Mass cytometry: a powerful tool for dissecting the immune landscape

Quantifying Intracellular Platinum Accumulation Using Inductively Coupled Mass Spectrometry

The platinum-based anticancer drug cisplatin and its analog carboplatin are the most used chemotherapeutic agents worldwide. It is estimated that approximately half of all cancer patients are treated with platinum drugs at some point during the therapy regimen. Cisplatin covalently binds to purine nucleobases to form DNA adducts. Cisplatin therapy is faced with two key challenges. First, despite the initial response, many patients develop cisplatin resistance. Reduced cellular accumulation of cisplatin is one common cause of therapy resistance. Second, cisplatin treatment causes general cytotoxicity, leading to severe side effects. Monitoring the subcellular concentration of platinum chemotherapeutics will help yield clinical efficacy with the minimum possible dose. Inductively coupled plasma-mass spectrometry (ICP-MS) is an analytical technique to quantify the elemental composition of various types of liquified bulk samples with high sensitivity. This article describes quantifying cisplatin accumulation in chromatin and total cell lysate using ICP-MS. The method involves treating cells with cisplatin, isolating RNA-free DNA, digesting samples, ICP-MS instrumentation, and data analysis. Although we describe these steps in one cancer cell line, the protocol can be adapted to any cell line or tissue. The protocol should be a valuable resource for investigators interested in accurate measurement of subcellular concentration of platinum and other metallo-drugs. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Cell culture conditions for A2780 cells and cisplatin treatment Basic Protocol 2: Isolating cellular fractions and sample quantitation Basic Protocol 3: Sample digestion, ICP-MS data collection, and analysis.

Hypoxia inhibits the iMo/cDC2/CD8+ TRMs immune axis in the tumor microenvironment of human esophageal cancer

BACKGROUND

Esophageal cancer (ESCA) is a form of malignant tumor associated with chronic inflammation and immune dysregulation. However, the specific immune status and key mechanisms of immune regulation in this disease require further exploration.

METHODS

To investigate the features of the human ESCA tumor immune microenvironment and its possible regulation, we performed mass cytometry by time of flight, single-cell RNA sequencing, multicolor fluorescence staining of tissue, and flow cytometry analyses on tumor and paracancerous tissue from treatment-naïve patients.

RESULTS

We depicted the immune landscape of the ESCA and revealed that CD8+ (tissue-resident memory CD8+ T cells (CD8+ TRMs) were closely related to disease progression. We also revealed the heterogeneity of CD8+ TRMs in the ESCA tumor microenvironment (TME), which was associated with their differentiation and function. Moreover, the subset of CD8+ TRMs in tumor (called tTRMs) that expressed high levels of granzyme B and immune checkpoints was markedly decreased in the TME of advanced ESCA. We showed that tTRMs are tumor effector cells preactivated in the TME. We then demonstrated that conventional dendritic cells (cDC2s) derived from intermediate monocytes (iMos) are essential for maintaining the proliferation of CD8+ TRMs in the TME. Our preliminary study showed that hypoxia can promote the apoptosis of iMos and impede the maturation of cDC2s, which in turn reduces the proliferative capacity of CD8+ TRMs, thereby contributing to the progression of cancer.

CONCLUSIONS

Our study revealed the essential antitumor roles of CD8+ TRMs and preliminarily explored the regulation of the iMo/cDC2/CD8+ TRM immune axis in the human ESCA TME.

PAD4 Inhibitor-Functionalized Layered Double Hydroxide Nanosheets for Synergistic Sonodynamic Therapy/Immunotherapy Of Tumor Metastasis

Sonodynamic therapy (SDT) is demonstrated to trigger the systemic immune response of the organism and facilitate the treatment of metastatic tumors. However, SDT-mediated neutrophil extracellular traps (NETs) formation can promote tumor cell spread, thus weakening the therapeutic effectiveness of metastatic tumors. Herein, the amorphous CoW-layered double hydroxide (a-CoW-LDH) nanosheets are functionalized with a peptidyl arginine deiminase 4 (PAD4) inhibitor, i.e., YW3-56, to construct a multifunctional nanoagent (a-LDH@356) for synergistic SDT/immunotherapy. Specifically, a-CoW-LDH nanosheets can act as a sonosensitizer to generate abundant reactive oxygen species (ROS) under US irradiation. After loading with YW3-56, a-LDH@356 plus US irradiation not only effectively induces ROS generation and immunogenic cell death, but also inhibits the elevation of citrullinated histone H3 (H3cit) and the release of NETs, enabling a synergistic enhancement of anti-tumor metastasis effect. Using 4T1 tumor model, it is demonstrated that combining a-CoW-LDH with YW3-56 stimulates an anti-tumor response by upregulating the proportion of immune-activated cells and inducing polarization of M1 macrophages, and inhibits immune escape by downregulating the expression of PD-1 on immune cells under US irradiation, which not only arrests primary tumor progression with a tumor inhibition rate of 69.5% but also prevents tumor metastasis with the least number of lung metastatic nodules.

Systemic and local immunosuppression in glioblastoma and its prognostic significance

The effectiveness of tumor therapy, especially immunotherapy and oncolytic virotherapy, critically depends on the activity of the host immune cells. However, various local and systemic mechanisms of immunosuppression operate in cancer patients. Tumor-associated immunosuppression involves deregulation of many components of immunity, including a decrease in the number of T lymphocytes (lymphopenia), an increase in the levels or ratios of circulating and tumor-infiltrating immunosuppressive subsets [e.g., macrophages, microglia, myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs)], as well as defective functions of subsets of antigen-presenting, helper and effector immune cell due to altered expression of various soluble and membrane proteins (receptors, costimulatory molecules, and cytokines). In this review, we specifically focus on data from patients with glioblastoma/glioma before standard chemoradiotherapy. We discuss glioblastoma-related immunosuppression at baseline and the prognostic significance of different subsets of circulating and tumor-infiltrating immune cells (lymphocytes, CD4+ and CD8+ T cells, Tregs, natural killer (NK) cells, neutrophils, macrophages, MDSCs, and dendritic cells), including neutrophil-to-lymphocyte ratio (NLR), focus on the immune landscape and prognostic significance of isocitrate dehydrogenase (IDH)-mutant gliomas, proneural, classical and mesenchymal molecular subtypes, and highlight the features of immune surveillance in the brain. All attempts to identify a reliable prognostic immune marker in glioblastoma tissue have led to contradictory results, which can be explained, among other things, by the unprecedented level of spatial heterogeneity of the immune infiltrate and the significant phenotypic diversity and (dys)functional states of immune subpopulations. High NLR is one of the most repeatedly confirmed independent prognostic factors for shorter overall survival in patients with glioblastoma and carcinoma, and its combination with other markers of the immune response or systemic inflammation significantly improves the accuracy of prediction; however, more prospective studies are needed to confirm the prognostic/predictive power of NLR. We call for the inclusion of dynamic assessment of NLR and other blood inflammatory markers (e.g., absolute/total lymphocyte count, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, systemic immune-inflammation index, and systemic immune response index) in all neuro-oncology studies for rigorous evaluation and comparison of their individual and combinatorial prognostic/predictive significance and relative superiority.

