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Senescence is an irreversible arrest of the cell cycle that can be characterized by markers of senescence such as p16, p21, and KI-67. The characterization of different senescence-associated phenotypes requires selection of the most relevant senescence markers to define reliable cytometric methodologies. Mass cytometry (a.k.a. Cytometry by time of flight, CyTOF) can monitor up to 40 different cell markers at the single-cell level and has the potential to integrate multiple senescence and other phenotypic markers to identify senescent cells within a complex tissue such as skeletal muscle, with greater accuracy and scalability than traditional bulk measurements and flow cytometry-based measurements. This article introduces an analysis framework for detecting putative senescent cells based on clustering, outlier detection, and Boolean logic for outliers. Results show that the pipeline can identify putative senescent cells in skeletal muscle with well-established markers such as p21 and potential markers such as GAPDH. It was also found that heterogeneity of putative senescent cells in skeletal muscle can partly be explained by their cell type. Additionally, autophagy-related proteins ATG4A, LRRK2, and GLB1 were identified as important proteins in predicting the putative senescent population, providing insights into the association between autophagy and senescence. It was observed that sex did not affect the proportion of putative senescent cells among total cells. However, age did have an effect, with a higher proportion observed in fibro/adipogenic progenitors (FAPs), satellite cells, M1 and M2 macrophages from old mice. Moreover, putative senescent cells from muscle of old and young mice show different expression levels of senescence-related proteins, with putative senescent cells of old mice having higher levels of p21 and GAPDH, whereas putative senescent cells of young mice had higher levels of IL-6. Overall, the analysis framework prioritizes multiple senescence-associated proteins to characterize putative senescent cells sourced from tissue made of different cell types., (© 2024 The Author(s). Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

Measurable residual disease (MRD) is detected in approximately a quarter of AML chemotherapy responders, serving as a predictor for relapse and shorter survival. Immunological control of residual disease is suggested to prevent relapse, but the mechanisms involved are not fully understood. We present a peripheral blood single cell immune profiling by mass cytometry using a 42-antibody panel with particular emphasis on markers of cellular immune response. Six healthy donors were compared with four AML patients with MRD (MRD + ) in first complete remission (CR1 MRD+ ). Three of four patients demonstrated a favorable genetic risk profile, while the fourth patient had an unfavorable risk profile (complex karyotype, TP53-mutation) and a high level of MRD. Unsupervised clustering using self-organizing maps and dimensional reduction analysis was performed for visualization and analysis of immune cell subsets. CD57 + natural killer (NK)-cell subsets were found to be less abundant in patients than in healthy donors. Both T and NK cells demonstrated elevated expression of activity and maturation markers (CD44, granzyme B, and phosho-STAT5 Y694) in patients. Although mass cytometry remains an expensive method with limited scalability, our data suggest the utility for employing a 42-plex profiling for cellular immune surveillance in whole blood, and possibly as a biomarker platform in future clinical trials. The findings encourage further investigations of single cell immune profiling in CR1 MRD+ AML-patients., (© 2024 The Author(s). Cytometry Part B: Clinical Cytometry published by Wiley Periodicals LLC on behalf of International Clinical Cytometry Society.)

Oral squamous cell carcinoma is the predominant subtype of head and neck squamous cell carcinoma, characterized by a challenging prognosis. In this study, we established a murine model of oral carcinogenesis using 4-nitroquinoline-1-oxide (4-NQO) induction to investigate the impact of immunotherapy on microenvironmental alterations. Mice in the precancerous condition were randomly divided into two groups: one receiving programmed death-1 (PD1) monoclonal antibody treatment and the other, control immunoglobulin G. Our observations showed that while PD1 blockade effectively delayed the progression of carcinogenesis, it did not completely impede or reverse it. To unravel the underlying reasons for the limited effectiveness of PD1 blockade, we collected tongue lesions and applied mass cytometry (CyTOF) and RNA sequencing (RNA-seq) to characterize the microenvironment. CyTOF analysis revealed an increased macrophage subset (expressing high levels of IFNγ and iNOS) alongside a diminished Th1-like subset (exhibiting low expression of TCF7) and three myeloid-derived suppressor cell subsets (displaying low expression of MHC Class II or IFNγ) following anti-PD1 treatment. Notably, we observed an increased presence of cancer-associated fibroblasts (CAFs) expressing collagen-related genes after PD1 blockade. Furthermore, we found a negative correlation between the infiltration levels of CAFs and CD8 + T cells. These findings were validated in murine tongue tissue slides, and publicly available multi-omics datasets. Our results suggest that CAFs may impair the therapeutic efficacy of PD1 blockade in oral carcinogenesis by the remodeling of the extracellular matrix., (© 2023 Wiley Periodicals LLC.)

Characterization of target abundance on cells has broad translational applications. Among the approaches for assessing membrane target expression is quantification of the number of target-specific antibody (Ab) bound per cell (ABC). ABC determination on relevant cell subsets in complex and limited biological samples necessitates multidimensional immunophenotyping, for which the high-order multiparameter capabilities of mass cytometry provide considerable advantages. In the present study, we describe the implementation of CyTOF® for the concomitant quantification of membrane markers on diverse types of immune cells in human whole blood. Specifically, our protocol relies on establishing B max of Ab saturable binding on cells, then converted into ABC according to a metal's transmission efficiency and number of metal atoms per Ab. Using this method, we calculated ABC values for CD4 and CD8 within the expected range for circulating T cells and in concordance with the ABC obtained in the same samples by flow cytometry. Furthermore, we successfully conducted multiplex measurements of the ABC for CD28, CD16, CD32a, and CD64, on >15 immune cell subsets in human whole blood samples. We developed a high-dimensional data analysis workflow enabling semi-automated B max calculation in all examined cell subsets to facilitate ABC reporting across populations. In addition, we investigated impacts of the type of metal isotope and acquisition batch effect on the ABC evaluation with CyTOF®. In summary, our findings demonstrate mass cytometry is a valuable tool for concurrent quantitative analysis of multiple targets in specific and rare cell types, thus increasing the numbers of biomeasures obtained from a single sample., (© 2023 Pfizer Inc. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

