A beginner's guide to supervised analysis for mass cytometry data in cancer biology

Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research.

Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper

Single-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional methods for annotating cytometry data face challenges in scalability, robustness, and accuracy. We propose a cytometry masked autoencoder (cyMAE), which automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies.

Seeing or believing in hyperplexed spatial proteomics via antibodies: New and old biases for an image-based technology

Hyperplexed in-situ targeted proteomics via antibody immunodetection (i.e., >15 markers) is changing how we classify cells and tissues. Differently from other high-dimensional single-cell assays (flow cytometry, single-cell RNA sequencing), the human eye is a necessary component in multiple procedural steps: image segmentation, signal thresholding, antibody validation, and iconographic rendering. Established methods complement the human image evaluation, but may carry undisclosed biases in such a new context, therefore we re-evaluate all the steps in hyperplexed proteomics. We found that the human eye can discriminate less than 64 out of 256 gray levels and has limitations in discriminating luminance levels in conventional histology images. Furthermore, only images containing visible signals are selected and eye-guided digital thresholding separates signal from noise. BRAQUE, a hyperplexed proteomic tool, can extract, in a marker-agnostic fashion, granular information from markers which have a very low signal-to-noise ratio and therefore are not visualized by traditional visual rendering. By analyzing a public human lymph node dataset, we also found unpredicted staining results by validated antibodies, which highlight the need to upgrade the definition of antibody specificity in hyperplexed immunostaining. Spatially hyperplexed methods upgrade and supplant traditional image-based analysis of tissue immunostaining, beyond the human eye contribution.

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.

Single-cell mass cytometry in immunological skin diseases

Immune-related skin diseases represent a collective of dermatological disorders intricately linked to dysfunctional immune system processes. These conditions are primarily characterized by an immoderate activation of the immune system or deviant immune responses, involving diverse immune components including immune cells, antibodies, and inflammatory mediators. However, the precise molecular dysregulation underlying numerous individual cases of these diseases and unique subsets respond under disease conditions remains elusive. Comprehending the mechanisms and determinants governing the homeostasis and functionality of diseases could offer potential therapeutic opportunities for intervention. Mass cytometry enables precise and high-throughput quantitative measurement of proteins within individual cells by utilizing antibodies labeled with rare heavy metal isotopes. Imaging mass cytometry employs mass spectrometry to obtain spatial information on cell-to-cell interactions within tissue sections, simultaneously utilizing more than 40 markers. The application of single-cell mass cytometry presents a unique opportunity to conduct highly multiplexed analysis at the single-cell level, thereby revolutionizing our understanding of cell population heterogeneity and hierarchy, cellular states, multiplexed signaling pathways, proteolysis products, and mRNA transcripts specifically in the context of many autoimmune diseases. This information holds the potential to offer novel approaches for the diagnosis, prognostic assessment, and monitoring responses to treatment, thereby enriching our strategies in managing the respective conditions. This review summarizes the present-day utilization of single-cell mass cytometry in studying immune-related skin diseases, highlighting its advantages and limitations. This technique will become increasingly prevalent in conducting extensive investigations into these disorders, ultimately yielding significant contributions to their accurate diagnosis and efficacious therapeutic interventions.

Comprehensive immune modulation mechanisms of Angong Niuhuang Wan in ischemic stroke: Insights from mass cytometry analysis

BACKGROUND

Angong Niuhuang Wan (AGNHW, ), is a classical medicinal formula in Traditional Chinese Medicine (TCM) that has been appreciated for its neuroprotective properties in ischemic cerebral injuries, yet its intricate mechanisms remain only partially elucidated.

AIMS

This study leverages advanced Mass cytometry (CyTOF) to analyze AGNHW's multifaceted immunomodulation effects in-depth, emphasizing previously underexplored areas.

RESULTS

AGNHW mitigated monocyte-derived macrophages (MoDM) infiltration in the brain, distinguishing its effects on those from microglia. While the vehicle group exhibited elevated inflammatory markers like CD4, CD8a, and CD44 in ischemic brains, the AGNHW-treated group attenuated their expressions, indicating AGNHW's potential to temper the post-ischemic inflammatory response. Systemically, AGNHW modulated fundamental immune cell dynamics, notably augmenting CD8+ T cells, B cells, monocytes, and neutrophil counts in the peripheral blood under post-stroke conditions. Intracellularly, AGNHW exhibited its targeted modulation of the signaling pathways, revealing a remarked inhibition of key markers like IκBα, indicating potential suppression of inflammatory responses in ischemic brain injuries.

