Validation of CyTOF Against Flow Cytometry for Immunological Studies and Monitoring of Human Cancer Clinical Trials

A Tandem-Locked Chemiluminescent Probe for Imaging of Tumor-Associated Macrophage Polarization

Tumor-associated macrophages (TAMs) play a role in both pro- and anti-tumor functions; and the targeted polarization from M2 to M1 TAMs has become an effective therapy option. Although detection of M1 TAMs is imperative to assess cancer immunotherapeutic efficacy, existing optical probes suffer from shallow tissue penetration depth and poor specificity toward M1 TAMs. Herein, we report a tandem-locked NIR chemiluminescent (CL) probe for specific detection of M1 TAMs. Through a combined molecular engineering approach via both atomic alternation and introduction of electron-withdrawing groups, near-infrared (NIR) chemiluminophores are screened out to possess record-long emission (over 800 nm), record-high CL quantum yield (2.7 % einstein/mol), and prolonged half-life (7.7 h). Based on an ideal chemiluminophore, the tandem-locked probe (DPDGN) is developed to only activate CL signal in the presence of both tumour (γ-glutamyl transpeptidase) and M1 macrophage biomarkers (nitric oxide). Such a tandem-lock design ensures its high specificity towards M1 macrophages in the tumor microenvironment over those in normal tissues or peripheral blood. Thus, DPDGN permits noninvasive imaging and tracking of M1 TAM in the tumor of living mice during R837 treatment, showing a good correlation with ex vivo methods. This study not only reports a new molecular approach towards highly efficient chemiluminophores but also reveals the first tandem-locked CL probes for enhanced imaging specificity.

Comparative assessment of cytometry by time-of-flight and full spectral flow cytometry based on a 33-color antibody panel

Mass cytometry and full spectrum flow cytometry have recently emerged as new promising single cell proteomic analysis tools that can be exploited to decipher the extensive diversity of immune cell repertoires and their implication in human diseases. In this study, we evaluated the performance of mass cytometry against full spectrum flow cytometry using an identical 33-color antibody panel on four healthy individuals. Our data revealed an overall high concordance in the quantification of major immune cell populations between the two platforms using a semi-automated clustering approach. We further showed a strong correlation of cluster assignment when comparing manual and automated clustering. Both comparisons revealed minor disagreements in the quantification and assignment of rare cell subpopulations. Our study showed that both single cell proteomic technologies generate highly overlapping results and substantiate that the choice of technology is not a primary factor for successful biological assessment of cell profiles but must be considered in a broader design framework of clinical studies.

Applications of Flow Cytometry in Drug Discovery and Translational Research

Flow cytometry is a mainstay technique in cell biology research, where it is used for phenotypic analysis of mixed cell populations. Quantitative approaches have unlocked a deeper value of flow cytometry in drug discovery research. As the number of drug modalities and druggable mechanisms increases, there is an increasing drive to identify meaningful biomarkers, evaluate the relationship between pharmacokinetics and pharmacodynamics (PK/PD), and translate these insights into the evaluation of patients enrolled in early clinical trials. In this review, we discuss emerging roles for flow cytometry in the translational setting that supports the transition and evaluation of novel compounds in the clinic.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

The Perspective of Using Flow Cytometry for Unpuzzling Hypoxia-Inducible Factors Signalling

Hypoxia-inducible factors (HIFs) are transcription factors that are responsible for adapting to the changes in oxygen levels in the cellular environment. HIF activity determines the expression of cellular proteins that control the development and physiology of the cells and pathophysiology of a disease. Understanding the role of specific HIF (HIF-1-3) in cellular function is essential for development of the HIF-targeted therapies. In this review, we have discussed the use of flow cytometry in analysing HIF function in cells. Proper understanding of HIF-signalling will help to design pharmacological interventions HIF-mediated therapy. We have discussed the role of HIF-signalling in various diseases such as cancer, renal and liver diseases, ulcerative colitis, arthritis, diabetes and diabetic complications, psoriasis, and wound healing. We have also discussed protocols that help to decipher the role of HIFs in these diseases that would eventually help to design promising therapies.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Flow Cytometry Assessment of Lymphocyte Populations Infiltrating Liver Tumors

Tissue-resident and recruited immune cells are essential mediators of natural and therapy-induced immunosurveillance of liver neoplasia. This idea has been recently reinforced by the clinical approval of immune checkpoint inhibitors for the immunotherapy of hepatocellular carcinoma and cholangiocarcinoma. Such research progress relies on the in-depth characterization of the immune populations that are present in pre-neoplastic and neoplastic hepatic lesions. A convenient technology for advancing along this path is high-dimensional cytometry.In this chapter, we present a protocol to assess the subtype and differentiation state of hepatic lymphocyte populations by multicolor immunofluorescence staining and flow cytometry. We detail the steps required for viability assessment and immune cell phenotyping of single-cell suspensions of liver cells by means of surface and intracellular staining of more than a dozen markers of interest. This protocol does not require prior removal of debris and dead cells and allows to process multiple samples in parallel. The procedure includes the use of a fixative-resistant viability dye that allows cell fixation and permeabilization after cell surface staining and before intracellular staining and data acquisition on a flow cytometer. Moreover, we provide a panel of fluorochrome-labeled antibodies designed for the characterization of lymphocytic subsets that can be adapted to distinct experimental settings. Finally, we present an overview of the post-staining pipeline, including data acquisition on a flow cytometer and tools for post-acquisition analyses.

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

Application of mass cytometry to characterize hematopoietic stem cells in apheresis products of patients with hematological malignancies

INTRODUCTION

Hematopoietic stem cell transplantation (HSCT) is a widely used therapy, but its success largely depends on the number and quality of stem cells collected. Current evidence shows the complexity of the hematopoietic system, which implies that, in the quality assurance of the apheresis product, the hematopoietic stem cells are adequately characterized and quantified, in which mass cytometry (MC) can provide its advantages in high-dimensional analysis.

OBJECTIVE

This research aimed to characterize and enumerate CD45dim/CD34+ stem cells using the MC in apheresis product yields from patients with chronic lymphoid malignant diseases undergoing autologous transplantation at the Abu Dhabi Stem Cells Center.

