Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks

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.

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.

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.

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 .

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.