Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors

Automated Analysis of Neuronal Morphology in 2D Fluorescence Micrographs through an Unsupervised Semantic Segmentation of Neurons

Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering extensive distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and prone to bias. Most automated analyses of neurons rely on reconstruction of the neuron as a whole without a semantic analysis of each neurite. A fully-automated classification of all neurites still remains unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in two-dimensional fluorescence micrographs of cultured neurons with minimal requirements for user interaction. Single neurons are first reconstructed into binarized images using a Hessian-based segmentation algorithm to detect thin neurite structures combined with intensity- and shape-based reconstruction of the cell body. Neurites are then classified into axon, dendrites and their branches of increasing order using a geodesic distance transform of the cell skeleton. The software was benchmarked against a published dataset and reproduced the phenotype observed after manual annotation. Our tool promises accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.

Preserving Retinal Structure and Function with the Novel Nitroxide Antioxidant, DCTEIO

Oxidative stress is a major contributor to progressive neurodegenerative disease and may be a key target for the development of novel preventative and therapeutic strategies. Nitroxides have been successfully utilised to study changes in redox status (biological probes) and modulate radical-induced oxidative stress. This study investigates the efficacy of DCTEIO (5,6-dicarboxy-1,1,3,3-tetraethyllisoindolin-2-yloxyl), a stable, kinetically-persistent, nitroxide-based antioxidant, as a retinal neuroprotectant. The preservation of retinal function following an acute ischaemic/reperfusion (I/R) insult in the presence of DCTEIO was quantified by electroretinography (ERG). Inflammatory responses in retinal glia were analysed by GFAP and IBA-1 immunohistochemistry, and retinal integrity assessed by histology. A nitroxide probe combined with flow cytometry provided a rapid technique to assess oxidative stress and the mitigation offered by antioxidant compounds in cultured 661W photoreceptor cells. DCTEIO protected the retina from I/R-induced damage, maintaining retinal function. Histological analysis showed preservation of retinal integrity with reduced disruption and disorganisation of the inner and outer nuclear layers. I/R injury upregulated GFAP expression, indicative of retinal stress, which was significantly blunted by DCTEIO. The number of 'activated' microglia, particularly in the outer retina, in response to cellular stress was also significantly reduced by DCTEIO, potentially suggesting reduced inflammasome activation and cell death. DCTEIO mitigated oxidative stress in 661W retinal cell cultures, in a dose-dependent fashion. Together these findings demonstrate the potential of DCTEIO as a neuroprotective therapeutic for degenerative diseases of the CNS that involve an ROS-mediated component, including those of the retina e.g. age-related macular degeneration and glaucoma.

Gut-derived bacterial LPS attenuates incubation of methamphetamine craving via modulating microglia

BACKGROUND

The microbiota-gut-brain axis plays a critical role in the pathophysiology of neuropsychiatric disorders, and the compositions of gut microbiota are altered by addictive drugs. However, the role of gut microbiota in the incubation of methamphetamine (METH) craving remains poorly understood.

METHODS

16S rRNA gene sequencing was performed to assess the richness and diversity of gut microbiota in METH self-administration model. Hematoxylin and eosin staining was performed to evaluate the integrity of intestinal barrier. Immunofluorescence and three-dimensional reconstruction were performed to assess the morphologic changes of microglia. Serum levels of lipopolysaccharide (LPS) were determined using the rat enzyme-linked immunosorbent assay kits. Quantitative real-time PCR was performed to assess transcript levels of dopamine receptor, glutamate ionotropic AMPA receptor 3 and brain-derived neurotrophic factor.

RESULTS

METH self-administration induced gut microbiota dysbiosis, intestinal barrier damage and microglia activation in the nucleus accumbens core (NAcc), which was partially recovered after prolonged withdrawal. Microbiota depletion via antibiotic treatment increased LPS levels and induced a marked change in the microglial morphology in the NAcc, as indicated by the decreases in the lengths and numbers of microglial branches. Depleting the gut microbiota also prevented the incubation of METH craving and increased the population of Klebsiella oxytoca. Furthermore, Klebsiella oxytoca treatment or exogenous administration of the gram-negative bacterial cell wall component LPS increased serum and central LPS levels, induced microglial morphological changes and reduced the dopamine receptor transcription in the NAcc. Both treatments and NAcc microinjections of gut-derived bacterial LPS significantly decreased METH craving after prolonged withdrawal.

CONCLUSIONS

These data suggest that LPS from gut gram-negative bacteria may enter circulating blood, activate microglia in the brain and consequently decrease METH craving after withdrawal, which may have important implications for novel strategies to prevent METH addiction and relapse.

Cell morphologies in the nervous system: Glia steal the limelight

Neurons and glia have distinct yet interactive functions but are both characterized by branching morphology. Dendritic trees have been digitally traced for over 40 years in many animal species, anatomical regions, and neuron types. Recently, long-range axons also are being reconstructed throughout the brain of many organisms from invertebrates to primates. In contrast, less attention has been paid until lately to glial morphology. Thus, although glia and neurons are similarly abundant in the nervous systems of humans and most animal models, glia have traditionally been much less represented than neurons in morphological reconstruction repositories such as NeuroMorpho.Org. This is rapidly changing with the advent of high-throughput glia tracing. NeuroMorpho.Org introduced glial cells in 2017 and today they constitute nearly a third of the database content. It took NeuroMorpho.Org 10 years to collect the first 40,000 neurons and now that amount of data can be produced in a single publication. This not only demonstrates the spectacular technological progress in data production, but also demands a corresponding advancement in informatics processing. At the same time, these publicly available data also open new opportunities for quantitative analysis and computational modeling to identify universal or cell-type-specific design principles in the cellular architecture of nervous systems. As a first application, we demonstrated that supervised machine learning of tree geometry classifies neurons and glia with practically perfect accuracy. Furthermore, we discovered a new morphometric biomarker capable of robustly separating these cell classes across multiple species, brain regions, and experimental preparations, with only sparse sampling of branch measurements.

Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors

Neurons and glia are the two main cell classes in the nervous systems of most animals. Although functionally distinct, neurons and glia are both characterized by multiple branching arbors stemming from the cell bodies. Glial processes are generally known to form smaller trees than neuronal dendrites. However, the full extent of morphological differences between neurons and glia in multiple species and brain regions has not yet been characterized, nor is it known whether these cells can be reliably distinguished based on geometric features alone. Here, we show that multiple supervised learning algorithms deployed on a large database of morphological reconstructions can systematically classify neuronal and glial arbors with nearly perfect accuracy and precision. Moreover, we report multiple morphometric properties, both size related and size independent, that differ substantially between these cell types. In particular, we newly identify an individual morphometric measurement, Average Branch Euclidean Length that can robustly separate neurons from glia across multiple animal models, a broad diversity of experimental conditions, and anatomical areas, with the notable exception of the cerebellum. We discuss the practical utility and physiological interpretation of this discovery.

More than meets the eye: The role of microglia in healthy and diseased retina

Microglia are the main resident immune cells of the nervous system and as such they are involved in multiple roles ranging from tissue homeostasis to response to insults and circuit refinement. While most knowledge about microglia comes from brain studies, some mechanisms have been confirmed for microglia cells in the retina, the light-sensing compartment of the eye responsible for initial processing of visual information. However, several key pieces of this puzzle are still unaccounted for, as the characterization of retinal microglia has long been hindered by the reduced population size within the retina as well as the previous lack of technologies enabling single-cell analyses. Accumulating evidence indicates that the same cell type may harbor a high degree of transcriptional, morphological and functional differences depending on its location within the central nervous system. Thus, studying the roles and signatures adopted specifically by microglia in the retina has become increasingly important. Here, we review the current understanding of retinal microglia cells in physiology and in disease, with particular emphasis on newly discovered mechanisms and future research directions.

