HCA-vision: Automated neurite outgrowth analysis

TSC2 loss in neural progenitor cells suppresses translation of ASD/NDD-associated transcripts in an mTORC1- and MNK1/2-reversible fashion

Tuberous sclerosis complex (TSC) is an inherited neurodevelopmental disorder (NDD) with frequent manifestations of epilepsy and autism spectrum disorder (ASD). TSC is caused by inactivating mutations in TSC1 or TSC2 tumor suppressor genes, with encoded proteins hamartin (TSC1) and tuberin (TSC2) forming a functional complex inhibiting mechanistic target of rapamycin complex 1 (mTORC1) signaling. This has led to treatment with allosteric mTORC1 inhibitor rapamycin analogs ("rapalogs") for TSC tumors; however, rapalogs are ineffective for treating neurodevelopmental manifestations. mTORC1 signaling controls protein synthesis by regulating formation of the eIF4F complex, with further modulation by MNK1/2 kinases via phosphorylation of the eIF4F subunit eIF4E. While both these pathways modulate translation, comparing their impact on transcriptome-wide mRNA translation, as well as effects of inhibiting these pathways in TSC has not been explored. Here, employing CRISPR-modified, isogenic TSC2 patient-derived neural progenitor cells (NPCs), we have examined transcriptome-wide changes in mRNA translation upon TSC2 loss. Our results reveal dysregulated translation in TSC2 -Null NPCs, which significantly overlaps with the translatome from TSC1 -Null NPCs. Interestingly, numerous non-monogenic ASD-, NDD-and epilepsy-associated genes identified in patients harboring putative loss-of-function mutations, were translationally suppressed in TSC2 -Null NPCs. Importantly, translation of these ASD- and NDD-associated genes was reversed upon inhibition of either mTORC1 or MNK1/2 signaling using RMC-6272 or eFT-508, respectively. This study establishes the importance of mTORC1-eIF4F- and MNK-eIF4E-sensitive mRNA translation in TSC, ASD and other neurodevelopmental disorders laying the groundwork for evaluating drugs in clinical development that target these pathways as a treatment strategy for these disorders.

The potential of baicalin to enhance neuroprotection and mitochondrial function in a human neuronal cell model

Baicalin is a flavone glycoside derived from flowering plants belonging to the Scutellaria genus. Previous studies have reported baicalin's anti-inflammatory and neuroprotective properties in rodent models, indicating the potential of baicalin in neuropsychiatric disorders where alterations in numerous processes are observed. However, the extent of baicalin's therapeutic effects remains undetermined in a human cell model, more specifically, neuronal cells to mimic the brain environment in vitro. As a proof of concept, we treated C8-B4 cells (murine cell model) with three different doses of baicalin (0.1, 1 and 5 μM) and vehicle control (DMSO) for 24 h after liposaccharide-induced inflammation and measured the levels of TNF-α in the medium by ELISA. NT2-N cells (human neuronal-like cell model) underwent identical baicalin treatment, followed by RNA extraction, genome-wide mRNA expression profiles and gene set enrichment analysis (GSEA). We also performed neurite outgrowth assays and mitochondrial flux bioanalysis (Seahorse) in NT2-N cells. We found that in C8-B4 cells, baicalin at ≥ 1 μM exhibited anti-inflammatory effects, lowering TNF-α levels in the cell culture media. In NT2-N cells, baicalin positively affected neurite outgrowth and transcriptionally up-regulated genes in the tricarboxylic acid cycle and the glycolysis pathway. Similarly, Seahorse analysis showed increased oxygen consumption rate in baicalin-treated NT2-N cells, an indicator of enhanced mitochondrial function. Together, our findings have confirmed the neuroprotective and mitochondria enhancing effects of baicalin in human-neuronal like cells. Given the increased prominence of mitochondrial mechanisms in diverse neuropsychiatric disorders and the paucity of mitochondrial therapeutics, this suggests the potential therapeutic application of baicalin in human neuropsychiatric disorders where these processes are altered.

