OMIP 083: A 21-marker 18-color flow cytometry panel for in-depth phenotyping of human peripheral monocytes

Causal role of immune cells in uveitis: Mendelian randomization study

Background

Uveitis, characterized by inflammation of the iris, ciliary body, and choroid, presents a significant global clinical challenge, contributing substantially to visual impairment. Risk factors include autoimmune diseases and immune cell dysfunctions, yet many remain unidentified. Immune cells, notably T cells, B cells, and monocytes, play pivotal roles in uveitis pathogenesis. While biologic agents show promise, comprehensive studies on immune cell types in ocular diseases are lacking. Genome-wide association studies (GWAS) and Mendelian randomization (MR) present promising avenues to elucidate genetic susceptibilities and causal relationships between immune cell traits and uveitis risk.

Methods

Two-sample MR analysis was used to evaluate the causal relationship between 731 immune cells and uveitis, and genome-wide significance analysis was performed for genetic variation in 731 immune cells traits (P < 5 × 10-8). Immune characteristics include median fluorescence intensity (MFI), relative cell counts (RC), absolute cell counts (AC), and morphological parameters (MP), which were determined by published GWAS, and public data from the IEU Open GWAS database. The main analysis method of MR is inverse variance weighting (IVW). Heterogeneity and horizontal pleiotropy were also assessed.

Results

5 immunophenotypes, including CD62L-DC %DC, IgD+ CD38dim %B cell, CD3 on CM CD4+T cell, CD3 on CD45RA-CD4 +T cell, and CD3 on CD39+ CD4+ Treg may increase the risk of uveitis. 5 immunophenotypes, including CD11b on CD33dim HLA DR-Myeloid cell, HLA DR on CD33dim HLA DR+ CD11b-myeloid cell, CD14-CD16 + %monocyte, HLA DR on CD14-CD16 + monocyte and PDL-1 on CD14-CD16 + monocyte was negatively associated with the risk of uveitis. Among them, HLA DR on CD14-CD16 + monocyte (OR=0.921, 95%CI =0.875-0.970, P=0.001) and HLA DR on CD33dim HLA DR+ CD11b- (OR=0.879, 95%CI = 0.833-0.927, P=0.00) were negatively associated with the risk of uveitis in bi-direction.

Conclusion

These results indicate that 10 immune cells traits are significantly associated with the risk of developing uveitis and 2 of them were strongly associated with uveitis bi-directionally, after excluding the effects of confounding factors such as some immune diseases, which provided new ideas and therapeutic targets for the study of immune mechanism of uveitis.

Single-Cell Atlas of Atherosclerosis Patients by Cytof: Circulatory and Local Immune Disorders

Atherosclerosis (AS) is a common underlying pathology of coronary artery disease, peripheral artery disease, and stroke. The characteristics of immune cells within plaques and their functional relationships with blood are crucial in AS. In this study, Mass cytometry (CyTOF), RNA-sequencing and immunofluorescence were combined to comprehensively analyze plaque tissues and peripheral blood from 25 AS patients (22 for Mass cytometry and 3 for RNA-sequencing), as well as blood from 20 healthy individuals. The study identified a complexity of leukocytes in the plaque, including both defined anti-inflammatory and pro-inflammatory subsets such as M2-like CD163+ macrophages, Natural killer T cells (NKT), CD11b+ CD4+ T effector memory cells (Tem), and CD8+ terminally differentiated effector memory cells (TEMRA). Functionally activated cell subsets were also found in peripheral blood in AS patients, highlighting the vivid interactions between leukocytes in plaque and blood. The study provides an atlas of the immune landscape in atherosclerotic patients, where pro-inflammatory activation was found to be a major feature of peripheral blood. The study identified NKT, CD11b+ CD4+ Tem, CD8+ TEMRA and CD163+ macrophages as key players in the local immune environment.

Hybrid Macromolecular Constructs as a Platform for Spectral Nanoprobes for Advanced Cellular Barcoding in Flow Cytometry

Herein, an advanced bioconjugation technique to synthesize hybrid polymer-antibody nanoprobes tailored for fluorescent cell barcoding in flow cytometry-based immunophenotyping of leukocytes is applied. A novel approach of attachment combining two fluorescent dyes on the copolymer precursor and its conjugation to antibody is employed to synthesize barcoded nanoprobes of antibody polymer dyes allowing up to six nanoprobes to be resolved in two-dimensional cytometry analysis. The major advantage of these nanoprobes is the construct design in which the selected antibody is labeled with an advanced copolymer bearing two types of fluorophores in different molar ratios. The cells after antibody recognition and binding to the target antigen have a characteristic double fluorescence signal for each nanoprobe providing a unique position on the dot plot, thus allowing antibody-based barcoding of cellular samples in flow cytometry assays. This technique is valuable for cellular assays that require low intersample variability and is demonstrated by the live cell barcoding of clinical samples with B cell abnormalities. In total, the samples from six various donors were successfully barcoded using only two detection channels. This barcoding of clinical samples enables sample preparation and measurement in a single tube.

