Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning

Automatic dendritic spine segmentation in widefield fluorescence images reveal synaptic nanostructures distribution with super-resolution imaging

Dendritic spines are the main sites for synaptic communication in neurons, and alterations in their density, size, and shapes occur in many brain disorders. Current spine segmentation methods perform poorly in conditions with low signal-to-noise and resolution, particularly in the widefield images of thick (⍰ 10 μm) brain slices. Here, we combined two open-source machine-learning models to achieve automatic 3D spine segmentation in widefield diffraction-limited fluorescence images of neurons in thick brain slices. We validated the performance by comparison with manually segmented super-resolution images of spines reconstructed from direct stochastic optical reconstruction microscopy (dSTORM). Lastly, we show an application of our approach by combining spine segmentation from diffraction-limited images with dSTORM of synaptic protein PSD-95 in the same field-of-view. This allowed us to automatically analyze and quantify the nanoscale distribution of PSD-95 inside the spine. Importantly, we found the numbers, but not the average sizes, of synaptic nanomodules and nanodomains increase with spine size.

NEURD: automated proofreading and feature extraction for connectomics

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about 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 each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows to automate a variety of tasks that would otherwise require extensive manual effort, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and computation of other features. These features enable many downstream analyses of neural morphology and connectivity, making these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

SpineTool is an open-source software for analysis of morphology of dendritic spines

Dendritic spines form most excitatory synaptic inputs in neurons and these spines are altered in many neurodevelopmental and neurodegenerative disorders. Reliable methods to assess and quantify dendritic spines morphology are needed, but most existing methods are subjective and labor intensive. To solve this problem, we developed an open-source software that allows segmentation of dendritic spines from 3D images, extraction of their key morphological features, and their classification and clustering. Instead of commonly used spine descriptors based on numerical metrics we used chord length distribution histogram (CLDH) approach. CLDH method depends on distribution of lengths of chords randomly generated within dendritic spines volume. To achieve less biased analysis, we developed a classification procedure that uses machine-learning algorithm based on experts' consensus and machine-guided clustering tool. These approaches to unbiased and automated measurements, classification and clustering of synaptic spines that we developed should provide a useful resource for a variety of neuroscience and neurodegenerative research applications.

Human cortical amygdala dendrites and spines morphology under open‐source three‐dimensional reconstruction procedures

Visualizing nerve cells has been fundamental for the systematic description of brain structure and function in humans and other species. Different approaches aimed to unravel the morphological features of neuron types and diversity. The inherent complexity of the human nervous tissue and the need for proper histological processing have made studying human dendrites and spines challenging in postmortem samples. In this study, we used Golgi data and open-source software for 3D image reconstruction of human neurons from the cortical amygdaloid nucleus to show different dendrites and pleomorphic spines at different angles. Procedures required minimal equipment and generated high-quality images for differently shaped cells. We used the "single-section" Golgi method adapted for the human brain to engender 3D reconstructed images of the neuronal cell body and the dendritic ramification by adopting a neuronal tracing procedure. In addition, we elaborated 3D reconstructions to visualize heterogeneous dendritic spines using a supervised machine learning-based algorithm for image segmentation. These tools provided an additional upgrade and enhanced visual display of information related to the spatial orientation of dendritic branches and for dendritic spines of varied sizes and shapes in these human subcortical neurons. This same approach can be adapted for other techniques, areas of the central or peripheral nervous system, and comparative analysis between species.

Ultrastructural analysis of dendritic spine necks reveals a continuum of spine morphologies

Dendritic spines are membranous protrusions that receive essentially all excitatory inputs in most mammalian neurons. Spines, with a bulbous head connected to the dendrite by a thin neck, have a variety of morphologies that likely impact their functional properties. Nevertheless, the question of whether spines belong to distinct morphological subtypes is still open. Addressing this quantitatively requires clear identification and measurements of spine necks. Recent advances in electron microscopy enable large-scale systematic reconstructions of spines with nanometer precision in 3D. Analyzing ultrastructural reconstructions from mouse neocortical neurons with computer vision algorithms, we demonstrate that the vast majority of spine structures can be rigorously separated into heads and necks, enabling morphological measurements of spine necks. We then used a database of spine morphological parameters to explore the potential existence of different spine classes. Without exception, our analysis revealed unimodal distributions of individual morphological parameters of spine heads and necks, without evidence for subtypes of spines. The postsynaptic density size was strongly correlated with the spine head volume. The spine neck diameter, but not the neck length, was also correlated with the head volume. Spines with larger head volumes often had a spine apparatus and pairs of spines in a post-synaptic cell contacted by the same axon had similar head volumes. Our data reveal a lack of morphological subtypes of spines and indicate that the spine neck length and head volume must be independently regulated. These results have repercussions for our understanding of the function of dendritic spines in neuronal circuits.

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.

