High-Content Screening for Quantitative Cell Biology

It's Not the Destination, It's the Journey: Heterogeneity in mRNA Export Mechanisms

The process of creating a translation-competent mRNA is highly complex and involves numerous steps including transcription, splicing, addition of modifications, and, finally, export to the cytoplasm. Historically, much of the research on regulation of gene expression at the level of the mRNA has been focused on either the regulation of mRNA synthesis (transcription and splicing) or metabolism (translation and degradation). However, in recent years, the advent of new experimental techniques has revealed the export of mRNA to be a major node in the regulation of gene expression, and numerous large-scale and specific mRNA export pathways have been defined. In this chapter, we will begin by outlining the mechanism by which most mRNAs are homeostatically exported ("bulk mRNA export"), involving the recruitment of the NXF1/TAP export receptor by the Aly/REF and THOC5 components of the TREX complex. We will then examine various mechanisms by which this pathway may be controlled, modified, or bypassed in order to promote the export of subset(s) of cellular mRNAs, which include the use of metazoan-specific orthologs of bulk mRNA export factors, specific cis RNA motifs which recruit mRNA export machinery via specific trans-acting-binding factors, posttranscriptional mRNA modifications that act as "inducible" export cis elements, the use of the atypical mRNA export receptor, CRM1, and the manipulation or bypass of the nuclear pore itself. Finally, we will discuss major outstanding questions in the field of mRNA export heterogeneity and outline how cutting-edge experimental techniques are providing new insights into and tools for investigating the intriguing field of mRNA export heterogeneity.

Biophysics and the Genomic Sciences

It is now rare to find biological, or genetic investigations that do not rely on the tools, data, and thinking drawn from the genomic sciences. Much of this revolution is powered by contemporary sequencing approaches that readily deliver large, genome-wide data sets that not only provide genetic insights but also uniquely report molecular outcomes from experiments that biophysicists are increasingly using for potentiating structural and mechanistic investigations. In this perspective, I describe a path of how biophysical thinking greatly contributed to this revolution in ways that parallel advancements in computer science through discussion of several key inventions, described as "foundational devices." These discussions also point at the future of how biophysics and the genomic sciences may become more finely integrated for empowering new measurement paradigms for biological investigations.

Review of high-content screening applications in toxicology

High-content screening (HCS) technology combining automated microscopy and quantitative image analysis can address biological questions in academia and the pharmaceutical industry. Various HCS experimental applications have been utilized in the research field of in vitro toxicology. In this review, we describe several HCS application approaches used for studying the mechanism of compound toxicity, highlight some challenges faced in the toxicological community, and discuss the future directions of HCS in regards to new models, new reagents, data management, and informatics. Many specialized areas of toxicology including developmental toxicity, genotoxicity, developmental neurotoxicity/neurotoxicity, hepatotoxicity, cardiotoxicity, and nephrotoxicity will be examined. In addition, several newly developed cellular assay models including induced pluripotent stem cells (iPSCs), three-dimensional (3D) cell models, and tissues-on-a-chip will be discussed. New genome-editing technologies (e.g., CRISPR/Cas9), data analyzing tools for imaging, and coupling with high-content assays will be reviewed. Finally, the applications of machine learning to image processing will be explored. These new HCS approaches offer a huge step forward in dissecting biological processes, developing drugs, and making toxicology studies easier.

Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.

Optimization of Insect Odorant Receptor Trafficking and Functional Expression Via Transient Transfection in HEK293 Cells

Insect odorant receptors (ORs) show a limited functional expression in various heterologous expression systems including insect and mammalian cells. This may be in part due to the absence of key components driving the release of these proteins from the endoplasmic reticulum and directing them to the plasma membrane. In order to mitigate this problem, we took advantage of small export signals within the human HCN1 and Rhodopsin that have been shown to promote protein release from the endoplasmic reticulum and the trafficking of post-Golgi vesicles, respectively. Moreover, we designed a new vector based on a bidirectional expression cassette to drive the functional expression of the insect odorant receptor coreceptor (Orco) and an odor-binding OR, simultaneously. We show that this new method can be used to reliably express insect ORs in HEK293 cells via transient transfection and that is highly suitable for downstream applications using automated and high-throughput imaging platforms.

Microfluidic three-dimensional cell culture of stem cells for high-throughput analysis

Although the recent advances in stem cell engineering have gained a great deal of attention due to their high potential in clinical research, the applicability of stem cells for preclinical screening in the drug discovery process is still challenging due to difficulties in controlling the stem cell microenvironment and the limited availability of high-throughput systems. Recently, researchers have been actively developing and evaluating three-dimensional (3D) cell culture-based platforms using microfluidic technologies, such as organ-on-a-chip and organoid-on-a-chip platforms, and they have achieved promising breakthroughs in stem cell engineering. In this review, we start with a comprehensive discussion on the importance of microfluidic 3D cell culture techniques in stem cell research and their technical strategies in the field of drug discovery. In a subsequent section, we discuss microfluidic 3D cell culture techniques for high-throughput analysis for use in stem cell research. In addition, some potential and practical applications of organ-on-a-chip or organoid-on-a-chip platforms using stem cells as drug screening and disease models are highlighted.