Distinct human stem cell subpopulations drive adipogenesis and fibrosis in musculoskeletal injury

Fibroadipogenic progenitors (FAPs) maintain healthy skeletal muscle in homeostasis but drive muscle degeneration in chronic injuries by promoting adipogenesis and fibrosis. To uncover how these stem cells switch from a pro-regenerative to pro-degenerative role we perform single-cell mRNA sequencing of human FAPs from healthy and injured human muscles across a spectrum of injury, focusing on rotator cuff tears. We identify multiple subpopulations with progenitor, adipogenic, or fibrogenic gene signatures. We utilize full spectrum flow cytometry to identify distinct FAP subpopulations based on highly multiplexed protein expression. Injury severity increases adipogenic commitment of FAP subpopulations and is driven by the downregulation of DLK1. Treatment of FAPs both in vitro and in vivo with DLK1 reduces adipogenesis and fatty infiltration, suggesting that during injury, reduced DLK1 within a subpopulation of FAPs may drive degeneration. This work highlights how stem cells perform varied functions depending on tissue context, by dynamically regulating subpopulation fate commitment, which can be targeted improve patient outcomes after injury.

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.

Deep Immunophenotyping of Human Whole Blood by Standardized Multi-parametric Flow Cytometry Analyses

Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease. High-throughput flow cytometry has been used extensively to reveal changes in immune cell composition and function at the single-cell level. Here, we describe six optimized 11-color flow cytometry panels for deep immunophenotyping of human whole blood. A total of 51 surface antibodies, which are readily available and validated, were selected to identify the key immune cell populations and evaluate their functional state in a single assay. The gating strategies for effective flow cytometry data analysis are included in the protocol. To ensure data reproducibility, we provide detailed procedures in three parts, including (1) instrument characterization and detector gain optimization, (2) antibody titration and sample staining, and (3) data acquisition and quality checks. This standardized approach has been applied to a variety of donors for a better understanding of the complexity of the human immune system.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00092-9.

A 16-color full spectrum flow cytometric analysis for comprehensive evaluation of T-cell reconstitution in SIV-infected rhesus macaques

T-cell reconstitution is central in human immunodeficiency virus (HIV) infection/disease progression. Simian immunodeficiency virus (SIV)-infected rhesus macaques (Macaca mulatta) have been the most widely used animal model for HIV research so far. An effective flow cytometry panel is crucial for monitoring the T cell reconstitution in SIV infection progression. We developed this sixteen-color flow cytometry-based panel for a T cell subsets analysis by manual gating and, once successfully gated, to characterize T cells function in-depth in rhesus macaques. This panel included markers to characterize CD4+ T cells and CD8+ T cells, T regulatory cells (Tregs), and T cell differentiation status (CD45RA and CCR7). Additionally, we included antibodies that measure T cell activation and proliferation molecules (CD69, HLA-DR, CD38 and Ki67), antibodies that examine the expressions of key PD-1 pathway molecule (PD-1), SIV potential target (CD32) and the primary SIV co-receptor CCR5 (CD195). High-dimensional single cell analysis was also performed to identify CD3+ T cells immunophenotypes of SIV-infected rhesus macaques. We designed this panel to evaluate the responses of different T cell subsets to SIV in whole blood from SIV-infected rhesus macaques.

Ensuring Full Spectrum Flow Cytometry Data Quality for High-Dimensional Data Analysis

Full spectrum flow cytometry (FSFC) allows for the analysis of more than 40 parameters at the single-cell level. Compared to the practice of manual gating, high-dimensional data analysis can be used to fully explore single-cell datasets and reduce analysis time. As panel size and complexity increases so too does the detail and time required to prepare and validate the quality of the resulting data for use in downstream high-dimensional data analyses. To ensure data analysis algorithms can be used efficiently and to avoid artifacts, some important steps should be considered. These include data cleaning (such as eliminating variable signal change over time, removing cell doublets, and antibody aggregates), proper unmixing of full spectrum data, ensuring correct scale transformation, and correcting for batch effects. We have developed a methodical step-by-step protocol to prepare full spectrum high-dimensional data for use with high-dimensional data analyses, with a focus on visualizing the impact of each step of data preparation using dimensionality reduction algorithms. Application of our workflow will aid FSFC users in their efforts to apply quality control methods to their datasets for use in high-dimensional analysis, and help them to obtain valid and reproducible results. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Data cleaning Basic Protocol 2: Validating the quality of unmixing Basic Protocol 3: Data scaling Basic Protocol 4: Batch-to-batch normalization.

