Developmental Rewiring between Cerebellar Climbing Fibers and Purkinje Cells Begins with Positive Feedback Synapse Addition

Non-allometric expansion and enhanced compartmentalization of Purkinje cell dendrites in the human cerebellum

Purkinje cell (PC) dendrites are optimized to integrate the vast cerebellar input array and drive the sole cortical output. PCs are classically seen as stereotypical computational units, yet mouse PCs are morphologically diverse and those with multi-branched structure can receive non-canonical climbing fiber (CF) multi-innervation that confers independent compartment-specific signaling. While otherwise uncharacterized, human PCs are universally multi-branched. Do they exceed allometry to achieve enhanced integrative capacities relative to mouse PCs? To answer this, we used several comparative histology techniques in adult human and mouse to analyze cellular morphology, parallel fiber (PF) and CF input arrangement, and regional PC demographics. Human PCs are substantially larger than previously described; they exceed allometric constraint by cortical thickness and are the largest neuron in the brain with 6-7 cm total dendritic length. Unlike mouse, human PC dendrites ramify horizontally to form a multi-compartment motif that we show can receive multiple CFs. Human spines are denser (6.9 vs 4.9 spines/μm), larger (~0.36 vs 0.29 μm), and include an unreported 'spine cluster' structure-features that may be congruent with enhanced PF association and amplification as human-specific adaptations. By extrapolation, human PCs may receive 500,000 to 1 million synaptic inputs compared with 30-40,000 in mouse. Collectively, human PC morphology and input arrangement is quantitatively and qualitatively distinct from rodent. Multi-branched PCs are more prevalent in posterior and lateral cerebellum, co-varying with functional boundaries, supporting the hypothesis that this morphological motif permits expanded input multiplexing and may subserve task-dependent needs for input association.

Molecular and Anatomical Strengthening of "Winner" Climbing Fiber Synapses in Developing Mouse Purkinje Cells

Neural circuits are refined by strengthening frequently used or advantaged synapses while eliminating redundant connections. In neonatal mice, cerebellar Purkinje cells (PCs) are initially innervated by multiple climbing fibers (CFs) of similar strength. By postnatal day 7 (P7), one CF, the "winner," is selectively strengthened and begins dendritic translocation by P9, while both "winner" and "loser" CFs temporarily maintain somatic synapses. Although the functional differentiation of CF inputs is well understood, their structural differentiation is less clear. In this study, we examined "winner" CF synapses in dendrites and both "winner" and "loser" synapses in the soma using serial electron microscopy and immunohistochemistry in C57BL/6 mice. We found that "winner" CF synapses, both in the soma and dendrites, developed more complex pre- and postsynaptic structures than "loser" CFs, with an expanded area of postsynaptic density. Additionally, "winner" CF synapses expressed significantly higher levels of AMPA-type glutamate receptors. Notably, only dendritic "winner" synapses showed increased levels of Rab3-interacting molecule RIM, a key presynaptic regulator of neurotransmitter release. These findings reveal the molecular and structural features that enable "winner" CFs to reinforce their synaptic strength and innervation, allowing them to outcompete other inputs during early development.

Non-allometric expansion and enhanced compartmentalization of Purkinje cell dendrites in the human cerebellum

Purkinje cell (PC) dendrites are optimized to integrate the vast cerebellar input array and drive the sole cortical output. PCs are classically seen as stereotypical computational units, yet mouse PCs are morphologically diverse and those with multi-branched structure can receive non-canonical climbing fiber (CF) multi-innervation that confers independent compartment-specific signaling. While otherwise uncharacterized, human PCs are universally multi-branched. Do they exceed allometry to achieve enhanced integrative capacities relative to mouse PCs? To answer this, we used several comparative histology techniques in adult human and mouse to analyze cellular morphology, parallel fiber (PF) and CF input arrangement, and regional PC demographics. Human PCs are substantially larger than previously described; they exceed allometric constraint by cortical thickness and are the largest neuron in the brain with 6-7cm total dendritic length. Unlike mouse, human PC dendrites ramify horizontally to form a multi-compartment motif that we show can receive multiple CFs. Human spines are denser (6.9 vs 4.9 spines/μm), larger (~0.36 vs 0.29μm), and include an unreported 'spine cluster' structure-features that may be congruent with enhanced PF association and amplification as human-specific adaptations. By extrapolation, human PCs may receive 500,000 to 1 million synaptic inputs compared with 30-40,000 in mouse. Collectively, human PC morphology and input arrangement is quantitatively and qualitatively distinct from rodent. Multi-branched PCs are more prevalent in posterior and lateral cerebellum, co-varying with functional boundaries, supporting the hypothesis that this morphological motif permits expanded input multiplexing and may subserve task-dependent needs for input association.

