Quantitative neurite outgrowth measurement based on image segmentation with topological dependence

Bile Acids Induce Neurite Outgrowth in Nsc-34 Cells via TGR5 and a Distinct Transcriptional Profile

Increasing evidence supports a neuroprotective role for bile acids in major neurodegenerative disorders. We studied major human bile acids as signaling molecules for their two cellular receptors, farnesoid X receptor (FXR or NR1H4) and G protein-coupled bile acid receptor 1 (GPBAR1 or TGR5), as potential neurotrophic agents. Using quantitative image analysis, we found that 20 μM deoxycholic acid (DCA) could induce neurite outgrowth in NSC-34 cells that was comparable to the neurotrophic effects of the culture control 1 μM retinoic acid (RA), with lesser effects observed for chenodexoycholic acid (CDCA) at 20 μM, and similar though less robust neurite outgrowth in SH-SY5Y cells. Using chemical agonists and antagonists of FXR, LXR, and TGR5, we found that TGR5 agonism was comparable to DCA stimulation and stronger than RA, and that neither FXR nor liver X receptor (LXR) inhibition could block bile acid-induced neurite growth. RNA sequencing identified a core set of genes whose expression was regulated by DCA, CDCA, and RA. Our data suggest that bile acid signaling through TGR5 may be a targetable pathway to stimulate neurite outgrowth.

Ethanolic Fruit Extract of Emblica officinalis Suppresses Neuroinflammation in Microglia and Promotes Neurite Outgrowth in Neuro2a Cells

Inhibiting neuroinflammation and modulating neurite outgrowth could be a promising strategy to prevent neurological disorders. Emblica officinalis (EO) may be a potent agent against them. Although EO extract reportedly has anti-inflammatory properties in macrophages, there is limited knowledge about its neuroprotective activity by suppressing microglia-mediated proinflammatory cytokine production and inducing neurite outgrowth. The present study aimed to elucidate the effect of EO fruit extract on the lipopolysaccharide- (LPS-) induced neuroinflammation using microglial (BV2) and neuroblastoma (Neuro2a) cells. The results demonstrated that, in LPS-treated BV2 cells, EO fruit extract reduced nitric oxide, interleukin-6, and tumor necrotic factor-α production. It also enhanced the neurite length of Neuro2a cells, which was linked to the upregulation of TuJ1 and MAP2 expressions. In conclusion, these findings indicate that the ethanolic extract of EO fruits has promising neuroprotective potential to exhibit antineuroinflammation activity and accelerative effect on neurite outgrowth in vitro. Therefore, EO fruit extract can be considered a novel herbal medicine candidate for managing neuroinflammatory diseases.

Automated high content image analysis of dendritic arborization in primary mouse hippocampal and rat cortical neurons in culture

BACKGROUND

Primary neuronal cell cultures are useful for studying mechanisms that influence dendritic morphology during normal development and in response to various stressors. However, analyzing dendritic morphology is challenging, particularly in cultures with high cell density, and manual methods of selecting neurons and tracing dendritic arbors can introduce significant bias, and are labor-intensive. To overcome these challenges, semi-automated and automated methods are being developed, with most software solutions requiring computer-assisted dendrite tracing with subsequent quantification of various parameters of dendritic morphology, such as Sholl analysis. However fully automated approaches for classic Sholl analysis of dendritic complexity are not currently available.

NEW METHOD

The previously described Omnisphero software, was extended by adding new functions to automatically assess dendritic mass, total length of the dendritic arbor and the number of primary dendrites, branch points, and terminal tips, and to perform Sholl analysis.

RESULTS

The new functions for assessing dendritic morphology were validated using primary mouse hippocampal and rat cortical neurons transfected with a fluorescently tagged MAP2 cDNA construct. These functions allow users to select specific populations of neurons as a training set for subsequent automated selection of labeled neurons in high-density cultures.

COMPARISON WITH EXISTING SEMI-AUTOMATED METHODS

Compared to manual or semi-automated analyses of dendritic arborization, the new functions increase throughput while significantly decreasing researcher bias associated with neuron selection, tracing, and thresholding.

CONCLUSION

These results demonstrate the importance of using unbiased automated methods to mitigate experimenter-dependent bias in analyzing dendritic morphology.

