A high-content image-based method for quantitatively studying context-dependent cell population dynamics

A spheroid whole mount drug testing pipeline with machine-learning based image analysis identifies cell-type specific differences in drug efficacy on a single-cell level

BACKGROUND

The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures.

METHODS

Here, we developed a high-content pipeline ranging from the setup of novel tumor-fibroblast spheroid co-cultures over optical tissue clearing, whole mount staining, and 3D confocal microscopy to optimized 3D-image segmentation and a 3D-deep-learning model to automate the analysis of a range of cell-type-specific processes, such as cell proliferation, apoptosis, necrosis, drug susceptibility, nuclear morphology, and cell density.

RESULTS

This demonstrated that co-cultures of KP-4 tumor cells with CCD-1137Sk fibroblasts exhibited a growth advantage compared to tumor cell mono-cultures, resulting in higher cell counts following cytostatic treatments with paclitaxel and doxorubicin. However, cell-type-specific single-cell analysis revealed that this apparent benefit of co-cultures was due to a higher resilience of fibroblasts against the drugs and did not indicate a higher drug resistance of the KP-4 cancer cells during co-culture. Conversely, cancer cells were partially even more susceptible in the presence of fibroblasts than in mono-cultures.

CONCLUSION

In summary, this underlines that a novel cell-type-specific single-cell analysis method can reveal critical insights regarding the mechanism of action of drug substances in three-dimensional cell culture models.

Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.

Graphitic carbon nitride as a novel anticancer agent: potential mechanisms and efficacy in prostate cancer and glioblastoma treatment

Carbon-derived compounds are gaining traction in the scientific community because of their unique properties, such as conductivity and strength, and promising innovations in technology and medicine. Graphitic nitride carbon (g-C3N4) stands out among these compounds because of its potential in antitumor therapies. This study aimed to assess g-C3N4's antitumor potential and cytotoxic mechanisms. Prostate cancer (DU-145) and glioblastoma (U87) cell lines were used to evaluate antitumor effects, whereas RAW 264.7 and HFF-1 non-tumor cells were used for selectivity evaluation. The synthesized g-C3N4 particles underwent comprehensive characterization, including the assessment of particle size, morphology, and oxygen content, employing various techniques, such as X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, transmission electron microscopy, and atomic force microscopy. The results indicated that g-C3N4 significantly affected tumor cell proliferation and viability, exhibiting high cytotoxicity within 48 h. In non-tumor cells, minimal effects on proliferation were observed, except for damage to the cell membranes of RAW 264.7 cells. Moreover, g-C3N4 changed the cell morphology and ultrastructure, affecting cell migration in U87 cells and potentially enhancing migration in RAW 264.7 cells. Biochemical assays in Balb/C mice revealed alterations in alanine aminotransferase, aspartate aminotransferase, and amylase levels. In conclusion, g-C3N4 demonstrated promising antitumor effects with minimal toxicity to non-tumor cells, suggesting its potential in neoplasm treatment.

High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer

Therapy resistance in breast cancer is increasingly attributed to polyploid giant cancer cells (PGCCs), which arise through whole-genome doubling and exhibit heightened resilience to standard treatments. Characterized by enlarged nuclei and increased DNA content, these cells tend to be dormant under therapeutic stress, driving disease relapse. Despite their critical role in resistance, strategies to effectively target PGCCs are limited, largely due to the lack of high-throughput methods for assessing their viability. Traditional assays lack the sensitivity needed to detect PGCC-specific elimination, prompting the development of novel approaches. To address this challenge, we developed a high-throughput single-cell morphological analysis workflow designed to differentiate compounds that selectively inhibit non-PGCCs, PGCCs, or both. Using this method, we screened a library of 2,726 FDA Phase 1-approved drugs, identifying promising anti-PGCC candidates, including proteasome inhibitors, FOXM1, CHK, and macrocyclic lactones. Notably, RNA-Seq analysis of cells treated with the macrocyclic lactone Pyronaridine revealed AXL inhibition as a potential strategy for targeting PGCCs. Although our single-cell morphological analysis pipeline is powerful, empirically testing all existing compounds is impractical and inefficient. To overcome this limitation, we trained a machine learning model to predict anti-PGCC efficacy in silico, integrating chemical fingerprints and compound descriptions from prior publications and databases. The model demonstrated a high correlation with experimental outcomes and predicted efficacious compounds in an expanded library of over 6,000 drugs. Among the top-ranked predictions, we experimentally validated two compounds as potent PGCC inhibitors. These findings underscore the synergistic potential of integrating high-throughput empirical screening with machine learning-based virtual screening to accelerate the discovery of novel therapies, particularly for targeting therapy-resistant PGCCs in breast cancer.