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.

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.

High-dimensional analysis reveals an immune atlas and novel neutrophil clusters in the lungs of model animals with Actinobacillus pleuropneumoniae-induced pneumonia

Due to the increase in bacterial resistance, improving the anti-infectious immunity of the host is rapidly becoming a new strategy for the prevention and treatment of bacterial pneumonia. However, the specific lung immune responses and key immune cell subsets involved in bacterial infection are obscure. Actinobacillus pleuropneumoniae (APP) can cause porcine pleuropneumonia, a highly contagious respiratory disease that has caused severe economic losses in the swine industry. Here, using high-dimensional mass cytometry, the major immune cell repertoire in the lungs of mice with APP infection was profiled. Various phenotypically distinct neutrophil subsets and Ly-6C+ inflammatory monocytes/macrophages accumulated post-infection. Moreover, a linear differentiation trajectory from inactivated to activated to apoptotic neutrophils corresponded with the stages of uninfected, onset, and recovery of APP infection. CD14+ neutrophils, which mainly increased in number during the recovery stage of infection, were revealed to have a stronger ability to produce cytokines, especially IL-10 and IL-21, than their CD14- counterparts. Importantly, MHC-II+ neutrophils with antigen-presenting cell features were identified, and their numbers increased in the lung after APP infection. Similar results were further confirmed in the lungs of piglets infected with APP and Klebsiella pneumoniae infection by using a single-cell RNA-seq technique. Additionally, a correlation analysis between cluster composition and the infection process yielded a dynamic and temporally associated immune landscape where key immune clusters, including previously unrecognized ones, marked various stages of infection. Thus, these results reveal the characteristics of key neutrophil clusters and provide a detailed understanding of the immune response to bacterial pneumonia.

Integrated Proteomics and Single-Cell Mass Cytometry Analysis Dissects the Immune Landscape of Ankylosing Spondylitis

Mass cytometry is a powerful single-cell technology widely adopted to depict immune cell heterogeneity in different contexts. However, this method is only capable of examining several dozens of proteins simultaneously and requires a prior knowledge of the markers to be analyzed. Here we propose that the integration of mass cytometry with shot-gun proteomics may serve as a valuable tool to achieve an in-depth understanding of the immune system. By implementing such a strategy, we investigated the immune landscape of ankylosing spondylitis (AS), a chronic inflammatory arthritis with unclear etiology. The proteome alteration in peripheral blood mononuclear cells (PBMCs) was investigated by quantitative proteomics, and then mass cytometry analysis was conducted to decipher the immunome by considering the signaling molecules identified with differential expression by proteomics. As a result, we identified a wide spectrum of proteins dysregulated in AS, e.g., upregulation of glycolytic enzymes, downregulation of lipid transporters, and dysregulation of chemokine signaling molecules involved in proinflammatory cytokine production and leucocyte migration. Moreover, the single-cell analysis showed the upregulation of chemokine signaling regulators in subclusters of both innate and adaptive immune cells in AS. In addition, correlation analysis unveiled the interplay among Phenograph-identified subclusters of monocytes, CD4+ T cells, and CD8+ T cells. Taken together, our findings demonstrated that the integration of mass spectrometry-based proteomics and single-cell mass cytometry may serve as a useful tool to reveal clinically relevant information regarding useful targets and cellular phenotypes that could be further exploited to develop novel therapeutic strategies.

In-silico generation of high-dimensional immune response data in patients using a deep neural network

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.

Strategies of neutrophil diversification

Neutrophils are formidable defenders. Their vast numbers, constant production, high cytotoxicity and capacity to produce extracellular traps, underlie their ability to efficiently protect in a microorganism-rich world. However, neutrophils are much more than immune sentinels, as evidenced by the expanding repertoire of functions discovered in the context of tissue homeostasis, regeneration or chronic pathologies. In this Perspective, we discuss general functional features of the neutrophil compartment that may be relevant in most, if not all, physiological scenarios in which they participate, including specialization in naïve tissues, transcriptional noise in the bloodstream as a potential strategy for diversification and functional bias in inflammatory sites. We intentionally present the reader with more questions than answers and propose models and approaches that we hope will shed new light onto the biology of these fascinating cells and spark new directions of research.

Immune Profiling Reveals Decreases in Circulating Regulatory and Exhausted T Cells in Human Hypertension

Evidence from nonhuman animal models demonstrates an important role for immune cells in hypertension, but immune cell changes in human hypertension are less clear. Using mass cytometry, we demonstrate novel and selective reductions in CCR10+ regulatory T cells (Tregs) and PD-1+CD57-CD8+ memory T cells. RNA sequencing reveals that CCR10+ Tregs exhibit gene expression changes consistent with enhanced immunosuppressive function. In addition, CITE-Seq demonstrates that PD-1+CD57-CD8+ memory T cells exhibit features of T-cell exhaustion. Taken together, these results provide novel evidence for decreases in anti-inflammatory and/or hypofunctional T-cell populations that may contribute to enhanced inflammation in human hypertension.

GLIS1 intervention enhances anti-PD1 therapy for hepatocellular carcinoma by targeting SGK1-STAT3-PD1 pathway

BACKGROUND

GLI-similar 1 (GLIS1) is one of of Krüppel-like zinc finger proteins, which are either stimulators or inhibitors of genetic transcription. Nevertheless, its effects on T cell were elusive.

METHODS

In this study, we intend to explore the effects of GLIS1 on modulating the anticancer potency of CD8⁺ T cells in hepatocellular carcinoma (HCC). The expression of GLIS1 in CD8 peripheral blood mononuclear cell and CD8 tumor-infiltrating lymphocytes of HCC tissues was validated by quantificational real-time-PCR and flow cytometry. The anticancer potency of CD8⁺ T cells with GLIS1 knock down was confirmed in C57BL/6 mouse model and HCC patient-derived xenograft mice model. GLIS1^-/- C57BL/6 mice was applied to explore the effects GLIS1 on tumor immune microenvironment. Chromatin immunoprecipitation and RNA transcriptome sequencing analysis were both performed in GLIS1-knock down of CD8⁺ T cells.