Neuropeptide Y increases differentiation of human olfactory receptor neurons through the Y1 receptor

Olfactory dysfunction significantly impedes the life quality of patients. Neuropeptide Y (NPY) is not only a neurotrophic factor in the rodent olfactory system but also an orexigenic peptide that regulates feeding behavior. NPY increases the olfactory receptor neurons (ORNs) responsivity during starvation; however, whether NPY can promote differentiation of human ORNs remains unexplored. This study investigates the effect of NPY on the differentiation of human olfactory neuroepithelial cells in vitro. Human olfactory neuroepithelium explants were cultured on tissue culture polystyrene dishes for 21 days. Then, cells were cultured with or without NPY at the concentration of 0.5 ng/mℓ for 7 days. The effects of treatment were assessed by phase contrast microscopy, immunocytochemistry and western blot analysis. The further mechanism was evaluated with NPY Y1 receptor-selected antagonist BIBP3226. NPY-treated olfactory neuroepithelial cells exhibited thin bipolar shape, low circularity, low spread area, and long processes. The expression levels of Ascl1, βIII tubulin, GAP43 and OMP were significantly higher in NPY-treated cells than in controls (p < 0.05). NPY-treated olfactory neuroepithelial cells expressed more components of signal transduction apparatuses, G_olf and ADCY3, than those without NPY treatment. Western blot analysis also further confirmed these findings (p < 0.05). Additionally, the expression levels of Ascl1, βIII2 tubulin, GAP43, OMP, ADCY3, and Golf in BIBP3226 + NPY and controls were comparable (p > 0.05). NPY not only increases expressions of protein markers of human olfactory neuronal progenitor cells, but also promotes differentiation of ORN and enhances formation of components of olfactory-specific signal transduction pathway through Y1 receptors.

Micropatterned nanolayers immobilized with nerve growth factor for neurite formation of PC12 cells

BACKGROUND

Nerve regeneration is important for the treatment of degenerative diseases and neurons injured by accidents. Nerve growth factor (NGF) has been previously conjugated to materials for promotion of neurogenesis.

MATERIALS AND METHODS

Photoreactive gelatin was prepared by chemical coupling of gelatin with azidobenzoic acid (P-gel), and then NGF was immobilized on substrates in the presence or absence of micropatterned photomasks. UV irradiation induced crosslinking reactions of P-gel with itself, NGF, and the plate for immobilization.

RESULTS

By adjustment of the P-gel concentration, the nanometer-order height of micropatterns was controlled. NGF was quantitatively immobilized with increasing amounts of P-gel. Immobilized NGF induced neurite outgrowth of PC12 cells, a cell line derived from a pheochromocytoma of the rat adrenal medulla, at the same level as soluble NGF. The immobilized NGF showed higher thermal stability than the soluble NGF and was repeatedly used without loss of biological activity. The 3D structure (height of the formed micropattern) regulated the behavior of neurite guidance. As a result, the orientation of neurites was regulated by the stripe pattern width.

CONCLUSION

The micropattern-immobilized NGF nanolayer biochemically and topologically regulated neurite formation.

Mining Big Neuron Morphological Data

The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.

Automated analysis of images for molecular quantification in immunohistochemistry

The quantification of the expression of different molecules is a key question in both basic and applied sciences. While protein quantification through molecular techniques leads to the loss of spatial information and resolution, immunohistochemistry is usually associated with time-consuming image analysis and human bias. In addition, the scarce automatic software analysis is often proprietary and expensive and relies on a fixed threshold binarization. Here we describe and share a set of macros ready for automated fluorescence analysis of large batches of fixed tissue samples using FIJI/ImageJ. The quantification of the molecules of interest are based on an automatic threshold analysis of immunofluorescence images to automatically identify the top brightest structures of each image. These macros measure several parameters commonly quantified in basic neuroscience research, such as neuropil density and fluorescence intensity of synaptic puncta, perisomatic innervation and col-localization of different molecules and analysis of the neurochemical phenotype of neuronal subpopulations. In addition, these same macro functions can be easily modified to improve similar analysis of fluorescent probes in human biopsies for diagnostic purposes based on the expression patterns of several molecules.

Discovery of a novel ROCK2 inhibitor with anti-migration effects via docking and high-content drug screening

Rho-associated protein kinase (ROCK) mediated the reorganization of the actin cytoskeleton and has been implicated in the spread and metastatic process of cancer. In this study, structure-based high-throughput virtual screening was used to identify candidate compounds targeting ROCK2 from a chemical library. Moreover, high-content screening based on neurite outgrowth of SH-SY5Y cells (a human neuroblastoma cell line) was used for accelerating the identification of compounds with characteristics of ROCK2 inhibitors. The effects of bioactive ROCK2 inhibitor candidates were further validated using other bioassays including cell migration and wound healing in SH-SY5Y cells. Through the combined virtual and high-content drug screening, the compound 1,3-benzodioxol-5-yl[1-(5-isoquinolinylmethyl)-3-piperidinyl]-methanone (BIPM) was identified as a novel and potent ROCK2 inhibitor. Exposure of SH-SY5Y cells to BIPM led to significant changes in neurite length, cell migration and actin stress fibers. Further experiments demonstrated that BIPM was able to significantly inhibit phosphorylation of cofilin, a regulatory protein of actin cytoskeleton. These results suggest that BIPM could be considered as a promising scaffold for the further development of ROCK2 inhibitors for anti-cancer metastasis.

