cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

Controlled human hookworm infection remodels plasmacytoid dendritic cells and regulatory T cells towards profiles seen in natural infections in endemic areas

Hookworm infection remains a significant public health concern, particularly in low- and middle-income countries, where mass drug administration has not stopped reinfection. Developing a vaccine is crucial to complement current control measures, which necessitates a thorough understanding of host immune responses. By leveraging controlled human infection models and high-dimensional immunophenotyping, here we investigated the immune remodeling following infection with 50 Necator americanus L3 hookworm larvae in four naïve volunteers over two years of follow-up and compared the profiles with naturally infected populations in endemic areas. Increased plasmacytoid dendritic cell frequency and diminished responsiveness to Toll-like receptor 7/8 ligand were observed in both controlled and natural infection settings. Despite the increased CD45RA+ regulatory T cell (Tregs) frequencies in both settings, markers of Tregs function, including inducible T-cell costimulatory (ICOS), tumor necrosis factor receptor 2 (TNFR2), and latency-associated peptide (LAP), as well as in vitro Tregs suppressive capacity were higher in natural infections. Taken together, this study provides unique insights into the immunological trajectories following a first-in-life hookworm infection compared to natural infections.

Multiparametric identification of putative senescent cells in skeletal muscle via mass cytometry

Senescence is an irreversible arrest of the cell cycle that can be characterized by markers of senescence such as p16, p21, and KI-67. The characterization of different senescence-associated phenotypes requires selection of the most relevant senescence markers to define reliable cytometric methodologies. Mass cytometry (a.k.a. Cytometry by time of flight, CyTOF) can monitor up to 40 different cell markers at the single-cell level and has the potential to integrate multiple senescence and other phenotypic markers to identify senescent cells within a complex tissue such as skeletal muscle, with greater accuracy and scalability than traditional bulk measurements and flow cytometry-based measurements. This article introduces an analysis framework for detecting putative senescent cells based on clustering, outlier detection, and Boolean logic for outliers. Results show that the pipeline can identify putative senescent cells in skeletal muscle with well-established markers such as p21 and potential markers such as GAPDH. It was also found that heterogeneity of putative senescent cells in skeletal muscle can partly be explained by their cell type. Additionally, autophagy-related proteins ATG4A, LRRK2, and GLB1 were identified as important proteins in predicting the putative senescent population, providing insights into the association between autophagy and senescence. It was observed that sex did not affect the proportion of putative senescent cells among total cells. However, age did have an effect, with a higher proportion observed in fibro/adipogenic progenitors (FAPs), satellite cells, M1 and M2 macrophages from old mice. Moreover, putative senescent cells from muscle of old and young mice show different expression levels of senescence-related proteins, with putative senescent cells of old mice having higher levels of p21 and GAPDH, whereas putative senescent cells of young mice had higher levels of IL-6. Overall, the analysis framework prioritizes multiple senescence-associated proteins to characterize putative senescent cells sourced from tissue made of different cell types.

Cataloging circulating CD3+CD56+ T cells through a series of stimulating (NKG2D, DNAM-1) and inhibitory (PD-1, TIGIT, and Tim-3) immune checkpoint receptors in women diagnosed with precancerous cervical lesions or invasive cervical carcinoma

Persistent human papillomavirus infection is associated with the development of premalignant lesions that can eventually lead to cervical cancer. In this study, we evaluated the expression of activating (NKG2D, DNAM-1) and inhibitory immune checkpoints receptors (PD-1, TIGIT, and Tim-3) in peripheral blood NKT-like (CD3+CD56+) lymphocytes from patients with cervical carcinoma (CC, n = 19), high-grade lesions (HG, n = 8), low-grade lesions (LG, n = 19) and healthy donors (HD, n = 17) using multiparametric flow cytometry. Dimensional data analysis showed four clusters within the CD3+CD56+ cells with different patterns of receptor expression. We observed upregulation of CD16 in CC and HG patients in one of the clusters. In another, TIGIT was upregulated, while DNAM-1 was downregulated. Throughout manual gating, we observed that NKT-like cells expressing activating receptors also co-express inhibitory receptors (PD-1 and TIGIT), which can affect the activation of these cells. A deeper characterization of the functional state of the cells may help to clarify their role in cervical cancer, as will the characterization of the NKT-like cells as cytotoxic CD8+ T cells or members of type I or type II NKT cells.