RESULTS

GLIS1 was upregulated in exhausted CD8⁺ T cells in HCC. GLIS1 downregulation in CD8⁺ T cells repressed cancer development, elevated the infiltrate ability of CD8⁺ T cells, mitigated CD8⁺ T cell exhaustion and ameliorated the anti-PD1 reaction of CD8⁺ T cells in HCC. The causal link beneath this included transcriptional regulation of SGK1-STAT3-PD1 pathway by GLIS1, thereby maintaining the abundant PD1 expression on the surface of CD8⁺ T cells.

CONCLUSION

Our study revealed that GLIS1 promoted CD8⁺ T cell exhaustion in HCC through transcriptional regulating SGK1-STAT3-PD1 pathway. Downregulating the expression of GLIS1 in CD8⁺ T cells exerted an effect with anti-PD1 treatment synergistically, revealing a prospective method for HCC immune therapy.

Cytofluorometric characterization of the myeloid compartment of irradiated mouse tumors

The use of ionizing radiation (IR) is a cornerstone for the treatment of cancer and radiotherapy (RT) is used in roughly 50% of cancer patients. It is now well established that RT exerts widespread effects on the tumor stroma, including the immune environment. Together with its deeply characterized effects on the lymphoid compartment, RT also deeply affects the myeloid cell compartment. Fluorescence-activated flow cytometry is one of the most widely used technologies in immunology, allowing the multiparametric analysis of cells on a cell-by-cell basis. Here, we provide a detailed flow cytometry protocol to analyze the myeloid cell populations of human papillomavirus (HPV)-positive TC1/Luc tumors engrafted in the oral mucosa of immunocompetent mice, and to evaluate their modulations in response to RT. The same method, with slight modifications, can be used to study the tumor myeloid cells from a variety of other mouse tumors.

Revisiting transplant immunology through the lens of single-cell technologies

Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell- and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT.

Using mass cytometry for the analysis of samples of the human airways

Mass cytometry (MC) is a powerful method for mapping complex cellular systems at single-cell levels, based on the detection of cellular proteins. Numerous studies have been performed using human blood, but there is a lack of protocols describing the processing and labeling of bronchoalveolar lavage fluid (BALF) and nasal polyps (NP) for acquisition by MC. These specimens are essential in the investigation of immune cell characteristics in airway diseases such as asthma and chronic rhinosinusitis with NP (CRSwNP). Here we optimized a workflow for processing, labeling, and acquisition of BALF and NP cells by MC. Among three methods tested for NP digestion, combined enzymatic/mechanical processing yielded maximum cell recovery, viability and labeling patterns compared to the other methods. Treatment with DNAse improved sample acquisition by MC. In a final step, we performed a comparison of blood, BALF and NP cell composition using a 31-marker MC antibody panel, revealing expected differences between the different tissue but also heterogeneity among the BALF and NP samples. We here introduce an optimized workflow for the MC analysis of human NP and BALF, which enables comparative analysis of different samples in larger cohorts. A deeper understanding of immune cell characteristics in these samples may guide future researchers and clinicians to a better disease management.

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.

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.

High-Dimensional Immune Profiling by Mass Cytometry Revealed the Circulating Immune Cell Landscape in Patients With Intracranial Aneurysm

Background

Increasing evidence supports a critical role of chronic inflammation in intracranial aneurysm (IA). Understanding how the immunological alterations in IA provides opportunities for targeted treatment. However, there is a lack of comprehensive and detailed characterization of the changes in circulating immune cells in IA.

Objective

To perform a comprehensive and detailed characterization of the changes in circulating immune cells in patients with IA.

Methods

Peripheral blood mononuclear cell samples from IA patients (n = 26) and age-and sex-matched healthy controls (HCs, n = 20) were analyzed using high dimensional mass cytometry, and the frequency and phenotype of immune cell subtypes were assessed.

Results

We identified 28 cell clusters and found that the immune signature of IA consists of cluster changes. IA patients exhibited dysfunction of immunity, with dysregulation of CD4⁺ T-cell clusters, increased B cells and monocytes, and decreased CD8⁺ T cells, DNT cells, and DPT cells. Moreover, compared with findings in HC, IA was associated with enhanced lymphocyte and monocyte immune activation, with a higher expression of HLA-DR, CXCR3, and CX3CR1. In addition, the expression of TLR4, p-STAT3, and the exhaustion marker PD1 was increased in T cells, B cells, and NK cells in IA patients.

Conclusions

Our data provide an overview of the circulating immune cell landscape of IA patients, and reveal that the dysfunction of circulating immunity may play a potential role in the development of IA.

The influence of phytochemicals on cell heterogeneity in chronic inflammation-associated diseases: the prospects of single cell sequencing

Chronic inflammation-associated diseases include, but is not limited to cardiovascular disease, cancer, obesity, diabetes, etc. Cell heterogeneity is a prerequisite for understanding the physiological and pathological development of cell metabolism, and its response to external stimuli. Recently, dietary habits based on phytochemicals became increasingly recognized to play a pivotal role in chronic inflammation. Phytochemicals can relieve chronic inflammation by regulating inflammatory cell differentiation and immune cell response, but the influence of phytochemicals on cell heterogeneity from in vitro and ex vivo studies cannot simulate the complexity of cell differentiation in vivo due to the differences in cell lines and extracellular environment. Therefore, there is no consensus on the regulation mechanism of phytochemicals on chronic diseases based on cell heterogeneity. The purpose of this review is to summarize cell heterogeneity in common chronic inflammation-associated diseases and trace the effects of phytochemicals on cell differentiation in chronic diseases development. More importantly, by discussing the problems and challenges which hinder the study of cell heterogeneity in recent nutritional assessment experiments, we propose new prospects based on the drawbacks of existing research to optimize the research on the regulation mechanism of phytochemicals on chronic diseases. The need to explore precise measurements of cell heterogeneity is a key pillar in understanding the influence of phytochemicals on certain diseases. In the future, deeper understanding of cell-to-cell variation and the impact of food components and their metabolites on cell function by single-cell genomics and epigenomics with the focus on individual differences will open new avenues for the next generation of health care.

