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Human cortical interneurons have been challenging to study due to high diversity and lack of mature brain tissue platforms and genetic targeting tools. We employed rapid GABAergic neuron viral labeling plus unbiased Patch-seq sampling in brain slices to define the signature morpho-electric properties of GABAergic neurons in the human neocortex. Viral targeting greatly facilitated sampling of the SST subclass, including primate specialized double bouquet cells which mapped to two SST transcriptomic types. Multimodal analysis uncovered an SST neuron type with properties inconsistent with original subclass assignment; we instead propose reclassification into PVALB subclass. Our findings provide novel insights about functional properties of human cortical GABAergic neuron subclasses and types and highlight the essential role of multimodal annotation for refinement of emerging transcriptomic cell type taxonomies.One Sentence SummaryViral genetic labeling of GABAergic neurons in humanex vivobrain slices paired with Patch-seq recording yields an in-depth functional annotation of human cortical interneuron subclasses and types and highlights the essential role of multimodal functional annotation for refinement of emerging transcriptomic cell type taxonomies.

Neocortical layer 1 (L1) is a site of convergence between pyramidal neuron dendrites and feedback axons where local inhibitory signaling can profoundly shape cortical processing. Evolutionary expansion of human neocortex is marked by distinctive pyramidal neuron types with extensive branching in L1, but whether L1 interneurons are similarly diverse is underexplored. Using patch-seq recordings from human neurosurgically resected tissues, we identified four transcriptomically defined subclasses, unique subtypes within those subclasses and additional types with no mouse L1 homologue. Compared with mouse, human subclasses were more strongly distinct from each other across all modalities. Accompanied by higher neuron density and more variable cell sizes compared with mouse, these findings suggest L1 is an evolutionary hotspot, reflecting the increasing demands of regulating the expanding human neocortical circuit.One Sentence SummaryUsing transcriptomics and morpho-electric analyses, we describe innovations in human neocortical layer 1 interneurons.

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.

The Patch-seq approach is a powerful variation of the standard patch clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at a scale and quality that can be integrated with high-throughput dissociated cell transcriptomic data, we have optimized the technique by identifying and refining key factors that contribute to the efficient collection of high-quality data. To rapidly generate high-quality electrophysiology data, we developed patch clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized a substantial improvement in transcriptomic data quality when the nucleus was extracted following the recording. For morphology success, the importance of maximizing the neuron’s membrane integrity during the extraction of the nucleus was much more critical to success than varying the duration of the electrophysiology recording. We compiled the lab protocol with the analysis and acquisition software athttps://github.com/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate Patch-seq data across diverse mammalian species and that is compatible with recent large-scale publicly available Allen Institute Patch-seq datasets.

The neocortex is disproportionately expanded in human compared to mouse, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers that selectively make connections within the cortex and other telencephalic structures. Single-cell transcriptomic analyses of human and mouse cortex show an increased diversity of glutamatergic neuron types in supragranular cortex in human and pronounced gradients as a function of cortical depth. To probe the functional and anatomical correlates of this transcriptomic diversity, we describe a robust Patch-seq platform using neurosurgically-resected human tissues. We characterize the morphological and physiological properties of five transcriptomically defined human glutamatergic supragranular neuron types. Three of these types have properties that are specialized compared to the more homogeneous properties of transcriptomically defined homologous mouse neuron types. The two remaining supragranular neuron types, located exclusively in deep layer 3, do not have clear mouse homologues in supragranular cortex but are transcriptionally most similar to deep layer mouse intratelencephalic-projecting neuron types. Furthermore, we reveal the transcriptomic types in deep layer 3 that express high levels of non-phosphorylated heavy chain neurofilament protein that label long-range neurons known to be selectively depleted in Alzheimer’s disease. Together, these results demonstrate the power of transcriptomic cell type classification, provide a mechanistic underpinning for increased complexity of cortical function in human cortical evolution, and implicate discrete transcriptomic cell types as selectively vulnerable in disease.

Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t-types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.HighlightsPatch-seq data obtained from >3,700 GABAergic cortical interneuronsComprehensive characterization of morpho-electric features of transcriptomic types20 interneuron met-types that have congruent properties across data modalitiesDifferent Sst met-types preferentially innervate different cortical layers

Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t- types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.

Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.

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 profile morpho-electric properties of mammalian neurons systematically, 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 data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines.