Single-cell genomics-based immune and disease monitoring in blood malignancies

Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Peripheral immune cells in metastatic breast cancer patients display a systemic immunosuppressed signature consistent with chronic inflammation

Immunotherapies blocking the PD-1/PD-L1 checkpoint show some efficacy in metastatic breast cancer (mBC) but are often hindered by immunosuppressive mechanisms. Understanding these mechanisms is crucial for personalized treatments, with peripheral blood monitoring representing a practical alternative to repeated biopsies. In the present study, we performed a comprehensive mass cytometry analysis of peripheral blood immune cells in 104 patients with HER2 negative mBC and 20 healthy donors (HD). We found that mBC patients had significantly elevated monocyte levels and reduced levels of CD4+ T cells and plasmacytoid dendritic cells, when compared to HD. Furthermore, mBC patients had more effector T cells and regulatory T cells, increased expression of immune checkpoints and other activation/exhaustion markers, and a shift to a Th2/Th17 phenotype. Furthermore, T-cell phenotypes identified by mass cytometry correlated with functionality as assessed by IFN-γ production. Additional analysis indicated that previous chemotherapy and CDK4/6 inhibition impacted the numbers and phenotype of immune cells. From 63 of the patients, fresh tumor samples were analyzed by flow cytometry. Paired PBMC-tumor analysis showed moderate correlations between peripheral CD4+ T and NK cells with their counterparts in tumors. Further, a CD4+ T cell cluster in PBMCs, that co-expressed multiple checkpoint receptors, was negatively associated with CD4+ T cell tumor infiltration. In conclusion, the identified systemic immune signatures indicate an immune-suppressed environment in mBC patients who had progressed/relapsed on standard treatments, and is consistent with ongoing chronic inflammation. These activated immuno-suppressive mechanisms may be investigated as therapeutic targets, and for use as biomarkers of response or treatment resistance.

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).

Immunometabolic Adaptation of CD19-Targeted CAR T Cells in the Central Nervous System Microenvironment of Patients Promotes Memory Development

Metabolic reprogramming is a hallmark of T-cell activation, and metabolic fitness is fundamental for T-cell-mediated antitumor immunity. Insights into the metabolic plasticity of chimeric antigen receptor (CAR) T cells in patients could help identify approaches to improve their efficacy in treating cancer. Here, we investigated the spatiotemporal immunometabolic adaptation of CD19-targeted CAR T cells using clinical samples from CAR T-cell-treated patients. Context-dependent immunometabolic adaptation of CAR T cells demonstrated the link between their metabolism, activation, differentiation, function, and local microenvironment. Specifically, compared with the peripheral blood, low lipid availability, high IL15, and low TGFβ in the central nervous system microenvironment promoted immunometabolic adaptation of CAR T cells, including upregulation of a lipolytic signature and memory properties. Pharmacologic inhibition of lipolysis in cerebrospinal fluid led to decreased CAR T-cell survival. Furthermore, manufacturing CAR T cells in cerebrospinal fluid enhanced their metabolic fitness and antileukemic activity. Overall, this study elucidates spatiotemporal immunometabolic rewiring of CAR T cells in patients and demonstrates that these adaptations can be exploited to maximize the therapeutic efficacy of CAR T cells.

SIGNIFICANCE

The spatiotemporal immunometabolic landscape of CD19-targeted CAR T cells from patients reveals metabolic adaptations in specific microenvironments that can be exploited to maximize the therapeutic efficacy of CAR T cells.

