High-throughput fluorescence imaging approaches for drug discovery using in vitro and in vivo three-dimensional models

Development of Polymethine Dyes for NIR-II Fluorescence Imaging and Therapy

Fluorescence imaging in the second near-infrared window (NIR-II) is burgeoning because of its higher imaging fidelity in monitoring physiological and pathological processes than clinical visible/the second near-infrared window fluorescence imaging. Notably, the imaging fidelity is heavily dependent on fluorescence agents. So far, indocyanine green, one of the polymethine dyes, with good biocompatibility and renal clearance is the only dye approved by the Food and Drug Administration, but it shows relatively low NIR-II brightness. Importantly, tremendous efforts are devoted to synthesizing polymethine dyes for imaging preclinically and clinically. They have shown feasibility in the customization of structure and properties to fulfill various needs in imaging and therapy. Herein, a timely update on NIR-II polymethine dyes, with a special focus on molecular design strategies for fluorescent, photoacoustic, and multimodal imaging, is offered. Furthermore, the progress of polymethine dyes in sensing pathological biomarkers and even reporting drug release is illustrated. Moreover, the NIR-II fluorescence imaging-guided therapies with polymethine dyes are summarized regarding chemo-, photothermal, photodynamic, and multimodal approaches. In addition, artificial intelligence is pointed out for its potential to expedite dye development. This comprehensive review will inspire interest among a wide audience and offer a handbook for people with an interest in NIR-II polymethine dyes.

PyrAtes: Modular Organic Salts with Large Stokes Shifts for Fluo-rescence Microscopy

The deployment of small-molecule fluorescent agents plays an ever-growing role in medicine and drug development. Herein, we complement the portfolio of powerful fluorophores, reporting the serendipitous discovery and development of a novel class with an imidazo[1,2-a]pyridinium triflate core, which we term PyrAtes. These fluorophores are synthesized in a single step from readily available materials (>60 examples) and display Stokes shifts as large as 240 nm, while also reaching NIR-I emissions at λmax as long as 720 nm. Computational studies allow the development of a platform for the prediction of λmax and λEm. Furthermore, we demonstrate the compatibility of these novel fluorophores with live cell imaging in HEK293 cells, suggesting PyrAtes as potent intracellular markers.

Scaffold-based 3D cell culture models in cancer research

Three-dimensional (3D) cell cultures have emerged as valuable tools in cancer research, offering significant advantages over traditional two-dimensional (2D) cell culture systems. In 3D cell cultures, cancer cells are grown in an environment that more closely mimics the 3D architecture and complexity of in vivo tumors. This approach has revolutionized cancer research by providing a more accurate representation of the tumor microenvironment (TME) and enabling the study of tumor behavior and response to therapies in a more physiologically relevant context. One of the key benefits of 3D cell culture in cancer research is the ability to recapitulate the complex interactions between cancer cells and their surrounding stroma. Tumors consist not only of cancer cells but also various other cell types, including stromal cells, immune cells, and blood vessels. These models bridge traditional 2D cell cultures and animal models, offering a cost-effective, scalable, and ethical alternative for preclinical research. As the field advances, 3D cell cultures are poised to play a pivotal role in understanding cancer biology and accelerating the development of effective anticancer therapies. This review article highlights the key advantages of 3D cell cultures, progress in the most common scaffold-based culturing techniques, pertinent literature on their applications in cancer research, and the ongoing challenges.

Application and progress of highcontent imaging in molecular biology

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries

Objective and Impact Statement: We developed a generalized computational approach to design uniform, high-intensity excitation light for low-cost, quantitative fluorescence imaging of in vitro, ex vivo, and in vivo samples with a single device. Introduction: Fluorescence imaging is a ubiquitous tool for biomedical applications. Researchers extensively modify existing systems for tissue imaging, increasing the time and effort needed for translational research and thick tissue imaging. These modifications are application-specific, requiring new designs to scale across sample types. Methods: We implemented a computational model to simulate light propagation from multiple sources. Using a global optimization algorithm and a custom cost function, we determined the spatial positioning of optical fibers to generate 2 illumination profiles. These results were implemented to image core needle biopsies, preclinical mammary tumors, or tumor-derived organoids. Samples were stained with molecular probes and imaged with uniform and nonuniform illumination. Results: Simulation results were faithfully translated to benchtop systems. We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings. The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model. Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used. Conclusion: We demonstrate the significance of using a computationally designed illumination for in vitro, ex vivo, and in vivo fluorescence imaging. Application-specific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features. This approach is generalizable across light sources, biological applications, and detectors.