Translatome analysis of tuberous sclerosis complex 1 patient-derived neural progenitor cells reveals rapamycin-dependent and independent alterations

BACKGROUND

Tuberous sclerosis complex (TSC) is an inherited neurocutaneous disorder caused by mutations in the TSC1 or TSC2 genes, with patients often exhibiting neurodevelopmental (ND) manifestations termed TSC-associated neuropsychiatric disorders (TAND) including autism spectrum disorder (ASD) and intellectual disability. Hamartin (TSC1) and tuberin (TSC2) proteins form a complex inhibiting mechanistic target of rapamycin complex 1 (mTORC1) signaling. Loss of TSC1 or TSC2 activates mTORC1 that, among several targets, controls protein synthesis by inhibiting translational repressor eIF4E-binding proteins. Using TSC1 patient-derived neural progenitor cells (NPCs), we recently reported early ND phenotypic changes, including increased cell proliferation and altered neurite outgrowth in TSC1-null NPCs, which were unaffected by the mTORC1 inhibitor rapamycin.

METHODS

Here, we used polysome profiling, which quantifies changes in mRNA abundance and translational efficiencies at a transcriptome-wide level, to compare CRISPR-edited TSC1-null with CRISPR-corrected TSC1-WT NPCs generated from one TSC donor (one clone/genotype). To assess the relevance of identified gene expression alterations, we performed polysome profiling in postmortem brains from ASD donors and age-matched controls. We further compared effects on translation of a subset of transcripts and rescue of early ND phenotypes in NPCs following inhibition of mTORC1 using the allosteric inhibitor rapamycin versus a third-generation bi-steric, mTORC1-selective inhibitor RMC-6272.

RESULTS

Polysome profiling of NPCs revealed numerous TSC1-associated alterations in mRNA translation that were largely recapitulated in human ASD brains. Moreover, although rapamycin treatment partially reversed the TSC1-associated alterations in mRNA translation, most genes related to neural activity/synaptic regulation or ASD were rapamycin-insensitive. In contrast, treatment with RMC-6272 inhibited rapamycin-insensitive translation and reversed TSC1-associated early ND phenotypes including proliferation and neurite outgrowth that were unaffected by rapamycin.

CONCLUSIONS

Our work reveals ample mRNA translation alterations in TSC1 patient-derived NPCs that recapitulate mRNA translation in ASD brain samples. Further, suppression of TSC1-associated but rapamycin-insensitive translation and ND phenotypes by RMC-6272 unveils potential implications for more efficient targeting of mTORC1 as a superior treatment strategy for TAND.

Significant Sex Differences in the Efficacy of the CSF1R Inhibitor-PLX5622 on Rat Brain Microglia Elimination

Microglia play pivotal roles in central nervous system development, homeostasis, responses to trauma, and neurodegenerative and neuropsychiatric disorders with significant sex-bias in their symptoms and prevalence. Survival of the microglia in adult brains depends on the expression of the colony-stimulating factor 1 receptor (CSF1R). The inhibition of CSF1R by brain-permeant PLX5622 in the chow eliminates, within 5–10 days, ~90% of the microglia in female and male mice, thereby enabling the investigation of the roles of the microglia in health and pathological mice models. Because of a prevailing “impression” that PLX5622 is ineffective in rats, it has hardly been used in studies of adult rats. Here, we report that effective microglia elimination by PLX5622-chow in rats is highly sex-dependent. Our observations provide missing information for the limited use and interpretation of PLX5622 in biomedical studies of the microglia in rat models. The sex differences that are too often overlooked must be carefully considered and clearly emphasized.

An automated method for precise axon reconstruction from recordings of high-density micro-electrode arrays

Objective

Neurons communicate with each other by sending action potentials through their axons. The velocity of axonal signal propagation describes how fast electrical action potentials can travel. This velocity can be affected in a human brain by several pathologies, including multiple sclerosis, traumatic brain injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural electrical activity. The high density of the recording electrodes enables to image the activity of individual neurons down to subcellular resolution, which includes the propagation of axonal signals. However, axon reconstruction, to date, mainly relies on manual approaches to select the electrodes and channels that seemingly record the signals along a specific axon, while an automated approach to track multiple axonal branches in extracellular action-potential recordings is still missing.