CONCLUSION

This study offers a comprehensive portrait of AGNHW's immunomodulation effects on ischemic stroke, illuminating its dual sites of action-both cerebral and systemic-and its nuanced modulation of cellular and molecular dynamics.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

CD57-positive CD8 + T cells define the response to anti-programmed cell death protein-1 immunotherapy in patients with advanced non-small cell lung cancer

Immune checkpoint inhibitors have transformed the treatment landscape of non-small cell lung cancer (NSCLC). However, accurately identifying patients who will benefit from immunotherapy remains a challenge. This study aimed to discover potential biomarkers for predicting immunotherapy response in NSCLC patients. Single-cell mass cytometry (CyTOF) was utilized to analyze immune cell subsets in peripheral blood mononuclear cells (PBMCs) obtained from NSCLC patients before and 12 weeks after single-agent immunotherapy. The CyTOF findings were subsequently validated using flow cytometry and multiplex immunohistochemistry/immunofluorescence in PBMCs and tumor tissues, respectively. RNA sequencing (RNA-seq) was performed to elucidate the underlying mechanisms. In the CyTOF cohort (n = 20), a high frequency of CD57+CD8+ T cells in PBMCs was associated with durable clinical benefit from immunotherapy in NSCLC patients (p = 0.034). This association was further confirmed in an independent cohort using flow cytometry (n = 27; p < 0.001), with a determined cutoff value of 12.85%. The cutoff value was subsequently validated in another independent cohort (AUC = 0.733). We also confirmed the CyTOF findings in pre-treatment formalin-fixed and paraffin-embedded tissues (n = 90; p < 0.001). RNA-seq analysis revealed 475 differentially expressed genes (DEGs) between CD57+CD8+ T cells and CD57-CD8+ T cells, with functional analysis identifying DEGs significantly enriched in immune-related signaling pathways. This study highlights CD57+CD8+ T cells as a promising biomarker for predicting immunotherapy success in NSCLC patients.

Elasto-inertial microfluidic separation of microspheres with submicron resolution at high-throughput

Elasto-inertial microfluidic separation offers many advantages including high throughput and separation resolution. Even though the separation efficiency highly depends on precise control of the flow conditions, no concrete guidelines have been reported yet in elasto-inertial microfluidics. Here, we propose a dimensionless analysis for precise estimation of the microsphere behaviors across the interface of Newtonian and viscoelastic fluids. Reynolds number, modified Weissenberg number, and modified elastic number are used to investigate the balance between inertial and elastic lift forces. Based on the findings, we introduce a new dimensionless number defined as the width of the Newtonian fluid stream divided by microsphere diameter. The proposed dimensionless analysis allows us to predict whether the microspheres migrate across the co-flow interface. The theoretical estimation is found to be in good agreement with the experimental results using 2.1- and 3.2-μm-diameter polystyrene microspheres in a co-flow of water and polyethylene oxide solution. Based on the theoretical estimation, we also realize submicron separation of the microspheres with 2.1 and 2.5 μm in diameter at high throughput, high purity (>95%), and high recovery rate (>97%). The applicability of the proposed method was validated by separation of platelets from similar-sized Escherichia coli (E.coli).

A novel mass cytometry protocol optimized for immunophenotyping of low-frequency antigen-specific T cells

Understanding antigen-specific T-cell responses, for example, following virus infections or allergen exposure, is of high relevance for the development of vaccines and therapeutics. We aimed on optimizing immunophenotyping of T cells after antigen stimulation by improving staining procedures for flow and mass cytometry. Our method can be used for primary cells of both mouse and human origin for the detection of low-frequency T-cell response using a dual-barcoding system for individual samples and conditions. First, live-cell barcoding was performed using anti-CD45 antibodies prior to an in vitro T-cell stimulation assay. Second, to discriminate between stimulation conditions and prevent cell loss, sample barcoding was combined with a commercial barcoding solution. This dual-barcoding approach is cell sparing and, therefore, particularly relevant for samples with low cell numbers. To further reduce cell loss and to increase debarcoding efficiency of multiplexed samples, we combined our dual-barcoding approach with a new centrifugation-free washing system by laminar flow (Curiox™). Finally, to demonstrate the benefits of our established protocol, we assayed virus-specific T-cell response in SARS-CoV-2-vaccinated and SARS-CoV-2-infected patients and compared with healthy non-exposed individuals by a high-parameter CyTOF analysis. We could reveal a heterogeneity of phenotypes among responding CD4, CD8, and gd-T cells following antigen-specific stimulations. Our protocol allows to assay antigen-specific responses of minute populations of T cells to virus-derived peptides, allergens, or other antigens from the same donor sample, in order to investigate qualitative and quantitative differences.

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.

Mass cytometry as a tool in target validation and drug discovery

Mass cytometry provides highly multiparametric data at a single cell level, coupling the specificity and sensitivity of time-of-flight mass spectrometry with the single-cell throughput of flow cytometry. It offers great value in interrogating the potentially heterogenous impact that a drug may have on a biological system, allowing an investigator to capture not just changes in cell behavior, but how these changes may differ between cell subtypes. In this chapter, we review the technical details of the platform as well as its limitations, before describing our approach to planning and running a mass cytometry experiment. A series of method modules, spanning the staining process through to data cleaning, are described that are then combined to create three separate experiments. The first experiment illustrates a core process in mass cytometry: the validation and titration of a metal-conjugated antibody reporter. The second experiment explores the impact of a kinase inhibitor on cell cycle and apoptosis pathways of a human myeloma cell line. And the third experiment exploits the multiparametric capability of mass cytometry, by exploring the differential expression changes in a transcription factor upon drug treatment across the cellular compartments of a peripheral blood mononuclear cell sample.

