Visualizing cellular imaging data using PhenoPlot

Do the same chlorinated organophosphorus flame retardants that cause cytotoxicity and DNA damage share the same pathway?

Organophosphorus flame retardants (OPFRs) have been frequently detected with relatively high concentrations in various environmental media and are considered emerging environmental pollutants. However, their biological effect and underlying mechanism is still unclear, and whether chlorinated OPFRs (Cl-OPFRs) cause adverse outcomes with the same molecular initial events or share the same key events (KEs) remains unknown. In this study, in vitro bioassays were conducted to analyze the cytotoxicity, mitochondrial impairment, DNA damage and molecular mechanisms of two Cl-OPFRs. The results showed that these two Cl-OPFRs, which have similar structures, induced severe cellular and molecular damages via different underlying mechanisms. Both tris(2-chloroethyl) phosphate (TCEP) and tris(1-chloro-2-propyl) (TCPP) induced oxidative stress-mediated mitochondrial impairment and DNA damage, as shown by the overproduction of intracellular reactive oxygen species (ROS) and mitochondrial superoxide. Furthermore, the DNA damage caused by TCPP resulted in p53/p21-mediated cell cycle arrest, as evidenced by flow cytometry and real-time PCR. At the cellular and molecular levels, TCPP increased the sub-G1 apoptotic peak and upregulated the p53/Bax apoptosis pathway, possibly resulted in apoptosis associated with its stronger cytotoxicity. Although structurally similar to TCPP, TCEP did not induce mitochondrial impairment and DNA damage by the same KEs. These results provide insight into the toxicity of Cl-OPFRs with similar structures but different mechanisms, which is of great significance for constructing adverse outcome pathways or determining intermediate KEs.

Morphological entropy encodes cellular migration strategies on multiple length scales

Cell migration is crucial for numerous physiological and pathological processes. A cell adapts its morphology, including the overall and nuclear morphology, in response to various cues in complex microenvironments, such as topotaxis and chemotaxis during migration. Thus, the dynamics of cellular morphology can encode migration strategies, from which diverse migration mechanisms can be inferred. However, deciphering the mechanisms behind cell migration encoded in morphology dynamics remains a challenging problem. Here, we present a powerful universal metric, the Cell Morphological Entropy (CME), developed by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology at multiple length scales through the deviation from a perfectly circular shape, is illustrated using a variety of normal and tumor cell lines in different in vitro microenvironments. Our results show how geometric constraints affect the MDA-MB-231 cell nucleus, the emerging interactions of MCF-10A cells migrating on collagen gel, and the critical transition from proliferation to invasion in tumor spheroids. The analysis demonstrates that the CME-based approach provides an effective and physically interpretable tool to measure morphology in real-time across multiple length scales. It provides deeper insight into cell migration and contributes to the understanding of different behavioral modes and collective cell motility in more complex microenvironments.

Aging and injury affect nuclear shape heterogeneity in tendon

Tissue level properties are commonly studied using histological stains assessed with qualitative scoring methods. As qualitative evaluation is typically insensitive, quantitative analysis provides additional information about pathological mechanisms, but cannot capture structural heterogeneity across cell subpopulations. However, molecular analyses of cell and nuclear behavior have identified that cell and more recently also nuclear shape are highly associated with cell function and malfunction. This study combined a Visually Aided Morpho-Phenotyping Image Recognition analysis that automatically segments cells based on their shape with an added capacity to further discriminate between cells in certain protein-rich extracellular matrix regions. We used tendon as a model system given the enormous changes in organization and cell and nuclear shape they undergo during aging and injury. Our results uncover that multiple shape modes of nuclei exist during maturity and aging in rat tendon and that distinct subgroups of cell nuclei shapes exist in proteoglycan-rich regions during aging. With injury, several immunomarkers (αSMA, CD31, CD146) were associated with more rounded shape modes. In human tendons, the cell nuclei at sites of injury were found to be more rounded relative to uninjured tissues. To conclude, the tendon tissue changes occurring during aging and injury could be associated with a variation in cell nuclear morphology and the appearance of various region-specific subpopulations. Thus, the methodologies developed allow for a deeper understanding of cell heterogeneity during tendon aging and injury and may be extended to study further clinical applications.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