Approach

In this article, we propose a fully automated approach to reconstruct axons from extracellular electrical-potential landscapes, so-called “electrical footprints” of neurons. After an initial electrode and channel selection, the proposed method first constructs a graph based on the voltage signal amplitudes and latencies. Then, the graph is interrogated to extract possible axonal branches. Finally, the axonal branches are pruned, and axonal action-potential propagation velocities are computed.

Main results

We first validate our method using simulated data from detailed reconstructions of neurons, showing that our approach is capable of accurately reconstructing axonal branches. We then apply the reconstruction algorithm to experimental recordings of HD-MEAs and show that it can be used to determine axonal morphologies and signal-propagation velocities at high throughput.

Significance

We introduce a fully automated method to reconstruct axonal branches and estimate axonal action-potential propagation velocities using HD-MEA recordings. Our method yields highly reliable and reproducible velocity estimations, which constitute an important electrophysiological feature of neuronal preparations.

Evaluation of chemical compounds that inhibit neurite outgrowth using GFP-labeled iPSC-derived human neurons

Due to the increasing number of drugs and untested environmental compounds introduced into commercial use, there is recognition for a need to develop reliable and efficient screening methods to identify compounds that may adversely impact the nervous system. One process that has been implicated in neurodevelopment is neurite outgrowth; the disruption of which can result in adverse outcomes that persist later in life. Here, we developed a green fluorescent protein (GFP) labeled neurite outgrowth assay in a high-content, high-throughput format using induced pluripotent stem cell (iPSC) derived human spinal motor neurons and cortical glutamatergic neurons. The assay was optimized for use in a 1536-well plate format. Then, we used this assay to screen a set of 84 unique compounds that have previously been screened in other neurite outgrowth assays. This library consists of known developmental neurotoxicants, environmental compounds with unknown toxicity, and negative controls. Neurons were cultured for 40 h and then treated with compounds at 11 concentrations ranging from 1.56 nM to 92 μM for 24 and 48 h. Effects of compounds on neurite outgrowth were evaluated by quantifying total neurite length, number of segments, and maximum neurite length per cell. Among the 84 tested compounds, neurite outgrowth in cortical neurons and motor neurons were selectively inhibited by 36 and 31 compounds, respectively. Colchicine, rotenone, and methyl mercuric (II) chloride inhibited neurite outgrowth in both cortical and motor neurons. It is interesting to note that some compounds like parathion and bisphenol AF had inhibitory effects on neurite outgrowth specifically in the cortical neurons, while other compounds, such as 2,2',4,4'-tetrabromodiphenyl ether and caffeine, inhibited neurite outgrowth in motor neurons. The data gathered from these studies show that GFP-labeled iPSC-derived human neurons are a promising tool for identifying and prioritizing compounds with developmental neurotoxicity potential for further hazard characterization.

Machine intelligence for nerve conduit design and production

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering.

Review of high-content screening applications in toxicology

High-content screening (HCS) technology combining automated microscopy and quantitative image analysis can address biological questions in academia and the pharmaceutical industry. Various HCS experimental applications have been utilized in the research field of in vitro toxicology. In this review, we describe several HCS application approaches used for studying the mechanism of compound toxicity, highlight some challenges faced in the toxicological community, and discuss the future directions of HCS in regards to new models, new reagents, data management, and informatics. Many specialized areas of toxicology including developmental toxicity, genotoxicity, developmental neurotoxicity/neurotoxicity, hepatotoxicity, cardiotoxicity, and nephrotoxicity will be examined. In addition, several newly developed cellular assay models including induced pluripotent stem cells (iPSCs), three-dimensional (3D) cell models, and tissues-on-a-chip will be discussed. New genome-editing technologies (e.g., CRISPR/Cas9), data analyzing tools for imaging, and coupling with high-content assays will be reviewed. Finally, the applications of machine learning to image processing will be explored. These new HCS approaches offer a huge step forward in dissecting biological processes, developing drugs, and making toxicology studies easier.

