Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features

Bayesian network classifiers for categorizing cortical gABAergic interneurons

Abstract An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1–F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts’ assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell’s label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1–F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52 % accuracy, and single out the number of branches at 180 µm from the soma, the convex hull 2D area, and axonal features F1–F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons

Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior in Mice

The advent and recent advances of single-cell RNA sequencing (scRNA-seq) have yielded transformative insights into our understanding of cellular diversity in the central nervous system (CNS) with unprecedented detail. However, due to current experimental and computational limitations on defining transcriptomic cell types (T-types) and the multiple phenotypic features of cell types in the CNS, an integrative and multimodal approach should be required for the comprehensive classification of cell types. To this end, performing multimodal analysis of scRNA-seq in hypothalamus would be very beneficial in that hypothalamus, controlling homeostatic and innate survival behaviors which known to be highly conserved across a wide range of species and encoded in hard-wired brain circuits, is likely to display the more straightforward relationship between transcriptomic identity, axonal projections, and behavioral activation, respectively. In my dissertation, I have been focused on the cell type characterizations of a hypothalamic node controlling innate social behavior in mice, the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl). VMHvl only contains ~4,000 neurons per hemisphere in mice but due to its behavioral, anatomical, and molecular heterogeneity, which T-types in VMHvl are related to connectivity and behavioral function is largely unknown. In Chapter II, I described my main thesis work to perform scRNA-seq in VMHvl using two independent platforms: SMART-seq2 (~4,500 neurons sequenced) and 10x (~78,000 neurons sequenced). Specifically, 17 joint VMHvl T-types including several sexually dimorphic clusters were identified by canonical correlation analysis (CCA) in Seurat, and the majority of them were validated by multiplexed single-molecule FISH (seqFISH). Correspondence between transcriptomic identity, and axonal projections or behavioral activation, respectively, was also investigated. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity. In Chapter III, I will discuss about future directions for a deeper and better understanding of VMHvl cell types. Briefly, my previous data from whole-cell patch clamp recording in VMHvl slices suggested that there were at least 4 distinct electrophysiological cell types (E-types). Additionally, two distinct neuromodulatory effects on VMHvl were observed (persistently activated by vasopressin/oxytocin vs. silenced by nitric oxide) by monitoring populational activities using two-photon Ca2+ imaging in slices. Based on the results from the first part and combined with advanced molecular techniques (e.g. Patch-seq and CRISPR-Cas9), we can further dissect out the cellular diversity in VMHvl and their functional implications.</p

Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior

The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms—SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)—and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity

Statistical analysis of neuronal data: Development of quantitative frameworks and application to microelectrode array analysis and cell type classification

With increasing amounts of data being collected in various fields of neuroscience, there is a growing need for robust techniques for the analysis of this information. This thesis focuses on the evaluation and development of quantitative frameworks for the analysis and classification of neuronal data from a variety of contexts. Firstly, I investigate methods for analysing spontaneous neuronal network activity recorded on microelectrode arrays (MEAs). I perform an unbiased evaluation of the existing techniques for detecting ‘bursts’ of neuronal activity in these types of recordings, and provide recommendations for the robust analysis of bursting activity in a range of contexts using both existing and adapted burst detection methods. These techniques are then used to analyse bursting activity in novel recordings of human induced pluripotent stem cell-derived neuronal networks. Results from this review of burst analysis methods are then used to inform the development of a framework for characterising the activity of neuronal networks recorded on MEAs, using properties of bursting as well as other common features of spontaneous activity. Using this framework, I examine the ontogeny of spontaneous network activity in in vitro neuronal networks from various brain regions, recorded on both single and multi-well MEAs. I also develop a framework for classifying these recordings according to their network type, based on quantitative features of their activity patterns. Next, I take a multi-view approach to classifying neuronal cell types using both the morphological and electrophysiological features of cells. I show that a number of multi-view clustering algorithms can more reliably differentiate between neuronal cell types in two existing data sets, compared to single-view clustering techniques applied to either the morphological or electrophysiological ‘view’ of the data, or a concatenation of the two views. To close, I examine the properties of the cell types identified by these methods.Supported by a Wellcome Trust PhD Studentship and a National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre Studentshi

Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.https://elifesciences.org/articles/67490Published versio

Toward a comprehensive account of orientation selectivity in the retina.

Retinal Ganglion Cells (RGCs) form functionally distinct signaling channels that selectively encode features of the visual input including direction of motion, contrast polarity, size, and color. A highly conserved visual channel amongst vertebrates conveys orientation selectivity, i.e., the selective firing of neuronal cells in response to elongated stimuli along a preferred orientation. Orientation selectivity is an apparent critical computation and several studies have reported aspects of it, including cell type identity in anatomical reconstructions, and functional characterization of at least four different identified RGC types. But how cell types in the different studies relate is not well resolved; the mechanisms that generate the orientation selective responses in mice remain incompletely understood; and the retinofugal projections of OS RGC types are unknown. The goal of this study was to comprehensively characterize Orientation Selective (OS) RGC types in the mouse retina, and to elucidate the mechanisms that contribute to their tuning properties. We used population calcium imaging and hierarchical clustering to identify orientation selective RCGs in retinal explants. We then targeted these cells for detailed morphological and electrophysiological study. Our survey of RGC populations and subsequent morphological analysis distinguished 10 morphological types with apparent OS tuning. Electrophysiological analysis of 5 types identified specific tuning mechanisms, including a type with tuned excitation and inhibition, and a type with just tuned inhibition. Retrograde tracing from dLGN indicates that OS cells project to the shell region of the dorsal Lateral Geniculate Nucleus (dLGN), indicating that at least some OS RGC types contribute to dLGN OS tuning. This work provides new insight into the morphology and function of RGC types that exhibit OS properties. Additional studies will be necessary to further solidify the full complement of OS types in the retina and resolve their detailed circuit-level mechanisms, synaptic partners, molecular profiles, and retinofugal projections