Automated image analysis for high-content screening and analysis

Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers

Molecular biomarkers are changes measured in biological samples that reflect disease states. Such markers can help clinicians identify types of cancer or stages of progression, and they can guide in tailoring specific therapies. Many efforts to identify biomarkers consider genes that mutate between normal and cancerous tissues or changes in protein or RNA expression levels. Here we define location biomarkers, proteins that undergo changes in subcellular location that are indicative of disease. To discover such biomarkers, we have developed an automated pipeline to compare the subcellular location of proteins between two sets of immunohistochemistry images. We used the pipeline to compare images of healthy and tumor tissue from the Human Protein Atlas, ranking hundreds of proteins in breast, liver, prostate, and bladder based on how much their location was estimated to have changed. The performance of the system was evaluated by determining whether proteins previously known to change location in tumors were ranked highly. We present a number of candidate location biomarkers for each tissue, and identify biochemical pathways that are enriched in proteins that change location. The analysis technology is anticipated to be useful not only for discovering new location biomarkers but also for enabling automated analysis of biomarker distributions as an aid to determining diagnosis.

Real-time cytotoxicity assays in human whole blood

A live cell-based whole blood cytotoxicity assay (WCA) that allows access to temporal information of the overall cell cytotoxicity is developed with high-throughput cell positioning technology. The targeted tumor cell populations are first preprogrammed to immobilization into an array format, and labeled with green fluorescent cytosolic dyes. Following the cell array formation, antibody drugs are added in combination with human whole blood. Propidium iodide (PI) is then added to assess cell death. The cell array is analyzed with an automatic imaging system. While cytosolic dye labels the targeted tumor cell populations, PI labels the dead tumor cell populations. Thus, the percentage of target cancer cell killing can be quantified by calculating the number of surviving targeted cells to the number of dead targeted cells. With this method, researchers are able to access time-dependent and dose-dependent cell cytotoxicity information. Remarkably, no hazardous radiochemicals are used. The WCA presented here has been tested with lymphoma, leukemia, and solid tumor cell lines. Therefore, WCA allows researchers to assess drug efficacy in a highly relevant ex vivo condition.

Autocrine signaling is a key regulatory element during osteoclastogenesis

Osteoclasts are responsible for bone destruction in degenerative, inflammatory and metastatic bone disorders. Although osteoclastogenesis has been well-characterized in mouse models, many questions remain regarding the regulation of osteoclast formation in human diseases. We examined the regulation of human precursors induced to differentiate and fuse into multinucleated osteoclasts by receptor activator of nuclear factor kappa-B ligand (RANKL). High-content single cell microscopy enabled the time-resolved quantification of both the population of monocytic precursors and the emerging osteoclasts. We observed that prior to induction of osteoclast fusion, RANKL stimulated precursor proliferation, acting in part through an autocrine mediator. Cytokines secreted during osteoclastogenesis were resolved using multiplexed quantification combined with a Partial Least Squares Regression model to identify the relative importance of specific cytokines for the osteoclastogenesis outcome. Interleukin 8 (IL-8) was identified as one of RANKL-induced cytokines and validated for its role in osteoclast formation using inhibitors of the IL-8 cognate receptors CXCR1 and CXCR2 or an IL-8 blocking antibody. These insights demonstrate that autocrine signaling induced by RANKL represents a key regulatory component of human osteoclastogenesis.

A rapid and effective vignetting correction for quantitative microscopy

We describe a novel and versatile algorithm for vignetting correction and demonstrate its usefulness for quantitative microscopy.

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.

Delivery of proteases in aqueous two-phase systems enables direct purification of stem cell colonies from feeder cell co-cultures for differentiation into functional cardiomyocytes

Patterning of bioactive enzymes with subcellular resolution is achieved by dispensing droplets of dextran (DEX) onto polyethylene glycol (PEG)-covered cells though a glass capillary needle connected to a pneumatic pump. This technique is applied to purify colonies of induced pluripotent stem cells (iPSCs) from mouse embryonic fibroblast (MEF) feeder cultures and inefficiently induced iPSC colonies by selectively dissociating the iPSCs with proteases.

Visualizing chemical structure-subcellular localization relationships using fluorescent small molecules as probes of cellular transport

Background

To study the chemical determinants of small molecule transport inside cells, it is crucial to visualize relationships between the chemical structure of small molecules and their associated subcellular distribution patterns. For this purpose, we experimented with cells incubated with a synthetic combinatorial library of fluorescent, membrane-permeant small molecule chemical agents. With an automated high content screening instrument, the intracellular distribution patterns of these chemical agents were microscopically captured in image data sets, and analyzed off-line with machine vision and cheminformatics algorithms. Nevertheless, it remained challenging to interpret correlations linking the structure and properties of chemical agents to their subcellular localization patterns in large numbers of cells, captured across large number of images.