Validation and Optimization of an Image-Based Screening Method Applied to the Study of Neuronal Processes on Nanogrooves

Research on neuronal differentiation and neuronal network guidance induced through nanotopographical cues generates large datasets, and therefore the analysis of such data can be aided by automatable, unbiased image screening tools. To link such tools, we present an image-based screening method to evaluate the influence of nanogroove pattern dimensions on neuronal differentiation. This new method consists of combining neuronal feature detection software, here HCA-Vision, and a Frangi vesselness algorithm to calculate neurite alignment values and quantify morphological aspects of neurons, which are measured via neurite length, neuronal polarity, and neurite branching, for differentiated SH-SY5Y cells cultured on nanogrooved polydimethylsiloxane (PDMS) patterns in the 200–2000 nm range. The applicability of this method is confirmed by our results, which find that the level of alignment is dependent on nanogroove dimensions. Furthermore, the screening method reveals that differentiation and alignment are correlated. In particular, patterns with groove widths >200 nm and with a low ridge width to pattern period ratio have a quantifiable influence on alignment, neurite length, and polarity. In summary, the novel combination of software that forms a base for this statistical analysis method demonstrates good potential for evaluating tissue microarchitecture, which depends on subtle design variation in substrate topography. Using the screening method, we obtained automated and sensitive quantified readouts from large datasets.

Modulated DISP3/PTCHD2 expression influences neural stem cell fate decisions

Neural stem cells (NSCs) are defined by their dual ability to self-renew through mitotic cell division or differentiate into the varied neural cell types of the CNS. DISP3/PTCHD2 is a sterol-sensing domain-containing protein, highly expressed in neural tissues, whose expression is regulated by thyroid hormone. In the present study, we used a mouse NSC line to investigate what effect DISP3 may have on the self-renewal and/or differentiation potential of the cells. We demonstrated that NSC differentiation triggered significant reduction in DISP3 expression in the resulting astrocytes, neurons and oligodendrocytes. Moreover, when DISP3 expression was disrupted, the NSC "stemness" was suppressed, leading to a larger population of cells undergoing spontaneous neuronal differentiation. Conversely, overexpression of DISP3 resulted in increased NSC proliferation. When NSCs were cultured under differentiation conditions, we observed that the lack of DISP3 augmented the number of NSCs differentiating into each of the neural cell lineages and that neuronal morphology was altered. In contrast, DISP3 overexpression resulted in impaired cell differentiation. Taken together, our findings imply that DISP3 may help dictate the NSC cell fate to either undergo self-renewal or switch to the terminal differentiation cell program.

Modeling Axonal Defects in Hereditary Spastic Paraplegia with Human Pluripotent Stem Cells

BACKGROUND

Cortical motor neurons, also known as upper motor neurons, are large projection neurons whose axons convey signals to lower motor neurons to control the muscle movements. Degeneration of cortical motor neuron axons is implicated in several debilitating disorders, including hereditary spastic paraplegia (HSP) and amyotrophic lateral sclerosis (ALS). Since the discovery of the first HSP gene, SPAST that encodes spastin, over 70 distinct genetic loci associated with HSP have been identified. How the mutations of these functionally diverse genes result in axonal degeneration and why certain axons are affected in HSP remains largely unknown. The development of induced pluripotent stem cell (iPSC) technology has provided researchers an excellent resource to generate patient-specific human neurons to model human neuropathologic processes including axonal defects.

METHODS

In this article, we will frst review the pathology and pathways affected in the common forms of HSP subtypes by searching the PubMed database. We will then summurize the findings and insights gained from studies using iPSC-based models, and discuss the challenges and future directions.

RESULTS

HSPs, a heterogeneous group of genetic neurodegenerative disorders, are characterized by lower extremity weakness and spasticity that result from retrograde axonal degeneration of cortical motor neurons. Recently, iPSCs have been generated from several common forms of HSP including SPG4, SPG3A, and SPG11 patients. Neurons derived from HSP iPSCs exhibit disease-relevant axonal defects, such as impaired neurite outgrowth, increased axonal swellings, and reduced axonal transport.

CONCLUSION

These patient-derived neurons offer unique tools to study the pathogenic mechanisms and explore the treatments for rescuing axonal defects in HSP, as well as other diseases involving axonopathy.

