High-throughput flow cytometry for drug discovery: principles, applications, and case studies

Quantum measurement enables single biomarker sensitivity in flow cytometry

We present the first unambiguous experimental method enabling single-fluorophore sensitivity in a flow cytometer using quantum properties of single-photon emitters. We use a quantum measurement based on the second-order coherence function to prove that the optical signal is produced by individual biomarkers traversing the interrogation volume of the flow cytometer from the first principles. This observation enables the use of the quantum toolbox for rapid detection, enumeration, and sorting of single fluorophores in large cell populations as well as a 'photons-to-moles' calibration of this measurement modality.

DNA framework signal amplification platform-based high-throughput systemic immune monitoring

Systemic immune monitoring is a crucial clinical tool for disease early diagnosis, prognosis and treatment planning by quantitative analysis of immune cells. However, conventional immune monitoring using flow cytometry faces huge challenges in large-scale sample testing, especially in mass health screenings, because of time-consuming, technical-sensitive and high-cost features. However, the lack of high-performance detection platforms hinders the development of high-throughput immune monitoring technology. To address this bottleneck, we constructed a generally applicable DNA framework signal amplification platform (DSAP) based on post-systematic evolution of ligands by exponential enrichment and DNA tetrahedral framework-structured probe design to achieve high-sensitive detection for diverse immune cells, including CD4+, CD8+ T-lymphocytes, and monocytes (down to 1/100 μl). Based on this advanced detection platform, we present a novel high-throughput immune-cell phenotyping system, DSAP, achieving 30-min one-step immune-cell phenotyping without cell washing and subset analysis and showing comparable accuracy with flow cytometry while significantly reducing detection time and cost. As a proof-of-concept, DSAP demonstrates excellent diagnostic accuracy in immunodeficiency staging for 107 HIV patients (AUC > 0.97) within 30 min, which can be applied in HIV infection monitoring and screening. Therefore, we initially introduced promising DSAP to achieve high-throughput immune monitoring and open robust routes for point-of-care device development.

AliGater: a framework for the development of bioinformatic pipelines for large-scale, high-dimensional cytometry data

Motivation

AliGater is an open-source framework to accelerate the development of bioinformatic pipelines for the analysis of large-scale, high-dimensional flow cytometry data. AliGater provides a Python package for automatic feature extraction workflows, as well as building blocks to construct analysis pipelines.

Results

We illustrate the use of AliGater in a high-resolution flow cytometry-based genome-wide association study on 46 immune cell populations in 14 288 individuals.

Availability and implementation

Source code and documentation at https://github.com/LudvigEk/aligater and https://aligater.readthedocs.io.

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress

Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are clinically relevant for diagnosis, risk stratification and targeted drug development have been generated. Recent studies highlight the potential of implementing genomic-based and phenotypic-based screens in clinics to improve survival in patients with refractory AML. In this review, we will discuss successful applications as well as challenges of most up-to-date high-throughput technologies, including artificial intelligence (AI) approaches, in the development of personalized medicine for AML, and recent clinical studies for evaluating the utility of integrating genomics-guided and drug sensitivity testing-guided treatment approaches for AML patients.

High-throughput screening for drug discovery targeting the cancer cell-microenvironment interactions in hematological cancers

INTRODUCTION

The interactions between leukemic blasts and cells within the bone marrow environment affect oncogenesis, cancer stem cell survival, as well as drug resistance in hematological cancers. The importance of this interaction is increasingly being recognized as a potentially important target for future drug discoveries and developments. Recent innovations in the high throughput drug screening-related technologies, novel ex-vivo disease-models, and freely available machine-learning algorithms are advancing the drug discovery process by targeting earlier undruggable proteins, complex pathways, as well as physical interactions (e.g. leukemic cell-bone microenvironment interaction).

AREA COVERED

In this review, the authors discuss the recent methodological advancements and existing challenges to target specialized hematopoietic niches within the bone marrow during leukemia and suggest how such methods can be used to identify drugs targeting leukemic cell-bone microenvironment interactions.

