Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between them1. Neural cell types have previously been defined by morphology2,3, electrophysiology4, transcriptomic expression5,6, connectivity7-9 or a combination of such modalities10-12. The Patch-seq technique enables the characterization of morphology, electrophysiology and transcriptomic properties from individual cells13-15. These properties were integrated to define 28 inhibitory, morpho-electric-transcriptomic (MET) cell types in mouse visual cortex16, which do not include synaptic connectivity. Conversely, large-scale electron microscopy (EM) enables morphological reconstruction and a near-complete description of a neuron's local synaptic connectivity, but does not include transcriptomic or electrophysiological information. Here, we leveraged morphological information from Patch-seq to predict the transcriptomically defined cell subclass and/or MET-type of inhibitory neurons within a large-scale EM dataset. We further analysed Martinotti cells-a somatostatin (Sst)-positive17 morphological cell type18,19-which were classified successfully into Sst MET-types with distinct axon myelination and synaptic output connectivity patterns. We demonstrate that morphological features can be used to link cell types across experimental modalities, enabling further comparison of connectivity to gene expression and electrophysiology. We observe unique connectivity rules for predicted Sst cell types.

In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function.

The distinctive physiology of striatal medium spiny neurons (MSNs) underlies their ability to integrate sensory and motor input. In rodents, MSNs have a hyperpolarized resting potential and low input resistance. When activated, they have a delayed onset of spiking and regular spike rate. Here, we show that in the macaque putamen, latency to spike is reduced and spike rate adaptation is increased relative to mouse. We use whole-cell brain slice recordings and recover single-cell gene expression using Patch-seq to distinguish macaque MSN cell types. Species differences in the expression of ion channel genes including the calcium-activated chloride channel, ANO2, and an auxiliary subunit of the A-type potassium channel, DPP10, are correlated with species differences in spike rate adaptation and latency to the first spike, respectively. These surprising divergences in physiology better define the strengths and limitations of mouse models for understanding neuronal and circuit function in the primate basal ganglia.

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.

Abstract Human intelligence is a highly heritable trait, and GWAS have yielded numerous associated genes. These genes overlap with human accelerated regions (HARs) implicated in human brain evolution and might act on the same biological processes. Here, we investigated whether genes associated with human cognition and HAR genes are similarly expressed in adult human cortical neurons and brain areas of cognition, and how their expression relates to neuronal function and structure. We find that these gene sets are preferentially expressed in L3 excitatory neurons in middle temporal gyrus (MTG). Furthermore, neurons with higher expression had larger dendrites and faster action potential kinetics, properties previously linked to intelligence. Finally, we identify a subset of genes associated with dendritic length, with predominantly synaptic functions and high abundance of HARs. Our results indicate that mechanisms underlying human brain evolution and interindividual differences in intelligence might share genetic origin and manifest in specific neuronal types.

Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined.

Neocortical neuronal cell types differ in their intrinsic electrical properties, leading to input - output characteristics tuned to their function. To understand how gene expression in a given cell type gives rise to these electrical characteristics, we established a data generation pipeline using the ‘Patch-seq’ technique - a powerful approach to characterize the electrophysiological, morphological and transcriptomic features from single neurons. We first capture the neuron's passive and active electrical properties in current clamp using a standard set of square pulse, ramp, and noise stimuli. We then extract the cellular contents for downstream RNA-seq processing - ultimately mapping the cell to its transcriptomic type. To narrow the gap between gene expression and electrical behavior, we also characterize macroscopic ion channel properties using the outside-out ‘nucleated’ voltage clamp configuration. Here we use voltage clamp protocols designed to isolate and characterize the fast and slow inactivating potassium ion channel currents. In the mouse primary visual cortex, we find differences in potassium channel current density amplitude and kinetics in cells representing the major inhibitory transcriptomic cell subclasses - Pvalb, Sst, VIP, and Lamp5. Within a major subclass, we find that some specific transcriptomic types can be distinguished by their electrophysiological properties, while others overlap. Interestingly, properties of the macroscopic potassium current correlate with action potential kinetics and the expression of certain ion channels. This richer dataset drives both perturbation experiments to establish a causal link between ion channel currents and active properties as well as a refined taxonomy that incorporates active ion channel currents to further discriminate between cell types.

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.

Rats exhibit ‘empathy’ making them a model to understand the neural underpinnings of such behavior. We show data consistent with these findings, but also that behavior and dopamine (DA) release reflects subjective rather than objective evaluation of appetitive and aversive events that occur to another. We recorded DA release in two paradigms: one that involved cues predictive of unavoidable shock to the conspecific and another that allowed the rat to refrain from reward when there were harmful consequences to the conspecific. Behavior and DA reflected pro-social interactions in that DA suppression was reduced during cues that predicted shock in the presence of the conspecific and that DA release observed on self-avoidance trials was present when the conspecific was spared. However, DA also increased when the conspecific was shocked instead of the recording rat and DA release during conspecific avoidance trials was lower than when the rat avoided shock for itself.

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

Recent computational models of sign tracking (ST) and goal tracking (GT) have accounted for observations that dopamine (DA) is not necessary for all forms of learning and have provided a set of predictions to further their validity. Among these, a central prediction is that manipulating the intertrial interval (ITI) during autoshaping should change the relative ST-GT proportion as well as DA phasic responses. Here, we tested these predictions and found that lengthening the ITI increased ST, i.e., behavioral engagement with conditioned stimuli (CS) and cue-induced phasic DA release. Importantly, DA release was also present at the time of reward delivery, even after learning, and DA release was correlated with time spent in the food cup during the ITI. During conditioning with shorter ITIs, GT was prominent (i.e., engagement with food cup), and DA release responded to the CS while being absent at the time of reward delivery after learning. Hence, shorter ITIs restored the classical DA reward prediction error (RPE) pattern. These results validate the computational hypotheses, opening new perspectives on the understanding of individual differences in Pavlovian conditioning and DA signaling.

Dopamine (DA) is critical for reward processing, but significantly less is known about its role in punishment avoidance. Using a combined approach-avoidance task, we measured phasic DA release in the nucleus accumbens (NAc) of rats during presentation of cues that predicted reward, punishment or neutral outcomes and investigated individual differences based on avoidance performance. Here we show that DA release within a single microenvironment is higher for reward and avoidance cues compared with neutral cues and positively correlated with poor avoidance behaviour. We found that DA release delineates trial-type during sessions with good avoidance but is non-selective during poor avoidance, with high release correlating with poor performance. These data demonstrate that phasic DA is released during cued approach and avoidance within the same microenvironment and abnormal processing of value signals is correlated with poor performance.