Assaying Microglia Functions In Vitro

Microglia, the main immune modulators of the central nervous system, have key roles in both the developing and adult brain. These functions include shaping healthy neuronal networks, carrying out immune surveillance, mediating inflammatory responses, and disposing of unwanted material. A wide variety of pathological conditions present with microglia dysregulation, highlighting the importance of these cells in both normal brain function and disease. Studies into microglial function in the context of both health and disease thus have the potential to provide tremendous insight across a broad range of research areas. In vitro culture of microglia, using primary cells, cell lines, or induced pluripotent stem cell derived microglia, allows researchers to generate reproducible, robust, and quantifiable data regarding microglia function. A broad range of assays have been successfully developed and optimised for characterizing microglial morphology, mediation of inflammation, endocytosis, phagocytosis, chemotaxis and random motility, and mediation of immunometabolism. This review describes the main functions of microglia, compares existing protocols for measuring these functions in vitro, and highlights common pitfalls and future areas for development. We aim to provide a comprehensive methodological guide for researchers planning to characterise microglial functions within a range of contexts and in vitro models.

The Shape of μ—How Morphological Analyses Shape the Study of Microglia

Microglia, the innate immune cells of the CNS parenchyma, serve as the first line of defense in a myriad of neurodevelopmental, neurodegenerative, and neuroinflammatory conditions. In response to the peripheral inflammation, circulating mediators, and other external signals that are produced by these conditions, microglia dynamically employ different transcriptional programs as well as morphological adaptations to maintain homeostasis. To understand these cells’ function, the field has established a number of essential analysis approaches, such as gene expression, cell quantification, and morphological reconstruction. Although high-throughput approaches are becoming commonplace in regard to other types of analyses (e.g., single-cell scRNA-seq), a similar standard for morphological reconstruction has yet to be established. In this review, we offer an overview of microglial morphological analysis methods, exploring the advantages and disadvantages of each, highlighting a number of key studies, and emphasizing how morphological analysis has significantly contributed to our understanding of microglial function in the CNS parenchyma. In doing so, we advocate for the use of unbiased, automated morphological reconstruction approaches in future studies, in order to capitalize on the valuable information embedded in the cellular structures microglia inhabit.

Efficient metadata mining of web-accessible neural morphologies

Advancements in neuroscience research have led to steadily accelerating data production and sharing. The online community repository of neural reconstructions NeuroMorpho.Org grew from fewer than 1000 digitally traced neurons in 2006 to more than 140,000 cells today, including glia that now constitute 10.1% of the content. Every reconstruction consists of a detailed 3D representation of branch geometry and connectivity in a standardized format, from which a collection of morphometric features is extracted and stored. Moreover, each entry in the database is accompanied by rich metadata annotation describing the animal subject, anatomy, and experimental details. The rapid expansion of this resource in the past decade was accompanied by a parallel rise in the complexity of the available information, creating both opportunities and challenges for knowledge mining. Here, we introduce a new summary reporting functionality, allowing NeuroMorpho.Org users to efficiently download digests of metadata and morphometrics from multiple groups of similar cells for further analysis. We demonstrate the capabilities of the tool for both glia and neurons and present an illustrative statistical analysis of the resulting data.

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.

Antioxidant thioether core-crosslinked nanoparticles prevent the bilateral spread of secondary injury to protect spatial learning and memory in a controlled cortical impact mouse model of traumatic brain injury

