Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation

Morphometric quantification of a pseudohyphae forming Saccharomyces cerevisiae strain using in situ microscopy and image analysis

Yeast morphology and counting are highly important in fermentation as they are often associated with productivity and can be influenced by process conditions. At present, time-consuming and offline methods are utilized for routine analysis of yeast morphology and cell counting using a haemocytometer. In this study, we demonstrate the application of an in situ microscope to obtain a fast stream of pseudohyphae images from agitated sample suspensions of a Saccharomyces cerevisiae strain, whose morphology in cell clusters is frequently found in the bioethanol fermentation industry. The large statistics of microscopic images allow for online determination of the principal morphological characteristics of the pseudohyphae, including the number of constituent cells, cell-size, number of branches, and length of branches. The distributions of these feature values are calculated online, constituting morphometric monitoring of the pseudohyphae population. By providing representative data, the proposed system can improve the effectiveness of morphological characterization, which in turn can help to improve the understanding and control of bioprocesses in which pseudohyphal-like morphologies are found.

Machine Learning for Analysis of Microscopy Images: A Practical Guide

The explosive growth of machine learning has provided scientists with insights into data in ways unattainable using prior research techniques. It has allowed the detection of biological features that were previously unrecognized and overlooked. However, because machine-learning methodology originates from informatics, many cell biology labs have experienced difficulties in implementing this approach. In this article, we target the rapidly expanding audience of cell and molecular biologists interested in exploiting machine learning for analysis of their research. We discuss the advantages of employing machine learning with microscopy approaches and describe the machine-learning pipeline. We also give practical guidelines for building models of cell behavior using machine learning. We conclude with an overview of the tools required for model creation, and share advice on their use. © 2020 by John Wiley & Sons, Inc.

Acute ethanol exposure reduces serotonin receptor 1A internalization by increasing ubiquitination and degradation of β-arrestin2

Acute alcohol exposure alters the trafficking and function of many G-protein-coupled receptors (GPCRs) that are associated with aberrant behavioral responses to alcohol. However, the molecular mechanisms underlying alcohol-induced changes in GPCR function remain unclear. β-Arrestin is a key player involved in the regulation of GPCR internalization and thus controls the magnitude and duration of GPCR signaling. Although β-arrestin levels are influenced by various drugs of abuse, the effect of alcohol exposure on β-arrestin expression and β-arrestin-mediated GPCR trafficking is poorly understood. Here, we found that acute ethanol exposure increases β-arrestin2 degradation via its increased ubiquitination in neuroblastoma-2a (N2A) cells and rat prefrontal cortex (PFC). β-Arrestin2 ubiquitination was likely mediated by the E3 ligase MDM2 homolog (MDM2), indicated by an increased coupling between β-arrestin2 and MDM2 in response to acute ethanol exposure in both N2A cells and rat PFC homogenates. Importantly, ethanol-induced β-arrestin2 reduction was reversed by siRNA-mediated MDM2 knockdown or proteasome inhibition in N2A cells, suggesting β-arrestin2 degradation is mediated by MDM2 through the proteasomal pathway. Using serotonin 5-HT1A receptors (5-HT1ARs) as a model receptor system, we found that ethanol dose-dependently inhibits 5-HT1AR internalization and that MDM2 knockdown reverses this effect. Moreover, ethanol both reduced β-arrestin2 levels and delayed agonist-induced β-arrestin2 recruitment to the membrane. We conclude that β-arrestin2 dysregulation by ethanol impairs 5-HT1AR trafficking. Our findings reveal a critical molecular mechanism underlying ethanol-induced alterations in GPCR internalization and implicate β-arrestin as a potential player mediating behavioral responses to acute alcohol exposure.

Digging deep into Golgi phenotypic diversity with unsupervised machine learning

Structural alterations of the Golgi apparatus may lead to phenotypes that human vision cannot easily discriminate. In this work, we present a high-content analysis framework including an unsupervised clustering step to automatically uncover Golgi phenotypic diversity. We use this deep phenotyping to quantitatively compare the effects of gene depletion.