Mapping of structural arrangement of cells and collective calcium transients: an integrated framework combining live cell imaging using confocal microscopy and UMAP-assisted HDBSCAN-based approach

Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.

Acquiring structural and mechanical information of a fibrous network through deep learning

Fibrous networks play an essential role in the structure and properties of a variety of biological and engineered materials, such as cytoskeletons, protein filament-based hydrogels, and entangled or crosslinked polymer chains. Therefore, insight into the structural features of these fibrous networks and their constituent filaments is critical for discovering the structure-property-function relationships of these material systems. In this paper, a fibrous network-deep learning system (FN-DLS) is established to extract fibrous network structure information from atomic force microscopy images. FN-DLS accurately assesses the structural and mechanical characteristics of fibrous networks, such as contour length, number of nodes, persistence length, mesh size and fractal dimension. As an open-source system, FN-DLS is expected to serve a vast community of scientists working on very diverse disciplines and pave the way for new approaches on the study of biological and synthetic polymer and filament networks found in current applied and fundamental sciences.

Fluorescent toys 'n' tools lighting the way in fungal research

Although largely overlooked compared to bacterial infections, fungal infections pose a significant threat to the health of humans and other organisms. Many pathogenic fungi, especially Candida species, are extremely versatile and flexible in adapting to various host niches and stressful situations. This leads to high pathogenicity and increasing resistance to existing drugs. Due to the high level of conservation between fungi and mammalian cells, it is hard to find fungus-specific drug targets for novel therapy development. In this respect, it is vital to understand how these fungi function on a molecular, cellular as well as organismal level. Fluorescence imaging allows for detailed analysis of molecular mechanisms, cellular structures and interactions on different levels. In this manuscript, we provide researchers with an elaborate and contemporary overview of fluorescence techniques that can be used to study fungal pathogens. We focus on the available fluorescent labelling techniques and guide our readers through the different relevant applications of fluorescent imaging, from subcellular events to multispecies interactions and diagnostics. As well as cautioning researchers for potential challenges and obstacles, we offer hands-on tips and tricks for efficient experimentation and share our expert-view on future developments and possible improvements.

Quantitative Confocal Microscopy for Grouping of Dose-Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine

Fluorescent calcium (Ca²⁺) imaging is one of the preferred methods to record cellular activity during in vitro preclinical studies, high-content drug screening, and toxicity analysis. Visualization and analysis for dose-response data obtained using high-resolution imaging remain challenging, due to the inherent heterogeneity present in the Ca²⁺ spiking. To address this challenge, we propose measurement of cytosolic Ca²⁺ ions using spinning-disk confocal microscopy and machine learning-based analytics that is scalable. First, we implemented uniform manifold approximation and projection (UMAP) for visualizing the multivariate time-series dataset in the two-dimensional (2D) plane using Python. The dataset was obtained through live imaging experiments with norepinephrine-induced Ca²⁺ oscillation in HeLa cells for a large range of doses. Second, we demonstrate that the proposed framework can be used to depict the grouping of the spiking pattern for lower and higher drug doses. To the best of our knowledge, this is the first attempt at UMAP visualization of the time-series dose response and identification of the Ca²⁺ signature during lytic death. Such quantitative microscopy can be used as a component of a high-throughput data analysis workflow for toxicity analysis.