Quantitative 3D analysis of complex single border cell behaviors in coordinated collective cell migration

Understanding the mechanisms of collective cell migration is crucial for cancer metastasis, wound healing and many developmental processes. Imaging a migrating cluster in vivo is feasible, but the quantification of individual cell behaviours remains challenging. We have developed an image analysis toolkit, CCMToolKit, to quantify the Drosophila border cell system. In addition to chaotic motion, previous studies reported that the migrating cells are able to migrate in a highly coordinated pattern. We quantify the rotating and running migration modes in 3D while also observing a range of intermediate behaviours. Running mode is driven by cluster external protrusions. Rotating mode is associated with cluster internal cell extensions that could not be easily characterized. Although the cluster moves slower while rotating, individual cells retain their mobility and are in fact slightly more active than in running mode. We also show that individual cells may exchange positions during migration.

GAIN: A graphical method to automatically analyze individual neurite outgrowth

BACKGROUND

Neurite outgrowth is a metric widely used to assess the success of in vitro neural stem cell differentiation or neuron reprogramming protocols and to evaluate high-content screening assays for neural regenerative drug discovery. However, neurite measurements are tedious to perform manually, and there is a paucity of freely available, fully automated software to determine neurite measurements and neuron counting. To provide such a tool to the neurobiology, stem cell, cell engineering, and neuroregenerative communities, we developed an algorithm for performing high-throughput neurite analysis in immunofluorescent images.

NEW METHOD

Given an input of paired neuronal nuclear and cytoskeletal microscopy images, the GAIN algorithm calculates neurite length statistics linked to individual cells or clusters of cells. It also provides an estimate of the number of nuclei in clusters of overlapping cells, thereby increasing the accuracy of neurite length statistics for higher confluency cultures. GAIN combines image processing for neuronal cell bodies and neurites with an algorithm for resolving neurite junctions.

RESULTS

GAIN produces a table of neurite lengths from cell body to neurite tip per cell cluster in an image along with a count of cells per cluster.

COMPARISON WITH EXISTING METHODS

GAIN's performance compares favorably with the popular ImageJ plugin NeuriteTracer for counting neurons, and provides the added benefit of assigning neurites to their respective cell bodies.

CONCLUSIONS

In summary, GAIN provides a new tool to improve the robust assessment of neural cells by image-based analysis.

Large-scale image-based screening and profiling of cellular phenotypes

Cellular phenotypes are observable characteristics of cells resulting from the interactions of intrinsic and extrinsic chemical or biochemical factors. Image-based phenotypic screens under large numbers of basal or perturbed conditions can be used to study the influences of these factors on cellular phenotypes. Hundreds to thousands of phenotypic descriptors can also be quantified from the images of cells under each of these experimental conditions. Therefore, huge amounts of data can be generated, and the analysis of these data has become a major bottleneck in large-scale phenotypic screens. Here, we review current experimental and computational methods for large-scale image-based phenotypic screens. Our focus is on phenotypic profiling, a computational procedure for constructing quantitative and compact representations of cellular phenotypes based on the images collected in these screens. © 2016 International Society for Advancement of Cytometry.

NeuronCyto II: An automatic and quantitative solution for crossover neural cells in high throughput screening

Microscopy is a fundamental technology driving new biological discoveries. Today microscopy allows a large number of images to be acquired using, for example, High Throughput Screening (HTS) and 4D imaging. It is essential to be able to interrogate these images and extract quantitative information in an automated fashion. In the context of neurobiology, it is important to automatically quantify the morphology of neurons in terms of neurite number, length, branching and complexity, etc. One major issue in quantification of neuronal morphology is the “crossover” problem where neurites cross and it is difficult to assign which neurite belongs to which cell body. In the present study, we provide a solution to the “crossover” problem, the software package NeuronCyto II. NeuronCyto II is an interactive and user‐friendly software package for automatic neurite quantification. It has a well‐designed graphical user interface (GUI) with only a few free parameters allowing users to optimize the software by themselves and extract relevant quantitative information routinely. Users are able to interact with the images and the numerical features through the Result Inspector. The processing of neurites without crossover was presented in our previous work. Our solution for the “crossover” problem is developed based on our recently published work with directed graph theory. Both methods are implemented in NeuronCyto II. The results show that our solution is able to significantly improve the reliability and accuracy of the neurons displaying “crossover.” NeuronCyto II is freely available at the website: https://sites.google.com/site/neuroncyto/, which includes user support and where software upgrades will also be placed in the future. © 2016 The Authors. Cytometry Part A Published by Wiley Periodicals, Inc. on behalf of ISAC.

