An end-to-end software solution for the analysis of high-throughput single-cell migration data

The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory-and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their highthroughput data processing

Time-Lapse Microscopy

Time-lapse microscopy is a powerful, versatile and constantly developing tool for real-time imaging of living cells. This review outlines the advances of time-lapse microscopy and refers to the most interesting reports, thus pointing at the fact that the modern biology and medicine are entering the thrilling and promising age of molecular cinematography

Comparative Study of Contact Repulsion in Control and Mutant Macrophages Using a Novel Interaction Detection

In this paper, a novel method for interaction detection is presented to compare the contact dynamics of macrophages in the Drosophila embryo. The study is carried out by a framework called macrosight, which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing the motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot mutants, a candidate protein that is hypothesised to regulate contact dynamics between migrating cells. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments with computational analysis

Classification of bacterial motility using machine learning

Cells can display a diverse set of motility behaviors, and these behaviors may reflect a cell’s functional state. Automated, and accurate cell motility analysis is essential to cell studies where the analysis of motility pattern is required. The results of such analysis can be used for diagnostic or curative decisions. Deep learning area has made astonishing progresses in the past several years. For computer vision tasks, different convolutional neural networks (CNN) and optimizers have been proposed to fix some problems. For time sequence data, recurrent neural networks (RNN) have been widely used. This project leveraged on these recent advances to find the proper neural network for bacterial motility trajectory analysis for genotypic classification. This thesis was trying to answer two questions: (1) Which machine learning model can effectively classify the genotype of bacterial based on their motility patterns? And (2) Which motility parameters can best predict the bacterial genotype? The first question is addressed in the result 1 and 2 using different data formats and different machine learning models. The second question is addressed in result 3. Accordingly, this thesis is divided into three parts: (1) different traditional machine learning models are tested for predicting bacterial genotype using the coordinates’ sequences extracted from microscopic videos. (2) bacterial genotype classification task is solved by using deep neural network and the raw videos. (3) different motility parameters are tested to find out the best for predicting bacterial genotypes. It is found that neural network gives highest accuracy in classifying bacterial genotype using coordinates’ sequences. Deep neural network with CNN-RNN can effectively classify the bacterial genotype using video data. Among popular motility parameters, some of them predict the bacterial genotype 20% better than others. The broader impact of this project is to automate trajectory analysis process and enable high-throughput trajectory analysis for research and clinical uses

Towards cellular hydrodynamics: collective migration in artificial microstructures

The collective migration of cells governs many biological processes, including embryonic development, wound healing and cancer progression. Observed phenomena are not simply the sum of the individual motion of many isolated cells, but rather emerge as a consequence of their interactions. The movements in epithelial cell sheets display rich phenomenology, such as the occurrence of vortices spanning several cell diameters and the transition from fluid-like behavior at low densities to glass-like behavior at high densities. In this thesis, collective invasion of epithelial cell sheets into microchannels was studied on a phenomenological level within the scope of theoretical approaches to active fluids. In a first project, the motion profile of a cell layer in straight channels was investigated using single cell tracking and particle image velocimetry (PIV) on timelapse microscopy data. A defined plug-flow like velocity profile was observed across the channels. The cell density profile is well-described by the Fisher-Kolmogorov reaction-diffusion equation, which includes active migration and the contribution of proliferation. This study revealed a change in the short scale noise behavior in the presence of this global invasion into a channel. For a closer look at the system’s proliferation component, the effect of an underlying global migration direction on the orientation of the cells’ division axes was examined. We found strong alignment of the axes’ orientation with the imposed movement direction. Specifically, the strongest correlations were observed between the orientation of the cells’ division axes and the local strain rate tensor’s main axis. This is in agreement with the notion that stresses in the migrating cell sheet orient the cell divisions. Expanding the assay of invasion into straight channels, we introduced a constriction, which the cell sheet needs to pass through in order to progress. A plateau of low velocities was observed in the region ahead of the constriction, which was attributed to an increase in local cell density accompanied by jamming. These results were compared to an active isotropic-nematic mixture model. The suitability of this model to describe this assay could be ruled out, however, as it showed qualitatively very different behavior than the experiments. Finally, the frequency of topological nearest-neighbor T1 transitions within a cell sheet was investigated in minimal model systems. In order to study the smallest possible fundamental unit for such transitions, groups of four cells were confined to cloverleaf patterns, which could be shown to inhibit the onset of collective rotation states. Results showed that T1 transitions occurred more frequently for groups of cells with a lower average length of the cell-cell junction that shrinks in the process of this transition. These results are consistent with the notion that the energy barrier which needs to be overcome by the cells in order to perform this transition, scales with the original length of the shrinking junction. Taken together, the results of this thesis contribute to a better understanding of the flow fields for collective cell migration processes in confined geometries. In addition to the insights the phenomenological observations in this work could provide directly, they will also continue to prove useful as a standard for validating detailed theoretical models

