INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function.
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DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images
Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet.
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- Award ID(s):
- 1707999
- NSF-PAR ID:
- 10097226
- Date Published:
- Journal Name:
- PLOS computational biology
- Volume:
- 15
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1007012
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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INTRODUCTION The analysis of the human brain is a central goal of neuroscience, but for methodological reasons, research has focused on model organisms, the mouse in particular. Because substantial homology was found at the level of ion channels, transcriptional programs, and basic neuronal types, a strong similarity of neuronal circuits across species has also been assumed. However, a rigorous test of the configuration of local neuronal circuitry in mouse versus human—in particular, in the gray matter of the cerebral cortex—is missing. The about 1000-fold increase in number of neurons is the most obvious evolutionary change of neuronal network properties from mouse to human. Whether the structure of the local cortical circuitry has changed as well is, however, unclear. Recent data from transcriptomic analyses has indicated an increase in the proportion of inhibitory interneurons from mouse to human. But what the effect of such a change is on the circuit configurations found in the human cerebral cortex is not known. This is, however, of particular interest also to the study of neuropsychiatric disorders because in these, the alteration of inhibitory-to-excitatory synaptic balance has been identified as one possible mechanistic underpinning. RATIONALE We used recent methodological improvements in connectomics to acquire data from one macaque and two human individuals, using biopsies of the temporal, parietal, and frontal cortex. Human tissue was obtained from neurosurgical interventions related to tumor removal, in which access path tissue was harvested that was not primarily affected by the underlying disease. A key concern in the analysis of human patient tissue has been the relation to epilepsy surgery, when the underlying disease has required often year-long treatment with pharmaceuticals, plausibly altering synaptic connectivity. Therefore, the analysis of nonepileptic surgery tissue seemed of particular importance. We also included data from one macaque individual, who was not known to have any brain-related pathology. RESULTS We acquired three-dimensional electron microscopy data from temporal and frontal cortex of human and temporal and parietal cortex of macaque. From these, we obtained connectomic reconstructions and compared these with five connectomes from mouse cortex. On the basis of these data, we were able to determine the effect of the about 2.5-fold expansion of the interneuron pool in macaque and human cortex compared with that of mouse. Contrary to expectation, the inhibitory-to-excitatory synaptic balance on pyramidal neurons in macaque and human cortex was not substantially altered. Rather, the interneuron pool was selectively expanded for bipolar-type interneurons, which prefer the innervation of other interneurons, and which further increased their preference for interneuron innervation from mouse to human. These changes were each multifold, yielding in effect an about 10-fold expanded interneuron-to-interneuron network in the human cortex that is only sparsely present in mouse. The total amount of synaptic input to pyramidal neurons, however, did not change according to the threefold thickening of the cortex; rather, a modest increase from about 12,000 synaptic inputs in mouse to about 15,000 in human was found. CONCLUSION The principal cells of the cerebral cortex, pyramidal neurons, maintain almost constant inhibitory-to-excitatory input balance and total synaptic input across 100 million years of evolutionary divergence, which is particularly noteworthy with the concomitant 1000-fold expansion of the neuronal network size and the 2.5-fold increase of inhibitory interneurons from mouse to human. Rather, the key network change from mouse to human is an expansion of almost an order of magnitude of an interneuron-to-interneuron network that is virtually absent in mouse but constitutes a substantial part of the human cortical network. Whether this new network is primarily created through the expansion of existing neuronal types, or is related to the creation of new interneuron subtypes, requires further study. The discovery of this network component in human cortex encourages detailed analysis of its function in health and disease. Connectomic screening across mammalian species: Comparison of five mouse, two macaque, and two human connectomic datasets from the cerebral cortex. ( A ) Automated reconstructions of all neurons with their cell bodies in the volume shown, using random colors. The analyzed connectomes comprised a total of ~1.6 million synapses. Arrows indicate evolutionary divergence: the last common ancestor between human and mouse, approximately 100 million years ago, and the last common ancestor between human and macaque, about 20 million years ago. ( B ) Illustration of the about 10-fold expansion of the interneuron-to-interneuron network from mouse to human.more » « less
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Abstract Motivation Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.
Results We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.
Availability and implementation Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.
Supplementary information Supplementary data are available at Bioinformatics online.