Results

To address this challenge, we constructed a Multidimensional Online Virtual Image Display (MOVID) visualization platform using off-the-shelf hardware and software components. For analysis, the image data set acquired from cells incubated with a combinatorial library of fluorescent molecular probes was sorted based on quantitative relationships between the chemical structures, physicochemical properties or predicted subcellular distribution patterns. MOVID enabled visual inspection of the sorted, multidimensional image arrays: Using a multipanel desktop liquid crystal display (LCD) and an avatar as a graphical user interface, the resolution of the images was automatically adjusted to the avatar’s distance, allowing the viewer to rapidly navigate through high resolution image arrays, zooming in and out of the images to inspect and annotate individual cells exhibiting interesting staining patterns. In this manner, MOVID facilitated visualization and interpretation of quantitative structure-localization relationship studies. MOVID also facilitated direct, intuitive exploration of the relationship between the chemical structures of the probes and their microscopic, subcellular staining patterns.

Conclusion

MOVID can provide a practical, graphical user interface and computer-assisted image data visualization platform to facilitate bioimage data mining and cheminformatics analysis of high content, phenotypic screening experiments.

3D time series analysis of cell shape using Laplacian approaches

Background

Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes.

Results

We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells.

Conclusions

The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations.

Evaluation of the effectiveness of simple nuclei-segmentation methods on Caenorhabditis elegans embryogenesis images

BACKGROUND

For the analysis of spatio-temporal dynamics, various automated processing methods have been developed for nuclei segmentation. These methods tend to be complex for segmentation of images with crowded nuclei, preventing the simple reapplication of the methods to other problems. Thus, it is useful to evaluate the ability of simple methods to segment images with various degrees of crowded nuclei.

RESULTS

Here, we selected six simple methods from various watershed based and local maxima detection based methods that are frequently used for nuclei segmentation, and evaluated their segmentation accuracy for each developmental stage of the Caenorhabditis elegans. We included a 4D noise filter, in addition to 2D and 3D noise filters, as a pre-processing step to evaluate the potential of simple methods as widely as possible. By applying the methods to image data between the 50- to 500-cell developmental stages at 50-cell intervals, the error rate for nuclei detection could be reduced to ≤ 2.1% at every stage until the 350-cell stage. The fractions of total errors throughout the stages could be reduced to ≤ 2.4%. The error rates improved at most of the stages and the total errors improved when a 4D noise filter was used. The methods with the least errors were two watershed-based methods with 4D noise filters. For all the other methods, the error rate and the fraction of errors could be reduced to ≤ 4.2% and ≤ 4.1%, respectively. The minimum error rate for each stage between the 400- to 500-cell stages ranged from 6.0% to 8.4%. However, similarities between the computational and manual segmentations measured by volume overlap and Hausdorff distance were not good. The methods were also applied to Drosophila and zebrafish embryos and found to be effective.

CONCLUSIONS

The simple segmentation methods were found to be useful for detecting nuclei until the 350-cell stage, but not very useful after the 400-cell stage. The incorporation of a 4D noise filter to the simple methods could improve their performances. Error types and the temporal biases of errors were dependent on the methods used. Combining multiple simple methods could also give good segmentations.

Finding the shape-shifter genes

Cell morphological plasticity is controlled by intracellular signalling pathways. By combining large-scale imaging with quantitative analysis, an RNA interference (RNAi) screen in Drosophila melanogaster haemocytes reveals that most targeted genes regulate transitions between discrete shapes. Loss of gene function changes shape frequencies or reduces diversity, rather than producing new morphologies.

Crimean-Congo hemorrhagic fever virus utilizes a clathrin- and early endosome-dependent entry pathway

The early events in Crimean-Congo hemorrhagic fever virus (CCHFV) have not been completely characterized. Earlier work indicated that CCHFV likely enters cells by clathrin-mediated endocytosis (CME). Here we provide confirmatory evidence for CME entry by showing that CCHFV infection is inhibited in cells treated with Pitstop 2, a drug that specifically and reversibly interferes with the dynamics of clathrin-coated pits. Additionally, we show that CCHFV infection is inhibited by siRNA depletion of the clathrin pit associated protein AP-2. Following CME entry, we show that CCHFV has a pH-dependent entry step, with virus inactivation occurring at pH 6.0 and below. To more precisely define the endosomal trafficking of CCHFV, we show for the first time that overexpression of the dominant negative forms of Rab5 protein but not Rab7 protein inhibits CCHFV infection. These results indicate that CCHFV likely enters cells through the early endosomal compartment.