Demands and technical developments of clinical flow cytometry with emphasis in quantitative, spectral, and imaging capabilities

As the gold-standard method for single-cell analysis, flow cytometry enables high-throughput and multiple-parameter characterization of individual biological cells. This review highlights the demands for clinical flow cytometry in laboratory hematology (e.g., diagnoses of minimal residual disease and various types of leukemia), summarizes state-of-the-art clinical flow cytometers (e.g., FACSLyricTM by Becton Dickinson, DxFLEX by Beckman Coulter), then considers innovative technical improvements in flow cytometry (including quantitative, spectral, and imaging approaches) to address the limitations of clinical flow cytometry in hematology diagnosis. Finally, driven by these clinical demands, future developments in clinical flow cytometry are suggested.

Increased monocyte distribution width in COVID-19 and sepsis arises from a complex interplay of altered monocyte cellular size and subset frequency

Introduction

Monocyte distribution width (MDW), a parameter generated alongside full blood counts (FBC) in new‐generation haematology analysers, has been proposed as a diagnostic test for severe infection/sepsis. It represents the standard deviation (SD) of the monocyte mean volume (MMV).

Methods

This study aimed to compare monocyte volumetric parameters retrieved by the UniCel DxH 900 haematology analyser (MMV and MDW) against corresponding parameters from the same sample measured using flow cytometry (forward scatter [FSC] mean and SD) in combination with phenotypic characterization of monocyte subtypes. We analysed blood samples from healthy individuals (n = 11) and patients with conditions associated with elevated MDW: sepsis (n = 26) and COVID‐19 (n = 15).

Results

Between‐instrument comparisons of monocyte volume parameters (MMV vs. FSC‐mean) showed relatively good levels of correlation, but comparisons across volume variability parameters (MDW vs. FSC‐SD) were poor. Stratification on sample type revealed this lack of correlation only within the sepsis group. Flow cytometry analysis revealed that in healthy controls intermediate monocytes are the largest and non‐classical the smallest cells. In each disease state, however, each monocyte subset undergoes different changes in volume and frequency that together determine the overall configuration of the monocyte population. Increased MDW was associated with reduced classical monocyte frequency and increased intermediate monocyte size. In COVID‐19, the range of monocyte sizes (smallest to largest) reduced, whereas in sepsis it increased.

Conclusion

Increased MDW in COVID‐19 and sepsis has no single flow cytometric phenotypic correlate. It represents—within a single value—the delicate equipoise between monocyte subset frequency and size.

Using Full-Spectrum Flow Cytometry to Phenotype Memory T and NKT Cell Subsets with Optimized Tissue-Specific Preparation Protocols

Full-spectrum flow cytometry is now routinely used in many laboratories internationally, and the demand for this technology is rapidly increasing. With capacity to use larger and more complex staining panels, standardized protocols are required for optimal panel design and analysis. Importantly, for ex vivo analysis, tissue preparation methods also need to be optimized to ensure samples are truly representative of tissues in situ. This is particularly relevant given the recent interest in adaptive immune cells that form residency in specific organs. Here we provide optimized protocols for tissue processing and phenotyping of memory T cells and natural killer T (NKT) cell subsets from liver, lung, spleen, and lymph node using full-spectrum flow cytometry. We provide a 21-color antibody panel for identification of different memory subsets, including tissue-resident memory T (T_RM ) cells, which are increasingly regarded as important effectors in adaptive immunity. We show that processing procedures can affect outcomes, with liver T_RM cells particularly sensitive to heat, such that accurate evaluation requires fast processing at defined temperatures. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Processing mouse liver for flow cytometric analysis of memory T and NKT cell subsets Basic Protocol 2: Processing mouse spleen for flow cytometric analysis of memory T and NKT cell subsets Basic Protocol 3: Processing mouse lungs for flow cytometric analysis of memory T and NKT cell subsets Basic Protocol 4: Processing mouse lymph nodes for flow cytometric analysis of memory T and NKT cell subsets Basic Protocol 5: Staining and flow cytometric analysis of samples for memory T and NKT cell subsets Support Protocol: Obtaining cell counts from flow cytometry data.