On the Variability in Cell and Nucleus Shapes

Cell morphology is an important regulator of cell function. Many abnormalities in cellular behavior can be discerned from changes in the shape of the cell and its organelles, typically the nucleus. Two major challenges for developing such phenotypic assays are reconstructing 3D surfaces of individual cells and nuclei from confocal images and developing characterizations of these surfaces for comparisons. We demonstrate two algorithms - 3D active contours and 3D condensed-attention UNet - to segment cells and nuclei from confocal images. The cell and nuclear surfaces are then converted into vectors using a reversible, spherical transform - i.e., shapes can be recovered from the vectors. Typical methods for characterizing shapes using size, shape, and image parameters such as area, volume, shape factor, solidity, and pixel intensities are not amenable to such reverse transformation. Our vector representation's principal component analysis shows that the significant modes of variability among cell and nucleus shapes are scaling and flattening. We benchmark these modes using a known mechanical model for nucleus morphology. Subsequent modes alter the eccentricity of the nucleus and translate and rotate it with respect to the cell. Our vector-space representation of cell and nucleus shape helps physically interpret the variability sources. It may further help to guide mechanical models and identify molecular mechanisms driving cell and nuclear shape changes.

Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses

Cell morphology is profoundly influenced by cellular interactions with microenvironmental factors such as the extracellular matrix (ECM). Upon adhesion to specific ECM, various cell types are known to exhibit different but distinctive morphologies, suggesting that ECM-dependent cell morphological responses may harbour rich information on cellular signalling states. However, the inherent morphological complexity of cellular and subcellular structures has posed an ongoing challenge for automated quantitative analysis. Since multi-channel fluorescence microscopy provides robust molecular specificity important for the biological interpretations of observed cellular architecture, here we develop a deep learning-based analysis pipeline for the classification of cell morphometric phenotypes from multi-channel fluorescence micrographs, termed SE-RNN (residual neural network with squeeze-and-excite blocks). We demonstrate SERNN-based classification of distinct morphological signatures observed when fibroblasts or epithelial cells are presented with different ECM. Our results underscore how cell shapes are non-random and established the framework for classifying cell shapes into distinct morphological signature in a cell-type and ECM-specific manner.

ShapoGraphy: A User-Friendly Web Application for Creating Bespoke and Intuitive Visualisation of Biomedical Data

Effective visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns from complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by an interactive web-based application for creating customisable quantitative pictorial representations to facilitate the understanding and analysis of image datasets (www.shapography.com). ShapoGraphy enables the user to create a structure of interest as a set of shapes. Each shape can encode different variables that are mapped to the shape dimensions, colours, symbols, or outline. We illustrate the utility of ShapoGraphy using various image data, including high dimensional multiplexed data. Our results show that ShapoGraphy allows a better understanding of cellular phenotypes and relationships between variables. In conclusion, ShapoGraphy supports scientific discovery and communication by providing a rich vocabulary to create engaging and intuitive representations of diverse data types.

BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations

MOTIVATION

High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language.

RESULTS

Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets.

AVAILABILITY AND IMPLEMENTATION

The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

AgentVis: Visual Analysis of Agent Behavior With Hierarchical Glyphs

Glyphs representing complex behavior provide a useful and common means of visualizing multivariate data. However, due to their complex shape, overlapping, and occlusion of glyphs is a common and prominent limitation. This limits the number of discreet data tuples that can be displayed in a given image. Using a real-world application, glyphs are used to depict agent behavior in a call center. However, many call centers feature thousands of agents. A standard approach representing thousands of agents with glyphs does not scale. To accommodate the visualization incorporating thousands of glyphs we develop clustering of overlapping glyphs into a single parent glyph. This hierarchical glyph represents the mean value of all child agent glyphs, removing overlap and reduTcing visual clutter. Multi-variate clustering techniques are explored and developed in collaboration with domain experts in the call center industry. We implement dynamic control of glyph clusters according to zoom level and customized distance metrics, to utilize image space with reduced overplotting and cluttering. We demonstrate our technique with examples and a usage scenario using real-world call-center data to visualize thousands of call center agents, revealing insight into their behavior and reporting feedback from expert call-center analysts.