A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography

The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.

Neuroprotective effects of apigenin against inflammation, neuronal excitability and apoptosis in an induced pluripotent stem cell model of Alzheimer's disease

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases, yet current therapeutic treatments are inadequate due to a complex disease pathogenesis. The plant polyphenol apigenin has been shown to have anti-inflammatory and neuroprotective properties in a number of cell and animal models; however a comprehensive assessment has not been performed in a human model of AD. Here we have used a human induced pluripotent stem cell (iPSC) model of familial and sporadic AD, in addition to healthy controls, to assess the neuroprotective activity of apigenin. The iPSC-derived AD neurons demonstrated a hyper-excitable calcium signalling phenotype, elevated levels of nitrite, increased cytotoxicity and apoptosis, reduced neurite length and increased susceptibility to inflammatory stress challenge from activated murine microglia, in comparison to control neurons. We identified that apigenin has potent anti-inflammatory properties with the ability to protect neurites and cell viability by promoting a global down-regulation of cytokine and nitric oxide (NO) release in inflammatory cells. In addition, we show that apigenin is able to protect iPSC-derived AD neurons via multiple means by reducing the frequency of spontaneous Ca(2+) signals and significantly reducing caspase-3/7 mediated apoptosis. These data demonstrate the broad neuroprotective action of apigenin against AD pathogenesis in a human disease model.

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.

High-content analysis of α-synuclein aggregation and cell death in a cellular model of Parkinson's disease

BACKGROUND

Alpha-synuclein (α-SYN) aggregates represent a key feature of Parkinson's disease, but the exact relationship between α-SYN aggregation and neurodegeneration remains incompletely understood. Therefore, the availability of a cellular assay that allows medium-throughput analysis of α-SYN-linked pathology will be of great value for studying the aggregation process and for advancing α-SYN-based therapies.

NEW METHOD

Here we describe a high-content neuronal cell assay that simultaneously measures oxidative stress-induced α-SYN aggregation and apoptosis.

RESULTS

We optimized an automated and reproducible assay to quantify both α-SYN aggregation and cell death in human SH-SY5Y neuroblastoma cells.

COMPARISON WITH EXISTING METHODS

Quantification of α-SYN aggregates in cells has typically relied on manual imaging and counting or cell-free assays, which are time consuming and do not allow a concurrent analysis of cell viability. Our high-content analysis method for quantification of α-SYN aggregation allows simultaneous measurements of multiple cell parameters at a single-cell level in a fast, objective and automated manner.

CONCLUSIONS

The presented analysis approach offers a rapid, objective and multiparametric approach for the screening of compounds and genes that might alter α-SYN aggregation and/or toxicity.

Novel High Content Screen Detects Compounds That Promote Neurite Regeneration from Cochlear Spiral Ganglion Neurons

The bipolar spiral ganglion neurons (SGN) carry sound information from cochlear hair cells to the brain. After noise, antibiotic or toxic insult to the cochlea, damage to SGN and/or hair cells causes hearing impairment. Damage ranges from fiber and synapse degeneration to dysfunction and loss of cells. New interventions to regenerate peripheral nerve fibers could help reestablish transfer of auditory information from surviving or regenerated hair cells or improve results from cochlear implants, but the biochemical mechanisms to target are largely unknown. Presently, no drugs exist that are FDA approved to stimulate the regeneration of SGN nerve fibers. We designed an original phenotypic assay to screen 440 compounds of the NIH Clinical Collection directly on dissociated mouse spiral ganglia. The assay detected one compound, cerivastatin, that increased the length of regenerating neurites. The effect, mimicked by other statins at different optimal concentrations, was blocked by geranylgeraniol. These results demonstrate the utility of screening small compound libraries on mixed cultures of dissociated primary ganglia. The success of this screen narrows down a moderately sized library to a single compound which can be elevated to in-depth in vivo studies, and highlights a potential new molecular pathway for targeting of hearing loss drugs.