METHODS

An analytical and cross-sectional study was performed on 31 apheresis products from 15 patients diagnosed with multiple myeloma (n = 9) and non-Hodgkin lymphomas (n = 6) eligible for HSCT. The MC was employed using the MaxPar Kit for stem cell immunophenotyping. The analysis was performed manually in the Kaluza and unsupervised by machine learning in Cytobank Premium.

RESULTS

An excellent agreement was found between mass and flow cytometry for the relative and absolute counts of CD45dim/CD34+ cells (Bland-Altman bias: -0.029 and -64, respectively), seven subpopulations were phenotyped and no lineage bias was detected for any of the methods used in the pool of collected cells. A CD34+/CD38+/CD138+ population was seen in the analyses performed on four patients with multiple myeloma.

CONCLUSIONS

The MC helps to characterize subpopulations of stem cells in apheresis products. It also allows cell quantification by double platform. Unsupervised analysis allows results completion and validation of the manual strategy. The proposed methodology can be extended to apheresis products for purposes other than HSCT.

Preanalytical conditions for multiparameter platelet flow cytometry

Background

Flow cytometry is an important technique for understanding multiple aspects of blood platelet biology. Despite the widespread use of the platform for assessing platelet function, the optimization and careful consideration of preanalytical conditions, sample processing techniques, and data analysis strategies should be regularly assessed. When set up and designed with optimal conditions, it can ensure the acquisition of robust and reproducible flow cytometry data. However, these parameters are rarely described despite their importance.

Objectives

We aimed to characterize the effects of several preanalytical variables on the analysis of blood platelets by multiparameter fluorescent flow cytometry.

Methods

We assessed anticoagulant choice, sample material, sample processing, and storage times on 4 distinct and commonly used markers of platelet activation, including fibrinogen binding, expression of CD62P and CD42b, and phosphatidylserine exposure.

Results

The use of suboptimal conditions led to increases in basal platelet activity and reduced sensitivities to stimulation; however, the use of optimal conditions protected the platelets from artifactual stimulation and preserved basal activity and sensitivity to activation.

Conclusion

The optimal preanalytical conditions identified here for the measurement of platelet phenotype by flow cytometry suggest a framework for future development of multiparameter platelet assays for high-quality data sets and advanced analysis.

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.

Design, optimisation and standardisation of a high-dimensional spectral flow cytometry workflow assessing T-cell immunophenotype in patients with melanoma

Objectives

Despite the success of immune checkpoint blockade, most metastatic melanoma patients fail to respond to therapy or experience severe toxicity. Assessment of biomarkers and immunophenotypes before or early into treatment will help to understand favourable responses and improve therapeutic outcomes.

Methods

We present a high-dimensional approach for blood T-cell profiling using three multi-parameter cytometry panels: (1) a TruCount panel for absolute cell counts, (2) a 27-colour spectral panel assessing T-cell markers and (3) a 20-colour spectral panel evaluating intracellular cytokine expression. Pre-treatment blood mononuclear cells from patients and healthy controls were cryopreserved before staining across 11 batches. Batch effects were tracked using a single-donor control and the suitability of normalisation was assessed. The data were analysed using manual gating and high-dimensional strategies.

Results

Batch-to-batch variation was minimal, as demonstrated by the dimensionality reduction of batch-control samples, and normalisation did not improve manual or high-dimensional analysis. Application of the workflow demonstrated the capacity of the panels and showed that patients had fewer lymphocytes than controls (P = 0.0027), due to lower naive CD4+ (P = 0.015) and CD8+ (P = 0.011) T cells and follicular helper T cells (P = 0.00076). Patients showed trends for higher proportions of Ki67 and IL-2-expressing cells within CD4+ and CD8+ memory subsets, and increased CD57 and EOMES expression within TCRγδ+ T cells.

Conclusion

Our optimised high-parameter spectral cytometry approach provided in-depth profiling of blood T cells and found differences in patient immunophenotype at baseline. The robustness of our workflow, as demonstrated by minimal batch effects, makes this approach highly suitable for the longitudinal evaluation of immunotherapy effects.

Clinical Cytometry for Platelets and Platelet Disorders

Clinical flow cytometry tests for inherited and acquired platelet disorders are useful diagnostic tools but are not widely available. Flow cytometric methods are available to detect inherited glycoprotein deficiencies, granule release (secretion defects), drug-induced thrombocytopenias, presence of antiplatelet antibodies, and pharmacodynamic inhibition by antiplatelet agents. New tests take advantage of advanced multicolor cytometers and allow identification of novel platelet subsets by high-dimensional immunophenotyping. Studies are needed to evaluate the value of these new tests for diagnosis and monitoring of therapy in patients with platelet disorders.

High-Throughput Single-Cell Analysis of Nanoparticle-Cell Interactions

Understanding nanoparticle-cell interactions at single-nanoparticle and single-cell resolutions is crucial to improving the design of next-generation nanoparticles for safer, more effective, and more efficient applications in nanomedicine. This review focuses on recent advances in the continuous high-throughput analysis of nanoparticle-cell interactions at the single-cell level. We highlight and discuss the current trends in continual flow high-throughput methods for analyzing single cells, such as advanced flow cytometry techniques and inductively coupled plasma mass spectrometry methods, as well as their intersection in the form of mass cytometry. This review further discusses the challenges and opportunities with current single-cell analysis approaches and provides proposed directions for innovation in the high-throughput analysis of nanoparticle-cell interactions.

Characterization of papillary and clear cell renal cell carcinoma through imaging mass cytometry reveals distinct immunologic profiles

Objective

To characterize and further compare the immune cell populations of the tumor microenvironment (TME) in both clear cell and papillary renal cell carcinoma (RCC) using heavy metal-labeled antibodies in a multiplexed imaging approach (imaging mass cytometry).

Materials and methods

Formalin-fixed paraffin-embedded (FFPE) baseline tumor tissues from metastatic patients with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were retrospectively requisitioned from an institutional biorepository. Pretreated FFPE samples from 33 RCC patients (10 ccRCC, 23 pRCC) were accessioned and stained for imaging mass cytometry (IMC) analysis. Clinical characteristics were curated from an institutional RCC database. FFPE samples were prepared and stained with heavy metal-conjugated antibodies for IMC. An 11-marker panel of tumor stromal and immune markers was used to assess and quantify cellular relationships in TME compartments. To validate our time-of-flight (CyTOF) analysis, we cross-validated findings with The Cancer Genome Atlas Program (TCGA) analysis and utilized the CIBERSORTx tool to examine the abundance of main immune cell types in pRCC and ccRCC patients.