Permanent deconstruction of intracellular primary cilia in differentiating granule cell neurons

Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While granule cell cilia are essential during early developmental stages, they become infrequent upon maturation. Here, we provide nanoscopic resolution of cilia in situ using large-scale electron microscopy volumes and immunostaining of mouse cerebella. In many granule cells, we found intracellular cilia, concealed from the external environment. Cilia were disassembled in differentiating granule cell neurons-in a process we call cilia deconstruction-distinct from premitotic cilia resorption in proliferating progenitors. In differentiating granule cells, cilia deconstruction involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Unlike ciliated neurons in other brain regions, our results show the deconstruction of concealed cilia in differentiating granule cells, which might prevent mitogenic hedgehog responsiveness. Ciliary deconstruction could be paradigmatic of cilia removal during differentiation in other tissues.

A neural algorithm for computing bipartite matchings

Finding optimal bipartite matchings-e.g., matching medical students to hospitals for residency, items to buyers in an auction, or papers to reviewers for peer review-is a fundamental combinatorial optimization problem. We found a distributed algorithm for computing matchings by studying the development of the neuromuscular circuit. The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and fibers are densely connected, but after development, each fiber is typically matched (i.e., connected) to exactly one neuron. We cast this synaptic pruning process as a distributed matching (or assignment) algorithm, where motor neurons "compete" with each other to "win" muscle fibers. We show that this algorithm is simple to implement, theoretically sound, and effective in practice when evaluated on real-world bipartite matching problems. Thus, insights from the development of neural circuits can inform the design of algorithms for fundamental computational problems.

Paroxysmal dystonia results from the loss of RIM4 in Purkinje cells

Full-length RIM1 and 2 are key components of the presynaptic active zone that ubiquitously control excitatory and inhibitory neurotransmitter release. Here, we report that the function of the small RIM isoform RIM4, consisting of a single C2 domain, is strikingly different from that of the long isoforms. RIM4 is dispensable for neurotransmitter release but plays a postsynaptic, cell type-specific role in cerebellar Purkinje cells that is essential for normal motor function. In the absence of RIM4, Purkinje cell intrinsic firing is reduced and caffeine-sensitive, and dendritic integration of climbing fibre input is disturbed. Mice lacking RIM4, but not mice lacking RIM1/2, selectively in Purkinje cells exhibit a severe, hours-long paroxysmal dystonia. These episodes can also be induced by caffeine, ethanol or stress and closely resemble the deficits seen with mutations of the PNKD (paroxysmal non-kinesigenic dystonia) gene. Our data reveal essential postsynaptic functions of RIM proteins and show non-overlapping specialized functions of a small isoform despite high homology to a single domain in the full-length proteins.

Molecular and cellular mechanisms of developmental synapse elimination in the cerebellum: Involvement of autism spectrum disorder-related genes

Neural circuits are initially created with excessive synapse formation until around birth and undergo massive reorganization until they mature. During postnatal development, necessary synapses strengthen and remain, whereas unnecessary ones are weakened and eventually eliminated. These events, collectively called "synapse elimination" or "synapse pruning", are thought to be fundamental for creating functionally mature neural circuits in adult animals. In the cerebellum of neonatal rodents, Purkinje cells (PCs) receive synaptic inputs from multiple climbing fibers (CFs). Then, inputs from a single CF are strengthened and those from the other CFs are eliminated, and most PCs become innervated by single CFs by the end of the third postnatal week. These events are regarded as a representative model of synapse elimination. This review examines the molecular and cellular mechanisms of CF synapse elimination in the developing cerebellum and argues how autism spectrum disorder (ASD)-related genes are involved in CF synapse development. We introduce recent studies to update our knowledge, incorporate new data into the known scheme, and discuss the remaining issues and future directions.