Simultaneous in situ visualization and quantitation of dual antigens adsorbed on adjuvants using high content analysis

Aim: Traditional antigenicity assay requires antigen recovery from the particulate adjuvants prior to analysis. An in situ method was developed for interrogating vaccine antigens with monoclonal antibodies while being adsorbed on adjuvants. Materials & methods: The fluorescence imaging-based high content analysis was used to visualize the antigen distribution on adjuvant agglomerates and to analyze the antigenicity for adsorbed antigens. Results: Simultaneous visualization and quantitation were achieved for dual antigens in a bivalent human papillomavirus vaccine with uniquely labeled antibodies. Good agreement was observed between the in situ multiplexed assays with well-established sandwich enzyme-linked immunosorbent assays. Conclusion: The streamlined procedures and the amenability for multiplexing make the in situ antigenicity analysis a favorable choice for in vitro functional assessment of bionanoparticles as vaccine antigens.

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.

To understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

High-throughput time-resolved morphology screening in bacteria reveals phenotypic responses to antibiotics

Image-based high-throughput screening strategies for quantifying morphological phenotypes have proven widely successful. Here we describe a combined experimental and multivariate image analysis approach for systematic large-scale phenotyping of morphological dynamics in bacteria. Using off-the-shelf components and software, we established a workflow for high-throughput time-resolved microscopy. We then screened the single-gene deletion collection of Escherichia coli for antibiotic-induced morphological changes. Using single-cell quantitative descriptors and supervised classification methods, we measured how different cell morphologies developed over time for all strains in response to the β-lactam antibiotic cefsulodin. 191 strains exhibit significant variations under antibiotic treatment. Phenotypic clustering provided insights into processes that alter the antibiotic response. Mutants with stable bulges show delayed lysis, contributing to antibiotic tolerance. Lipopolysaccharides play a crucial role in bulge stability. This study demonstrates how multiparametric phenotyping by high-throughput time-resolved imaging and computer-aided cell classification can be used for comprehensively studying dynamic morphological transitions in bacteria.

Divide and conquer: real-time maximum likelihood fitting of multiple emitters for super-resolution localization microscopy

Multi-emitter localization has great potential for maximizing the imaging speed of super-resolution localization microscopy. However, the slow image analysis speed of reported multi-emitter localization algorithms limits their usage in mostly off-line image processing with small image size. Here we adopt the well-known divide and conquer strategy in computer science and present a fitting-based method called QC-STORM for fast multi-emitter localization. Using simulated and experimental data, we verify that QC-STORM is capable of providing real-time full image processing on raw images with 100 µm × 100 µm field of view and 10 ms exposure time, with comparable spatial resolution as the popular fitting-based ThunderSTORM and the up-to-date non-iterative WindSTORM. This study pushes the development and practical use of super-resolution localization microscopy in high-throughput or high-content imaging of cell-to-cell differences or discovering rare events in a large cell population.

Fluorescence spectral shape analysis for nucleotide identification

We report a conjugated polyelectrolyte fluorescence-based biosensor P-C-3 and a general methodology to evaluate spectral shape recognition to identify biomolecules using artificial intelligence. By using well-defined analytes, we demonstrate that the fluorescence spectral shape of P-C-3 is sensitive to minor structural changes and exhibits distinct signature patterns for different analytes. A method was also developed to select useful features to reduce computational complexity and prevent overfitting of the data. It was found that the normalized intensity of 3 to 5 selected wavelengths was sufficient for the fluorescence biosensor to classify 13 distinct nucleotides and distinguish as little as single base substitutions at distinct positions in the primary sequence of oligonucleotides rapidly with nearly 100% classification accuracy. Photophysical studies led to a model to explain the mechanism of these fluorescence spectral shape changes, which provides theoretical support for applying this method in complicated biological systems. Using the feature selection algorithm to measure the relative intensity of a few selected wavelengths significantly reduces measurement time, demonstrating the potential for fluorescence spectrum shape analysis in high-throughput and high-content screening.

Automated cell boundary and 3D nuclear segmentation of cells in suspension

To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.

High-content screen in human pluripotent cells identifies miRNA-regulated pathways controlling pluripotency and differentiation

Background

By post-transcriptionally regulating multiple target transcripts, microRNAs (miRNAs or miR) play important biological functions. H1 embryonic stem cells (hESCs) and NTera-2 embryonal carcinoma cells (ECCs) are two of the most widely used human pluripotent model cell lines, sharing several characteristics, including the expression of miRNAs associated to the pluripotent state or with differentiation. However, how each of these miRNAs functionally impacts the biological properties of these cells has not been systematically evaluated.

Methods

We investigated the effects of 31 miRNAs on NTera-2 and H1 hESCs, by transfecting miRNA mimics. Following 3–4 days of culture, cells were stained for the pluripotency marker OCT4 and the G2 cell-cycle marker Cyclin B1, and nuclei and cytoplasm were co-stained with Hoechst and Cell Mask Blue, respectively. By using automated quantitative fluorescence microscopy (i.e., high-content screening (HCS)), we obtained several morphological and marker intensity measurements, in both cell compartments, allowing the generation of a multiparametric miR-induced phenotypic profile describing changes related to proliferation, cell cycle, pluripotency, and differentiation.

Results

Despite the overall similarities between both cell types, some miRNAs elicited cell-specific effects, while some related miRNAs induced contrasting effects in the same cell. By identifying transcripts predicted to be commonly targeted by miRNAs inducing similar effects (profiles grouped by hierarchical clustering), we were able to uncover potentially modulated signaling pathways and biological processes, likely mediating the effects of the microRNAs on the distinct groups identified. Specifically, we show that miR-363 contributes to pluripotency maintenance, at least in part, by targeting NOTCH1 and PSEN1 and inhibiting Notch-induced differentiation, a mechanism that could be implicated in naïve and primed pluripotent states.

Conclusions

We present the first multiparametric high-content microRNA functional screening in human pluripotent cells. Integration of this type of data with similar data obtained from siRNA screenings (using the same HCS assay) could provide a large-scale functional approach to identify and validate microRNA-mediated regulatory mechanisms controlling pluripotency and differentiation.

Electronic supplementary material

The online version of this article (10.1186/s13287-019-1318-6) contains supplementary material, which is available to authorized users.

Growth-restricting effects of siRNA transfections: a largely deterministic combination of off-target binding and hybridization-independent competition

Perturbation of gene expression by means of synthetic small interfering RNAs (siRNAs) is a powerful way to uncover gene function. However, siRNA technology suffers from sequence-specific off-target effects and from limitations in knock-down efficiency. In this study, we assess a further problem: unintended effects of siRNA transfections on cellular fitness/proliferation. We show that the nucleotide compositions of siRNAs at specific positions have reproducible growth-restricting effects on mammalian cells in culture. This is likely distinct from hybridization-dependent off-target effects, since each nucleotide residue is seen to be acting independently and additively. The effect is robust and reproducible across different siRNA libraries and also across various cell lines, including human and mouse cells. Analyzing the growth inhibition patterns in correlation to the nucleotide sequence of the siRNAs allowed us to build a predictor that can estimate growth-restricting effects for any arbitrary siRNA sequence. Competition experiments with co-transfected siRNAs further suggest that the growth-restricting effects might be linked to an oversaturation of the cellular miRNA machinery, thus disrupting endogenous miRNA functions at large. We caution that competition between siRNA molecules could complicate the interpretation of double-knockdown or epistasis experiments, and potential interactions with endogenous miRNAs can be a factor when assaying cell growth or viability phenotypes.

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 fluorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy.Abbreviations: Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.

Next-Generation Phenotypic Screening in Early Drug Discovery for Infectious Diseases

Cell-based phenotypic screening has proven to be valuable, notably in recapitulating relevant biological conditions, for example, the host cell/pathogen niche. However, the corresponding methodological complexity is not readily compatible with high-throughput pipelines, and fails to inform either molecular target or mechanism of action, which frustrates conventional drug-discovery roadmaps. We review the state-of-the-art and emerging technologies that suggest new strategies for harnessing value from the complexity of phenotypic screening and augmenting powerful utility for translational drug discovery. Advances in cellular, molecular, and bioinformatics technologies are converging at a cutting edge where the complexity of phenotypic screening may no longer be considered a hinderance but rather a catalyst to chemotherapeutic discovery for infectious diseases.

An artificial triazole backbone linkage provides a split-and-click strategy to bioactive chemically modified CRISPR sgRNA

As the applications of CRISPR-Cas9 technology diversify and spread beyond the laboratory to diagnostic and therapeutic use, the demands of gRNA synthesis have increased and access to tailored gRNAs is now restrictive. Enzymatic routes are time-consuming, difficult to scale-up and suffer from polymerase-bias while existing chemical routes are inefficient. Here, we describe a split-and-click convergent chemical route to individual or pools of sgRNAs. The synthetic burden is reduced by splitting the sgRNA into a variable DNA/genome-targeting 20-mer, produced on-demand and in high purity, and a fixed Cas9-binding chemically-modified 79-mer, produced cost-effectively on large-scale, a strategy that provides access to site-specific modifications that enhance sgRNA activity and in vivo stability. Click ligation of the two components generates an artificial triazole linkage that is tolerated in functionally critical regions of the sgRNA and allows efficient DNA cleavage in vitro as well as gene-editing in cells with no unexpected off-target effects.

EU-OPENSCREEN: A Novel Collaborative Approach to Facilitate Chemical Biology

Compound screening in biological assays and subsequent optimization of hits is indispensable for the development of new molecular research tools and drug candidates. To facilitate such discoveries, the European Research Infrastructure EU-OPENSCREEN was founded recently with the support of its member countries and the European Commission. Its distributed character harnesses complementary knowledge, expertise, and instrumentation in the discipline of chemical biology from 20 European partners, and its open working model ensures that academia and industry can readily access EU-OPENSCREEN's compound collection, equipment, and generated data. To demonstrate the power of this collaborative approach, this perspective article highlights recent projects from EU-OPENSCREEN partner institutions. These studies yielded (1) 2-aminoquinazolin-4(3 H)-ones as potential lead structures for new antimalarial drugs, (2) a novel lipodepsipeptide specifically inducing apoptosis in cells deficient for the pVHL tumor suppressor, (3) small-molecule-based ROCK inhibitors that induce definitive endoderm formation and can potentially be used for regenerative medicine, (4) potential pharmacological chaperones for inborn errors of metabolism and a familiar form of acute myeloid leukemia (AML), and (5) novel tankyrase inhibitors that entered a lead-to-candidate program. Collectively, these findings highlight the benefits of small-molecule screening, the plethora of assay designs, and the close connection between screening and medicinal chemistry within EU-OPENSCREEN.

Capture and display of antibodies secreted by hybridoma cells enables fluorescent on-cell screening

Hybridoma methods for monoclonal antibody (mAb) cloning are a mainstay of biomedical research, but they are hindered by the need to maintain hybridomas in oligoclonal pools during antibody screening. Here, we describe a system in which hybridomas specifically capture and display the mAbs they secrete: On-Cell mAb Screening (OCMS™). In OCMS™, mAbs displayed on the cell surface can be rapidly assayed for expression level and binding specificity using fluorescent antigens with high-content (image-based) methods or flow cytometry. OCMS™ demonstrated specific mAb binding to poliovirus and rabies virus by forming a cell surface IgG “cap”, as a universal assay for anti-viral mAbs. We produced and characterized OCMS™-enabled hybridomas secreting mAbs that neutralize poliovirus and used fluorescence microscopy to identify and clone a human mAb specific for the human N-methyl-D-aspartate receptor. Lastly, we used OCMS™ to assess expression and antigen binding of a recombinant mAb produced in 293T cells. As a novel method to physically associate mAbs with the hybridomas that secrete them, OCMS™ overcomes a central challenge to hybridoma mAb screening and offers new paradigms for mAb discovery and production.

Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm

Current drug discovery procedures require fast and effective quantification of the pharmacological response evoked in living cells by agonist compounds. In the case of G-protein coupled receptors (GPCRs), the efficacy of a particular drug to initiate the endocytosis process is related to the formation of endocytic vesicles or endosomes and their subsequent internalisation within intracellular compartments that can be observed with high spatial and temporal resolution by fluorescence microscopy techniques. Recently, an algorithm has been proposed to evaluate the pharmacological response by estimating the number of endosomes per cell on time series of images. However, the algorithm was limited by the dependence on some manually set parameters and in some cases the quality of the image does not allow a reliable detection of the endosomes. Here we propose a simple, fast and automated image analysis method—the Δm algorithm- to quantify a pharmacological response with data obtained from fluorescence microscopy experiments. This algorithm does not require individual object detection and computes the relative increment of the third order moment in fluorescence microscopy images after filtering with the Laplacian of Gaussian function. It was tested on simulations demonstrating its ability to discriminate different experimental situations according to the number and the fluorescence signal intensity of the simulated endosomes. Finally and in order to validate this methodology with real data, the algorithm was applied to several time-course experiments based on the endocytosis of the mu opioid receptor (MOP) initiated by different agonist compounds. Each drug displayed a different Δm sigmoid time-response curve and statistically significant differences were observed among drugs in terms of efficacy and kinetic parameters.

Defining host-pathogen interactions employing an artificial intelligence workflow

For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capacity to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella enterica Typhimurium we demonstrate HRMAn's capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses. HRMAn thus presents the only intelligent solution operating at human capacity suitable for both single image and high content image analysis.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

High-content analysis of in vitro hepatocyte injury induced by various hepatotoxicants

In vitro prediction of hepatotoxicity can enhance the performance of non-clinical animal testing for identifying chemical hazards. In this study, we assessed high-content analysis (HCA) using multi-parameter cell-based assays as an in vitro hepatotoxicity testing model using various hepatotoxicants and human hepatocytes such as HepG2 cells and human primary hepatocytes (hPHs). Both hepatocyte types were exposed separately to multiple doses of ten hepatotoxicants associated with liver injury whose mechanisms of action have been described. HCA data were obtained using fluorescence probes for nuclear size (Hoechst), mitochondrial membrane potential (TMRM), cytosolic free calcium (Fluo-4AM), and lipid peroxidation (BODIPY). Cellular alterations were observed in response to all hepatotoxicants tested. The most sensitive parameter was TMRM, with high sensitivity at a low dose, next was BODIPY, followed by Fluo-4AM. HCA data from HepG2 cells and hPHs were generally concordant, although some inconsistencies were noted. Both hepatocyte types showed mild or severe mitochondrial impairment and lipid peroxidation in response to several hepatotoxicants. The results demonstrate that the application of HCA to in vitro hepatotoxicity testing enables more efficient hazard identification, and further, they suggest that certain parameters could serve as sensitive endpoints for predicting the hepatotoxic potential of chemical compounds.

How Surrogate and Chemical Genetics in Model Organisms Can Suggest Therapies for Human Genetic Diseases

Genetic diseases are both inherited and acquired. Many genetic diseases fall under the paradigm of orphan diseases, a disease found in < 1 in 2000 persons. With rapid and cost-effective genome sequencing becoming the norm, many causal mutations for genetic diseases are being rapidly determined. In this regard, model organisms are playing an important role in validating if specific mutations identified in patients drive the observed phenotype. An emerging challenge for model organism researchers is the application of genetic and chemical genetic platforms to discover drug targets and drugs/drug-like molecules for potential treatment options for patients with genetic disease. This review provides an overview of how model organisms have contributed to our understanding of genetic disease, with a focus on the roles of yeast and zebrafish in gene discovery and the identification of compounds that could potentially treat human genetic diseases.

Extracting and Integrating Protein Localization Changes from Multiple Image Screens of Yeast Cells

The evaluation of protein localization changes in cells under diverse chemical and genetic perturbations is now possible due to the increasing quantity of screens that systematically image thousands of proteins in an organism. Integrating information from different screens provides valuable contextual information about the protein function. For example, proteins that change localization in response to many different stressful environmental perturbations may have different roles than those that only change in response to a few. We developed, to our knowledge, the first protocol that permits the quantitative comparison and clustering of protein localization changes across multiple screens. Our analysis allows for the exploratory analysis of proteins according to their pattern of localization changes across many different perturbations, potentially discovering new roles by association.

Silver/silver chloride nanoparticles inhibit the proliferation of human glioblastoma cells

Glioblastomas (GBM) are aggressive brain tumors with very poor prognosis. While silver nanoparticles represent a potential new strategy for anticancer therapy, the silver/silver chloride nanoparticles (Ag/AgCl-NPs) have microbicidal activity, but had not been tested against tumor cells. Here, we analyzed the effect of biogenically produced Ag/AgCl-NPs (from yeast cultures) on the proliferation of GBM02 glioblastoma cells (and of human astrocytes) by automated, image-based high-content analysis (HCA). We compared the effect of 0.1-5.0 µg mL^-1 Ag/AgCl-NPs with that of 9.7-48.5 µg mL^-1 temozolomide (TMZ, chemotherapy drug currently used to treat glioblastomas), alone or in combination. At higher concentrations, Ag/AgCl-NPs inhibited GBM02 proliferation more effectively than TMZ (up to 82 and 62% inhibition, respectively), while the opposite occurred at lower concentrations (up to 23 and 53% inhibition, for Ag/AgCl-NPs and TMZ, respectively). The combined treatment (Ag/AgCl-NPs + TMZ) inhibited GBM02 proliferation by 54-83%. Ag/AgCl-NPs had a reduced effect on astrocyte proliferation compared with TMZ, and Ag/AgCl-NPs + TMZ inhibited astrocyte proliferation by 5-42%. The growth rate and population doubling time analyses confirmed that treatment with Ag/AgCl-NPs was more effective against GBM02 cells than TMZ (~ 67-fold), and less aggressive to astrocytes, while Ag/AgCl-NP + TMZ treatment was no more effective against GBM02 cells than Ag/AgCl-NPs monotherapy. Taken together, our data indicate that 2.5 µg mL^-1 Ag/AgCl-NPs represents the safest dose tested here, which affects GBM02 proliferation, with limited effect on astrocytes. Our findings show that HCA is a useful approach to evaluate the antiproliferative effect of nanoparticles against tumor cells.

Machine learning and image-based profiling in drug discovery

The increase in imaging throughput, new analytical frameworks and high-performance computational resources open new avenues for data-rich phenotypic profiling of small molecules in drug discovery. Image-based profiling assays assessing single-cell phenotypes have been used to explore mechanisms of action, target efficacy and toxicity of small molecules. Technological advances to generate large data sets together with new machine learning approaches for the analysis of high-dimensional profiling data create opportunities to improve many steps in drug discovery. In this review, we will discuss how recent studies applied machine learning approaches in functional profiling workflows with a focus on chemical genetics. While their utility in image-based screening and profiling is predictably evident, examples of novel insights beyond the status quo based on the applications of machine learning approaches are just beginning to emerge. To enable discoveries, future studies also need to develop methodologies that lower the entry barriers to high-throughput profiling experiments by streamlining image-based profiling assays and providing applications for advanced learning technologies such as easy to deploy deep neural networks.

Identification of individual cells from z-stacks of bright-field microscopy images

Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features. This fast, robust method can be used to identify different cell morphologies, including the features of E. coli, S. cerevisiae and epithelial cells, even in mixed cultures. Our method demonstrates the potential of acquiring and processing Z-stacks for single-layer, single-cell imaging and segmentation.

Microscopy in Infectious Disease Research-Imaging Across Scales

A comprehensive understanding of host-pathogen interactions requires quantitative assessment of molecular events across a wide range of spatiotemporal scales and organizational complexities. Due to recent technical developments, this is currently only achievable with microscopy. This article is providing a general perspective on the importance of microscopy in infectious disease research, with a focus on new imaging modalities that promise to have a major impact in biomedical research in the years to come. Every major technological breakthrough in light microscopy depends on, and is supported by, advancements in computing and information technologies. Bioimage acquisition and analysis based on machine learning will pave the way toward more robust, automated and objective implementation of new imaging modalities and in biomedical research in general. The combination of novel imaging technologies with machine learning and near-physiological model systems promises to accelerate discoveries and breakthroughs in our understanding of infectious diseases, from basic research all the way to clinical applications.

Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices

Van der Waals heterostructures are comprised of stacked atomically thin two-dimensional crystals and serve as novel materials providing unprecedented properties. However, the random natures in positions and shapes of exfoliated two-dimensional crystals have required the repetitive manual tasks of optical microscopy-based searching and mechanical transferring, thereby severely limiting the complexity of heterostructures. To solve the problem, here we develop a robotic system that searches exfoliated two-dimensional crystals and assembles them into superlattices inside the glovebox. The system can autonomously detect 400 monolayer graphene flakes per hour with a small error rate (<7%) and stack four cycles of the designated two-dimensional crystals per hour with few minutes of human intervention for each stack cycle. The system enabled fabrication of the superlattice consisting of 29 alternating layers of the graphene and the hexagonal boron nitride. This capacity provides a scalable approach for prototyping a variety of van der Waals superlattices.

Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins

The evaluation of protein localization changes on a systematic level is a powerful tool for understanding how cells respond to environmental, chemical, or genetic perturbations. To date, work in understanding these proteomic responses through high-throughput imaging has catalogued localization changes independently for each perturbation. To distinguish changes that are targeted responses to the specific perturbation or more generalized programs, we developed a scalable approach to visualize the localization behavior of proteins across multiple experiments as a quantitative pattern. By applying this approach to 24 experimental screens consisting of nearly 400,000 images, we differentiated specific responses from more generalized ones, discovered nuance in the localization behavior of stress-responsive proteins, and formed hypotheses by clustering proteins that have similar patterns. Previous approaches aim to capture all localization changes for a single screen as accurately as possible, whereas our work aims to integrate large amounts of imaging data to find unexpected new cell biology.

Whole-organism phenotypic screening for anti-infectives promoting host health

To date, antibiotics have been identified on the basis of their ability to kill bacteria or inhibit their growth rather than directly for their capacity to improve clinical outcomes of infected patients. Although historically successful, this approach has led to the development of an antibiotic armamentarium that suffers from a number of shortcomings, including the inevitable emergence of resistance and, in certain infections, suboptimal efficacy leading to long treatment durations, infection recurrence, or high mortality and morbidity rates despite apparent bacterial sterilization. Conventional antibiotics fail to address the complexities of in vivo bacterial physiology and virulence, as well as the role of the host underlying the complex, dynamic interactions that cause disease. New interventions are needed, aimed at host outcome rather than microbiological cure. Here we review the role of screening models for cellular and whole-organism infection, including worms, flies, zebrafish, and mice, to identify novel therapeutic strategies and discuss their future implications.

Quantitative Algorithm-Based Paired Imaging Measurement for Antibody-Triggered Endocytosis in Cultured Cells

Antibody-triggered endocytosis (ATE) is a biological mechanism on which many therapeutic strategies are grounded, such as delivery of antibody-drug conjugates (ADCs). Current methods monitoring ATE include confocal Z-stack analysis, acid wash, antibody quenching, and pH-sensitive dye labeling. However, those generate less quantifiable results with low throughput. Here we report a new method referred to as "paired imaging measurement" to analyze ATE using a quantitative algorithm in conjunction with high-content imaging. With two sequential measurements of cell surface antibody employing live cell staining and total antibody by immunostaining before and after cell permeabilization, intracellular antibody undergoing endocytosis can be quantified indirectly. Antibodies against CD98 and transferrin receptor were tested on hCMEC/D3 and hiPSC-derived endothelial cells. The maximal response and potency of endocytosed antibodies were generated with good assay robustness (Z' > 0.6) and >5-fold signal/background ratio. Antibody endocytosis response ranking is consistent between batches ( R² > 0.9). The obtained results were confirmed by other traditional methods. In conclusion, we have developed a novel method using a quantitative imaging algorithm in conjunction with live cell staining for high-throughput investigation of ATE.

Assessment of cytosolic free calcium changes during ceramide-induced cell death in MDA-MB-231 breast cancer cells expressing the calcium sensor GCaMP6m

Alterations in Ca²⁺ signaling can regulate key cancer hallmarks such as proliferation, invasiveness and resistance to cell death. Changes in the regulation of intracellular Ca²⁺ and specific components of Ca²⁺ influx are a feature of several cancers and/or cancer subtypes, including the basal-like breast cancer subtype, which has a poor prognosis. The development of genetically encoded calcium indicators, such as GCaMP6, represents an opportunity to measure changes in intracellular free Ca²⁺ during processes relevant to breast cancer progression that occur over long periods (e.g. hours), such as cell death. This study describes the development of a MDA-MB-231 breast cancer cell line stably expressing GCaMP6m. The cell line retained the key features of this aggressive basal-like breast cancer cell line. Using this model, we defined alterations in relative cytosolic free Ca²⁺ ([Ca²⁺]_CYT) when the cells were treated with C2-ceramide. Cell death was measured simultaneously via assessment of propidium iodide permeability. Treatment with ceramide produced delayed and heterogeneous sustained increases in [Ca²⁺]_CYT. Where cell death occurred, [Ca²⁺]_CYT increases preceded cell death. The sustained increases in [Ca²⁺]_CYT were not related to the rapid morphological changes induced by ceramide. Silencing of the plasma membrane Ca²⁺ ATPase isoform 1 (PMCA1) was associated with an augmentation in ceramide-induced increases in [Ca²⁺]_CYT and also cell death. This work demonstrates the utility of GCaMP6 Ca²⁺ indicators for investigating [Ca²⁺]_CYT changes in breast cancer cells during events relevant to tumor progression, which occur over hours rather than minutes.

High-Throughput Screening Raman Spectroscopy Platform for Label-Free Cellomics

We present a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular fingerprinting of tens of thousands eukaryotic cells. The newly proposed label-free HTS-RS platform combines automated imaging microscopy with Raman spectroscopy to enable a rapid label-free screening of cells and can be applied to a large number of biomedical and clinical applications. The potential of the new approach is illustrated by two applications. (1) HTS-RS-based differential white blood cell count. A classification model was trained using Raman spectra of 52 218 lymphocytes, 48 220 neutrophils, and 7 294 monocytes from four volunteers. The model was applied to determine a WBC differential for two volunteers and three patients, producing comparable results between HTS-RS and machine counting. (2) HTS-RS-based identification of circulating tumor cells (CTCs) in 1:1, 1:9, and 1:99 mixtures of Panc1 cells and leukocytes yielded ratios of 55:45, 10:90, and 3:97, respectively. Because the newly developed HTS-RS platform can be transferred to many existing Raman devices in all laboratories, the proposed implementation will lead to a significant expansion of Raman spectroscopy as a standard tool in biomedical cell research and clinical diagnostics.

Microfluidic systems for high-throughput and high-content screening using the nematode Caenorhabditis elegans

In a typical high-throughput drug screening (HTS) process, up to millions of chemical compounds are applied to cells cultured in well plates, aiming to find molecules that exhibit a robust dose-response, as evidenced for example by a fluorescence signal. In high-content screening (HCS), one goes a step further by linking the tested compounds to phenotypic information, obtained, for instance, from microscopic cell images, thereby creating richer data sets that also require more advanced analysis methods. The nematode Caenorhabditis elegans came into the screening picture due to the wide availability of its mutants and human disease models, its relatively easy culture and short life cycle. Being a whole-organism model, it allows drug testing under physiological conditions at multi-tissue levels and provides additional observable phenotypes with respect to cell models, related, for instance, to development, aging, behavior or motility. Worm-based HTS studies in liquid environments on microwell plates have been demonstrated, while microfluidic devices allowed surpassing the performance of plates by enabling more versatile and accurate assays, precise and dynamic dosing of compounds, and readouts down to single-animal resolution. In this review, we discuss microfluidic devices for C. elegans analysis and related studies, published in the period from 2012 to 2017. After an introduction to the different screening approaches, we first focus on microfluidic systems with potential for screening applications. Various enabling technologies, e.g. electrophysiological on-chip recordings or laser axotomy, have been implemented, as well as techniques for reversible worm immobilization and high-resolution imaging, combined with algorithms for automated experimentation and analysis. Several devices for developmental or behavioral assays, and worm sorting based on different phenotypes, have been proposed too. In a subsequent section, we review the application of microfluidic-based systems for medium- and high-throughput screens, including neurobiology and neurodegeneration studies, aging and developmental assays, toxicity and pathogenesis screens, as well as behavioral and motility assays. A thorough analysis of this work reveals a trend towards microfluidic systems more and more capable of offering high-quality analyses of large worm populations, based on multi-phenotypic and/or longitudinal readouts, with clear potential for their application in larger HTS/HCS contexts.

