Automated dendritic spine detection using convolutional neural networks on maximum intensity projected microscopic volumes

NEURD offers automated proofreading and feature extraction for connectomics

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

DeepD3, an open framework for automated quantification of dendritic spines

Dendritic spines are the seat of most excitatory synapses in the brain, and a cellular structure considered central to learning, memory, and activity-dependent plasticity. The quantification of dendritic spines from light microscopy data is usually performed by humans in a painstaking and error-prone process. We found that human-to-human variability is substantial (inter-rater reliability 82.2±6.4%), raising concerns about the reproducibility of experiments and the validity of using human-annotated 'ground truth' as an evaluation method for computational approaches of spine identification. To address this, we present DeepD3, an open deep learning-based framework to robustly quantify dendritic spines in microscopy data in a fully automated fashion. DeepD3's neural networks have been trained on data from different sources and experimental conditions, annotated and segmented by multiple experts and they offer precise quantification of dendrites and dendritic spines. Importantly, these networks were validated in a number of datasets on varying acquisition modalities, species, anatomical locations and fluorescent indicators. The entire DeepD3 open framework, including the fully segmented training data, a benchmark that multiple experts have annotated, and the DeepD3 model zoo is fully available, addressing the lack of openly available datasets of dendritic spines while offering a ready-to-use, flexible, transparent, and reproducible spine quantification method.

Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging

Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to simultaneously image large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for 3D spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data acquired by two-photon imaging in vivo. The core of our pipeline is a deep convolutional neural network that was pretrained on a general-purpose image library and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labeled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection reaching a performance slightly below that of the human experts. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.

Current Best Practices for Analysis of Dendritic Spine Morphology and Number in Neurodevelopmental Disorder Research

Quantitative methods for assessing neural anatomy have rapidly evolved in neuroscience and provide important insights into brain health and function. However, as new techniques develop, it is not always clear when and how each may be used to answer specific scientific questions posed. Dendritic spines, which are often indicative of synapse formation and neural plasticity, have been implicated across many brain regions in neurodevelopmental disorders as a marker for neural changes reflecting neural dysfunction or alterations. In this Perspective we highlight several techniques for staining, imaging, and quantifying dendritic spines as well as provide a framework for avoiding potential issues related to pseudoreplication. This framework illustrates how others may apply the most rigorous approaches. We consider the cost-benefit analysis of the varied techniques, recognizing that the most sophisticated equipment may not always be necessary for answering some research questions. Together, we hope this piece will help researchers determine the best strategy toward using the ever-growing number of techniques available to determine neural changes underlying dendritic spine morphology in health and neurodevelopmental disorders.

3dSpAn: An interactive software for 3D segmentation and analysis of dendritic spines

Three-dimensional segmentation and analysis of dendritic spine morphology involve two major challenges: 1) how to segment individual spines from the dendrites and 2) how to quantitatively assess the morphology of individual spines. To address these two issues, we developed software called 3dSpAn (3-dimensional Spine Analysis), based on implementing a previously published method, 3D multi-scale opening algorithm in shared intensity space. 3dSpAn consists of four modules: a) Preprocessing and Region of Interest (ROI) selection, b) Intensity thresholding and seed selection, c) Multi-scale segmentation, and d) Quantitative morphological feature extraction. In this article, we present the results of segmentation and morphological analysis for different observation methods and conditions, including in vitro and ex vivo imaging with confocal microscopy, and in vivo observations using high-resolution two-photon microscopy. In particular, we focus on software usage, the influence of adjustable parameters on the obtained results, user reproducibility, accuracy analysis, and also include a qualitative comparison with a commercial benchmark. 3dSpAn software is freely available for non-commercial use at www.3dSpAn.org .

Enhancing motor learning by increasing the stability of newly formed dendritic spines in the motor cortex

Dendritic spine dynamics are thought to be substrates for motor learning and memory, and altered spine dynamics often lead to impaired performance. Here, we describe an exception to this rule by studying mice lacking paired immunoglobulin receptor B (PirB^-/-). Pyramidal neuron dendrites in PirB^-/- mice have increased spine formation rates and density. Surprisingly, PirB^-/- mice learn a skilled reaching task faster than wild-type (WT) littermates. Furthermore, stabilization of learning-induced spines is elevated in PirB^-/- mice. Mechanistically, single-spine uncaging experiments suggest that PirB is required for NMDA receptor (NMDAR)-dependent spine shrinkage. The degree of survival of newly formed spines correlates with performance, suggesting that increased spine stability is advantageous for learning. Acute inhibition of PirB function in M1 of adult WT mice increases the survival of learning-induced spines and enhances motor learning. These results demonstrate that there are limits on motor learning that can be lifted by manipulating PirB, even in adulthood.

Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.

The serotonin reuptake blocker citalopram destabilizes fictive locomotor activity in salamander axial circuits through 5-HT1A receptors

Serotoninergic (5-HT) neurons are powerful modulators of spinal locomotor circuits. Most studies on 5-HT modulation focused on the effect of exogenous 5-HT and these studies provided key information about the cellular mechanisms involved. Less is known about the effects of increased release of endogenous 5-HT with selective serotonin reuptake inhibitors. In mammals, such molecules were shown to destabilize the fictive locomotor output of spinal limb networks through 5-HT_1A receptors. However, in tetrapods little is known about the effects of increased 5-HT release on the locomotor output of axial networks, which are coordinated with limb circuits during locomotion from basal vertebrates to mammals. Here, we examined the effect of citalopram on fictive locomotion generated in axial segments of isolated spinal cords in salamanders, a tetrapod where raphe 5-HT reticulospinal neurons and intraspinal 5-HT neurons are present as in other vertebrates. Using electrophysiological recordings of ventral roots, we show that fictive locomotion generated by bath-applied glutamatergic agonists is destabilized by citalopram. Citalopram-induced destabilization was prevented by a 5-HT_1A receptor antagonist, whereas a 5-HT_1A receptor agonist destabilized fictive locomotion. Using immunofluorescence experiments, we found 5-HT-positive fibers and varicosities in proximity with motoneurons and glutamatergic interneurons that are likely involved in rhythmogenesis. Our results show that increasing 5-HT release has a deleterious effect on axial locomotor activity through 5-HT_1A receptors. This is consistent with studies in limb networks of turtle and mouse, suggesting that this part of the complex 5-HT modulation of spinal locomotor circuits is common to limb and axial networks in limbed vertebrates.NEW & NOTEWORTHY Little is known about the modulation exerted by endogenous serotonin on axial locomotor circuits in tetrapods. Using axial ventral root recordings in salamanders, we found that a serotonin reuptake blocker destabilized fictive locomotor activity through 5-HT_1A receptors. Our anatomical results suggest that serotonin is released on motoneurons and glutamatergic interneurons possibly involved in rhythmogenesis. Our study suggests that common serotoninergic mechanisms modulate axial motor circuits in amphibians and limb motor circuits in reptiles and mammals.