Permanent deconstruction of intracellular primary cilia in differentiating granule cell neurons

Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While essential during early developmental stages, the fate of granule cell cilia is unknown. Here, we provide nanoscopic resolution of ciliary dynamics in situ by studying developmental changes in granule cell cilia using large-scale electron microscopy volumes and immunostaining of mouse cerebella. We found that many granule cell primary cilia were intracellular and concealed from the external environment. Cilia were disassembed in differentiating granule cell neurons in a process we call cilia deconstruction that was distinct from pre-mitotic cilia resorption in proliferating progenitors. In differentiating granule cells, ciliary loss involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Cilia did not reform from the docked centrioles, rather, in adult mice granule cell neurons remained unciliated. Many neurons in other brain regions require cilia to regulate function and connectivity. In contrast, our results show that granule cell progenitors had concealed cilia that underwent deconstruction potentially to prevent mitogenic hedgehog responsiveness. The ciliary deconstruction mechanism we describe could be paradigmatic of cilia removal during differentiation in other tissues.

CellWalker: a user-friendly and modular computational pipeline for morphological analysis of microscopy images

SUMMARY

The implementation of computational tools for analysis of microscopy images has been one of the most important technological innovations in biology, providing researchers unmatched capabilities to comprehend cell shape and connectivity. While numerous tools exist for image annotation and segmentation, there is a noticeable gap when it comes to morphometric analysis of microscopy images. Most existing tools often measure features solely on 2D serial images, which can be difficult to extrapolate to 3D. For this reason, we introduce CellWalker, a computational toolbox that runs inside Blender, an open-source computer graphics software. This add-on improves the morphological analysis by seamlessly integrating analysis tools into the Blender workflow, providing visual feedback through a powerful 3D visualization, and leveraging the resources of Blender's community. CellWalker provides several morphometric analysis tools that can be used to calculate distances, volume, surface areas and to determine cross-sectional properties. It also includes tools to build skeletons, calculate distributions of subcellular organelles. In addition, this python-based tool contains 'visible-source' IPython notebooks accessories for segmentation of 2D/3D microscopy images using deep learning and visualization of the segmented images that are required as input to CellWalker. Overall, CellWalker provides practical tools for segmentation and morphological analysis of microscopy images in the form of an open-source and modular pipeline which allows a complete access to fine-tuning of algorithms through visible-source code while still retaining a result-oriented interface.

AVAILABILITY AND IMPLEMENTATION

CellWalker source code is available on GitHub (https://github.com/utraf-pasteur-institute/Cellwalker-blender and https://github.com/utraf-pasteur-institute/Cellwalker-notebooks) under a GPL-3 license.

Correlative light and volume electron microscopy to study brain development

Recent advances in volume electron microscopy (EM) have been driving our thorough understanding of the brain architecture. Volume EM becomes increasingly powerful when cells and their subcellular structures that are imaged in light microscopy are correlated to those in ultramicrographs obtained with EM. This correlative approach, called correlative light and volume electron microscopy (vCLEM), is used to link three-dimensional ultrastructural information with physiological data such as intracellular Ca2+ dynamics. Genetic tools to express fluorescent proteins and/or an engineered form of a soybean ascorbate peroxidase allow us to perform vCLEM using natural landmarks including blood vessels without immunohistochemical staining. This immunostaining-free vCLEM has been successfully employed in two-photon Ca2+ imaging in vivo as well as in studying complex synaptic connections in thalamic neurons that receive a variety of specialized inputs from the cerebral cortex. In this mini-review, we overview how volume EM and vCLEM have contributed to studying the developmental processes of the brain. We also discuss potential applications of genetic manipulation of target cells using clustered regularly interspaced short palindromic repeats-associated protein 9 and subsequent volume EM to the analysis of protein localization as well as to loss-of-function studies of genes regulating brain development. We give examples for the combinatorial usage of genetic tools with vCLEM that will further enhance our understanding of regulatory mechanisms underlying brain development.