Targeting stress induction of GRP78 by cardiac glycoside oleandrin dually suppresses cancer and COVID-19

BACKGROUND

Despite recent therapeutic advances, combating cancer resistance remains a formidable challenge. The 78-kilodalton glucose-regulated protein (GRP78), a key stress-inducible endoplasmic reticulum (ER) chaperone, plays a crucial role in both cancer cell survival and stress adaptation. GRP78 is also upregulated during SARS-CoV-2 infection and acts as a critical host factor. Recently, we discovered cardiac glycosides (CGs) as novel suppressors of GRP78 stress induction through a high-throughput screen of clinically relevant compound libraries. This study aims to test the possibility that agents capable of blocking stress induction of GRP78 could dually suppress cancer and COVID-19.

RESULTS

Here we report that oleandrin (OLN), is the most potent among the CGs in inhibiting acute stress induction of total GRP78, which also results in reduced cell surface and nuclear forms of GRP78 in stressed cells. The inhibition of stress induction of GRP78 is at the post-transcriptional level, independent of protein degradation and autophagy and may involve translational control as OLN blocks stress-induced loading of ribosomes onto GRP78 mRNAs. Moreover, the human Na+/K+-ATPase α3 isoform is critical for OLN suppression of GRP78 stress induction. OLN, in nanomolar range, enhances apoptosis, sensitizes colorectal cancer cells to chemotherapeutic agents, and reduces the viability of patient-derived colon cancer organoids. Likewise, OLN, suppresses GRP78 expression and impedes tumor growth in an orthotopic breast cancer xenograft model. Furthermore, OLN blocks infection by SARS-CoV-2 and its variants and enhances existing anti-viral therapies. Notably, GRP78 overexpression mitigates OLN-mediated cancer cell apoptotic onset and suppression of virus release.

CONCLUSION

Our findings validate GRP78 as a target of OLN anti-cancer and anti-viral activities. These proof-of-principle studies support further investigation of OLN as a readily accessible compound to dually combat cancer and COVID-19.

Live Cell Painting: image-based profiling in live cells using Acridine Orange

Image-based profiling has been used to analyze cell health, drug mechanism of action, CRISPR-edited cells, and overall cytotoxicity. Cell Painting is a broadly used image-based assay that uses morphological features to capture how cells respond to treatments. However, this method requires cell fixation for staining, which prevents examining live cells. To address this limitation, here we present Live Cell Painting (LCP), a high-content method based on Acridine orange, a metachromatic dye that labels different organelles and cellular structures. We began by showing that LCP can be applied to follow acidic vesicle redistribution of cells exposed to acidic vesicles inhibitors. Next, we show that LCP can identify subtle changes in cells exposed to silver nanoparticles that are not detected by techniques such as MTT assay. In drug treatments, LCP was helpful in assessing the dose-response relationship and creating profiles that allow clustering of drugs that cause liver injury. Here, we present an affordable and easy-to-use image-based assay capable of assessing overall cell health and showing promise for use in various applications such as assessing drugs and nanoparticles. We envisage the use of Live Cell Painting as an initial screening of overall cell health while providing insights into new biological questions.

Examination of the role of mutualism in immune evasion

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumour requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumour implemented as a 2D-hexagonal lattice model suggests tumour survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumours with equivalent growth rates and no mutualism are more likely to be eliminated than tumours with mutualism. Tumours with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumours, mutualism is critical for early tumorigenesis.

High content screening strategies for large-scale compound libraries with a focus on high-containment viruses

A majority of viral diseases do not have FDA-approved drugs. The recent outbreaks caused by SARS-CoV-2, monkeypox, and Sudan ebolavirus have exposed the critical need for rapid screening and identification of antiviral compounds against emerging/re-emerging viral pathogens. A high-content screening (HCS) platform is becoming an essential part of the drug discovery process, thanks to developments in image acquisition and analysis. While HCS has several advantages, its full potential has not been realized in antiviral drug discovery compared to conventional drug screening approaches, such as fluorescence or luminescence-based microplate assays. Therefore, this review aims to summarize HCS workflow, strategies, and developments in image-based drug screening, focusing on high-containment viruses.

Single-cell morphological and transcriptome analysis unveil inhibitors of polyploid giant breast cancer cells in vitro

Considerable evidence suggests that breast cancer therapeutic resistance and relapse can be driven by polyploid giant cancer cells (PGCCs). The number of PGCCs increases with the stages of disease and therapeutic stress. Given the importance of PGCCs, it remains challenging to eradicate them. To discover effective anti-PGCC compounds, there is an unmet need to rapidly distinguish compounds that kill non-PGCCs, PGCCs, or both. Here, we establish a single-cell morphological analysis pipeline with a high throughput and great precision to characterize dynamics of individual cells. In this manner, we screen a library to identify promising compounds that inhibit all cancer cells or only PGCCs (e.g., regulators of HDAC, proteasome, and ferroptosis). Additionally, we perform scRNA-Seq to reveal altered cell cycle, metabolism, and ferroptosis sensitivity in breast PGCCs. The combination of single-cell morphological and molecular investigation reveals promising anti-PGCC strategies for breast cancer treatment and other malignancies.