Acute erythroid leukemia (AEL) is a disease continuum between Myelodysplastic syndrome (MDS) and Acute myeloid leukemia (AML) with the cellular hallmark of uncontrolled proliferation and impaired differentiation of erythroid progenitor cells. First described by Giovanni di Guglielmo in 1917 AEL accounts for less than 5% of all de novo AML cases. There have been efforts to characterize AEL at a molecular level, describing recurrent alterations in TP53, NPM1 and FLT3 genes. A genomic analysis of AEL cases confirmed its complexity. Despite these advances, the biology underlying erythroid proliferations remains unclear and the prognosis is dismal with a median survival of only 3 months for pure erythroid leukemia (PEL). Marker combinations suitable for the identification and characterization of leukemic stem cell (LSC) candidates, monitoring measurable residual disease (MRD) during chemotherapy treatment and the development of innovative targeted therapies are missing. Here, we developed a mass cytometry panel for an in-depth characterization of erythroid and myeloid blast cell populations from human AEL bone marrow samples in comparison to other AML subtypes and healthy counterparts. A total of 8 AEL samples were analyzed and compared to 28 AML samples from different molecular subtypes, healthy bone marrow counterparts (n = 5) and umbilical cord blood (n = 6) using high-dimensional mass cytometry. Identification of erythroid and myeloid blast populations in high-dimensional mass cytometry data enabled a refined view on erythroblast differentiation stages present in AEL erythroid blasts and revealed immunophenotypical profiles specific to AEL. Profiling of phenotypic LSCs revealed aberrant erythroid marker expression in the CD34 + CD38 - stem cell compartment. In addition, the identification of novel candidate surface marker combinations and aberrancies might enhance clinical diagnostics of AEL. We present a high-parameter mass cytometry approach feasible for immunophenotypical analysis of blast and stem cell populations in myeloid neoplasms with erythroid predominance laying the foundation for more precise experimental approaches in the future., (© 2023 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

Waldenström macroglobulinemia (WM) is a rare subtype of non-Hodgkin lymphoma characterized by malignant lymphoplasmacytic cells in the bone marrow (BM). To dissect the pathophysiology of WM, we evaluated clonal cells by mapping of B cell lymphomagenesis with adaptive and innate immune tumor microenvironment (TME) in the BM of WM patients using mass cytometry (CyTOF). In-depth immunophenotypic profiling of WM cells exhibited profound expansion of clonal cells in both unswitched and switched memory B cells and also plasma cells with aberrant expression variations. WM B lymphomagenesis was associated with reduction of most B cell precursors assessed with the same clonally restricted light chain and phenotypic changes. The immune TME was infiltrated by mature monocytes, neutrophils and adaptive T cells, preferentially subsets of effector T helper, effector CTL and effector memory CTL cells that were associated with superior overall survival (OS), in contrast to progenitors of T cells and myeloid/monocytic lineage subsets that were suppressed in WM cohort. Moreover, decrease in immature B and NKT cells was related to worse OS in WM patients. Innate and adaptive immune subsets of WM TME were modulated by immune checkpoints, including PD-1/PD-L1&PD-L2, TIGIT/PVR, CD137/CD137-L, CTLA-4, BTLA and KIR expression. The response of ibrutinib treatment to the reduction of clonal memory B cell was associated with high levels of immature B cells and effector memory CTL cells. Our study demonstrates that CyTOF technology is a powerful approach for characterizing the pathophysiology of WM at various stages, predicting patient risk and monitoring the effectiveness of treatment strategies., (© 2022 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

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., (© 2022 International Society for Advancement of Cytometry.)

Background: Myelodysplastic syndromes (MDS) at risk of transformation to acute myeloid leukemia (AML) are difficult to identify. The bone marrows of MDS patients harbor specific hematopoietic stem and progenitor cell (HSPC) abnormalities that may be associated with sub-types and risk-groups. Leukemia-associated characteristics of such cells may identify MDS patients at risk of progression to AML and provide insight in the pathobiology of MDS., Methods: Bone marrow samples from healthy donors (n = 10), low risk (n = 12) and high risk (n = 13) MDS patients were collected, in addition, AML samples for 5 out of 6 MDS patients that progressed. Mass cytometry was applied to assess expression of stem cell subset and leukemia-associated immunophenotype markers., Results: We analyzed the data using FlowSOM to cluster cells with similar expression of 10 commonly used stem cell markers. Metaclusters (n = 20) of these clusters represented populations of cells with a related phenotype, largely resembling known stem cell subsets. Within specific subsets, intra-cellular expression levels of pCREB, IkBα, or pS6 differed significantly between healthy bone marrow (HBM) and MDS or consecutive secondary AML samples. CD34, CD44, and CD49f expression was significantly increased in high risk MDS and AML-associated metaclusters. We identified MDS/sAML cells with aberrant phenotypes when compared to HBM. Such cells were observed in clusters of both primary MDS and secondary AML samples., Conclusions: High-dimensional mass cytometry and computational data analyses enabled characterization of HSPC subsets in MDS and identification of leukemia stem cell populations based on their immunophenotype. Stem cells in MDS that display leukemia-associated features may predict the risk of developing AML., (© 2022 The Authors. Cytometry Part B: Clinical Cytometry published by Wiley Periodicals LLC on behalf of International Clinical Cytometry Society.)