Computational methods to create and analyze a digital gene expression atlas of embryo development from microscopy images

Abstract The creation of atlases, or digital models where information from different subjects can be combined, is a field of increasing interest in biomedical imaging. When a single image does not contain enough information to appropriately describe the organism under study, it is then necessary to acquire images of several individuals, each of them containing complementary data with respect to the rest of the components in the cohort. This approach allows creating digital prototypes, ranging from anatomical atlases of human patients and organs, obtained for instance from Magnetic Resonance Imaging, to gene expression cartographies of embryo development, typically achieved from Light Microscopy. Within such context, in this PhD Thesis we propose, develop and validate new dedicated image processing methodologies that, based on image registration techniques, bring information from multiple individuals into alignment within a single digital atlas model. We also elaborate a dedicated software visualization platform to explore the resulting wealth of multi-dimensional data and novel analysis algo-rithms to automatically mine the generated resource in search of bio¬logical insights. In particular, this work focuses on gene expression data from developing zebrafish embryos imaged at the cellular resolution level with Two-Photon Laser Scanning Microscopy. Disposing of quantitative measurements relating multiple gene expressions to cell position and their evolution in time is a fundamental prerequisite to understand embryogenesis multi-scale processes. However, the number of gene expressions that can be simultaneously stained in one acquisition is limited due to optical and labeling constraints. These limitations motivate the implementation of atlasing strategies that can recreate a virtual gene expression multiplex. The developed computational tools have been tested in two different scenarios. The first one is the early zebrafish embryogenesis where the resulting atlas constitutes a link between the phenotype and the genotype at the cellular level. The second one is the late zebrafish brain where the resulting atlas allows studies relating gene expression to brain regionalization and neurogenesis. The proposed computational frameworks have been adapted to the requirements of both scenarios, such as the integration of partial views of the embryo into a whole embryo model with cellular resolution or the registration of anatom¬ical traits with deformable transformation models non-dependent on any specific labeling. The software implementation of the atlas generation tool (Match-IT) and the visualization platform (Atlas-IT) together with the gene expression atlas resources developed in this Thesis are to be made freely available to the scientific community. Lastly, a novel proof-of-concept experiment integrates for the first time 3D gene expression atlas resources with cell lineages extracted from live embryos, opening up the door to correlate genetic and cellular spatio-temporal dynamics. La creación de atlas, o modelos digitales, donde la información de distintos sujetos puede ser combinada, es un campo de creciente interés en imagen biomédica. Cuando una sola imagen no contiene suficientes datos como para describir apropiadamente el organismo objeto de estudio, se hace necesario adquirir imágenes de varios individuos, cada una de las cuales contiene información complementaria respecto al resto de componentes del grupo. De este modo, es posible crear prototipos digitales, que pueden ir desde atlas anatómicos de órganos y pacientes humanos, adquiridos por ejemplo mediante Resonancia Magnética, hasta cartografías de la expresión genética del desarrollo de embrionario, típicamente adquiridas mediante Microscopía Optica. Dentro de este contexto, en esta Tesis Doctoral se introducen, desarrollan y validan nuevos métodos de procesado de imagen que, basándose en técnicas de registro de imagen, son capaces de alinear imágenes y datos provenientes de múltiples individuos en un solo atlas digital. Además, se ha elaborado una plataforma de visualization específicamente diseñada para explorar la gran cantidad de datos, caracterizados por su multi-dimensionalidad, que resulta de estos métodos. Asimismo, se han propuesto novedosos algoritmos de análisis y minería de datos que permiten inspeccionar automáticamente los atlas generados en busca de conclusiones biológicas significativas. En particular, este trabajo se centra en datos de expresión genética del desarrollo embrionario del pez cebra, adquiridos mediante Microscopía dos fotones con resolución celular. Disponer de medidas cuantitativas que relacionen estas expresiones genéticas con las posiciones celulares y su evolución en el tiempo es un prerrequisito fundamental para comprender los procesos multi-escala característicos de la morfogénesis. Sin embargo, el número de expresiones genéticos que pueden ser simultáneamente etiquetados en una sola adquisición es reducido debido a limitaciones tanto ópticas como del etiquetado. Estas limitaciones requieren la implementación de estrategias de creación de atlas que puedan recrear un multiplexado virtual de expresiones genéticas. Las herramientas computacionales desarrolladas han sido validadas en dos escenarios distintos. El primer escenario es el desarrollo embrionario temprano del pez cebra, donde el atlas resultante permite constituir un vínculo, a nivel celular, entre el fenotipo y el genotipo de este organismo modelo. El segundo escenario corresponde a estadios tardíos del desarrollo del cerebro del pez cebra, donde el atlas resultante permite relacionar expresiones genéticas con la regionalización del cerebro y la formación de neuronas. La plataforma computacional desarrollada ha sido adaptada a los requisitos y retos planteados en ambos escenarios, como la integración, a resolución celular, de vistas parciales dentro de un modelo consistente en un embrión completo, o el alineamiento entre estructuras de referencia anatómica equivalentes, logrado mediante el uso de modelos de transformación deformables que no requieren ningún marcador específico. Está previsto poner a disposición de la comunidad científica tanto la herramienta de generación de atlas (Match-IT), como su plataforma de visualización (Atlas-IT), así como las bases de datos de expresión genética creadas a partir de estas herramientas. Por último, dentro de la presente Tesis Doctoral, se ha incluido una prueba conceptual innovadora que permite integrar los mencionados atlas de expresión genética tridimensionales dentro del linaje celular extraído de una adquisición in vivo de un embrión. Esta prueba conceptual abre la puerta a la posibilidad de correlar, por primera vez, las dinámicas espacio-temporales de genes y células