EXPERT OPINION

The recent development in cell-cell communication scoring technology and culture conditions can speed up the drug discovery by targeting the cell-microenvironment interaction. However, to accelerate this process, collecting clinical-relevant patient tissues, developing culture model systems, and implementing computational algorithms, especially trained to predict drugs and their combination targeting the cancer cell-bone microenvironment interaction are needed.

An overview of multiplexed analyses of CAR T-cell therapies: insights and potential

INTRODUCTION

Cancer immunotherapy is a rapidly growing field with exponential advancement in engineered immune cell-based therapies. For instance, an engineered chimeric antigen receptor (CAR) can be introduced in T-cells or other immune cells and adoptively transferred to target and kill cancer cells in hematologic malignancies or solid tumors. The first CAR-T-cell (CAR-T) therapy has been developed against CD19, a B-cell marker expressed on lymphoma and lymphoblastic leukemia. To allow for personalized treatment, proteomics approaches could provide insights into biomarkers for CAR-T therapy efficacy and toxicity.

AREAS COVERED

We researched the most recent technology methods of biomarker evaluation used in the laboratory and clinical setting. Publications of CAR-T biomarkers were then systematically reviewed to provide a narrative of the most validated biomarkers of CAR-T efficacy and toxicity. Examples of biomarkers include CAR-T functionality and phenotype as well as interleukin-6 and other cytokines.

EXPERT COMMENTARY

Biomarkers of CAR-T efficacy and toxicity have been identified, but still need to be validated and standardized across institutions. Moreover, few are used in the clinical setting due to limitations in real-time technology. Expansion of biomarker research could provide better understanding of patient response and risk of life-threatening side effects with potential for improved precision medicine.

The pursuit of precision mitochondrial medicine: Harnessing preclinical cellular and animal models to optimize mitochondrial disease therapeutic discovery

Mitochondria share extensive evolutionary conservation across nearly all living species. This homology allows robust insights to be gained into pathophysiologic mechanisms and therapeutic targets for the heterogeneous class of primary mitochondrial diseases (PMDs) through the study of diverse in vitro cellular and in vivo animal models. Dramatic advances in genetic technologies, ranging from RNA interference to achieve graded knock-down of gene expression to CRISPR/Cas-based gene editing that yields a stable gene knock-out or targeted mutation knock-in, have enabled the ready establishment of mitochondrial disease models for a plethora of individual nuclear gene disorders. These models are complemented and extended by the use of pharmacologic inhibitor-based stressors to characterize variable degrees, onset, duration, and combinations of acute on chronic mitochondrial dysfunction in individual respiratory chain enzyme complexes or distinct biochemical pathways within mitochondria. Herein is described the rationale for, and progress made in, "therapeutic cross-training," a novel approach meant to improve the validity and rigor of experimental conclusions when testing therapies by studying treatment effects in multiple, evolutionarily-distinct species, including Caenorhabditis elegans (invertebrate, worm), Danio rerio (vertebrate, zebrafish), Mus musculus (mammal, mouse), and/or human patient primary fibroblast cell line models of PMD. The goal of these preclinical studies is to identify lead therapies from candidate molecules or library screens that consistently demonstrate efficacy, with minimal toxicity, in specific subtypes of mitochondrial disease. Conservation of in vitro and in vivo therapeutic effects of lead molecules across species has proven extensive, where molar concentrations found to be toxic or efficacious in one species are often consistent with therapeutic effects at similar doses seen in other mitochondrial disease models. Phenotypic outcome studies in all models are prioritized at the level of survival and function, to reflect the ultimate goal of developing highly potent therapies for human mitochondrial disease. Lead compounds that demonstrate significant benefit on gross phenotypes may be further scrutinized in these same models to decipher their cellular targets, mechanism(s), and detailed biochemical effects. High-throughput, automated technologic advances will be discussed that enable efficient, parallel screening in a diverse array of mitochondrial disease disorders and overarching subclasses of compounds, concentrations, libraries, and combinations. Overall, this therapeutic cross-training approach has proven valuable to identify compounds with optimal potency and safety profiles among major biochemical subtypes or specific genetic etiologies of mitochondrial disease. This approach further supports rational prioritization of lead compounds, target concentrations, and specific disease phenotypes, outcomes, and subgroups to optimally inform the design of clinical trials that test their efficacy in human mitochondrial disease subjects.