Extraction of protein profiles from primary neurons using active contour models and wavelets

The function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means. We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites. We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses.

c-Jun N-terminal kinase in synergistic neurite outgrowth in PC12 cells mediated through P90RSK

Background

Synergistic multi-ligand treatments that can induce neuronal differentiation offer valuable strategies to regulate and modulate neurite outgrowth. Whereas the signaling pathways mediating single ligand-induced neurite outgrowth, such as Akt, extracellular signal-regulated kinase (Erk), c-Jun N-terminal kinase (JNK), and p38 mitogen-activated protein kinase (P38), have been extensively studied, the mechanisms underlying multi-ligand synergistic neurite outgrowth are poorly understood. In an attempt to gain insight into synergistic neurite outgrowth, PC12 cells were treated with one of three combinations: pituitary adenylate cyclase-activating peptide (PACAP) with epidermal growth factor (EP), basic fibroblast growth factor (FP), or nerve growth factor (NP) and then challenged with the appropriate kinase inhibitors to assess the signaling pathways involved in the process.

Results

Response surface analyses indicated that synergistic neurite outgrowth was regulated by distinct pathways in these systems. Synergistic increases in the phosphorylation of Erk and JNK, but not Akt or P38, were observed with the three growth factor-PACAP combinations. Unexpectedly, we identified a synergistic increase in JNK phosphorylation, which was involved in neurite outgrowth in the NP and FP, but not EP, systems. Inhibition of JNK using the SP600125 inhibitor reduced phosphorylation of 90 kDa ribosomal S6 kinase (P90RSK) in the NP and FP, but not EP, systems. This suggested the involvement of P90RSK in mediating the differential effects of JNK in synergistic neurite outgrowth.

Conclusions

Taken together, these findings reveal the involvement of distinct signaling pathways in regulating neurite outgrowth in response to different synergistic growth factor-PACAP treatments. Our findings demonstrate a hitherto unrecognized mechanism of JNK-P90RSK in mediating synergistic neurite outgrowth induced by the co-treatment of growth factors and PACAP.

HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening

Background

High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images.

Results

We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons.

Conclusions

Few automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.

Evaluation of a human neurite growth assay as specific screen for developmental neurotoxicants

Organ-specific in vitro toxicity assays are often highly sensitive, but they lack specificity. We evaluated here examples of assay features that can affect test specificity, and some general procedures are suggested on how positive hits in complex biological assays may be defined. Differentiating human LUHMES cells were used as potential model for developmental neurotoxicity testing. Forty candidate toxicants were screened, and several hits were obtained and confirmed. Although the cells had a definitive neuronal phenotype, the use of a general cell death endpoint in these cultures did not allow specific identification of neurotoxicants. As alternative approach, neurite growth was measured as an organ-specific functional endpoint. We found that neurite extension of developing LUHMES was specifically inhibited by diverse compounds such as colchicine, vincristine, narciclasine, rotenone, cycloheximide, or diquat. These compounds reduced neurite growth at concentrations that did not compromise cell viability, and neurite growth was affected more potently than the integrity of developed neurites of mature neurons. A ratio of the EC50 values of neurite growth inhibition and cell death of >4 provided a robust classifier for compounds associated with a developmental neurotoxic hazard. Screening of unspecific toxicants in the test system always yielded ratios <4. The assay identified also compounds that accelerated neurite growth, such as the rho kinase pathway modifiers blebbistatin or thiazovivin. The negative effects of colchicine or rotenone were completely inhibited by a rho kinase inhibitor. In summary, we suggest that assays using functional endpoints (neurite growth) can specifically identify and characterize (developmental) neurotoxicants.

Automated Analysis of Neurite Outgrowth in Mouse Retinal Explants

Despite intensive research efforts over the past years, regeneration of injured axons in the central nervous system remains elusive. In the quest for neurostimulatory agents that promote regeneration, well-defined models and analysis methods are required. Tissue explant cultures closely resemble the in vivo situation, making them ideal to study the effect of compounds on the neuro-glial network. This study reports the optimization of an explant culture technique using retinas of neonatal mice and the development of an analysis script that allows for rapid and automated analysis of neurite outgrowth from these explants. The key features of this script (i.e., local thresholding and form selection) allow for swift and unbiased detection of neurite outgrowth. The novel analysis method is compared with two commonly used manual methods and successfully validated by performing dose-response studies with molecules known to either inhibit (anti–β1-integrin antibody) or stimulate (brain-derived neurotrophic factor and ciliary neurotrophic factor) neurite outgrowth from retinal explants. Finally, the new analysis script is used to study whether retinal explant origin has any effect on neurite outgrowth.

