Classification of GABAergic interneurons by leading neuroscientists

Thyroid Hormone Transporters MCT8 and OATP1C1 Are Expressed in Pyramidal Neurons and Interneurons in the Adult Motor Cortex of Human and Macaque Brain

Monocarboxylate transporter 8 (MCT8) and organic anion transporter polypeptide 1C1 (OATP1C1) are thyroid hormone (TH) transmembrane transporters that play an important role in the availability of TH for neural cells, allowing their proper development and function. It is important to define which cortical cellular subpopulations express those transporters to explain why MCT8 and OATP1C1 deficiency in humans leads to dramatic alterations in the motor system. By means of immunohistochemistry and double/multiple labeling immunofluorescence in adult human and monkey motor cortices, we demonstrate the presence of both transporters in long-projection pyramidal neurons and in several types of short-projection GABAergic interneurons in both species, suggesting a critical position of these transporters for modulating the efferent motor system. MCT8 is present at the neurovascular unit, but OATP1C1 is only present in some of the large vessels. Both transporters are expressed in astrocytes. OATP1C1 was unexpectedly found, only in the human motor cortex, inside the Corpora amylacea complexes, aggregates linked to substance evacuation towards the subpial system. On the basis of our findings, we propose an etiopathogenic model that emphasizes these transporters' role in controlling excitatory/inhibitory motor cortex circuits in order to understand some of the severe motor disturbances observed in TH transporter deficiency syndromes.

microRNA-dependent regulation of gene expression in GABAergic interneurons

Information processing within neuronal circuits relies on their proper development and a balanced interplay between principal and local inhibitory interneurons within those circuits. Gamma-aminobutyric acid (GABA)ergic inhibitory interneurons are a remarkably heterogeneous population, comprising subclasses based on their morphological, electrophysiological, and molecular features, with differential connectivity and activity patterns. microRNA (miRNA)-dependent post-transcriptional control of gene expression represents an important regulatory mechanism for neuronal development and plasticity. miRNAs are a large group of small non-coding RNAs (21-24 nucleotides) acting as negative regulators of mRNA translation and stability. However, while miRNA-dependent gene regulation in principal neurons has been described heretofore in several studies, an understanding of the role of miRNAs in inhibitory interneurons is only beginning to emerge. Recent research demonstrated that miRNAs are differentially expressed in interneuron subclasses, are vitally important for migration, maturation, and survival of interneurons during embryonic development and are crucial for cognitive function and memory formation. In this review, we discuss recent progress in understanding miRNA-dependent regulation of gene expression in interneuron development and function. We aim to shed light onto mechanisms by which miRNAs in GABAergic interneurons contribute to sculpting neuronal circuits, and how their dysregulation may underlie the emergence of numerous neurodevelopmental and neuropsychiatric disorders.

Using 2D and 3D pluripotent stem cell models to study neurotropic viruses

Understanding the impact of viral pathogens on the human central nervous system (CNS) has been challenging due to the lack of viable human CNS models for controlled experiments to determine the causal factors underlying pathogenesis. Human embryonic stem cells (ESCs) and, more recently, cellular reprogramming of adult somatic cells to generate human induced pluripotent stem cells (iPSCs) provide opportunities for directed differentiation to neural cells that can be used to evaluate the impact of known and emerging viruses on neural cell types. Pluripotent stem cells (PSCs) can be induced to neural lineages in either two- (2D) or three-dimensional (3D) cultures, each bearing distinct advantages and limitations for modeling viral pathogenesis and evaluating effective therapeutics. Here we review the current state of technology in stem cell-based modeling of the CNS and how these models can be used to determine viral tropism and identify cellular phenotypes to investigate virus-host interactions and facilitate drug screening. We focus on several viruses (e.g., human immunodeficiency virus (HIV), herpes simplex virus (HSV), Zika virus (ZIKV), human cytomegalovirus (HCMV), SARS-CoV-2, West Nile virus (WNV)) to illustrate key advantages, as well as challenges, of PSC-based models. We also discuss how human PSC-based models can be used to evaluate the safety and efficacy of therapeutic drugs by generating data that are complementary to existing preclinical models. Ultimately, these efforts could facilitate the movement towards personalized medicine and provide patients and physicians with an additional source of information to consider when evaluating available treatment strategies.