Novel Techniques and Future Perspective for Investigating Critical-Size Bone Defects

A critical-size bone defect is a challenging clinical problem in which a gap between bone ends will not heal and will become a nonunion. The current treatment is to harvest and transplant an autologous bone graft to facilitate bone bridging. To develop less invasive but equally effective treatment options, one needs to first have a comprehensive understanding of the bone healing process. Therefore, it is imperative to leverage the most advanced technologies to elucidate the fundamental concepts of the bone healing process and develop innovative therapeutic strategies to bridge the nonunion gap. In this review, we first discuss the current animal models to study critical-size bone defects. Then, we focus on four novel analytic techniques and discuss their strengths and limitations. These four technologies are mass cytometry (CyTOF) for enhanced cellular analysis, imaging mass cytometry (IMC) for enhanced tissue special imaging, single-cell RNA sequencing (scRNA-seq) for detailed transcriptome analysis, and Luminex assays for comprehensive protein secretome analysis. With this new understanding of the healing of critical-size bone defects, novel methods of diagnosis and treatment will emerge.

Adaptation of Imaging Mass Cytometry to Explore the Single Cell Alloimmune Landscape of Liver Transplant Rejection

Rejection continues to be an important cause of graft loss in solid organ transplantation, but deep exploration of intragraft alloimmunity has been limited by the scarcity of clinical biopsy specimens. Emerging single cell immunoprofiling technologies have shown promise in discerning mechanisms of autoimmunity and cancer immunobiology. Within these applications, Imaging Mass Cytometry (IMC) has been shown to enable highly multiplexed, single cell analysis of immune phenotypes within fixed tissue specimens. In this study, an IMC panel of 10 validated markers was developed to explore the feasibility of IMC in characterizing the immune landscape of chronic rejection (CR) in clinical tissue samples obtained from liver transplant recipients. IMC staining was highly specific and comparable to traditional immunohistochemistry. A single cell segmentation analysis pipeline was developed that enabled detailed visualization and quantification of 109,245 discrete cells, including 30,646 immune cells. Dimensionality reduction identified 11 unique immune subpopulations in CR specimens. Most immune subpopulations were increased and spatially related in CR, including two populations of CD45+/CD3+/CD8+ cytotoxic T-cells and a discrete CD68+ macrophage population, which were not observed in liver with no rejection (NR). Modeling via principal component analysis and logistic regression revealed that single cell data can be utilized to construct statistical models with high consistency (Wilcoxon Rank Sum test, p=0.000036). This study highlights the power of IMC to investigate the alloimmune microenvironment at a single cell resolution during clinical rejection episodes. Further validation of IMC has the potential to detect new biomarkers, identify therapeutic targets, and generate patient-specific predictive models of clinical outcomes in solid organ transplantation.

Determining clinically relevant features in cytometry data using persistent homology

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as ‘elbows’. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.

Identifying differences between cytometry data seen as a point cloud can be complicated by random variations in data collection and data sources. We apply persistent homology used in topological data analysis to describe the shape and structure of the data representing immune cells in healthy donors and COVID-19 patients. By looking at how the shape and structure differ between healthy donors and COVID-19 patients, we are able to definitively conclude how these groups differ despite random variations in the data. Furthermore, these results are novel in their ability to capture shape and structure of cytometry data, something not described by other analyses.

Metabolism Characteristics of Th17 and Regulatory T Cells in Autoimmune Diseases

The abnormal number and functional deficiency of immune cells are the pathological basis of various diseases. Recent years, the imbalance of Th17/regulatory T (Treg) cell underlies the occurrence and development of inflammation in autoimmune diseases (AID). Currently, studies have shown that material and energy metabolism is essential for maintaining cell survival and normal functions and the altered metabolic state of immune cells exists in a variety of AID. This review summarizes the biology and functions of Th17 and Treg cells in AID, with emphasis on the advances of the roles and regulatory mechanisms of energy metabolism in activation, differentiation and physiological function of Th17 and Treg cells, which will facilitate to provide targets for the treatment of immune-mediated diseases.

Mechanistic and Biomarker Studies to Demonstrate Immune Tolerance in Multiple Sclerosis

The induction of specific immunological tolerance represents an important therapeutic goal for multiple sclerosis and other autoimmune diseases. Sound knowledge of the target antigens, the underlying pathomechanisms of the disease and the presumed mechanisms of action of the respective tolerance-inducing approach are essential for successful translation. Furthermore, suitable tools and assays to evaluate the induction of immune tolerance are key aspects for the development of such treatments. However, investigation of the mechanisms of action underlying tolerance induction poses several challenges. The optimization of sensitive, robust methods which allow the assessment of low frequency autoreactive T cells and the long-term reduction or change of their responses, the detection of regulatory cell populations and their immune mediators, as well as the validation of specific biomarkers indicating reduction of inflammation and damage, are needed to develop tolerance-inducing approaches successfully to patients. This short review focuses on how to demonstrate mechanistic proof-of-concept in antigen-specific tolerance-inducing therapies in MS.

An Immune Atlas of Nephrolithiasis: Single-Cell Mass Cytometry on SIRT3 Knockout and Calcium Oxalate-Induced Renal Injury

Background

As a common urological disease with a high recurrence rate, nephrolithiasis caused by CaOx may elicit a strong immunologic response. We present a CyTOF-based atlas of the immune landscape in nephrolithiasis models to understand how the immune system contributes to, and is affected by, the underlying response caused by SIRT3 knockout and CaOx inducement.

Materials and Methods

We performed a large-scale CyTOF analysis of immune cell abundance profiles in nephrolithiasis. The immunophenotyping data were collected from four different mouse models, including the SIRT3 wild-type or knockout, including and excluding CaOx inducement. Unsupervised analysis strategies, such as SPADE and viSNE, revealed the intrarenal resident immune components and the immune alterations caused by SIRT3 knockout and CaOx-induced renal injury.

Results

An overview analysis of the immune landscape identified T cells and macrophages as the main immune cell population in nephrolithiasis models. Highly similar phenotypes were observed among CD4⁺ and CD8⁺ T cell subsets, including cells expressing Ki67, Ly6C, Siglec-F, and TCRβ. Macrophages expressed a characteristic panel of markers with varied expression levels including MHC II, SIRPα, CD11c, Siglec-F, F4/80, CD64, and CD11b, indicating more subtle differences in marker expression than T cells. The SIRT3^KO/CaOx and SIRT3^WT/CaOx groups exhibited global differences in the intrarenal immune landscape, whereas only small differences existed between the SIRT3^KO/CaOx and SIRT3^KO/Ctrl groups. Among the major immune lineages, the response of CD4⁺ T cells, NK cells, monocytes, and M1 to CaOx inducement was regulated by SIRT3 expression in contrast to the expression changes of B cells, DCs, and granulocytes caused by CaOx inducement. The panel of immune markers influenced by CaOx inducement significantly varied with and without SIRT3 knockout.