A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei

Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm ( https://github.com/kukionfr/VAMPIRE_open ). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.

High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells

Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.

Potential of electrospun cationic BSA fibers to guide osteogenic MSC differentiation via surface charge and fibrous topography

Large or complex bone fractures often need clinical treatments for sufficient bone repair. New treatment strategies have pursued the idea of using mesenchymal stromal cells (MSCs) in combination with osteoinductive materials to guide differentiation of MSCs into bone cells ensuring complete bone regeneration. To overcome the challenge of developing such materials, fundamental studies are needed to analyze and understand the MSC behavior on modified surfaces of applicable materials for bone healing. For this purpose, we developed a fibrous scaffold resembling the bone/bone marrow extracellular matrix (ECM) based on protein without addition of synthetic polymers. With this biomimetic in vitro model we identified the fibrous structure as well as the charge of the material to be responsible for its effects on MSC differentiation. Positive charge was introduced via cationization that additionally supported the stability of the scaffold in cell culture, and acted as nucleation point for mineralization during osteogenesis. Furthermore, we revealed enhanced focal adhesion formation and osteogenic differentiation of MSCs cultured on positively charged protein fibers. This pure protein-based and chemically modifiable, fibrous ECM model allows the investigation of MSC behavior on biomimetic materials to unfold new vistas how to direct cells' differentiation for the development of new bone regenerating strategies.

Recent advances in tissue imaging for cancer research

Image analysis in clinical research has evolved at fast pace in the last decade. This review discusses basic concepts ranging from immunohistochemistry to advanced techniques such as multiplex imaging, digital pathology, flow cytometry and intravital microscopy. Tissue imaging ex vivo is still one of the gold-standards in the field due to feasibility. We describe here different protocols and applications of digital analysis providing basic and clinical researchers with an overview on how to analyse tissue images. In vivo imaging is not easily accessible to researchers; however, it provides invaluable dynamic information. Overall, we discuss a plethora of techniques that - when combined - constitute a powerful platform for basic and translational cancer research.

Mathematical modeling of drug-induced receptor internalization in the HER2-positive SKBR3 breast cancer cell-line

About 20% of breast cancer tumors over-express the HER2 receptor. Trastuzumab, an approved drug to treat this type of breast cancer, is a monoclonal antibody directly binding at the HER2 receptor and ultimately inhibiting cancer cell growth. The goal of our study was to understand the early impact of trastuzumab on HER2 internalization and recycling in the HER2-overexpressing breast cancer cell line SKBR3. To this end, fluorescence microscopy, monitoring the amount of HER2 expression in the plasma membrane, was combined with mathematical modeling to derive the flux of HER2 receptors from and to the membrane. We constructed a dynamic multi-compartment model based on ordinary differential equations. To account for cancer cell heterogeneity, a first, dynamic model was expanded to a second model including two distinct cell phenotypes, with implications for different conformational states of HER2, i.e. monomeric or homodimeric. Our mathematical model shows that the hypothesis of fast constitutive HER2 recycling back to the plasma membrane does not match the experimental data. It conclusively describes the experimental observation that trastuzumab induces sustained receptor internalization in cells with membrane ruffles. It is also concluded that for rare, non-ruffled (flat) cells, HER2 internalization occurs three orders of magnitude slower than for the bulk, ruffled cell population.