The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportunities to explore this organelle organization and the gene network underlying it. To date, image-based Golgi screens have based on a single parameter or supervised analysis with predefined Golgi structural classes. Here, we report the use of multiparametric data extracted from a single marker and a computational unsupervised analysis framework to explore Golgi phenotypic diversity more extensively. In contrast with the three visually definable phenotypes, our framework reproducibly identified 10 Golgi phenotypes. They were used to quantify and stratify phenotypic similarities among genetic perturbations. The derived phenotypic network partially overlaps previously reported protein–protein interactions as well as suggesting novel functional interactions. Our workflow suggests the existence of multiple stable Golgi organizational states and provides a proof of concept for the classification of drugs and genes using fine-grained phenotypic information.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Detection and tracking of overlapping cell nuclei for large scale mitosis analyses

Background

Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods.

Results

Here, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters.

Conclusions

The proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.

Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach

Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively.

An advanced image analysis tool for the quantification and characterization of breast cancer in microscopy images

The paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images. The proposed tool utilizes adaptive thresholding and a Support Vector Machines classifier. The segmentation results are enhanced through a Majority Voting and a Watershed technique, while an object labeling algorithm has been developed for the fast and accurate validation of the recognized cells. Expert pathologists evaluated the tool and the reported results are satisfying and reproducible.

Machine learning in cell biology - teaching computers to recognize phenotypes

Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

Multi-scale Gaussian representation and outline-learning based cell image segmentation

Background

High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation.

Methods

We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information.

Results and conclusions

We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images

More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms.

Enhancing automated micrograph-based evaluation of LPS-stimulated macrophage spreading

To evaluate macrophage spreading in immunofluorescence images of macrophages for surface protein CD11b and nuclear counterstaining with DAPI, it is necessary to measure the size of the macrophages at different time points after stimulation. Manual evaluation of fluorescent micrographs is usually a time-consuming and error-prone task, with poor reproducibility. Automatic image analysis methods can be used to improve the results. The quality of the analysis with these methods mainly depends on the quality of the image segmentation. A segmentation and quantification scheme based on shading correction, k-means clustering, and fast marching level sets has been developed for the purpose. An initial application of this approach showed that separating touching and overlapping cells in particular suffers severely in the inevitably blurred conditions, leading to partly erroneous measurements of macrophage spreading. An alternative method of segmentation in fluorescent micrographs was therefore investigated and evaluated in this study. The proposed approach uses a methodology that separates foreground objects from background objects on the basis of Boykov's graph cuts. In this process, a rough estimation of background pixels is used for background seeds. To identify foreground seeds, a difference of Gaussian band pass filter based workflow is developed. Information on foreground and background seeds is then used for a gradient magnitude based graph cut resulting in a robust figure-ground separation method. In addition, a fast marching level set approach is used in the post-processing step, which makes it possible to split touching cells by incorporating information about the cell nuclei. An evaluation based on a total of 553 manually labeled macrophages depicted in 21 micrographs showed that the proposed method significantly improves segmentation and splitting performance for fluorescent micrographs of LPS-stimulated macrophages and reduces the rate of error in automated analysis of macrophage spreading in comparison with alternative methods.

Smart markers for watershed-based cell segmentation

Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain-specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have potential to greatly improve segmentation results. In this work, we propose a new approach for the effective segmentation of live cells from phase contrast microscopy. This approach introduces a new set of "smart markers" for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain-specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1,954 cells. The experimental results demonstrate that this approach, which uses the proposed definition of smart markers, is quite effective in identifying better markers compared to its counterparts. This will, in turn, be effective in improving the segmentation performance of a marker-controlled watershed algorithm.

Comparison of parameter-adapted segmentation methods for fluorescence micrographs

Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells.

Parallel imaging microfluidic cytometer

By adding an additional degree of freedom from multichannel flow, the parallel microfluidic cytometer (PMC) combines some of the best features of fluorescence-activated flow cytometry (FCM) and microscope-based high-content screening (HCS). The PMC (i) lends itself to fast processing of large numbers of samples, (ii) adds a 1D imaging capability for intracellular localization assays (HCS), (iii) has a high rare-cell sensitivity, and (iv) has an unusual capability for time-synchronized sampling. An inability to practically handle large sample numbers has restricted applications of conventional flow cytometers and microscopes in combinatorial cell assays, network biology, and drug discovery. The PMC promises to relieve a bottleneck in these previously constrained applications. The PMC may also be a powerful tool for finding rare primary cells in the clinic. The multichannel architecture of current PMC prototypes allows 384 unique samples for a cell-based screen to be read out in ∼6-10 min, about 30 times the speed of most current FCM systems. In 1D intracellular imaging, the PMC can obtain protein localization using HCS marker strategies at many times for the sample throughput of charge-coupled device (CCD)-based microscopes or CCD-based single-channel flow cytometers. The PMC also permits the signal integration time to be varied over a larger range than is practical in conventional flow cytometers. The signal-to-noise advantages are useful, for example, in counting rare positive cells in the most difficult early stages of genome-wide screening. We review the status of parallel microfluidic cytometry and discuss some of the directions the new technology may take.