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.

An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions

The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic.

High-content imaging as a tool to quantify and characterize malaria parasites

In 2021, Plasmodium falciparum was responsible for 619,000 reported malaria-related deaths. Resistance has been detected to every clinically used antimalarial, urging the development of novel antimalarials with uncompromised mechanisms of actions. High-content imaging allows researchers to collect and quantify numerous phenotypic properties at the single-cell level, and machine learning-based approaches enable automated classification and clustering of cell populations. By combining these technologies, we developed a method capable of robustly differentiating and quantifying P. falciparum asexual blood stages. These phenotypic properties also allow for the quantification of changes in parasite morphology. Here, we demonstrate that our analysis can be used to quantify schizont nuclei, a phenotype that previously had to be enumerated manually. By monitoring stage progression and quantifying parasite phenotypes, our method can discern stage specificity of new compounds, thus providing insight into the compound's mode of action.

Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data

Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.

Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

Genetic Tools for Studying the Roles of Sphingolipids in Viral Infections

Sphingolipids are a critical family of membrane lipids with diverse functions in eukaryotic cells, and a growing body of literature supports that these lipids play essential roles during the lifecycles of viruses. While small molecule inhibitors of sphingolipid synthesis and metabolism are widely used, the advent of CRISPR-based genomic editing techniques allows for nuanced exploration into the manners in which sphingolipids influence various stages of viral infections. Here we describe some of these critical considerations needed in designing studies utilizing genomic editing techniques for manipulating the sphingolipid metabolic pathway, as well as the current body of literature regarding how viruses depend on the products of this pathway. Here, we highlight the ways in which sphingolipids affect viruses as these pathogens interact with and influence their host cell and describe some of the many open questions remaining in the field.

Multiplex quadruple bioluminescent assay system

Bioluminescence (BL) is unique cold body radiation of light, generated by luciferin-luciferase reactions and commonly used in various bioassays and molecular imaging. However, most of the peak emissions of BL populate the blue-yellow region and have broad spectral bandwidths and thus superimpose each other, causing optical cross-leakages in multiplex assays. This study synthesized a new series of coelenterazine (CTZ) analogues, named K-series, that selectively illuminates marine luciferases with unique, blue-shifted spectral properties. The optical property and specificity of the K-series CTZ analogues were characterized by marine luciferases, with K2 and K5 found to specifically luminesce with ALuc- and RLuc-series marine luciferases, respectively. The results confirmed that the luciferase specificity and color variation of the CTZ analogues minimize the cross-leakages of BL signals and enable high-throughput screening of specific ligands in the mixture. The specificity and color variation of the substrates were further tailored to marine luciferases (or single-chain bioluminescent probes) to create a multiplex quadruple assay system with four integrated, single-chain bioluminescent probes, with each probe designed to selectively luminesce only with its specific ligand (first authentication) and a specific CTZ analogue (second authentication). This unique multiplex quadruple bioluminescent assay system is an efficient optical platform for specific and high-throughput imaging of multiple optical markers in bioassays without optical cross-leakages.

Optimizing CRISPR/Cas9 Editing of Repetitive Single Nucleotide Variants

CRISPR/Cas9, base editors and prime editors comprise the contemporary genome editing toolbox. Many studies have optimized the use of CRISPR/Cas9, as the original CRISPR genome editing system, in substituting single nucleotides by homology directed repair (HDR), although this remains challenging. Studies describing modifications that improve editing efficiency fall short of isolating clonal cell lines or have not been validated for challenging loci or cell models. We present data from 95 transfections using a colony forming and an immortalized cell line comparing the effect on editing efficiency of donor template modifications, concentration of components, HDR enhancing agents and cold shock. We found that in silico predictions of guide RNA efficiency correlated poorly withactivity in cells. Using NGS and ddPCR we detected editing efficiencies of 5–12% in the transfected populations which fell to 1% on clonal cell line isolation. Our data demonstrate the variability of CRISPR efficiency by cell model, target locus and other factors. Successful genome editing requires a comparison of systems and modifications to develop the optimal protocol for the cell model and locus. We describe the steps in this process in a flowchart for those embarking on genome editing using any system and incorporate validated HDR-boosting modifications for those using CRISPR/Cas9.