The Role of Inhibitory Interneurons in Circuit Assembly and Refinement Across Sensory Cortices

Sensory information is transduced into electrical signals in the periphery by specialized sensory organs, which relay this information to the thalamus and subsequently to cortical primary sensory areas. In the cortex, microcircuits constituted by interconnected pyramidal cells and inhibitory interneurons, distributed throughout the cortical column, form the basic processing units of sensory information underlying sensation. In the mouse, these circuits mature shortly after birth. In the first postnatal week cortical activity is characterized by highly synchronized spontaneous activity. While by the second postnatal week, spontaneous activity desynchronizes and sensory influx increases drastically upon eye opening, as well as with the onset of hearing and active whisking. This influx of sensory stimuli is fundamental for the maturation of functional properties and connectivity in neurons allocated to sensory cortices. In the subsequent developmental period, spanning the first five postnatal weeks, sensory circuits are malleable in response to sensory stimulation in the so-called critical periods. During these critical periods, which vary in timing and duration across sensory areas, perturbations in sensory experience can alter cortical connectivity, leading to long-lasting modifications in sensory processing. The recent advent of intersectional genetics, in vivo calcium imaging and single cell transcriptomics has aided the identification of circuit components in emergent networks. Multiple studies in recent years have sought a better understanding of how genetically-defined neuronal subtypes regulate circuit plasticity and maturation during development. In this review, we discuss the current literature focused on postnatal development and critical periods in the primary auditory (A1), visual (V1), and somatosensory (S1) cortices. We compare the developmental trajectory among the three sensory areas with a particular emphasis on interneuron function and the role of inhibitory circuits in cortical development and function.

Multi-scale light microscopy/electron microscopy neuronal imaging from brain to synapse with a tissue clearing method, ScaleSF

The mammalian brain is organized over sizes that span several orders of magnitude, from synapses to the entire brain. Thus, a technique to visualize neural circuits across multiple spatial scales (multi-scale neuronal imaging) is vital for deciphering brain-wide connectivity. Here, we developed this technique by coupling successive light microscopy/electron microscopy (LM/EM) imaging with a glutaraldehyde-resistant tissue clearing method, ScaleSF. Our multi-scale neuronal imaging incorporates (1) brain-wide macroscopic observation, (2) mesoscopic circuit mapping, (3) microscopic subcellular imaging, and (4) EM imaging of nanoscopic structures, allowing seamless integration of structural information from the brain to synapses. We applied this technique to three neural circuits of two different species, mouse striatofugal, mouse callosal, and marmoset corticostriatal projection systems, and succeeded in simultaneous interrogation of their circuit structure and synaptic connectivity in a targeted way. Our multi-scale neuronal imaging will significantly advance the understanding of brain-wide connectivity by expanding the scales of objects.

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Successive light microscopy/electron microscopy in optically cleared tissues

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Multi-scale neuronal imaging from the entire brain to individual synapses

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Simultaneous interrogation of neural circuit structure and synaptic connectivity

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Zooming-in to scarce synaptic contacts with the successive imaging

Optimal responsiveness and information flow in networks of heterogeneous neurons

Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.

Deep Learning-Based Classification of GAD67-Positive Neurons Without the Immunosignal

Excitatory neurons and GABAergic interneurons constitute neural circuits and play important roles in information processing. In certain brain regions, such as the neocortex and the hippocampus, there are fewer interneurons than excitatory neurons. Interneurons have been quantified via immunohistochemistry, for example, for GAD67, an isoform of glutamic acid decarboxylase. Additionally, the expression level of other proteins varies among cell types. For example, NeuN, a commonly used marker protein for postmitotic neurons, is expressed differently across brain regions and cell classes. Thus, we asked whether GAD67-immunopositive neurons can be detected using the immunofluorescence signals of NeuN and the fluorescence signals of Nissl substances. To address this question, we stained neurons in layers 2/3 of the primary somatosensory cortex (S1) and the primary motor cortex (M1) of mice and manually labeled the neurons as either cell type using GAD67 immunosignals. We then sought to detect GAD67-positive neurons without GAD67 immunosignals using a custom-made deep learning-based algorithm. Using this deep learning-based model, we succeeded in the binary classification of the neurons using Nissl and NeuN signals without referring to the GAD67 signals. Furthermore, we confirmed that our deep learning-based method surpassed classic machine-learning methods in terms of binary classification performance. Combined with the visualization of the hidden layer of our deep learning algorithm, our model provides a new platform for identifying unbiased criteria for cell-type classification.

Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks

Pyramidal neurons are the most common neurons in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. We compared human temporal cortex and mouse visual cortex pyramidal neurons from the Allen Cell Types Database in terms of their electrophysiology and dendritic morphology. We found that, among other differences, human pyramidal neurons had a higher action potential threshold voltage, a lower input resistance, and larger dendritic arbors. We learned Gaussian Bayesian networks from the data in order to identify correlations and conditional independencies between the variables and compare them between the species. We found strong correlations between electrophysiological and morphological variables in both species. In human cells, electrophysiological variables were correlated even with morphological variables that are not directly related to dendritic arbor size or diameter, such as mean bifurcation angle and mean branch tortuosity. Cortical depth was correlated with both electrophysiological and morphological variables in both species, and its effect on electrophysiology could not be explained in terms of the morphological variables. For some variables, the effect of cortical depth was opposite in the two species. Overall, the correlations among the variables differed strikingly between human and mouse neurons. Besides identifying correlations and conditional independencies, the learned Bayesian networks might be useful for probabilistic reasoning regarding the morphology and electrophysiology of pyramidal neurons.