Sea urchin spermatozoa as case study

"Digitization and robotization of laboratory equipment has recently contributed to the generation of high content of data and its metadata. While this seems like an advantage for science's celerity, the analysis of such data became the limiting step { a very narrow bottleneck. Such is the case for imaging data acquisition and its analysis. After collecting Gigabytes of images, researchers spend several orders of magnitude of more time to determine the regions of interest (ROIs) (e.g. cell) and to measure relevant attributes (e.g. mean uorescence intensity). This manual curation of data promotes another issue that is related with the reproducibility of the analysis, e.g., the same researcher will hardly select the exact same ROIs in the same data set. Furthermore, there is also the possibility of bias in the selection of which cells to use in the analysis by biased determination of the ROIs.(---)

Developing Highly Multiplexed Technology for High-throughput Super-resolution Fluorescence Microscopy

High-Throughput imaging can reconstruct complex signalling networks, reveal unknown interactions and capture rare cellular events. Simultaneously, the development of Single Molecule Localization Super Resolution Microscopy has enabled molecular-level structural information to be obtained in a single cell. But the increase in resolution comes at a trade-off for the amount of molecular species that can be imaged and the time it takes to acquire data, all of which limit the applicability of super-resolution to high-throughput work-flows. The present work details a framework to address this. It combines three independent approaches: a microscope hardware design approach to increase the amount of data that can be obtained in a Super-Resolution experiment; an optofluidics platform that can be wholly synchronized with most microscopes; and a sequential labelling framework to increase the number of species that can be imaged in Super-Resolution in a single cell. The hardware design is validated by performing Single Molecule Localization of cytoskeleton components and its throughput is shown to be up to an order of magnitude larger than a corresponding commercial system. We demonstrate a complete optofluidics platform to integrate microfluidics with a microscope, enabling live imaging, drug application, fixation, and staining in single cells synchronized with imaging protocols. Finally, we show an efficient sequential labelling protocol that is compatible with the optofluidics platform, enabling several molecular species to be imaged in the same cells. Overall, our approach increases the speed and amount of data that can be acquired in a single of Super-Resolution experiment, as well as, by performing on-line fixation, considerably improves our capacity for High-Throughput experiments in Super-Resolution imaging