Flow cytometric single cell-based assay to simultaneously detect cell death, cell cycling, DNA content and cell senescence

Cell death, cell cycle arrest and cellular senescence are three distinct cellular responses that can be induced by oncogene activation and diverse anti-cancer agents, and this often requires the action of the tumour suppressor TP53. Within a cell population, or even within an individual cell, these processes are not necessarily mutually exclusive. It is therefore important to measure all these processes simultaneously. However, current assays generally visualise only one or at best two responses, often only detecting the dominant one. Here, we present a novel flow cytometric assay that allows simultaneous assessment of cell viability and cell cycling through measurement of DNA content and DNA synthesis, and markers of cell senescence at the single cell level. We demonstrate that this assay can be performed on both human and murine cells, that are either cancerous or non-transformed, and can help to dissect complex cell fate decisions. We believe that this experimental tool will be useful for the study of diverse biological processes.

Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response

Three-quarters of compounds that enter clinical trials fail to make it to market due to safety or efficacy concerns. This statistic strongly suggests a need for better screening methods that result in improved translatability of compounds during the preclinical testing period. Patient-derived organoids have been touted as a promising 3D preclinical model system to impact the drug discovery pipeline, particularly in oncology. However, assessing drug efficacy in such models poses its own set of challenges, and traditional cell viability readouts fail to leverage some of the advantages that the organoid systems provide. Consequently, phenotypically evaluating complex 3D cell culture models remains difficult due to intra- and inter-patient organoid size differences, cellular heterogeneities, and temporal response dynamics. Here, we present an image-based high-content assay that provides object level information on 3D patient-derived tumor organoids without the need for vital dyes. Leveraging computer vision, we segment and define organoids as independent regions of interest and obtain morphometric and textural information per organoid. By acquiring brightfield images at different timepoints in a robust, non-destructive manner, we can track the dynamic response of individual organoids to various drugs. Furthermore, to simplify the analysis of the resulting large, complex data files, we developed a web-based data visualization tool, the Organoizer, that is available for public use. Our work demonstrates the feasibility and utility of using imaging, computer vision and machine learning to determine the vital status of individual patient-derived organoids without relying upon vital dyes, thus taking advantage of the characteristics offered by this preclinical model system.

Multiplex Hextuple Luciferase Assaying

We recently expanded the commonly used dual luciferase assaying method toward multiplex hextuple luciferase assaying, allowing monitoring the activity of five experimental pathways against one control at the same time. In doing so, while our expanded assay utilizes a total of six orthogonal luciferases instead of two, this assay, conveniently, still utilizes the well-established reagents and principles of the widely used dual luciferase assay. Three quenchable D-luciferin-consuming luciferases are measured after addition of D-Luciferin substrate, followed by quenching of their bioluminescence (BL) and the measurement of three coelenterazine (CTZ)-consuming luciferases after addition of CTZ substrate, all in the same vessel. Here, we provide detailed protocols on how to perform such multiplex hextuple luciferase assaying to monitor cellular signal processing upstream of five transcription factors and their corresponding transcription factor-binding motifs, using a constitutive promoter as normalization control. The first protocol is provided on how to perform cell culture in preparation toward genetic or pharmaceutical perturbations, as well as transfecting a multiplex hextuple luciferase reporter vector encoding all luciferase reporter units needed for multiplex hextuple luciferase assaying. The second protocol details on how to execute multiplex hextuple luciferase assaying using a microplate reader appropriately equipped to detect the different BLs emitted by all six luciferases. Finally, the third protocol provides details on analyzing, plotting, and interpreting the data obtained by the microplate reader.

From imaging a single cell to implementing precision medicine: an exciting new era

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

Assessing the antiproliferative effect of biogenic silver chloride nanoparticles on glioblastoma cell lines by quantitative image-based analysis

Glioblastoma is the most life‐threatening tumour of the central nervous system. Temozolomide (TMZ) is the first‐choice oral drug for the treatment of glioblastoma, although it shows low efficacy. Silver nanoparticles (AgNPs) have been shown to exhibit biocidal activity in a variety of microorganisms, including some pathogenic microorganisms. Herein, the antiproliferative effect of AgCl‐NPs on glioblastoma cell lines (GBM02 and GBM11) and on astrocytes was evaluated through automated quantitative image‐based analysis (HCA) of the cells. The cells were treated with 0.1‐5.0 μg/ml AgCl‐NPs or with 9.7‐48.5 μg/ml TMZ. Cells that received combined treatment were also analysed. At a maximum tested concentration of AgCl‐NPs, GBM02 and GBM11, the growth decreased by 93% and 40%, respectively, following 72 h of treatment. TMZ treatment decreased the proliferation of GBM02 and GBM11 cells by 58% and 34%, respectively. Combinations of AgCl‐NPs and TMZ showed intermediate antiproliferative effects; the lowest concentrations caused an inhibition similar to that obtained with TMZ, and the highest concentrations caused inhibition similar to that obtained with AgCl‐NPs alone. No significant changes in astrocyte proliferation were observed. The authors’ findings showed that HCA is a fast and reliable approach that can be used to evaluate the antiproliferative effect of the nanoparticles at the single‐cell level and that AgCl‐NPs are promising agents for glioblastoma treatment.