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.

High-content neurite development study using optically patterned substrates

The study of neurite guidance in vitro relies on the ability to reproduce the distribution of attractive and repulsive guidance molecules normally expressed in vivo. The identification of subtle variations in the neurite response to changes in the spatial distribution of extracellular molecules can be achieved by monitoring the behavior of cells on protein gradients. To do this, automated high-content screening assays are needed to quantify the morphological changes resulting from growth on gradients of guidance molecules. Here, we present the use of laser-assisted protein adsorption by photobleaching (LAPAP) to allow the fabrication of large-scale substrate-bound laminin-1 gradients to study neurite extension. We produced thousands of gradients of different slopes and analyzed the variations in neurite attraction of neuron-like cells (RGC-5). An image analysis algorithm processed bright field microscopy images, detecting each cell and quantifying the soma centroid and the initiation, terminal and turning angles of the longest neurite.

Quantifying amyloid beta (Aβ)-mediated changes in neuronal morphology in primary cultures: implications for phenotypic screening

Alzheimer's disease (AD) is a devastating neurodegenerative disease affecting millions of people. The amyloid hypothesis suggests that the pathogenesis of AD is related to the accumulation of amyloid beta (Aβ) in the brain. Herein, the authors quantify Aβ-mediated changes in neuronal morphology in primary cultures using the Cellomics neuronal profiling version 3.5 (NPv3.5) BioApplication. We observed that Aβ caused a 33% decrease in neurite length in primary human cortical cultures after 24 h of treatment compared with control-treated cultures. We also determined that quantifying changes of neuronal morphology was a more sensitive indicator of nonlethal cell injury than traditional cytotoxicity assays. Aβ-mediated neuronal deficits observed in human cortical cultures were also observed in primary rat hippocampal cultures, where we demonstrated that the integrin-blocking antibody, 17E6, completely abrogated Aβ-mediated cytotoxicity. Finally, we showed that Aβ challenge to 21 days in vitro rat hippocampal cultures reduced synapsin staining to 14% of control-treated cultures. These results are consistent with the finding that loss of presynaptic integrity is one of the initial deficits observed in AD. The implementation of phenotypic screens to identify compounds that block Aβ-mediated cytotoxicity in primary neuronal cultures may lead to the development of novel strategies to prevent AD.

NeuriteQuant: an open source toolkit for high content screens of neuronal morphogenesis

Background

To date, some of the most useful and physiologically relevant neuronal cell culture systems, such as high density co-cultures of astrocytes and primary hippocampal neurons, or differentiated stem cell-derived cultures, are characterized by high cell density and partially overlapping cellular structures. Efficient analytical strategies are required to enable rapid, reliable, quantitative analysis of neuronal morphology in these valuable model systems.

Results

Here we present the development and validation of a novel bioinformatics pipeline called NeuriteQuant. This tool enables fully automated morphological analysis of large-scale image data from neuronal cultures or brain sections that display a high degree of complexity and overlap of neuronal outgrowths. It also provides an efficient web-based tool to review and evaluate the analysis process. In addition to its built-in functionality, NeuriteQuant can be readily extended based on the rich toolset offered by ImageJ and its associated community of developers. As proof of concept we performed automated screens for modulators of neuronal development in cultures of primary neurons and neuronally differentiated P19 stem cells, which demonstrated specific dose-dependent effects on neuronal morphology.

Conclusions

NeuriteQuant is a freely available open-source tool for the automated analysis and effective review of large-scale high-content screens. It is especially well suited to quantify the effect of experimental manipulations on physiologically relevant neuronal cultures or brain sections that display a high degree of complexity and overlap among neurites or other cellular structures.