An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors

Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.

Harmonized analysis of PBMC by mass cytometry

Mass cytometry permits the high dimensional analysis of cellular systems at single-cell resolution with high throughput in various areas of biomedical research. Here, we provide a state-of-the-art protocol for the analysis of human peripheral blood mononuclear cells (PBMC) by mass cytometry. We focus on the implementation of measures promoting the harmonization of large and complex studies to aid robustness and reproducibility of immune phenotyping data.

Application of Mass Cytometry Platforms to Solid Organ Transplantation

Transplantation serves as the cornerstone of treatment for patients with end-stage organ disease. The prevalence of complications, such as allograft rejection, infection, and malignancies, underscores the need to dissect the complex interactions of the immune system at the single-cell level. In this review, we discuss studies using mass cytometry or cytometry by time-of-flight, a cutting-edge technology enabling the characterization of immune populations and cell-to-cell interactions in granular detail. We review the application of mass cytometry in human and experimental animal studies in the context of transplantation, uncovering invaluable contributions of the tool to understanding rejection and other transplant-related complications. We discuss recent innovations that have the potential to streamline and standardize mass cytometry workflows for application to multisite clinical trials. Additionally, we introduce imaging mass cytometry, a technique that couples the power of mass cytometry with spatial context, thereby mapping cellular interactions within tissue microenvironments. The synergistic integration of mass cytometry and imaging mass cytometry data with other omics data sets and high-dimensional data platforms to further define immune dynamics is discussed. In conclusion, mass cytometry technologies, when integrated with other tools and data, shed light on the intricate landscape of the immune response in transplantation. This approach holds significant potential for enhancing patient outcomes by advancing our understanding and facilitating the development of new diagnostics and therapeutics.

T-cell States, Repertoire, and Function in Classical Hodgkin Lymphoma Revealed through Single-Cell Analyses

The classical Hodgkin lymphoma (cHL) environment is comprised of a dense and complex immune cell infiltrate interspersed with rare malignant Hodgkin-Reed-Sternberg (HRS) cells. HRS cells are actively surveilled by endogenous T cells, but data linking phenotypic and functional T-cell states with clonality at the single-cell level in cHL is lacking. To address this knowledge gap, we performed paired single-cell RNA and T-cell receptor sequencing on 14 cHL and 5 reactive lymphoid tissue specimens. Conventional CD4+ T cells dominated the cHL landscape. However, recurrent clonal expansion within effector and exhausted CD8+ T-cell and regulatory T-cell clusters was uniquely observed in cHL specimens. Multiplex flow cytometric analysis revealed that most lymphoma-resident T cells produced effector cytokines upon ex vivo restimulation, arguing against a profound dysfunctional T-cell state in cHL. Our results raise new questions about the nature of T cells that mediate the antilymphoma response following programmed cell death protein 1 (PD-1) blockade therapy in cHL.

Mesothelial cells with mesenchymal features enhance peritoneal dissemination by forming a protumorigenic microenvironment

Malignant ascites accompanied by peritoneal dissemination contain various factors and cell populations as well as cancer cells; however, how the tumor microenvironment is shaped in ascites remains unclear. Single-cell proteomic profiling and a comprehensive proteomic analysis are conducted to comprehensively characterize malignant ascites. Here, we find defects in immune effectors along with immunosuppressive cell accumulation in ascites of patients with gastric cancer (GC) and identify five distinct subpopulations of CD45(-)/EpCAM(-) cells. Mesothelial cells with mesenchymal features in CD45(-)/EpCAM(-) cells are the predominant source of chemokines involved in immunosuppressive myeloid cell (IMC) recruitment. Moreover, mesothelial-mesenchymal transition (MMT)-induced mesothelial cells strongly express extracellular matrix (ECM)-related genes, including tenascin-C (TNC), enhancing metastatic colonization. These findings highlight the definite roles of the mesenchymal cell population in the development of a protumorigenic microenvironment to promote peritoneal dissemination.

Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings

Clinical studies are conducted to better understand the pathological mechanism of diseases and to find biomarkers associated with disease activity, drug response, or outcome prediction. Mass cytometry (MC) is a high-throughput single-cell technology that measures hundreds of cells per second with more than 40 markers per cell. Thus, it is a suitable tool for immune monitoring and biomarker discovery studies. Working in translational and clinical settings requires a careful experimental design to minimize, monitor, and correct the variations introduced during sample collection, preparation, acquisition, and analysis. In this review, we will focus on these important aspects of MC-related experiments and data curation in the context of translational clinical research projects.

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.

CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data

To gain a better understanding of the complex human immune system, it is necessary to measure and interpret numerous cellular protein expressions at the single cell level. Mass cytometry is a relatively new technology that offers unprecedented information about the protein expression of a single cell. Conversely, the analysis of high-dimensional and multiparametric mass cytometric data sets presents a new computational challenge. For instance, conventional "manual gating" analysis was inefficient and unreliable for multiparametric phenotyping of the heterogeneous immune cellular system; consequently, automated methods have been developed to address the high dimensionality of mass cytometry data and enhance the reproducibility of the analysis. Here, we present CyGate, a semiautomated method for classifying single cells into their respective cell types. CyGate learns a gating strategy from a reference data set, trains a model for cell classification, and then automatically analyzes additional data sets using the trained model. CyGate also supports the machine learning framework for the classification of "ungated" cells, which are typically disregarded by automated methods. CyGate's utility was demonstrated by its high performance in cell type classification and the lowest generalization error on various public data sets when compared to the state-of-the-art semiautomated methods. Notably, CyGate had the shortest execution time, allowing it to scale with a growing number of samples. CyGate is available at https://github.com/seungjinna/cygate.

Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study

The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.

CRB1-Associated Retinal Dystrophy Patients Have Expanded Lewis Glycoantigen-Positive T Cells

Purpose

Eye inflammation may occur in patients with inherited retinal dystrophies (IRDs) and is seen frequently in IRDs associated with mutations in the CRB1 gene. The purpose of this study was to determine the types of inflammatory cells involved in IRDs, by deep profiling the composition of peripheral blood mononuclear cells of patients with a CRB1-associated IRD.

Methods

This study included 33 patients with an IRD with confirmed CRB1 mutations and 32 healthy controls. A 43-parameter flow cytometry analysis was performed on peripheral blood mononuclear cells isolated from venous blood. FlowSOM and manual Boolean combination gating were used to identify and quantify immune cell subsets.

Results

Comparing patients with controls revealed a significant increase in patients in the abundance of circulating CD4+ T cells and CD8+ T cells that express sialyl Lewis X antigen. Furthermore, we detected a decrease in plasmacytoid dendritic cells and an IgA+CD24+CD38+ transitional B-cell subset in patients with an IRD.

Conclusions

Patients with a CRB1-associated IRD show marked changes in blood leukocyte composition, affecting lymphocyte and dendritic cell populations. These results implicate inflammatory pathways in the disease manifestations of IRDs.

Merging and imputation of flow cytometry data: A critical assessment

Although most modern techniques and analysis methods in multiparameter flow cytometry (MFC) allow for increased dimensionality for the characterization and quantification of cell populations, most MFC applications depend on flow cytometers measuring relatively small (<16) numbers of parameters. When more markers than the available parameters need to be acquired, these are commonly distributed over multiple independent measurements that include a backbone of common markers. Several methods have been proposed to impute values for combinations of markers that were not measured simultaneously. These imputation methods are frequently used without proper validation and knowledge of their effects on data analysis. We evaluated the performance of existing imputation software (Infinicyt, CyTOFmerge, CytoBackBone, and cyCombine) in approximating known measured expression data in terms of similarity in visual appearance, cell expression, and gating in different datasets by splitting MFC samples into separate measurements with partially overlapping markers and re-calculating missing marker expression. Out of the assessed packages, CyTOFmerge showed the most accurate approximation of the known expression in terms of similar expression values and concordance with manual gating, with a mean F-score between 0.53 and 0.87 when retrieving cell populations in different datasets. Performance remained inadequate for all methods, with only limited similarity at the cell level. In conclusion, the use of imputed MFC data should take such limitations into account and include independent validation of results to justify conclusions.