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.

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.

Obesity alters monocyte developmental trajectories to enhance metastasis

Obesity is characterized by chronic systemic inflammation and enhances cancer metastasis and mortality. Obesity promotes breast cancer metastasis to lung in a neutrophil-dependent manner; however, the upstream regulatory mechanisms of this process remain unknown. Here, we show that obesity-induced monocytes underlie neutrophil activation and breast cancer lung metastasis. Using mass cytometry, obesity favors the expansion of myeloid lineages while restricting lymphoid cells within the peripheral blood. RNA sequencing and flow cytometry revealed that obesity-associated monocytes resemble professional antigen-presenting cells due to a shift in their development and exhibit enhanced MHCII expression and CXCL2 production. Monocyte induction of the CXCL2-CXCR2 axis underlies neutrophil activation and release of neutrophil extracellular traps to promote metastasis, and enhancement of this signaling axis is observed in lung metastases from obese cancer patients. Our findings provide mechanistic insight into the relationship between obesity and cancer by broadening our understanding of the interactive role that myeloid cells play in this process.

A taxonomy of multiple regression methods for immunologists

Multiple regression is a powerful tool in the immunologist's toolbox. This paper defines multiple regression, discusses availability and accessibility, provides some additional helpful definitions, treats the topics of transformation and extreme value screening, and establishes the paper's scope and philosophy. Then eleven methods of multiple regression are detailed, giving strengths and limitations. Throughout an emphasis is placed on application to immunological assays. A flowchart to guide selection of multiple regression methods is provided.

Multicolor flow cytometry-based immunophenotyping for preclinical characterization of nanotechnology-based formulations: an insight into structure activity relationship and nanoparticle biocompatibility profiles

Introduction

Immunophenotyping, which is the identification of immune cell subsets based on antigen expression, is an integral technique used to determine changes of cell composition and activation in various disease states or as a response to different stimuli. As nanoparticles are increasingly utilized for diagnostic and therapeutic applications, it is important to develop methodology that allows for the evaluation of their immunological impact. Therefore, the development of techniques such as immunophenotyping are desirable. Currently, the most common technique used to perform immunophenotyping is multicolor flow cytometry.

Methods

We developed two distinct multicolor flow cytometry immunophenotyping panels which allow for the evaluation of the effects of nanoparticles on the composition and activation status of treated human peripheral blood mononuclear cells. These two panels assess the presence of various lymphoid and myeloid-derived cell populations as well as aspects of their activation statuses-including proliferation, adhesion, co-stimulation/presentation, and early activation-after treatment with controls or nanoparticles. To conduct assay performance qualification and determine the applicability of this method to preclinical characterization of nanoparticles, we used clinical-grade nanoformulations (AmBisome, Doxil and Feraheme) and research-grade PAMAM dendrimers of different sizes (G3, G4 and G5) and surface functionalities (amine-, carboxy- and hydroxy-).

Results and Discussion

We found that formulations possessing intrinsic fluorescent properties (e.g., Doxil and AmBisome) interfere with accurate immunophenotyping; such interference may be partially overcome by dilution. In the absence of interference (e.g., in the case of dendrimers), nanoparticle size and surface functionalities determine their effects on the cells with large amine-terminated dendrimers being the most reactive.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent (x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention.

Significance

This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis

Summary

While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface.

Availability and implementation

{tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Deep Immune Profiling of Multiple Myeloma at Diagnosis and under Lenalidomide Maintenance Therapy

The bone marrow microenvironment interacts with malignant cells and regulates cancer survival and immune evasion in multiple myeloma (MM). We investigated the immune profiles of longitudinal bone marrow samples from patients with newly diagnosed MM (n = 18) using cytometry by time-of-flight. The results before and during treatment were compared between patients with good (GR, n = 11) and bad (BR, n = 7) responses to lenalidomide/bortezomib/dexamethasone-based treatment. Before treatment, the GR group had a lower tumor cell burden and a higher number of T cells with a phenotype shifted toward CD8+ T cells expressing markers attributed to cytotoxicity (CD45RA and CD57), a higher abundance of CD8+ terminal effector cells, and a lower abundance of CD8+ naïve T cells. On natural killer (NK) cells, increased expression of CD56 (NCAM), CD57, and CD16 was seen at baseline in the GR group, indicating their maturation and cytotoxic potential. During lenalidomide-based treatment, the GR patients showed an increase in effector memory CD4+ and CD8+ T-cell subsets. These findings support distinct immune patterns in different clinical contexts, suggesting that deep immune profiling could be used for treatment guidance and warrants further exploration.

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

Characterization of myelodysplastic syndromes hematopoietic stem and progenitor cells using mass cytometry

BACKGROUND

Myelodysplastic syndromes (MDS) at risk of transformation to acute myeloid leukemia (AML) are difficult to identify. The bone marrows of MDS patients harbor specific hematopoietic stem and progenitor cell (HSPC) abnormalities that may be associated with sub-types and risk-groups. Leukemia-associated characteristics of such cells may identify MDS patients at risk of progression to AML and provide insight in the pathobiology of MDS.

