Molecular and cellular mechanisms of teneurin signaling in synaptic partner matching.

In developing brains, axons exhibit remarkable precision in selecting synaptic partners among many non-partner cells. Evolutionarily conserved teneurins are transmembrane proteins that instruct synaptic partner matching. However, how intracellular signaling pathways execute teneurins' functions is unclear. Here, we use in situ proximity labeling to obtain the intracellular interactome of a teneurin (Ten-m) in the Drosophila brain. Genetic interaction studies using quantitative partner matching assays in both olfactory receptor neurons (ORNs) and projection neurons (PNs) reveal a common pathway: Ten-m binds to and negatively regulates a RhoGAP, thus activating the Rac1 small GTPases to promote synaptic partner matching. Developmental analyses with single-axon resolution identify the cellular mechanism of synaptic partner matching: Ten-m signaling promotes local F-actin levels and stabilizes ORN axon branches that contact partner PN dendrites. Combining spatial proteomics and high-resolution phenotypic analyses, this study advanced our understanding of both cellular and molecular mechanisms of synaptic partner matching.

Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks.

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+ activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing.

Understanding neural circuits requires deciphering interactions among myriad cell types defined by spatial organization, connectivity, gene expression, and other properties. Resolving these cell types requires both single-neuron resolution and high throughput, a challenging combination with conventional methods. Here, we introduce barcoded anatomy resolved by sequencing (BARseq), a multiplexed method based on RNA barcoding for mapping projections of thousands of spatially resolved neurons in a single brain and relating those projections to other properties such as gene or Cre expression. Mapping the projections to 11 areas of 3,579 neurons in mouse auditory cortex using BARseq confirmed the laminar organization of the three top classes (intratelencephalic [IT], pyramidal tract-like [PT-like], and corticothalamic [CT]) of projection neurons. In depth analysis uncovered a projection type restricted almost exclusively to transcriptionally defined subtypes of IT neurons. By bridging anatomical and transcriptomic approaches at cellular resolution with high throughput, BARseq can potentially uncover the organizing principles underlying the structure and formation of neural circuits.

Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain.

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

Visual Projection Neurons Mediating Directed Courtship in Drosophila.

Many animals rely on vision to detect, locate, and track moving objects. In Drosophila courtship, males primarily use visual cues to orient toward and follow females and to select the ipsilateral wing for courtship song. Here, we show that the LC10 visual projection neurons convey essential visual information during courtship. Males with LC10 neurons silenced are unable to orient toward or maintain proximity to the female and do not predominantly use the ipsilateral wing when singing. LC10 neurons preferentially respond to small moving objects using an antagonistic motion-based center-surround mechanism. Unilateral activation of LC10 neurons recapitulates the orienting and ipsilateral wing extension normally elicited by females, and the potency with which LC10 induces wing extension is enhanced in a state of courtship arousal controlled by male-specific P1 neurons. These data suggest that LC10 is a major pathway relaying visual input to the courtship circuits in the male brain.

Classifying Drosophila Olfactory Projection Neuron Subtypes by Single-Cell RNA Sequencing.

The definition of neuronal type and how it relates to the transcriptome are open questions. Drosophila olfactory projection neurons (PNs) are among the best-characterized neuronal types: different PN classes target dendrites to distinct olfactory glomeruli, while PNs of the same class exhibit indistinguishable anatomical and physiological properties. Using single-cell RNA sequencing, we comprehensively characterized the transcriptomes of most PN classes and unequivocally mapped transcriptomes to specific olfactory function for six classes. Transcriptomes of closely related PN classes exhibit the largest differences during circuit assembly but become indistinguishable in adults, suggesting that neuronal subtype diversity peaks during development. Transcription factors and cell-surface molecules are the most differentially expressed genes between classes and are highly informative in encoding cell identity, enabling us to identify a new lineage-specific transcription factor that instructs PN dendrite targeting. These findings establish that neuronal transcriptomic identity corresponds with anatomical and physiological identity defined by connectivity and function.