Organoids in image-based phenotypic chemical screens

Image-based phenotypic screening relies on the extraction of multivariate information from cells cultured under a large variety of conditions. Technical advances in high-throughput microscopy enable screening in increasingly complex and biologically relevant model systems. To this end, organoids hold great potential for high-content screening because they recapitulate many aspects of parent tissues and can be derived from patient material. However, screening is substantially more difficult in organoids than in classical cell lines from both technical and analytical standpoints. In this review, we present an overview of studies employing organoids for screening applications. We discuss the promises and challenges of small-molecule treatments in organoids and give practical advice on designing, running, and analyzing high-content organoid-based phenotypic screens.

Apoptosis assessment in high-content and high-throughput screening assays

Here the authors describe the development of AUTOptosis, an economical and rapid apoptosis monitoring method suitable for high-content and high-throughput screening assays. AUTOptosis is based on the quantification of nuclei intensity via staining with Hoechst 33342. First, the authors calibrated the method using standard apoptosis inducers in multiple cell lines. Next, the authors validated the applicability of this approach to high-content screening using a small library of compounds and compared it with the terminal deoxynucleotidyl transferase dUTP nick end labeling gold standard. Finally, the authors demonstrated the specificity of the method by using AUTOposis to detect apoptosis triggered by Mycobacterium tuberculosis intracellular infections.

High-throughput microscopy reveals the impact of multifactorial environmental perturbations on colorectal cancer cell growth

BACKGROUND

Colorectal cancer (CRC) mortality is principally due to metastatic disease, with the most frequent organ of metastasis being the liver. Biochemical and mechanical factors residing in the tumor microenvironment are considered to play a pivotal role in metastatic growth and response to therapy. However, it is difficult to study the tumor microenvironment systematically owing to a lack of fully controlled model systems that can be investigated in rigorous detail.

RESULTS

We present a quantitative imaging dataset of CRC cell growth dynamics influenced by in vivo-mimicking conditions. They consist of tumor cells grown in various biochemical and biomechanical microenvironmental contexts. These contexts include varying oxygen and drug concentrations, and growth on conventional stiff plastic, softer matrices, and bioengineered acellular liver extracellular matrix. Growth rate analyses under these conditions were performed via the cell phenotype digitizer (CellPD).

CONCLUSIONS

Our data indicate that the growth of highly aggressive HCT116 cells is affected by oxygen, substrate stiffness, and liver extracellular matrix. In addition, hypoxia has a protective effect against oxaliplatin-induced cytotoxicity on plastic and liver extracellular matrix. This expansive dataset of CRC cell growth measurements under in situ relevant environmental perturbations provides insights into critical tumor microenvironment features contributing to metastatic seeding and tumor growth. Such insights are essential to dynamical modeling and understanding the multicellular tumor-stroma dynamics that contribute to metastatic colonization. It also establishes a benchmark dataset for training and testing data-driven dynamical models of cancer cell lines and therapeutic response in a variety of microenvironmental conditions.

The ATPase ATP6V1A facilitates rabies virus replication by promoting virion uncoating and interacting with the viral matrix protein

Rabies virus (RABV) matrix protein (M) plays crucial roles in viral transcription, replication, assembly, and budding; however, its function during the early stage of virus replication remains unknown. Here, we mapped the protein interactome between RABV M and human host factors using a proteomic approach, finding a link to the V-type proton ATPase catalytic subunit A (ATP6V1A), which is located in the endosomes where RABV first enters. By downregulating or upregulating ATP6V1A expression in HEK293T cells, we found that ATP6V1A facilitated RABV replication. We further found that ATP6V1A was involved in the dissociation of incoming viral M proteins during viral uncoating. Coimmunoprecipitation demonstrated that M interacted with the full length or middle domain of ATP6V1A, which was dependent on the lysine residue at position 256 and the glutamic acid residue at position 279. RABV growth and uncoating in ATP6V1A-depleted cells was restored by trans-complementation with the full length or interaction domain of ATP6V1A. Moreover, stably overexpressed ATP6V1A enhanced RABV growth in Vero cells, which are used for the production of rabies vaccine. Our findings identify a new partner for RABV M proteins and establish a new role of ATP6V1A by promoting virion uncoating during RABV replication.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

Simultaneous Examination of Cellular Pathways using Multiplex Hextuple Luciferase Assaying