METHODS

Bone marrow samples from healthy donors (n = 10), low risk (n = 12) and high risk (n = 13) MDS patients were collected, in addition, AML samples for 5 out of 6 MDS patients that progressed. Mass cytometry was applied to assess expression of stem cell subset and leukemia-associated immunophenotype markers.

RESULTS

We analyzed the data using FlowSOM to cluster cells with similar expression of 10 commonly used stem cell markers. Metaclusters (n = 20) of these clusters represented populations of cells with a related phenotype, largely resembling known stem cell subsets. Within specific subsets, intra-cellular expression levels of pCREB, IkBα, or pS6 differed significantly between healthy bone marrow (HBM) and MDS or consecutive secondary AML samples. CD34, CD44, and CD49f expression was significantly increased in high risk MDS and AML-associated metaclusters. We identified MDS/sAML cells with aberrant phenotypes when compared to HBM. Such cells were observed in clusters of both primary MDS and secondary AML samples.

CONCLUSIONS

High-dimensional mass cytometry and computational data analyses enabled characterization of HSPC subsets in MDS and identification of leukemia stem cell populations based on their immunophenotype. Stem cells in MDS that display leukemia-associated features may predict the risk of developing AML.

Reagents for Mass Cytometry

Mass cytometry (cytometry by time-of-flight detection [CyTOF]) is a bioanalytical technique that enables the identification and quantification of diverse features of cellular systems with single-cell resolution. In suspension mass cytometry, cells are stained with stable heavy-atom isotope-tagged reagents, and then the cells are nebulized into an inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) instrument. In imaging mass cytometry, a pulsed laser is used to ablate ca. 1 μm² spots of a tissue section. The plume is then transferred to the CyTOF, generating an image of biomarker expression. Similar measurements are possible with multiplexed ion bean imaging (MIBI). The unit mass resolution of the ICP-TOF-MS detector allows for multiparametric analysis of (in principle) up to 130 different parameters. Currently available reagents, however, allow simultaneous measurement of up to 50 biomarkers. As new reagents are developed, the scope of information that can be obtained by mass cytometry continues to increase, particularly due to the development of new small molecule reagents which enable monitoring of active biochemistry at the cellular level. This review summarizes the history and current state of mass cytometry reagent development and elaborates on areas where there is a need for new reagents. Additionally, this review provides guidelines on how new reagents should be tested and how the data should be presented to make them most meaningful to the mass cytometry user community.

Macrophage Phenotyping in Atherosclerosis by Proteomics

Macrophages are heterogeneous and plastic cells, able to adapt their phenotype and functions to changes in the microenvironment. They are involved in several homeostatic processes and also in many human diseases, including atherosclerosis, where they participate in all the stages of the disease. For these reasons, macrophages have been studied extensively using different approaches, including proteomics. Proteomics, indeed, may be a powerful tool to better understand the behavior of these cells, and a careful analysis of the proteome of different macrophage phenotypes can help to better characterize the role of these phenotypes in atherosclerosis and provide a broad view of proteins that might potentially affect the course of the disease. In this review, we discuss the different proteomic techniques that have been used to delineate the proteomic profile of macrophage phenotypes and summarize some results that can help to elucidate the roles of macrophages and develop new strategies to counteract the progression of atherosclerosis and/or promote regression.

Protocol for large scale whole blood immune monitoring by mass cytometry and Cyto Quality Pipeline

Mass cytometry (MC) is a powerful large-scale immune monitoring technology. To maximize MC data quality, we present a protocol for whole blood analysis together with an R package, Cyto Quality Pipeline (CytoQP), which minimizes the experimental artifacts and batch effects to ensure data reproducibility. We describe the steps to stimulate, fix, and freeze blood samples before acquisition to make them suitable for retrospective studies. We then detail the use of barcoding and reference samples to facilitate multicenter and multi-batch experiments. For complete details on the use and execution of this protocol, please refer to Rybakowska et al. (2021a) and (2021b).

Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison

High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.

Facets of ICP-MS and their potential in the medical sciences-Part 2: nanomedicine, immunochemistry, mass cytometry, and bioassays

Inductively coupled-plasma mass spectrometry (ICP-MS) has transformed our knowledge on the role of trace and major elements in biology and has emerged as the most versatile technique in elemental mass spectrometry. The scope of ICP-MS has dramatically changed since its inception, and nowadays, it is a mature platform technology that is compatible with chromatographic and laser ablation (LA) systems. Over the last decades, it kept pace with various technological advances and was inspired by interdisciplinary approaches which endorsed new areas of applications. While the first part of this review was dedicated to fundamentals in ICP-MS, its hyphenated techniques and the application in biomonitoring, isotope ratio analysis, elemental speciation analysis, and elemental bioimaging, this second part will introduce relatively current directions in ICP-MS and their potential to provide novel perspectives in the medical sciences. In this context, current directions for the characterisation of novel nanomaterials which are considered for biomedical applications like drug delivery and imaging platforms will be discussed while considering different facets of ICP-MS including single event analysis and dedicated hyphenated techniques. Subsequently, immunochemistry techniques will be reviewed in their capability to expand the scope of ICP-MS enabling analysis of a large range of biomolecules alongside elements. These methods inspired mass cytometry and imaging mass cytometry and have the potential to transform diagnostics and treatment by offering new paradigms for personalised medicine. Finally, the interlacing of immunochemistry methods, single event analysis, and functional nanomaterials has opened new horizons to design novel bioassays which promise potential as assets for clinical applications and larger screening programs and will be discussed in their capabilities to detect low-level proteins and nucleic acids.