Automated and reproducible cell identification in mass cytometry using neural networks

The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration.

GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker's intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.

Cyclone: an accessible pipeline to analyze, evaluate, and optimize multiparametric cytometry data

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data

Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/ .

Computational Methods for Single-Cell Proteomics

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

Single-Cell Multiomics

Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.

Clonally expanded CD38hi cytotoxic CD8 T cells define the T cell infiltrate in checkpoint inhibitor-associated arthritis

Immune checkpoint inhibitor (ICI) therapies used to treat cancer, such as anti-PD-1 antibodies, can induce autoimmune conditions in some individuals. The T cell mechanisms mediating such iatrogenic autoimmunity and their overlap with spontaneous autoimmune diseases remain unclear. Here, we compared T cells from the joints of 20 patients with an inflammatory arthritis induced by ICI therapy (ICI-arthritis) with two archetypal autoimmune arthritides, rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Single-cell transcriptomic and antigen receptor repertoire analyses highlighted clonal expansion of an activated effector CD8 T cell population in the joints and blood of patients with ICI-arthritis. These cells were identified as CD38hiCD127- CD8 T cells and were uniquely enriched in ICI-arthritis joints compared with RA and PsA and also displayed an elevated interferon signature. In vitro, type I interferon induced CD8 T cells to acquire the ICI-associated CD38hi phenotype and enhanced cytotoxic function. In a cohort of patients with advanced melanoma, ICI therapy markedly expanded circulating CD38hiCD127- T cells, which were frequently bound by the therapeutic anti-PD-1 drug. In patients with ICI-arthritis, drug-bound CD8 T cells in circulation showed marked clonal overlap with drug-bound CD8 T cells from synovial fluid. These results suggest that ICI therapy directly targets CD8 T cells in patients who develop ICI-arthritis and induces an autoimmune pathology that is distinct from prototypical spontaneous autoimmune arthritides.

Human immune and gut microbial parameters associated with inter-individual variations in COVID-19 mRNA vaccine-induced immunity

COVID-19 mRNA vaccines induce protective adaptive immunity against SARS-CoV-2 in most individuals, but there is wide variation in levels of vaccine-induced antibody and T-cell responses. However, the mechanisms underlying this inter-individual variation remain unclear. Here, using a systems biology approach based on multi-omics analyses of human blood and stool samples, we identified several factors that are associated with COVID-19 vaccine-induced adaptive immune responses. BNT162b2-induced T cell response is positively associated with late monocyte responses and inversely associated with baseline mRNA expression of activation protein 1 (AP-1) transcription factors. Interestingly, the gut microbial fucose/rhamnose degradation pathway is positively correlated with mRNA expression of AP-1, as well as a gene encoding an enzyme producing prostaglandin E2 (PGE2), which promotes AP-1 expression, and inversely correlated with BNT162b2-induced T-cell responses. These results suggest that baseline AP-1 expression, which is affected by commensal microbial activity, is a negative correlate of BNT162b2-induced T-cell responses.

Cyclone: an accessible pipeline to analyze, evaluate and optimize multiparametric cytometry data

In the past decade, high-dimensional single cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation which are computationally intense and difficult to evaluate and optimize. Here we present Cyclone, an analysis pipeline integrating dimensionality reduction, clustering, evaluation and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification, but also enables the unsupervised identification of lymphocytes and mononuclear phagocytes subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on variety of cytometry datasets which will further power immunology research and provide a scaffold for biological discovery.