Multiplex experimentation that can assay multiple cellular signaling pathways in the same cells requires orthogonal genetically encoded reporters that report over large dynamic ranges. Luciferases are cost-effective, versatile candidates whose output signals can be sensitively detected in a multiplex fashion. Commonly used dual luciferase reporter assays detect one luciferase that is coupled to a single cellular pathway and a second that is coupled to a control pathway for normalization purposes. We have expanded this approach to multiplex hextuple luciferase assays that can report on five cellular signaling pathways and one control, each of which is encoded by a unique luciferase. Light emission by the six luciferases can be distinguished by the use of two distinct substrates, each specific for three luciferases, followed by spectral decomposition of the light emitted by each of the three luciferase enzymes with bandpass filters. Here, we present detailed protocols on how to perform multiplex hextuple luciferase assaying to monitor pathway fluxes through transcriptional response elements for five specific signaling pathways (i.e., c-Myc, NF-κβ, TGF-β, p53, and MAPK/JNK) using the constitutive CMV promoter as normalization control. Protocols are provided for preparing reporter vector plasmids for multiplex reporter assaying, performing cell culture and multiplex luciferase reporter vector plasmid transfection, executing multiplex luciferase assays, and analyzing and interpreting data obtained by a plate reader appropriately equipped to detect the different luminescences. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Preparation of vectors for multiplex hextuple luciferase assaying Basic Protocol 2: Cell culture work for multiplex hextuple luciferase assays Basic Protocol 3: Transfection of luciferase reporter plasmids followed by drug and recombinant protein treatments Basic Protocol 4: Performing the multiplex hextuple luciferase assay.

Anti-EGFR Therapy Induces EGF Secretion by Cancer-Associated Fibroblasts to Confer Colorectal Cancer Chemoresistance

Targeted agents have improved the efficacy of chemotherapy for cancer patients, however, there remains a lack of understanding of how these therapies affect the unsuspecting bystanders of the stromal microenvironment. Cetuximab, a monoclonal antibody therapy targeting the epidermal growth factor receptor (EGFR), is given in combination with chemotherapy as the standard of care for a subset of metastatic colorectal cancer patients. The overall response to this treatment is underwhelming and, while genetic mutations that confer resistance have been identified, it is still not known why this drug is ineffective for some patients. We discovered that cancer-associated fibroblasts (CAFs), a major cellular subset of the tumor stroma, can provide a source of cancer cell resistance. Specifically, we observed that upon treatment with cetuximab, CAFs increased their secretion of EGF, which was sufficient to render neighboring cancer cells resistant to cetuximab treatment through sustained mitogen-activated protein kinases (MAPK) signaling. Furthermore, we show the cetuximab-induced EGF secretion to be specific to CAFs and not to cancer cells or normal fibroblasts. Altogether, this work emphasizes the importance of the tumor microenvironment and considering the potential unintended consequences of therapeutically targeting cancer-driving proteins on non-tumorigenic cell types.

Monitoring cell endocytosis of liposomes by real-time electrical impedance spectroscopy

Evaluation and understanding the effect of drug delivery in in vitro systems is fundamental in drug discovery. We present an assay based on real-time electrical impedance spectroscopy (EIS) measurements that can be used to follow the internalisation and cytotoxic effect of a matrix metalloproteinase (MMP)-sensitive liposome formulation loaded with oxaliplatin (OxPt) on colorectal cancer cells. The EIS response identified two different cellular processes: (i) a negative peak in the cell index (CI) within the first 5 h, due to onset of liposome endocytosis, followed by (ii) a subsequent CI increase, due to the reattachment of cells until the onset of cytotoxicity with a decrease in CI. Free OxPt or OxPt-loaded Stealth liposomes did not show this two-stage EIS response; the latter can be due to the fact that Stealth cannot be cleaved by MMPs and thus is not taken up by the cells. Real-time bright-field imaging supported the EIS data, showing variations in cell adherence and cell morphology after exposure to the different liposome formulations. A drastic decrease in cell coverage as well as rounding up of cells during the first 5 h of exposure to OxPt-loaded (MMP)-sensitive liposome formulation is reflected by the first negative EIS response, which indicates the onset of liposome endocytosis. Graphical abstract.