A Graph-Theoretical Approach for Tracing Filamentary Structures in Neuronal and Retinal Images

The aim of this study is about tracing filamentary structures in both neuronal and retinal images. It is often crucial to identify single neurons in neuronal networks, or separate vessel tree structures in retinal blood vessel networks, in applications such as drug screening for neurological disorders or computer-aided diagnosis of diabetic retinopathy. Both tasks are challenging as the same bottleneck issue of filament crossovers is commonly encountered, which essentially hinders the ability of existing systems to conduct large-scale drug screening or practical clinical usage. To address the filament crossovers' problem, a two-step graph-theoretical approach is proposed in this paper. The first step focuses on segmenting filamentary pixels out of the background. This produces a filament segmentation map used as input for the second step, where they are further separated into disjointed filaments. Key to our approach is the idea that the problem can be reformulated as label propagation over directed graphs, such that the graph is to be partitioned into disjoint sub-graphs, or equivalently, each of the neurons (vessel trees) is separated from the rest of the neuronal (vessel) network. This enables us to make the interesting connection between the tracing problem and the digraph matrix-forest theorem in algebraic graph theory for the first time. Empirical experiments on neuronal and retinal image datasets demonstrate the superior performance of our approach over existing methods.

High-throughput histopathological image analysis via robust cell segmentation and hashing

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells .

Light sheet fluorescence microscopy (LSFM): past, present and future

Light sheet fluorescence microscopy (LSFM) has emerged as an important imaging modality to follow biology in live 3D samples over time with reduced phototoxicity and photobleaching. In particular, LSFM has been instrumental in revealing the detail of early embryonic development of Zebrafish, Drosophila, and C. elegans. Open access projects, DIY-SPIM, OpenSPIM, and OpenSPIN, now allow LSFM to be set-up easily and at low cost. The aim of this paper is to facilitate the set-up and use of LSFM by reviewing and comparing open access projects, image processing tools and future challenges.

Loss of α-catenin elicits a cholestatic response and impairs liver regeneration

The liver is unique in its capacity to regenerate after injury, during which hepatocytes actively divide and establish cell-cell contacts through cell adhesion complexes. Here, we demonstrate that the loss of α-catenin, a well-established adhesion component, dramatically disrupts liver regeneration. Using a partial hepatectomy model, we show that regenerated livers from α-catenin knockdown mice are grossly larger than control regenerated livers, with an increase in cell size and proliferation. This increased proliferation correlated with increased YAP activation, implicating α-catenin in the Hippo/YAP pathway. Additionally, α-catenin knockdown mice exhibited a phenotype reminiscent of clinical cholestasis, with drastically altered bile canaliculi, elevated levels of bile components and signs of jaundice and inflammation. The disrupted regenerative capacity is a result of actin cytoskeletal disorganisation, leading to a loss of apical microvilli, dilated lumens in the bile canaliculi, and leaky tight junctions. This study illuminates a novel, essential role for α-catenin in liver regeneration.

Automatic Myonuclear Detection in Isolated Single Muscle Fibers Using Robust Ellipse Fitting and Sparse Representation

Accurate and robust detection of myonuclei in isolated single muscle fibers is required to calculate myonuclear domain size. However, this task is challenging because: 1) shape and size variations of the nuclei, 2) overlapping nuclear clumps, and 3) multiple z-stack images with out-of-focus regions. In this paper, we have proposed a novel automatic detection algorithm to robustly quantify myonuclei in isolated single skeletal muscle fibers. The original z-stack images are first converted into one all-in-focus image using multi-focus image fusion. A sufficient number of ellipse fitting hypotheses are then generated from the myonuclei contour segments using heteroscedastic errors-in-variables (HEIV) regression. A set of representative training samples and a set of discriminative features are selected by a two-stage sparse model. The selected samples with representative features are utilized to train a classifier to select the best candidates. A modified inner geodesic distance based mean-shift clustering algorithm is used to produce the final nuclei detection results. The proposed method was extensively tested using 42 sets of z-stack images containing over 1,500 myonuclei. The method demonstrates excellent results that are better than current state-of-the-art approaches.

Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model

Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology, and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution, and diversity in cell size, and morphology. We believe this method would speed drawing the neural circuits and boost brain studies.

Cytometry in the brain: studying differentiation to diagnostic applications in brain disease and regeneration therapy

During brain development, a population of uniform embryonic cells migrates and differentiates into a large number of neural phenotypes - origin of the enormous complexity of the adult nervous system. Processes of cell proliferation, differentiation and programmed death of no longer required cells, do not occur only during embryogenesis, but are also maintained during adulthood and are affected in neurodegenerative and neuropsychiatric disease states. As neurogenesis is an endogenous response to brain injury, visible as proliferation (of to this moment silent stem or progenitor cells), its further stimulation can present a treatment strategy in addition to stem cell transfer for cell regeneration therapy. Concise techniques for studying such events in vitro and in vivo permit understanding of underlying mechanisms. Detection of subtle physiological alterations in brain cell proliferation and neurogenesis can be explored, that occur during environmental stimulation, exercise and ageing. Here, we have collected achievements in the field of basic research on applications of cytometry, including automated imaging for quantification of morphological or fluorescence-based parameters in cell cultures, towards imaging of three-dimensional brain architecture together with DNA content and proliferation data. Multi-parameter and more recently in vivo flow cytometry procedures, have been developed for quantification of phenotypic diversity and cell processes that occur during brain development as well as in adulthood, with importance for therapeutic approaches.

A rapid and efficient 2D/3D nuclear segmentation method for analysis of early mouse embryo and stem cell image data

Segmentation is a fundamental problem that dominates the success of microscopic image analysis. In almost 25 years of cell detection software development, there is still no single piece of commercial software that works well in practice when applied to early mouse embryo or stem cell image data. To address this need, we developed MINS (modular interactive nuclear segmentation) as a MATLAB/C++-based segmentation tool tailored for counting cells and fluorescent intensity measurements of 2D and 3D image data. Our aim was to develop a tool that is accurate and efficient yet straightforward and user friendly. The MINS pipeline comprises three major cascaded modules: detection, segmentation, and cell position classification. An extensive evaluation of MINS on both 2D and 3D images, and comparison to related tools, reveals improvements in segmentation accuracy and usability. Thus, its accuracy and ease of use will allow MINS to be implemented for routine single-cell-level image analyses.

Positive and negative regulation of Gli activity by Kif7 in the zebrafish embryo

Loss of function mutations of Kif7, the vertebrate orthologue of the Drosophila Hh pathway component Costal2, cause defects in the limbs and neural tubes of mice, attributable to ectopic expression of Hh target genes. While this implies a functional conservation of Cos2 and Kif7 between flies and vertebrates, the association of Kif7 with the primary cilium, an organelle absent from most Drosophila cells, suggests their mechanisms of action may have diverged. Here, using mutant alleles induced by Zinc Finger Nuclease-mediated targeted mutagenesis, we show that in zebrafish, Kif7 acts principally to suppress the activity of the Gli1 transcription factor. Notably, we find that endogenous Kif7 protein accumulates not only in the primary cilium, as previously observed in mammalian cells, but also in cytoplasmic puncta that disperse in response to Hh pathway activation. Moreover, we show that Drosophila Costal2 can substitute for Kif7, suggesting a conserved mode of action of the two proteins. We show that Kif7 interacts with both Gli1 and Gli2a and suggest that it functions to sequester Gli proteins in the cytoplasm, in a manner analogous to the regulation of Ci by Cos2 in Drosophila. We also show that zebrafish Kif7 potentiates Gli2a activity by promoting its dissociation from the Suppressor of Fused (Sufu) protein and present evidence that it mediates a Smo dependent modification of the full length form of Gli2a. Surprisingly, the function of Kif7 in the zebrafish embryo appears restricted principally to mesodermal derivatives, its inactivation having little effect on neural tube patterning, even when Sufu protein levels are depleted. Remarkably, zebrafish lacking all Kif7 function are viable, in contrast to the peri-natal lethality of mouse kif7 mutants but similar to some Acrocallosal or Joubert syndrome patients who are homozygous for loss of function KIF7 alleles.

