Development of a customizable mouse backbone spectral flow cytometry panel to delineate immune cell populations in normal and tumor tissues.

Introduction: In vivo studies of cancer biology and assessment of therapeutic efficacy are critical to advancing cancer research and ultimately improving patient outcomes. Murine cancer models have proven to be an invaluable tool in pre-clinical studies. In this context, multi-parameter flow cytometry is a powerful method for elucidating the profile of immune cells within the tumor microenvironment and/or play a role in hematological diseases. However, designing an appropriate multi-parameter panel to comprehensively profile the increasing diversity of immune cells across different murine tissues can be extremely challenging. Methods: To address this issue, we designed a panel with 13 fixed markers that define the major immune populations -referred to as the backbone panel- that can be profiled in different tissues but with the option to incorporate up to seven additional fluorochromes, including any marker specific to the study in question. Results: This backbone panel maintains its resolution across different spectral flow cytometers and organs, both hematopoietic and non-hematopoietic, as well as tumors with complex immune microenvironments. Discussion: Having a robust backbone that can be easily customized with pre-validated drop-in fluorochromes saves time and resources and brings consistency and standardization, making it a versatile solution for immuno-oncology researchers. In addition, the approach presented here can serve as a guide to develop similar types of customizable backbone panels for different research questions requiring high-parameter flow cytometry panels.

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022. The collected flow cytometry data were used to train an artificial intelligence using deep learning. The trained AI demonstrated a 94.6% sensitivity in detecting acute myeloid leukemia (AML) patients and a 98.2% sensitivity for B-lymphoblastic leukemia (B-ALL) patients. The sensitivities of physiological cells were at least 80%, with variable performance for pathological cells. In conclusion, the AI, trained with ResNet-50 and EverFlow, shows promising results in identifying patients with AML and B-ALL, as well as classifying physiological cells.

Isolation of Murine Adipose-Derived Stromal/Stem Cells for Adipogenic and Osteogenic Differentiation or Flow Cytometry-Based Analysis.

Murine models of obesity or reduced adiposity are a valuable resource for understanding the role of adipocyte dysfunction in metabolic disorders. Adipose tissue stromal vascular cells or primary adipocytes derived from murine adipose tissue and grown in culture are essential tools for studying the mechanisms underlying adipocyte development and function. Herein, we describe methods for the isolation, expansion, and long-term storage of murine adipose-derived stromal/stem cells, along with protocols for inducing adipogenesis to white or beige adipocytes in this cell population and osteogenic differentiation. Isolation of the adipose stromal vascular fraction cells for flow cytometric analysis is also described.

Detection of Cancer Stem Cells in Normal and Dysplastic/Leukemic Human Blood.

The hierarchical organization of the leukemic stem cells (LSCs) is identical to that of healthy counterpart cells. It may be split into roughly three stages: a small number of pluripotent stem cells at the top, few lineage-restricted cells in the middle, and several terminally differentiated blood cells at the bottom. Although LSCs can differentiate into the hematopoietic lineage, they can also accumulate as immature progenitor cells, also known as blast cells. Since blast cells are uncommon in healthy bloodstreams, their presence might be a sign of cancer. For instance, a 20% blast cutoff in peripheral blood or bone marrow is formally used to distinguish acute myeloid leukemia from myelodysplastic neoplasms, which is essential to plan the patients' management. Many techniques may be useful for blast enumeration: one of them is flow cytometry, which can perform analyses on many cells by detecting the expression of cell surface markers. Leukemic and non-leukemic blast cells might indeed be characterized by the same surface markers, but these markers are usually differently expressed. Here we propose to use CD45, in combination with CD34 and other cell surface markers, to identify and immunophenotype blast cells in patient-derived samples.

Phenotypic Analysis of Hematopoietic Stem and Progenitor Cell Populations in Acute Myeloid Leukemia Based on Spectral Flow Cytometry, a 20-Color Panel, and Unsupervised Learning Algorithms.