Results

Patients with ccRCC had a longer median overall survival than did those with pRCC (67.7 vs 26.8 mo, respectively). Significant differences were identified in the proportion of CD4+ T cells between disease subtypes (ccRCC 14.1%, pRCC 7.0%, p<0.01). Further, the pRCC cohort had significantly more PanCK+ tumor cells than did the ccRCC cohort (24.3% vs 9.5%, respectively, p<0.01). There were no significant differences in macrophage composition (CD68+) between cohorts. Our results demonstrated a significant correlation between the CyTOF and TCGA analyses, specifically validating that ccRCC patients exhibit higher levels of CD4+ T cells (ccRCC 17.60%, pRCC 15.7%, p<0.01) and CD8+ T cells (ccRCC 17.83%, pRCC 11.15%, p<0.01). The limitation of our CyTOF analysis was the large proportion of cells that were deemed non-characterizable.

Conclusions

Our findings emphasize the need to investigate the TME in distinct RCC histological subtypes. We observed a more immune infiltrative phenotype in the TME of the ccRCC cohort than in the pRCC cohort, where a tumor-rich phenotype was noted. As practical predictive biomarkers remain elusive across all subtypes of RCC, further studies are warranted to analyze the biomarker potential of such TME classifications.

Circulating immune cell dynamics as outcome predictors for immunotherapy in non-small cell lung cancer

The use of immune checkpoint inhibitors (ICIs) continues to transform the therapeutic landscape of non-small cell lung cancer (NSCLC), with these drugs now being evaluated at every stage of the disease. In contrast to these advances, little progress has been made with respect to reliable predictive biomarkers that can inform clinicians on therapeutic efficacy. All current biomarkers for outcome prediction, including PD-L1, tumor mutational burden or complex immune gene expression signatures, require access to tumor tissue. Besides the invasive nature of the sampling procedure, other disadvantages of tumor tissue biopsies are the inability to capture the complete spatial heterogeneity of the tumor and the difficulty to perform longitudinal follow-up on treatment. A concept emerges in which systemic immune events developing at a distance from the tumor reflect local response or resistance to immunotherapy. The importance of this cancer 'macroenvironment', which can be deciphered by comprehensive analysis of peripheral blood immune cell subsets, has been demonstrated in several cutting-edge preclinical reports, and is corroborated by intriguing data emerging from ICI-treated patients. In this review, we will provide the biological rationale underlying the potential of blood immune cell-based biomarkers in guiding treatment decision in immunotherapy-eligible NSCLC patients. Finally, we will describe new techniques that will facilitate the discovery of more immune cell subpopulations with potential to become predictive biomarkers, and reflect on ways and the remaining challenges to bring this type of analysis to the routine clinical care in the near future.

Ribonuclease 1 Enhances Antitumor Immunity against Breast Cancer by Boosting T cell Activation

The secretory enzyme human ribonuclease 1 (RNase1) is involved in innate immunity and anti-inflammation, achieving host defense and anti-cancer effects; however, whether RNase1 contributes to adaptive immune response in the tumor microenvironment (TME) remains unclear. Here, we established a syngeneic immunocompetent mouse model in breast cancer and demonstrated that ectopic RNase1 expression significantly inhibited tumor progression. Overall changes in immunological profiles in the mouse tumors were analyzed by mass cytometry and showed that the RNase1-expressing tumor cells significantly induced CD4⁺ Th1 and Th17 cells and natural killer cells and reduced granulocytic myeloid-derived suppressor cells, supporting that RNase1 favors an antitumor TME. Specifically, RNase1 increased expression of T cell activation marker CD69 in a CD4⁺ T cell subset. Notably, analysis of cancer-killing potential revealed that T cell-mediated antitumor immunity was enhanced by RNase1, which further collaborated with an EGFR-CD3 bispecific antibody to protect against breast cancer cells across molecular subtypes. Our results uncover a tumor-suppressive role of RNase1 through adaptive immune response in breast cancer in vivo and in vitro, providing a potential treatment strategy of combining RNase1 with cancer immunotherapies for immunocompetent patients.

Performance of spectral flow cytometry and mass cytometry for the study of innate myeloid cell populations

Introduction

Monitoring of innate myeloid cells (IMC) is broadly applied in basic and translational research, as well as in diagnostic patient care. Due to their immunophenotypic heterogeneity and biological plasticity, analysis of IMC populations typically requires large panels of markers. Currently, two cytometry-based techniques allow for the simultaneous detection of ≥40 markers: spectral flow cytometry (SFC) and mass cytometry (MC). However, little is known about the comparability of SFC and MC in studying IMC populations.

Methods

We evaluated the performance of two SFC and MC panels, which contained 21 common markers, for the identification and subsetting of blood IMC populations. Based on unsupervised clustering analysis, we systematically identified 24 leukocyte populations, including 21 IMC subsets, regardless of the cytometry technique.

Results

Overall, comparable results were observed between the two technologies regarding the relative distribution of these cell populations and the staining resolution of individual markers (Pearson's ρ=0.99 and 0.55, respectively). However, minor differences were observed between the two techniques regarding intra-measurement variability (median coefficient of variation of 42.5% vs. 68.0% in SFC and MC, respectively; p<0.0001) and reproducibility, which were most likely due to the significantly longer acquisition times (median 16 min vs. 159 min) and lower recovery rates (median 53.1% vs. 26.8%) associated with SFC vs. MC.

Discussion

Altogether, our results show a good correlation between SFC and MC for the identification, enumeration and characterization of IMC in blood, based on large panels (>20) of antibody reagents.

Current Methods for Identifying Plasma Membrane Proteins as Cancer Biomarkers

Plasma membrane proteins are a special class of biomolecules present on the cellular membrane. They provide the transport of ions, small molecules, and water in response to internal and external signals, define a cell's immunological identity, and facilitate intra- and intercellular communication. Since they are vital to almost all cellular functions, their mutants, or aberrant expression is linked to many diseases, including cancer, where they are a part of cancer cell-specific molecular signatures and phenotypes. In addition, their surface-exposed domains make them exciting biomarkers for targeting by imaging agents and drugs. This review looks at the challenges in identifying cancer-related cell membrane proteins and the current methodologies that solve most of the challenges. We classified the methodologies as biased, i.e., search cells for the presence of already known membrane proteins. Second, we discuss the unbiased methods that can identify proteins without prior knowledge of what they are. Finally, we discuss the potential impact of membrane proteins on the early detection and treatment of cancer.