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INTRODUCTION Neurons are by far the most diverse of all cell types in animals, to the extent that “cell types” in mammalian brains are still mostly heterogeneous groups, and there is no consensus definition of the term. The Drosophila optic lobes, with approximately 200 well-defined cell types, provides a tractable system with which to address the genetic basis of neuronal type diversity. We previously characterized the distinct developmental gene expression program of each of these types using single-cell RNA sequencing (scRNA-seq), with one-to-one correspondence to the known morphological types. RATIONALE The identity of fly neurons is determined by temporal and spatial patterning mechanisms in stem cell progenitors, but it remained unclear how these cell fate decisions are implemented and maintained in postmitotic neurons. It was proposed in Caenorhabditis elegans that unique combinations of terminal selector transcription factors (TFs) that are continuously expressed in each neuron control nearly all of its type-specific gene expression. This model implies that it should be possible to engineer predictable and complete switches of identity between different neurons just by modifying these sustained TFs. We aimed to test this prediction in the Drosophila visual system. RESULTS Here, we used our developmental scRNA-seq atlases to identify the potential terminal selector genes in all optic lobe neurons. We found unique combinations of, on average, 10 differentially expressed and stably maintained (across all stages of development) TFs in each neuron. Through genetic gain- and loss-of-function experiments in postmitotic neurons, we showed that modifications of these selector codes are sufficient to induce predictable switches of identity between various cell types. Combinations of terminal selectors jointly control both developmental (e.g., morphology) and functional (e.g., neurotransmitters and their receptors) features of neurons. The closely related Transmedullary 1 (Tm1), Tm2, Tm4, and Tm6 neurons (see the figure) share a similar code of terminal selectors, but can be distinguished from each other by three TFs that are continuously and specifically expressed in one of these cell types: Drgx in Tm1, Pdm3 in Tm2, and SoxN in Tm6. We showed that the removal of each of these selectors in these cell types reprograms them to the default Tm4 fate. We validated these conversions using both morphological features and molecular markers. In addition, we performed scRNA-seq to show that ectopic expression of pdm3 in Tm4 and Tm6 neurons converts them to neurons with transcriptomes that are nearly indistinguishable from that of wild-type Tm2 neurons. We also show that Drgx expression in Tm1 neurons is regulated by Klumpfuss, a TF expressed in stem cells that instructs this fate in progenitors, establishing a link between the regulatory programs that specify neuronal fates and those that implement them. We identified an intronic enhancer in the Drgx locus whose chromatin is specifically accessible in Tm1 neurons and in which Klu motifs are enriched. Genomic deletion of this region knocked down Drgx expression specifically in Tm1 neurons, leaving it intact in the other cell types that normally express it. We further validated this concept by demonstrating that ectopic expression of Vsx (visual system homeobox) genes in Mi15 neurons not only converts them morphologically to Dm2 neurons, but also leads to the loss of their aminergic identity. Our results suggest that selector combinations can be further sculpted by receptor tyrosine kinase signaling after neurogenesis, providing a potential mechanism for postmitotic plasticity of neuronal fates. Finally, we combined our transcriptomic datasets with previously generated chromatin accessibility datasets to understand the mechanisms that control brain wiring downstream of terminal selectors. We built predictive computational models of gene regulatory networks using the Inferelator framework. Experimental validations of these networks revealed how selectors interact with ecdysone-responsive TFs to activate a large and specific repertoire of cell surface proteins and other effectors in each neuron at the onset of synapse formation. We showed that these network models can be used to identify downstream effectors that mediate specific cellular decisions during circuit formation. For instance, reduced levels of cut expression in Tm2 neurons, because of its negative regulation by pdm3 , controls the synaptic layer targeting of their axons. Knockdown of cut in Tm1 neurons is sufficient to redirect their axons to the Tm2 layer in the lobula neuropil without affecting other morphological features. CONCLUSION Our results support a model in which neuronal type identity is primarily determined by a relatively simple code of continuously expressed terminal selector TFs in each cell type throughout development. Our results provide a unified framework of how specific fates are initiated and maintained in postmitotic neurons and open new avenues to understanding synaptic specificity through gene regulatory networks. The conservation of this regulatory logic in both C. elegans and Drosophila makes it likely that the terminal selector concept will also be useful in understanding and manipulating the neuronal diversity of mammalian brains. Terminal selectors enable predictive cell fate reprogramming. Tm1, Tm2, Tm4, and Tm6 neurons of the Drosophila visual system share a core set of TFs continuously expressed by each cell type (simplified). The default Tm4 fate is overridden by the expression of a single additional terminal selector to generate Tm1 ( Drgx ), Tm2 ( pdm3 ), or Tm6 ( SoxN ) fates.more » « less
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AMPA receptors (AMPARs) mediate the majority of fast excitatory transmission in the brain. Regulation of AMPAR levels at synapses controls synaptic strength and underlies information storage and processing. Many proteins interact with the intracellular domain of AMPARs to regulate their trafficking and synaptic clustering. However, a growing number of extracellular factors important for glutamatergic synapse development, maturation and function have emerged that can also regulate synaptic AMPAR levels. This mini-review highlights extracellular protein factors that regulate AMPAR trafficking to control synapse development and plasticity. Some of these factors regulate AMPAR clustering and mobility by interacting with the extracellular N-terminal domain of AMPARs whereas others regulate AMPAR trafficking indirectly via their respective signaling receptors. While several of these factors are secreted from neurons, others are released from non-neuronal cells such as glia and muscle. Although it is apparent that secreted factors can act locally on neurons near their sites of release to coordinate individual synapses, it is less clear if they can diffuse over longer ranges to coordinate related synapses within a circuit or region of the brain. Given that there are hundreds of factors that can be secreted from neuronal and non-neuronal cells, it will not be surprising if more extracellular factors that modulate AMPARs and glutamatergic synapses are discovered. Many open questions remain including where and when the factors are expressed, what regulates their secretion from different cell types, what controls their diffusion, stability, and range of action, and how their cognate receptors influence intracellular signaling to control AMPAR trafficking.
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