Characterization and quantification of necrotic tissues and morphology in multicellular ovarian cancer tumor spheroids using optical coherence tomography

The three-dimensional (3D) tumor spheroid model is a critical tool for high-throughput ovarian cancer research and anticancer drug development in vitro. However, the 3D structure prevents high-resolution imaging of the inner side of the spheroids. We aim to visualize and characterize 3D morphological and physiological information of the contact multicellular ovarian tumor spheroids growing over time. We intend to further evaluate the distinctive evolutions of the tumor spheroid and necrotic tissue volumes in different cell numbers and determine the most appropriate mathematical model for fitting the growth of tumor spheroids and necrotic tissues. A label-free and noninvasive swept-source optical coherence tomography (SS-OCT) imaging platform was applied to obtain two-dimensional (2D) and 3D morphologies of ovarian tumor spheroids over 18 days. Ovarian tumor spheroids of two different initial cell numbers (5,000- and 50,000- cells) were cultured and imaged (each day) over the time of growth in 18 days. Four mathematical models (Exponential-Linear, Gompertz, logistic, and Boltzmann) were employed to describe the growth kinetics of the tumor spheroids volume and necrotic tissues. Ovarian tumor spheroids have different growth curves with different initial cell numbers and their growths contain different stages with various growth rates over 18 days. The volumes of 50,000-cells spheroids and the corresponding necrotic tissues are larger than that of the 5,000-cells spheroids. The formation of necrotic tissue in 5,000-cells numbers is slower than that in the 50,000-cells ones. Moreover, the Boltzmann model exhibits the best fitting performance for the growth of tumor spheroids and necrotic tissues. Optical coherence tomography (OCT) can serve as a promising imaging modality to visualize and characterize morphological and physiological features of multicellular ovarian tumor spheroids. The Boltzmann model integrating with 3D OCT data of ovarian tumor spheroids provides great potential for high-throughput cancer research in vitro and aiding in drug development.

Optically superior fluorescent probes for selective imaging of cells, tumors, and reactive chemical species

Fluorescent chemical probes have become powerful tools to study biological events in living cells. They provide a great opportunity to quantitatively and qualitatively analyze the physiological and biochemical properties of living cells in real time. The ability of researchers to manipulate these probes for a desired specific purpose has turned many heads in the scientific community. Despite a slow start, fluorescent probe research has seen exponential growth over the last decade in the world. This change required some adventurous and creative scientists from different fields-like biology, medicine, and chemistry-to come together to facilitate the constant expansion of this field. This review article introduces some fundamental concepts related to fluorescent probe designing and development. It also summarizes various fluorescent probes with superior optical properties used in fields like cell biology, cellular imaging, medical research, and cancer diagnosis. It is hoped that this article will encourage more young and creative scientists to contribute their talents to this field.

Targeted esterase-induced dye (TED) loading supports direct calcium imaging in eukaryotic cell-free systems

Calcium imaging is an important functional tool for analysing ion channels, transporters and pumps for drug screening in living cells. Depicted eukaryotic cell-free systems utilize microsomes, derived from the endoplasmic reticulum to incorporate the synthesized membrane proteins-like ion channels. Carboxylesterase is required to cleave the acetoxymethyl ester moiety of the chemical calcium indicators in order to ensure its immobility across the endoplasmic reticulum membrane. Absence or an inadequate amount of carboxylesterase in the endoplasmic reticulum of different eukaryotic cells poses a hindrance to perform calcium imaging in microsomes. In this work, we try to overcome this drawback and adapt the cell-based calcium imaging principle to a cell-free protein synthesis platform. Carboxylesterase synthesized in a Spodoptera frugiperda Sf21 lysate translation system is established as a viable calcium imaging tool in microsomes. Cell-free synthesized carboxylesterase inside microsomes is validated with esterase and dye loading assays. Native proteins from the endoplasmic reticulum, such as ryanodine channels and calcium ATPase, are analysed. Cell-free synthesized transient receptor potential channels are used as model proteins to demonstrate the realization of this concept.

Carboxylesterase, the key enzyme to handle ester-based dyes, is synthesized in microsomes using eukaryotic cell-free protein synthesis platform and established as a viable calcium imaging tool to analyze native and cell-free synthesized ion channels.

Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review

Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.

Microfluidic channel integrated with a lattice lightsheet microscopic system for continuous cell imaging

In this study, a continuous cell-imaging system with subcellular resolution was developed by integrating a microfluidic platform with lattice lightsheet microscopy (LLSM). To reduce aberrations of the lightsheet propagating into the device, a microfluidic channel sealed with a water refractive index-matched thin film was fabricated. When the lightsheet emerged from the water-immersed objectives and penetrated through the water refractive-matched thin film into the microfluidic channel at an incident angle, less light scattering and fewer aberrations were found. Suspended cells flowed across the lattice lightsheet, and an imaging system with the image plane perpendicular to the lightsheet was used to sequentially acquire cell images. By applying a thinner lattice lightsheet, higher-resolution, higher-contrast images were obtained. Furthermore, three-dimensional cell images could be achieved by reconstructing sequential two-dimensional cell images.