Automated image segmentation of haematoxylin and eosin stained skeletal muscle cross-sections

The ability to accurately and efficiently quantify muscle morphology is essential to determine the physiological relevance of a variety of muscle conditions including growth, atrophy and repair. There is agreement across the muscle biology community that important morphological characteristics of muscle fibres, such as cross-sectional area, are critical factors that determine the health and function (e.g. quality) of the muscle. However, at this time, quantification of muscle characteristics, especially from haematoxylin and eosin stained slides, is still a manual or semi-automatic process. This procedure is labour-intensive and time-consuming. In this paper, we have developed and validated an automatic image segmentation algorithm that is not only efficient but also accurate. Our proposed automatic segmentation algorithm for haematoxylin and eosin stained skeletal muscle cross-sections consists of two major steps: (1) A learning-based seed detection method to find the geometric centres of the muscle fibres, and (2) a colour gradient repulsive balloon snake deformable model that adopts colour gradient in Luv colour space. Automatic quantification of muscle fibre cross-sectional areas using the proposed method is accurate and efficient, providing a powerful automatic quantification tool that can increase sensitivity, objectivity and efficiency in measuring the morphometric features of the haematoxylin and eosin stained muscle cross-sections.

Segmenting the etiological agent of schistosomiasis for high-content screening

Schistosomiasis is a parasitic disease with a global health impact second only to malaria. The World Health Organization has classified schistosomiasis as an illness for which new therapies are urgently needed. However, the causative parasite is refractory to current high-throughput drug screening due to the diversity and complexity of shape, appearance and movement-based phenotypes exhibited in response to putative drugs. Currently, there is no automated image-based approach capable of relieving this deficiency. We propose and validate an image segmentation algorithm designed to overcome the distinct challenges posed by schistosomes and macroparasites in general, including irregular shapes and sizes, dense groups of touching parasites and the unpredictable effects of drug exposure. Our approach combines a region-based distributing function with a novel edge detector derived from phase congruency and grayscale thinning by threshold superposition. The method is sufficiently rapid, robust and accurate to be used for quantitative analysis of diverse parasite phenotypes in high-throughput and high-content screening.

Enhancing automated micrograph-based evaluation of LPS-stimulated macrophage spreading

To evaluate macrophage spreading in immunofluorescence images of macrophages for surface protein CD11b and nuclear counterstaining with DAPI, it is necessary to measure the size of the macrophages at different time points after stimulation. Manual evaluation of fluorescent micrographs is usually a time-consuming and error-prone task, with poor reproducibility. Automatic image analysis methods can be used to improve the results. The quality of the analysis with these methods mainly depends on the quality of the image segmentation. A segmentation and quantification scheme based on shading correction, k-means clustering, and fast marching level sets has been developed for the purpose. An initial application of this approach showed that separating touching and overlapping cells in particular suffers severely in the inevitably blurred conditions, leading to partly erroneous measurements of macrophage spreading. An alternative method of segmentation in fluorescent micrographs was therefore investigated and evaluated in this study. The proposed approach uses a methodology that separates foreground objects from background objects on the basis of Boykov's graph cuts. In this process, a rough estimation of background pixels is used for background seeds. To identify foreground seeds, a difference of Gaussian band pass filter based workflow is developed. Information on foreground and background seeds is then used for a gradient magnitude based graph cut resulting in a robust figure-ground separation method. In addition, a fast marching level set approach is used in the post-processing step, which makes it possible to split touching cells by incorporating information about the cell nuclei. An evaluation based on a total of 553 manually labeled macrophages depicted in 21 micrographs showed that the proposed method significantly improves segmentation and splitting performance for fluorescent micrographs of LPS-stimulated macrophages and reduces the rate of error in automated analysis of macrophage spreading in comparison with alternative methods.

Live imaging, identifying, and tracking single cells in complex populations in vivo and ex vivo

Advances in optical imaging technologies combined with the use of genetically encoded fluorescent proteins have enabled the visualization of stem cells over extensive periods of time in vivo and ex vivo. The generation of genetically encoded fluorescent protein reporters that are fused with subcellularly localized proteins, such as human histone H2B, has made it possible to direct fluorescent protein reporters to specific subcellular structures and identify single cells in complex populations. This facilitates the visualization of cellular behaviors such as division, movement, and apoptosis at a single-cell resolution and, in principle, allows the prospective and retrospective tracking towards determining the lineage of each cell.