Cryogenic electron microscopy and single-particle analysis.

About 20 years ago, the first three-dimensional (3D) reconstructions at subnanometer (<10-Å) resolution of an icosahedral virus assembly were obtained by cryogenic electron microscopy (cryo-EM) and single-particle analysis. Since then, thousands of structures have been determined to resolutions ranging from 30 Å to near atomic (<4 Å). Almost overnight, the recent development of direct electron detectors and the attendant improvement in analysis software have advanced the technology considerably. Near-atomic-resolution reconstructions can now be obtained, not only for megadalton macromolecular complexes or highly symmetrical assemblies but also for proteins of only a few hundred kilodaltons. We discuss the developments that led to this breakthrough in high-resolution structure determination by cryo-EM and point to challenges that lie ahead.

Generating neuronal diversity in the mammalian cerebral cortex.

The neocortex is the part of the brain responsible for execution of higher-order brain functions, including cognition, sensory perception, and sophisticated motor control. During evolution, the neocortex has developed an unparalleled neuronal diversity, which still remains partly unclassified and unmapped at the functional level. Here, we broadly review the structural blueprint of the neocortex and discuss the current classification of its neuronal diversity. We then cover the principles and mechanisms that build neuronal diversity during cortical development and consider the impact of neuronal class-specific identity in shaping cortical connectivity and function.

Pan-cancer ion transport signature reveals functional regulators of glioblastoma aggression.

Ion channels, transporters, and other ion-flux controlling proteins, collectively comprising the "ion permeome", are common drug targets, however, their roles in cancer remain understudied. Our integrative pan-cancer transcriptome analysis shows that genes encoding the ion permeome are significantly more often highly expressed in specific subsets of cancer samples, compared to pan-transcriptome expectations. To enable target selection, we identified 410 survival-associated IP genes in 33 cancer types using a machine-learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and are associated with poor prognosis in glioblastoma, the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived glioblastoma cells induces transcriptome-wide changes involving neuron projection and proliferation pathways, impairs cell viability and tumor sphere formation in vitro, perturbs tunneling nanotube dynamics, and extends the survival of glioblastoma-bearing mice. Thus, aberrant activation of genes encoding ion transport proteins appears as a pan-cancer feature defining tumor heterogeneity, which can be exploited for mechanistic insights and therapy development.

Deep sequencing of Phox2a nuclei reveals five classes of anterolateral system neurons.

The anterolateral system (ALS) is a major ascending pathway from the spinal cord that projects to multiple brain areas and underlies the perception of pain, itch, and skin temperature. Despite its importance, our understanding of this system has been hampered by the considerable functional and molecular diversity of its constituent cells. Here, we use fluorescence-activated cell sorting to isolate ALS neurons belonging to the Phox2a-lineage for single-nucleus RNA sequencing. We reveal five distinct clusters of ALS neurons (ALS1-5) and document their laminar distribution in the spinal cord using in situ hybridization. We identify three clusters of neurons located predominantly in laminae I-III of the dorsal horn (ALS1-3) and two clusters with cell bodies located in deeper laminae (ALS4 and ALS5). Our findings reveal the transcriptional logic that underlies ALS neuronal diversity in the adult mouse and uncover the molecular identity of two previously identified classes of projection neurons. We also show that these molecular signatures can be used to target groups of ALS neurons using retrograde viral tracing. Overall, our findings provide a valuable resource for studying somatosensory biology and targeting subclasses of ALS neurons.

Learning critically drives parkinsonian motor deficits through imbalanced striatal pathway recruitment.

Dopamine (DA) loss in Parkinson's disease (PD) causes debilitating motor deficits. However, dopamine is also widely linked to reward prediction and learning, and the contribution of dopamine-dependent learning to movements that are impaired in PD-which often do not lead to explicit rewards-is unclear. Here, we used two distinct motor tasks to dissociate dopamine's acute motoric effects vs. its long-lasting, learning-mediated effects. In dopamine-depleted mice, motor task performance gradually worsened with task exposure. Task experience was critical, as mice that remained in the home cage during the same period were relatively unimpaired when subsequently probed on the task. Repeated dopamine replacement treatments acutely rescued deficits and gradually induced long-term rescue that persisted despite treatment withdrawal. Surprisingly, both long-term rescue and parkinsonian performance decline were task specific, implicating dopamine-dependent learning. D1R activation potently induced acute rescue that gradually consolidated into long-term rescue. Conversely, reduced D2R activation potently induced parkinsonian decline. In dopamine-depleted mice, either D1R activation or D2R activation prevented parkinsonian decline, and both restored balanced activation of direct vs. indirect striatal pathways. These findings suggest that reinforcement and maintenance of movements-even movements not leading to explicit rewards-are fundamental functions of dopamine and provide potential mechanisms for the hitherto unexplained "long-duration response" by dopaminergic therapies in PD.

The roles of surround inhibition for the intrinsic function of the striatum, analyzed in silico.

The basal ganglia are important for action initiation, selection, and motor learning. The input level, the striatum, receives input preferentially from the cortex and thalamus and is to 95% composed of striatal projection neurons (SPNs) with sparse GABAergic collaterals targeting distal dendrites of neighboring SPNs, in a distance-dependent manner. The remaining 5% are GABAergic and cholinergic interneurons. Our aim here is to investigate the role of surround inhibition for the intrinsic function of the striatum. Large-scale striatal networks of 20 to 40 thousand neurons were simulated with detailed multicompartmental models of different cell types, corresponding to the size of a module of the dorsolateral striatum, like the forelimb area (mouse). The effect of surround inhibition on dendritic computation and network activity was investigated, while groups of SPNs were activated. The SPN-induced surround inhibition in distal dendrites shunted effectively the corticostriatal EPSPs. The size of dendritic plateau-like potentials within the specific dendritic segment was both reduced and enhanced by inhibition, due to the hyperpolarized membrane potential of SPNs and the reversal-potential of GABA. On a population level, the competition between two subpopulations of SPNs was found to depend on the distance between the two units, the size of each unit, the activity level in each subgroup and the dopaminergic modulation of the dSPNs and iSPNs. The SPNs provided the dominating source of inhibition within the striatum, while the fast-spiking interneuron mainly had an initial effect due to short-term synaptic plasticity as shown in with ablation of the synaptic interaction.

Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms.

The number of noisy images required for molecular reconstruction in single-particle cryoelectron microscopy (cryo-EM) is governed by the autocorrelations of the observed, randomly oriented, noisy projection images. In this work, we consider the effect of imposing sparsity priors on the molecule. We use techniques from signal processing, optimization, and applied algebraic geometry to obtain theoretical and computational contributions for this challenging nonlinear inverse problem with sparsity constraints. We prove that molecular structures modeled as sums of Gaussians are uniquely determined by the second-order autocorrelation of their projection images, implying that the sample complexity is proportional to the square of the variance of the noise. This theory improves upon the nonsparse case, where the third-order autocorrelation is required for uniformly oriented particle images and the sample complexity scales with the cube of the noise variance. Furthermore, we build a computational framework to reconstruct molecular structures which are sparse in the wavelet basis. This method combines the sparse representation for the molecule with projection-based techniques used for phase retrieval in X-ray crystallography.

Effective Regulation of Auditory Processing by Parvalbumin Interneurons in the Tail of the Striatum.