The secondary phase of traumatic brain injury (TBI) is partly caused by the release of excess reactive oxygen species (ROS) from the primary injury. However, there are currently no therapies that have been shown to reduce the secondary spread of injury beyond the primary insult. Nanoparticles offer the ability to rapidly accumulate and be retained in injured brain for improved target engagement. Here, we utilized systemically administered antioxidant thioether core-cross-linked nanoparticles (NP1) that scavenge and inactivate ROS to reduce this secondary spread of injury in a mild controlled cortical impact (CCI) mouse model of TBI. We found that NP1 treatment protected CCI mice from injury induced learning and memory deficits observed in the Morris water maze (MWM) test at 1-month post-CCI. This protection was likely a result of NP1-mediated reduction in oxidative stress in the ipsilateral hemisphere as determined by immunofluorescence imaging of markers of oxidative stress and the spread of neuroinflammation into the contralateral hippocampus as determined by immunofluorescence imaging of activated microglia and neuron-astrocyte-microglia triad formation. These data suggest NP1-mediated reduction in post-traumatic oxidative stress correlates with the reduction in the spread of injury to the contralateral hippocampus to protect spatial memory and learning in CCI mice. Therefore, these materials may offer an improved treatment strategy to reduce the secondary spread of TBI.

Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks

Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. We present a direct method that generates readouts for a comprehensive panel of biomarkers from serial whole-brain slices, characterizing all major brain cell types, at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions from each slice. We use iterative cycles of optimized 10-plex immunostaining with 10-color epifluorescence imaging to accumulate highly enriched image datasets from individual whole-brain slices, from which seamless signal-corrected mosaics are reconstructed. Specific fluorescent signals of interest are isolated computationally, rejecting autofluorescence, imaging noise, cross-channel bleed-through, and cross-labeling. Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by simultaneously profiling multiple biological processes in their native anatomical context.

Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method

Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.

Segmentation, Tracing, and Quantification of Microglial Cells from 3D Image Stacks

Microglia play a central role in modulating synaptic structure and physiology, learning and memory processes. They exhibit morphological changes to perform these roles, therefore the morphological study of microglia can help to understand their functionality. Many promising methods are proposed to automatically segment the blood vessels or reconstruct the neuronal morphology. However, they often fail to accurately capture microglia organizations due to the striking structural differences. This requires a more sophisticated approach of reconstruction taking into account the varying nature of branch structures and soma sizes. To this end, we propose an automated method to reconstruct microglia, and quantify their features from 2D/3D image datasets. We first employ multilevel thresholding to segment soma volumes(3D)/areas(2D) and recognize foreground voxels/pixels. Seed points sampled from the foreground, are connected to form the skeleton of the branches via the tracing process. The reconstructed data is quantified and written in SWC standard file format. We have applied our method to 3D image datasets of microglia, then evaluated the results using ground truth data, and compared them to those achieved via the state-of-the-art methods. Our method outperforms the others both in accuracy and computational time.

Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites

Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods

Purpose

Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone.

Methods

488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset.

Results

The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset.

Conclusion

Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

Characterizing microglia activation: a spatial statistics approach to maximize information extraction

Microglia play an important role in the pathology of CNS disorders, however, there remains significant uncertainty about the neuroprotective/degenerative role of these cells due to a lack of techniques to adequately assess their complex behaviour in response to injury. Advancing microscopy techniques, transgenic lines and well-characterized molecular markers, have made histological assessment of microglia populations more accessible. However, there is a distinct lack of tools to adequately extract information from these images to fully characterise microglia behaviour. This, combined with growing economic pressures and the ethical need to minimise the use of laboratory animals, led us to develop tools to maximise the amount of information obtained. This study describes a novel approach, combining image analysis with spatial statistical techniques. In addition to monitoring morphological parameters and global changes in microglia density, nearest neighbour distance, and regularity index, we used cluster analyses based on changes in soma size and roundness to yield novel insights into the behaviour of different microglia phenotypes in a murine optic nerve injury model. These methods should be considered a generic tool to quantitatively assess microglia activation, to profile phenotypic changes into microglia subpopulations, and to map spatial distributions in virtually every CNS region and disease state.

