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

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 origin of universal cell shape variability in confluent epithelial monolayers

Cell shape is fundamental in biology. The average cell shape can influence crucial biological functions, such as cell fate and division orientation. But cell-to-cell shape variability is often regarded as noise. In contrast, recent works reveal that shape variability in diverse epithelial monolayers follows a nearly universal distribution. However, the origin and implications of this universality remain unclear. Here, assuming contractility and adhesion are crucial for cell shape, characterized via aspect ratio (r), we develop a mean-field analytical theory for shape variability. We find that all the system-specific details combine into a single parameter α that governs the probability distribution function (PDF) of r; this leads to a universal relation between the standard deviation and the average of r. The PDF for the scaled r is not strictly but nearly universal. In addition, we obtain the scaled area distribution, described by the parameter μ. Information of α and μ together can distinguish the effects of changing physical conditions, such as maturation, on different system properties. We have verified the theory via simulations of two distinct models of epithelial monolayers and with existing experiments on diverse systems. We demonstrate that in a confluent monolayer, average shape determines both the shape variability and dynamics. Our results imply that cell shape distribution is inevitable, where a single parameter describes both statics and dynamics and provides a framework to analyze and compare diverse epithelial systems. In contrast to existing theories, our work shows that the universal properties are consequences of a mathematical property and should be valid in general, even in the fluid regime.

In vivo 3D profiling of site-specific human cancer cell morphotypes in zebrafish

Tissue microenvironments affect the functional states of cancer cells, but determining these influences in vivo has remained a challenge. We present a quantitative high-resolution imaging assay of single cancer cells in zebrafish xenografts to probe functional adaptation to variable cell-extrinsic cues and molecular interventions. Using cell morphology as a surrogate readout of cell functional states, we examine environmental influences on the morphotype distribution of Ewing Sarcoma, a pediatric cancer associated with the oncogene EWSR1-FLI1 and whose plasticity is thought to determine disease outcome through non-genomic mechanisms. Computer vision analysis reveals systematic shifts in the distribution of 3D morphotypes as a function of cell type and seeding site, as well as tissue-specific cellular organizations that recapitulate those observed in human tumors. Reduced expression of the EWSR1-FLI1 protein product causes a shift to more protrusive cells and decreased tissue specificity of the morphotype distribution. Overall, this work establishes a framework for a statistically robust study of cancer cell plasticity in diverse tissue microenvironments.

Cell Architecture-Dependent Constraints: Critical Safeguards to Carcinogenesis

Animal cells display great diversity in their shape. These morphological characteristics result from crosstalk between the plasma membrane and the force-generating capacities of the cytoskeleton macromolecules. Changes in cell shape are not merely byproducts of cell fate determinants, they also actively drive cell fate decisions, including proliferation and differentiation. Global and local changes in cell shape alter the transcriptional program by a multitude of mechanisms, including the regulation of physical links between the plasma membrane and the nuclear envelope and the mechanical modulation of cation channels and signalling molecules. It is therefore not surprising that anomalies in cell shape contribute to several diseases, including cancer. In this review, we discuss the possibility that the constraints imposed by cell shape determine the behaviour of normal and pro-tumour cells by organizing the whole interconnected regulatory network. In turn, cell behaviour might stabilize cells into discrete shapes. However, to progress towards a fully transformed phenotype and to acquire plasticity properties, pro-tumour cells might need to escape these cell shape restrictions. Thus, robust controls of the cell shape machinery may represent a critical safeguard against carcinogenesis.

Insights Into the Biogenesis and Emerging Functions of Lipid Droplets From Unbiased Molecular Profiling Approaches

Lipid droplets (LDs) are spherical, single sheet phospholipid-bound organelles that store neutral lipids in all eukaryotes and some prokaryotes. Initially conceived as relatively inert depots for energy and lipid precursors, these highly dynamic structures play active roles in homeostatic functions beyond metabolism, such as proteostasis and protein turnover, innate immunity and defense. A major share of the knowledge behind this paradigm shift has been enabled by the use of systematic molecular profiling approaches, capable of revealing and describing these non-intuitive systems-level relationships. Here, we discuss these advances and some of the challenges they entail, and highlight standing questions in the field.

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer.

Computational Methods for Single-Cell Imaging and Omics Data Integration

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

Cell shape-based chemical screening reveals an epigenetic network mediated by focal adhesions

Adapter proteins CRK and CRKL participate in a variety of signaling pathways, including cell adhesion, and fate regulation of mammalian cells. However, the molecular functions of CRK/CRKL in epigenetic regulation remain largely unknown. Here, we developed a pipeline to evaluate cell morphology using high-content image analysis combined with chemical screening of kinase and epigenetic modulators. We found that CRK/CRKL modulates gene regulatory networks associated with cell morphology through epigenetic alteration in mouse embryonic fibroblasts. Integrated epigenome and transcriptome analyses revealed that CRK/CRKL is involved in super-enhancer activity and upregulation of Cdt1, Rin1, and Spp1 expression for the regulation of cell morphology. Screening of a library of 80 epigenetic inhibitors showed that histone H3 modifiers, euchromatic histone methyltransferase 2 and mitogen- and stress-activated kinase 1, may be important for CRK/CRKL-mediated morphological changes. Taken together, our results indicate that CRK/CRKL plays a critical role in gene regulatory networks through epigenetic modification. DATABASES: Chromatin immunoprecipitation sequencing and RNA sequencing data were deposited in the DNA Data Bank of Japan under DRA011080 and DRA011081 accession numbers, respectively.