Exploiting geometric similarity for statistical quantification of fluorescence spatial patterns in bacterial colonies

Background

Currently the combination of molecular tools, imaging techniques and analysis software offer the possibility of studying gene activity through the use of fluorescent reporters and infer its distribution within complex biological three-dimensional structures. For example, the use of Confocal Scanning Laser Microscopy (CSLM) is a regularly-used approach to visually inspect the spatial distribution of a fluorescent signal. Although a plethora of generalist imaging software is available to analyze experimental pictures, the development of tailor-made software for every specific problem is still the most straightforward approach to perform the best possible image analysis. In this manuscript, we focused on developing a simple methodology to satisfy one particular need: automated processing and analysis of CSLM image stacks to generate 3D fluorescence profiles showing the average distribution detected in bacterial colonies grown in different experimental conditions for comparison purposes.

Results

The presented method processes batches of CSLM stacks containing three-dimensional images of an arbitrary number of colonies. Quasi-circular colonies are identified, filtered and projected onto a normalized orthogonal coordinate system, where a numerical interpolation is performed to obtain fluorescence values within a spatially fixed grid. A statistically representative three-dimensional fluorescent pattern is then generated from this data, allowing for standardized fluorescence analysis regardless of variability in colony size. The proposed methodology was evaluated by analyzing fluorescence from GFP expression subject to regulation by a stress-inducible promoter.

Conclusions

This method provides a statistically reliable spatial distribution profile of fluorescence detected in analyzed samples, helping the researcher to establish general correlations between gene expression and spatial allocation under differential experimental regimes. The described methodology was coded into a MATLAB script and shared under an open source license to make it accessible to the whole community.

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.

High-throughput imaging of zebrafish embryos using a linear-CCD-based flow imaging system

High-throughput imaging and screening is essential for biomedical research and drug discovery using miniature model organisms such as zebrafish. This study introduces a high-speed imaging system which illuminates zebrafish embryos flowing through a capillary tube with a sheet of light and captures them using a linear charge-coupled device (CCD). This system can image dozens of zebrafish embryos per second. An image algorithm was developed to recognize each embryo and to perform automatic analysis. We distinguished dead and living embryos according to the gray level distribution and conducted statistics of morphological characteristics of embryos at different growing stages.

Rat-strain dependent changes of dendritic and spine morphology in the hippocampus after cocaine self-administration

We previously showed that cocaine self-administration increases spine density in CA1 hippocampal neurons in Lewis (LEW) but not in Fischer 344 (F344) rats. Dendritic spine morphology is intimately related to its function. Thus, we conducted a 3D morphological analysis of CA1 dendrites and dendritic spines in these two strains of rats. Strain-specific differences were observed prior to cocaine self-administration: LEW rats had significantly larger dendritic diameters but lower spine density than the F344 strain. After cocaine self-administration, proximal dendritic volume, dendritic surface area and spine density were increased in LEW rats, where a higher percentage of larger spines were also observed. In addition, we found a strong positive correlation between dendritic volume and spine morphology, and a moderate correlation between dendritic volume and spine density in cocaine self-administered LEW rats, an effect that was not evident in any other condition. By contrast, after cocaine self-administration, F334 rats showed decreased spine head volumes. Our findings suggest that genetic differences could play a key role in the structural plasticity induced by cocaine in CA1 pyramidal neurons. These cocaine-induced alterations could be related to differences in the memory processing of drug reward cues that could potentially explain differential individual vulnerability to cocaine addiction.

Application of clinical bioinformatics in lung cancer-specific biomarkers

The fact that lung cancer is a heterogeneous disease suggests that there is a high likelihood that effective lung cancer biomarkers will need to address patient-specific molecular defects, clinical characters, and aspects of the tumor microenvironment. In this transition, clinical bioinformatics tools and resources are the most appropriate means to improve the analysis, as major biological databases are now containing clinical data alongside genomics, proteomics, and other biological data. Clinical bioinformatics comprises a series of concepts and approaches that have been used successfully both to delineate novel biological mechanisms and to drive translational advances in individualized healthcare. In this article, we outline several of emerging clinical bioinformatics-based strategies as they apply specifically to lung cancer.

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Amongst the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardization of both image acquisition and image analysis, it has become possible to extract statistically relevant readouts from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.

Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer's disease, Parkinson's diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines.

Haptically assisted connection procedure for the reconstruction of dendritic spines

Dendritic spines are thin protrusions that cover the dendritic surface of numerous neurons in the brain and whose function seems to play a key role in neural circuits. The correct segmentation of those structures is difficult due to their small size and the resulting spines can appear incomplete. This paper presents a four-step procedure for the complete reconstruction of dendritic spines. The haptically driven procedure is intended to work as an image processing stage before the automatic segmentation step giving the final representation of the dendritic spines. The procedure is designed to allow both the navigation and the volume image editing to be carried out using a haptic device. A use case employing our procedure together with a commercial software package for the segmentation stage is illustrated. Finally, the haptic editing is evaluated in two experiments; the first experiment concerns the benefits of the force feedback and the second checks the suitability of the use of a haptic device as input. In both cases, the results shows that the procedure improves the editing accuracy.