The analysis of hematopoietic stem and progenitor cell populations (HSPCs) is fundamental in the understanding of normal hematopoiesis as well as in the management of malignant diseases, such as leukemias, and in their diagnosis and follow-up, particularly the measurement of treatment efficiency with the detection of measurable residual disease (MRD). In this study, I designed a 20-color flow cytometry panel tailored for the comprehensive analysis of HSPCs using a spectral cytometer. My investigation encompassed the examination of forty-six samples derived from both normal human bone marrows (BMs) and patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) along with those subjected to chemotherapy and BM transplantation. By comparing my findings to those obtained through conventional flow cytometric analyses utilizing multiple tubes, I demonstrate that my innovative 20-color approach enables a more in-depth exploration of HSPC subpopulations and the detection of MRD with at least comparable sensitivity. Furthermore, leveraging advanced analytical tools such as t-SNE and FlowSOM learning algorithms, I conduct extensive cross-sample comparisons with two-dimensional gating approaches. My results underscore the efficacy of these two methods as powerful unsupervised alternatives for manual HSPC subpopulation analysis. I expect that in the future, complex multi-dimensional flow cytometric data analyses, such as those employed in this study, will be increasingly used in hematologic diagnostics.

Immunophenotyping, Part I: Instrument Calibration and Reagent Qualification for Immunophenotyping Analysis of Human Peripheral Blood Mononuclear Cell Cultures.

Nanoparticles are increasingly used in biomedical applications to influence the way the immune system reacts to tumors and infectious disease-causing agents. Nanoparticles not-intended for immunomodulation can also influence immune responses by affecting immune cell subsets' viability and/or activity. While immunophenotyping is commonly used to assess the effects of drugs and nanoparticles on immune cell subsets, no standardized approach exists due to the breadth of available cell models and instrumentation. In this chapter, we describe a protocol for flow cytometer calibration and reagent qualification prior to its use in the immunophenotyping experiment. The strategies described herein can be adapted to other instruments. The subsequent chapter-immunophenotyping part II (Chap. 25 )-provides detailed instructions for applying this methodology to analyze nanoparticle effects on subsets of immune cells present in peripheral blood.

Immunophenotyping, Part II: Analysis of Nanoparticle Effects on the Composition and Activation Status of Human Peripheral Blood Mononuclear Cells.

The use of nanoparticles as drug delivery carriers requires analysis of their safety, which among other tests, includes immunotoxicity. Nanoparticles are also increasingly used for applications intended to specifically activate, inhibit, or modify the immune system's responses to improve the treatment of inflammatory and autoimmune disorders, cancer immunotherapy, and vaccines targeting cancer cells and viral and bacterial pathogens. In addition to the safety, the analysis of nanoparticles intended for immune system targeting includes mechanistic immunology investigations. Immunophenotyping provides researchers with a tool to assess the immune cell viability and activation status. These results provide mechanistic insights into nanoparticle efficacy and toxicity and therefore are of interest to the biomedical nanotechnology field. However, no standardized approaches exist due to the breadth of methods and instruments available for this analysis. This chapter provides detailed instructions for applying this methodology to analyze nanoparticle effects on subsets of immune cells present in peripheral blood. While this experimental strategy is specific to the NovoCyte 3005 flow cytometer, it can be adapted to other instruments. Instructions for instrument setup, calibration, and antibody qualification are described in this book's Chapter 24 , Immunophenotyping, part I.

Protocol to measure apoptosis-associated speck-like protein containing a CARD specks in human cerebrospinal fluid via imaging flow cytometry.