Facets of ICP-MS and their potential in the medical sciences-Part 1: fundamentals, stand-alone and hyphenated techniques

Since its inception in the early 80s, inductively coupled plasma-mass spectrometry has developed to the method of choice for the analysis of elements in complex biological systems. High sensitivity paired with isotopic selectivity and a vast dynamic range endorsed ICP-MS for the inquiry of metals in the context of biomedical questions. In a stand-alone configuration, it has optimal qualities for the biomonitoring of major, trace and toxicologically relevant elements and may further be employed for the characterisation of disrupted metabolic pathways in the context of diverse pathologies. The on-line coupling to laser ablation (LA) and chromatography expanded the scope and application range of ICP-MS and set benchmarks for accurate and quantitative speciation analysis and element bioimaging. Furthermore, isotopic analysis provided new avenues to reveal an altered metabolism, for the application of tracers and for calibration approaches. In the last two decades, the scope of ICP-MS was further expanded and inspired by the introduction of new instrumentation and methodologies including novel and improved hardware as well as immunochemical methods. These additions caused a paradigm shift for the biomedical application of ICP-MS and its impact in the medical sciences and enabled the analysis of individual cells, their microenvironment, nanomaterials considered for medical applications, analysis of biomolecules and the design of novel bioassays. These new facets are gradually recognised in the medical communities and several clinical trials are underway. Altogether, ICP-MS emerged as an extremely versatile technique with a vast potential to provide novel insights and complementary perspectives and to push the limits in the medical disciplines. This review will introduce the different facets of ICP-MS and will be divided into two parts. The first part will cover instrumental basics, technological advances, and fundamental considerations as well as traditional and current applications of ICP-MS and its hyphenated techniques in the context of biomonitoring, bioimaging and elemental speciation. The second part will build on this fundament and describe more recent directions with an emphasis on nanomedicine, immunochemistry, mass cytometry and novel bioassays.

Combining Isoprenoid Probes with Antibody Markers for Mass Cytometric Analysis of Prenylation in Single Cells

Protein prenylation is an essential post-translational modification that plays a key role in facilitating protein localization. Aberrations in protein prenylation have been indicated in multiple disease pathologies including progeria, some forms of cancer, and Alzheimer's disease. While there are single-cell methods to study prenylation, these methods cannot simultaneously assess prenylation and other cellular changes in the complex cell environment. Here, we report a novel method to monitor, at the single-cell level, prenylation and expression of autophagy markers. An isoprenoid analogue containing a terminal alkyne, substrate of prenylation enzymes, was metabolically incorporated into cells in culture. Treatment with a terbium reporter containing an azide functional group, followed by copper-catalyzed azide-alkyne cycloaddition, covalently attached terbium ions to prenylated proteins within cells. In addition, simultaneous treatment with a holmium-containing analogue of the reporter, without an azide functional group, was used to correct for non-specific retention at the single-cell level. This procedure was compatible with other mass cytometric sample preparation steps that use metal-tagged antibodies. We demonstrate that this method reports changes in levels of prenylation in competitive and inhibitor assays, while tracking autophagy molecular markers with metal-tagged antibodies. The method reported here makes it possible to track prenylation along with other molecular pathways in single cells of complex systems, which is essential to elucidate the role of this post-translational modification in disease, cell response to pharmacological treatments, and aging.

An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data

Mass cytometry (CyTOF) has become a method of choice for in-depth characterization of tissue heterogeneity in health and disease, and is currently implemented in multiple clinical trials, where higher quality standards must be met. Currently, preprocessing of raw files is commonly performed in independent standalone tools, which makes it difficult to reproduce. Here, we present an R pipeline based on an updated version of CATALYST that covers all preprocessing steps required for downstream mass cytometry analysis in a fully reproducible way. This new version of CATALYST is based on Bioconductor’s SingleCellExperiment class and fully unit tested. The R-based pipeline includes file concatenation, bead-based normalization, single-cell deconvolution, spillover compensation and live cell gating after debris and doublet removal. Importantly, this pipeline also includes different quality checks to assess machine sensitivity and staining performance while allowing also for batch correction. This pipeline is based on open source R packages and can be easily be adapted to different study designs. It therefore has the potential to significantly facilitate the work of CyTOF users while increasing the quality and reproducibility of data generated with this technology.