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.

Subcellular omics: a new frontier pushing the limits of resolution, complexity and throughput

We argue that the study of single-cell subcellular organelle omics is needed to understand and regulate cell function. This requires and is being enabled by new technology development.

The evolving use of measurable residual disease in chronic lymphocytic leukemia clinical trials

Measurable residual disease (MRD) status in chronic lymphocytic leukemia (CLL), assessed on and after treatment, correlates with increased progression-free and overall survival benefit. More recently, MRD assessment has been included in large clinical trials as a primary outcome and is increasingly used in routine practice as a prognostic tool, a therapeutic goal, and potentially a trigger for early intervention. Modern therapy for CLL delivers prolonged remissions, causing readout of traditional trial outcomes such as progression-free and overall survival to be inherently delayed. This represents a barrier for the rapid incorporation of novel drugs to the overall therapeutic armamentarium. MRD offers a dynamic and robust platform for the assessment of treatment efficacy in CLL, complementing traditional outcome measures and accelerating access to novel drugs. Here, we provide a comprehensive review of recent major clinical trials of CLL therapy, focusing on small-molecule inhibitors and monoclonal antibody combinations that have recently emerged as the standard frontline and relapse treatment options. We explore the assessment and reporting of MRD (including novel techniques) and the challenges of standardization and provide a comprehensive review of the relevance and adequacy of MRD as a clinical trial endpoint. We further discuss the impact that MRD data have on clinical decision-making and how it can influence a patient's experience. Finally, we evaluate how upcoming trial design and clinical practice are evolving in the face of MRD-driven outcomes.

Innate and Adaptive Immunity during SARS-CoV-2 Infection: Biomolecular Cellular Markers and Mechanisms

The coronavirus 2019 (COVID-19) pandemic was caused by a positive sense single-stranded RNA (ssRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, other human coronaviruses (hCoVs) exist. Historical pandemics include smallpox and influenza, with efficacious therapeutics utilized to reduce overall disease burden through effectively targeting a competent host immune system response. The immune system is composed of primary/secondary lymphoid structures with initially eight types of immune cell types, and many other subtypes, traversing cell membranes utilizing cell signaling cascades that contribute towards clearance of pathogenic proteins. Other proteins discussed include cluster of differentiation (CD) markers, major histocompatibility complexes (MHC), pleiotropic interleukins (IL), and chemokines (CXC). The historical concepts of host immunity are the innate and adaptive immune systems. The adaptive immune system is represented by T cells, B cells, and antibodies. The innate immune system is represented by macrophages, neutrophils, dendritic cells, and the complement system. Other viruses can affect and regulate cell cycle progression for example, in cancers that include human papillomavirus (HPV: cervical carcinoma), Epstein-Barr virus (EBV: lymphoma), Hepatitis B and C (HB/HC: hepatocellular carcinoma) and human T cell Leukemia Virus-1 (T cell leukemia). Bacterial infections also increase the risk of developing cancer (e.g., Helicobacter pylori). Viral and bacterial factors can cause both morbidity and mortality alongside being transmitted within clinical and community settings through affecting a host immune response. Therefore, it is appropriate to contextualize advances in single cell sequencing in conjunction with other laboratory techniques allowing insights into immune cell characterization. These developments offer improved clarity and understanding that overlap with autoimmune conditions that could be affected by innate B cells (B1+ or marginal zone cells) or adaptive T cell responses to SARS-CoV-2 infection and other pathologies. Thus, this review starts with an introduction into host respiratory infection before examining invaluable cellular messenger proteins and then individual immune cell markers.

Technology meets TILs: Deciphering T cell function in the -omics era

T cells are at the center of cancer immunology because of their ability to recognize mutations in tumor cells and directly mediate cancer cell killing. Immunotherapies to rejuvenate exhausted T cell responses have transformed the clinical management of several malignancies. In parallel, the development of novel multidimensional analysis platforms, such as single-cell RNA sequencing and high-dimensional flow cytometry, has yielded unprecedented insights into immune cell biology. This convergence has revealed substantial heterogeneity of tumor-infiltrating immune cells in single tumors, across tumor types, and among individuals with cancer. Here we discuss the opportunities and challenges of studying the complex tumor microenvironment with -omics technologies that generate vast amounts of data, highlighting the opportunities and limitations of these technologies with a particular focus on interpreting high-dimensional studies of CD8+ T cells in the tumor microenvironment.

Single-cell ICP-MS to address the role of trace elements at a cellular level

The heterogeneity properties shown by cells or unicellular organisms have led to the development of analytical methods at the single-cell level. In this sense, considering the importance of trace elements in these biological systems, the inductively coupled plasma mass spectrometer (ICP-MS) configured for analyzing single cell has presented a high potential to assess the evaluation of elements in cells. Moreover, advances in instrumentation, such as coupling laser ablation to the tandem configuration (ICP-MS/MS), or alternative mass analyzers (ICP-SFMS and ICP-TOFMS), brought significant benefits, including sensitivity improvement, high-resolution imaging, and the cell fingerprint. From this perspective, the single-cell ICP-MS has been widely reported in studies involving many fields, from oncology to environmental research. Hence, it has contributed to finding important results, such as elucidating nanoparticle toxicity at the cellular level and vaccine development. Therefore, in this review, the theory of single-cell ICP-MS analysis is explored, and the applications in this field are pointed out. In addition, the instrumentation advances for single-cell ICP-MS are addressed.

Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer death worldwide. Immunotherapy with immune checkpoint inhibitors (ICI) has significantly improved outcomes in some patients, however 80-85% of patients receiving immunotherapy develop primary resistance, manifesting as a lack of response to therapy. Of those that do have an initial response, disease progression may occur due to acquired resistance. The make-up of the tumour microenvironment (TME) and the interaction between tumour infiltrating immune cells and cancer cells can have a large impact on the response to immunotherapy. Robust assessment of the TME with accurate and reproducible methods is vital to understanding mechanisms of immunotherapy resistance. In this paper we will review the evidence of several methodologies to assess the TME, including multiplex immunohistochemistry, imaging mass cytometry, flow cytometry, mass cytometry and RNA sequencing.