We developed a highly reproducible 32-marker mass cytometry panel able to measure all canonical immune lineages and perform detailed characterization of both CD4 and CD8 T cells in human peripheral blood mononuclear cells. In this panel, we identify six different T cell memory subsets, as well as markers of activation, cell cycling, and survival. In addition, this panel classifies all major CD4 T cell helper subsets. This panel enables detailed monitoring of CD4 and CD8 T cells in the context of infectious disease, cancer or autoimmunity with limited patient sample use. Detailed methods for standardization and optimization of the panel can be found in Supporting Information., (© 2022 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

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., (© 2022 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.)

In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high-dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries. As these issues can be difficult to detect manually, an automated approach is recommended. In order to filter these erroneous events out, we created a novel quality control algorithm, Peak Extraction And Cleaning Oriented Quality Control (PeacoQC), that allows for automated cleaning of cytometry data. The algorithm will determine density peaks per channel on which it will remove low quality events based on their position in the isolation tree and on their mean absolute deviation distance to these density peaks. To evaluate PeacoQC's cleaning capability, it was compared to three other existing quality control algorithms (flowAI, flowClean and flowCut) on a wide variety of datasets. In comparison to the other algorithms, PeacoQC was able to filter out all different types of anomalies in flow, mass and spectral cytometry data, while the other methods struggled with at least one type. In the quantitative comparison, PeacoQC obtained the highest median balanced accuracy and a similar running time compared to the other algorithms while having a better scalability for large files. To ensure that the parameters chosen in the PeacoQC algorithm are robust, the cleaning tool was run on 16 public datasets. After inspection, only one sample was found where the parameters should be further optimized. The other 15 datasets were analyzed correctly indicating a robust parameter choice. Overall, we present a fast and accurate quality control algorithm that outperforms existing tools and ensures high-quality data that can be used for further downstream analysis. An R implementation is available., (© 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.)

As the size and complexity of high-dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre)., (© 2021 International Society for Advancement of Cytometry.)

The identification and discrimination of viable cells is important to understand how experimental variables may influence biochemical processes such as cell metabolism, cell cycle, and signaling pathways. Cisplatin is commonly used as a viability stain in mass cytometry studies, however, recent work by Mei et al. has demonstrated that cisplatin can also be used to label antibodies, complicating the simultaneous use of the platinum measurement channels for both antibody and viability staining. This study demonstrates that other metal salts (hafnium chloride, niobium chloride, and zirconium chloride) can serve as substitutes for cisplatin in viability staining. These stains yield similar fractions of live and dead cells and stain the same dead cells in parallel high parameter analyses. In addition, this study demonstrates how a variety of protein antigen viability markers (pRb, Ki-67, Histone H1, cleaved PARP, and GAPDH) can be used to discriminate live and dead cell populations, without the need for a separate viability staining step. As few as two of these protein antigen viability markers can help identify live and dead cell populations in fixed samples and can identify the same viable cells in high dimensional analyses with or without use of viability stain information. This study demonstrates several alternative approaches to mass cytometry viability assessment that can facilitate use of platinum isotopes for antibody staining and enables identification of live and dead cell populations in samples for which a separate viability stain is not practical., (© 2021 International Society for Advancement of Cytometry.)

Signal intensity measured in a mass cytometry (CyTOF) channel can often be affected by the neighboring channels due to technological limitations. Such signal artifacts are known as spillover effects and can substantially limit the accuracy of cell population clustering. Current approaches reduce these effects by using additional beads for normalization purposes known as single-stained controls. While effective in compensating for spillover effects, incorporating single-stained controls can be costly and require customized panel design. This is especially evident when executing large-scale immune profiling studies. We present a novel statistical method, named CytoSpill that independently quantifies and compensates the spillover effects in CyTOF data without requiring the use of single-stained controls. Our method utilizes knowledge-guided modeling and statistical techniques, such as finite mixture modeling and sequential quadratic programming, to achieve optimal error correction. We evaluated our method using five publicly available CyTOF datasets obtained from human peripheral blood mononuclear cells (PBMCs), C57BL/6J mouse bone marrow, healthy human bone marrow, chronic lymphocytic leukemia patient, and healthy human cord blood samples. In the PBMCs with known ground truth, our method achieved comparable results to experiments that incorporated single-stained controls. In datasets without ground-truth, our method not only reduced spillover on likely affected markers, but also led to the discovery of potentially novel subpopulations expressing functionally meaningful, cluster-specific markers. CytoSpill (developed in R) will greatly enhance the execution of large-scale cellular profiling of tumor immune microenvironment, development of novel immunotherapy, and the discovery of immune-specific biomarkers. The implementation of our method can be found at https://github.com/KChen-lab/CytoSpill.git., (© 2020 International Society for Advancement of Cytometry.)

Mass-tag cell barcoding has increased the throughput, multiplexing, and robustness of multiple cytometry approaches. Previously, we adapted mass cytometry for cells to analyze synaptosome preparations (mass synaptometry or SynTOF), extending mass cytometry to these smaller, anuclear particles. To improve throughput and individual event resolution, we report here the application of palladium-based barcoding in human synaptosomes. Up to 20 individual samples, each with a unique combinatorial barcode, were pooled for labeling with an antibody cocktail. Our synaptosome protocol used six palladium-based barcoding reagents, and in combination with sequential gating increased the identification of presynaptic events approximately fourfold. These same parameters also efficiently resolved two other anuclear particles: human red blood cells and platelets. The addition of palladium-based mass-tag barcoding to our approach improves mass cytometry of synaptic particles., (© 2021 International Society for Advancement of Cytometry.)

Whole blood is often collected for large-scale immune monitoring studies to track changes in cell frequencies and responses using flow (FC) or mass cytometry (MC). In order to preserve sample composition and phenotype, blood samples should be analyzed within 24 h after bleeding, restricting the recruitment, analysis protocols, as well as biobanking. Herein, we have evaluated two whole blood preservation protocols that allow rapid sample processing and long-term stability. Two fixation buffers were used, Phosphoflow Fix and Lyse (BD) and Proteomic Stabilizer (PROT) to fix and freeze whole blood samples for up to 6 months. After analysis by an 8-plex panel by FC and a 26-plex panel by MC, manual gating of circulating leukocyte populations and cytokines was performed. Additionally, we tested the stability of a single sample over a 13-months period using 45 consecutive aliquots and a 34-plex panel by MC. We observed high correlation and low bias toward any cell population when comparing fresh and 6 months frozen blood with FC and MC. This correlation was confirmed by hierarchical clustering. Low coefficients of variation (CV) across studied time points indicate good sample preservation for up to 6 months. Cytokine detection stability was confirmed by low CVs, with some differences between fresh and fixed conditions. Thirteen months regular follow-up of PROT samples showed remarkable sample stability. Whole blood can be preserved for phenotyping and cytokine-response studies provided the careful selection of a compatible antibody panel. However, possible changes in cell morphology, differences in antibody affinity, and changes in cytokine-positive cell frequencies when compared to fresh blood should be considered. Our setting constitutes a valuable tool for multicentric and retrospective studies. © 2020 International Society for Advancement of Cytometry., (© 2020 International Society for Advancement of Cytometry.)

Mass cytometry (CyTOF) represents one of the most powerful tools in immune phenotyping, allowing high throughput quantification of over 40 parameters at single-cell resolution. However, wide deployment of CyTOF-based immune phenotyping studies are limited by complex experimental workflows and the need for specialized CyTOF equipment and technical expertise. Furthermore, differences in cell isolation and enrichment protocols, antibody reagent preparation, sample staining, and data acquisition protocols can all introduce technical variation that can confound integrative analyses of large data-sets of samples processed across multiple labs. Here, we present a streamlined whole blood CyTOF workflow which addresses many of these sources of experimental variation and facilitates wider adoption of CyTOF immune monitoring across sites with limited technical expertise or sample-processing resources or equipment. Our workflow utilizes commercially available reagents including the Fluidigm MaxPar Direct Immune Profiling Assay (MDIPA), a dry tube 30-marker immunophenotyping panel, and SmartTube Proteomic Stabilizer, which allows for simple and reliable fixation and cryopreservation of whole blood samples. We validate a workflow that allows for streamlined staining of whole blood samples with minimal processing requirements or expertise at the site of sample collection, followed by shipment to a central CyTOF core facility for batched downstream processing and data acquisition. We apply this workflow to characterize 184 whole blood samples collected longitudinally from a cohort of 72 hospitalized COVID-19 patients and healthy controls, highlighting dynamic disease-associated changes in circulating immune cell frequency and phenotype., (© 2021 International Society for Advancement of Cytometry.)

The advent of time-of-flight mass cytometry (CyTOF) has enabled high dimensional and unbiased examination of the immune system to simultaneous interrogate a multitude of parameters and gain a better understanding of immunologic data from clinical trial samples. Here we describe the development and validation of a 33-marker mass cytometry workflow for measuring gastrointestinal (GI) trafficking peripheral blood mononuclear cells (PBMCs) in patients with celiac disease (CeD). This panel builds upon identification of well-characterized immune cells and expands to include markers modulated in response to gluten challenge in patients with CeD. The CeD panel was optimized and validated according to accepted industry practice for validation of flow cytometry assays and builds upon established sample processing workflows for mass cytometry studies. Several critical parameters were evaluated during the assay development phase of this study including optimization of the sample processing steps, antibody specificity, and ensuring the panel as a whole performed to expectation. The panel was then validated using a fit-for-purpose approach tailored to the intended use of the data in the clinical trial. Validation included assessment of analytical parameters essential to understanding the reliability and robustness of the CeD panel such as intra-assay precision, inter-assay precision, inter-operator precision and sample processing stability. Together, this validated mass cytometry workstream provides robust and reproducible high-dimensional analysis of human peripheral blood immune cells to characterize patient samples from clinical trials., (© 2021 International Clinical Cytometry Society.)

Cell cycle analysis by mass cytometry (MC) is hampered by the poor resolution of the Iridium-labeled DNA intercalator compared to DNA-specific fluorescent dyes. We report here a minimum cell cycle panel for MC consisting of Ir-intercalator (DNA content), IdU (S phase), anti-pS28HistoneH3 (mitosis), anti-CDT1 (G 1 phase) and anti-Geminin (non-G 1 phases). Cell cycle distributions obtained by MC were not significantly different from fluorescence flow cytometry results (r 2 = 0.98, P < 0.001). Further subdivision of the G 1 and G 2 phases could be done with anti-pS780RB1 (late G 1 ) and anti-PLK1 (late G 2 ), respectively. A disadvantage of MC is that aggregates of cells cannot easily be removed while retaining all single cells. We have developed an analysis pipeline including unsupervised clustering by FlowSOM and subsequent single-cell gating. When performed on cells stained with the cell cycle panel, this analysis pipeline successfully identified debris, dead/apoptotic cells, nonsingle-cell populations and the major cell cycle phases. The presented cell cycle panel and analysis pipeline thus achieves true single-cell analysis at the same time as any additional channels in the panel are open for phenotyping and cell cycle-resolved expression or modification analysis. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry., (© 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.)

The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry., (© 2020 International Society for Advancement of Cytometry.)

The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single-cell level. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize, and interpret these high-dimensional data sets. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high-dimensional data sets using expert gating, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high-dimensional clustering or dimensionality reduction algorithms such as t-SNE, UMAP, or FlowSOM. However, large scale studies combined with an in-depth technical analysis will be needed to assess differences between these technologies in more detail. © 2020 International Society for Advancement of Cytometry., (© 2020 International Society for Advancement of Cytometry.)

Diffuse large B-cell lymphoma (DLBCL) is the most common histologic subtype of non-Hodgkin lymphoma and is notorious for its clinical heterogeneity. Patient outcomes can be predicted by cell-of-origin (COO) classification, demonstrating that the underlying transcriptional signature of malignant B-cells informs biological behavior in the context of standard combination chemotherapy regimens. In the current study, we used mass cytometry (CyTOF) to examine tumor phenotypes at the protein level with single cell resolution in a collection of 27 diagnostic DLBCL biopsy specimens from treatment naïve patients. We found that malignant B-cells from each patient occupied unique regions in 37-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Interestingly, variable MHC class II expression was found to be the greatest contributor to phenotypic diversity. Within individual tumors, a subset of cases showed multiple phenotypic subpopulations, and in one case, we were able to demonstrate direct correspondence between protein-level phenotypic subsets and DNA mutation-defined subclones. In summary, CyTOF analysis can resolve both intertumoral and intratumoral heterogeneity among primary samples and reveals that each case of DLBCL is unique and may be comprised of multiple, genetically distinct subclones. © 2019 International Society for Advancement of Cytometry., (© 2019 International Society for Advancement of Cytometry.)

High-dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high-content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross-sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single-cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure internal consistency between clinical samples based on shared controls across various study batches. Data from the shared controls is used to learn the appropriate transformations for each batch (e.g., each analysis day). Importantly, some sources of technical variation are strongly influenced by the amount of protein expressed on specific cell types, requiring several population-specific transformations to normalize cells from a heterogeneous sample. To address this, our approach first identifies the overall cellular distribution using a clustering step, and calculates subset-specific transformations on the control samples by computing their quantile distributions and aligning them with splines. These transformations are then applied to all other clinical samples in the batch to remove the batch-specific variations. We evaluated the algorithm on a customized data set with two shared controls across batches. One control sample was used for calculation of the normalization transformations and the second control was used as a blinded test set and evaluated with Earth Mover's distance. Additional results are provided using two real-world clinical data sets. Overall, our method compared favorably to standard normalization procedures. The algorithm is implemented in the R package "CytoNorm" and available via the following link: www.github.com/saeyslab/CytoNorm © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry., (© 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.)

Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry., (© 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.)

Mass cytometry is a powerful tool for high-dimensional single cell characterization. Since the introduction of the first commercial CyTOF mass cytometer by DVS Sciences in 2009, mass cytometry technology has matured and become more widely utilized, with sequential platform upgrades designed to address specific limitations and to expand the capabilities of the platform. Fluidigm's third-generation Helios mass cytometer introduced a number of upgrades over the previous CyTOF2. One of these new features is a modified narrow bore sample injector that generates smaller ion clouds, which is expected to improve sensitivity and throughput. However, following rigorous testing, we find that the narrow-bore sample injector may have unintended negative consequences on data quality and result in lower median and higher coefficients of variation in many antibody-associated signal intensities. We describe an alternative Helios acquisition protocol using a wider bore injector, which largely mitigates these data quality issues. We directly compare these two protocols in a multisite study of 10 Helios instruments across 7 institutions and show that the modified protocol improves data quality and reduces interinstrument variability. These findings highlight and address an important source of technical variability in mass cytometry experiments that is of particular relevance in the setting of multicenter studies. © 2019 International Society for Advancement of Cytometry., (© 2019 International Society for Advancement of Cytometry.)

Mass cytometry is increasingly employed in larger immune profiling studies involving data acquisitions across several days and multiple sites. For gaining a maximum of information from respective data by computational analyses, several techniques have been developed to minimize noise in mass cytometric data sets, such as sample banking, standardized instrument setup, sample barcoding, and signal normalization. However, the repeated preparation of cocktails composed of isotope-tagged antibodies remained a significant source of error. We here show that premixed antibody cocktails fail to deliver expected staining patterns when stored at 4°C for 4 weeks. As a solution, we developed and tested a cryopreservation method for highly multiplexed antibody cocktails for mass cytometry including lanthanide, palladium, and platinum conjugates that yielded stable staining patterns for at least 9 months when stored at temperatures below -80°C. Using frozen aliquots of antibody cocktails is an economic and flexible approach to significantly improve data consistency in large mass cytometry studies with repetitive staining/measurement cycles spanning several days or involving multiple data acquisition sites. © 2019 International Society for Advancement of Cytometry., (© 2019 International Society for Advancement of Cytometry.)

Mass cytometry (MCM; CyTOF) utilizes isotopically purified metal-tagged antibodies for single-cell analysis and can analyze more than 40 parameters simultaneously with minimum signal spillover to other mass channels as compared to fluorescent flow cytometry. In spite of this improvement, various factors such as metal oxidation, abundance sensitivity related spillover, and metal impurities can cause measurable amounts of spillover in MCM that can potentially lead to misinterpretation of data. Linear spillover can be corrected by applying compensation; however, we demonstrate that at high signal intensities, MCM channel spillovers are frequently nonlinear. This report describes a simple method to correct for nonlinear signal spillover (due to abundance sensitivity, isotopic contamination, or oxide formation) that can occur at high signal intensity through the use of unlabeled competitor antibodies to the specific metal-tagged antibodies causing spillover. This method significantly decreased high signal intensity and nonlinear spillover to other mass channels while maintaining saturating antibody concentrations, thereby facilitating accurate staining and compensation. In contrast, the common method of using under-titrated antibodies to overcome spillover lead to staining intensity that varied with cell numbers and antigen abundance. We demonstrate that this technique reduces total signal without significantly altering immunophenotypic or functional measurement of relative antigen levels and could be used to enable improved linear compensation of signal spillovers from high abundance antigens. STATEMENT OF SIGNIFICANCE: Mass cytometry is becoming a well-established technology for comprehensive analysis of complex biological samples, due to its ability to enable measurement of more than 40 simultaneous parameters. Due to the use of isotopically pure metal-tagged antibodies, measurement channel spillover in mass cytometry is drastically lower than in fluorescent cytometry but can still occur due to metal oxidation, isotopic impurities, or abundance sensitivity when mass signals have high intensity. We show in this report that high abundance antigens with high signal intensity exhibit non-linear mass channel spillovers that cannot be easily compensated. We also demonstrate a simple method for the use of unlabeled competitor antibody to decrease antigen signal intensity while maintaining antigen abundance to allow for more accurate linear compensation. This method performs more consistently than the commonly used approach of using under-titrated antibodies. We believe that this report has immediate practical utility for researchers using mass cytometry and can be broadly utilized to enable compensation of mass cytometry data when needed. We thus feel that this article merits publication as a Brief Report in Cytometry Part A. © 2019 International Society for Advancement of Cytometry., (© 2019 International Society for Advancement of Cytometry.)

Background: Mass cytometry can differentiate more channels than conventional flow cytometry. However, for clinical use, standardization and agreement with well-established methods is paramount. We compared mass cytometry to standard clinical flow cytometry., Methods: Mass and flow cytometry were performed in parallel on peripheral blood samples from 25 healthy individuals. Antibody staining was performed on the same samples at the same time, and analyzed for granulocyte, monocyte, lymphocyte, T, B, NK, CD4 and CD8 percentages. Validation parameters included comparison to flow cytometry, inter- and intra-assay precision and establishment of reference intervals., Results: There was a positive correlation between mass and flow cytometry for the eight populations studied (R 2 between 0.26 and 0.97). Slopes of the best-fit lines varied from 0.50 to 1.21 (fluorescence/mass). No significant differences in variance were found (F-test, P > 0.05). However, paired t-tests were significantly different for four of the eight markers (granulocytes, NK cells, T cells and CD4 cells), resulting in different reference intervals. Signal intensities were correlated for monocytes, lymphocytes, T, CD4 and CD8 cells (R 2 = 0.41-0.57). The mass cytometry intra-assay precisions were 0.7-8.5% and inter-assay precisions 1.5-13.8%., Conclusion: Mass and flow cytometry evaluations of whole blood for major cell populations correlate with similar precision and signal intensity. However, for clinical use, separate reference interval studies are required. Cell population identification should rely on gating strategies that take advantage of the characteristics offered by each method. © 2019 International Clinical Cytometry Society., (© 2019 International Clinical Cytometry Society.)

Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry., (© 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.)

Recent years have seen an increased interest in employing data analysis techniques for the automated identification of cell populations in the field of cytometry. These techniques highly depend on the use of a distance metric, a function that quantifies the distances between single-cell measurements. In most cases, researchers simply use the Euclidean distance metric. In this article, we exploit the availability of single-cell labels to find an optimal Mahalanobis distance metric derived from the data. We show that such a Mahalanobis distance metric results in an improved identification of cell populations compared with the Euclidean distance metric. Once determined, it can be used for the analysis of multiple samples that were measured under the same experimental setup. We illustrate this approach for cytometry data from two different origins, that is, flow cytometry applied to microbial cells and mass cytometry for the analysis of human blood cells. We also illustrate that such a distance metric results in an improved identification of cell populations when clustering methods are employed. Generally, these results imply that the performance of data analysis techniques can be improved by using a more advanced distance metric. © 2019 International Society for Advancement of Cytometry., (© 2019 International Society for Advancement of Cytometry.)

We describe here a simple and efficient antibody titration approach for cell-surface markers and intracellular cell signaling targets for mass cytometry. The iterative approach builds upon a well-characterized backbone panel of antibodies and analysis using bioinformatic tools such as SPADE. Healthy peripheral blood and bone marrow cells are stained with a pre-optimized "backbone" antibody panel in addition to the progressively diluted (titrated) antibodies. Clustering based on the backbone panel enables the titration of each antibody against a rich hematopoietic background and assures that nonspecific binding and signal spillover can be quantified accurately. Using a slightly expanded backbone panel, antibodies quantifying changes in transcription factors and phosphorylated antigens are titrated on ex vivo stimulated cells to optimize sensitivity and evaluate baseline expression. Based on this information, complex panels of antibodies can be thoroughly optimized for use on healthy whole blood and bone marrow and are easily adaptable to the investigation of samples from for example clinical studies. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry., (© 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.)

CD40 expression is required for germinal center (GC) formation and function, but the kinetics and magnitude of signaling following CD40 engagement remain poorly characterized in human B cells undergoing GC reactions. Here, differences in CD40 expression and signaling responses were compared across differentiation stages of mature human tonsillar B cells. A combination of mass cytometry and phospho-specific flow cytometry was used to quantify protein expression and CD40L-induced signaling in primary human naïve, GC, and memory B cells. Protein expression signatures of cell subsets were quantified using viSNE and Marker Enrichment Modeling (MEM). This approach revealed enriched expression of CD40 protein in GC B cells, compared to naïve and memory B cells. Despite this, GC B cells responded to CD40L engagement with lower phosphorylation of NFκB p65 during the first 30 min following CD40L activation. Before CD40L stimulation, GC B cells expressed higher levels of suppressor protein IκBα than naïve and memory B cells. Following CD40 activation, IκBα was rapidly degraded and reached equivalently low levels in naïve, GC, and memory B cells at 30 min following CD40L. Quantifying CD40 signaling responses as a function of bound ligand revealed a correlation between bound CD40L and degree of induced NFκB p65 phosphorylation, whereas comparable IκBα degradation occurred at all measured levels of CD40L binding. These results characterize cell-intrinsic signaling differences that exist in mature human B cells undergoing GC reactions. © 2019 International Society for Advancement of Cytometry., (© 2019 International Society for Advancement of Cytometry.)

Receptor occupancy, the ratio between amount of drug bound and amount of total receptor on single cells, is a biomarker for treatment response to therapeutic monoclonal antibodies. Receptor occupancy is traditionally measured by flow cytometry. However, spectral overlap in flow cytometry limits the number of markers that can be measured simultaneously. This restricts receptor occupancy assays to the analysis of major cell types, although rare cell populations are of potential therapeutic relevance. We therefore developed a receptor occupancy assay suitable for mass cytometry. Measuring more markers than currently available in flow cytometry allows simultaneous receptor occupancy assessment and high-parameter immune phenotyping in whole blood, which should yield new insights into disease activity and therapeutic effects. However, varying sensitivity across the mass cytometer detection range may lead to misinterpretation of the receptor occupancy when drug and receptor are detected in different channels. In this report, we describe a method for optimization of mass cytometry receptor occupancy measurements by using antibody-binding quantum simply cellular (QSC) beads for standardization across channels with different sensitivities. We evaluated the method in a mass cytometry-based receptor occupancy assay for natalizumab, a therapeutic antibody used in multiple sclerosis treatment that binds to α4-integrin, which is expressed on leukocyte cell surfaces. Peripheral blood leukocytes from a treated patient were stained with a panel containing metal-conjugated antibodies for detection of natalizumab and α4-integrin. QSC beads with known antibody binding capacity were stained with the same metal-conjugated antibodies and were used to standardize the signal intensity in the leukocyte sample before calculating receptor occupancy. We found that QSC bead standardization across channels corrected for sensitivity differences for detection of drug and receptor and generated more accurate results than observed without standardization. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry., (© 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.)

Mass cytometry enables the measurement of up to 50 features on single cell. This has catalyzed a shift toward multidimensional data analysis methods, rather than the manual gating strategies as traditionally for in flow cytometry data. This shift means that data scientists are involved in the analysis process to an increasing degree. As the data is analyzed in a more unbiased fashion, where noisy or uninformative observations are not easily excluded, a deeper knowledge of the origin, noise, and modalities of the data is therefore needed to embark on useful data analysis. In this primer, we introduce the idiosyncrasies of mass cytometry data with a focus on the technical properties of how data generated with the CyTOF® system, and the characteristics of protein expression in the cells of the hematopoietic continuum, specifically targeted toward data scientists. We also provide a comprehensive online repository of scripts, tutorials, and example data. © 2018 International Society for Advancement of Cytometry., (© 2018 International Society for Advancement of Cytometry.)

Background: Background: Mass cytometry (CyTOF) is a powerful tool for analyzing cellular networks at the single cell level. Due to the high-dimensional nature of this approach, analysis algorithms have been developed to visualize and interpret mass cytometry data. In this study, we applied these approaches to a cohort of patients with secondary acute myeloid leukemia (sAML)., Methods: We utilized mass cytometry to interrogate localization and intensity of thrombopoietin-mediated intracellular signaling in sAML. Extracellular and intracellular phenotypes were dissected using SPADE, viSNE, and PhenoGraph., Results: Healthy controls exhibited highly localized signaling responses largely restricted to the hematopoietic stem/progenitor cell (HSPC) compartment. In contrast, sAML samples contained subpopulations outside the HSPC compartment exhibiting thrombopoietin (TPO) sensitivity comparable to or greater than immunophenotypically defined HSPCs. We employed unsupervised clustering by PhenoGraph to elucidate distinct subpopulations within these heterogeneous samples. One metacluster composed almost exclusively of Lin - CD61 + CD34 - CD38 - CD45 low cells was identified. This subpopulation was not readily identified by established manual gating approaches, and generally exhibited greater STAT phosphorylation in response to TPO stimulation than did Lin - CD61 - CD34 + CD38 - cells. Lin - CD61 + CD34 - CD38 - CD45 low cells were identified in three additional sAML patients analyzed independently using a manual gating approach based upon PhenoGraph results. Each patient exhibited a similar TPO hypersensitivity to the PhenoGraph metacluster., Conclusions: The identification of this cellular subpopulation highlights the limitations of manual gating in sAML. Our study demonstrates the potential for mass cytometry to elucidate rare subpopulations in highly heterogeneous tumors by utilizing unsupervised high dimensional analysis. © 2018 International Clinical Cytometry Society., (© 2018 International Clinical Cytometry Society.)