Comparative toxicity study of a novel non-ionic surfactant, vanillin ethoxylates, and nonylphenol ethoxylates in Chinese hamster ovary cells in vitro

Surfactants such as alkylphenol polyethoxylates (APEOs) and nonylphenol ethoxylates (NPEOs) are commonly used worldwide, but the majority of these compounds, together with their metabolites, have been reported to induce severe biological toxicity. Here, we evaluated for the first time the cytotoxicity, genotoxicity and mitochondrial damage in Chinese hamster ovary (CHO-K1) cells caused by a novel non-ionic surfactant, vanillin ethoxylates (VAEOs), an alternative to APEOs. In parallel, the same in vitro bioassays were conducted on NPEOs along with their metabolic byproducts 4-nonylphenol (4-NP) and vanillin. The results showed that the cytotoxic potency order was NPEOs > 4-NP > VAEOs>vanillin using CCK-8 assays. Also, 4-NP showed potential direct DNA damage in SOS/umu tests, whereas NPEOs, VAEOs and vanillin showed no positive result with and without S9 addition. In addition, none of the test compounds showed obvious genotoxic effects with low olive tail moment value using comet assays. However, all test compounds were shown to cause mitochondrial impairment by increasing mitochondrial mass and decreasing mitochondrial membrane potential in a concentration-dependent manner. And further analysis of reactive oxygen species (ROS) and mitochondrial superoxide (MNSOD) measurement showed that mitochondrial impairment was induced by oxidative stress with intracellular ROS and MNSOD overproduction. It's worth noting that VAEOs and vanillin cause relative lower cytotoxic, genotoxic and mitochondrial damage effects than NPEOs and 4-NP, indicating that VAEOs have the potential to substitute NPEOs as suitable surfactants. Take together, this study elucidates the toxicity profiles of VAEOs and NPEOs relatively comprehensively, and further toxicity analyses are suggested in the population, community and ecosystem.

RefCell: multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'

BACKGROUND

Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs.

RESULTS

Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample.

CONCLUSIONS

We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.

Aryl- and alkyl-phosphorus-containing flame retardants induced mitochondrial impairment and cell death in Chinese hamster ovary (CHO-k1) cells

Phosphorus-containing flame retardants (PFRs) are increasingly in demand worldwide as replacements for brominated flame retardants (BFRs), but insufficient available toxicological information on PFRs makes assessing their health risks challenging. Mitochondria are important targets of various environmental pollutants, and mitochondrial dysfunction may lead to many common diseases. In the present study, mitochondria impairment-related endpoints were measured by a high content screening (HCS) assay for 11 selected non-halogen PFRs in Chinese hamster ovary (CHO-k1) cells. A cluster analysis was used to categorize these PFRs into three groups according to their structural characteristics and results from the HCS assay. Two groups, containing long-chain alkyl-PFRs and all aryl-PFRs, were found to cause mitochondrial impairment but showed different mechanisms of toxicity. Due to the high correlation between cell death and mitochondrial impairment, two PFRs with different structures, trihexyl phosphate (THP) and cresyl diphenyl phosphate (CDP), were selected and compared with chlorpyrifos (CPF) to elucidate their mechanism of inducing cell death. THP (an alkyl-PFR) was found to utilize a similar pathway as CPF to induce apoptosis. However, cell death induced by CDP (an aryl-PFR) was different from classical necrosis based on experiments to discriminate among the different modes of cell death. These results confirm that mitochondria might be important targets for some PFRs and that differently structured PFRs could function via distinct mechanisms of toxicity.

High-content screening assay-based discovery of paullones as novel podocyte-protective agents

Podocyte dysfunction and loss is an early event and a hallmark of proteinuric kidney diseases. A podocyte's normal function is maintained via its unique cellular architecture that relies on an intracellular network of filaments, including filamentous actin (F-actin) and microtubules, that provides mechanical support. Damage to this filamentous network leads to changes in cellular morphology and results in podocyte injury, dysfunction, and death. Conversely, stabilization of this network protects podocytes and ameliorates proteinuria. This suggests that stabilization of podocyte architecture via its filamentous network could be a key therapeutic strategy for proteinuric kidney diseases. However, development of podocyte-directed therapeutics, especially those that target the cell's filamentous network, is still lacking, partly because of unavailability of appropriate cellular assays for use in a drug discovery environment. Here, we describe a new high-content screening-based methodology and its implementation on podocytes to identify paullone derivatives as a novel group of podocyte-protective compounds. We find that three compounds, i.e., kenpaullone, 1-azakenpaullone, and alsterpaullone, dose dependently protect podocytes from puromycin aminonucleoside (PAN)-mediated injury in vitro by reducing PAN-induced changes in both the filamentous actin and microtubules, with alsterpaullone providing maximal protection. Mechanistic studies further show that alsterpaullone suppressed PAN-induced activation of signaling downstream of GSK3β and p38 mitogen-activated protein kinase. In vivo it reduced ADR-induced glomerular injury in a zebrafish model. Together, these results identify paullone derivatives as novel podocyte-protective agents for future therapeutic development.

A Bioinformatics 3D Cellular Morphotyping Strategy for Assessing Biomaterial Scaffold Niches

Many biomaterial scaffolds have been advanced to provide synthetic cell niches for tissue engineering and drug screening applications; however, current methods for comparing scaffold niches focus on cell functional outcomes or attempt to normalize materials properties between different scaffold formats. We demonstrate a three-dimensional (3D) cellular morphotyping strategy for comparing biomaterial scaffold cell niches between different biomaterial scaffold formats. Primary human bone marrow stromal cells (hBMSCs) were cultured on 8 different biomaterial scaffolds, including fibrous scaffolds, hydrogels, and porous sponges, in 10 treatment groups to compare a variety of biomaterial scaffolds and cell morphologies. A bioinformatics approach was used to determine the 3D cellular morphotype for each treatment group by using 82 shape metrics to analyze approximately 1000 cells. We found that hBMSCs cultured on planar substrates yielded planar cell morphotypes, while those cultured in 3D scaffolds had elongated or equiaxial cellular morphotypes with greater height. Multivariate analysis was effective at distinguishing mean shapes of cells in flat substrates from cells in scaffolds, as was the metric L1-depth (the cell height along its shortest axis after aligning cells with a characteristic ellipsoid). The 3D cellular morphotyping technique enables direct comparison of cellular microenvironments between widely different types of scaffolds and design of scaffolds based on cell structure-function relationships.

3D screening device for the evaluation of cell response to different electrospun microtopographies

Micro- and nano-topographies of scaffold surfaces play a pivotal role in tissue engineering applications, influencing cell behavior such as adhesion, orientation, alignment, morphology and proliferation. In this study, a novel microfabrication method based on the combination of soft-lithography and electrospinning for the production of micro-patterned electrospun scaffolds was proposed. Subsequently, a 3D screening device for electrospun meshes with different micro-topographies was designed, fabricated and biologically validated. Results indicated that the use of defined patterns could induce specific morphological variations in human mesenchymal stem cell cytoskeletal organization, which could be related to differential activity of signaling pathways.

STATEMENT OF SIGNIFICANCE

We introduce a novel and time saving method to fabricate 3D micropatterns with controlled micro-architectures on electrospun meshes using a custom made collector and a PDMS mold with the desired topography. A possible application of this fabrication technique is represented by a 3D screening system for patterned electrospun meshes that allows the screening of different scaffold/electrospun parameters on cell activity. In addition, what we have developed in this study could be modularly applied to existing platforms. Considering the different patterned geometries, the cell morphological data indicated a change in the cytoskeletal organization with a close correspondence to the patterns, as shown by phenoplot and boxplot analysis, and might hint at the differential activity of cell signaling. The 3D screening system proposed in this study could be used to evaluate topographies favoring cell alignment, proliferation and functional performance, and has the potential to be upscaled for high-throughput.

Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics

The associations between clinical phenotypes (tumor grade, survival) and cell phenotypes, such as shape, signaling activity, and gene expression, are the basis for cancer pathology, but the mechanisms explaining these relationships are not always clear. The generation of large data sets containing information regarding cell phenotypes and clinical data provides an opportunity to describe these mechanisms. Here, we develop an image-omics approach to integrate quantitative cell imaging data, gene expression, and protein-protein interaction data to systematically describe a "shape-gene network" that couples specific aspects of breast cancer cell shape to signaling and transcriptional events. The actions of this network converge on NF-κB, and support the idea that NF-κB is responsive to mechanical stimuli. By integrating RNAi screening data, we identify components of the shape-gene network that regulate NF-κB in response to cell shape changes. This network was also used to generate metagene models that predict NF-κB activity and aspects of morphology such as cell area, elongation, and protrusiveness. Critically, these metagenes also have predictive value regarding tumor grade and patient outcomes. Taken together, these data strongly suggest that changes in cell shape, driven by gene expression and/or mechanical forces, can promote breast cancer progression by modulating NF-κB activation. Our findings highlight the importance of integrating phenotypic data at the molecular level (signaling and gene expression) with those at the cellular and tissue levels to better understand breast cancer oncogenesis.

Multiparametric Analysis of Cell Shape Demonstrates that β-PIX Directly Couples YAP Activation to Extracellular Matrix Adhesion

Mechanical signals from the extracellular matrix (ECM) and cellular geometry regulate the nuclear translocation of transcriptional regulators such as Yes-associated protein (YAP). Elucidating how physical signals control the activity of mechanosensitive proteins poses a technical challenge, because perturbations that affect cell shape may also affect protein localization indirectly. Here, we present an approach that mitigates confounding effects of cell-shape changes, allowing us to identify direct regulators of YAP localization. This method uses single-cell image analysis and statistical models that exploit the naturally occurring heterogeneity of cellular populations. Through systematic depletion of all human kinases, Rho family GTPases, GEFs, and GTPase activating proteins (GAPs), together with targeted chemical perturbations, we found that β-PIX, a Rac1/Ccd42 GEF, and PAK2, a Rac1/Cdc42 effector, drive both YAP activation and cell-ECM adhesion turnover during cell spreading. Our observations suggest that coupling YAP to adhesion dynamics acts as a mechano-timer, allowing cells to rapidly tune gene expression in response to physical signals.

Cellular Deconstruction: Finding Meaning in Individual Cell Variation

The advent of single cell transcriptome analysis has permitted the discovery of cell-to-cell variation in transcriptome expression of even presumptively identical cells. We hypothesize that this variability reflects a many-to-one relation between transcriptome states and the phenotype of a cell. In this relation, the molecular ratios of the subsets of RNA are determined by the stoichiometric constraints of the cell systems, which underdetermine the transcriptome state. Furthermore, the variability is, in part, induced by the tissue context and is important for system-level function. This theory is analogous to theories of literary deconstruction, where multiple 'signifiers' work in opposition to one another to create meaning. By analogy, transcriptome phenotypes should be defined as subsets of RNAs comprising selected RNA systems where the system-associated RNAs are balanced with each other to produce the associated cellular function. This idea provides a framework for understanding cellular heterogeneity in phenotypic responses to variant conditions, such as disease challenge.

On the importance of modelling the internal spatial dynamics of biological cells

Spatial effects such as cell shape have very often been considered negligible in models of cellular pathways, and many existing simulation infrastructures do not take such effects into consideration. Recent experimental results are reversing this judgement by showing that very small spatial variations can make a big difference in the fate of a cell. This is particularly the case when considering eukaryotic cells, which have a complex physical structure and many subtle control mechanisms, but bacteria are also interesting for the huge variation in shape both between species and in different phases of their lifecycle. In this work we perform simulations that measure the effect of three common bacterial shapes on the behaviour of model cellular pathways. To perform these experiments we develop ReDi-Cell, a highly scalable GPGPU cell simulation infrastructure for the modelling of cellular pathways in spatially detailed environments. ReDi-Cell is validated against known-good simulations, prior to its use in new work. We then use ReDi-Cell to conduct novel experiments that demonstrate the effect that three common bacterial shapes (Cocci, Bacilli and Spirilli) have on the behaviour of model cellular pathways. Pathway wavefront shape, pathway concentration gradients, and chemical species distribution are measured in the three different shapes. We also quantify the impact of internal cellular clutter on the same pathways. Through this work we show that variations in the shape or configuration of these common cell shapes alter model cell behaviour.

Microscopy-Based High-Content Screening

Image-based screening is used to measure a variety of phenotypes in cells and whole organisms. Combined with perturbations such as RNA interference, small molecules, and mutations, such screens are a powerful method for gaining systematic insights into biological processes. Screens have been applied to study diverse processes, such as protein-localization changes, cancer cell vulnerabilities, and complex organismal phenotypes. Recently, advances in imaging and image-analysis methodologies have accelerated large-scale perturbation screens. Here, we describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.

Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images

Determination of fundamental mechanisms of disease often hinges on histopathology visualization and quantitative image analysis. Currently, the analysis of multi-channel fluorescence tissue images is primarily achieved by manual measurements of tissue cellular content and sub-cellular compartments. Since the current manual methodology for image analysis is a tedious and subjective approach, there is clearly a need for an automated analytical technique to process large-scale image datasets. Here, we introduce Nuquantus (Nuclei quantification utility software) - a novel machine learning-based analytical method, which identifies, quantifies and classifies nuclei based on cells of interest in composite fluorescent tissue images, in which cell borders are not visible. Nuquantus is an adaptive framework that learns the morphological attributes of intact tissue in the presence of anatomical variability and pathological processes. Nuquantus allowed us to robustly perform quantitative image analysis on remodeling cardiac tissue after myocardial infarction. Nuquantus reliably classifies cardiomyocyte versus non-cardiomyocyte nuclei and detects cell proliferation, as well as cell death in different cell classes. Broadly, Nuquantus provides innovative computerized methodology to analyze complex tissue images that significantly facilitates image analysis and minimizes human bias.

Recent advances in quantitative high throughput and high content data analysis

INTRODUCTION

High throughput screening has become a basic technique with which to explore biological systems. Advances in technology, including increased screening capacity, as well as methods that generate multiparametric readouts, are driving the need for improvements in the analysis of data sets derived from such screens.

AREAS COVERED

This article covers the recent advances in the analysis of high throughput screening data sets from arrayed samples, as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Screening multiple genomic reagents targeting any given gene creates additional challenges and so methods that prioritize individual gene targets have been developed. The article reviews many of the open source data analysis methods that are now available and which are helping to define a consensus on the best practices to use when analyzing screening data.

EXPERT OPINION

As data sets become larger, and more complex, the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. In addition, advanced statistical and machine learning algorithms that can help identify patterns, correlations and the best features in massive data sets will be required. The ease of use for these tools will be important, as they will need to be used iteratively by laboratory scientists to improve the outcomes of complex analyses.

Visualizing quantitative microscopy data: History and challenges

Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.

Evaluation of the direct effects of poly(dopamine) on the in vitro response of human osteoblastic cells

Functional poly(dopamine) coatings promise to become an efficient strategy to endow biomaterials with enhanced bioactive properties.

Fluorescence anisotropy (polarization): from drug screening to precision medicine

INTRODUCTION

Fluorescence anisotropy (FA) is one of the major established methods accepted by industry and regulatory agencies for understanding the mechanisms of drug action and selecting drug candidates utilizing a high-throughput format.

AREAS COVERED

This review covers the basics of FA and complementary methods, such as fluorescence lifetime anisotropy and their roles in the drug discovery process. The authors highlight the factors affecting FA readouts, fluorophore selection and instrumentation. Furthermore, the authors describe the recent development of a successful, commercially valuable FA assay for long QT syndrome drug toxicity to illustrate the role that FA can play in the early stages of drug discovery.

EXPERT OPINION

Despite the success in drug discovery, the FA-based technique experiences competitive pressure from other homogeneous assays. That being said, FA is an established yet rapidly developing technique, recognized by academic institutions, the pharmaceutical industry and regulatory agencies across the globe. The technical problems encountered in working with small molecules in homogeneous assays are largely solved, and new challenges come from more complex biological molecules and nanoparticles. With that, FA will remain one of the major work-horse techniques leading to precision (personalized) medicine.