Teachable, high-content analytics for live-cell, phase contrast movies

CL-Quant is a new solution platform for broad, high-content, live-cell image analysis. Powered by novel machine learning technologies and teach-by-example interfaces, CL-Quant provides a platform for the rapid development and application of scalable, high-performance, and fully automated analytics for a broad range of live-cell microscopy imaging applications, including label-free phase contrast imaging. The authors used CL-Quant to teach off-the-shelf universal analytics, called standard recipes, for cell proliferation, wound healing, cell counting, and cell motility assays using phase contrast movies collected on the BioStation CT and BioStation IM platforms. Similar to application modules, standard recipes are intended to work robustly across a wide range of imaging conditions without requiring customization by the end user. The authors validated the performance of the standard recipes by comparing their performance with truth created manually, or by custom analytics optimized for each individual movie (and therefore yielding the best possible result for the image), and validated by independent review. The validation data show that the standard recipes' performance is comparable with the validated truth with low variation. The data validate that the CL-Quant standard recipes can provide robust results without customization for live-cell assays in broad cell types and laboratory settings.

High-content analysis in monastrol suppressor screens. A neural network-based classification approach

OBJECTIVES

High-content screening (HCS) via automated fluorescent microscopy is a powerful technology for the effective expression of cellular processes. However, HCS will generally produce tremendous image datasets, which leads to difficulties of handling and analyzing. We proposed an automatic classification approach for simultaneous feature extraction and cell phenotype recognition of monoaster and bipolar cells in HCS system.

METHODS

The proposed approach was composed of image segmentation, feature extraction, and classification. The image segmentation was based on the Laplacian of Gaussian (LoG) edge detection method. For the reduction of noise effect on cellular images, we employed an adaptive threshold in microtubule channel. The principal component analysis was used in the feature selection process. The classification was performed with a back-propagation neural network (BPNN). Using the current approach, the cell phases were distinguished from three-channel acquisitions of cellular images and the numbers of bipolar and monoaster cells were automatically counted.

RESULTS

The validity of this approach was examined by the application of screening the response of drug compounds in suppressing Monastrol. Our results indicate that the proposed algorithm could improve the recognition rates of monoaster and bipolar cells to 97.98% and 93.12%, respectively, compared with 97.02% and 86.96% obtained from the same samples by multi-phenotypic mitotic analysis (MMA).

CONCLUSIONS

We have shown that BPNN is a valuable tool to classify cell phenotype. To further improve the classification performance, more test data, more optimized feature selection approaches, and advanced classifier may be required and will be investigated in future works.

A computational framework for studying neuron morphology from in vitro high content neuron-based screening

High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.

Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis

Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cell-cycle analysis software package, DCELLIQ, which is freely available.

Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time

Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.

Automated quantification of DNA demethylation effects in cells via 3D mapping of nuclear signatures and population homogeneity assessment

Today's advanced microscopic imaging applies to the preclinical stages of drug discovery that employ high-throughput and high-content three-dimensional (3D) analysis of cells to more efficiently screen candidate compounds. Drug efficacy can be assessed by measuring response homogeneity to treatment within a cell population. In this study, topologically quantified nuclear patterns of methylated cytosine and global nuclear DNA are utilized as signatures of cellular response to the treatment of cultured cells with the demethylating anti-cancer agents: 5-azacytidine (5-AZA) and octreotide (OCT). Mouse pituitary folliculostellate TtT-GF cells treated with 5-AZA and OCT for 48 hours, and untreated populations, were studied by immunofluorescence with a specific antibody against 5-methylcytosine (MeC), and 4,6-diamidino-2-phenylindole (DAPI) for delineation of methylated sites and global DNA in nuclei (n = 163). Cell images were processed utilizing an automated 3D analysis software that we developed by combining seeded watershed segmentation to extract nuclear shells with measurements of Kullback-Leibler's (K-L) divergence to analyze cell population homogeneity in the relative nuclear distribution patterns of MeC versus DAPI stained sites. Each cell was assigned to one of the four classes: similar, likely similar, unlikely similar, and dissimilar. Evaluation of the different cell groups revealed a significantly higher number of cells with similar or likely similar MeC/DAPI patterns among untreated cells (approximately 100%), 5-AZA-treated cells (90%), and a lower degree of same type of cells (64%) in the OCT-treated population. The latter group contained (28%) of unlikely similar or dissimilar (7%) cells. Our approach was successful in the assessment of cellular behavior relevant to the biological impact of the applied drugs, i.e., the reorganization of MeC/DAPI distribution by demethylation. In a comparison with other metrics, K-L divergence has proven to be a more valuable and robust tool for categorization of individual cells within a population, with potential applications in epigenetic drug screening.