Climbing fiber multi-innervation of mouse Purkinje dendrites with arborization common to human

Canonically, each Purkinje cell (PC) in the adult cerebellum receives only one climbing fiber (CF) from the inferior olive. Underlying current theories of cerebellar function is the notion that this highly conserved one-to-one relationship renders Purkinje dendrites into a single computational compartment. However, we discovered that multiple primary dendrites are a near-universal morphological feature in humans. Using tract tracing, immunolabeling, and in vitro electrophysiology, we found that in mice ~25% of mature multibranched cells receive more than one CF input. Two-photon calcium imaging in vivo revealed that separate dendrites can exhibit distinct response properties to sensory stimulation, indicating that some multibranched cells integrate functionally independent CF-receptive fields. These findings indicate that PCs are morphologically and functionally more diverse than previously thought.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous system’s organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/ . With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning

3D electron microscopy (EM) connectomics image volumes are surpassing 1 mm3, providing information-dense, multi-scale visualizations of brain circuitry and necessitating scalable analysis techniques. We present SynapseCLR, a self-supervised contrastive learning method for 3D EM data, and use it to extract features of synapses from mouse visual cortex. SynapseCLR feature representations separate synapses by appearance and functionally important structural annotations. We demonstrate SynapseCLR's utility for valuable downstream tasks, including one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using manual annotation of only 0.2% of the dataset's synapses. In particular, excitatory versus inhibitory neuronal types can be assigned with >99.8% accuracy to individual synapses and highly truncated neurites, enabling neurite-enhanced connectomics analysis. Finally, we present a data-driven, unsupervised study of synaptic structural variation on the representation manifold, revealing its intrinsic axes of variation and showing that representations contain inhibitory subtype information.

3D reconstruction of the cerebellar germinal layer reveals tunneling connections between developing granule cells

The difficulty of retrieving high-resolution, in vivo evidence of the proliferative and migratory processes occurring in neural germinal zones has limited our understanding of neurodevelopmental mechanisms. Here, we used a connectomic approach using a high-resolution, serial-sectioning scanning electron microscopy volume to investigate the laminar cytoarchitecture of the transient external granular layer (EGL) of the developing cerebellum, where granule cells coordinate a series of mitotic and migratory events. By integrating image segmentation, three-dimensional reconstruction, and deep-learning approaches, we found and characterized anatomically complex intercellular connections bridging pairs of cerebellar granule cells throughout the EGL. Connected cells were either mitotic, migratory, or transitioning between these two cell stages, displaying a chronological continuum of proliferative and migratory events never previously observed in vivo at this resolution. This unprecedented ultrastructural characterization poses intriguing hypotheses about intercellular connectivity between developing progenitors and its possible role in the development of the central nervous system.

Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

Automated Synapse Detection Method for Cerebellar Connectomics

The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.

The Brain Observatory Storage Service and Database (BossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery

Technological advances in imaging and data acquisition are leading to the development of petabyte-scale neuroscience image datasets. These large-scale volumetric datasets pose unique challenges since analyses often span the entire volume, requiring a unified platform to access it. In this paper, we describe the Brain Observatory Storage Service and Database (BossDB), a cloud-based solution for storing and accessing petascale image datasets. BossDB provides support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API). A key feature is the scalable indexing of spatial data and automatic and manual annotations to facilitate data discovery. Our project is open source and can be easily and cost effectively used for a variety of modalities and applications, and has effectively worked with datasets over a petabyte in size.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

Interactions Between Purkinje Cells and Granule Cells Coordinate the Development of Functional Cerebellar Circuits

Cerebellar development has a remarkably protracted morphogenetic timeline that is coordinated by multiple cell types. Here, we discuss the intriguing cellular consequences of interactions between inhibitory Purkinje cells and excitatory granule cells during embryonic and postnatal development. Purkinje cells are central to all cerebellar circuits, they are the first cerebellar cortical neurons to be born, and based on their cellular and molecular signaling, they are considered the master regulators of cerebellar development. Although rudimentary Purkinje cell circuits are already present at birth, their connectivity is morphologically and functionally distinct from their mature counterparts. The establishment of the Purkinje cell circuit with its mature firing properties has a temporal dependence on cues provided by granule cells. Granule cells are the latest born, yet most populous, neuronal type in the cerebellar cortex. They provide a combination of mechanical, molecular and activity-based cues that shape the maturation of Purkinje cell structure, connectivity and function. We propose that the wiring of Purkinje cells for function falls into two developmental phases: an initial phase that is guided by intrinsic mechanisms and a later phase that is guided by dynamically-acting cues, some of which are provided by granule cells. In this review, we highlight the mechanisms that granule cells use to help establish the unique properties of Purkinje cell firing.