Binge alcohol alters exercise-driven neuroplasticity

Exercise is increasingly being used as a treatment for alcohol use disorders (AUD), but the interactive effects of alcohol and exercise on the brain remain largely unexplored. Alcohol damages the brain, in part by altering glial functioning. In contrast, exercise promotes glial health and plasticity. In the present study, we investigated whether binge alcohol would attenuate the effects of subsequent exercise on glia. We focused on the medial prefrontal cortex (mPFC), an alcohol-vulnerable region that also undergoes neuroplastic changes in response to exercise. Adult female Long-Evans rats were gavaged with ethanol (25% w/v) every 8h for 4days. Control animals received an isocaloric, non-alcohol diet. After 7days of abstinence, rats remained sedentary or exercised for 4weeks. Immunofluorescence was then used to label microglia, astrocytes, and neurons in serial tissue sections through the mPFC. Confocal microscope images were processed using FARSIGHT, a computational image analysis toolkit capable of automated analysis of cell number and morphology. We found that exercise increased the number of microglia in the mPFC in control animals. Binged animals that exercised, however, had significantly fewer microglia. Furthermore, computational arbor analytics revealed that the binged animals (regardless of exercise) had microglia with thicker, shorter arbors and significantly less branching, suggestive of partial activation. We found no changes in the number or morphology of mPFC astrocytes. We conclude that binge alcohol exerts a prolonged effect on morphology of mPFC microglia and limits the capacity of exercise to increase their numbers.

Automated tracing of microglia using multilevel thresholding and minimum spanning trees

Microglia are immune cells of the central nervous system. Good knowledge about their morphology leads us to a better understanding of their functionality. In this article we propose an automated method to trace microglia in microscopy images. Our approach is powered by two main algorithms: multilevel thresholding (MT) and minimum spanning tree (MST). MT quantizes intensities of image pixels to several levels, then we sample each level with different rates to produce seed points. Each seed point belongs to a level and has a specific value, therefore they can be prioritized. The tree structure of microglia is known a priori, so we apply the MST to the prioritized seed points to preserve this feature. Results show that our proposed method is fast and accurate in reconstruction of large images.

A methodological pipeline for serial-section imaging and tissue realignment for whole-brain functional and connectivity assessment

BACKGROUND

Understanding the neurobiological basis of cognition and behavior, and disruptions to these processes following injury and disease, requires a large-scale assessment of neural populations, and knowledge of their patterns of connectivity.

NEW METHOD

We present an analysis platform for large-scale investigation of functional and neuroanatomical connectivity in rodents. Retrograde tracers were injected and in a subset of animals behavioral tests to drive immediate-early gene expression were administered. This approach allows users to perform whole-brain assessment of function and connection in a semi-automated quantitative manner. Brains were cut in the coronal plane, and an image of the block face was acquired. Wide-field fluorescent scans of whole sections were acquired and analyzed using Matlab software.

RESULTS

The toolkit utilized open-source and custom platforms to accommodate a largely automated analysis pipeline in which neuronal boundaries are automatically segmented, the position of segmented neurons are co-registered with a corresponding image acquired during sectioning, and a 3-D representation of neural tracer (and other products) throughout the entire brain is generated.

COMPARISON WITH EXISTING METHODS

Current whole brain connectivity measures primarily target mice and use anterograde tracers. Our focus on segmented units of interest (e.g., NeuN labeled neurons) and restricting measures to these units produces a flexible platform for a variety of whole brain analyses (measuring activation, connectivity, markers of disease, etc.).

CONCLUSIONS

This open-source toolkit allows an investigator to visualize and quantify whole brain data in 3-D, and additionally provides a framework that can be rapidly integrated with user-specific analyses and methodologies.

Quantifying Modes of 3D Cell Migration

Although it is widely appreciated that cells migrate in a variety of diverse environments in vivo, we are only now beginning to use experimental workflows that yield images with sufficient spatiotemporal resolution to study the molecular processes governing cell migration in 3D environments. Since cell migration is a dynamic process, it is usually studied via microscopy, but 3D movies of 3D processes are difficult to interpret by visual inspection. In this review, we discuss the technologies required to study the diversity of 3D cell migration modes with a focus on the visualization and computational analysis tools needed to study cell migration quantitatively at a level comparable to the analyses performed today on cells crawling on flat substrates.