Flow Cytometry - Sophisticated Tool for Basic Research or/and Routine Diagnosis; Impact of the Complementarity in Both Pre- as Well as Clinical Studies

Flow cytometry is a sophisticated technology used widely in both basic research and as a routine tool in clinical diagnosis. The technology has progressed from single parameter detection in the 1970s and 1980s to high end multicolor analysis, with currently 30 parameters detected simultaneously, allowing the identification and purification of rare subpopulations of cells of interest. Flow cytometry continues to evolve and expand to facilitate the investigation of new diagnostic and therapeutic avenues. The present review gives an overview of basic theory and instrumentation, presents and compares the advantages and disadvantages of conventional, spectral and imaging flow cytometry as well as mass cytometry. Current methodologies and applications in both research, pre- and clinical settings are discussed, as well as potential limitations and future evolution. This finding encourages the reader to promote such relationship between basic science, diagnosis and multidisciplinary approach since the standard methods have limitations (e.g., in differentiating the cells after staining). Moreover, such path inspires future cytometry specialists develop new/alternative frontiers between pre- and clinical diagnosis and be more flexible in designing the study for both human as well as veterinary medicine.

Biomarkers in Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) is a severe medical condition characterized by elevated pulmonary vascular resistance (PVR), right ventricular (RV) failure, and death in the absence of appropriate treatment. The progression and prognosis are strictly related to the etiology, biochemical parameters, and treatment response. The gold-standard test remains right-sided heart catheterization, but dynamic monitoring of systolic pressure in the pulmonary artery is performed using echocardiography. However, simple and easily accessible non-invasive assays are also required in order to monitor this pathology. In addition, research in this area is in continuous development. In recent years, more and more biomarkers have been studied and included in clinical guidelines. These biomarkers can be categorized based on their associations with inflammation, endothelial cell dysfunction, cardiac fibrosis, oxidative stress, and metabolic disorders. Moreover, biomarkers can be easily detected in blood and urine and correlated with disease severity, playing an important role in diagnosis, prognosis, and disease progression.

Mass Cytometry Reveals Classical Monocytes, NK Cells, and ICOS+ CD4+ T Cells Associated with Pembrolizumab Efficacy in Patients with Lung Cancer

PURPOSE

Immune checkpoint inhibitors (ICI) have revolutionized the treatment of non-small cell lung cancer (NSCLC), but predictive biomarkers of their efficacy are imperfect. The primary objective is to evaluate circulating immune predictors of pembrolizumab efficacy in patients with advanced NSCLC.

EXPERIMENTAL DESIGN

We used high-dimensional mass cytometry (CyTOF) in baseline blood samples of patients with advanced NSCLC treated with pembrolizumab. CyTOF data were analyzed by machine-learning algorithms (Citrus, tSNE) and confirmed by manual gating followed by principal component analysis (between-group analysis).

RESULTS

We analyzed 27 patients from the seminal KEYNOTE-001 study (median follow-up of 60.6 months). We demonstrate that blood baseline frequencies of classical monocytes, natural killer (NK) cells, and ICOS+ CD4+ T cells are significantly associated with improved objective response rates, progression-free survival, and overall survival (OS). In addition, we report that a baseline immune peripheral score combining these three populations strongly predicts pembrolizumab efficacy (OS: HR = 0.25; 95% confidence interval = 0.12-0.51; P < 0.0001).

CONCLUSIONS

As this immune monitoring is easy in routine practice, we anticipate our findings may improve prediction of ICI benefit in patients with advanced NSCLC.

A roadmap for translational cancer glycoimmunology at single cell resolution

Cancer cells can evade immune responses by exploiting inhibitory immune checkpoints. Immune checkpoint inhibitor (ICI) therapies based on anti-CTLA-4 and anti-PD-1/PD-L1 antibodies have been extensively explored over the recent years to unleash otherwise compromised anti-cancer immune responses. However, it is also well established that immune suppression is a multifactorial process involving an intricate crosstalk between cancer cells and the immune systems. The cancer glycome is emerging as a relevant source of immune checkpoints governing immunosuppressive behaviour in immune cells, paving an avenue for novel immunotherapeutic options. This review addresses the current state-of-the-art concerning the role played by glycans controlling innate and adaptive immune responses, while shedding light on available experimental models for glycoimmunology. We also emphasize the tremendous progress observed in the development of humanized models for immunology, the paramount contribution of advances in high-throughput single-cell analysis in this context, and the importance of including predictive machine learning algorithms in translational research. This may constitute an important roadmap for glycoimmunology, supporting careful adoption of models foreseeing clinical translation of fundamental glycobiology knowledge towards next generation immunotherapies.

Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing

Glioma is the most common type of primary CNS tumor, composed of cells that resemble normal glial cells. Recent genetic studies have provided insight into the inter-tumoral heterogeneity of gliomas, resulting in the updated 2021 WHO classification of gliomas. Thorough understanding of inter-tumoral heterogeneity has already improved the prognosis and treatment outcomes of some types of gliomas. Currently, the challenge for researchers is to study the intratumoral cell heterogeneity of newly defined glioma subtypes. Cancer stem cells (CSCs) present in gliomas and many other tumors are an example of intratumoral heterogeneity of great importance. In this review, we discuss the modern concept of glioma stem cells and recent single-cell sequencing-driven progress in the research of intratumoral glioma cell heterogeneity. The particular emphasis was placed on the recently revealed variations of the cell composition of the subtypes of the adult-type diffuse gliomas, including astrocytoma, oligodendroglioma and glioblastoma. The novel data explain the inconsistencies in earlier glioma stem cell research and also provide insight into the development of more effective targeted therapy and the cell-based immunotherapy of gliomas. Separate sections are devoted to the description of single-cell sequencing approach and its role in the development of cell-based immunotherapies for glioma.