Conclusion

In a CaOx-induced nephrolithiasis model, SIRT3 has a critical role in regulating the immune system, especially in reducing inflammatory injury. The characteristic panel of altered immune clusters and markers provides novel insights leading to improved prediction and management of nephrolithiasis.

Crosstalk Between the Tumor Microenvironment and Cancer Cells: A Promising Predictive Biomarker for Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) have changed the landscape of cancer treatment and are emerging as promising curative treatments in different type of cancers. However, only a small proportion of patients have benefited from ICIs and there is an urgent need to find robust biomarkers for individualized immunotherapy and to explore the causes of immunotherapy resistance. In this article, we review the roles of immune cells in the tumor microenvironment (TME) and discuss the effects of ICIs on these cell populations. We discuss the potential of the functional interaction between the TME and cancer cells as a predictive biomarker for ICIs. Furthermore, we outline the potential personalized strategies to improve the effectiveness of ICIs with precision.

Biomarker Technologies to Support Early Clinical Immuno-oncology Development: Advances and Interpretation

Today, there is a huge effort to develop cancer immunotherapeutics capable of combating cancer cells as well as the biological environment in which they can grow, adapt, and survive. For such treatments to benefit more patients, there is a great need to dissect the complex interplays between tumor cells and the host's immune system. Monitoring mechanisms of resistance to immunotherapeutics can delineate the evolution of key players capable of driving an efficacious antitumor immune response. In doing so, simultaneous and systematic interrogation of multiple biomarkers beyond single biomarker approaches needs to be undertaken. Zooming into cell-to-cell interactions using technological advancements with unprecedented cellular resolution such as single-cell spatial transcriptomics, advanced tissue histology approaches, and new molecular immune profiling tools promises to provide a unique level of molecular granularity of the tumor environment and may support better decision-making during drug development. This review will focus on how such technological tools are applied in clinical settings, to inform the underlying tumor-immune biology of patients and offer a deeper understanding of cancer immune responsiveness to immuno-oncology treatments.

New applications of advanced instrumental techniques for the characterization of food allergenic proteins

Current approaches based on electrophoretic, chromatographic or immunochemical principles have allowed characterizing multiple allergens, mapping their epitopes, studying their mechanisms of action, developing detection and diagnostic methods and therapeutic strategies for the food and pharmaceutical industry. However, some of the common structural features related to the allergenic potential of food proteins remain unknown, or the pathological mechanism of food allergy is not yet fully understood. In addition, it is also necessary to evaluate new allergens from novel protein sources that may pose a new risk for consumers. Technological development has allowed the expansion of advanced technologies for which their whole potential has not been entirely exploited and could provide novel contributions to still unexplored molecular traits underlying both the structure of food allergens and the mechanisms through which they sensitize or elicit adverse responses in human subjects, as well as improving analytical techniques for their detection. This review presents cutting-edge instrumental techniques recently applied when studying structural and functional aspects of proteins, mechanism of action and interaction between biomolecules. We also exemplify their role in the food allergy research and discuss their new possible applications in several areas of the food allergy field.

Immune cell phenotypes associated with disease severity and long-term neutralizing antibody titers after natural dengue virus infection

Prior immunological exposure to dengue virus can be both protective and disease-enhancing during subsequent infections with different dengue virus serotypes. We provide here a systematic, longitudinal analysis of B cell, T cell, and antibody responses in the same patients. Antibody responses as well as T and B cell activation differentiate primary from secondary responses. Hospitalization is associated with lower frequencies of activated, terminally differentiated T cells and higher percentages of effector memory CD4 T cells. Patients with more severe disease tend to have higher percentages of plasmablasts. This does not translate into long-term antibody titers, since neutralizing titers after 6 months correlate with percentages of specific memory B cells, but not with acute plasmablast activation. Overall, our unbiased analysis reveals associations between cellular profiles and disease severity, opening opportunities to study immunopathology in dengue disease and the potential predictive value of these parameters.

Determining clinically relevant features in cytometry data using persistent homology

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. Our method identifies proteins of interest by a decision-tree based classifier and passes them to a kernel-density estimator (KDE) for sampling points from the density distribution. We then compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as 'elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or in the presence of large batch effects.

Author summary

Identifying differences between cytometry data seen as a point cloud can be complicated by random variations in data collection and data sources. We apply persistent homology used in topological data analysis to describe the shape and structure of the data representing immune cells in healthy donors and COVID-19 patients. By looking at how the shape and structure differ between healthy donors and COVID-19 patients, we are able to definitively conclude how these groups differ despite random variations in the data. Furthermore, these results are novel in their ability to capture shape and structure of cytometry data, something not described by other analyses.

CyTOF Analysis Reveals a Distinct Immunosuppressive Microenvironment in IDH Mutant Anaplastic Gliomas

The immune microenvironment is important for the development, progression, and prognosis of anaplastic glioma (AG). This complex milieu has not been fully elucidated, and a high-dimensional analysis is urgently required. Utilizing mass cytometry (CyTOF), we performed an analysis of immune cells from 5 patients with anaplastic astrocytoma, IDH-mutant (AAmut) and 10 patients with anaplastic oligodendroglioma, IDH-mutant and 1p/19q codeletion (AOD) and their paired peripheral blood mononuclear cells (PBMCs). Based on a panel of 33 biomarkers, we demonstrated the tumor-driven immune changes in the AG immune microenvironment. Our study confirmed that mononuclear phagocytes and T cells are the most abundant immunocytes in the AG immune microenvironment. Glioma-associated microglia/macrophages in both AAmut and AOD samples showed highly immunosuppressive characteristics. Compared to those in the PBMCs, the ratios of immune checkpoint-positive exhausted CD4+ T cells and CD8+ T cells were higher at the AG tumor sites. The AAmut immune milieu exhibits more immunosuppressive characteristics than that in AOD.