A novel cell segmentation method and cell phase identification using Markov model

Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.

Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection

The identification of eukaryotic genes involved in virus entry and replication is important for understanding viral infection. Our goal is to develop a siRNA-based screening system using cell arrays and high-throughput (HT) fluorescence microscopy. A central issue is efficient, robust, and automated single-cell-based analysis of massive image datasets. We have developed an image analysis approach that comprises (i) a novel, gradient-based thresholding scheme for cell nuclei segmentation which does not require subsequent postprocessing steps for separation of clustered nuclei, (ii) quantification of the virus signal in the neighborhood of cell nuclei, (iii) localization of regions with transfected cells by combining model-based circle fitting and grid fitting, (iv) cell classification as infected or noninfected, and (v) image quality control (e.g., identification of out-of-focus images). We compared the results of our nucleus segmentation approach with a previously developed scheme of adaptive thresholding with subsequent separation of nuclear clusters. Our approach, which does not require a postprocessing step for the separation of nuclear clusters, correctly segmented 97.1% of the nuclei, whereas the previous scheme achieved 95.8%. Using our algorithm for the detection of out-of-focus images, we obtained a high discrimination power of 99.4%. Our overall approach has been applied to more than 55,000 images of cells infected by either hepatitis C or dengue virus. Reduced infection rates were correctly detected in positive siRNA controls, as well as for siRNAs targeting, for example, cellular genes involved in viral infection. Our image analysis approach allows for the automatic and accurate determination of changes in viral infection based on high-throughput single-cell-based siRNA cell array imaging experiments.

BlobFinder, a tool for fluorescence microscopy image cytometry

Images can be acquired at high rates with modern fluorescence microscopy hardware, giving rise to a demand for high-speed analysis of image data. Digital image cytometry, i.e., automated measurements and extraction of quantitative data from images of cells, provides valuable information for many types of biomedical analysis. There exists a number of different image analysis software packages that can be programmed to perform a wide array of useful measurements. However, the multi-application capability often compromises the simplicity of the tool. Also, the gain in speed of analysis is often compromised by time spent learning complicated software. We provide a free software called BlobFinder that is intended for a limited type of application, making it easy to use, easy to learn and optimized for its particular task. BlobFinder can perform batch processing of image data and quantify as well as localize cells and point like source signals in fluorescence microscopy images, e.g., from FISH, in situ PLA and padlock probing, in a fast and easy way.

A fast, fully automated cell segmentation algorithm for high-throughput and high-content screening

High-throughput, high-content screening (HT-HCS) of large compound libraries for drug discovery imposes new constraints on image analysis algorithms. Time and robustness are paramount while accuracy is intrinsically statistical. In this article, a fast and fully automated algorithm for cell segmentation is proposed. The algorithm is based on a strong attachment to the data that provide robustness and have been validated on the HT-HCS of large compound libraries and different biological assays. We present the algorithm and its performance, a description of its advantages and limitations, and a discussion of its range of application.

Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening

The premise of today's drug development is that the mechanism of a disease is highly dependent upon underlying signaling and cellular pathways. Such pathways are often composed of complexes of physically interacting genes, proteins, or biochemical activities coordinated by metabolic intermediates, ions, and other small solutes and are investigated with molecular biology approaches in genomics, proteomics, and metabonomics. Nevertheless, the recent declines in the pharmaceutical industry's revenues indicate such approaches alone may not be adequate in creating successful new drugs. Our observation is that combining methods of genomics, proteomics, and metabonomics with techniques of bioimaging will systematically provide powerful means to decode or better understand molecular interactions and pathways that lead to disease and potentially generate new insights and indications for drug targets. The former methods provide the profiles of genes, proteins, and metabolites, whereas the latter techniques generate objective, quantitative phenotypes correlating to the molecular profiles and interactions. In this paper, we describe pathway reconstruction and target validation based on the proposed systems biologic approach and show selected application examples for pathway analysis and drug screening.

Lessons from genetics: interpreting complex phenotypes in RNAi screens

Mammalian cell biology is witnessing a new era in which cellular processes are explained through dynamic networks of interacting cellular components. In this fast-pacing field, where image-based RNAi screening is taking a central role, there is a strong need to improve ways to capture such interactions in space and time. Cell biologists traditionally depict these events by confining themselves to the level of a single cell, or to many population-averaged cells. Similarly, classical geneticists observe and interpret phenotypes in a single organism to delineate signaling processes, but have also described genetic phenomena in populations of organisms. The analogy in the two approaches inspired us to draw parallels with, and take lessons from concepts in classical genetics.

Workflow and methods of high-content time-lapse analysis for quantifying intracellular calcium signals

Calcium ions (Ca2+) play a fundamental role in a variety of physiological functions in many cell types by acting as a secondary messenger. Variation of intracellular Ca2+ concentration ([Ca2+]i) is often observed when the cell is stimulated. However, it is a challenging task to automatically quantify intracellular [Ca2+]i in a population of cells. In this study, we present a workflow including specific algorithms for the automated intracellular calcium signal analysis using high-content, time-lapse cellular images. The experimental validations indicate the effectiveness of the proposed workflow and algorithms. We applied the workflow to analyze the intracellular calcium signals induced by different concentrations of H2O2 in the cell lines transfected by presenilin-1 (PS-1) that is known to be closely related to the familial Alzheimer's disease (FAD). The analysis results imply an important role of mutant PS-1, but not normal human PS-1 and mutant human amyloid precursor protein (APP), in enhancing intracellular calcium signaling induced by H2O2.

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles

Background

High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study.

Results

The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system.

Conclusion

The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

In situ detection of phosphorylated platelet-derived growth factor receptor beta using a generalized proximity ligation method

Improved methods are needed for in situ characterization of post-translational modifications in cell lines and tissues. For example, it is desirable to monitor the phosphorylation status of individual receptor tyrosine kinases in samples from human tumors treated with inhibitors to evaluate therapeutic responses. Unfortunately the leading methods for observing the dynamics of tissue post-translational modifications in situ, immunohistochemistry and immunofluorescence, exhibit limited sensitivity and selectivity. Proximity ligation assay is a novel method that offers improved selectivity through the requirement of dual recognition and increased sensitivity by including DNA amplification as a component of detection of the target molecule. Here we therefore established a generalized in situ proximity ligation assay to investigate phosphorylation of platelet-derived growth factor receptor beta (PDGFRbeta) in cells stimulated with platelet-derived growth factor BB. Antibodies specific for immunoglobulins from different species, modified by attachment of DNA strands, were used as secondary proximity probes together with a pair of primary antibodies from the corresponding species. Dual recognition of receptors and phosphorylated sites by the primary antibodies in combination with the secondary proximity probes was used to generate circular DNA strands; this was followed by signal amplification by replicating the DNA circles via rolling circle amplification. We detected tyrosine phosphorylated PDGFRbeta in human embryonic kidney cells stably overexpressing human influenza hemagglutinin-tagged human PDGFRbeta in porcine aortic endothelial cells transfected with the beta-receptor, but not in cells transfected with the alpha-receptor, and also in immortalized human foreskin fibroblasts, BJ hTert, endogenously expressing the PDGFRbeta. We furthermore visualized tyrosine phosphorylated PDGFRbeta in tissue sections from fresh frozen human scar tissue undergoing wound healing. The method should be of great value to study signal transduction, screen for effects of pharmacological agents, and enhance the diagnostic potential in histopathology.

An automated feedback system with the hybrid model of scoring and classification for solving over-segmentation problems in RNAi high content screening

BACKGROUND

High content screening (HCS) via automated fluorescence microscopy is a powerful technology for generating cellular images that are rich in phenotypic information. RNA interference is a revolutionary approach for silencing gene expression and has become an important method for studying genes through RNA interference-induced cellular phenotype analysis. The convergence of the two technologies has led to large-scale, image-based studies of cellular phenotypes under systematic perturbations of RNA interference. However, existing high content screening image analysis tools are inadequate to extract content regarding cell morphology from the complex images, thus they limit the potential of genome-wide RNA interference high content screening screening for simple marker readouts. In particular, over-segmentation is one of the persistent problems of cell segmentation; this paper describes a new method to alleviate this problem.

METHODS

To solve the issue of over-segmentation, we propose a novel feedback system with a hybrid model for automated cell segmentation of images from high content screening. A Hybrid learning model is developed based on three scoring models to capture specific characteristics of over-segmented cells. Dead nuclei are also removed through a statistical model.

RESULTS

Experimental validation showed that the proposed method had 93.7% sensitivity and 94.23% specificity. When applied to a set of images of F-actin-stained Drosophila cells, 91.3% of over-segmented cells were detected and only 2.8% were under-segmented.

CONCLUSIONS

The proposed feedback system significantly reduces over-segmentation of cell bodies caused by over-segmented nuclei, dead nuclei, and dividing cells. This system can be used in the automated analysis system of high content screening images.

Context based mixture model for cell phase identification in automated fluorescence microscopy

Background

Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task.

Results

The data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Maximum Margin Criterion (MMC), Stepwise Discriminate Analysis based Feature Selection (SDAFS), and Genetic Algorithm based Feature Selection (GAFS). Then, we propose a Context Based Mixture Model (CBMM) for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM), Neural Network (NN), and K-Nearest Neighbor (KNN). Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The experimental results show that CBMM outperforms all other classifies in identifying prophase and has the best overall performance.

Conclusion

The application of feature reduction techniques can improve the prediction accuracy significantly. CBMM can effectively utilize the contextual information and has the best overall performance when combined with any of the previously mentioned feature reduction techniques.

CellProfiler: image analysis software for identifying and quantifying cell phenotypes

CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

Automated quantification of quantum-dot-labelled epidermal growth factor receptor internalization via multiscale image segmentation

The ability to monitor epidermal growth factor receptor (EGFr) internalization specifically, and cellular protein concentrations and activation states in general, has been recently improved by the use of appropriately functionalized quantum dots (QDs), as a result of the long-lasting fluorescence, brightness and multicolour of these nanoparticles. However, important quantitative information about locational proteomics is based on the analysis of the properties of many cells and cell cultures on a per-cell basis, rather than tracking individual events within one cell. Moreover, relative positional information is often gained from traditional staining protocols of distinct cellular compartments that are prone to noise, fading and low contrast. We apply a novel multiscale image segmentation based on region growing to classify automatically objects in fixed cell preparations and to define regional zones in all cells prior to QD concentration measures. This allows rapid quantitative description of EGFr internalization as it changes with incubation time. The capabilities realizable by simultaneous application of confocal imaging and functionalized QDs in conjunction with advanced image analysis are a prerequisite for automated and multiplexed cytomics assays.

Automated Analysis of the Mitotic Phases of Human Cells in 3D Fluorescence Microscopy Image Sequences

The evaluation of fluorescence microscopy images acquired in high-throughput cell phenotype screens constitutes a substantial bottleneck and motivates the development of automated image analysis methods. Here we introduce a computational scheme to process 3D multi-cell time-lapse images as they are produced in large-scale RNAi experiments. We describe an approach to automatically segment, track, and classify cell nuclei into different mitotic phases. This enables automated analysis of the duration of single phases of the cell life cycle and thus the identification of cell cultures that show an abnormal mitotic behavior. Our scheme proves a high accuracy, suggesting a promising future for automating the evaluation of high-throughput experiments.

Cell microarrays and RNA interference chip away at gene function

The recent development of cell microarrays offers the potential to accelerate high-throughput functional genetic studies. The widespread use of RNA interference (RNAi) has prompted several groups to fabricate RNAi cell microarrays that make possible discrete, in-parallel transfection with thousands of RNAi reagents on a microarray slide. Though still a budding technology, RNAi cell microarrays promise to increase the efficiency, economy and ease of genome-wide RNAi screens in metazoan cells.

Towards automated cellular image segmentation for RNAi genome-wide screening

The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.