WIS-NeuroMath enables versatile high throughput analyses of neuronal processes

Automated analyses of neuronal morphology are important for quantifying connectivity and circuitry in vivo, as well as in high content imaging of primary neuron cultures. The currently available tools for quantification of neuronal morphology either are highly expensive commercial packages or cannot provide automated image quantifications at single cell resolution. Here, we describe a new software package called WIS-NeuroMath, which fills this gap and provides solutions for automated measurement of neuronal processes in both in vivo and in vitro preparations. Diverse image types can be analyzed without any preprocessing, enabling automated and accurate detection of neurites followed by their quantification in a number of application modules. A cell morphology module detects cell bodies and attached neurites, providing information on neurite length, number of branches, cell body area, and other parameters for each cell. A neurite length module provides a solution for images lacking cell bodies, such as tissue sections. Finally, a ganglion explant module quantifies outgrowth by identifying neurites at different distances from the ganglion. Quantification of a diverse series of preparations with WIS-NeuroMath provided data that were well matched with parallel analyses of the same preparations in established software packages such as MetaXpress or NeuronJ. The capabilities of WIS-NeuroMath are demonstrated in a range of applications, including in dissociated and explant cultures and histological analyses on thin and whole-mount sections. WIS-NeuroMath is freely available to academic users, providing a versatile and cost-effective range of solutions for quantifying neurite growth, branching, regeneration, or degeneration under different experimental paradigms.

Automatic cell counting in vivo in the larval nervous system of Drosophila

Identification and counting of cells is necessary to test biological hypotheses, for instance of nervous system formation, disease, degeneration, injury and regeneration, but manual counting is time consuming, tedious, and subject to bias. The fruit fly Drosophila is a widely used model organism to analyse gene function, and most research is carried out in the intact animal or in whole organs, rather than in cell culture. Inferences on gene function require that cell counts are known from these sample types. Image processing and pattern recognition techniques are appropriate tools to automate cell counting. However, counting cells in Drosophila is a complex task: variations in immunohistochemical markers and developmental stages result in images of very different properties, rendering it challenging to identify true cells. Here, we present a technique for counting automatically larval glial cells in three dimensions, from confocal microscopy serial optical sections. Local outlier thresholding and domes are combined to find the cells. Shape descriptors extracted from a data set are used to characterize cells and avoid oversegmentation. Morphological operators are employed to divide cells that could otherwise be missed. The method is accurate and very fast, and treats all samples equally and objectively, rendering all data comparable across specimens. Our method is also applicable to identify cells labelled with other nuclear markers and in sections of mouse tissues.

Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set

Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMAs) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm that can reliably separate touching cells in hematoxylin-stained breast TMA specimens that have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach that utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on 234 image patches exhibiting dense overlap and containing more than 2200 cells. It was also tested on whole slide images including blood smears and TMAs containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) that resulted in significant speedup over the C/C++ implementation.

Comparison of parameter-adapted segmentation methods for fluorescence micrographs

Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells.

Comparison of segmentation algorithms for fluorescence microscopy images of cells

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability.

NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery

Background

Automatic quantification of neuronal morphology from images of fluorescence microscopy plays an increasingly important role in high-content screenings. However, there exist very few freeware tools and methods which provide automatic neuronal morphology quantification for pharmacological discovery.

Results

This study proposes an effective quantification method, called NeurphologyJ, capable of automatically quantifying neuronal morphologies such as soma number and size, neurite length, and neurite branching complexity (which is highly related to the numbers of attachment points and ending points). NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image processing and analysis platform. The high performance of NeurphologyJ arises mainly from an elegant image enhancement method. Consequently, some morphology operations of image processing can be efficiently applied. We evaluated NeurphologyJ by comparing it with both the computer-aided manual tracing method NeuronJ and an existing ImageJ-based plugin method NeuriteTracer. Our results reveal that NeurphologyJ is comparable to NeuronJ, that the coefficient correlation between the estimated neurite lengths is as high as 0.992. NeurphologyJ can accurately measure neurite length, soma number, neurite attachment points, and neurite ending points from a single image. Furthermore, the quantification result of nocodazole perturbation is consistent with its known inhibitory effect on neurite outgrowth. We were also able to calculate the IC50 of nocodazole using NeurphologyJ. This reveals that NeurphologyJ is effective enough to be utilized in applications of pharmacological discoveries.

Conclusions

This study proposes an automatic and fast neuronal quantification method NeurphologyJ. The ImageJ plugin with supports of batch processing is easily customized for dealing with high-content screening applications. The source codes of NeurphologyJ (interactive and high-throughput versions) and the images used for testing are freely available (see Availability).