Distinct functional developments of surviving and eliminated presynaptic terminals

For neuronal circuits in the brain to mature, necessary synapses must be maintained and redundant synapses eliminated through experience-dependent mechanisms. However, the functional differentiation of these synapse types during the refinement process remains elusive. Here, we addressed this issue by distinct labeling and direct recordings of presynaptic terminals fated for survival and for elimination in the somatosensory thalamus. At surviving terminals, the number of total releasable vesicles was first enlarged, and then calcium channels and fast-releasing synaptic vesicles were tightly coupled in an experience-dependent manner. By contrast, transmitter release mechanisms did not mature at terminals fated for elimination, irrespective of sensory experience. Nonetheless, terminals fated for survival and for elimination both exhibited developmental shortening of action potential waveforms that was experience independent. Thus, we dissected experience-dependent and -independent developmental maturation processes of surviving and eliminated presynaptic terminals during neuronal circuit refinement.

Postnatal connectomic development of inhibition in mouse barrel cortex

Brain circuits in the neocortex develop from diverse types of neurons that migrate and form synapses. Here we quantify the circuit patterns of synaptogenesis for inhibitory interneurons in the developing mouse somatosensory cortex. We studied synaptic innervation of cell bodies, apical dendrites, and axon initial segments using three-dimensional electron microscopy focusing on the first 4 weeks postnatally (postnatal days P5 to P28). We found that innervation of apical dendrites occurs early and specifically: Target preference is already almost at adult levels at P5. Axons innervating cell bodies, on the other hand, gradually acquire specificity from P5 to P9, likely via synaptic overabundance followed by antispecific synapse removal. Chandelier axons show first target preference by P14 but develop full target specificity almost completely by P28, which is consistent with a combination of axon outgrowth and off-target synapse removal. This connectomic developmental profile reveals how inhibitory axons in the mouse cortex establish brain circuitry during development.

Synaptic Plasticity Induced by Differential Manipulation of Tonic and Phasic Motoneurons in Drosophila

Structural and functional plasticity induced by neuronal competition is a common feature of developing nervous systems. However, the rules governing how postsynaptic cells differentiate between presynaptic inputs are unclear. In this study, we characterized synaptic interactions following manipulations of tonic Ib or phasic Is glutamatergic motoneurons that coinnervate postsynaptic muscles of male or female Drosophila melanogaster larvae. After identifying drivers for each neuronal subtype, we performed ablation or genetic manipulations to alter neuronal activity and examined the effects on synaptic innervation and function at neuromuscular junctions. Ablation of either Ib or Is resulted in decreased muscle response, with some functional compensation occurring in the Ib input when Is was missing. In contrast, the Is terminal failed to show functional or structural changes following loss of the coinnervating Ib input. Decreasing the activity of the Ib or Is neuron with tetanus toxin light chain resulted in structural changes in muscle innervation. Decreased Ib activity resulted in reduced active zone (AZ) number and decreased postsynaptic subsynaptic reticulum volume, with the emergence of filopodial-like protrusions from synaptic boutons of the Ib input. Decreased Is activity did not induce structural changes at its own synapses, but the coinnervating Ib motoneuron increased the number of synaptic boutons and AZs it formed. These findings indicate that tonic Ib and phasic Is motoneurons respond independently to changes in activity, with either functional or structural alterations in the Ib neuron occurring following ablation or reduced activity of the coinnervating Is input, respectively.SIGNIFICANCE STATEMENT Both invertebrate and vertebrate nervous systems display synaptic plasticity in response to behavioral experiences, indicating that underlying mechanisms emerged early in evolution. How specific neuronal classes innervating the same postsynaptic target display distinct types of plasticity is unclear. Here, we examined whether Drosophila tonic Ib and phasic Is motoneurons display competitive or cooperative interactions during innervation of the same muscle, or compensatory changes when the output of one motoneuron is altered. We established a system to differentially manipulate the motoneurons and examined the effects of cell type-specific changes to one of the inputs. Our findings indicate Ib and Is motoneurons respond differently to activity mismatch or loss of the coinnervating input, with the Ib subclass responding robustly compared with Is motoneurons.