Apoptosis-associated speck-like protein containing a c-terminal caspase activation and recruitment domain (ASC) specks are elevated in the cerebrospinal fluid (CSF) of Alzheimer's disease and related dementias (AD/ADRDs) patients. Here, we present a flow cytometry protocol to quantify ASC specks. We describe steps for fluorescently labeling ASC specks using antibody technology, visualizing with imaging flow cytometry, and gating based on physical characteristics. CSF ASC specks levels positively correlate with phosphorylated tau (Thr181) and negatively correlate with amyloid ß ratio (42/40), thus serving as a neuroinflammatory biomarker for diagnosing AD/ADRDs. For complete details on the use and execution of this protocol, please refer to Jiang et al.1.

Using the Sysmex UF-4000 urine flow cytometer for rapid diagnosis of urinary tract infection in the clinical microbiological laboratory.

BACKGROUND: Urinary tract infections are responsible for a significant worldwide disease burden. Performing urine culture is time consuming and labor intensive. Urine flow cytometry might provide a quick and reliable method to screen for urinary tract infection. METHODS: We analyzed routinely collected urine samples received between 2020 and 2022 from both inpatients and outpatients. The UF-4000 urine flow cytometer was implemented with an optimal threshold for positivity of ≥100 bacteria/µL. We thereafter validated the prognostic value to detect the presence of urinary tract infection (UTI) based on bacterial (BACT), leukocyte (WBC), and yeast-like cell (YLC) counts combined with the bacterial morphology (UF gram-flag). RESULTS: In the first phase, in 2019, the UF-4000 was implemented using 970 urine samples. In the second phase, between 2020 and 2022, the validation was performed in 42,958 midstream urine samples. The UF-4000 screen resulted in a 37% (n = 15,895) decrease in performed urine cultures. Uropathogens were identified in 18,673 (69%) positively flagged urine samples. BACT > 10.000/µL combined with a gram-negative flag had a >90% positive predictive value for the presence of gram-negative uropathogens. The absence of gram-positive flag or YLC had high negative predictive values (99% and >99%, respectively) and are, therefore, best used to rule out the presence of gram-positive bacteria or yeast. WBC counts did not add to the prediction of uropathogens. CONCLUSION: Implementation of the UF-4000 in routine practice decreased the number of cultured urine samples by 37%. Bacterial cell counts were highly predictive for the presence of UTI, especially when combined with the presence of a gram-negative flag.

Immunophenotyping of canine T cell activation and proliferation by combined protein and RNA flow cytometry.

The limited availability of canine-reactive monoclonal antibodies restricts the analyses of immune cell subsets and their functions by flow cytometry. The PrimeFlow™ RNA Assay may serve as a potential solution to close this gap. Here we report a blood immunophenotyping method utilizing combined protein- and RNA-based flow cytometry to characterize canine T cell activation and proliferation within individual cells. In this assay, CD69 expression was detected by an RNA probe and CD25 and Ki67 were detected by antibodies. Canine peripheral blood mononuclear cells (PBMCs) were stimulated with three agents with different modes of action, anti-CD3/CD28 antibodies, phytohemagglutinin, or phorbol myristate acetate /ionomycin. Robust T cell activation (CD25+ and/or CD69+) and proliferation (Ki67+) were detected. Both CD69 and CD25 appear to be robust and sensitive T cell activation markers with early induction and low background expression. Upon stimulation, T cell proliferation occurred later than T cell activation and was associated with CD25 expression. This canine T cell activation and proliferation immunophenotyping method was evaluated in 5 independent experiments using PBMCs from 10 different beagle dogs with satisfactory assay performance. This method can greatly facilitate the evaluation of immune disease pathogenesis and immunotoxicity risk assessment in nonclinical drug development in canine.

Practical 10-Color T-Cell Panel for Phenotyping Diverse Populations Using Spectral Flow Cytometry: A Beginner's Guide.