A deep learning and novelty detection framework for rapid phenotyping in high-content screening

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening.

Assay Development for High Content Quantification of Sod1 Mutant Protein Aggregate Formation in Living Cells

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that can be caused by inherited mutations in the gene encoding copper-zinc superoxide dismutase 1 (SOD1). The structural instability of SOD1 and the detection of SOD1-positive inclusions in familial-ALS patients supports a potential causal role for misfolded and/or aggregated SOD1 in ALS pathology. In this study, we describe the development of a cell-based assay designed to quantify the dynamics of SOD1 aggregation in living cells by high content screening approaches. Using lentiviral vectors, we generated stable cell lines expressing wild-type and mutant A4V SOD1 tagged with yellow fluorescent protein and found that both proteins were expressed in the cytosol without any sign of aggregation. Interestingly, only SOD1 A4V stably expressed in HEK-293, but not in U2OS or SH-SY5Y cell lines, formed aggregates upon proteasome inhibitor treatment. We show that it is possible to quantify aggregation based on dose-response analysis of various proteasome inhibitors, and to track aggregate-formation kinetics by time-lapse microscopy. Our approach introduces the possibility of quantifying the effect of ALS mutations on the role of SOD1 in aggregate formation as well as screening for small molecules that prevent SOD1 A4V aggregation.

Toward High-Throughput and Multiplexed Imaging of Genome Organization

Dr. Eric Joyce from the Department of Genetics at the University of Pennsylvania was awarded The President's Innovation award at the annual Society of Biomolecular Imaging and Informatics meeting held in Boston, September 2016. Chromosome interactions are a fundamental aspect of nuclear organization that can activate and silence genes or even direct chromosome rearrangements. However, the molecular mechanisms underlying how chromosomal segments find each other and form stable interactions within cells remain unknown. To address this gap, we have recently developed two technologies that use fluorescent in situ hybridization (FISH) to interrogate chromosome positioning at single-cell resolution. The first is a technology for high-throughput FISH, and the other, called Oligopaints, is a new type of probe that reduces the cost and increases the resolution of FISH. Here, I review our use of these two technologies to uncover and characterize the molecular mechanisms that govern chromosome pairing in Drosophila. I further describe how these methods should benefit a broad spectrum of research fields, including those focusing on chromatin looping, compaction, replication, homologous recombination, and DNA repair.

Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning

In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.

Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning

In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Workflows for microscopy image analysis and cellular phenotyping

In large scale biological experiments, like high-throughput or high-content cellular screening, the amount and the complexity of images to be analyzed are steadily increasing. To handle and process these images, well defined image processing and analysis steps need to be performed by applying dedicated workflows. Multiple software tools have emerged with the aim to facilitate creation of such workflows by integrating existing methods, tools, and routines, and by adapting them to different applications and questions, as well as making them reusable and interchangeable. In this review, we describe workflow systems for the integration of microscopy image analysis techniques with focus on KNIME and Galaxy.

Live-single-cell phenotypic cancer biomarkers-future role in precision oncology?

The promise of precision and personalized medicine is rooted in accurate, highly sensitive, and specific disease biomarkers. This is particularly true for cancer-a disease characterized by marked tumor heterogeneity and diverse molecular signatures. Although thousands of biomarkers have been described, only a very small number have been successfully translated into clinical use. Undoubtedly, there is need for rapid, quantitative, and more cost effective biomarkers for tumor diagnosis and prognosis, to allow for better risk stratification and aid clinicians in making personalized treatment decisions. This is particularly true for cancers where specific biomarkers are either not available (e.g., renal cell carcinoma) or where current biomarkers tend to classify individuals into broad risk categories unable to accurately assess individual tumor aggressiveness and adverse pathology potential (e.g., prostate cancer), thereby leading to problems of over-diagnosis and over-treatment of indolent cancer and under-treatment of aggressive cancer. This perspective highlights an emerging class of cancer biomarkers-live-single-cell phenotypic biomarkers, as compared to genomic biomarkers, and their potential application for cancer diagnosis, risk-stratification, and prognosis.