NKB cells: A double-edged sword against inflammatory diseases

Interferon-γ (IFN-γ)-producing natural killer (NK) cells and innate lymphoid cells (ILCs) activate the adaptive system’s B and T cells in response to pathogenic invasion; however, how these cells are activated during infections is not yet fully understood. In recent years, a new lymphocyte population referred to as “natural killer-like B (NKB) cells”, expressing the characteristic markers of innate NK cells and adaptive B cells, has been identified in both the spleen and mesenteric lymph nodes during infectious and inflammatory pathologies. NKB cells produce IL-18 and IL-12 cytokines during the early phases of microbial infection, differentiating them from conventional NK and B cells. Emerging evidence indicates that NKB cells play key roles in clearing microbial infections. In addition, NKB cells contribute to inflammatory responses during infectious and inflammatory diseases. Hence, the role of NKB cells in disease pathogenesis merits further study. An in-depth understanding of the phenotypic, effector, and functional properties of NKB cells may pave the way for the development of improved vaccines and therapeutics for infectious and inflammatory diseases.

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.

Exploring glioblastoma stem cell heterogeneity: Immune microenvironment modulation and therapeutic opportunities

Despite its growing use in cancer treatment, immunotherapy has been virtually ineffective in clinical trials for gliomas. The inherently cold tumor immune microenvironment (TIME) in gliomas, characterized by a high ratio of pro-tumor to anti-tumor immune cell infiltrates, acts as a seemingly insurmountable barrier to immunotherapy. Glioma stem cells (GSCs) within these tumors are key contributors to this cold TIME, often functioning indirectly through activation and recruitment of pro-tumor immune cell types. Furthermore, drivers of GSC plasticity and heterogeneity (e.g., reprogramming transcription factors, epigenetic modifications) are associated with induction of immunosuppressive cell states. Recent studies have identified GSC-intrinsic mechanisms, including functional mimicry of immune suppressive cell types, as key determinants of anti-tumor immune escape. In this review, we cover recent advancements in our understanding of GSC-intrinsic mechanisms that modulate GSC-TIME interactions and discuss cutting-edge techniques and bioinformatics platforms available to study immune modulation at high cellular resolution with exploration of both malignant (i.e., GSC) and non-malignant (i.e., immune) cell fractions. Finally, we provide insight into the therapeutic opportunities for targeting immunomodulatory GSC-intrinsic mechanisms to potentiate immunotherapy response in gliomas.

Mass cytometry-based peripheral blood analysis as a novel tool for early detection of solid tumours: a multicentre study

OBJECTIVE

Early detection of a tumour remains an unmet medical need, and approaches with high sensitivity and specificity are urgently required. Mass cytometry time-of-flight (CyTOF) is a powerful technique to profile immune cells and could be applied to tumour detection. We attempted to establish diagnostic models for hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC).

DESIGN

We performed CyTOF analysis for 2348 participants from 15 centres, including 1131 participants with hepatic diseases, 584 participants with pancreatic diseases and 633 healthy volunteers. Diagnostic models were constructed through random forest algorithm and validated in subgroups.

RESULTS

We determined the disturbance of systemic immunity caused by HCC and PDAC, and calculated a peripheral blood immune score (PBIScore) based on the constructed model. The PBIScore exhibited good performance in detecting HCC and PDAC, with both sensitivity and specificity being around 80% in the validation cohorts. We further established an integrated PBIScore (iPBIScore) by combining PBIScore and alpha-fetoprotein or carbohydrate antigen 19-9. The iPBIScore for HCC had an area under the curve (AUC) of 0.99, 0.97 and 0.96 in training, internal validation and external validation cohorts, respectively. Similarly, the iPBIScore for PDAC showed an AUC of 0.99, 0.98 and 0.97 in the training, internal validation and external validation cohorts, respectively. In early-stage and tumour-marker-negative patients, our iPBIScore-based models also showed an AUC of 0.95-0.96 and 0.81-0.92, respectively.

CONCLUSION

Our study proved that the alterations of peripheral immune cell subsets could assist tumour detection, and provide a ready-to-use detection model for HCC and PDAC.

Mass cytometry immunostaining protocol for multiplexing clinical samples

This is a cytometry by time-of-flight (CyTOF) staining protocol for hematopoietic-derived cells, that leverages live-cell barcoding using receptor-type tyrosine-protein phosphatase C (CD45) antibodies conjugated to metal isotopes in combination with DNA-based palladium barcoding to multiplex up to 40 samples. In this protocol, DNA-based barcoding is performed before surface and intracellular immunostaining, which reduces the batch effects that result from day-to-day variations in staining and instrument sensitivity. This protocol also reduces antibody consumption and eliminates the need for repeated instrument adjustment.

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Mass cytometry immunostaining for 40+ samples to reduce batch-to-batch variation

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Barcoding samples before immunostaining ensures consistency across all samples

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Reduction in antibody volume consumption and data acquisition time

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Immune characterization of suicidal behavior in female adolescents

Background

To address the need to identify potential markers of suicide behavior for adolescents (ages 12-18 years), mass cytometry was used to explore the cellular mechanisms that may underpin immune dysregulation in adolescents with recent suicidal behavior.

Methods

Peripheral blood mononuclear cell (PBMC) samples from 10 female adolescents with a recent suicide attempt and 4 healthy female adolescents were used. A panel of 30 antibodies was analyzed using mass cytometry. We used two complementary approaches to 1) identify the cell types that significantly differed between the two groups, and 2) explore differences in the expression profile of markers on the surface of these cells. Mass cytometry data were investigated using (Center for Disease Control, 2021) Opt-SNE for dimension reduced (Curtin and Heron, 2019), FlowSOM for clustering, and (Bridge et al., 2006) EgdeR and SAM for statistical analyses.

Results

Opt-SNE (a data driven clustering analysis) identified 15 clusters of distinct cell types. From these 15 clusters, cluster 5 (classical monocytes) had statistically lower abundance in suicidal adolescents as compared to healthy controls, whereas cluster 7 (gamma-delta T cells) had statistically higher abundance in suicidal adolescents compared to healthy control. Furthermore, across the 15 cell types, chemokine receptors, CXCR3 (cluster 5) and CXCR5 (clusters 4, 5, 7, and 9), had an elevated expression profile in those with a recent suicide attempt versus healthy controls.

Conclusion

This report demonstrates the utility of high dimensional cell phenotyping in psychiatric disorders and provides preliminary evidence for distinct immune dysfunctions in adolescents with recent suicide attempts as compared to healthy controls.

Identification of Pathogenic Immune Cell Subsets Associated With Checkpoint Inhibitor-Induced Myocarditis

BACKGROUND

Immune checkpoint inhibitors (ICIs) are monoclonal antibodies used to activate the immune system against tumor cells. Despite therapeutic benefits, ICIs have the potential to cause immune-related adverse events such as myocarditis, a rare but serious side effect with up to 50% mortality in affected patients. Histologically, patients with ICI myocarditis have lymphocytic infiltrates in the heart, implicating T cell-mediated mechanisms. However, the precise pathological immune subsets and molecular changes in ICI myocarditis are unknown.

METHODS

To identify immune subset(s) associated with ICI myocarditis, we performed time-of-flight mass cytometry on peripheral blood mononuclear cells from 52 individuals: 29 patients with autoimmune adverse events (immune-related adverse events) on ICI, including 8 patients with ICI myocarditis, and 23 healthy control subjects. We also used multiomics single-cell technology to immunophenotype 30 patients/control subjects using single-cell RNA sequencing, single-cell T-cell receptor sequencing, and cellular indexing of transcriptomes and epitopes by sequencing with feature barcoding for surface marker expression confirmation. To correlate between the blood and the heart, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing on MRL/Pdcd1^-/- (Murphy Roths large/programmed death-1-deficient) mice with spontaneous myocarditis.

RESULTS

Using these complementary approaches, we found an expansion of cytotoxic CD8⁺ T effector cells re-expressing CD45RA (Temra CD8⁺ cells) in patients with ICI myocarditis compared with control subjects. T-cell receptor sequencing demonstrated that these CD8⁺ Temra cells were clonally expanded in patients with myocarditis compared with control subjects. Transcriptomic analysis of these Temra CD8⁺ clones confirmed a highly activated and cytotoxic phenotype. Longitudinal study demonstrated progression of these Temra CD8⁺ cells into an exhausted phenotype 2 months after treatment with glucocorticoids. Differential expression analysis demonstrated elevated expression levels of proinflammatory chemokines (CCL5/CCL4/CCL4L2) in the clonally expanded Temra CD8⁺ cells, and ligand receptor analysis demonstrated their interactions with innate immune cells, including monocytes/macrophages, dendritic cells, and neutrophils, as well as the absence of key anti-inflammatory signals. To complement the human study, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing in Pdcd1^-/- mice with spontaneous myocarditis and found analogous expansions of cytotoxic clonal effector CD8⁺ cells in both blood and hearts of such mice compared with controls.

CONCLUSIONS

Clonal cytotoxic Temra CD8⁺ cells are significantly increased in the blood of patients with ICI myocarditis, corresponding to an analogous increase in effector cytotoxic CD8⁺ cells in the blood/hearts of Pdcd1^-/- mice with myocarditis. These expanded effector CD8⁺ cells have unique transcriptional changes, including upregulation of chemokines CCL5/CCL4/CCL4L2, which may serve as attractive diagnostic/therapeutic targets for reducing life-threatening cardiac immune-related adverse events in ICI-treated patients with cancer.

Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy

Type I interferons (IFN-Is) are central regulators of anti-tumor immunity and responses to immunotherapy, but they also drive the feedback inhibition underlying therapeutic resistance. In the present study, we developed a mass cytometry approach to quantify IFN-I-stimulated protein expression across immune cells and used multi-omics to uncover pre-therapy cellular states encoding responsiveness to inflammation. Analyzing peripheral blood cells from multiple cancer types revealed that differential responsiveness to IFN-Is before anti-programmed cell death protein 1 (PD1) treatment was highly predictive of long-term survival after therapy. Unexpectedly, IFN-I hyporesponsiveness efficiently predicted long-term survival, whereas high responsiveness to IFN-I was strongly associated with treatment failure and diminished survival time. Peripheral IFN-I responsive states were not associated with tumor inflammation, identifying a disconnect between systemic immune potential and 'cold' or 'hot' tumor states. Mechanistically, IFN-I responsiveness was epigenetically imprinted before therapy, poising cells for differential inflammatory responses and dysfunctional T cell effector programs. Thus, we identify physiological cell states with clinical importance that can predict success and long-term survival of PD1-blocking immunotherapy.

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

SLAMF Receptor Expression Identifies an Immune Signature That Characterizes Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown etiology, linked to alterations in both the innate and the adaptive immune system. Due to the heterogeneity of the clinical presentation, the diagnosis of SLE remains complicated and is often made years after the first symptoms manifest, delaying treatment, and worsening the prognosis. Several studies have shown that signaling lymphocytic activation molecule family (SLAMF) receptors are aberrantly expressed and dysfunctional in SLE immune cells, contributing to the altered cellular function observed in these patients. The aim of this study was to determine whether altered co-/expression of SLAMF receptors on peripheral blood mononuclear cells (PBMC) identifies SLE characteristic cell populations. To this end, single cell mass cytometry and bioinformatic analysis were exploited to compare SLE patients to healthy and autoimmune diseases controls. First, the expression of each SLAMF receptor on all PBMC populations was investigated. We observed that SLAMF1+ B cells (referred to as SLEB1) were increased in SLE compared to controls. Furthermore, the frequency of SLAMF4+ monocytes and SLAMF4+ NK were inversely correlated with disease activity, whereas the frequency SLAMF1+ CD4+ TDEM cells were directly correlated with disease activity. Consensus clustering analysis identified two cell clusters that presented significantly increased frequency in SLE compared to controls: switch memory B cells expressing SLAMF1, SLAMF3, SLAMF5, SLAMF6 (referred to as SLESMB) and circulating T follicular helper cells expressing the same SLAMF receptors (referred to as SLEcTFH). Finally, the robustness of the identified cell populations as biomarkers for SLE was evaluated through ROC curve analysis. The combined measurement of SLEcTFH and SLEB1 or SLESMB cells identified SLE patients in 90% of cases. In conclusion, this study identified an immune signature for SLE based on the expression of SLAMF receptors on PBMC, further highlighting the involvement of SLAMF receptors in the pathogenesis of SLE.