Issues with Cancer Spheroid Models in Therapeutic Drug Screening

In vitro screening for anti-cancer agents currently uses mainly cell lines in 2D culture. It is generally assumed that 3D culture, namely spheroids, represents physiologically more relevant models for tumors. Unfortunately, drug testing in spheroids is not as easy and reproducible as in 2D culture because there are factors that limit the universal use of spheroids as screening platforms. Technical problems in the generation of uniform spheroids, cell/tumor-specific differences in the ability to form spheroids, and more complex readout parameters are the main reasons for differences between spheroid data. The review discusses requirements for cancer spheroids to be representative models, suitable methodologies to generate spheroids for the screening and readout parameters for the evaluation of anti-cancer agents.

Image-based high-content screening in drug discovery

While target-based drug discovery strategies rely on the precise knowledge of the identity and function of the drug targets, phenotypic drug discovery (PDD) approaches allow the identification of novel drugs based on knowledge of a distinct phenotype. Image-based high-content screening (HCS) is a potent PDD strategy that characterizes small-molecule effects through the quantification of features that depict cellular changes among or within cell populations, thereby generating valuable data sets for subsequent data analysis. However, these data can be complex, making image analysis from large HCS campaigns challenging. Technological advances in image acquisition, processing, and analysis as well as machine-learning (ML) approaches for the analysis of multidimensional data sets have rendered HCS as a viable technology for small-molecule drug discovery. Here, we discuss HCS concepts, current workflows as well as opportunities and challenges of image-based phenotypic screening and data analysis.

Application of Three-dimensional (3D) Tumor Cell Culture Systems and Mechanism of Drug Resistance

The in-vitro experimental model for the development of cancer therapeutics has always been challenging. Recently, the scientific revolution has improved cell culturing techniques by applying three dimensional (3D) culture system, which provides a similar physiologically relevant in-vivo model for studying various diseases including cancer. In particular, cancer cells exhibiting in-vivo behavior in a model of 3D cell culture is a more accurate cell culture model to test the effectiveness of anticancer drugs or characterization of cancer cells in comparison with two dimensional (2D) monolayer. This study underpins various factors that cause resistance to anticancer drugs in forms of spheroids in 3D in-vitro cell culture and also outlines key challenges and possible solutions for the future development of these systems.

High-throughput screening in multicellular spheroids for target discovery in the tumor microenvironment

INTRODUCTION

Solid tumors are highly influenced by a complex tumor microenvironment (TME) that cannot be modeled with conventional two-dimensional (2D) cell culture. In addition, monolayer culture conditions tend to induce undesirable molecular and phenotypic cellular changes. The discrepancy between in vitro and in vivo is an important factor accounting for the high failure rate in drug development. Three-dimensional (3D) multicellular tumor spheroids (MTS) more closely resemble the in vivo situation in avascularized tumors.

AREAS COVERED

This review describes the use of MTS for anti-cancer drug discovery, with an emphasis on high-throughput screening (HTS) compatible assays. In particular, we focus on how these assays can be used for target discovery in the context of the TME.

EXPERT OPINION

Arrayed MTS in microtiter plates are HTS compatible but remain more expensive and time consuming than their 2D culture counterpart. It is therefore imperative to use assays with multiplexed readouts, in order to maximize the information that can be gained with the screen. In this context, high-content screening allowing to uncover microenvironmental dependencies is the true added value of MTS-based screening compared to 2D culture-based screening. Hit translation in animal models will, however, be key to allow a broader use of MTS-based screening in industry.