Mapping Cell Phenomics with Multiparametric Flow Cytometry Assays

Phenomics explores the complex interactions among genes, epigenetics, symbiotic microorganisms, diet, and environmental exposure based on the physical, chemical, and biological characteristics of individuals and groups. Increasingly efficient and comprehensive phenotyping techniques have been integrated into modern phenomics-related research. Multicolor flow cytometry technology provides more measurement parameters than conventional flow cytometry. Based on detailed descriptions of cell phenotypes, rare cell populations and cell subsets can be distinguished, new cell phenotypes can be discovered, and cell apoptosis characteristics can be detected, which will expand the potential of cell phenomics research. Based on the enhancements in multicolor flow cytometry hardware, software, reagents, and method design, the present review summarizes the recent advances and applications of multicolor flow cytometry in cell phenomics, illuminating the potential of applying phenomics in future studies.

Simplified mass cytometry protocol for in-plate staining, barcoding, and cryopreservation of human PBMC samples in clinical trials

With the increasing use of mass cytometry in clinical research, a simplified and standardized protocol for immunophenotyping human peripheral blood mononuclear cells (PBMCs) in clinical trials is needed. We present a simplified in-plate staining protocol for up to 80 samples, for laboratories of all mass cytometry expertise levels, aimed to generate reproducible datasets for large clinical cohorts. In this protocol, we provide details on the requirements to obtain meaningful results, spanning from sample quality, barcoding, and batch-freezing of stained samples.

Immunophenotyping: Analytical approaches and role in preclinical development of nanomedicines

Pharmaceutical products can activate immune cells, suppress their function, or change the immune responses to traditional immunologically active agonists such as those present in microbes. Therefore, the assessment of immunostimulation, immunosuppression, and immunomodulation comprises the backbone of immunotoxicity studies of new drug entities. Depending on physicochemical properties (e.g., size, charge, surface functionalities, hydrophobicity), nanoparticles can be immunostimulatory, immunosuppressive, and immunomodulatory. Various methods and experimental frameworks have been established to support preclinical translational studies of nanotechnology-based drug products. Immunophenotyping after the exposure of cells or preclinical animal models to nanoparticles can provide critical information about the changes in both the numbers of immune cells and their activation status. However, this methodology is underutilized in preclinical studies of engineered nanomaterials. Herein, we review current literature about varieties of instrumentation and methods utilized for immunophenotyping, discuss their advantages and limitations, and propose a roadmap for applying immunophenotyping to support preclinical immunological characterization of nanotechnology-based formulations.

CyTOF® for the Masses

Mass cytometry has revolutionized immunophenotyping, particularly in exploratory settings where simultaneous breadth and depth of characterization of immune populations is needed with limited samples such as in preclinical and clinical tumor immunotherapy. Mass cytometry is also a powerful tool for single-cell immunological assays, especially for complex and simultaneous characterization of diverse intratumoral immune subsets or immunotherapeutic cell populations. Through the elimination of spectral overlap seen in optical flow cytometry by replacement of fluorescent labels with metal isotopes, mass cytometry allows, on average, robust analysis of 60 individual parameters simultaneously. This is, however, associated with significantly increased complexity in the design, execution, and interpretation of mass cytometry experiments. To address the key pitfalls associated with the fragmentation, complexity, and analysis of data in mass cytometry for immunologists who are novices to these techniques, we have developed a comprehensive resource guide. Included in this review are experiment and panel design, antibody conjugations, sample staining, sample acquisition, and data pre-processing and analysis. Where feasible multiple resources for the same process are compared, allowing researchers experienced in flow cytometry but with minimal mass cytometry expertise to develop a data-driven and streamlined project workflow. It is our hope that this manuscript will prove a useful resource for both beginning and advanced users of mass cytometry.

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

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

The Route of Vaccine Administration Determines Whether Blood Neutrophils Undergo Long-Term Phenotypic Modifications

Innate immunity modulates adaptive immunity and defines the magnitude, quality, and longevity of antigen-specific T- and B- cell immune memory. Various vaccine and administration factors influence the immune response to vaccination, including the route of vaccine delivery. We studied the dynamics of innate cell responses in blood using a preclinical model of non-human primates immunized with a live attenuated vaccinia virus, a recombinant Modified vaccinia virus Ankara (MVA) expressing a gag-pol-nef fusion of HIV-1, and mass cytometry. We previously showed that it induces a strong, early, and transient innate response, but also late phenotypic modifications of blood myeloid cells after two months when injected subcutaneously. Here, we show that the early innate effector cell responses and plasma inflammatory cytokine profiles differ between subcutaneous and intradermal vaccine injection. Additionally, we show that the intradermal administration fails to induce more highly activated/mature neutrophils long after immunization, in contrast to subcutaneous administration. Different batches of antibodies, staining protocols and generations of mass cytometers were used to generate the two datasets. Mass cytometry data were analyzed in parallel using the same analytical pipeline based on three successive clustering steps, including SPADE, and categorical heatmaps were compared using the Manhattan distance to measure the similarity between cell cluster phenotypes. Overall, we show that the vaccine per se is not sufficient for the late phenotypic modifications of innate myeloid cells, which are evocative of innate immune training. Its route of administration is also crucial, likely by influencing the early innate response, and systemic inflammation, and vaccine biodistribution.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

Mass Cytometry for the Characterization of Individual Cell Types in Ovarian Solid Tumors