Parvalbumin (PV) interneurons in the auditory cortex (AC) play a crucial role in shaping auditory processing, including receptive field formation, temporal precision enhancement, and gain regulation. PV interneurons are also the primary inhibitory neurons in the tail of the striatum (TS), which is one of the major descending brain regions in the auditory nervous system. However, the specific roles of TS-PV interneurons in auditory processing remain elusive. In this study, morphological and slice recording experiments in both male and female mice revealed that TS-PV interneurons, compared with AC-PV interneurons, were present in fewer numbers but exhibited longer projection distances, which enabled them to provide sufficient inhibitory inputs to spiny projection neurons (SPNs). Furthermore, TS-PV interneurons received dense auditory input from both the AC and medial geniculate body (MGB), particularly from the MGB, which rendered their auditory responses comparable to those of AC-PV interneurons. Optogenetic manipulation experiments demonstrated that TS-PV interneurons were capable of bidirectionally regulating the auditory responses of SPNs. Our findings suggest that PV interneurons can effectively modulate auditory processing in the TS and may play a critical role in auditory-related behaviors.

DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning.

The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify 3D stack content, and to rapidly and robustly predict binary masks containing the target content, e.g. tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as a fully documented Python package.

SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction.

Synergistic drug combinations can improve the therapeutic effect and reduce the drug dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen synergistic drug combinations. However, the in vitro method is time-consuming and expensive. With the rapid growth of high-throughput data, computational methods are becoming efficient tools to predict potential synergistic drug combinations. Considering the limitations of the previous computational methods, we developed a new model named Siamese Network and Random Matrix Projection for AntiCancer Drug Combination prediction (SNRMPACDC). Firstly, the Siamese convolutional network and random matrix projection were used to process the features of the two drugs into drug combination features. Then, the features of the cancer cell line were processed through the convolutional network. Finally, the processed features were integrated and input into the multi-layer perceptron network to get the predicted score. Compared with the traditional method of splicing drug features into drug combination features, SNRMPACDC improved the interpretability of drug combination features to a certain extent. In addition, the introduction of convolutional networks can better extract the potential information in the features. SNRMPACDC achieved the root mean-squared error of 15.01 and the Pearson correlation coefficient of 0.75 in 5-fold cross-validation of regression prediction for response data. In addition, SNRMPACDC achieved the AUC of 0.91 ± 0.03 and the AUPR of 0.62 ± 0.05 in 5-fold cross-validation of classification prediction of synergistic or not. These results are almost better than all the previous models. SNRMPACDC would be an effective approach to infer potential anticancer synergistic drug combinations.

Roles of odorant receptors during olfactory glomerular map formation.

The organization of the olfactory glomerular map involves the convergence of olfactory sensory neurons (OSNs) expressing the same odorant receptor (OR) into glomeruli in the olfactory bulb (OB). A remarkable feature of the olfactory glomerular map formation is that the identity of OR instructs the topography of the bulb, resulting in thousands of discrete glomeruli in mice. Several lines of evidence indicate that ORs control the expression levels of various kinds of transmembrane proteins to form glomeruli at appropriate regions of the OB. In this review, we will discuss how the OR identity is decoded by OSNs into gene expression through intracellular regulatory mechanisms.

Cbln1 Directs Axon Targeting by Corticospinal Neurons Specifically toward Thoraco-Lumbar Spinal Cord.

Corticospinal neurons (CSN) are centrally required for skilled voluntary movement, which necessitates that they establish precise subcerebral connectivity with the brainstem and spinal cord. However, molecular controls regulating specificity of this projection targeting remain largely unknown. We previously identified that developing CSN subpopulations exhibit striking axon targeting specificity in the spinal white matter. These CSN subpopulations with segmentally distinct spinal projections are also molecularly distinct; a subset of differentially expressed genes between these distinct CSN subpopulations regulate differential axon projection targeting. Rostrolateral CSN extend axons exclusively to bulbar-cervical segments (CSNBC-lat), while caudomedial CSN (CSNmedial) are more heterogeneous, with distinct, intermingled subpopulations extending axons to either bulbar-cervical or thoraco-lumbar segments. Here, we report, in male and female mice, that Cerebellin 1 (Cbln1) is expressed specifically by CSN in medial, but not lateral, sensorimotor cortex. Cbln1 shows highly dynamic temporal expression, with Cbln1 levels in CSN highest during the period of peak axon extension toward thoraco-lumbar segments. Using gain-of-function experiments, we identify that Cbln1 is sufficient to direct thoraco-lumbar axon extension by CSN. Misexpression of Cbln1 in CSNBC-lat either by in utero electroporation, or by postmitotic AAV-mediated gene delivery, redirects these axons past their normal bulbar-cervical targets toward thoracic segments. Further, Cbln1 overexpression in postmitotic CSNBC-lat increases the number of CSNmedial axons that extend past cervical segments into the thoracic cord. Collectively, these results identify that Cbln1 functions as a potent molecular control over thoraco-lumbar CSN axon extension, part of an integrated network of controls over segmentally-specific CSN axon projection targeting.SIGNIFICANCE STATEMENT Corticospinal neurons (CSN) exhibit remarkable diversity and precision of axonal projections to targets in the brainstem and distinct spinal segments; the molecular basis for this targeting diversity is largely unknown. CSN subpopulations projecting to distinct targets are also molecularly distinguishable. Distinct subpopulations degenerate in specific motor neuron diseases, further suggesting that intrinsic molecular differences might underlie differential vulnerability to disease. Here, we identify a novel molecular control, Cbln1, expressed by CSN extending axons to thoraco-lumbar spinal segments. Cbln1 is sufficient, but not required, for CSN axon extension toward distal spinal segments, and Cbln1 expression is controlled by recently identified, CSN-intrinsic regulators of axon extension. Our results identify that Cbln1, together with other regulators, coordinates segmentally precise CSN axon targeting.

Multivariate pattern analysis: a method and software to reveal, quantify, and visualize predictive association patterns in multicollinear data.

BACKGROUND: Strongly multicollinear covariates, such as those typically represented in metabolomics applications, represent a challenge for multivariate regression analysis. These challenges are commonly circumvented by reducing the number of covariates to a subset of linearly independent variables, but this strategy may lead to loss of resolution and thus produce models with poorer interpretative potential. The aim of this work was to implement and illustrate a method, multivariate pattern analysis (MVPA), which can handle multivariate covariates without compromising resolution or model quality. RESULTS: MVPA has been implemented in an open-source R package of the same name, mvpa. To facilitate the usage and interpretation of complex association patterns, mvpa has also been integrated into an R shiny app, mvpaShiny, which can be accessed on www.mvpashiny.org . MVPA utilizes a general projection algorithm that embraces a diversity of possible models. The method handles multicollinear and even linear dependent covariates. MVPA separates the variance in the data into orthogonal parts within the frame of a single joint model: one part describing the relations between covariates, outcome, and explanatory variables and another part describing the "net" predictive association pattern between outcome and explanatory variables. These patterns are visualized and interpreted in variance plots and plots for pattern analysis and ranking according to variable importance. Adjustment for a linear dependent covariate is performed in three steps. First, partial least squares regression with repeated Monte Carlo resampling is used to determine the number of predictive PLS components for a model relating the covariate to the outcome. Second, postprocessing of this PLS model by target projection provided a single component expressing the predictive association pattern between the outcome and the covariate. Third, the outcome and the explanatory variables were adjusted for the covariate by using the target score in the projection algorithm to obtain "net" data. We illustrate the main features of MVPA by investigating the partial mediation of a linearly dependent metabolomics descriptor on the association pattern between a measure of insulin resistance and lifestyle-related factors. CONCLUSIONS: Our method and implementation in R extend the range of possible analyses and visualizations that can be performed for complex multivariate data structures. The R packages are available on github.com/liningtonlab/mvpa and github.com/liningtonlab/mvpaShiny.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.