Morphological change tracking of dendritic spines based on structural features

The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

Histological evidence suggests that the estrous cycle exerts a powerful effect on CA1 neurons in mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice over multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with heightened density during periods of greater estradiol (proestrus). These morphological changes were accompanied by greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed enhanced place field stability during proestrus, evident at the single-cell and population level. These results establish the estrous cycle as a driver of large-scale structural and functional plasticity in hippocampal circuits essential for learning and memory.

Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling

Understanding the intracellular dynamics of brain cells entails performing three-dimensional molecular simulations incorporating ultrastructural models that can capture cellular membrane geometries at nanometer scales. While there is an abundance of neuronal morphologies available online, e.g. from NeuroMorpho.Org, converting those fairly abstract point-and-diameter representations into geometrically realistic and simulation-ready, i.e. watertight, manifolds is challenging. Many neuronal mesh reconstruction methods have been proposed; however, their resulting meshes are either biologically unplausible or non-watertight. We present an effective and unconditionally robust method capable of generating geometrically realistic and watertight surface manifolds of spiny cortical neurons from their morphological descriptions. The robustness of our method is assessed based on a mixed dataset of cortical neurons with a wide variety of morphological classes. The implementation is seamlessly extended and applied to synthetic astrocytic morphologies that are also plausibly biological in detail. Resulting meshes are ultimately used to create volumetric meshes with tetrahedral domains to perform scalable in silico reaction-diffusion simulations for revealing cellular structure-function relationships. Availability and implementation: Our method is implemented in NeuroMorphoVis, a neuroscience-specific open source Blender add-on, making it freely accessible for neuroscience researchers.

Impaired synaptic function and hyperexcitability of the pyramidal neurons in the prefrontal cortex of autism-associated Shank3 mutant dogs

BACKGROUND

SHANK3 gene is a highly replicated causative gene for autism spectrum disorder and has been well characterized in multiple Shank3 mutant rodent models. When compared to rodents, domestic dogs are excellent animal models in which to study social cognition as they closely interact with humans and exhibit similar social behaviors. Using CRISPR/Cas9 editing, we recently generated a dog model carrying Shank3 mutations, which displayed a spectrum of autism-like behaviors, such as social impairment and heightened anxiety. However, the neural mechanism underlying these abnormal behaviors remains to be identified.

METHODS

We used Shank3 mutant dog models to examine possible relationships between Shank3 mutations and neuronal dysfunction. We studied electrophysiological properties and the synaptic transmission of pyramidal neurons from acute brain slices of the prefrontal cortex (PFC). We also examined dendrite elaboration and dendritic spine morphology in the PFC using biocytin staining and Golgi staining. We analyzed the postsynaptic density using electron microscopy.

RESULTS

We established a protocol for the electrophysiological recording of canine brain slices and revealed that excitatory synaptic transmission onto PFC layer 2/3 pyramidal neurons in Shank3 heterozygote dogs was impaired, and this was accompanied by reduced dendrite complexity and spine density when compared to wild-type dogs. Postsynaptic density structures were also impaired in Shank3 mutants; however, pyramidal neurons exhibited hyperexcitability.

LIMITATIONS

Causal links between impaired PFC pyramidal neuron function and behavioral alterations remain unclear. Further experiments such as manipulating PFC neuronal activity or restoring synaptic transmission in Shank3 mutant dogs are required to assess PFC roles in altered social behaviors.

CONCLUSIONS

Our study demonstrated the feasibility of using canine brain slices as a model system to study neuronal circuitry and disease. Shank3 haploinsufficiency causes morphological and functional abnormalities in PFC pyramidal neurons, supporting the notion that Shank3 mutant dogs are new and valid animal models for autism research.

DXplorer: A Unified Visualization Framework for Interactive Dendritic Spine Analysis Using 3D Morphological Features

Dendritic spines are dynamic, submicron-scale protrusions on neuronal dendrites that receive neuronal inputs. Morphological changes in the dendritic spine often reflect alterations in physiological conditions and are indicators of various neuropsychiatric conditions. However, owing to the highly dynamic and heterogeneous nature of spines, accurate measurement and objective analysis of spine morphology are major challenges in neuroscience research. Most conventional approaches for analyzing dendritic spines are based on two-dimensional (2D) images, which barely reflect the actual three-dimensional (3D) shapes. Although some recent studies have attempted to analyze spines with various 3D-based features, it is still difficult to objectively categorize and analyze spines based on 3D morphology. Here, we propose a unified visualization framework for an interactive 3D dendritic spine analysis system, DXplorer, that displays 3D rendering of spines and plots the high-dimensional features extracted from the 3D mesh of spines. With this system, users can perform the clustering of spines interactively and explore and analyze dendritic spines based on high-dimensional features. We propose a series of high-dimensional morphological features extracted from a 3D mesh of dendritic spines. In addition, an interactive machine learning classifier with visual exploration and user feedback using an interactive 3D mesh grid view ensures a more precise classification based on the spine phenotype. A user study and two case studies were conducted to quantitatively verify the performance and usability of the DXplorer. We demonstrate that the system performs the entire analytic process effectively and provides high-quality, accurate, and objective analysis.

