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

Quantification of Neurite Degeneration through use of an Optimized and Automated Method

Neurite degeneration is a cellular dysfunction commonly associated with neurodegenerative pathologies such as Alzheimer’s disease and Parkinson’s disease (PD). One common method of scoring neurite degeneration in micrographs involves calculation of a degeneration index (DI) using neurite fragment measurements obtained via the particle analyzer plugin of FIJI software. However, this method can be time consuming and subject to inaccuracies related to inadequate contrast. Here we describe a modified method for performing DI measurements with enhanced efficiency, accessibility, and accuracy compared to existing techniques. We developed a macro to automate the analysis process, enabling rapid and objective measurements of multiple images. We have also increased the accuracy of measurements by modifying selection criteria for neurite fragments, as well as by determining optimal procedures for contrast enhancement and removal of non-neurite materials from images. Moreover, we demonstrate how this method may be applied to measure neurite degeneration in an in vitro model of PD. To model neurite degeneration associated with PD, we treated Lund Human Mesencephalic (LUHMES) cells with 4-hydroxynonenal or 6-hydroxydopamine, compounds that induce oxidative stress. We describe culture methods, cell densities, and drug concentrations that yield consistent and accurate measurements of neurite degeneration, and we demonstrate use of our optimized method in an experiment assessing the effects of c-Jun N-terminal Kinase (JNK) on neurite degeneration. Since neurite degeneration is a key, early-stage event associated with PD, this optimized and automated method may be used to gain novel insights into molecular interactions underlying PD progression

A Framework for Modeling the Growth and Development of Neurons and Networks

The development of neural tissue is a complex organizing process, in which it is difficult to grasp how the various localized interactions between dividing cells leads relentlessly to global network organization. Simulation is a useful tool for exploring such complex processes because it permits rigorous analysis of observed global behavior in terms of the mechanistic axioms declared in the simulated model. We describe a novel simulation tool, CX3D, for modeling the development of large realistic neural networks such as the neocortex, in a physical 3D space. In CX3D, as in biology, neurons arise by the replication and migration of precursors, which mature into cells able to extend axons and dendrites. Individual neurons are discretized into spherical (for the soma) and cylindrical (for neurites) elements that have appropriate mechanical properties. The growth functions of each neuron are encapsulated in set of pre-defined modules that are automatically distributed across its segments during growth. The extracellular space is also discretized, and allows for the diffusion of extracellular signaling molecules, as well as the physical interactions of the many developing neurons. We demonstrate the utility of CX3D by simulating three interesting developmental processes: neocortical lamination based on mechanical properties of tissues; a growth model of a neocortical pyramidal cell based on layer-specific guidance cues; and the formation of a neural network in vitro by employing neurite fasciculation. We also provide some examples in which previous models from the literature are re-implemented in CX3D. Our results suggest that CX3D is a powerful tool for understanding neural development

An Instruction Language for Self-Construction in the Context of Neural Networks

Biological systems are based on an entirely different concept of construction than human artifacts. They construct themselves by a process of self-organization that is a systematic spatio-temporal generation of, and interaction between, various specialized cell types. We propose a framework for designing gene-like codes for guiding the self-construction of neural networks. The description of neural development is formalized by defining a set of primitive actions taken locally by neural precursors during corticogenesis. These primitives can be combined into networks of instructions similar to biochemical pathways, capable of reproducing complex developmental sequences in a biologically plausible way. Moreover, the conditional activation and deactivation of these instruction networks can also be controlled by these primitives, allowing for the design of a “genetic code” containing both coding and regulating elements. We demonstrate in a simulation of physical cell development how this code can be incorporated into a single progenitor, which then by replication and differentiation, reproduces important aspects of corticogenesis

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

Background: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. Results: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. Conclusions: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

Background: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. Results: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. Conclusions: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences

Computational Modelling of Cancer Systems: From Individual to Collective Cell Behaviour

Debido a su complejidad, el cáncer sigue siendo una de las principales causas de muerte a nivel mundial. La creación de prácticas preventivas adecuadas y terapias innovadoras está limitada por la falta de comprensión de los mecanismos básicos que causan el cáncer. Como tal, se deben desarrollar métodos nuevos y más efectivos que avancen nuestra comprensión del cáncer. En los últimos años, se ha visto un aumento en el uso de modelos computacionales para explicar procesos biológicos que son costosos y difíciles de explorar en entornos experimentales. Estos métodos permiten la traducción de mecanismos biológicos en ecuaciones y suposiciones matemáticas que pueden evaluarse utilizando herramientas informáticas para producir nuevas hipótesis. Además, las tecnologías computacionales se están volviendo más potentes debido a la disponibilidad de datos y la amplia capacidad de procesamiento.El objetivo global de esta tesis es diseñar e implementar modelos computacionales de cáncer, comenzando con comportamientos simples y aislados y progresando hacia fenómenos más complejos. Se abordan tres campos de investigación específicos para lograr este objetivo general: (i) motilidad unicelular, (ii) crecimiento tumoral y (iii) formación de patrones. En el primer objetivo, se presenta un modelo computacional para simular la motilidad celular individual que considera las propiedades mecánicas y químicas del microambiente. Posteriormente, este trabajo fue ampliado para tener en cuenta las interacciones célula-célula y reproducir el crecimiento de estructuras tumorales multicelulares. Por último, todos los eventos biológicos mencionados anteriormente fueron considerados y se añadió la diferenciación celular como el bloque de construcción final de esta tesis para simular la formación de patrones espaciales.Además, esta tesis analiza la relevancia de integrar datos experimentales y métodos computacionales para mejorar la precisión biológica y confirmar los resultados del modelo. En particular, muestra cómo se pueden usar técnicas de calibración y optimización para considerar datos empíricos en el diseño y validación de modelos. Los resultados experimentales cualitativos y cuantitativos, tanto de la literatura como de nuevos experimentos, se reproducen en este artículo para mostrar diferentes enfoques en la integración de datos.En general, esta tesis proporciona un modelo de cómo se pueden utilizar los métodos computacionales para analizar y comprender problemas complejos en la biología del cáncer.Demuestra explícitamente cómo los componentes del modelo pueden representar ciertos aspectos de la biología del cáncer, que pueden mejorarse y reproducirse utilizando datos experimentales. En consecuencia, los comportamientos complejos, como el crecimiento tumoral y la formación de patrones, resultan de la intrincada interacción entre los componentes del modelo.<br /