Biology subjects

Ascoli, G. A.

Publications and source records attributed to Ascoli, G. A..

30 records · Page 2Linked to original sources

bioRxiv2021

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Recent advances in neuroscience make the extraction of full neuronal morphology at whole brain dataset available. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of more than one thousand neurons including their dendrites and full axons, and detected million scale putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

neuroscience↗

bioRxiv2020

A multimodal cell census and atlas of the mammalian primary motor cortex

We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

neuroscience↗

bioRxiv2020

Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding

This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) where neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding (ASE) of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation-maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (n ~ 212 - 215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of dimensional embedding and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.

bioinformatics↗

bioRxiv2020

The mouse cortico-tectal projectome

The superior colliculus (SC) is a midbrain structure that receives diverse and robust cortical inputs to drive a range of cognitive and sensorimotor behaviors. However, it remains unclear how descending cortical inputs arising from higher-order associative areas coordinate with SC sensorimotor networks to influence its outputs. In this study, we constructed a comprehensive map of all cortico-tectal projections and identified four collicular zones with differential cortical inputs: medial (SC.m), centromedial (SC.cm), centrolateral (SC.cl) and lateral (SC.l). Computational analyses revealed that cortico-tectal projections are organized as multiple subnetworks that are consistent with previously identified cortico-cortical and cortico-striatal subnetworks. Furthermore, we delineated the brain-wide input/output organization of each collicular zone and described a subset of their constituent neuronal cell types based on distinct connectional and morphological features. Altogether, this work provides a novel structural foundation for the integrative role of the SC in controlling cognition, orientation, and other sensorimotor behaviors.

neuroscience↗

bioRxiv2020

Itinerant complexity in networks of intrinsically bursting neurons

Active neurons can be broadly classified by their intrinsic oscillation patterns into two classes characterized by periodic spiking or periodic bursting. Here we show that networks of identical bursting neurons with inhibitory pulsatory coupling exhibit itinerant dynamics. Using the relative phases of bursts between neurons, we numerically demonstrate that the network exhibits endogenous transitions among multiple modes of transient synchrony. This is true even for bursts consisting of two spikes. In contrast, our simulations reveal that identical singlet-spiking neurons do not exhibit such complexity in the network. These results suggest a role for bursting dynamics in realizing itinerant complexity in neural circuits.

neuroscience↗

bioRxiv2019

Distinct roles of microtubules and actin filaments in defining dendritic architecture

Microtubules and F-actin have long been recognized as key regulators of dendritic morphology. Nevertheless, precisely ascertaining their distinct influences on dendritic trees have been hampered until now by the lack of direct, arbor-wide cytoskeletal quantification. We pair live confocal imaging of fluorescently labeled dendritic arborization (da) neurons in Drosophila larvae with complete multi-signal neural tracing to separately measure microtubules and F-actin. We demonstrate that dendritic arbor length is highly interrelated with local microtubule quantity, whereas local F-actin enrichment is associated with dendritic branching. Computational simulation of arbor structure solely constrained by experimentally observed subcellular distributions of these cytoskeletal components generated synthetic morphological and molecular patterns statistically equivalent to those of real da neurons, corroborating the efficacy of local microtubule and F-actin in describing dendritic architecture. The analysis and modeling outcomes hold true for the simplest (Class I), most complex (Class IV), and genetically altered (Formin3 overexpression) da neuron types. SUPPORT: NIH R01 NS39600 and NS086082 and BICCN U01 MH114829.

neuroscience↗

bioRxiv2019

Connectivity characterization of the mouse basolateral amygdalar complex

The basolateral amygdalar complex (BLA) is implicated in behavioral processing ranging from fear acquisition to addiction. Newer methods like optogenetics have enabled the association of circuit-specific functionality to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. In this work, we applied computational analysis techniques to the results of our circuit-tracing experiments to create a foundational, comprehensive, multiscale connectivity atlas of the mouse BLA. The analyses identified three domains within the classically defined anterior BLA (BLAa) that house target-specific projection neurons with distinguishable cell body and dendritic morphologies. Further, we identify brain-wide targets of projection neurons located in the three BLAa domains as well as in the posterior BLA (BLAp), ventral BLA (BLAv), lateral (LA), and posterior basomedial (BMAp) nuclei. Projection neurons that provide input to each nucleus are also identifed. Functional characterization of some projection-defined BLA neurons were demonstrated via optogenetic and recording experiments. Hypotheses relating function to connection-defined BLA cell types are proposed.

neuroscience↗

bioRxiv2019

Molecular Expression Profiles of Morphologically Defined Hippocampal Neuron Types: Empirical Evidence and Relational Inferences

Gene and protein expressions are key determinants of cellular function. Neurons are the building blocks of brain circuits, yet the relationship between their molecular identity and the spatial distribution of their dendritic inputs and axonal outputs remain incompletely understood. The open-source knowledge base Hippocampome.org amasses such transcriptomic data from the scientific literature for morphologically defined neuron types in the rodent hippocampal formation: dentate gyrus (DG), CA3, CA2, CA1, subiculum (SUB), and entorhinal cortex (EC). Positive, negative, or mixed expression reports were initially obtained from published articles directly connecting molecular evidence to neurons with known axonal and dendritic patterns across hippocampal layers. Here, we supplement this information by collating, formalizing, and leveraging relational expression inferences (REIs) that link a gene or protein expression or lack thereof to that of another molecule or to an anatomical location. With these additional interpretations, we freely release online a comprehensive human- and machine-readable molecular profile for the more than 100 neuron types in Hippocampome.org. Analysis of these data ascertains the ability to distinguish unequivocally most neuron types in each of the major subdivisions of the hippocampus based on currently known biochemical markers. Moreover, grouping neuron types by expression similarity reveals eight super-families characterized by a few defining molecules.\n\nSignificance StatementThe molecular composition of cells underlies their structure, activity, and function. Neurons are arguably the most diverse cell types with their characteristic tree-like shapes mediating synaptic communication throughout the brain. Biochemical marker data are available online for hundreds of morphologically identified neuron types in the mammalian hippocampus, including expression of calcium-binding proteins, receptors, and enzymes. Here, we augment this evidence by systematically applying logical rules empirically derived from the published literature (e.g. \"presence of molecule X implies lack of molecule Y\"). The resulting substantially expanded expression profiles provide nearly unique molecular identities for most known hippocampal neuron types while revealing previously unrecognized genetic similarities across anatomical regions and morphological phenotypes.

neuroscience↗

bioRxiv2019

Simple models of quantitative firing phenotypes in hippocampal neurons: comprehensive coverage of intrinsic diversity

Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with/without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In a previous work, Hippocampome.org, a comprehensive knowledgebase of hippocampal neuron types, systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a comprehensive set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. Furthermore, we observed that adding dendritic compartments to point-neuron models, in general, allowed soma to reproduce features of bursting patterns and abrupt non-linearities in some frequency adapting patterns slightly more accurately. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the dynamical interactions of various types at the mesoscopic level.\n\nAuthor SummaryThe neurons in the hippocampus show enormous diversity in their intrinsic activity patterns. A comprehensive characterization of various intrinsic types using a neuronal modeling system is necessary to simulate biologically realistic networks of brain regions. Morphologically detailed neuronal modeling frameworks often limit the scalability of such network simulations due to the specification of hundreds of rules governing each neurons intrinsic dynamics. In this work, we have accomplished a comprehensive mapping of experimentally identified intrinsic dynamics in a simple modeling system with only two governing rules. We have created over a hundred point-neuron models that reflect the intrinsic differences among the hippocampal neuron types both qualitatively and quantitatively. In addition, we compactly extended our point-neurons to include up to four compartments, which will allow anatomically finer-grained connections among the neurons in a network. Our compact model representations, which are freely available in Hippocampome.org, will allow future researchers to investigate dynamical interactions among various intrinsic types and emergent integrative properties using scalable, yet biologically realistic network simulations.

neuroscience↗

bioRxiv2017

Formin3 regulates dendritic architecture via microtubule stabilization and is required for somatosensory nociceptive behavior

The acquisition, maintenance and modulation of dendritic architecture are critical to neuronal form, plasticity and function. Morphologically, dendritic shape impacts functional connectivity and is largely mediated by organization and dynamics of cytoskeletal fibers that provide the underlying scaffold and tracks for intracellular trafficking. Identifying molecular factors that regulate dendritic cytoskeletal architecture is therefore important in understanding mechanistic links between cytoskeletal organization and neuronal function. In a neurogenomic-driven genetic screen of cytoskeletal regulatory molecules, we identified Formin3 (Form3) as a critical regulator of cytoskeletal architecture in Drosophila nociceptive sensory neurons. Form3 is a member of the conserved Formin family of multi-functional cytoskeletal regulators and time course analyses reveal Form3 is cell-autonomously required for maintenance of complex dendritic arbors. Cytoskeletal imaging demonstrates form3 mutants exhibit a specific destabilization of the dendritic microtubule (MT) cytoskeleton, together with defective dendritic trafficking of mitochondria, satellite Golgi and the TRPA channel Painless. Biochemical studies reveal Form3 directly interacts with MTs via FH1-FH2 domains and promotes MT stabilization via acetylation. Neurologically, mutations in human Inverted Formin 2 (INF2; ortholog of form3) have been causally linked to Charcot-Marie-Tooth (CMT) disease. CMT sensory neuropathies lead to impaired peripheral sensitivity. Defects in form3 function in nociceptive neurons results in a severe impairment in noxious heat evoked behaviors. Expression of the INF2 FH1-FH2 domains rescues form3 defects in MT stabilization and nocifensive behavior revealing conserved functions in regulating the cytoskeleton and sensory behavior thereby providing novel mechanistic insights into potential etiologies of CMT sensory neuropathies.\n\nSignificance StatementMechanisms governing cytoskeletal architecture are critical in regulating neural function as aberrations are linked to a broad spectrum of neurological and neurocognitive disorders. Formins are important cytoskeletal regulators however their mechanistic roles in neuronal architecture are poorly understood. We demonstrate mutations in Drosophila formin3 lead to progressive destabilization of the dendritic microtubule cytoskeleton resulting in severely reduced arborization coupled to impaired organelle and ion channel trafficking, as well as nociceptive sensitivity. INF2 mutations are implicated in CMT sensory neuropathies, and INF2 expression can rescue microtubule and nociceptive behavioral defects in form3 mutants. While CMT sensory neuropathies have been linked to defects in axonal development and myelination, our studies connect dendritic cytoskeletal defects with peripheral insensitivity suggesting possible alternative etiological bases.

neuroscience↗

bioRxiv2017

Genetic Single Neuron Anatomy reveals fine granularity of cortical interneuron subtypes

Parsing diverse nerve cells into biological types is necessary for understanding neural circuit organization. Morphology is an intuitive criterion for neuronal classification and a proxy of connectivity, but morphological diversity and variability often preclude resolving the granularity of discrete cell groups from population continuum. Combining genetic labeling with high-resolution, large volume light microscopy, we established a platform of genetic single neuron anatomy that resolves, registers and quantifies complete neuron morphologies in the mouse brain. We discovered that cortical axo-axonic cells (AACs), a cardinal GABAergic interneuron type that controls pyramidal neuron (PyN) spiking at axon initial segment, consist of multiple subtypes distinguished by laminar position, dendritic and axonal arborization patterns. Whereas the laminar arrangements of AAC dendrites reflect differential recruitment by input streams, the laminar distribution and local geometry of AAC axons enable differential innervation of PyN ensembles. Therefore, interneuron types likely consist of fine-grained subtypes with distinct input-output connectivity patterns.

neuroscience↗

bioRxiv2017

Firing pattern classification and phenotyping in a knowledge base of hippocampal neuron types

Systematically organizing the anatomical, molecular, and physiological properties of cortical neurons is important for understanding their computational functions. Hippocampome.org defines 122 neuron types in the rodent hippocampal formation (dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex) based on their somatic, axonal, and dendritic locations, putative excitatory/inhibitory outputs, molecular marker expression, and biophysical properties such as time constant and input resistance. Here we augment the electrophysiological data of this knowledge base by collecting, quantifying, and analyzing the firing responses to depolarizing current injections for every hippocampal neuron type from available published experiments. We designed and implemented objective protocols to classify firing patterns based on both transient and steady-state activity. Specifically, we identified 5 transients (delay, adapting spiking, rapidly adapting spiking, transient stuttering, and transient slow-wave bursting) and 4 steady states (non-adapting spiking, persistent stuttering, persistent slow-wave bursting, and silence). By characterizing the set of all firing responses reported for hippocampal neurons, this automated classification approach revealed 9 unique families of firing pattern phenotypes while distinguishing potential new neuronal subtypes. Several novel statistical associations also emerged between firing responses and other electrophysiological properties, morphological features, and molecular marker expression. The firing pattern parameters, complete experimental conditions (including solution and stimulus details), digitized spike times, exact reference to the original empirical evidence, and analysis scripts are released open-source through Hippocampome.org for all neuron types, greatly enhancing the existing search and browse capabilities. This information, collated online in human-and machine-accessible form, will help design and interpret both experiments and hippocampal model simulations.\n\nSignificance StatementComprehensive characterization of nerve cells is essential for understanding signal processing in biological neuronal networks. Firing patterns are important identification characteristics of neurons and play crucial roles in information coding in neural systems. Building upon the comprehensive knowledge base Hippocampome.org, we developed and implemented automated protocols to classify all known firing responses exhibited by each neuron type of the rodent hippocampus based on analysis of transient and steady-state activity. This approach identified the most distinguishing elements of every firing phenotype and revealed previously unnoticed statistical associations of firing responses with other electrophysiological, morphological, and molecular properties. The resulting data, freely released online, constitute a powerful resource for designing and interpreting experiments as well as developing and testing hippocampal models.

neuroscience↗