Automated Sholl analysis of digitized neuronal morphology at multiple scales: Whole cell Sholl analysis versus Sholl analysis of arbor subregions

The morphology of dendrites and the axon determines how a neuron processes and transmits information. Neurite morphology is frequently analyzed by Sholl analysis or by counting the total number of neurites and branch tips. However, the time and resources required to perform such analysis by hand is prohibitive for the processing of large data sets and introduces problems with data auditing and reproducibility. Furthermore, analyses performed by hand or using course-grained morphometric data extraction tools can obscure subtle differences in data sets because they do not store the data in a form that facilitates the application of multiple analytical tools. To address these shortcomings, we have developed a program (titled "Bonfire") to facilitate digitization of neurite morphology and subsequent Sholl analysis. Our program builds upon other available open-source morphological analysis tools by performing Sholl analysis on subregions of the neuritic arbor, enabling the detection of local level changes in dendrite and axon branching behavior. To validate this new tool, we applied Bonfire analysis to images of hippocampal neurons treated with 25 ng/ml brain-derived neurotrophic factor (BDNF) and untreated control neurons. Consistent with prior findings, conventional Sholl analysis revealed that global exposure to BDNF increases the number of neuritic intersections proximal to the soma. Bonfire analysis additionally uncovers that BDNF treatment affects both root processes and terminal processes with no effect on intermediate neurites. Taken together, our data suggest that global exposure of hippocampal neurons to BDNF results in a reorganization of neuritic segments within their arbors, but not necessarily a change in their number or length. These findings were only made possible by the neurite-specific Sholl data returned by Bonfire analysis.

Automated analysis of neuronal morphology, synapse number and synaptic recruitment

The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis.

Neuron tracing in perspective

The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.

HCA-vision: Automated neurite outgrowth analysis

Automating the analysis of neurons in culture represents a key aspect of the search for neuroactive compounds. A number of commercial neurite analysis software packages tend to measure some basic features such as total neurite length and number of branching points. However, with only these measurements, some differences between neurite morphologies that are clear to a human observer cannot be identified. The authors have developed a suite of image analysis tools that will allow researchers to produce quality analyses at primary screening rates. The suite provides sensitive and information-rich measurements of neurons and neurites. It can discriminate subtle changes in complex neurite arborization even when neurons and neurites are dense. This allows users to selectively screen for compounds triggering different types of neurite outgrowth behavior. In mixed cell populations, neurons can be filtered and separated from other brain cell types so that neurite analysis can be performed only on neurons. It supports batch processing with a built-in database to store the batch-processing results, a batch result viewer, and an ad hoc query builder for users to retrieve features of interest. The suite of tools has been deployed into a software package called HCA-Vision. The free version of the software package is available at http://www.hca-vision.com.

Evolving generalized Voronoi diagrams for accurate cellular image segmentation

Analyzing cellular morphologies on a cell-by-cell basis is vital for drug discovery, cell biology, and many other biological studies. Interactions between cells in their culture environments cause cells to touch each other in acquired microscopy images. Because of this phenomenon, cell segmentation is a challenging task, especially when the cells are of similar brightness and of highly variable shapes. The concept of topological dependence and the maximum common boundary (MCB) algorithm are presented in our previous work (Yu et al., Cytometry Part A 2009;75A:289-297). However, the MCB algorithm suffers a few shortcomings, such as low computational efficiency and difficulties in generalizing to higher dimensions. To overcome these limitations, we present the evolving generalized Voronoi diagram (EGVD) algorithm. Utilizing image intensity and geometric information, EGVD preserves topological dependence easily in both 2D and 3D images, such that touching cells can be segmented satisfactorily. A systematic comparison with other methods demonstrates that EGVD is accurate and much more efficient.

DeadEasy neurons: automatic counting of HB9 neuronal nuclei in Drosophila

Research into the genetic basis of nervous system development and neurodegenerative diseases requires counting neurons to find out the extent of neurogenesis or neuronal loss. Drosophila is a widely used model organism for in vivo studies. However, counting neurons throughout the nervous system of the intact animal is humanly unfeasible. Automatic methods for cell counting in intact Drosophila are desirable. Here, we show a method called DeadEasy Neurons to count the number of neurons stained with anti-HB9 antibodies in Drosophila embryos. DeadEasy Neurons employs image filtering and mathematical morphology techniques in 2D and 3D, followed by identification of nuclei in 3D based on minimum volume, to count automatically the number of HB9 neurons in vivo. The resultant method has been validated for Drosophila embryos and we show here how it can be used to address biological questions. Counting neurons with DeadEasy is very fast, extremely accurate, and objective, and it enables analyses otherwise humanly unmanageable. DeadEasy Neurons can be modified by the user for other applications, and it will be freely available as an ImageJ plug-in. DeadEasy Neurons will be of interest to the microscopy, image processing, Drosophila, neurobiology, and biomedical communities.