Mass cytometry analysis of blood from peanut-sensitized tolerant and clinically allergic infants

IgE-mediated food allergies in infants are a significant health concern, with peanut allergy being of particular interest due to its prevalence and severity. Among individuals who produce peanut-specific IgE some experience no adverse reaction on peanut consumption. This asymptomatic phenotype is known as sensitized tolerance. To elucidate the immune environment of peanut sensitized tolerant and clinically allergic one-year-olds, high-dimensional mass cytometry was conducted as part of the HealthNuts study. The resulting data includes peripheral blood mononuclear cells from 36 participants encompassing non-allergic, peanut sensitized with tolerance, and clinically peanut allergic infants. The raw mass cytometry data is described here and freely available for reuse through the Immunology Database and Analysis Portal (ImmPort). Additional allergy information and serum vitamin D levels of the participants were measured and are also included in the data upload. These high-dimensional mass cytometry data, when combined with clinical information, offer a broad immune profile of peanut allergic and sensitized tolerant infants.

Integrated machine learning approaches for flow cytometric quantification of myeloid-derived suppressor cells in acute sepsis

Highly heterogeneous cell populations require multiple flow cytometric markers for appropriate phenotypic characterization. This exponentially increases the complexity of 2D scatter plot analyses and exacerbates human errors due to variations in manual gating of flow data. We describe a semi-automated workflow, based entirely on the Flowjo Graphical User Interface (GUI), that involves the stepwise integration of several, newly available machine learning tools for the analysis of myeloid-derived suppressor cells (MDSCs) in septic and non-septic critical illness. Supervised clustering of flow cytometric data showed correlation with, but significantly different numbers of, MDSCs as compared with the cell numbers obtained by manual gating. Neither quantification method predicted 30-day clinical outcomes in a cohort of 16 critically ill and septic patients and 5 critically ill and non-septic patients. Machine learning identified a significant decrease in the proportion of PMN-MDSC in critically ill and septic patients as compared with healthy controls. There was no difference between the proportion of these MDSCs in septic and non-septic critical illness.

Evidence of premature lymphocyte aging in people with low anti-spike antibody levels after BNT162b2 vaccination

SARS-CoV-2 vaccines have unquestionably blunted the overall impact of the COVID-19 pandemic, but host factors such as age, sex, obesity, and other co-morbidities can affect vaccine efficacy. We identified individuals in a relatively healthy population of healthcare workers (CORALE study cohort) who had unexpectedly low peak anti-spike receptor binding domain (S-RBD) antibody levels after receiving the BNT162b2 vaccine. Compared to matched controls, "low responders" had fewer spike-specific antibody-producing B cells after the second and third/booster doses. Moreover, their spike-specific T cell receptor (TCR) repertoire had less depth and their CD4+ and CD8+T cell responses to spike peptide stimulation were less robust. Single cell transcriptomic evaluation of peripheral blood mononuclear cells revealed activation of aging pathways in low responder B and CD4+T cells that could underlie their attenuated anti-S-RBD antibody production. Premature lymphocyte aging may therefore contribute to a less effective humoral response and could reduce vaccination efficacy.

High-throughput mass cytometry staining for deep phenotyping of human natural killer cells

This protocol details the step-by-step procedure for in-depth immune phenotyping of peripheral blood natural killer (NK) cells from clinical samples by mass cytometry. The protocol consists of three main steps: PBMC incubation with a mix of metal-conjugated antibodies for extracellular phenotyping followed by fixation, permeabilization and incubation with a mix of metal-conjugated antibodies for staining of intracellular proteins, and sample acquisition on a mass cytometer. High-dimensional analysis enables the visualization of NK cell subsets and their phenotypical characteristics. For complete details on the use and execution of this protocol, please refer to Chretien et al. (2021).