Multi-Platform Single-Cell Analysis Identifies Immune Cell Types Enhanced in Pulmonary Fibrosis

Immune cells have been implicated in Idiopathic Pulmonary Fibrosis (IPF), but the phenotypes and effector mechanisms of these cells remain incompletely characterized. We performed mass cytometry to quantify immune/inflammatory cell subsets in lungs of 12 patients with IPF and 15 organ donors without chronic lung disease and utilized existing single-cell RNA-sequencing (scRNA-seq) data to investigate transcriptional profiles of immune cells over-represented in IPF. Among myeloid cells, we found increased numbers of alveolar macrophages (AMØs) and dendritic cells (DCs) in IPF, as well as a subset of monocyte-derived DC. In contrast, monocyte-like cells and interstitial macrophages were reduced in IPF. Transcriptomic profiling identified an enrichment for interferon-γ (IFN-γ) response pathways in AMØs and DCs from IPF, as well as antigen processing in DCs and phagocytosis in AMØs. Among T cells, we identified three subset of memory T cells that were increased in IPF, including CD4+ and CD8+ resident memory T cells (TRM), and CD8+ effector memory (TEMRA) cells. The response to IFN-γ pathway was enriched in CD4 TRM and CD8 TRM cells in IPF, along with T cell activation and immune response-regulating signaling pathways. Increased AMØs, DCs, and memory T cells were present in IPF lungs compared to control subjects. In IPF, these cells possess an activation profile indicating increased IFN-γ signaling and up-regulation of adaptive immunity in the lungs. Together, these studies highlight critical features of the immunopathogenesis of IPF.

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.

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

Single-cell mass cytometry, also known as cytometry by time of flight is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use. A deep learning with graphic cluster (DGCyTOF) visualization is developed in identifying canonical and new cell types. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis, Factor Analysis, Independent Component Analysis, Isometric Feature Mapping, t-distributed Stochastic Neighbor Embedding, and Uniform Manifold Approximation and Projection with k-means clustering and Gaussian mixture clustering. We observed that DGCyTOF outperformed all the other methods in accuracy, speed and visualization. The application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

Uncovering Pharmacological Opportunities for Cancer Stem Cells-A Systems Biology View

Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells. Despite the importance of understanding CSC biology, our knowledge of how neighboring immune and tumor cell populations collectively shape CSC stemness is incomplete. Here, we provide a systems biology perspective on the crucial roles of cellular population identification and dissection of cell regulatory states. By reviewing state-of-the-art single-cell technologies, we show how innovative systems-based analysis enables a deeper understanding of the stemness of the tumor niche and the influence of intratumoral cancer cell and immune cell compositions. We also summarize strategies for refining CSC systems biology, and the potential role of this approach in the development of improved anticancer treatments. Because CSCs are amenable to cellular transitions, we envision how systems pharmacology can become a major engine for discovery of novel targets and drug candidates that can modulate state transitions for tumor cell reprogramming. Our aim is to provide deeper insights into cancer stemness from a systems perspective. We believe this approach has great potential to guide the development of more effective personalized cancer therapies that can prevent CSC-mediated relapse.

Characterization of Definitive Regulatory B Cell Subsets by Cell Surface Phenotype, Function and Context

Regulatory B cell or “Breg” is a broad term that represents the anti-inflammatory activity of B cells, but does not describe their individual phenotypes, specific mechanisms of regulation or relevant disease contexts. Thus, given the variety of B cell regulatory mechanisms reported in human disease and their animal models, a more thorough and comprehensive identification strategy is needed for tracking and comparing B cell subsets between research groups and in clinical settings. This review summarizes the discovery process and mechanism of action for well-defined regulatory B cell subsets with an emphasis on the mouse model of multiple sclerosis experimental autoimmune encephalomyelitis. We discuss the importance of conducting thorough B cell phenotyping along with mechanistic studies prior to defining a particular subset of B cells as Breg. Since virtually all B cell subsets can exert regulatory activity, it is timely for their definitive identification across studies.

Nutritional Regulation of Mammary Tumor Microenvironment

The mammary gland is a heterogeneous organ comprising of immune cells, surrounding adipose stromal cells, vascular cells, mammary epithelial, and cancer stem cells. In response to nutritional stimuli, dynamic interactions amongst these cell populations can be modulated, consequently leading to an alteration of the glandular function, physiology, and ultimately disease pathogenesis. For example, obesity, a chronic over-nutritional condition, is known to disrupt homeostasis within the mammary gland and increase risk of breast cancer development. In contrast, emerging evidence has demonstrated that fasting or caloric restriction can negatively impact mammary tumorigenesis. However, how fasting induces phenotypic and functional population differences in the mammary microenvironment is not well understood. In this review, we will provide a detailed overview on the effect of nutritional conditions (i.e., overnutrition or fasting) on the mammary gland microenvironment and its impact on mammary tumor progression.

Single-cell immune signature for detecting early-stage HCC and early assessing anti-PD-1 immunotherapy efficacy

Background

The early diagnosis of hepatocellular carcinoma (HCC) can greatly improve patients’ 5-year survival rate, and the early efficacy assessment is important for oncologists to harness the anti-programmed cell death protein 1 (PD-1) immunotherapy in patients with advanced HCC. The lack of effective predicting biomarkers not only leads to delayed detection of the disease but also results in ineffective immunotherapy and limited clinical survival benefit.

Methods

We exploited the single-cell approach (cytometry by time of flight (CyTOF)) to analyze peripheral blood mononuclear cells from multicohorts of human samples. Immune signatures for different stages of patients with HCC were systematically profiled and statistically compared. Furthermore, the dynamic changes of peripheral immune compositions for both first-line and second-line patients with HCC after anti-PD-1 monotherapy were also evaluated and systematically compared.

Results

We identified stage-specific immune signatures for HCC and constructed a logistic AdaBoost-SVM classifier based on these signatures. The classifier provided superior performance in predicting early-stage HCC over the commonly used serum alpha-fetoprotein level. We also revealed the treatment stage-specific immune signatures from peripheral blood and their dynamical changing patterns, all of which were integrated to achieve early discrimination of patients with non-durable benefit for both first-line and second-line anti-PD-1 monotherapies.

Conclusions

Our newly identified single-cell peripheral immune signatures provide promising non-invasive biomarkers for early detection of HCC and early assessment for anti-PD-1 immunotherapy efficacy in patients with advanced HCC. These new findings can potentially facilitate early diagnosis and novel immunotherapy for patients with HCC in future practice and further guide the utility of CyTOF in clinical translation of cancer research.

Trial registration numbers

NCT02576509 and NCT02989922.