Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant

NeuriteQuant: An open source toolkit for high content screens of neuronal Morphogenesis

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Vincristine-Induced Peripheral Neuropathy: Assessing Preventable Strategies in Paediatric Acute Lymphoblastic Leukaemia

Background: Acute Lymphoblastic Leukaemia is the most common cancer experienced by children with overall survival rates now exceeding 90%. However, most children will experience vincristine-induced peripheral neuropathy (VIPN) during treatment resulting in sensory-motor abnormalities. To date, there are no approved preventative therapeutics or mitigation strategies for VIPN. This body of work set out to: (1) establish a high-throughput and high-content assay with the capacity to identify neuroprotective compounds, (2) test the feasibility of repurposing olesoxime as a neuroprotectant, and (3) compare traditional statistical methods with machine learning models to identify patients at risk of VIPN. Methods: (1) In vitro neuronal cultures were exposed to vincristine to recapitulate the VIPN phenotype and olesoxime assessed as a positive control. The neurotoxicity assay was miniaturised in 384-well microplates with automation steps to reduce manual handling. (2) Olesoxime and vincristine were applied to proliferating malignant cell lines to ensure the efficacy of vincristine was maintained. (3) Machine learning algorithms were developed using data from a local retrospective cohort to predict VIPN. Results: (1) Neurite length was reduced in a dose-responsive manner with vincristine. Assay miniaturisation and automation steps helped facilitate a high-throughput workflow. An optimised multiplexed dye solution enabled image acquisition and neurite quantification. Further, olesoxime was found to protect neurites and deemed suitable as a positive control (2) Cell viability assays confirmed olesoxime did not interfere with vincristine efficacy in leukemia cells. (3) Machine learning algorithms showed equivalency to traditional univariate analysis. The observation of severe class imbalance meant that patients who were least susceptible to VIPN could be identified. Conclusions: This body of work demonstrates the successful development of a neurotoxicity assay suitable for neuroprotectant drug discovery. Olesoxime was found suitable as a positive control in the assay. Further, viability studies indicated that vincristine retains it efficacy with olesoxime, opening the possibility of its use as an adjunctive therapy. Finally, this work developed machine learning models with the capacity to identify patients with VIPN-free survival. The utility of this model may mean that it can be used to stratify patients prospectively in the clinic based on favourable clinical features

Automated computation of arbor densities: a step toward identifying neuronal cell types

The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference

Computer vision profiling of neurite outgrowth mordphodynamics reveals spatio-temporal modularity of Rho GTPase signaling