Characterizing Highly Cited Papers in Mass Cytometry through H-Classics

Mass cytometry (CyTOF) is a relatively novel technique for the multiparametric analysis of single-cell features with an increasing central role in cell biology, immunology, pharmacology, and biomedicine. This technique mixes the fundamentals of flow cytometry with mass spectrometry and is mainly used for in-depth studies of the immune system and diseases with a significant immune load, such as cancer, autoimmune diseases, and viral diseases like HIV or the recently emerged COVID-19, produced by the SARS-CoV-2 coronavirus. The objective of this study was to provide a useful insight into the evolution of the mass cytometry research field, revealing the knowledge structure (conceptual and social) and authors, countries, sources, documents, and organizations that have made the most significant contribution to its development. We retrieved 937 articles from the Web of Science (2010-2019), analysed 71 Highly Cited Papers (HCP) through the H-Classics methodology and computed the data by using Bibliometrix R package. HCP sources corresponded to high-impact journals, such as Nature Biotechnology and Cell, and its production was concentrated in the US, and specifically Stanford University, affiliation of the most relevant authors in the field. HCPs analysis confirmed great interest in the study of the immune system and complex data processing in the mass cytometry research field.

Mass Cytometry Tags: Where Chemistry Meets Single-Cell Analysis

Mass cytometry is a highly multiparametric proteomic technology that allows the measurement and quantification of nearly 50 markers with single-cell resolution. Mass cytometry reagents are probes tagged with metal isotopes of defined mass and act as reporters. Metals are detected using inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS). Many different types of mass-tag reagents have been developed to afford myriad applications. We have classified these compounds into polymer-based mass-tag reagents, nonpolymer-based mass-tag reagents, and inorganic nanoparticles. Metal-chelating polymers (MCPs) are widely used to profile and quantify cellular biomarkers; however, both the range of metals that can be detected and the metal signals have to be improved. Several strategies such as the inclusion of chelating agents or highly branched polymers may overcome these issues. Biocompatible materials such as polystyrene and inorganic nanoparticles are also of profound interest in mass cytometry. While polystyrene allows the inclusion of a wide variety of metals, the high metal content of inorganic nanoparticles offers an excellent opportunity to increase the signal from the metals to detect low-abundance biomarkers. Nonpolymer-based mass-tag reagents offer multiple applications: cell detection, cell cycle property determination, biomarker detection, and mass-tag cellular barcoding (MCB). Recent developments have been achieved in live cell barcoding by targeting proteins (CD45, b2m, and CD298), by using small and nonpolar probes or by ratiometric barcoding. From this perspective, the principal applications, strengths, and shortcomings of mass-tag reagents are highlighted, summarized, and discussed, with special emphasis on mass-tag reagents for MCB. Thereafter, the future perspectives of mass-tag reagents are discussed considering the current state-of-the-art technologies.

Potentiality of multiple modalities for single-cell analyses to evaluate the tumor microenvironment in clinical specimens

Single-cell level analysis is powerful tool to assess the heterogeneity of cellular components in tumor microenvironments (TME). In this study, we investigated immune-profiles using the single-cell analyses of endoscopically- or surgically-resected tumors, and peripheral blood mononuclear cells from gastric cancer patients. Furthermore, we technically characterized two distinct platforms of the single-cell analysis; RNA-seq-based analysis (scRNA-seq), and mass cytometry-based analysis (CyTOF), both of which are broadly embraced technologies. Our study revealed that the scRNA-seq analysis could cover a broader range of immune cells of TME in the biopsy-resected small samples of tumors, detecting even small subgroups of B cells or Treg cells in the tumors, although CyTOF could distinguish the specific populations in more depth. These findings demonstrate that scRNA-seq analysis is a highly-feasible platform for elucidating the complexity of TME in small biopsy tumors, which would provide a novel strategies to overcome a therapeutic difficulties against cancer heterogeneity in TME.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Spatially Resolved Analytical Chemistry in Intact, Living Tissues

Tissues are an exciting frontier for bioanalytical chemistry, one in which spatial distribution is just as important as total content. Intact tissue preserves the native cellular and molecular organization and the cell-cell contacts found in vivo. Live tissue, in particular, offers the potential to analyze dynamic events in a spatially resolved manner, leading to fundamental biological insights and translational discoveries. In this Perspective, we provide a tutorial on the four fundamental challenges for the bioanalytical chemist working in living tissue samples as well as best practices for mitigating them. The challenges include (i) the complexity of the sample matrix, which contributes myriad interfering species and causes nonspecific binding of reagents; (ii) hindered delivery and mixing; (iii) the need to maintain physiological conditions; and (iv) tissue reactivity. This framework is relevant to a variety of methods for spatially resolved chemical analysis, including optical imaging, inserted sensors and probes such as electrodes, and surface analyses such as sensing arrays. The discussion focuses primarily on ex vivo tissues, though many considerations are relevant in vivo as well. Our goal is to convey the exciting potential of analytical chemistry to contribute to understanding the functions of live, intact tissues.

Phenotyping of Adaptive Immune Responses in Inflammatory Diseases

Immunophenotyping on the molecular and cellular level is a central aspect for characterization of patients with inflammatory diseases, both to better understand disease etiopathogenesis and based on this to develop diagnostic and prognostic biomarkers which allow patient stratification and tailor-made treatment strategies. Technology-driven developments have considerably expanded the range of analysis tools. Especially the analysis of adaptive immune responses, often regarded as central though mostly poorly characterized disease drivers, is a major focus of personalized medicine. The identification of the disease-relevant antigens and characterization of corresponding antigen-specific lymphocytes in individual patients benefits significantly from recent developments in cytometry by sequencing and proteomics. The aim of this workshop was to identify the important developments for state-of-the-art immunophenotyping for clinical application and precision medicine. We focused here on recent key developments in analysis of antigen-specific lymphocytes, sequencing, and proteomics approaches, their relevance in precision medicine and the discussion of the major challenges and opportunities for the future.

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.

Partial absence of PD-1 expression by tumor-infiltrating EBV-specific CD8+ T cells in EBV-driven lymphoepithelioma-like carcinoma

Objectives

Lymphoepithelioma‐like carcinoma (LELC) is an uncommon lung cancer, typically observed in young, non‐smoking Asian populations. LELC is associated with Epstein–Barr virus (EBV) infection of lung tumor cells of epithelial origin, suggesting a carcinogenic role of EBV as observed in nasopharyngeal carcinoma (NPC). Here, we studied the antigen specificity and phenotype of EBV‐specific CD8⁺ T cells in blood and tumor of one LELC patient positive for EBV infection in lung tumor cells.

Methods

Using multiplex MHC class I tetramers, mass cytometry and mRNA sequencing, we studied EBV‐specific CD8⁺ T cells at the transcriptomic and phenotypic levels in blood and tumor tissues of the LELC patient.