Flow cytometry stands as the most employed high-throughput single-cell analysis technique, facilitating the profiling of remarkably diverse samples, such as blood, bone marrow and body fluids. In addition, it allows for the discrimination of diverse immune cell subsets, including infrequently encountered types like T regulatory cells and exhausted CD28Null T cells. However, analyzing rare immune cell subsets with conventional flow cytometry poses challenges stemming from factors like fluorophore overlap, compensation issues, and limited flexibility in fluorophore selection. Therefore, spectral flow cytometry offers advantages over traditional flow cytometry. It measures the full emission spectrum and then separates it to identify different fluorochromes. This enables the use of fluorochromes with significant overlap in a single test, allowing for the analysis of more protein markers. Following this, spectral technology employs precise calculations to separate individual fluorochromes, thereby enabling the detection and elimination of autofluorescent signals originating from cells within the entire emission spectrum. This capability is pivotal in achieving deep phenotyping of immune cells with the requisite sensitivity and resolution essential for monitoring the immune systems of patients with compromised immunity, such as cancer and autoimmune disorders. Additionally, it allows for the exploration of interactions between distinct immune subsets. In this context, we introduce an optimized protocol utilizing spectral flow cytometry for precise T-cell characterization and differentiation, encompassing the assessment of their activation states. Furthermore, this protocol extends its applicability to the identification of less common circulating T-cell populations, notably T-regulatory and CD28Null T cells, following autofluorescence correction within the spectrum. This protocol provides a set of steps and reagents for the surface and intracellular staining of human T cells using whole peripheral blood. The spectral-based design of this panel allows for its applicability to other spectral machines, providing a versatile and efficient tool for T-cell analysis. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Achieving optimal staining through effective antibody titration Basic Protocol 2: Single-cell staining Basic Protocol 3: Comprehensive panel staining post-titration and spectral library integration.

An open-source FACS automation system for high-throughput cell biology.

Recent advances in gene editing are enabling the engineering of cells with an unprecedented level of scale. To capitalize on this opportunity, new methods are needed to accelerate the different steps required to manufacture and handle engineered cells. Here, we describe the development of an integrated software and hardware platform to automate Fluorescence-Activated Cell Sorting (FACS), a central step for the selection of cells displaying desired molecular attributes. Sorting large numbers of samples is laborious, and, to date, no automated system exists to sequentially manage FACS samples, likely owing to the need to tailor sorting conditions ("gating") to each individual sample. Our platform is built around a commercial instrument and integrates the handling and transfer of samples to and from the instrument, autonomous control of the instrument's software, and the algorithmic generation of sorting gates, resulting in walkaway functionality. Automation eliminates operator errors, standardizes gating conditions by eliminating operator-to-operator variations, and reduces hands-on labor by 93%. Moreover, our strategy for automating the operation of a commercial instrument control software in the absence of an Application Program Interface (API) exemplifies a universal solution for other instruments that lack an API. Our software and hardware designs are fully open-source and include step-by-step build documentation to contribute to a growing open ecosystem of tools for high-throughput cell biology.

SeParate: multiway fluorescence-activated droplet sorting based on integration of serial and parallel triaging concepts.

Fluorescence-activated droplet sorting (FADS) has emerged as a versatile high-throughput sorting tool that is, unlike most fluorescence-activated cell sorting (FACS) platforms, capable of sorting droplet-compartmentalized cells, cell secretions, entire enzymatic reactions and more. Recently, multiplex FADS platforms have been developed for the sorting of multi-fluorophore populations towards different outlets in addition to the standard, more commonly used, 2-way FADS platform. These multiplex FADS platforms consist of either multiple 2-way junctions one after the other (i.e. serial sorters) or of one junction sorting droplets in more than 2 outlets (i.e. parallel sorters). In this work, we present SeParate, a novel platform based on integrating s̲e̲rial and p̲a̲r̲allel sorting principles for accura̲t̲e̲ multiplex droplet sorting that is able to mitigate limitations of current multiplex sorters. We show the SeParate platform and its capability in highly accurate 4-way sorting of a multi-fluorophore population into four subpopulations with the potential to expand to more. More specifically, the SeParate platform was thoroughly validated using mixed populations of fluorescent beads and picoinjected droplets, yielding sorting accuracies up to 100% and 99.9%, respectively. Finally, transfected HEK-293T cells were sorted employing two different optical setups, resulting in an accuracy up to 99.5%. SeParate's high accuracy for a diverse set of samples, including highly variable biological specimens, together with its scalability beyond the demonstrated 4-way sorting, warrants a broad applicability for multi-fluorophore studies in life sciences, environmental sciences and others.