Chapter 17: bioimage informatics for systems pharmacology

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

High-content screening and analysis of the Golgi complex

High-content screening (HCS) as a methodological tool has evolved relatively recently, largely driven by the demand for in depth spatial and temporal information from intact cells exposed to a range of chemical and/or genomic perturbations. The technology is based around automated fluorescence microscopy in combination with advanced imaging processing and analysis tools, which together can provide quantitative information as a first-level description of complex cellular events. HCS and high-content analysis are particularly powerful when combined with perturbation techniques such as RNA interference (RNAi), as this allows large families of genes to be interrogated with respect to a biological pathway or process of interest. In this methodology chapter, we describe an approach by which HCS can be applied to study the morphological state of the Golgi complex in cultured mammalian cells. We provide a detailed protocol for the highly parallel downregulation of gene activity using RNAi in 384-well plates and describe an automated image analysis routine that could be used to quantify Golgi complex in a genome-wide RNAi context.

OXPHOS mutations and neurodegeneration

Mitochondrial oxidative phosphorylation (OXPHOS) sustains organelle function and plays a central role in cellular energy metabolism. The OXPHOS system consists of 5 multisubunit complexes (CI-CV) that are built up of 92 different structural proteins encoded by the nuclear (nDNA) and mitochondrial DNA (mtDNA). Biogenesis of a functional OXPHOS system further requires the assistance of nDNA-encoded OXPHOS assembly factors, of which 35 are currently identified. In humans, mutations in both structural and assembly genes and in genes involved in mtDNA maintenance, replication, transcription, and translation induce 'primary' OXPHOS disorders that are associated with neurodegenerative diseases including Leigh syndrome (LS), which is probably the most classical OXPHOS disease during early childhood. Here, we present the current insights regarding function, biogenesis, regulation, and supramolecular architecture of the OXPHOS system, as well as its genetic origin. Next, we provide an inventory of OXPHOS structural and assembly genes which, when mutated, induce human neurodegenerative disorders. Finally, we discuss the consequences of mutations in OXPHOS structural and assembly genes at the single cell level and how this information has advanced our understanding of the role of OXPHOS dysfunction in neurodegeneration.

Practical course on "imaging infection: from single molecules to animals"

A 2-week long theoretical and practical course on innovative microscopy in the field of microbial infection was organized in Pretoria, South Africa. Talks from lecturers from such fields as super-resolution microscopy, fluorescence and bioluminescence imaging, high throughput microscopy assays and image analysis were followed by practicals on cutting-edge microscopes.

Shedding light on filovirus infection with high-content imaging

Microscopy has been instrumental in the discovery and characterization of microorganisms. Major advances in high-throughput fluorescence microscopy and automated, high-content image analysis tools are paving the way to the systematic and quantitative study of the molecular properties of cellular systems, both at the population and at the single-cell level. High-Content Imaging (HCI) has been used to characterize host-virus interactions in genome-wide reverse genetic screens and to identify novel cellular factors implicated in the binding, entry, replication and egress of several pathogenic viruses. Here we present an overview of the most significant applications of HCI in the context of the cell biology of filovirus infection. HCI assays have been recently implemented to quantitatively study filoviruses in cell culture, employing either infectious viruses in a BSL-4 environment or surrogate genetic systems in a BSL-2 environment. These assays are becoming instrumental for small molecule and siRNA screens aimed at the discovery of both cellular therapeutic targets and of compounds with anti-viral properties. We discuss the current practical constraints limiting the implementation of high-throughput biology in a BSL-4 environment, and propose possible solutions to safely perform high-content, high-throughput filovirus infection assays. Finally, we discuss possible novel applications of HCI in the context of filovirus research with particular emphasis on the identification of possible cellular biomarkers of virus infection.

Functional genomic and high-content screening for target discovery and deconvolution

INTRODUCTION

Functional genomic screens apply knowledge gained from the sequencing of the human genome toward rapid methods of identifying genes involved in cellular function based on a specific phenotype. This approach has been made possible through advances in both molecular biology and automation. The utility of this approach has been further enhanced through the application of image-based high-content screening: an automated microscopy and quantitative image analysis platform. These approaches can significantly enhance the acquisition of novel targets for drug discovery.

AREAS COVERED

Both the utility and potential issues associated with functional genomic screening approaches are discussed in this review, along with examples that illustrate both. The considerations for high-content screening applied to functional genomics are also presented.

EXPERT OPINION

Functional genomic screening and high-content screening are extremely useful in the identification of new drug targets. However, the technical, experimental, and computational parameters have an enormous influence on the results. Thus, although new targets are identified, caution should be applied to the interpretation of screening data in isolation. Genomic screens should be viewed as an integral component of a target identification campaign that requires both the acquisition of orthogonal data, as well as a rigorous validation strategy.

Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images

We review state-of-the-art computational methods for constructing, from image data, generative statistical models of cellular and nuclear shapes and the arrangement of subcellular structures and proteins within them. These automated approaches allow consistent analysis of images of cells for the purposes of learning the range of possible phenotypes, discriminating between them, and informing further investigation. Such models can also provide realistic geometry and initial protein locations to simulations in order to better understand cellular and subcellular processes. To determine the structures of cellular components and how proteins and other molecules are distributed among them, the generative modeling approach described here can be coupled with high throughput imaging technology to infer and represent subcellular organization from data with few a priori assumptions. We also discuss potential improvements to these methods and future directions for research.

Fiji: an open-source platform for biological-image analysis

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Innovation in biological microscopy: current status and future directions

The current revolution in biological microscopy stems from the realisation that advances in optics and computational tools and automation make the modern microscope an instrument that can access all scales relevant to modern biology – from individual molecules all the way to whole tissues and organisms and from single snapshots to time-lapse recordings sampling from milliseconds to days. As these and more new technologies appear, the challenges of delivering them to the community grows as well. I discuss some of these challenges, and the examples where openly shared technology have made an impact on the field.

Cell perturbation screens for target identification by RNAi

Over the last decade, cell-based screening has become a powerful method in target identification and plays an important role both in basic research and drug discovery. The availability of whole genome sequences and improvements in cell-based screening techniques opened new avenues for high-throughput experiments. Large libraries of RNA interference reagents available for many organisms allow the dissection of broad spectrum of cellular processes. Here, we describe the current state of the large-scale phenotype screening with a focus on cell-based screens. We underline the importance and provide details of screen design, scalability, performance, data analysis, and hit prioritization. Similar to classical high-throughput in vitro screens with defined-target approaches in the past, cell-based screens depend on a successful establishment of robust phenotypic assays, the ability to quantitatively measure phenotypic changes and bioinformatics methods for data analysis, integration, and interpretation.

CellOrganizer: Image-derived models of subcellular organization and protein distribution

This chapter describes approaches for learning models of subcellular organization from images. The primary utility of these models is expected to be from incorporation into complex simulations of cell behaviors. Most current cell simulations do not consider spatial organization of proteins at all, or treat each organelle type as a single, idealized compartment. The ability to build generative models for all proteins in a proteome and use them for spatially accurate simulations is expected to improve the accuracy of models of cell behaviors. A second use, of potentially equal importance, is expected to be in testing and comparing software for analyzing cell images. The complexity and sophistication of algorithms used in cell-image-based screens and assays (variously referred to as high-content screening, high-content analysis, or high-throughput microscopy) is continuously increasing, and generative models can be used to produce images for testing these algorithms in which the expected answer is known.

High-content screening of peptide-based non-viral gene delivery systems

High-content screening (HCS) uses high-capacity automated fluorescence imaging for the quantitative analysis of single cells and cell populations. Here, we developed an HCS assay for rapid screening of non-viral gene delivery systems as exemplified by the screening of a small library of peptide-based transfectants. These peptides were simultaneously screened for transfection efficiency, cytotoxicity, induction of cell permeability and the capacity to transfect non-dividing cells. We demonstrated that HCS is a valuable extension to the already existing screening methods for the in vitro evaluation of non-viral gene delivery systems with the added value that multiple parameters can be screened in parallel thereby obtaining more information from a single screening event, which will accelerate the development of novel gene delivery systems.

Comparison of Multivariate Data Analysis Strategies for High-Content Screening

High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data-processing strategies, the prediction accuracies and the Z′ factors of control compounds of a HCS cell cycle data set were monitored. As expected, dimension reduction led to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.

Light microscopic analysis of mitochondrial heterogeneity in cell populations and within single cells

Heterogeneity in the shapes of individual multicellular organisms is a daily experience. Likewise, even a quick glance through the ocular of a light microscope reveals the morphological heterogeneities in genetically identical cultured cells, whereas heterogeneities on the level of the organelles are much less obvious. This short review focuses on intracellular heterogeneities at the example of the mitochondria and their analysis by fluorescence microscopy. The overall mitochondrial shape as well as mitochondrial dynamics can be studied by classical (fluorescence) light microscopy. However, with an organelle diameter generally close to the resolution limit of light, the heterogeneities within mitochondria cannot be resolved with conventional light microscopy. Therefore, we briefly discuss here the potential of subdiffraction light microscopy (nanoscopy) to study inner-mitochondrial heterogeneities.