Neurite outgrowth is essential to build the neuronal processes that produce axons and dendrites that connect the adult brain. In cultured cells, the neurite outgrowth process is highly dynamic, and consists of a series of repetitive morphogenetic sub-processes (MSPs), such as neurite initiation, elongation, branching, growth cone motility and collapse (da Silva and Dotti 2002). Neurons also actively migrate, which might in part reflect neuronal migration during brain development. Each of the different MSPs inherent to neurite outgrowth and cell migration is likely to be regulated by precise spatio-temporal signaling networks that control cytoskeletal dynamics, trafficking and adhesion events. These MSPs can occur on a range of time and length scales. For example, microtubule bundling in the neurite shaft can be maintained during hours, while growth cone filopodia dynamically explore their surrounding on time scales of seconds and length scales of single microns. This implies that a correct understanding of these processes will require analysis with an adequate spatio-temporal resolution. The Rho family of GTPases are signaling switches that regulate a wide variety of cellular processes, such as actin and adhesion dynamics, gene transcription, and neuronal differentiation (Boguski and McCormick 1993). Rho GTPases are activated by guanine nucleotide exchange factors (GEFs), and are switched off by GTPase activating proteins (GAPs). Upon activation, Rho GTPases can associate with effectors to initiate a downstream response. Current models propose that Rac1 and Cdc42 regulate neurite extension, while RhoA controls growth cone collapse and neurite retraction (da Silva and Dotti 2002). However, until now the effects of Rho GTPases on neurite outgrowth have mostly been assessed using protein mutants in steady-state experiments, most often at late differentiation stages, which do not provide any insight about the different MSPs during neurite outgrowth. However, our proteomic analysis of biochemically-purified neurites from N1E-115 neuronal-like cells (Pertz et al. 2008), has suggested the existence of an unexpectedly complex 220 proteins signaling network consisting of multiple GEFs, GAPs, Rho GTPases, effectors and additional interactors. This is inconsistent with the simplistic view that classical experiments have provided before. In order to gain insight into the complexity of this Rho GTPase signaling network, we performed a siRNA screen that targets each of these 220 proteins individually. We hypothesized that specific spatio-temporal Rho GTPase signaling networks control different MSPs occurring during neurite outgrowth, and therefore designed an integrated approach to capture the whole morphodynamic continuum of this process. Perturbations of candidates that lead to a similar phenotype might be part of a given spatio-temporal signaling network. This approach consisted of: 1) A high content microscopy platform that allowed us to produce 8000 timelapse movies of 660 siRNA perturbations; 2) A custom built, computer vision approach that allowed us to automatically segment and track neurite and soma morphodynamics in the timelapse movies (collaboration with the group of Pascal Fua, EPFL, Lausanne); 3) A sophisticated statistical analysis pipeline that allowed the extraction of morphological and morphodynamic signatures (MDSs) relevant to each siRNA perturbation (collaboration with the group of Francois Fleuret, IDIAP). Analysis of our dataset revealed that each siRNA perturbation led to a quantifiable phenotype, emphasizing the quality of our proteomic dataset. Hierarchical clustering of the MDSs revealed the existence of 24 phenoclusters that provide information about neurite length, branching, number of neurites, soma migration speed, and a panel of additional morphological and morphodynamic features that are more difficult to grasp using visual inspection. This complex phenotypic space can more easily be understood when classified according to the first 4 features. Our screen then suggests the existence of 4 major morphodynamic phenotypes that define distinct stages of the neurite outgrowth process. These consist of phenotypes with short neurites, multiple short neurites, long neurites, and long and branched neurites. Further subdivision using the other features provides more information, with cell migration features being very often affected. This implies a high overlap between the signaling machinery that regulates the neurite outgrowth and cell migration processes. The high phenotypical redundancy (24 clusters for 220 candidate genes) provides only limited information to deduce unambiguous signaling networks regulating distinct MSPs. Further knowledge acquired from other approaches we used to study Rho GTPase signaling (FRET biosensors, and other live cell imaging techniques), made us realize that some morphodynamic phenotypes can only be understood when growth cone dynamics are inspected at a much higher resolution. For this purpose, we decided to further investigate a defined subset of genes using high resolution live cell imaging and a custom built growth cone segmentation and tracking pipeline for accurate quantification (collaboration with the group of Gaudenz Danuser, Harvard Medical School, Boston). These results shed light into how distinct cytoskeletal networks enabling growth cone advance can globally impact the neurite outgrowth process. A clear understanding of spatio-temporal Rho GTPase signaling will therefore require multi-scale approaches. Our results provide the first insight into the complexity of spatio-temporal Rho GTPase signaling during neurite outgrowth. The technologies we devised and our initial results, pave the way for a systems biology understanding of these complex signaling systems

Establishment of a human cell-based in vitro battery to assess developmental neurotoxicity hazard of chemicals

Developmental neurotoxicity (DNT) is a major safety concern for all chemicals of the human exposome. However, DNT data from animal studies are available for only a small percentage of manufactured compounds. Test methods with a higher throughput than current regulatory guideline methods, and with improved human relevance are urgently needed. We therefore explored the feasibility of DNT hazard assessment based on new approach methods (NAMs). An in vitro battery (IVB) was assembled from ten individual NAMs that had been developed during the past years to probe effects of chemicals on various fundamental neurodevelopmental processes. All assays used human neural cells at different developmental stages. This allowed us to assess disturbances of: (i) proliferation of neural progenitor cells (NPC); (ii) migration of neural crest cells, radial glia cells, neurons and oligodendrocytes; (iii) differentiation of NPC into neurons and oligodendrocytes; and (iv) neurite outgrowth of peripheral and central neurons. In parallel, cytotoxicity measures were obtained. The feasibility of concentration-dependent screening and of a reliable biostatistical processing of the complex multi-dimensional data was explored with a set of 120 test compounds, containing subsets of pre-defined positive and negative DNT compounds. The battery provided alerts (hit or borderline) for 24 of 28 known toxicants (82% sensitivity), and for none of the 17 negative controls. Based on the results from this screen project, strategies were developed on how IVB data may be used in the context of risk assessment scenarios employing integrated approaches for testing and assessment (IATA).European Food Safety Authority (EFSA-Q-2018-00308), the Danish Environmental Protection Agency (EPA), Denmark, under the grant number MST-667-00205, the State Ministry of Baden-Wuerttemberg, Germany, for Economic Affairs, Labour and Tourism (NAM-Accept), the project CERST (Center for Alternatives to Animal Testing) of the Ministry for culture and science of the State of North-Rhine Westphalia, Germany (file number 233–1.08.03.03- 121972/131–1.08.03.03–121972), the European Chemical Industry Council Long-Range Research Initiative (Cefic LRI) under the project name AIMT11 and the BMBF (NeuroTool). It has also received funding from the European Union's Horizon 2020 research and innovation program under grant agreements No. 964537 (RISK-HUNT3R), No. 964518 (ToxFree), No. 101057014 (PARC) and No. 825759 (ENDpoiNTs)

