A Novel Validation Algorithm Allows for Automated Cell Tracking and the Extraction of Biologically Meaningful Parameters

Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high reliability and statistical significance. These include the distribution of life/cycle times and cell areas, as well as of the symmetry of cell divisions and motion analyses. The new algorithm thus allows for the quantification and parameterization of cell culture with unprecedented accuracy. To evaluate our validation algorithm, two large reference data sets were manually created. These data sets comprise more than 320,000 unstained adult pancreatic stem cells from rat, including 2592 mitotic events. The reference data sets specify every cell position and shape, and assign each cell to the correct branch of its genealogic tree. We provide these reference data sets for free use by others as a benchmark for the future improvement of automated tracking methods

Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy

A variety of biological and pharmaceutical studies, such as for anti-cancer drugs, require the quantification of cell responses over long periods of time. This is performed with time-lapse video microscopy that gives a long sequence of frames. For this purpose, phase contrast imaging is commonly used since it is minimally invasive. The cell responses of interest in this study are the mitotic cell divisions. Their manual measurements are tedious, subjective, and restrictive. This study introduces an automated method for these measurements. The method starts with preprocessing for restoration and reconstruction of the phase contrast time-lapse sequences. The data are first restored from intensity non-uniformities. Subsequently, the circular symmetry of the contour of the mitotic cells in phase contrast images is used by applying a Circle Hough Transform (CHT) to reconstruct the entire cells. The CHT is also enhanced with the ability to “vote” exclusively towards the center of curvature. The CHT image sequence is then registered for misplacements between successive frames. The sequence is subsequently processed to detect cell centroids in individual frames and use them as starting points to form spatiotemporal trajectories of cells along the positive as well as along the negative time directions, that is, anti-causally. The connectivities of different trajectories enhanced by the symmetry of the trajectories of the daughter cells provide as topological by-products the events of cell divisions together with the corresponding entries into mitoses as well as exits from cytokineses. The experiments use several experimental video sequences from three different cell lines with many cells undergoing mitoses and divisions. The quantitative validations of the results of the processing demonstrate the high performance and efficiency of the method

Comparison of Cost Function Against Positional Analysis for Automated Tracking of Three-Cell Interactions

Localization and tracking of cells generates raw digital information from microscopy images, including images of stained nuclei and highly precise determination of central positions of cells, which can be analyzed for investigation of cell motility. In a previous study by this group, an algorithm termed automated contour-based tracking for in vitro environments (ACTIVE) was established for tracking large cell populations for long periods of time. For the two-cell interaction events on which ACTIVE was initially focused, error rate was reduced as much as 43% compared to a traditional positional analysis algorithm by Kilfoil and colleagues. In the present thesis, we investigated whether the ACTIVE algorithm could be improved when applied to a more complicated condition: three-cell interactions. To determine whether modification of the ACTIVE algorithms could allow ACTIVE to outperform the Kilfoil benchmark method when applied not only to two-cell interaction cases but also to three-cell interaction cases, two approaches were developed and studied: 1) optimization of the existing ACTIVE cost-function weighting factors by orthogonal design with addition of two new factors, velocity and directionality, and detection of ranges and effects for all factors, and 2) modification of the circumstances under which the Kilfoil approach and the cost function approach were executed. We found the position factor to be the most important and accurate among all the factors, and optimized all factors. What is more, the directionality was determined to be the second most significant factor of the cost function for correctly tracking cells. However, modification of neither the position nor directionality factor could achieve higher accuracy than the Kilfoil method. Having evaluated the new strategy that combines both the cost function and the Kilfoil method, we found that the new strategy did not result in higher accuracy for three-cell interactions, as compared to the pure Kilfoil benchmark method. The accuracy of the new strategy was 6% lower on average than the Kilfoil method. Although the results of the present work do not yet achieve a method for analysis of three-cell interactions that outperforms purely positional analysis, the work provides a method for optimization of the cost function and new understanding of characteristics of three-cell interactions that lead to reduced accuracy in the cost function and/or positional (Kilfoil) approaches

ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.

Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances.This work has been partially supported by the National Grant TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness, Madrid Regional Government and Universidad Carlos III de Madrid through the project SHARON-CM-UC3M, RTI2018- 095497-B-I00 from Ministerio de Ciencia e Innovación (MICINN) and HR17_00527 from Fundación La Caixa to A.H. M.M-M. is supported by the Spanish Ministry of Education, Culture and Sports FPU Grant FPU18/02825. M.P-S. is supported by a Federation of European Biochemical Societies long-term fellowship. J.S. is supported by a fellowship (PRE2019-089130) from MICINN.S

Mikrosystembasierte Zellkultivierung und Zellmanipulation zur Applikation mechanischer Reize auf Zellen

Die übergeordnete Fragestellung der vorliegenden Arbeit ist biomedizinischer Art und seit mehr als 150 Jahren anhängig: Wie verhalten sich Zellen unter definierter Belastung? In vivo ist die Beobachtung zellulärer Prozesse bisher nicht ohne invasive Methoden möglich. Das verlangt nach einer Lösung in vitro, welche die natürlichen Bedingungen adäquat nachahmt und gleichzeitig optimale Bedingungen für die Beobachtung und Beeinflussung der Prozesse bietet. Dass sich dafür Mikrosysteme mit einer angepassten Peripherie eignen, wird in dieser Arbeit nachgewiesen.The motivating question underlying this work is generated by life sciences, pending for more than 150 years: How do cells behave under defined load? In vivo it is not possible to monitor subsiding cellular processes without the use of invasive methods. This demands for a solution in vitro, which mimics natural conditions adequately and offers optimized conditions for observation and manipulation at the same time. For this purpose BioMEMS (Bio Micro Electro Mechanical Systems) for cell are suitable. By means of analysis of state of the art for conventional macro and for micro system based cell cultivation and manipulation, requirements from cells and from users of such systems are defined. A micro system with a cultivation camber, tube connectors, an integrated scaffold, an optical and a mechanical access and other components forms the backbone of the entire system. It is completed by peripheral modules for supply of cells under adequate environmental conditions, observation of cells and processes and manipulation of cells and technical components. This configuration is explained in detail by exemplary realizations. Cell cultivation outside an incubator is feasible, securing biocompatibility. Considerations of the application of stimuli on cells are founded on this newly developed infrastructure. Existing macroscopic and microscopic methods may be adapted to the system, realizations are suggested. The performance of the entire system is discussed with reference to results of technical and biological tests. As result of the documented developmental process now a system exists, which after integration of cell-specific loading methods can be used by life scientists conduce to answer questions on cellular behavior.Zusätzliche Dateien: - Tabelle 5: Mikrozellkultivierungssysteme - Anhang A5: Literatur Zelldetektio

New algorithms for the analysis of live-cell images acquired in phase contrast microscopy

La détection et la caractérisation automatisée des cellules constituent un enjeu important dans de nombreux domaines de recherche tels que la cicatrisation, le développement de l'embryon et des cellules souches, l’immunologie, l’oncologie, l'ingénierie tissulaire et la découverte de nouveaux médicaments. Étudier le comportement cellulaire in vitro par imagerie des cellules vivantes et par le criblage à haut débit implique des milliers d'images et de vastes quantités de données. Des outils d'analyse automatisés reposant sur la vision numérique et les méthodes non-intrusives telles que la microscopie à contraste de phase (PCM) sont nécessaires. Comme les images PCM sont difficiles à analyser en raison du halo lumineux entourant les cellules et de la difficulté à distinguer les cellules individuelles, le but de ce projet était de développer des algorithmes de traitement d'image PCM dans Matlab® afin d’en tirer de l’information reliée à la morphologie cellulaire de manière automatisée. Pour développer ces algorithmes, des séries d’images de myoblastes acquises en PCM ont été générées, en faisant croître les cellules dans un milieu avec sérum bovin (SSM) ou dans un milieu sans sérum (SFM) sur plusieurs passages. La surface recouverte par les cellules a été estimée en utilisant un filtre de plage de valeurs, un seuil et une taille minimale de coupe afin d'examiner la cinétique de croissance cellulaire. Les résultats ont montré que les cellules avaient des taux de croissance similaires pour les deux milieux de culture, mais que celui-ci diminue de façon linéaire avec le nombre de passages. La méthode de transformée par ondelette continue combinée à l’analyse d'image multivariée (UWT-MIA) a été élaborée afin d’estimer la distribution de caractéristiques morphologiques des cellules (axe majeur, axe mineur, orientation et rondeur). Une analyse multivariée réalisée sur l’ensemble de la base de données (environ 1 million d’images PCM) a montré d'une manière quantitative que les myoblastes cultivés dans le milieu SFM étaient plus allongés et plus petits que ceux cultivés dans le milieu SSM. Les algorithmes développés grâce à ce projet pourraient être utilisés sur d'autres phénotypes cellulaires pour des applications de criblage à haut débit et de contrôle de cultures cellulaires.Automated cell detection and characterization is important in many research fields such as wound healing, embryo development, immune system studies, cancer research, parasite spreading, tissue engineering, stem cell research and drug research and testing. Studying in vitro cellular behavior via live-cell imaging and high-throughput screening involves thousands of images and vast amounts of data, and automated analysis tools relying on machine vision methods and non-intrusive methods such as phase contrast microscopy (PCM) are a necessity. However, there are still some challenges to overcome, since PCM images are difficult to analyze because of the bright halo surrounding the cells and blurry cell-cell boundaries when they are touching. The goal of this project was to develop image processing algorithms to analyze PCM images in an automated fashion, capable of processing large datasets of images to extract information related to cellular viability and morphology. To develop these algorithms, a large dataset of myoblasts images acquired in live-cell imaging (in PCM) was created, growing the cells in either a serum-supplemented (SSM) or a serum-free (SFM) medium over several passages. As a result, algorithms capable of computing the cell-covered surface and cellular morphological features were programmed in Matlab®. The cell-covered surface was estimated using a range filter, a threshold and a minimum cut size in order to look at the cellular growth kinetics. Results showed that the cells were growing at similar paces for both media, but their growth rate was decreasing linearly with passage number. The undecimated wavelet transform multivariate image analysis (UWT-MIA) method was developed, and was used to estimate cellular morphological features distributions (major axis, minor axis, orientation and roundness distributions) on a very large PCM image dataset using the Gabor continuous wavelet transform. Multivariate data analysis performed on the whole database (around 1 million PCM images) showed in a quantitative manner that myoblasts grown in SFM were more elongated and smaller than cells grown in SSM. The algorithms developed through this project could be used in the future on other cellular phenotypes for high-throughput screening and cell culture control applications

Spatio-temporal cell segmentation and tracking for automated screening

A growing number of screening applications require the automated monitoring of cell populations including cell segmentation, tracking, and measurement. We present general methods for cell segmentation and tracking that exploit the spatiotemporal nature of the task to constrain segmentation. The images are de-noised and segmented by combining wavelet coefficients at various levels, thus enabling extraction of cells in images with low contrast-to-noise ratios. Each track of clustered cells resulting from association of nearby cells in the spatio-temporal volume is then split into individual cells by evolving sets of contours from other slices. The hypothesis whether to split or merge objects making up the cluster is tested using learned features trained from single track cells. Due to the difficult nature of generating ground truth, we also present a framework for edit-based validation whereby the user corrects the edits made by the automatic system rather than generating the truth from scratch. The results show the promise of the approach and demonstrate the ability of the algorithms to provide meaningful measurements of cell response to drug treatment in low-dose Hoechst-stained cells. ©2008 IEEE