High-Dimensional Fluorescence Cytometry

Combinatorial antibody titrations for high-parameter flow cytometry

The objective of titrating fluorochrome-labeled antibodies is to identify the optimal concentration for a given marker-fluorochrome pair that results in the best possible separation between the positive and negative cell populations, while minimizing the background within the negative population. Best practices in flow cytometry dictate that each new lot of antibody should be titrated on the sample of interest. However, many researchers routinely use large (30+) color panels due to recent technical advancements in fluorescence-based cytometry instrumentation which quickly leads to an unmanageable number of individual titrations. In this technical note, we provide evidence that antibodies can be effectively titrated in groups rather than individually, resulting in considerable time and cost savings. This approach streamlines the process, without compromising data quality, thereby enhancing the efficiency of setting up high-parameter cytometry experiments.

Panel Design and Optimization for Full Spectrum Flow Cytometry

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large flow cytometry panels, reaching up to 40 markers at the single-cell level. Full spectrum flow cytometry, which measures the full emission range of all the fluorophores present in the panel instead of only the emission peaks, is now routinely used in laboratories around the world, and the demand for this technology is rapidly increasing. With the ability to use larger and more complex staining panels, optimized protocols are vital for achieving the best panel design, panel optimization, and high-dimensional data analysis outcomes. In addition, a better understanding of how to fully characterize the autofluorescence of the sample, coupled with an intelligent panel design approach, allows improved marker resolution on highly autofluorescent tissues or cells. Here, we provide optimized step-by-step protocols for full spectrum flow cytometry, covering panel design and optimization, autofluorescence evaluation and strategy selection, and methods for performing longitudinal studies.

Extensive flow cytometric immunophenotyping of human PBMC incorporating detection of chemokine receptors, cytokines and tetramers

Characterization of immune cells is essential to advance our understanding of immunology and flow cytometry is an important tool in this context. Addressing both cellular phenotype and antigen-specific functional responses of the same cells is valuable to achieve a more integrated understanding of immune cell behavior and maximizes information obtained from precious samples. Until recently, panel size was limiting, resulting in panels generally focused on either deep immunophenotyping or functional readouts. Ongoing developments in the field of (spectral) flow cytometry have made panels of 30⁺ markers more accessible, opening up possibilities for advanced integrated analyses. Here, we optimized immune phenotyping by co-detection of markers covering chemokine receptors, cytokines and specific T cell/peptide tetramer interaction using a 32-color panel. Such panels enable integrated analysis of cellular phenotypes and markers assessing the quality of immune responses and will contribute to our understanding of the immune system.

Platelet Phenotyping by Full Spectrum Flow Cytometry

Platelets play key roles in hemostasis, immunity, and inflammation, and tests of platelet phenotype and function are useful in studies of disease biology and pathology. Full spectrum flow cytometry offers distinct advantages over standard tests and enables the sensitive and simultaneous detection of many biomarkers. A typical assay provides a wealth of information on platelet biology and allows the assessment of in vivo activation and in vitro reactivity, as well as the discovery of novel phenotypes. Here, we describe the analysis of platelets by full spectrum flow cytometry and discuss a range of controls and methods for interpreting results. © 2023 Wiley Periodicals LLC. Basic Protocol: Platelet phenotyping by full spectrum flow cytometry Support Protocol 1: Spectral unmixing Support Protocol 2: Data preprocessing.

Methods to Identify Immune Cells in Tissues With a Focus on Skin as a Model

The skin protects our body from external challenges, insults, and pathogens and consists of two layers, epidermis and dermis. The immune cells of the skin are an integral part of protecting the body and essential for mediating skin immune homeostasis. They are distributed in the epidermal and dermal layers of the skin. Under homeostatic conditions, the mouse and human skin epidermis harbors immune cells such as Langerhans cells and CD8⁺ T cells, whereas the dermis contains dendritic cells (DCs), mast cells, macrophages, T cells, and neutrophils. Skin immune homeostasis is maintained through communication between epidermal and dermal cells and soluble factors. This communication is important for proper recruitment of immune cells in the skin to mount immune responses during infection/injury or in response to external/internal insults that alter the local cellular milieu. Imbalance in this crosstalk that occurs in association with inflammatory skin disorders such as psoriasis and atopic dermatitis can lead to alterations in the number and type of immune cells contributing to pathological manifestation in these disorders. Profiling changes in the immune cell type, localization, and number can provide important information about disease mechanisms and help design interventional therapeutic strategies. Toward this end, skin cells can be detected and characterized using basic techniques like immunofluorescence, immunohistochemistry, and flow cytometry, and recently developed methods of multiplexing. This article provides an overview on the basic techniques that are widely accessible to researchers to characterize immune cells of the skin. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Guidelines for standardizing T-cell cytometry assays to link biomarkers, mechanisms, and disease outcomes in type 1 diabetes

Cytometric immunophenotyping is a powerful tool to discover and implement T-cell biomarkers of type 1 diabetes (T1D) progression and response to clinical therapy. Although many discovery-based T-cell biomarkers have been described, to date, no such markers have been widely adopted in standard practice. The heterogeneous nature of T1D and lack of standardized assays and experimental design across studies is a major barrier to the broader adoption of T-cell immunophenotyping assays. There is an unmet need to harmonize the design of immunophenotyping assays, including those that measure antigen-agnostic cell populations, such that data collected from different clinical trial sites and T1D cohorts are comparable, yet account for cohort-specific features and different drug mechanisms of action. In these Guidelines, we aim to provide expert advice on how to unify aspects of study design and practice. We provide recommendations for defining cohorts, method implementation, as well as tools for data analysis and reporting by highlighting and building on selected successes. Harmonization of cytometry-based T-cell assays will allow researchers to better integrate findings across trials, ultimately enabling the identification and validation of biomarkers of disease progression and treatment response in T1D.

Immunological Assessment of Lung Responses to Inhalational Lipoprotein Vaccines Against Bacterial Pathogens

Lipopeptides or lipoproteins show potential as safe and effective subunit vaccines for protection against bacterial pathogens. Provided suitable adjuvants are selected, such as the TLR2-stimulating molecules Pam₂Cys and Pam₃Cys, these may be formulated as inhalational vaccines to optimize localized pulmonary immune responses. Here, we present methods to assess antigen-specific memory lymphocyte responses to novel vaccines, with a focus on immune responses in the lung tissue and bronchoalveolar space. We describe detection of T-cell responses via leukocyte restimulation, followed by intracellular cytokine staining and flow cytometry, enzyme-linked immunosorbent spot assay (ELISpot), and sustained leukocyte restimulation for detection of antigen-specific memory responses. We also detail assessment of antibody responses to vaccine antigens, via enzyme-linked immunosorbent assay (ELISA)-based detection. These methods are suitable for testing a wide range of pulmonary vaccines.

A gain and dynamic range independent index to quantify spillover spread to aid panel design in flow cytometry

In conventional flowcytometry one detector (primary) is dedicated for one fluorochrome. However, photons usually end up in other detectors too (fluorescence spillover). 'Compensation' is a process that corrects the spillover signal from all detectors except the primary detector. Post 'compensation', the photon counting error of spillover signals become evident as spreading of the data. The spreading induced by spillover impairs the ability to resolve stained cell population from the unstained one, potentially reducing or completely losing cell populations. For successful multi-color panel design, it is important to know the expected spillover to maximize the data resolution. The Spillover Spreading Matrix (SSM) can be used to estimate the spread, but the outcome is dependent on detector sensitivity. Simply, the same single stained sample produces different spillover spread values when detector(s) sensitivity is altered. Many researchers mistakenly use this artifact to "reduce" the spread by decreasing detector sensitivity. This can result in diminished capacity to resolve dimly expressing cell populations. Here, we introduce SQI (Spread Quantification Index), that can quantify the spillover spread independent of detector sensitivity and independent of dynamic range. This allows users to compare spillover spread between instruments having different types of detectors, which is not possible using SSM.

Panel Optimization for High-Dimensional Immunophenotyping Assays Using Full-Spectrum Flow Cytometry

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large flow cytometry panels reaching up to 43 colors at the single-cell level. However, as panel size and complexity increase, so too does the detail involved in designing and optimizing successful high-quality panels fit for downstream high-dimensional data analysis. In contrast to conventional flow cytometers, full-spectrum flow cytometers measure the entire emission spectrum of each fluorophore across all lasers. This allows for fluorophores with very similar emission maxima but unique overall spectral fingerprints to be used in conjunction, enabling relatively straightforward design of larger panels. Although a protocol for best practices in full-spectrum flow cytometry panel design has been published, there is still a knowledge gap in going from the theoretically designed panel to the necessary steps required for panel optimization. Here, we aim to guide users through the theory of optimizing a high-dimensional full-spectrum flow cytometry panel for immunophenotyping using comprehensive step-by-step protocols. These protocols can also be used to troubleshoot panels when issues arise. A practical application of this approach is exemplified with a 24-color panel designed for identification of conventional T-cell subsets in human peripheral blood. © 2021 Malaghan Institute of Medical Research, Cytek Biosciences. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Preparation and evaluation of optimal spectral reference controls Support Protocol 1: Antibody titration Support Protocol 2: Changing instrument settings Basic Protocol 2: Unmixing evaluation of fully stained sample Basic Protocol 3: Evaluation of marker resolution Support Protocol 3: Managing heterogeneous autofluorescence Basic Protocol 4: Assessment of data quality using expert gating and dimensionality reduction algorithms.

Panel Design and Optimization for High-Dimensional Immunophenotyping Assays Using Spectral Flow Cytometry

Technological advances in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large (20+ parameters) flow cytometry panels. However, as panel complexity and size increase, so does the difficulty involved in designing a high-quality panel, accessing the instrumentation capable of accommodating large numbers of parameters, and analyzing such high-dimensional data. A recent advancement is spectral flow cytometry, which in contrast to conventional flow cytometry distinguishes the full emission spectrum of each fluorophore across all lasers, rather than identifying only the peak of emission. Fluorophores with a similar emission maximum but distinct off-peak signatures can therefore be accommodated within the same flow cytometry panel, allowing greater flexibility in terms of panel design and fluorophore detection. Here, we highlight the specific characteristics of spectral flow cytometry and aim to guide users through the process of building, designing, and optimizing high-dimensional spectral flow cytometry panels using a comprehensive step-by-step protocol. Special considerations are also given for using highly overlapping dyes, and a logical selection process for optimal marker-fluorophore assignment is provided. © 2020 by John Wiley & Sons, Inc.

Full Spectrum Flow Cytometry as a Powerful Technology for Cancer Immunotherapy Research

The Nobel Prize-deserving concept of blocking inhibitory pathways in T cells, to unleash their anti-tumoral capacity, became one of the pillars of cancer treatment in the last decade and has resulted in durable clinical responses for multiple cancer types. Currently, two of the most important goals in cancer immunotherapy are to understand the mechanisms resulting in failure to checkpoint blockade and to identify predictive immunological biomarkers that correlate to treatment response, disease progression or adverse effects. The identification and validation of biomarkers for routine clinical use is not only critical to monitor disease or treatment progression, but also to personalize and develop new therapies. To achieve these goals, powerful research tools are needed. Flow cytometry stands as one of the most successful single-cell analytical tools used to characterize immune cell phenotypes to monitor solid tumors, hematological malignancies, minimal residual disease or metastatic progression. This technology has been fundamental in diagnosis, treatment and translational research in cancer clinical trials. Most recently, the need to evaluate simultaneously more features in each cell has pushed the field to implement more powerful adaptations beyond conventional flow cytometry, including Full Spectrum Flow Cytometry (FSFC). FSFC captures the full emission spectrum of fluorescent molecules using arrays of highly sensitive light detectors, and to date has enabled characterization of 40 parameters in a single sample. We will summarize the contributions of this technology to the advancement of research in immunotherapy studies and discuss best practices to obtain reliable, robust and reproducible FSFC results.

Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts

MOTIVATION

Many 'automated gating' algorithms now exist to cluster cytometry and single cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasise different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets.

RESULTS

We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimises (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain.

AVAILABILITY

Implementation of our Pareto front methodology and all scripts to reproduce this article are available at https://github.com/ghar1821/ParetoBench.

Algorithms for the selection of fluorescent reporters

Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as excitation and emission spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through.

Vaidyanathan et al. present a heuristic algorithm for the selection of fluorescent reporters in the context of single-cell analysis. They present a tool to enable biologists to design multi-colour fluorophore panels based on specific equipment’s configurations. The authors demonstrate the efficacy of their algorithm by comparing computational predictions with experimental observations.

New cytometry tools for immune monitoring during cancer immunotherapy

The success of cancer immunotherapy (CIT) in the past decade has brought renewed excitement and the need to better understand how the human immune system functions during health and disease. Advances in single cell technologies have also inspired the creation of a Human Cell Atlas to identify and describe every cell in the human body with the intention of elucidating how to "fix" the ones that fail normal function. For example, treatment of cancer patients with immune checkpoint blockade (ICB) antibodies can reinvigorate their T cells and produce durable clinical benefit in a subset of patients, but a number of resistance mechanisms exist that prohibit full benefit to all treated patients. Early detection of biomarkers of response and mechanisms of resistance are needed to identify the patients who can benefit most from ICB. A noninvasive approach to predict treatment outcomes early after immunotherapies is a longitudinal analysis of peripheral blood immune cells using flow cytometry. Here we review some of the advances in our understanding of how ICB antibodies can re-invigorate tumor-specific T cells and also highlight the recent advances in high complexity flow cytometry, including spectral cytometers, that allow longitudinal sampling and deep immune phenotyping in clinical settings. We encourage the scientific community to utilize advanced cytometry platforms and analyses for immune monitoring in order to optimize CIT treatments for maximum clinical benefit.

Evaluating spectral cytometry for immune profiling in viral disease

In conventional fluorescence cytometry, each fluorophore present in a panel is measured in a target detector, through the use of wide band-pass optical filters. In contrast, spectral cytometry uses a large number of detectors with narrow band-pass filters to measure a fluorophore's signal across the spectrum, creating a more detailed fluorescent signature for each fluorophore. The spectral approach shows promise in adding flexibility to panel design and improving the measurement of fluorescent signal. However, few comparisons between conventional and spectral systems have been reported to date. We therefore sought to compare a modern conventional cytometry system with a modern spectral system, and to assess the quality of resulting datasets from the point of view of a flow cytometry user. Signal intensity, spread, and resolution were compared between the systems. Subsequently, the different methods of separating fluorophore signals were compared, where compensation mathematically separates multiple overlapping fluorophores and unmixing relies on creating a detailed fluorescent signature across the spectrum to separate the fluorophores. Within the spectral data set, signal spread and resolution were comparable between compensation and unmixing. However, for some highly overlapping fluorophores, unmixing resolved the two fluorescence signals where compensation did not. Finally, data from mid- to large-size panels were acquired and were found to have comparable resolution for many fluorophores on both instruments, but reduced levels of spreading error on our spectral system improved signal resolution for a number of fluorophores, compared with our conventional system. Furthermore, autofluorescence extraction on the spectral system allowed for greater population resolution in highly autofluorescent samples. Overall, the implementation of a spectral cytometry approach resulted in data that are comparable to that generated on conventional systems, with a number of potential advantages afforded by the larger number of detectors, and the integration of the spectral unmixing approach. © 2020 International Society for Advancement of Cytometry.

High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data

The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single-cell level. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize, and interpret these high-dimensional data sets. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high-dimensional data sets using expert gating, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high-dimensional clustering or dimensionality reduction algorithms such as t-SNE, UMAP, or FlowSOM. However, large scale studies combined with an in-depth technical analysis will be needed to assess differences between these technologies in more detail. © 2020 International Society for Advancement of Cytometry.

Characterization of the tumor immune infiltrate by multiparametric flow cytometry and unbiased high-dimensional data analysis

The tumor microenvironment (TME) is a highly complex and dynamic ensemble of cells of which a variety of immune cells are a major component. The unparalleled results obtained with immunotherapeutic approaches have underscored the importance of examining the immune landscape of the TME. Recent technological advances have incorporated high-throughput techniques at the single cell level, such as single cell RNA sequencing, mass cytometry, and multi-parametric flow cytometry to the characterization of the TME. Among them, flow cytometry is the most broadly used both in research and clinical settings and multi-color analysis is now routinely performed. The high dimensionality of the data makes the traditional manual gating strategy in 2D scatter plots very difficult. New unbiased visualization techniques provide a solution to this problem. Here we describe the steps to characterize the immune cell compartment in the TME in mouse tumor models by high-parametric flow cytometry, from the experimental setup to the analysis methodology with special emphasis on the use of unsupervised algorithms.

Immunophenotyping of Human Regulatory T Cells

Regulatory T cells, also known as Tregs, play a pivotal role in maintaining homeostasis of the immune system and self-tolerance. Tregs express CD3, CD4, CD25, and FOXP3 but lack CD127. CD4 and CD3 identify helper T lymphocytes, of which Tregs are a subset. CD25 is IL-2Rα, an essential activation marker that is expressed in high levels on Tregs. FOXP3 is the canonical transcription factor, important in the development, maintenance, and identification of Tregs. CD127 is IL-7 receptor, expressed inversely with suppression, and is therefore downregulated on Tregs. Flow cytometry is a powerful tool that is capable of simultaneously measuring Tregs along with several markers associated with subpopulations of Tregs, activation, maturation, proliferation, and surrogates of functional suppression. This chapter describes a multicolor flow cytometry-based approach to measure human Tregs, including details for surface staining, fixation/permeabilization, intracellular/intranuclear staining, acquisition of samples on a flow cytometer, plus analysis and interpretation of resulting FCS files.

Analysis of the Murine Bone Marrow Hematopoietic System Using Mass and Flow Cytometry

The hematopoietic system produces erythrocytes (red blood cells), leukocytes (white blood cells), and thrombocytes (platelets) throughout the life of an organism. Long-lived hematopoietic stem cells give rise to early progenitors with multi-lineage potential that progressively differentiate into lineage-specific progenitors. Following lineage commitment, these progenitors proliferate and expand, before eventually differentiating into their mature forms. This process drives the up- and downregulation of a wide variety of surface and intracellular markers throughout differentiation, making cytometric analysis of this interconnected system challenging. Moreover, during inflammation, the hematopoietic system can be mobilized to re-prioritize the production of various lineages, in order to match increased demand, often at the expense of other lineages. As such, the response of the hematopoietic system in the bone marrow (BM) is a critical component of both immunity and disease. Because of the complexity of the hematopoietic system in steady state and disease, high-dimensional cytometry technologies are well suited to the exploration of these complex systems. Here we describe a protocol for the extraction of murine bone marrow, and preparation for examination using high-dimensional flow or mass cytometry. Additionally, we describe methods for performing cell cycle assays using bromodeoxyuridine (BrdU) or iododeoxyuridine (IdU). Finally, we describe an analytical method that allows for a system-level analysis of the hematopoietic system in steady state or inflammatory scenarios.

High-Dimensional Immunophenotyping with Fluorescence-Based Cytometry: A Practical Guidebook

Recent technological advances have greatly diversified the platforms that are available for high-dimensional single-cell immunophenotyping, including mass cytometry, single-cell RNA sequencing, and fluorescent-based flow cytometry. The latter is currently the most commonly used approach, and modern instrumentation allows for the measurement of up to 30 parameters, revealing deep insights into the complexity of the immune system.Here, we provide a practical guidebook for the successful design and execution of complex fluorescence-based immunophenotyping panels. We address common misconceptions and caveats, and also discuss challenges that are associated with the quality control and analysis of these data sets.

Multiparametric analysis of colorectal cancer immune responses

Colorectal cancer (CRC) is a heterogeneous disease, with a diverse and plastic immune cell infiltrate. These immune cells play an important role in regulating tumour growth - progression or elimination. Some populations of cells have a strong correlation with disease-free survival, making them useful prognostic markers. In particular, the infiltrate of CD3+ and CD8+ T cells into CRC tumours has been validated worldwide as a valuable indicator of patient prognosis. However, the heterogeneity of the immune response, both between patients with tumours of different molecular subtypes, and within the tumour itself, necessitates the use of multiparametric analysis in the investigation of tumour-specific immune responses. This review will outline the multiparametric analysis techniques that have been developed and applied to studying the role of immune cells in the tumour, with a focus on colorectal cancer. Because much of the data in this disease relates to T cell subsets and heterogeneity, we have used T cell populations as examples throughout. Flow and mass cytometry give a detailed representation of the cells within the tumour in a single-cell suspension on a per-cell basis. Imaging technologies, such as imaging mass cytometry, are used to investigate increasing numbers of markers whilst retaining the spatial and structural information of the tumour section and the infiltrating immune cells. Together, the analyses of multiple immune parameters can provide valuable information to guide clinical decision-making in CRC.