Results

Lymphoepithelioma‐like carcinoma lung tumor cells were positive for EBV infection. In both blood and tumor tissues, we detected two populations of EBV‐specific CD8⁺ T cells targeting the EBV lytic cycle proteins: BRLF1 and BMLF1. Transcriptomic analyses of these two populations in the tumor, which can be considered as tumor‐specific, revealed their distinct exhausted profile and polyclonal TCR repertoire. High‐dimensional phenotypical analysis revealed the distinct phenotype of these cells between blood and tumor tissues. In tumor tissue, EBV‐specific CD8⁺ TILs were phenotypically heterogeneous, but consistently expressed CD39. Unexpectedly, although the LELC tumor cells expressed abundant PD‐L1, these tumor‐specific CD8⁺ tumor‐infiltrating lymphocytes (TILs) mostly did not express PD‐1.

Conclusion

Epstein–Barr virus‐specific CD8⁺ TILs in EBV‐driven tumor are heterogeneous and partially lack PD‐1 expression, suggesting that anti‐PD1/PD‐L1 immunotherapy may not be an appropriate strategy for disinhibiting EBV‐specific cells in the treatment of LELC patients.

Exercise immunology: Future directions

Several decades of research in the area of exercise immunology have shown that the immune system is highly responsive to acute and chronic exercise training. Moderate exercise bouts enhance immunosurveillance and when repeated over time mediate multiple health benefits. Most of the studies prior to 2010 relied on a few targeted outcomes related to immune function. During the past decade, technologic advances have created opportunities for a multi-omics and systems biology approach to exercise immunology. This article provides an overview of metabolomics, lipidomics, and proteomics as they pertain to exercise immunology, with a focus on immunometabolism. This review also summarizes how the composition and diversity of the gut microbiota can be influenced by exercise, with applications to human health and immunity. Exercise-induced improvements in immune function may play a critical role in countering immunosenescence and the development of chronic diseases, and emerging omics technologies will more clearly define the underlying mechanisms. This review summarizes what is currently known regarding a multi-omics approach to exercise immunology and provides future directions for investigators.

Genomic Cytometry and New Modalities for Deep Single-Cell Interrogation

In the past few years, the rapid development of single-cell analysis techniques has allowed for increasingly in-depth analysis of DNA, RNA, protein, and epigenetic states, at the level of the individual cell. This unprecedented characterization ability has been enabled through the combination of cytometry, microfluidics, genomics, and informatics. Although traditionally discrete, when properly integrated, these fields create the synergistic field of Genomic Cytometry. In this review, we look at the individual methods that together gave rise to the broad field of Genomic Cytometry. We further outline the basic concepts that drive the field and provide a framework to understand this increasingly complex, technology-intensive space. Thus, we introduce Genomic Cytometry as an emerging field and propose that synergistic rationalization of disparate modalities of cytometry, microfluidics, genomics, and informatics under one banner will enable massive leaps forward in the understanding of complex biology. © 2020 International Society for Advancement of Cytometry.

Identification of non-cancer cells from cancer transcriptomic data

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Unravelling the heterogeneity and dynamic relationships of tumor-infiltrating T cells by single-cell RNA sequencing analysis

T cells are crucial for the success of immune-based cancer therapy. Reinvigorating antitumor T cell activity by blocking checkpoint inhibitory receptors has provided clinical benefits for many cancer patients. However, the efficacy of these treatments varies in cancer patients and the mechanisms underlying these diverse responses remain elusive. The density and status of tumor-infiltrating T cells have been shown to positively correlate with patient response to checkpoint blockades. Therefore, further understanding of the heterogeneity, clonal expansion, migration, and effector functions of tumor-infiltrating T cells will provide fundamental insights into antitumor immune responses. To this end, recent advances in single-cell RNA sequencing technology have enabled profound and extensive characterization of intratumoral immune cells and have improved our understanding of their dynamic relationships. Here, we summarize recent progress in single-cell RNA sequencing technology and current strategies to uncover heterogeneous tumor-infiltrating T cell subsets. In particular, we discuss how the coupling of deep transcriptome information with T cell receptor (TCR)-based lineage tracing has furthered our understanding of intratumoral T cell populations. We also discuss the functional implications of various T cell subsets in tumors and highlight the identification of novel T cell markers with therapeutic or prognostic potential.

Liquid biopsy and tumor heterogeneity in metastatic solid tumors: the potentiality of blood samples

In a large number of cancer types, treatment selection depends on the presence of specific tumor biomarkers. Due to the dynamic nature of cancer, very often these predictive biomarkers are not uniformly present in all cancer cells. Tumor heterogeneity represents indeed one of the main causes of therapeutic failure, and its decoding remains a major ongoing challenge in the field.

Liquid biopsy is the sampling and analysis of non-solid biological tissue often through rapid and non-invasive methods, which allows the assessment in real-time of the evolving landscape of cancer. Samples can be obtained from blood and most other bodily fluids. A blood-based liquid biopsy can capture circulating tumor cells and leukocytes, as well as circulating tumor-derived nucleic acids.

In this review, we discuss the current and possibly future applications of blood-based liquid biopsy in oncology, its advantages and its limitations in clinical practice. We specifically focused on its role as a tool to capture tumor heterogeneity in metastatic cancer patients.

Single-Cell Atlas Reveals Complexity of the Immunosuppressive Microenvironment of Initial and Recurrent Glioblastoma

The Glioblastoma (GBM) immune microenvironment plays a critical role in tumor development, progression, and prognosis. A comprehensive understanding of the intricate milieu and its interactions remains unclear, and single-cell analysis is crucially needed. Leveraging mass cytometry (CyTOF), we analyzed immunocytes from 13 initial and three recurrent GBM samples and their matched peripheral blood mononuclear cells (pPBMCs). Using a panel of 30 markers, we provide a high-dimensional view of the complex GBM immune microenvironment. Hematoxylin and eosin staining and polychromatic immunofluorescence were used for verification of the key findings. In the initial and recurrent GBMs, glioma-associated microglia/macrophages (GAMs) constituted 59.05 and 27.87% of the immunocytes, respectively; programmed cell death-ligand 1 (PD-L1), T cell immunoglobulin domain and mucin domain-3 (TIM-3), lymphocyte activation gene-3 (LAG-3), interleukin-10 (IL-10) and transforming growth factor-β (TGFβ) demonstrated different expression levels in the GAMs among the patients. GAMs could be subdivided into different subgroups with different phenotypes. Both the exhausted T cell and regulatory T (Treg) cell percentages were significantly higher in tumors than in pPBMCs. The natural killer (NK) cells that infiltrated into the tumor lesions expressed higher levels of CXC chemokine receptor 3 (CXCR3), as these cells expressed lower levels of interferon-γ (IFNγ). The immune microenvironment in the initial and recurrent GBMs displayed similar suppressive changes. Our study confirmed that GAMs, as the dominant infiltrating immunocytes, present great inter- and intra-tumoral heterogeneity and that GAMs, increased exhausted T cells, infiltrating Tregs, and nonfunctional NK cells contribute to local immune suppressive characteristics. Recurrent GBMs share similar immune signatures with the initial GBMs except the proportion of GAMs decreases.