A Perspective on Expanding Our Understanding of Cancer Treatments by Integrating Approaches from the Biological and Physical Sciences

Multicellular systems such as cancer suffer from immense complexity. It is imperative to capture the heterogeneity of these systems across scales to achieve a deeper understanding of the underlying biology and develop effective treatment strategies. In this perspective article, we will discuss how recent technologies and approaches from the biological and physical sciences have transformed traditional ways of measuring, interpreting, and treating cancer. During the SLAS 2019 Annual Meeting, SBI² hosted a Special Interest Group (SIG) on this topic. Academic and industry leaders engaged in discussions surrounding what biological model systems are appropriate to study cancer complexity, what assays are necessary to interrogate this complexity, and how physical sciences approaches may be useful to detangle this complexity. In particular, we examined the utility of mathematical models in predicting cancer progression and treatment response when tightly integrated with reproducible, quantitative, and dynamic biological measurements achieved using high-content imaging and analysis. The dialogue centered around the impetus for convergent biosciences, bringing new perspectives to cancer research to further understand this complex adaptive system and successfully intervene therapeutically.

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.

Examining multiple cellular pathways at once using multiplex hextuple luciferase assaying

Sensitive simultaneous assessment of multiple signaling pathways within the same cells requires orthogonal reporters that can assay over large dynamic ranges. Luciferases are such genetically encoded candidates due to their sensitivity, versatility, and cost-effectiveness. We expand luciferase multiplexing in post-lysis endpoint luciferase assays from two to six. Light emissions are distinguished by a combination of distinct substrates and emission spectra deconvolution. All six luciferase reporter units are stitched together into one plasmid facilitating delivery of all reporter units through a process we termed solotransfection, minimizing experimental errors. We engineer a multiplex hextuple luciferase assay to probe pathway fluxes through five transcriptional response elements against a control constitutive promoter. We can monitor effects of siRNA, ligand, and chemical compound treatments on their target pathways along with the four other probed cellular pathways. We demonstrate the effectiveness and adaptiveness of multiplex luciferase assaying, and its broad application across different research fields.

Multiplexed detection of luciferase-based sensors in the same sample is challenging and limited by the substrates’ emission spectra. Here the authors establish a system based on three different luciferases and sequential detection to achieve measurements of up to six parameters within the same experiment.

Estimating Bacterial Concentrations in Fibrous Substrates Through a Combination of Scanning Electron Microscopy and ImageJ

Conventional signal-based microanalytical techniques for estimating bacterial concentrations are often susceptible to false signals. A visual quantification, therefore, may compliment such techniques by providing additional information and support better management decisions in the event of outbreaks. Herein, we explore a method that combines electron microscopy (EM) and image-analysis techniques and allows both visualization and quantification of pathogenic bacteria adherent even to complex nonuniform substrates. Both the estimation and imaging parameters were optimized to reduce the estimation error ( E, %) to close to ±5%. The method was validated against conventional microbiological techniques such as the use of optical density, flow cytometry, and quantitative real-time PCR (qPCR). It could easily be tailored to estimate different species of pathogens, such as Escherichia coli O157, Listeria innocua, Staphylococcus aureus, Enterococcus faecalis, and Bacillus anthracis, on samples similar to those in real-time contamination scenarios. The present method is sensitive enough to detect ∼100 bacterial CFU/mL but has the potential to estimate even lower concentrations with increased imaging and computation times. Overall, this imaging-based method may greatly complement any signal-based pathogen-detection technique, especially in negating false signals, and therefore may significantly contribute to the field of analytical microbiology and biochemistry.

Long-term fluorescence hyperspectral imaging of on-chip treated co-culture tumour spheroids to follow clonal evolution

Multicellular tumour spheroids are an ideal in vitro tumour model to study clonal heterogeneity and drug resistance in cancer research because different cell types can be mixed at will. However, measuring the individual response of each cell population over time is challenging: current methods are either destructive, such as flow cytometry, or cannot image throughout a spheroid, such as confocal microscopy. Our group previously developed a wide-field fluorescence hyperspectral imaging system to study spheroids formed and cultured in microfluidic chips. In the present study, two subclones of a single parental ovarian cancer cell line transfected to express different fluorophores were produced and co-culture spheroids were formed on-chip using ratios forming highly asymmetric subpopulations. We performed a 3D proliferation assay on each cell population forming the spheroids that matched the 2D growth behaviour. Response assays to PARP inhibitors and platinum-based drugs were also performed to follow the clonal evolution of mixed populations. Our experiments show that hyperspectral imaging can detect spheroid response before observing a decrease in spheroid diameter. Hyperspectral imaging and microfluidic-based spheroid assays provide a versatile solution to study clonal heterogeneity, able to measure response in subpopulations presenting as little as 10% of the initial spheroid.