Long-term transverse imaging of the hippocampus with glass microperiscopes

The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.

A Deep Learning-Based Workflow for Dendritic Spine Segmentation

The morphological analysis of dendritic spines is an important challenge for the neuroscientific community. Most state-of-the-art techniques rely on user-supervised algorithms to segment the spine surface, especially those designed for light microscopy images. Therefore, processing large dendritic branches is costly and time-consuming. Although deep learning (DL) models have become one of the most commonly used tools in image segmentation, they have not yet been successfully applied to this problem. In this article, we study the feasibility of using DL models to automatize spine segmentation from confocal microscopy images. Supervised learning is the most frequently used method for training DL models. This approach requires large data sets of high-quality segmented images (ground truth). As mentioned above, the segmentation of microscopy images is time-consuming and, therefore, in most cases, neuroanatomists only reconstruct relevant branches of the stack. Additionally, some parts of the dendritic shaft and spines are not segmented due to dyeing problems. In the context of this research, we tested the most successful architectures in the DL biomedical segmentation field. To build the ground truth, we used a large and high-quality data set, according to standards in the field. Nevertheless, this data set is not sufficient to train convolutional neural networks for accurate reconstructions. Therefore, we implemented an automatic preprocessing step and several training strategies to deal with the problems mentioned above. As shown by our results, our system produces a high-quality segmentation in most cases. Finally, we integrated several postprocessing user-supervised algorithms in a graphical user interface application to correct any possible artifacts.

Recent advances in computational methods for measurement of dendritic spines imaged by light microscopy

Dendritic spines are small protrusions that receive most of the excitatory inputs to the pyramidal neurons in the neocortex and the hippocampus. Excitatory neural circuits in the neocortex and hippocampus are important for experience-dependent changes in brain functions, including postnatal sensory refinement and memory formation. Several lines of evidence indicate that synaptic efficacy is correlated with spine size and structure. Hence, precise and accurate measurement of spine morphology is important for evaluation of neural circuit function and plasticity. Recent advances in light microscopy and image analysis techniques have opened the way toward a full description of spine nanostructure. In addition, large datasets of spine nanostructure can be effectively analyzed using machine learning techniques and other mathematical approaches, and recent advances in super-resolution imaging allow researchers to analyze spine structure at an unprecedented level of precision. This review summarizes computational methods that can effectively identify, segment and quantitate dendritic spines in either 2D or 3D imaging. Nanoscale analysis of spine structure and dynamics, combined with new mathematical approaches, will facilitate our understanding of spine functions in physiological and pathological conditions.

Dendritic Spines Shape Analysis-Classification or Clusterization? Perspective

Dendritic spines are small protrusions from the dendrite membrane, where contact with neighboring axons is formed in order to receive synaptic input. Changes in size, shape, and density of synaptic spines are associated with learning and memory, and observed after drug abuse in a variety of neurodegenerative, neurodevelopmental, and psychiatric disorders. Due to the preeminent importance of synaptic spines, there have been major efforts into developing techniques that enable visualization and analysis of dendritic spines in cultured neurons, in fixed slices and in intact brain tissue. The classification of synaptic spines into predefined morphological groups is a standard approach in neuroscience research, where spines are divided into fixed categories such as thin, mushroom, and stubby subclasses. This study examines accumulated evidence that supports the existence of dendritic spine shapes as a continuum rather than separated classes. Using new approaches and software tools we reflect on complex dendritic spine shapes, positing that understanding of their highly dynamic nature is required to perform analysis of their morphology. The study discusses and compares recently developed algorithms that rely on clusterization rather than classification, therefore enabling new levels of spine shape analysis. We reason that improved methods of analysis may help to investigate a link between dendritic spine shape and its function, facilitating future studies of learning and memory as well as studies of brain disorders.

A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning

BACKGROUND

Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.

NEW METHOD

We developed and validated algorithms to quantitatively assess neurite outgrowth from living and unstained spinal cord slice cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) based on an adaptive thresholding approach called NeuriteSegmantation. We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures.

RESULTS

NeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs. The temporal pattern of axonal growth was successfully assessed. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning.

COMPARISON WITH EXISTING METHODS

The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 min. In addition, NeuriteSegmentation was compared to NeuriteJ®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.

CONCLUSION

The developed image analysis methods were more time-saving and user-independent than established approaches. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience.