Mass cytometry aka Cytometry by Time-Of-Flight (CyTOF) is one of several recently developed multiparametric single-cell technologies designed to address cellular heterogeneity within healthy and diseased tissue. Mass cytometry is an adaptation of flow cytometry in which antibodies are labeled with stable heavy metal isotopes and the readout is by time-of-flight mass spectrometry. With minimal spillover between channels, mass cytometry enables readouts of up to 60 parameters per single cell. Critically, mass cytometry can identify minority cell populations that are lost in bulk tissue analysis. Mass cytometry has been used to great effect for the study of immune cells. We have extended its use to examine single cells within disaggregated solid tissues, specifically freshly resected tubo-ovarian high-grade serous tumors. Here we detail our protocols designed to ensure the production of high-quality single-cell datasets. The methodology can be modified to accommodate the study of other solid tissues.

New concepts on immunology of Multiple Sclerosis

Multiple sclerosis (MS) is a chronic inflammatory and immune-driven demyelinating disease of the central nervous system (CNS). During the past decade, major advances have been made to understand the development of MS as well as its progressive stage. Here, we discuss some emerging concepts on immunology of MS, including the growing interest in the involvement of gut microbiota and the recent pathological concepts on the progression phase. Finally, we present some immuno-tools recently available that contribute to better understand diversity and function of the immune system.

Network for Biomarker Immunoprofiling for Cancer Immunotherapy: Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC)

PURPOSE

Immunoprofiling to identify biomarkers and integration with clinical trial outcomes are critical to improving immunotherapy approaches for patients with cancer. However, the translational potential of individual studies is often limited by small sample size of trials and the complexity of immuno-oncology biomarkers. Variability in assay performance further limits comparison and interpretation of data across studies and laboratories.

EXPERIMENTAL DESIGN

To enable a systematic approach to biomarker identification and correlation with clinical outcome across trials, the Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC) Network was established through support of the Cancer MoonshotSM Initiative of the National Cancer Institute (NCI) and the Partnership for Accelerating Cancer Therapies (PACT) with industry partners via the Foundation for the NIH.

RESULTS

The CIMAC-CIDC Network is composed of four academic centers with multidisciplinary expertise in cancer immunotherapy that perform validated and harmonized assays for immunoprofiling and conduct correlative analyses. A data coordinating center (CIDC) provides the computational expertise and informatics platforms for the storage, integration, and analysis of biomarker and clinical data.

CONCLUSIONS

This overview highlights strategies for assay harmonization to enable cross-trial and cross-site data analysis and describes key elements for establishing a network to enhance immuno-oncology biomarker development. These include an operational infrastructure, validation and harmonization of core immunoprofiling assays, platforms for data ingestion and integration, and access to specimens from clinical trials. Published in the same volume are reports of harmonization for core analyses: whole-exome sequencing, RNA sequencing, cytometry by time of flight, and IHC/immunofluorescence.

Making the most of high-dimensional cytometry data

High-dimensional cytometry represents an exciting new era of immunology research, enabling the discovery of new cells and prediction of patient responses to therapy. A plethora of analysis and visualization tools and programs are now available for both new and experienced users; however, the transition from low- to high-dimensional cytometry requires a change in the way users think about experimental design and data analysis. Data from high-dimensional cytometry experiments are often underutilized, because of both the size of the data and the number of possible combinations of markers, as well as to a lack of understanding of the processes required to generate meaningful data. In this article, we explain the concepts behind designing high-dimensional cytometry experiments and provide considerations for new and experienced users to design and carry out high-dimensional experiments to maximize quality data collection.

T lymphocyte and monocyte subsets are dysregulated in type 1 diabetes patients with peripheral neuropathic pain

Diabetic neuropathic pain is a common and devastating complication of type 1 diabetes, but the mechanism by which it develops and persists is yet to be fully elucidated. This study utilised high-dimensional suspension mass cytometry in a pilot cohort to investigate differences in peripheral blood immunophenotypes between type 1 diabetes patients with (n ​= ​9) and without (n ​= ​9) peripheral neuropathic pain. The abundance and activation of several leukocyte subsets were investigated with unsupervised clustering approaches FlowSOM and SPADE, as well as by manual gating. Major findings included a proportional increase in CD4+ central memory T cells and an absolute increase in classical monocytes, non-classical monocytes, and mature natural killer cells in type 1 diabetes patients with pain compared to those without pain. The expression of CD27, CD127, and CD39 was upregulated on select T cell populations, and the phosphorylated form of pro-inflammatory transcription factor MK2 was upregulated across most populations. These results provide evidence that distinct immunological signatures are associated with painful neuropathy in type 1 diabetes patients. Further research may link these changes to mechanisms by which pain in type 1 diabetes is initiated and maintained, paving the way for much needed targeted treatments.