Characterization of pulmonary immune responses to hyperoxia by high-dimensional mass cytometry analyses

Prolonged exposure to hyperoxia has deleterious effects on the lung, provoking both inflammation and alveolar injury. The elements of hyperoxic injury, which result in high rates of lethality in experimental models, are thought to include multicellular immune responses. To characterize these alterations in immune cell populations, we performed time-of-flight mass cytometry (CyTOF) analysis of CD45-expressing immune cells in whole lung parenchyma and the bronchoalveolar space of mice, exposed to 48 hours of hyperoxia together with normoxic controls. At the tested time point, hyperoxia exposure resulted in decreased abundance of immunoregulatory populations (regulatory B cells, myeloid regulatory cells) in lung parenchyma and markedly decreased proliferation rates of myeloid regulatory cells, monocytes and alveolar macrophages. Additionally, hyperoxia caused a shift in the phenotype of alveolar macrophages, increasing proportion of cells with elevated CD68, CD44, CD11c, PD-L1, and CD205 expression levels. These changes occurred in the absence of histologically evident alveolar damage and abundance of neutrophils in the parenchyma or alveolar space did not change at these time points. Collectively, these findings demonstrate that pulmonary response to hyperoxia involves marked changes in specific subsets of myeloid and lymphoid populations. These findings have important implications for therapeutic targeting in acute lung injury.

High Dimensional Mass Cytometry Analysis Reveals Characteristics of the Immunosuppressive Microenvironment in Diffuse Astrocytomas

The tumor immune microenvironment (TIME) plays a pivotal role in tumor development, progression, and prognosis. However, the characteristics of the TIME in diffuse astrocytoma (DA) are still unclear. Leveraging mass cytometry with a panel of 33 markers, we analyzed the infiltrating immune cells from 10 DA and 4 oligodendroglioma (OG) tissues and provided a single cell-resolution landscape of the intricate immune microenvironment. Our study profiled the composition of the TIME in DA and confirmed the presence of immune cells, such as glioma-associated microglia/macrophages (GAMs), CD8+ T cells, CD4+ T cells, regulatory T cells (Tregs), and natural killer cells. Increased percentages of PD-1+ CD8+ T cells, TIM-3+ CD4+ T cell subpopulations, Tregs and pro-tumor phenotype GAMs substantially contribute to the local immunosuppressive microenvironment in DA. DAs and OGs share similar compositions in terms of immune cells, while GAMs in DA exhibit more inhibitory characteristics than those in OG.

Tolerating Factor VIII: Recent Progress

Development of neutralizing antibodies against biotherapeutic agents administered to prevent or treat various clinical conditions is a longstanding and growing problem faced by patients, medical providers and pharmaceutical companies. The hemophilia A community has deep experience with attempting to manage such deleterious immune responses, as the lifesaving protein drug factor VIII (FVIII) has been in use for decades. Hemophilia A is a bleeding disorder caused by genetic mutations that result in absent or dysfunctional FVIII. Prophylactic treatment consists of regular intravenous FVIII infusions. Unfortunately, 1/4 to 1/3 of patients develop neutralizing anti-FVIII antibodies, referred to clinically as “inhibitors,” which result in a serious bleeding diathesis. Until recently, the only therapeutic option for these patients was “Immune Tolerance Induction,” consisting of intensive FVIII administration, which is extraordinarily expensive and fails in ~30% of cases. There has been tremendous recent progress in developing novel potential clinical alternatives for the treatment of hemophilia A, ranging from encouraging results of gene therapy trials, to use of other hemostatic agents (either promoting coagulation or slowing down anti-coagulant or fibrinolytic pathways) to “bypass” the need for FVIII or supplement FVIII replacement therapy. Although these approaches are promising, there is widespread agreement that preventing or reversing inhibitors remains a high priority. Risk profiles of novel therapies are still unknown or incomplete, and FVIII will likely continue to be considered the optimal hemostatic agent to support surgery and manage trauma, or to combine with other therapies. We describe here recent exciting studies, most still pre-clinical, that address FVIII immunogenicity and suggest novel interventions to prevent or reverse inhibitor development. Studies of FVIII uptake, processing and presentation on antigen-presenting cells, epitope mapping, and the roles of complement, heme, von Willebrand factor, glycans, and the microbiome in FVIII immunogenicity are elucidating mechanisms of primary and secondary immune responses and suggesting additional novel targets. Promising tolerogenic therapies include development of FVIII-Fc fusion proteins, nanoparticle-based therapies, oral tolerance, and engineering of regulatory or cytotoxic T cells to render them FVIII-specific. Importantly, these studies are highly applicable to other scenarios where establishing immune tolerance to a defined antigen is a clinical priority.

Examining multiple cellular pathways at once using multiplex hextuple luciferase assaying

Sensitive simultaneous assessment of multiple signaling pathways within the same cells requires orthogonal reporters that can assay over large dynamic ranges. Luciferases are such genetically encoded candidates due to their sensitivity, versatility, and cost-effectiveness. We expand luciferase multiplexing in post-lysis endpoint luciferase assays from two to six. Light emissions are distinguished by a combination of distinct substrates and emission spectra deconvolution. All six luciferase reporter units are stitched together into one plasmid facilitating delivery of all reporter units through a process we termed solotransfection, minimizing experimental errors. We engineer a multiplex hextuple luciferase assay to probe pathway fluxes through five transcriptional response elements against a control constitutive promoter. We can monitor effects of siRNA, ligand, and chemical compound treatments on their target pathways along with the four other probed cellular pathways. We demonstrate the effectiveness and adaptiveness of multiplex luciferase assaying, and its broad application across different research fields.

Multiplexed detection of luciferase-based sensors in the same sample is challenging and limited by the substrates’ emission spectra. Here the authors establish a system based on three different luciferases and sequential detection to achieve measurements of up to six parameters within the same experiment.