Quantitative 3-D morphometric analysis of individual dendritic spines

The observation and analysis of dendritic spines morphological changes poses a major challenge in neuroscience studies. The alterations of their density and/or morphology are indicators of the cellular processes involved in neural plasticity underlying learning and memory, and are symptomatic in neuropsychiatric disorders. Despite ongoing intense investigations in imaging approaches, the relationship between changes in spine morphology and synaptic function is still unknown. The existing quantitative analyses are difficult to perform and require extensive user intervention. Here, we propose a new method for (1) the three-dimensional (3-D) segmentation of dendritic spines using a multi-scale opening approach and (2) define 3-D morphological attributes of individual spines for the effective assessment of their structural plasticity. The method was validated using confocal light microscopy images of dendritic spines from dissociated hippocampal cultures and brain slices (1) to evaluate accuracy relative to manually labeled ground-truth annotations and relative to the state-of-the-art Imaris tool, (2) to analyze reproducibility of user-independence of the segmentation method, and (3) to quantitatively analyze morphological changes in individual spines before and after chemically induced long-term potentiation. The method was monitored and used to precisely describe the morphology of individual spines in real-time using consecutive images of the same dendritic fragment.

Dendritic spine classification using shape and appearance features based on two-photon microscopy

BACKGROUND

Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity.

NEW METHOD

We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features.

RESULTS

Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines.

COMPARISON WITH EXISTING METHODS

Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem.

CONCLUSIONS

Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.

2dSpAn: semiautomated 2-d segmentation, classification and analysis of hippocampal dendritic spine plasticity

MOTIVATION

Accurate and effective dendritic spine segmentation from the dendrites remains as a challenge for current neuroimaging research community. In this article, we present a new method (2dSpAn) for 2-d segmentation, classification and analysis of structural/plastic changes of hippocampal dendritic spines. A user interactive segmentation method with convolution kernels is designed to segment the spines from the dendrites. Formal morphological definitions are presented to describe key attributes related to the shape of segmented spines. Spines are automatically classified into one of four classes: Stubby, Filopodia, Mushroom and Spine-head Protrusions.

RESULTS

The developed method is validated using confocal light microscopy images of dendritic spines from dissociated hippocampal cultures for: (i) quantitative analysis of spine morphological changes, (ii) reproducibility analysis for assessment of user-independence of the developed software and (iii) accuracy analysis with respect to the manually labeled ground truth images, and also with respect to the available state of the art. The developed method is monitored and used to precisely describe the morphology of individual spines in real-time experiments, i.e. consequent images of the same dendritic fragment.

AVAILABILITY AND IMPLEMENTATION

The software and the source code are available at https://sites.google.com/site/2dspan/ under open-source license for non-commercial use.

CONTACT

subhadip@cse.jdvu.ac.in or j.wlodarczyk@nencki.gov.pl

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Postpartum corticosterone administration reduces dendritic complexity and increases the density of mushroom spines of hippocampal CA3 arbours in dams

Postpartum depression (PPD) affects approximately 15% of mothers after giving birth. A complete understanding of depression during the postpartum period has yet to be established, although disruptions in the hypothalamic-pituitary-adrenal axis and stress during the postpartum may be involved. To model these components in rats, we administered high corticosterone (CORT) postpartum, which increases immobility in the forced swim test (FST), and reduces maternal care, body weight and hippocampal cell proliferation in dams. The hippocampus is altered in response to chronic stress, exposure to high glucocorticoids and in major depression in humans. In the present study, we examined whether high CORT reduced dendritic complexity and spines in the CA3 region of the hippocampus. Additionally, housing complexity was manipulated so that dams and litters were housed either with tubes (complex) or without tubes (impoverished) to investigate the consequences of new animal care regulations. Dams received 40 mg/kg/day of CORT or oil starting on day 2 postpartum for 23 days. Maternal behaviours were assessed on postpartum days 2-8 and dams were tested using the FST on days 21 and 22. Dams were killed on day 24 and brains were processed for Golgi impregnation. Pyramidal cells in the CA3 subfield were traced using a camera lucida and analysed for branch points and dendritic complexity, as well as spine density and type on both basal and apical arbours. As previously established, high CORT postpartum reduced maternal care and increased immobility in the FST, which is a measure of depressive-like behaviour. High CORT postpartum reduced the complexity of basal arbours and increased mushroom spines on both apical and basal dendrites. Housing complexity had no effect on spines of CA3 pyramidal cells but modest effects on cell morphology. These data show that chronic high CORT in postpartum females alters hippocampal morphology and may provide insight regarding the neurobiological consequences of high stress or CORT during the postpartum period, as well as be relevant for postpartum stress or depression.

Methods of dendritic spine detection: from Golgi to high-resolution optical imaging

Dendritic spines, the bulbous protrusions that form the postsynaptic half of excitatory synapses, are one of the most prominent features of neurons and have been imaged and studied for over a century. In that time, changes in the number and morphology of dendritic spines have been correlated to the developmental process as well as the pathophysiology of a number of neurodegenerative diseases. Due to the sheer scale of synaptic connectivity in the brain, work to date has merely scratched the surface in the study of normal spine function and pathology. This review will highlight traditional approaches to the imaging of dendritic spines and newer approaches made possible by advances in microscopy, protein engineering, and image analysis. The review will also describe recent work that is leading researchers toward the possibility of a systematic and comprehensive study of spine anatomy throughout the brain.