Spectral Flow Cytometry Methods and Pipelines for Comprehensive Immunoprofiling of Human Peripheral Blood and Bone Marrow.

Profiling hematopoietic and immune cells provides important information about disease risk, disease status, and therapeutic responses. Spectral flow cytometry enables high-dimensional single-cell evaluation of large cohorts in a high-throughput manner. Here, we designed, optimized, and implemented new methods for deep immunophenotyping of human peripheral blood and bone marrow by spectral flow cytometry. Two blood antibody panels capture 48 cell-surface markers to assess more than 58 cell phenotypes, including subsets of T cells, B cells, monocytes, natural killer (NK) cells, and dendritic cells, and their respective markers of exhaustion, activation, and differentiation in less than 2 mL of blood. A bone marrow antibody panel captures 32 markers for 35 cell phenotypes, including stem/progenitor populations, T-cell subsets, dendritic cells, NK cells, and myeloid cells in a single tube. We adapted and developed innovative flow cytometric analysis algorithms, originally developed for single-cell genomics, to improve data integration and visualization. We also highlight technical considerations for users to ensure data fidelity. Our protocol and analysis pipeline accurately identifies rare cell types, discerns differences in cell abundance and phenotype across donors, and shows concordant immune landscape trends in patients with known hematologic malignancy. SIGNIFICANCE: This study introduces optimized methods and analysis algorithms that enhance capabilities in comprehensive immunophenotyping of human blood and bone marrow using spectral flow cytometry. This approach facilitates detection of rare cell types, enables measurement of cell variations across donors, and provides proof-of-concept in identifying known hematologic malignancies. By unlocking complexities of hematopoietic and immune landscapes at the single-cell level, this advancement holds potential for understanding disease states and therapeutic responses.

How to Measure "Spillover Spread".

Since the development of the first instrument in the late 1960s, flow cytometry (FC) has become a powerful tool in both the clinical and research space. As one of the earliest single-cell analytical techniques, flow cytometry can measure thousands of cells in minutes, allowing researchers an unprecedented understanding of the biology of their system of interest. There are commercial systems available that can measure over 40 different parameters at the same time. The most common assay, immunophenotyping, involves labeling cells with fluorescently conjugated antibodies. The process of fluorescence occurs when a fluorescent molecule first absorbs a photon of light, which promotes an electron to a higher energy state. This energy is released by the emission of a photon of lower energy (thus a higher wavelength). The emitted photon will be within a range of visible wavelengths. When measured on a flow cytometer, this results in the fluorescent signal being measured not just in the primary detector but also in one or more secondary detectors. Termed "spillover," this is when the fluorescent signal measured in a detector other than the intended one creates a problem in identifying the real signal. The process of compensation is used to address this spectral spillover. However, in correcting for the spillover by compensation, the spread of the data is revealed. This spread can be quantified, and, here, we discuss two methods that can be used to identify and measure this spectral spread for any combination of fluorochromes. The output of these methods is useful in experimental design and monitoring instrument quality control. Armed with this information, the researcher can better design polychromatic panels to minimize the impact of spread on their data.

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.

Practicalities of Cell Sorting.

Cell sorting is a technique commonly used in academic and biotechnology laboratories in order to separate out cells or particles of interest from heterogeneous populations. Cell sorters use the same principles as flow cytometry analyzers, but instead of cell populations passing to the waste of the instrument, they can be collected for further studies including DNA sequencing as well as other genomic, in vitro and in vivo experiments. This chapter aims to give an overview of cell sorting, the different types of cell sorters, details on how a cell sorter works, as well as protocols that are useful when embarking on a journey with cell sorting.