HCS Methodology for Helping in Lab Scale Image-Based Assays

High-content screening (HCS) automates image acquisition and analysis in microscopy. This technology considers the multiple parameters contained in the images and produces statistically significant results. The recent improvements in image acquisition throughput, image analysis, and machine learning (ML) have popularized this kind of experiments, emphasizing the need for new tools and know-how to help in its design, analysis, and data interpretation. This chapter summarizes HCS recommendations for lab scale assays and provides both macros for HCS-oriented image analysis and user-friendly tools for data mining processes. All the steps described herein are oriented to a wide variety of image cell-based experiments. The workflows are illustrated with practical examples and test images. Their use is expected to help analyze thousands of images, create graphical representations, and apply machine learning models on HCS.

Evaluation of OptiFlow™-MS/MS for bioanalysis of pharmaceutical drugs and metabolites

Aim: Microflow tandem mass spectrometry-based methods have been proposed as options to improve sensitivity and selectivity while improving sample utility and solvent consumption. Here, we evaluate a newly introduced microflow source, OptiFlow™, for quantitative performance. Results/methodology: We performed a comparison of the OptiFlow and IonDrive™ sources, respectively, on the same triple quadrupole mass spectrometer. The comparison used a neat cocktail of commercially available drugs and extracted plasma samples monitoring midazolam and alprazolam metabolites. Microflow produced a 2–4× signal increase for the neat drug cocktail and a 5–10× increase for extracted plasma samples. Conclusion: The OptiFlow method consistently gave increased signal response relative to the IonDrive method and enabled a better lower limit of quantitation for defining phamacokinetics.

A High-Throughput Automated Confocal Microscopy Platform for Quantitative Phenotyping of Nanoparticle Uptake and Transport in Spheroids

There is a high demand for advanced, image-based, automated high-content screening (HCS) approaches to facilitate phenotypic screening in 3D cell culture models. A major challenge lies in retaining the resolution of fine cellular detail but at the same time imaging multicellular structures at a large scale. In this study, a confocal microscopy-based HCS platform in optical multiwell plates that enables the quantitative morphological profiling of populations of nonuniform spheroids obtained from HT-29 human colorectal cancer cells is described. This platform is then utilized to demonstrate a quantitative dissection of the penetration of synthetic nanoparticles (NP) in multicellular 3D spheroids at multiple levels of scale. A pilot RNA interference-based screening validates this methodology and identifies a subset of RAB GTPases that regulate NP trafficking in these spheroids. This technology is suitable for high-content phenotyping in 3D cell-based screening, providing a framework for nanomedicine drug development as applied to translational oncology.

Patient-Derived Cancer Organoid Cultures to Predict Sensitivity to Chemotherapy and Radiation

PURPOSE

Cancer treatment is limited by inaccurate predictors of patient-specific therapeutic response. Therefore, some patients are exposed to unnecessary side effects and delays in starting effective therapy. A clinical tool that predicts treatment sensitivity for individual patients is needed.

EXPERIMENTAL DESIGN

Patient-derived cancer organoids were derived across multiple histologies. The histologic characteristics, mutation profile, clonal structure, and response to chemotherapy and radiation were assessed using bright-field and optical metabolic imaging on spheroid and single-cell levels, respectively.

RESULTS

We demonstrate that patient-derived cancer organoids represent the cancers from which they were derived, including key histologic and molecular features. These cultures were generated from numerous cancers, various biopsy sample types, and in different clinical settings. Next-generation sequencing reveals the presence of subclonal populations within the organoid cultures. These cultures allow for the detection of clonal heterogeneity with a greater sensitivity than bulk tumor sequencing. Optical metabolic imaging of these organoids provides cell-level quantification of treatment response and tumor heterogeneity allowing for resolution of therapeutic differences between patient samples. Using this technology, we prospectively predict treatment response for a patient with metastatic colorectal cancer.

CONCLUSIONS

These studies add to the literature demonstrating feasibility to grow clinical patient-derived organotypic cultures for treatment effectiveness testing. Together, these culture methods and response assessment techniques hold great promise to predict treatment sensitivity for patients with cancer undergoing chemotherapy and/or radiation.

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Tumor spheroids have been developed as a three-dimensional (3D) cell culture model in cancer research and anti-cancer drug discovery. However, currently, high-throughput imaging modalities utilizing bright field or fluorescence detection, are unable to resolve the overall 3D structure of the tumor spheroid due to limited light penetration, diffusion of fluorescent dyes and depth-resolvability. Recently, our lab demonstrated the use of optical coherence tomography (OCT), a label-free and non-destructive 3D imaging modality, to perform longitudinal characterization of multicellular tumor spheroids in a 96-well plate. OCT was capable of obtaining 3D morphological and physiological information of tumor spheroids growing up to about 600 µm in height. In this article, we demonstrate a high-throughput OCT (HT-OCT) imaging system that scans the whole multi-well plate and obtains 3D OCT data of tumor spheroids automatically. We describe the details of the HT-OCT system and construction guidelines in the protocol. From the 3D OCT data, one can visualize the overall structure of the spheroid with 3D rendered and orthogonal slices, characterize the longitudinal growth curve of the tumor spheroid based on the morphological information of size and volume, and monitor the growth of the dead-cell regions in the tumor spheroid based on optical intrinsic attenuation contrast. We show that HT-OCT can be used as a high-throughput imaging modality for drug screening as well as characterizing biofabricated samples.

Human iPS Cell-Derived Patient Tissues and 3D Cell Culture Part 2: Spheroids, Organoids, and Disease Modeling

Human induced pluripotent stem cells (HiPSCs) provide several advantages for drug discovery, but principally they provide a source of clinically relevant tissue. Furthermore, the use of HiPSCs cultured in three-dimensional (3D) systems, as opposed to traditional two-dimensional (2D) culture approaches, better represents the complex tissue architecture in vivo. The use of HiPSCs in 3D spheroid and organoid culture is now growing, but particularly when using myocardial, intestinal enteric nervous system, and retinal cell lines. However, organoid cell culture is perhaps making the most notable impact in research and drug discovery, in which 3D neuronal cell cultures allow direct modeling of cortical cell layering and neuronal circuit activity. Given the specific degeneration seen in discrete neuronal circuitry in Alzheimer’s disease (AD) and Parkinson’s disease (PD), HiPSC culture systems are proving to be a major advance. In the present review, the second part of a two-part review, we discuss novel methods in which 3D cell culture systems (principally organoids) are now being used to provide insights into disease mechanisms. (The use of HiPSCs in target identification was reviewed in detail in Part 1.)

Development of a Novel 3D Tumor-tissue Invasion Model for High-throughput, High-content Phenotypic Drug Screening

While much progress has been made in the war on cancer, highly invasive cancers such as pancreatic cancer remain difficult to treat and anti-cancer clinical trial success rates remain low. One shortcoming of the drug development process that underlies these problems is the lack of predictive, pathophysiologically relevant preclinical models of invasive tumor phenotypes. While present-day 3D spheroid invasion models more accurately recreate tumor invasion than traditional 2D models, their shortcomings include poor reproducibility and inability to interface with automated, high-throughput systems. To address this gap, a novel 3D tumor-tissue invasion model which supports rapid, reproducible setup and user-definition of tumor and surrounding tissue compartments was developed. High-cell density tumor compartments were created using a custom-designed fabrication system and standardized oligomeric type I collagen to define and modulate ECM physical properties. Pancreatic cancer cell lines used within this model showed expected differential invasive phenotypes. Low-passage, patient-derived pancreatic cancer cells and cancer-associated fibroblasts were used to increase model pathophysiologic relevance, yielding fibroblast-mediated tumor invasion and matrix alignment. Additionally, a proof-of-concept multiplex drug screening assay was applied to highlight this model's ability to interface with automated imaging systems and showcase its potential as a predictive tool for high-throughput, high-content drug screening.

Quantitative high-throughput phenotypic screening of pediatric cancer cell lines identifies multiple opportunities for drug repurposing

Drug repurposing approaches have the potential advantage of facilitating rapid and cost-effective development of new therapies. Particularly, the repurposing of drugs with known safety profiles in children could bypass or streamline toxicity studies. We employed a phenotypic screening paradigm on a panel of well-characterized cell lines derived from pediatric solid tumors against a collection of ∼3,800 compounds spanning approved drugs and investigational agents. Specifically, we employed titration-based screening where compounds were tested at multiple concentrations for their effect on cell viability. Molecular and cellular target enrichment analysis indicated that numerous agents across different therapeutic categories and modes of action had an antiproliferative effect, notably antiparasitic/protozoal drugs with non-classic antineoplastic activity. Focusing on active compounds with dosing and safety information in children according to the Children's Pharmacy Collaborative database, we identified compounds with therapeutic potential through further validation using 3D tumor spheroid models. Moreover, we show that antiparasitic agents induce cell death via apoptosis induction. This study demonstrates that our screening platform enables the identification of chemical agents with cytotoxic activity in pediatric cancer cell lines of which many have known safety/toxicity profiles in children. These agents constitute attractive candidates for efficacy studies in pre-clinical models of pediatric solid tumors.