Identification of pharmacological inhibitors of conventional protein secretion

The retention using selective hooks (RUSH) system allows to withhold a fluorescent biosensor such as green fluorescent protein (GFP) fused to a streptavidin-binding peptide (SBP) by an excess of streptavidin molecules that are addressed to different subcellular localizations. Addition of biotin competitively disrupts this interaction, liberating the biosensor from its hook. We constructed a human cell line co-expressing soluble secretory-SBP-GFP (ss-SBP-GFP) and streptavidin within the endoplasmic reticulum (ER) lumen and then used this system to screen a compound library for inhibitors of the biotin-induced release of ss-SBP-GFP via the conventional Golgi-dependent protein secretion pathway into the culture supernatant. We identified and validated a series of molecularly unrelated drugs including antianginal, antidepressant, anthelmintic, antipsychotic, antiprotozoal and immunosuppressive agents that inhibit protein secretion. These compounds vary in their capacity to suppress protein synthesis and to compromise ER morphology and Golgi integrity, as well as in the degree of reversibility of such effects. In sum, we demonstrate the feasibility and utility of a novel RUSH-based phenotypic screening assay.

mTORC2 contributes to the metabolic reprogramming in EGFR tyrosine-kinase inhibitor resistant cells in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) patients with activating EGFR mutations are often successfully treated with EGFR tyrosine kinase inhibitor (TKI) such as erlotinib; however, treatment resistance inevitably occurs. Given tumor metabolism of glucose and therapeutic response are intimately linked, we explored the metabolic differences between isogenic erlotinib-sensitive and -resistant NSCLC cell lines. We discovered that the growth of erlotinib-resistant cells is more sensitive to glucose deprivation. Seahorse metabolic assay revealed erlotinib-resistant cells have lower spare respiratory capacity (SRC), an indicator of metabolic flexibility, compared to erlotinib-sensitive cells. Additionally, we found downstream components of mTORC2 signaling to be phosphorylated in erlotinib-resistant cells. Knockdown of an mTORC2 component, Rictor, enhanced the SRC and rescued the growth rate of erlotinib-resistant cells during glucose deprivation. Among NSCLCs with activating EGFR mutations, gene sets involved in glucose metabolism were enriched in patients with high expression of p-NDGR1, a readout of mTORC2 activity. Furthermore, overall survival was negatively correlated with p-NDRG1. Our work uncovers a link between mTORC2 and metabolic reprogramming in EGFR TKI-resistant cells and highlights the significance of mTORC2 in the progression of EGFR-mutated NSCLC.

PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal "virtual laboratory" for such multicellular systems simulates both the biochemical microenvironment (the "stage") and many mechanically and biochemically interacting cells (the "players" upon the stage). PhysiCell-physics-based multicellular simulator-is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility "out of the box." The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a "cellular cargo delivery" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do not allow for accurate interpretation of this interplay. Using a quantitative imaging-based approach, we present a high-content protocol for characterizing the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations to changes in environmental stimuli. We highlight our ability to distinguish between cell types based upon either fluorescence intensity or inherent morphology features depending on the application. This platform allows for a more comprehensive characterization of subpopulation response to perturbation while utilizing shorter time, smaller amounts of reagents, and lower likelihood of error than traditional cell biology assays. However, in some cases, cell populations may be difficult to identify and quantitate based on complex cellular features and will require additional troubleshooting; we highlight some of these circumstances in the protocol. We demonstrate this application using response to drug in a cancer model; however, it can easily be applied more broadly to other physiological processes. This protocol allows one to identify subpopulations within a co-culture system and characterize the particular response of each to external stimuli.

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.

Quantifying differences in cell line population dynamics using CellPD

BACKGROUND

The increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing tools for quantitative analysis generally require expert knowledge.

RESULTS

CellPD (cell phenotype digitizer) facilitates quantitative phenotype analysis, allowing users to fit mathematical models of cell population dynamics without specialized training. CellPD requires one input (a spreadsheet) and generates multiple outputs including parameter estimation reports, high-quality plots, and minable XML files. We validated CellPD's estimates by comparing it with a previously published tool (cellGrowth) and with Microsoft Excel's built-in functions. CellPD correctly estimates the net growth rate of cell cultures and is more robust to data sparsity than cellGrowth. When we tested CellPD's usability, biologists (without training in computational modeling) ran CellPD correctly on sample data within 30 min. To demonstrate CellPD's ability to aid in the analysis of high throughput data, we created a synthetic high content screening (HCS) data set, where a simulated cell line is exposed to two hypothetical drug compounds at several doses. CellPD correctly estimates the drug-dependent birth, death, and net growth rates. Furthermore, CellPD's estimates quantify and distinguish between the cytostatic and cytotoxic effects of both drugs-analyses that cannot readily be performed with spreadsheet software such as Microsoft Excel or without specialized computational expertise and programming environments.

CONCLUSIONS

CellPD is an open source tool that can be used by scientists (with or without a background in computational or mathematical modeling) to quantify key aspects of cell phenotypes (such as cell cycle and death parameters). Early applications of CellPD may include drug effect quantification, functional analysis of gene knockout experiments, data quality control, minable big data generation, and integration of biological data with computational models.