Comprehensive Immunophenotyping by Polychromatic Cytometry.

The human immune system comprises a diverse array of cells involved in innate and adaptive immunity, and these immune cells coordinate immune responses against pathogens through intricate interactions. Multicolor flow cytometry is a powerful technique for qualitatively and quantitatively measuring the characteristics of immune cells, offering advantages, such as high-dimensional analysis, elucidation of cellular heterogeneity, understanding of pathogenesis, development of therapeutic strategies, and platform flexibility. Here, we demonstrate a new immunophenotyping panel that allows simultaneous evaluation of the characteristics of T and B cells. This panel enables tracking of changes in the immune status due to aging, environmental factors, pathogen infections, and vaccine administration. Additionally, it includes co-stimulatory molecules for assessing the activation state of immune cells and inhibitory checkpoint molecules for evaluating exhaustion status, thereby providing valuable insights into the features of human immune responses. These analyses contribute to understanding the pathophysiology of diseases and developing therapeutic strategies while offering crucial information for assessing the correlation of symptoms with infections and evaluating the efficacy of vaccines.

Monitoring Cell Proliferation by Dye Dilution: Considerations for Panel Design.

High dimensional studies that include proliferation dyes face two inherent challenges in panel design. First, the more rounds of cell division to be monitored based on dye dilution, the greater the starting intensity of the labeled parent cells must be in order to distinguish highly divided daughter cells from background autofluorescence. Second, the greater their starting intensity, the more difficult it becomes to avoid spillover of proliferation dye signal into adjacent spectral channels, with resulting limitations on the use of other fluorochromes and ability to resolve dim signals of interest. In the third and fourth editions of this series, we described the similarities and differences between protein-reactive and membrane-intercalating dyes used for general cell tracking, provided detailed protocols for optimized labeling with each dye type, and summarized characteristics to be tested by the supplier and/or user when validating either dye type for use as a proliferation dye. In this fifth edition, we review: (a) Fundamental assumptions and critical controls for dye dilution proliferation assays; (b) Methods to evaluate the effect of labeling on cell growth rate and test the fidelity with which dye dilution reports cell division; and. (c) Factors that determine how many daughter generations can be accurately included in proliferation modeling. We also provide an expanded section on spectral characterization, using data collected for three protein-reactive dyes (CellTrace™ Violet, CellTrace™ CFSE, and CellTrace™ Far Red) and three membrane-intercalating dyes (PKH67, PKH26, and CellVue® Claret) on three different cytometers to illustrate typical decisions and trade-offs required during multicolor panel design. Lastly, we include methods and controls for assessing regulatory T cell potency, a functional assay that incorporates the "know your dye" and "know your cytometer" principles described herein.

Image-Enabled Cell Sorting Using the BD CellView Technology.

This chapter is an extension of the original publication by Schraivogel et al. (Science 375:315-320, 2022) which described, for the first time, image-enabled and high-speed cell sorting based on the BD CellView technology. It summarizes the technical aspects of the instrument in an easy-to-digest form and provides example-based guidance toward implementation of the CellView-based image cell sorting technology. As an example, it explains how to use the image-enabled cell sorter to analyze the chemically induced fragmentation of the Golgi apparatus in HeLa cells-an experiment that was alluded to in the original publication but was not included in the manuscript due to space constraints. The chemically induced Golgi fragmentation sort illustrates an elegant example of the utility of image-enabled cell sorting as a significant expansion of the single-cell toolbox. It is such a striking phenotype when analyzed with image cytometry but undetectable when using conventional flow cytometry. Described in a straightforward and concise manner, this experiment serves as a standard system assurance for image-based cell sorters.