High-Content Monitoring of Drug Effects in a 3D Spheroid Model

A recent decline in the discovery of novel medications challenges the widespread use of 2D monolayer cell assays in the drug discovery process. As a result, the need for more appropriate cellular models of human physiology and disease has renewed the interest in spheroid 3D culture as a pertinent model for drug screening. However, despite technological progress that has significantly simplified spheroid production and analysis, the seeming complexity of the 3D approach has delayed its adoption in many laboratories. The present report demonstrates that the use of a spheroid model may be straightforward and can provide information that is not directly available with a standard 2D approach. We describe a cost-efficient method that allows for the production of an array of uniform spheroids, their staining with vital dyes, real-time monitoring of drug effects, and an ATP-endpoint assay, all in the same 96-well U-bottom plate. To demonstrate the method performance, we analyzed the effect of the preclinical anticancer drug MLN4924 on spheroids formed by VCaP and LNCaP prostate cancer cells. The drug has different outcomes in these cell lines, varying from cell cycle arrest and protective dormancy to senescence and apoptosis. We demonstrate that by using high-content analysis of spheroid arrays, the effect of the drug can be described as a series of EC50 values that clearly dissect the cytostatic and cytotoxic drug actions. The method was further evaluated using four standard cancer chemotherapeutics with different mechanisms of action, and the effect of each drug is described as a unique multi-EC50 diagram. Once fully validated in a wider range of conditions, this method could be particularly valuable for phenotype-based drug discovery.

Small-Molecule Screens: A Gateway to Cancer Therapeutic Agents with Case Studies of Food and Drug Administration-Approved Drugs

High-throughput screening (HTS) of small-molecule libraries accelerates the discovery of chemical leads to serve as starting points for probe or therapeutic development. With this approach, thousands of unique small molecules, representing a diverse chemical space, can be rapidly evaluated by biologically and physiologically relevant assays. The origins of numerous United States Food and Drug Administration-approved cancer drugs are linked to HTS, which emphasizes the value in this methodology. The National Institutes of Health Molecular Libraries Program made HTS accessible to the public sector, enabling the development of chemical probes and drug-repurposing initiatives. In this work, the impact of HTS in the field of oncology is considered among both private and public sectors. Examples are given for the discovery and development of approved cancer drugs. The importance of target validation is discussed, and common assay approaches for screening are reviewed. A rigorous examination of the PubChem database demonstrates that public screening centers are contributing to early-stage drug discovery in oncology by focusing on new targets and developing chemical probes. Several case studies highlight the value of different screening strategies and the potential for drug repurposing.

A Cell-Based Assay to Investigate Hypolipidemic Effects of Nonalcoholic Fatty Liver Disease Therapeutics

In the recent past, there has been a growing interest in developing nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) therapeutics. As a result, a need for in vitro cell models of human hepatic steatosis and high-throughput assays to measure intracellular lipid levels has arisen. To address this growing need, we optimized the conditions based on the current literature to fatten HepG2 hepatocytes by adding a mixture of saturated and unsaturated fatty acids (oleate/palmitate, 2:1 molar ratio) without inducing any overt cytotoxicity. Our results indicate that hepatocytes fatten in a concentration- (0.75-1.5 mM of fatty acids) and time-dependent manner, with a substantial increase in intracellular lipid levels seen within 6 h. Additionally, a method to quantify lipid levels in cells using a fluorescent reagent that is more sensitive than that in conventional assays and adaptable for high-throughput screening is presented. Lastly, the utility of the in vitro cell model and an assay based on AdipoRed to measure hypolipidemic effects of therapeutic drugs is demonstrated using fenofibrate, a molecule that was previously shown to lower lipid levels in the liver.

Assessing Drug Efficacy in a Miniaturized Pancreatic Cancer In Vitro 3D Cell Culture Model

Pancreatic cancer continues to have one of the poorest prognoses among all cancers. The drug discovery efforts for this disease have largely failed, with no significant improvement in survival outcomes for advanced pancreatic cancer patients over the past 20 years. Traditional in vitro cell culture techniques have been used extensively in both basic and early drug discovery; however, these systems offer poor models to assess emerging therapeutics. More predictive cell-based models, which better capture the cellular heterogeneity and complexities of solid pancreatic tumors, are urgently needed not only to improve drug discovery success but also to provide insight into the tumor biology. Pancreatic tumors are characterized by a unique micro-environment that is surrounded by a dense stroma. A complex network of interactions between extracellular matrix (ECM) components and the effects of cell-to-cell contacts may enhance survival pathways within in vivo tumors. This biological and physical complexity is lost in traditional cell monolayer models. To explore the predictive potential of a more complex cellular system, a three-dimensional (3D) micro-tumor assay was evaluated. Efficacy of six current chemotherapeutics was determined against a panel of primary and metastatic pancreatic tumor cell lines in a miniaturized ECM-based 3D cell culture system. Suitability for potential use in high-throughput screening applications was assessed, including ascertaining the effects that miniaturization and automation had on assay robustness. Cellular health was determined by utilizing an indirect population-based metabolic activity assay and a direct imaging-based cell viability assay.

Optical Coherence Tomography Detects Necrotic Regions and Volumetrically Quantifies Multicellular Tumor Spheroids

Three-dimensional (3D) tumor spheroid models have gained increased recognition as important tools in cancer research and anticancer drug development. However, currently available imaging approaches used in high-throughput screening drug discovery platforms, for example, bright-field, phase contrast, and fluorescence microscopies, are unable to resolve 3D structures deep inside (>50 μm) tumor spheroids. In this study, we established a label-free, noninvasive optical coherence tomography (OCT) imaging platform to characterize 3D morphologic and physiologic information of multicellular tumor spheroids (MCTS) growing from approximately 250 to 600 μm in height over 21 days. In particular, tumor spheroids of two cell lines, glioblastoma (U-87MG) and colorectal carcinoma (HCT116), exhibited distinctive evolutions in their geometric shapes at late growth stages. Volumes of MCTS were accurately quantified using a voxel-based approach without presumptions of their geometries. In contrast, conventional diameter-based volume calculations assuming perfect spherical shape resulted in large quantification errors. Furthermore, we successfully detected necrotic regions within these tumor spheroids based on increased intrinsic optical attenuation, suggesting a promising alternative of label-free viability tests in tumor spheroids. Therefore, OCT can serve as a promising imaging modality to characterize morphologic and physiologic features of MCTS, showing great potential for high-throughput drug screening. Cancer Res; 77(21); 6011-20. ©2017 AACR.

Cancer drug discovery: recent innovative approaches to tumor modeling

INTRODUCTION

Cell culture models have been at the heart of anti-cancer drug discovery programs for over half a century. Advancements in cell culture techniques have seen the rapid evolution of more complex in vitro cell culture models investigated for use in drug discovery. Three-dimensional (3D) cell culture research has become a strong focal point, as this technique permits the recapitulation of the tumor microenvironment. Biologically relevant 3D cellular models have demonstrated significant promise in advancing cancer drug discovery, and will continue to play an increasing role in the future.

AREAS COVERED

In this review, recent advances in 3D cell culture techniques and their application in tumor modeling and anti-cancer drug discovery programs are discussed. The topics include selection of cancer cells, 3D cell culture assays (associated endpoint measurements and analysis), 3D microfluidic systems and 3D bio-printing.

EXPERT OPINION

Although advanced cancer cell culture models and techniques are becoming commonplace in many research groups, the use of these approaches has yet to be fully embraced in anti-cancer drug applications. Furthermore, limitations associated with analyzing information-rich biological data remain unaddressed.

The development of high-content screening (HCS) technology and its importance to drug discovery

INTRODUCTION

High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development.

AREAS COVERED

Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology.

EXPERT OPINION

Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.