A Silica Coating Approach to Enhance Bioconjugation on Metal-Encoded Polystyrene Microbeads for Bead-Based Assays in Mass Cytometry

Bead-based assays in flow cytometry are multiplexed analytical techniques that allow rapid and simultaneous detection and quantification of a large number of analytes from small volumes of samples. The development of corresponding bead-based assays in mass cytometry (MC) is highly desirable since it could increase the number of analytes detected in a single assay. The microbeads for these assays have to be labeled with metal isotopes for MC detection. One must also be able to functionalize the bead surface with affinity reagents to capture the analytes. Metal-encoded polystyrene microbeads prepared by multi-stage dispersion polymerization can produce effective isotopic signals in MC with relatively small bead-to-bead variations. However, functionalizing this microbead surface with bioaffinity agents remains challenging, possibly due to the interference of the steric-stabilizing PVP corona on the microbead surface. Here, we report a systematic investigation of a silica coating approach to coat Eu-encoded microbeads with thin silica shells, to functionalize the surface with amino groups, and to introduce bioaffinity agents. We examine the effect of silica shell roughness on the bioconjugation capacity and the effect of silica shell thickness on signal quality in MC measurements. To limit non-specific binding, we converted the amino groups on the microbead surface to carboxylic acid groups. Antibodies were effectively attached to microbead by first conjugating NeutrAvidin to the carboxyl-modified bead surface and then attaching biotinylated antibodies to the NeutrAvidin-modified bead surface. The antibody-modified microbeads can specifically capture antigens, which were marked with isotopic labels, and generate strong signals in MC. These are promising results for the development of bead-based assays in MC.

Platelet Immunophenotyping by High-Dimensional Mass Cytometry

Platelets are small blood cells that contribute to hemostasis, immunity, and inflammation. Characterization of platelet surface markers allows for differentiation of activated platelets from resting platelets, diagnosis of platelet disorders, and investigation of platelet biology and pathology. In this article, we describe the use of mass cytometry or "CyTOF" (mass spectroscopy detection of metal-tagged antibodies on individual cells) to measure a large number of markers on each platelet and to identify platelet subsets based on the shared expression of multiple markers. This powerful new approach provides a vastly more detailed picture of platelet immunophenotypes than conventional flow cytometry and enables investigation of the roles of platelet subsets in health and disease. © 2021 Wiley Periodicals LLC. Basic Protocol: Platelet immunophenotyping by high-dimensional mass cytometry Support Protocol: Data preprocessing.

CyTOF: An Emerging Technology for Single-Cell Proteomics in the Mouse

The ability to analyze the proteome of single cells is critical for the advancement of studies of steady-state and pathological processes. Mass cytometry, or CyTOF, combines principles of mass spectrometry and flow cytometry to enable single-cell analysis of protein expression. CyTOF can simultaneously assess DNA content and proteins and has the capacity to measure 40 to 100 parameters in each cell. Applying this technology to tissues or cells on slides, termed imaging mass cytometry (IMC), allows for visualization of normal and diseased tissues in situ. The high-dimensional proteomic analysis that can be undertaken with CyTOF and IMC has the potential to enhance our understanding of complex and heterogeneous developmental and disease pathways. This article will describe the CyTOF experimental workflow, including reagent selection, sample preparation, and data analysis. CyTOF is compared to flow cytometry, focusing on the strengths and weaknesses of these powerful techniques. Importantly, we review key studies in mouse models of human disease that highlight the strength of CyTOF and IMC to drive discovery research and therapeutic advancement. © 2021 Wiley Periodicals LLC.

Predictive Markers of Immunogenicity and Efficacy for Human Vaccines

Vaccines represent one of the major advances of modern medicine. Despite the many successes of vaccination, continuous efforts to design new vaccines are needed to fight "old" pandemics, such as tuberculosis and malaria, as well as emerging pathogens, such as Zika virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccination aims at reaching sterilizing immunity, however assessing vaccine efficacy is still challenging and underscores the need for a better understanding of immune protective responses. Identifying reliable predictive markers of immunogenicity can help to select and develop promising vaccine candidates during early preclinical studies and can lead to improved, personalized, vaccination strategies. A systems biology approach is increasingly being adopted to address these major challenges using multiple high-dimensional technologies combined with in silico models. Although the goal is to develop predictive models of vaccine efficacy in humans, applying this approach to animal models empowers basic and translational vaccine research. In this review, we provide an overview of vaccine immune signatures in preclinical models, as well as in target human populations. We also discuss high-throughput technologies used to probe vaccine-induced responses, along with data analysis and computational methodologies applied to the predictive modeling of vaccine efficacy.

Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Imaging in Biology

Elemental imaging gives insight into the fundamental chemical makeup of living organisms. Every cell on Earth is comprised of a complex and dynamic mixture of the chemical elements that define structure and function. Many disease states feature a disturbance in elemental homeostasis, and understanding how, and most importantly where, has driven the development of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) as the principal elemental imaging technique for biologists. This review provides an outline of ICP-MS technology, laser ablation cell designs, imaging workflows, and methods of quantification. Detailed examples of imaging applications including analyses of cancers, elemental uptake and accumulation, plant bioimaging, nanomaterials in the environment, and exposure science and neuroscience are presented and discussed. Recent incorporation of immunohistochemical workflows for imaging biomolecules, complementary and multimodal imaging techniques, and image processing methods is also reviewed.

Data processing workflow for large-scale immune monitoring studies by mass cytometry

Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.