Evaluation of Cervical High-Grade Squamous Intraepithelial Lesions-Correlated Markers as Triage Strategy for Colposcopy After Co-Testing

Background

Colposcopy was referred in cases with severe abnormalities in co-testing. Although p16/Ki67 dual staining reduced the referral rate, its sensitivity and specificity need to be enhanced.

Methods

The expressions of p16, Ki-67, SMAD3, YAP1, RELA were evaluated in the colposcopy referral population. The inclusion criteria included 30–60 years and diagnosed with HPV16/18-positive, other HR-HPV-positive with ASCUS, LSIL, AGC (atypical glandular cell) in co-testing. Colposcopies, endocervical curettages of cervical biopsies were also collected. Cases were excluded if there were no biopsies, if the interval between a cervical screening test and biopsies was more than 6 months, or if insufficient tissue was available as a formalin-fixed paraffin-embedded block. The pathology was independently reviewed by two pathologists. Discrepant interpretations were adjudicated by a third pathologist.

Results

In total, 1194 of 1273 cases who were referred to colposcopy were evaluated in the present study. The sensitivity and specificity of p16+ combined with Ki-67+ for predicting CIN2+ were 62.1% and 89.5%, respectively. p16+ combined with YAP1+ and/or RELA+ provided a sensitivity and specificity of 70.9% and 89.5%, respectively, while 72.8% and 86.4% were achieved by p16+ combined with YAP1+ and/or SMAD3+ and/or RELA+. In HPV16/18+ and LSIL subgroups, the sensitivity and specificity of p16+ combined with Ki-67+ for predicting CIN2+ were 67.7% and 87.6%, respectively, for the former group and 58.6%, 88.8%, respectively, for the latter group. p16+, YAP1+/RELA+ showed a better performance for predicting CIN2+ with a better sensitivity and considerable specificity in the other HPV+ combined with ASCUS group than were achieved by p16+ combined with Ki-67+. RELA+ and the combination of p16 and RELA/YAP1 also provided the Max AUC area.

Conclusion

Our study shows that RELA and the combination of p16 and RELA/YAP1 achieved better sensitivity and specificity for detecting morphologically CIN2+ lesions.

Joint analysis of expression levels and histological images identifies genes associated with tissue morphology

Histopathological images are used to characterize complex phenotypes such as tumor stage. Our goal is to associate features of stained tissue images with high-dimensional genomic markers. We use convolutional autoencoders and sparse canonical correlation analysis (CCA) on paired histological images and bulk gene expression to identify subsets of genes whose expression levels in a tissue sample correlate with subsets of morphological features from the corresponding sample image. We apply our approach, ImageCCA, to two TCGA data sets, and find gene sets associated with the structure of the extracellular matrix and cell wall infrastructure, implicating uncharacterized genes in extracellular processes. We find sets of genes associated with specific cell types, including neuronal cells and cells of the immune system. We apply ImageCCA to the GTEx v6 data, and find image features that capture population variation in thyroid and in colon tissues associated with genetic variants (image morphology QTLs, or imQTLs), suggesting that genetic variation regulates population variation in tissue morphological traits.

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.

Cellular morphological features are predictive markers of cancer cell state

Even genetically identical cells have heterogeneous properties because of stochasticity in gene or protein expression. Single cell techniques that assay heterogeneous properties would be valuable for basic science and diseases like cancer, where accurate estimates of tumor properties is critical for accurate diagnosis and grading. Cell morphology is an emergent outcome of many cellular processes, potentially carrying information about cell properties at the single cell level. Here we study whether morphological parameters are sufficient for classification of single cells, using a set of 15 cell lines, representing three processes: (i) the transformation of normal cells using specific genetic mutations; (ii) metastasis in breast cancer and (iii) metastasis in osteosarcomas. Cellular morphology is defined as quantitative measures of the shape of the cell and the structure of the actin. We use a toolbox that calculates quantitative morphological parameters of cell images and apply it to hundreds of images of cells belonging to different cell lines. Using a combination of dimensional reduction and machine learning, we test whether these different processes have specific morphological signatures and whether single cells can be classified based on morphology alone. Using morphological parameters we could accurately classify cells as belonging to the correct class with high accuracy. Morphological signatures could distinguish between cells that were different only because of a different mutation on a parental line. Furthermore, both oncogenesis and metastasis appear to be characterized by stereotypical morphology changes. Thus, cellular morphology is a signature of cell phenotype, or state, at the single cell level.