Ferramentas de processamento digital de imagem para imagens neuronais

Mestrado em Biomedicina MolecularOs neurónios são celulas especializadas do Sistema Nervoso, cujas funções se baseiam na correta formação de três compartimentos subcelulares primários – corpo celular, axónio e dendrites – e na rede neuronal que formam para passar a informação entre si. A análise quantitativa das características destas estruturas pode ser usada para estudar a relação entre a morfologia e função neuronal, e monitorizar alterações que ocorram em células individuais ou ao nível da rede, que se possam correlacionar com doenças neurológicas. Nesta tese foi efetuada uma pesquisa de ferramentas digitais disponíveis dedicadas ao processamento e análise de imagens neuronais, com enfoque na sua aplicabilidade para analisar as nossas bioimagens neuronais de fluorescência adquiridas no dia-a-dia. Nos programas selecionados (NeuronJ, NeurphologyJ e NeuriteQuant) foi primeiro avaliada a necessidade de preprocessamento, e os programas foram subsequentemente utilizados em conjuntos de imagens de culturas primárias de córtex de rato para comparar a sua eficácia no processamento destas bioimagens. Os dados obtidos com os vários programas foram comparados com a análise manual usando o ImageJ como ferramenta de análise. Os resultados demonstraram que o programa que aparenta funcionar melhor com as nossas imagens de fluorescência é o NeuriteQuant, porque é automático e dá resultados globalmente semelhantes aos da análise manual, especialmente na avaliação do Comprimento das Neurites por célula. Uma das desvantagens é que a quantificação da ramificação das neurites não dá resultados satisfatórios e deve continuar a ser realizada manualmente. Também realizamos uma pesquisa de ferramentas de processamento de imagem dedicada a imagens de contraste de fase, mas poucos programas foram encontrados. Estas imagens são mais fáceis de obter e mais acessíveis economicamente, contudo são mais difíceis de analisar devido às suas características intrínsecas. Para contornar esta lacuna, estabeleceu-se e otimizou-se uma sequência de processamento e análise para melhor extrair informação neuronal relevante de imagens de contraste de fase utilizando o programa ImageJ. A sequência desenvolvida, na forma de uma macro do ImageJ designada NeuroNet, foi aplicada a imagens de contraste de fase de culturas neuronais em diferentes dias de diferenciação, na presença ou ausência de um inibidor farmacológico, com o objetivo de responder a uma questão científica. A macro NeuroNet desenvolvida provou ser útil para analisar estas bioimagens, existindo contudo espaço para ser aperfeiçoada.Neurons are specialized cells of the Nervous System, with their function being based on the formation of the three primary sub cellular compartments – soma, axons, and dendrites – and on the neuritic network they form to contact and pass information to each other. The quantitative analysis of the characteristics of these structures can be used to study the relation between neuronal morphology and function, and to monitor distortions occurring in individual cells or at the network level that may correlate with neurological diseases. In this thesis a survey of freely available digital tools dedicated to neuronal images processing and analysis was made with an interest in their applicability to analyse our routinely acquired neuronal fluorescent bioimages. The selected program´ (NeuronJ, NeurphologyJ and NeuriteQuant) preprocessing requirements were first evaluated, and the programs were subsequently applied to a set of images of rat cortical neuronal primary cultures in order to compare their effectiveness in bioimage processing. Data obtained with the various programs was compared to the manual analysis of the images using the ImageJ analysis tool. The result show that the program that seems to work better with our fluorescence images is NeuriteQuant, since it is automatic and gives overall results more similar to the manual analysis. This is particularly true for the evaluation of the Neurite Length per Cell. One of the drawbacks is that the quantification of neuritic ramification does not give satisfactory results and is better to be performed manually. We also performed a survey of digital image processing tools dedicated to phase contrast microphotographs, but very few programs were found. These images are easier to obtain and more affordable in economic terms, however they are harder to analyse due to their intrinsic characteristics. To surpass this gap we have established and optimized a sequence of steps to better extract relevant information of neuronal phase contrast images using ImageJ. The work-flow developed, in the form of an ImageJ macro named NeuroNet, was then used to answer a scientific question by applying it to phase contrast images of neuronal cultures at different differentiating days, in the presence or absence of a pharmacological inhibitor. The developed macro NeuroNet proved to be useful to analyse the images however there is still space to improvement

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process

Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model

Motivation: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns