recording, immunohistochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single cells. Here we present a detailed step-by-step protocol for obtaining high-quality morphological, electrophysiological, and transcriptomic data from single cells. Patch-seq enables researchers to explore the rich, multidimensional phenotypic variability among cells and to directly correlate gene expression with phenotype at the level of single cells.

Deep scRNA sequencing reveals a broadly applicable Regeneration Classifier and implicates antioxidant response in corticospinal axon regeneration

Despite substantial progress in understanding the biology of axon regeneration in the CNS, our ability to promote regeneration of the clinically important corticospinal tract (CST) after spinal cord injury remains limited. To understand regenerative heterogeneity, we conducted patch-based single-cell RNA sequencing on rare regenerating CST neurons at high depth following PTEN and SOCS3 deletion. Supervised classification with Garnett gave rise to a Regeneration Classifier, which can be broadly applied to predict the regenerative potential of diverse neuronal types across developmental stages or after injury. Network analyses highlighted the importance of antioxidant response and mitochondrial biogenesis. Conditional gene deletion validated a role for NFE2L2 (or NRF2), a master regulator of antioxidant response, in CST regeneration. Our data demonstrate a universal transcriptomic signature underlying the regenerative potential of vastly different neuronal populations and illustrate that deep sequencing of only hundreds of phenotypically identified neurons has the power to advance regenerative biology.

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.

Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs

In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.

Excitatory subtypes of the lateral amygdala neurons are differentially involved in regulation of synaptic plasticity and excitation/inhibition balance in aversive learning in mice

The amygdala plays a crucial role in aversive learning. In Pavlovian fear conditioning, sensory information about an emotionally neutral conditioned stimulus (CS) and an innately aversive unconditioned stimulus is associated with the lateral amygdala (LA), and the CS acquires the ability to elicit conditioned responses. Aversive learning induces synaptic plasticity in LA excitatory neurons from CS pathways, such as the medial geniculate nucleus (MGN) of the thalamus. Although LA excitatory cells have traditionally been classified based on their firing patterns, the relationship between the subtypes and functional properties remains largely unknown. In this study, we classified excitatory cells into two subtypes based on whether the after-depolarized potential (ADP) amplitude is expressed in non-ADP cells and ADP cells. Their electrophysiological properties were significantly different. We examined subtype-specific synaptic plasticity in the MGN-LA pathway following aversive learning using optogenetics and found significant experience-dependent plasticity in feed-forward inhibitory responses in fear-conditioned mice compared with control mice. Following aversive learning, the inhibition/excitation (I/E) balance in ADP cells drastically changed, whereas that in non-ADP cells tended to change in the reverse direction. These results suggest that the two LA subtypes are differentially regulated in relation to synaptic plasticity and I/E balance during aversive learning.

Single-cell analysis of chromatin accessibility in the adult mouse brain

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1-4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs-specifically, those identified from a subset of cortical excitatory neurons-are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

Connecting single-cell transcriptomes to projectomes in mouse visual cortex

The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.