Airway mechanical compression: its role in asthma pathogenesis and progression

The lung is a mechanically active organ, but uncontrolled or excessive mechanical forces disrupt normal lung function and can contribute to the development of disease. In asthma, bronchoconstriction leads to airway narrowing and airway wall buckling. A growing body of evidence suggests that pathological mechanical forces induced by airway buckling alone can perpetuate disease processes in asthma. Here, we review the data obtained from a variety of experimental models, including in vitro, ex vivo and in vivo approaches, which have been used to study the impact of mechanical forces in asthma pathogenesis. We review the evidence showing that mechanical compression alters the biological and biophysical properties of the airway epithelium, including activation of the epidermal growth factor receptor pathway, overproduction of asthma-associated mediators, goblet cell hyperplasia, and a phase transition of epithelium from a static jammed phase to a mobile unjammed phase. We also define questions regarding the impact of mechanical forces on the pathology of asthma, with a focus on known triggers of asthma exacerbations such as viral infection.

Surface Sugars Get Cells in Shape

Cell membranes can adopt a variety of shapes and curvatures, yet our understanding of the factors involved remains limited. In this issue of Cell, Shurer et al. (2019) demonstrate that the glycocalyx can regulate cell shape from the outside in.

KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens

Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge- and Context-driven Machine Learning (KCML), a framework that systematically predicts multiple context-specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale-crossing and context-dependent gene functions. KCML is highly generalisable and applicable to various large-scale genetic perturbation screens.

TISMorph: A tool to quantify texture, irregularity and spreading of single cells

A number of recent studies have shown that cell shape and cytoskeletal texture can be used as sensitive readouts of the physiological state of the cell. However, utilization of this information requires the development of quantitative measures that can describe relevant aspects of cell shape. In this paper we develop a toolbox, TISMorph, that calculates a set of quantitative measures to address this need. Some of the measures introduced here have been used previously, while others are new and have desirable properties for shape and texture quantification of cells. These measures, broadly classifiable into the categories of textural, irregularity and spreading measures, are tested by using them to discriminate between osteosarcoma cell lines treated with different cytoskeletal drugs. We find that even though specific classification tasks often rely on a few measures, these are not the same between all classification tasks, thus requiring the use of the entire suite of measures for classification and discrimination. We provide detailed descriptions of the measures, as well as the TISMorph package to implement them. Quantitative morphological measures that capture different aspects of cell morphology will help enhance large-scale image-based quantitative analysis, which is emerging as a new field of biological data.

Imaging cell morphology and physiology using X-rays

Morphometric measurements, such as quantifying cell shape, characterizing sub-cellular organization, and probing cell-cell interactions, are fundamental in cell biology and clinical medicine. Until quite recently, the main source of morphometric data on cells has been light- and electron-based microscope images. However, many technological advances have propelled X-ray microscopy into becoming another source of high-quality morphometric information. Here, we review the status of X-ray microscopy as a quantitative biological imaging modality. We also describe the combination of X-ray microscopy data with information from other modalities to generate polychromatic views of biological systems. For example, the amalgamation of molecular localization data, from fluorescence microscopy or spectromicroscopy, with structural information from X-ray tomography. This combination of data from the same specimen generates a more complete picture of the system than that can be obtained by a single microscopy method. Such multimodal combinations greatly enhance our understanding of biology by combining physiological and morphological data to create models that more accurately reflect the complexities of life.

How cells sense their own shape - mechanisms to probe cell geometry and their implications in cellular organization and function

Cells come in a variety of shapes that most often underlie their functions. Regulation of cell morphogenesis implies that there are mechanisms for shape sensing that still remain poorly appreciated. Global and local cell geometry features, such as aspect ratio, size or membrane curvature, may be probed by intracellular modules, such as the cytoskeleton, reaction-diffusion systems or molecular complexes. In multicellular tissues, cell shape emerges as an important means to transduce tissue-inherent chemical and mechanical cues into intracellular organization. One emergent paradigm is that cell-shape sensing is most often based upon mechanisms of self-organization, rather than determinism. Here, we review relevant work that has elucidated some of the core principles of how cellular geometry may be conveyed into spatial information to guide processes, such as polarity, signaling, morphogenesis and division-plane positioning.

Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem

Symmetry and symmetry breaking concepts from physics and biology are applied to the problem of cancer. Three categories of symmetry breaking in cancer are examined: combinatorial, geometric, and functional. Within these categories, symmetry breaking is examined for relevant cancer features, including epithelial-mesenchymal transition (EMT); tumor heterogeneity; tensegrity; fractal geometric and information structure; functional interaction networks; and network stabilizability and attack tolerance. The new cancer symmetry concepts are relevant to homeostasis loss in cancer and to its origin, spread, treatment and resistance. Symmetry and symmetry breaking could provide a new way of thinking and a pathway to a solution of the cancer problem.