CellCognition : time-resolved phenotype annotation in high-throughput live cell imaging

Author Posting. © The Authors, 2010. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Methods 7 (2010): 747-754, doi:10.1038/nmeth.1486.Fluorescence time-lapse imaging has become a powerful tool to investigate complex dynamic processes such as cell division or intracellular trafficking. Automated microscopes generate time-resolved imaging data at high throughput, yet tools for quantification of large-scale movie data are largely missing. Here, we present CellCognition, a computational framework to annotate complex cellular dynamics. We developed a machine learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states. The incorporation of time information into the annotation scheme was essential to suppress classification noise at state transitions, and confusion between different functional states with similar morphology. We demonstrate generic applicability in a set of different assays and perturbation conditions, including a candidate-based RNAi screen for mitotic exit regulators in human cells. CellCognition is published as open source software, enabling live imaging-based screening with assays that directly score cellular dynamics.Work in the Gerlich laboratory is supported by Swiss National Science Foundation (SNF) research grant 3100A0-114120, SNF ProDoc grant PDFMP3_124904, a European Young Investigator (EURYI) award of the European Science Foundation, an EMBO YIP fellowship, and a MBL Summer Research Fellowship to D.W.G., an ETH TH grant, a grant by the UBS foundation, a Roche Ph.D. fellowship to M.H.A.S, and a Mueller fellowship of the Molecular Life Sciences Ph.D. program Zurich to M.H. M.H. and M.H.A.S are fellows of the Zurich Ph.D. Program in Molecular Life Sciences. B.F. was supported by European Commission’s seventh framework program project Cancer Pathways. Work in the Ellenberg laboratory is supported by a European Commission grant within the Mitocheck consortium (LSHG-CT-2004-503464). Work in the Peter laboratory is supported by the ETHZ, Oncosuisse, SystemsX.ch (LiverX) and the SNF

Automated microscopy for high-content RNAi screening

Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs

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

Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens

<p>Abstract</p> <p>Background</p> <p>The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.</p> <p>Results</p> <p>Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using <it>Drosophila </it>embryos [Additional files <supplr sid="S1">1</supplr>, <supplr sid="S2">2</supplr>], dataset for cell cycle phase identification using HeLa cells [Additional files <supplr sid="S1">1</supplr>, <supplr sid="S3">3</supplr>, <supplr sid="S4">4</supplr>] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a <it>Drosophila </it>genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.</p> <p>Conclusion</p> <p>We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.</p

Time-Resolved Quantification of Centrosomes by Automated Image Analysis Suggests Limiting Component to Set Centrosome Size in C. Elegans Embryos

The centrosome is a dynamic organelle found in all animal cells that serves as a microtubule organizing center during cell division. Most of the centrosome components have been identified by genetic screens over the last decade, but little is known about how these components interact with each other to form a functional centrosome. Towards a better understanding of the molecular organization of the centrosome, we investigated the mechanism that regulates the size of the centrosome in the early C. elegans embryo. For this, we monitored fluorescently labeled centrosomes in living embryos and developed a suite of image analysis algorithms to quantify the centrosomes in the resulting 3D time-lapse images. In particular, we developed a novel algorithm involving a two-stage linking process for tracking entrosomes, which is a multi-object tracking task. This fully automated analysis pipeline enabled us to acquire time-resolved data of centrosome growth in a large number of embryos and could detect subtle phenotypes that were missed by previous assays based on manual image analysis. In a first set of experiments, we quantified centrosome size over development in wild-type embryos and made three essential observations. First, centrosome volume scales proportionately with cell volume. Second, beginning at the 4-cell stage, when cells are small, centrosome size plateaus during the cell cycle. Third, the total centrosome volume the embryo gives rise to in any one cell stage is approximately constant. Based on our observations, we propose a ‘limiting component’ model in which centrosome size is limited by the amounts of maternally derived centrosome components. In a second set of experiments, we tested our hypothesis by varying cell size, centrosome number and microtubule-mediated pulling forces. We then manipulated the amounts of several centrosomal proteins and found that the conserved centriolar and pericentriolar material protein SPD-2 is one such component that determines centrosome size

A Time-Series Method for Automated Measurement of Changes in Mitotic and Interphase Duration from Time-Lapse Movies

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments.Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment.This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division

New foreground markers for Drosophila cell segmentation using marker-controlled watershed

Image segmentation consists of partitioning the image into different objects of interest. For a biological image, the segmentation step is important to understand the biological process. However, it is a challenging task due to the presence of different dimensions for cells, intensity inhomogeneity, and clustered cells. The marker-controlled watershed (MCW) is proposed for segmentation, outperforming the classical watershed. Besides, the choice of markers for this algorithm is important and impacts the results. For this work, two foreground markers are proposed: kernels, constructed with the software Fiji and Obj.MPP markers, constructed with the framework Obj.MPP. The new proposed algorithms are compared to the basic MCW. Furthermore, we prove that Obj.MPP markers are better than kernels. Indeed, the Obj.MPP framework takes into account cell properties such as shape, radiometry, and local contrast. Segmentation results, using new markers and illustrated on real Drosophila dataset, confirm the good performance quality in terms of quantitative and qualitative evaluation

Polar Angle Detection and Image Combination Based Leukocyte Segmentation for Overlapping Cell Images

Leukocyte segmentation is one of the essential steps in an automatic leukocyte recognition system. Due to the complexity of the overlapping cell images, methods for leukocyte segmentation are still needed. In this paper, we first construct a combined image by saturation and green channels to extract the nucleus and in turn locate a cursory circular region of the leukocyte. Then the boundary of the leukocyte is represented by the polar coordinate. We determine the overlapping area by polar angle detection. Finally, another combined image is built based on the red and blue channels of the sub image covering the overlap to segment the leukocyte. The paper reports a promising segmentation for 60 microscopic cell images