Recent advances in high-throughput flow cytometry for drug discovery

INTRODUCTION

High-throughput flow cytometry (HTFC) has proven to be an important technology in drug discovery. The use of HTFC enables multi-parametric screening of suspension cells containing heterogenous cell populations and coated particles for screening proteins of interest. Novel targets, novel cell markers and compound clusters for drug development have been identified from HTFC screens.

AREAS COVERED

In this article, the authors focus on reviewing the recent HTFC applications reported during the last 5-6 years, including drug discovery screens and studies for immune, immune-oncology, infectious and inflammatory diseases. The main HTFC approaches, development of HTFC systems, and automated sample preparation systems for HTFC are also discussed.

EXPERT OPINION

The advance of HTFC technology coupled with automated sample acquisition and sample preparation has demonstrated its utility in screening large numbers of compounds using suspension cells, facilitated screening of disease-relevant human primary cells, and enabled deep understanding of mechanism of action by analyzing multiple parameters. The authors see HTFC as a very valuable tool in immune, immune-oncology, infectious and inflammatory diseases where immune cells play essential roles.

Development of classification models for predicting inhibition of mitochondrial fusion and fission using machine learning methods

Mitochondrial fusion and fission are processes to maintain mitochondrial function when cells respond to environment stresses. Disruption of mitochondrial fusion and fission influences cell health and can cause adverse events such as neurodegenerative disorders. It is critical to identify environmental chemicals that can disrupt mitochondrial fusion and fission. However, experimentally testing all the chemicals is not practical because experimental methods are time-consuming and costly. Quantitative structure-activity relationship (QSAR) modeling is an attractive approach for evaluation of chemicals disrupting potential on mitochondrial fusion and fission. In this study, QSAR models were developed for differentiating chemicals capable of inhibition of mitochondrial fusion and fission using machine learning algorithms (i.e. random forest, logistic regression, Bernoulli naive Bayes, and deep neural network). One hundred iterations of five-fold cross validations and external validations showed that the best model on mitochondrial fusion had area under the receiver operating characteristic curve (AUC) of 82.8% and 78.1%, respectively; and the best model for mitochondrial fission yielded AUC of 84.3% and 97.5%, respectively. Furthermore, 45 and 56 structural alerts were identified for inhibition of mitochondrial fusion and fission, respectively. The results demonstrated that the models and the structural alerts could be useful for screening chemicals that inhibit mitochondrial fusion and fission.

High-Throughput Screening: today's biochemical and cell-based approaches

High-throughput screening (HTS) provides starting chemical matter in the adventure of developing a new drug. In this review, we survey several HTS methods used today for hit identification, organized in two main flavors: biochemical and cell-based assays. Biochemical assays discussed include fluorescence polarization and anisotropy, FRET, TR-FRET, and fluorescence lifetime analysis. Binding-based methods are also surveyed, including NMR, SPR, mass spectrometry, and DSF. On the other hand, cell-based assays discussed include viability, reporter gene, second messenger, and high-throughput microscopy assays. We devote some emphasis to high-content screening, which is becoming very popular. An advisable stage after hit discovery using phenotypic screens is target deconvolution, and we provide an overview of current chemical proteomics, in silico, and chemical genetics tools. Emphasis is made on recent CRISPR/dCas-based screens. Lastly, we illustrate some of the considerations that inform the choice of HTS methods and point to some areas with potential interest for future research.

Identification of clinically approved small molecules that inhibit growth and affect transcript levels of developmentally regulated genes in the African trypanosome