Aging produces a decline in the amplitude and precision of 24 h behavioral, endocrine, and metabolic rhythms, which are regulated in mammals by a central circadian pacemaker within the suprachiasmatic nucleus (SCN) and local oscillators in peripheral tissues. Disruption of the circadian system, as experienced during transmeridian travel, can lead to adverse health consequences, particularly in the elderly. To test the hypothesis that age-related changes in the response to simulated jet lag will reflect altered circadian function, we examined re-entrainment of central and peripheral oscillators from young and old PER2::luciferase mice. As in previous studies, locomotor activity rhythms in older mice required more days to re-entrain following a shift than younger mice. At the tissue level, effects of age on baseline entrainment were evident, with older mice displaying earlier phases for the majority of peripheral oscillators studied and later phases for cells within most SCN subregions. Following a 6 h advance of the light:dark cycle, old mice displayed slower rates of re-entrainment for peripheral tissues but a larger, more rapid SCN response compared to younger mice. Thus, aging alters the circadian timing system in a manner that differentially affects the re-entrainment responses of central and peripheral circadian clocks. This pattern of results suggests that a major consequence of aging is a decrease in pacemaker amplitude, which would slow re-entrainment of peripheral oscillators and reduce SCN resistance to external perturbation.

In the retina, melatonin is secreted at night by rod/cone photoreceptors and serves as a dark-adaptive signal. Melatonin receptors have been found in many retinal neurons including melanopsin-containing intrinsically photosensitive retinal ganglion cells (ipRGCs), suggesting it could modulate the physiology of these inner retinal photoreceptors. Here, we investigated whether melatonin modulates the alpha-like M4-type ipRGCs, which are believed to mediate image-forming vision as well as non-image-forming photoresponses. Applying melatonin during daytime (when endogenous melatonin secretion is low) caused whole-cell-recorded M4 cells’ rod/cone-driven depolarizing photoresponses to become broader and larger, whereas the associated elevation in spike rate was reduced. Melanopsin-based light responses were not affected significantly. Nighttime application of the melatonin receptor antagonist luzindole also altered M4 cells’ rod/cone-driven light responses but in the opposite ways: the duration and amplitude of the graded depolarization were reduced, whereas the accompanying spiking increase was enhanced. These luzindole-induced changes confirmed that M4 cells are modulated by endogenous melatonin. Melatonin could induce the above effects by acting directly on M4 cells because immunohistochemistry detected MT1 receptors in these cells, although it could also act presynaptically. Interestingly, the daytime and nighttime recordings showed significant differences in resting membrane potential, spontaneous spike rate and rod/cone-driven light responses, suggesting that M4 cells are under circadian control. This is the first report of a circadian variation in ipRGCs’ resting properties and synaptic input, and of melatoninergic modulation of ipRGCs.

In the mammalian retina, some ganglion cells express the photopigment melanopsin and function as photoreceptors. Five morphological types of these intrinsically photosensitive retinal ganglion cells (ipRGCs), M1–M5, have been identified in mice. Whereas M1 specializes in non-image-forming visual functions and drives such behaviors as the pupillary light reflex and circadian photoentrainment, the other types appear to contribute to image-forming as well as non-image-forming vision. Recent work has begun to reveal physiological diversity among some of the ipRGC types, including differences in photosensitivity, firing rate, and membrane resistance. To gain further insights into these neurons' functional differences, we conducted a comprehensive survey of the electrophysiological properties of all five morphological types. Compared with the other types, M1 had the highest membrane resistance, longest membrane time constant, lowest spike frequencies, widest action potentials, most positive spike thresholds, smallest hyperpolarization-activated inwardly-rectifying current-induced “sagging” responses to hyperpolarizing currents, and the largest effects of voltage-gated K+ currents on membrane potentials. M4 and M5 were at the other end of the spectrum for most of these measures, while M2 and M3 tended to be in the middle of this spectrum. Additionally, M1 and M2 cells generated more diverse voltage-gated Ca2+ currents than M3–M5. In conclusion, M1 cells are significantly different from all other ipRGCs in most respects, possibly reflecting the unique physiological requirements of non-image-forming vision. Furthermore, the non-M1 ipRGCs are electrophysiologically heterogeneous, implicating these cells' diverse functional roles in both non-image-forming vision and pattern vision.