Trypanosoma brucei are unicellular parasites endemic to Sub-Saharan Africa that cause fatal disease in humans and animals. Infection with these parasites is caused by the bite of the tsetse fly vector, and parasites living extracellularly in the blood of infected animals evade the host immune system through antigenic variation. Existing drugs for Human and Animal African Trypanosomiasis are difficult to administer and can have serious side effects. Resistance to some drugs is also increasing, creating an urgent need for alternative trypanosomiasis therapeutics. We screened a library of 1,585 U.S. or foreign-approved drugs and identified 154 compounds that inhibit trypanosome growth. As all of these compounds have already undergone testing for human toxicity, they represent good candidates for repurposing as trypanosome therapeutics. In addition to identifying drugs that inhibit trypanosome growth, we wished to identify small molecules that can induce bloodstream form parasites to differentiate into forms adapted for the insect vector. These insect stage parasites lack the immune evasion mechanisms prevalent in bloodstream forms, making them vulnerable to the host immune system. To identify drugs that increase transcript levels of an invariant, insect-stage specific surface protein called procyclin, we engineered bloodstream reporter parasites that express Green Fluorescent Protein (GFP) following induction or stabilization of the procyclin transcript. Using these bloodstream reporter strains in combination with automated flow cytometry, we identified eflornithine, spironolactone, and phenothiazine as small molecules that increase abundance of procyclin transcript. Both eflornithine and spironolactone also affect transcript levels for a subset of differentiation associated genes. While we failed to identify compounds that increase levels of procyclin protein on the cell surface, this study is proof of principle that these fluorescent reporter parasites represent a useful tool for future small molecule or genetic screens aimed at identifying molecules or processes that initiate remodeling of the parasite surface during life cycle stage transitions.

The Untapped Opportunity and Challenge of Immunometabolism: A New Paradigm for Drug Discovery

Here, we explore the manipulation of immune cell metabolism as a strategy in target discovery and drug development for immune-mediated diseases. Comparing exploitation of metabolic pathways to kill tumor cells for cancer treatment with the reprogramming of immune cells to treat autoimmune diseases highlights differences that confer several advantages to the latter (including a more favorable therapeutic index and greater target stability). We discuss technological capabilities and gaps, including the challenge of relating in vitro observations to in vivo biology. Finally, we conclude by identifying future opportunities that will move the field forward and accelerate drug discovery.

American tegumentary leishmaniasis diagnosis using L. (V.) braziliensis fixed promastigotes: a comparative performance of serological tests and spontaneous cure identification

BACKGROUND

The present study aimed to demonstrate the applicability of a flow cytometry-based serology approach to identify spontaneous cure by the detection of immunoglobulin G, and also, the diagnosis and cure criterion by the IgG1 isotype in American Tegumentary Leishmaniasis - ATL caused by L. (V.) braziliensis. Also, a comparison between flow cytometry with the serological conventional technique was performed.

METHODS

Forty five individuals were included in study. They were assessed in two moments: First, 8 subjects spontaneously cured of ATL, 8 healthy individuals and 15 patients who had a positive diagnosis for ATL were selected before treatment to identify spontaneous cure by immunoglobulin G detection. Secondly, 14 patients who were positive for ATL were selected and had their blood collected before and 1, 2 and 5 years after treatment, respectively, for the diagnostic tests (ELISA and flow cytometry) and cure criterion evaluation using the IgG1 isotype.

RESULTS

The analysis of the mean percentage of positive fluorescent parasites (PPFP) along with the titration curves of IgG anti-fixed promastigotes of L.(V.)braziliensis, confirmed the applicability of this method for monitoring spontaneous cure in ATL with outstanding co-positivity (100%) and co-negativity (100%) performance indexes. Regarding the results of the comparison between flow cytometry and ELISA it was seen that there was a better accuracy of the first one in relation to the other. When IgG1 applicability was evaluated, it was observed that before treatment, 36.8% of the patients were negative; in patients 1 year post-treatment, 82.3%; 2 years post-treatment, 27.2% and in patients 5 years post-treatment, 87.5%. The overall analysis of the results suggests that flow cytometry can be applied to ATL detection, and that the use of IgG1 isotype has possibilities to contribute as a more specific diagnostic method.

CONCLUSIONS

Therefore, this area has great perspectives use for the diagnosis and cure criterion, and also it can be scaled up with the possibility to characterize the different clinical stages of the disease. Together, these findings demonstrate the applicability of a flow cytometry-based serology approach and opens up new avenues of research with this technique, such as the understanding the humoral response in ATL patients.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Quantification of BCL-2 Family Members by Flow Cytometry

Flow cytometry is a powerful technique for the detection and quantification of cell surface and intracellular proteins. It enables the ability to measure the expression levels of specific proteins in a cell population of interest without the need to physically separate out the cells from within a heterogeneous population by using the appropriate cell-specific markers. It also requires fewer cells than other traditional techniques such as Western blotting. Here we describe a robust and reproducible method to measure the expression levels of the BCL-2 family members, BCL-2, BCL-X_L, and MCL-1 by quantitative flow cytometry (QFCM) using validated antibodies.

Determination of an accurate size distribution of nanoparticles using particle tracking analysis corrected for the adverse effect of random Brownian motion

The particle tracking analysis (PTA) method has been widely used to determine the size of nanoparticles from their Brownian motion, using the Stokes-Einstein assumption. However, the size distribution obtained by PTA is broader than the true distribution because of the uncertainties in determining the diffusion coefficients, and a correction of such a broadening effect is essential to obtain reliable results. In order to transform the apparent broadened size distribution from the PTA method to the true size distribution, we begin by approximating the true size distribution as a gamma distribution determined by a shape parameter and a scale parameter, and then we perform a hybrid analysis based on the maximum likelihood estimation and Bayesian parameter inference that accounts for the uncertainties in determining the diffusion coefficients. To examine the accuracy of our analysis, we compared the size distributions of polystyrene-latex spherical nanoparticles obtained by this transformation process, dynamic light scattering, and asymmetric flow field-flow fractionation with multi-angle light scattering (AF4-MALS). The transformed size distribution resulting from applying our Brownian motion correction to apparent PTA data agrees well with that obtained from AF4-MALS, indicating the success of the correction in obtaining the true nanoparticle size distribution.

Automated High-Throughput Flow Cytometry for High-Content Screening in Antibody Development

The tedious sample preparation for flow cytometry limits the throughput and thus its usage as a primary screening method despite its sensitivity and accuracy. With the growing focus on utilizing antibodies as a therapeutic modality in drug discovery, it is critical to develop a high-throughput flow cytometry (HTFC) workflow to cope with the increasing need to support antibody discovery programs. We have developed a seamless HTFC sample preparation and readout workflow using the HighRes modular robotic system and the IntelliCyt iQue Screener PLUS. To fully utilize the advantages offered by flow cytometry, we typically multiplex multiple cell lines of interest in one well to simultaneously quantitate on-target activity and nonspecific activity along with measurement of antibody concentration. The ability to measure multiple parameters coupled with speed and increased accuracy provides gains in productivity and helps speed up antibody lead discovery.

Miniaturized High-Throughput Multiparameter Flow Cytometry Assays Measuring In Vitro Human Dendritic Cell Maturation and T-Cell Activation in Mixed Lymphocyte Reactions

Enhancing antitumor activities of the human immune system is a clinically proven approach with the advent of monoclonal antibodies recognizing programmed cell death protein-1 (PD1) receptors on immune cell surfaces. Historically, using flow cytometry as a means to assess next-generation agent activities was underused, largely due to limits on cell number and assay sensitivity. Here, we leveraged an IntelliCyt high-throughput flow cytometry platform to monitor human dendritic cell maturation and lymphocyte proliferation in mixed lymphocyte reactions. Specifically, we established flow cytometry-based immunophenotyping and screening methodologies capable of measuring T-cell activation as a result of cell-associated antigens presented on dendritic cell surfaces, as indicated by cell proliferation, cytokine secretion, and surface marker expression. Together, the overall novelty of this 384-well platform is its capability to measure multiple functional readouts in one well and consistently evaluate large numbers of compounds in a single study, as well as its ability to show increased assay sensitivity requiring considerably fewer primary cells and less reagents compared to more traditional 96-well flow cytometry methods.

Expediting Antibody Discovery with a Cell and Bead Multiplexed Competition Assay

With advances in molecular engineering and humanization, monoclonal antibodies are one of the fastest-growing classes of biopharmaceuticals. During antibody discovery, antibody from hybridoma or primary B-cell supernatants is screened for the desired binding characteristics, and secondary screens measure antibody function and concentration, identify immunoglobulin G (IgG) isotype, and assess cell health. In order to expedite the antibody discovery process, we developed a high-throughput, multiplexed cell and bead-based competition assay that identifies and quantitates mouse IgG isotypes and assesses cell health. No differences in assay performance were observed between single and multiplex formats. The linear range of the assay was from 0.5 to 50 µg/mL, and washing was not required, decreasing assay time and variability. Slight modifications to the protocol allowed quantification of dilute antibody supernatants (0.1–5 µg/mL). Using hybridoma cultures, we showed that cell viability measurements in the assay did not interfere with the bead-based IgG measurements. The assay described here is a simple mix-and-read, no-dilution screen that can reduce the time to antibody cloning and production. The high-content data can differentiate monoclonal and polyclonal wells, determine IgG quantity for downstream functional assays, provide isotype information, and monitor cell proliferation and viability.

A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery

The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery. Over the past 5 years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.

Application of High-Throughput Flow Cytometry in Early Drug Discovery: An AstraZeneca Perspective

Flow cytometry is a powerful tool providing multiparametric analysis of single cells or particles. The introduction of faster plate-based sampling technologies on flow cytometers has transformed the technology into one that has become attractive for higher throughput drug discovery screening. This article describes AstraZeneca's perspectives on the deployment and application of high-throughput flow cytometry (HTFC) platforms for small-molecule high-throughput screening (HTS), structure-activity relationship (SAR) and phenotypic screening, and antibody screening. We describe the overarching HTFC workflow, including the associated automation and data analysis, along with a high-level overview of our HTFC assay portfolio. We go on to discuss the practical challenges encountered and solutions adopted in the course of our deployment of HTFC, as well as future enhancements and expansion of the technology to new areas of drug discovery.

A Scalable Pipeline for High-Throughput Flow Cytometry

Flow cytometry (FC) provides high-content data for a variety of applications, including phenotypic analysis of cell surface and intracellular markers, characterization of cell supernatant or lysates, and gene expression analysis. Historically, sample preparation, acquisition, and analysis have presented as a bottleneck for running such types of assays at scale. This article will outline the solutions that have been implemented at Novartis which have allowed high-throughput FC to be successfully conducted and analyzed for a variety of cell-based assays. While these experiments were generally conducted to measure phenotypic responses from a well-characterized and information-rich small molecular probe library known as the Mechanism-of-Action (MoA) Box, they are broadly applicable to any type of test sample. The article focuses on application of automated methods for FC sample preparation in 384-well assay plates. It also highlights a pipeline for analyzing large volumes of FC data, covering a visualization approach that facilitates review of screen-level data by dynamically embedding FlowJo (FJ) workspace images for each sample into a Spotfire file, directly linking them to the metric being observed. Finally, an application of these methods to a screen for MHC-I expression upregulators is discussed.

Comparing Flow Cytometry QBeads PlexScreen Assays with Other Immunoassays for Determining Multiple Analytes

Immunoassays, utilizing the affinity of antibodies to their antigens, are powerful techniques and have been widely used for quantifying analytes, such as cytokines, in biological samples in the clinic and in drug discovery. Various immunoassays have been developed to fit for different purposes. Recently, bead-based flow cytometry assays have emerged as interesting options for multiplex quantification of analytes. In this study, we compared high-throughput flow cytometry multiplex iQue QBeads PlexScreen assays with several other commonly used immunoassays, including MSD, Luminex, ELISA, HTRF, and AlphaLISA assays. Head-to-head comparisons of quantification data of the following cytokines were made: (1) IL-2, IL-4, IL-6, IL-13, IL-17A, IFNγ, KC/GRO, RANTES, and TNFα in mouse bronchoalveolar lavage fluid samples; (2) IL-10 and TNFα in supernatants from a THP-1 cell assay; (3) IL-6, IL-10, IL-12p70, and TNFα in supernatants from a human monocyte-derived dendritic cell assay; and (4) IL-2 in supernatants from a human CD4+ cell assay. The results demonstrated a good assay correlation between the iQue and the compared assays for the cytokine studied. Although overall good assay correlations were observed, our results showed that the iQue assay generated different absolute cytokine values for some cytokines in the same sample sets compared with other assays.