Background: Mass cytometry leverages inductively coupled mass spectrometry to perform high dimensional single cell analyses using antibodies tagged with rare earth isotopes that are considered to be largely absent in biological samples. We have recently noted an unusual exception to this rule while analyzing tissue samples from patients undergoing surgical resection for early stage lung cancer, and here we present a detailed cytometric characterization of cerium in a clinical patient sample., Methods: We performed a CyTOF analysis on cell suspensions derived from matched blood, tumor lesion, and non-involved lung tissue from an active smoker undergoing surgical resection for early stage lung adenocarcinoma. The samples were stained with a 31-parameter antibody panel to allow a detailed characterization of the cellular heterogeneity of the samples. The data were visualized using viSNE, major immune subsets were identified based on canonical marker expression patterns, and single cell cerium levels were evaluated across each of these defined subsets., Results: High dimensional immune cell mapping revealed that high levels of cerium were specifically associated with a phenotypically distinct subset of lung macrophages that were most prevalent in noninvolved lung tissue, whereas tumor associated macrophages showed relatively lower levels of cerium. We hypothesize that these findings reflect alveolar macrophage phagocytosis of inhaled cerium derived from cigarette flint lighters., Conclusions: These results demonstrate the first high-dimensional single cell characterization of environmental metal exposure associated with smoking, and offer a demonstration of the unique potential for applying mass cytometry to the field of environmental toxicology. © 2017 International Clinical Cytometry Society., (© 2017 International Clinical Cytometry Society.)

Cell cycle analysis is a recognized and important application of flow cytometry and, more recently, mass cytometry (MCM). Both technologies have been utilized for analysis of the cell cycle state of ex vivo samples from patients with hematologic malignancies. Clinical samples are frequently stored for hours at room temperature or cryogenically frozen before processing and analysis; however, how these processing methods alter cell cycle state is not well described. To understand how storage time and temperature affect the analysis of cell cycle distribution by MCM, two leukemia cell lines, HL-60 and MOLM13, and primary human cells from three human bone marrow aspirates were stored and frozen under a variety of conditions that are likely to be encountered in a clinical setting. Our findings indicate that short delays in sample processing (less than 1 h), have little to no effect on cell cycle distribution, while longer delays or cryopreservation cause significant disruptions to the cell cycle fraction characterized by consistent reductions in IdU incorporation and variable alterations in other cell cycle phases. Analysis of the recovery of cryopreserved leukemia cell lines and marrow cells demonstrated that cell cycle alterations persist for at least 48 h after thawing. Our findings demonstrate that accurate cell cycle analysis requires that samples be processed rapidly after collection, and that cryopreservation significantly alters cell cycle fractions. Measurement of IdU incorporation was the most sensitive to both delays in processing and cryopreservation, while estimation of the total cycling cell fraction using Ki-67 or phosphorylated retinoblastoma protein were least altered by the conditions tested. These findings provide guidance for the ideal approach to collection of samples for cell cycle analysis and can aid interpretation of cell cycle data from samples that cannot be collected under ideal circumstances. © 2018 International Society for Advancement of Cytometry., (© 2018 International Society for Advancement of Cytometry.)

Calcium (Ca 2+ ) signaling controls T-cell activation and functions. Ca 2+ concentrations are locally detected and controlled by Ca 2+ -sensors (STIM1 and 2 detecting the depletion from ER stores channels) and Ca 2+ -channels (ORAI1-3 in the cell membrane and VDAC1 in the outer mitochondrial membrane). We first validated and titrated antibodies to assess the expression of these Ca 2+ -sensors and -channels in human and murine cells, and further devised a 18-antibodies mass cytometry panel to characterize their expression in primary murine lymphocyte subsets., (© 2018 International Society for Advancement of Cytometry.)

Mass cytometry (or CyTOF) is an atomic mass spectrometry-based single-cell immunoassay technology, which has provided an increasingly systematic and sophisticated view in basic biological and clinical studies. Using elemental reporters composed of stable heavy metal isotopes, more than 50 cellular parameters are measured simultaneously. However, this current multiplexing does not meet the theoretical capability of CyTOF instrumentation with 135 detectable channels, primarily due to the limitation of available chemistries for conjugating elemental mass tags to affinity reagents. To address this issue, we develop herein additional metallic mass tag based on bismuth-209 ( 209 Bi) for efficient conjugation to monoclonal antibody. This enables the use of an addtional channel m/z = 209 of CyTOF for single-cell immunoassays. Bismuth has nearly the same charge-to-radius ratio as lanthanide elements; thus, bismuth(III) cations ( 209 Bi 3+ ) could coordinate with DTPA chelators in the same geometry of O- and N-donor groups as that of lanthanide. In this report, the coordination chemistry of 209 Bi 3+ with DTPA chelators and Maxpar® X8 polymers were investigated in details. Accordingly, the protocols of conjugating antibody with bismuth mass tag were provided. A method based on UV-Vis absorbance at 280 nm of 209 Bi 3+ -labeling DTPA complexes was developed to evaluate the stoichiometric ratio of 209 Bi 3+ cations to the conjugated antibody. Side-by-side single-cell analysis experiments with bismuth- and lanthanide-tagged antibodies were carried out to compare the analytical sensitivities. The measurement accuracy of bismuth-tagged antibody was validated within in vitro assay using primary human natural killer cells. Furthermore, bismuth-tagged antibodies were successfully employed in cell cycle measurements and high-dimensional phenotyping immunoassays. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

Mass cytometry uniquely enables high-dimensional single-cell analysis of complex populations. This recently developed technology is based on inductively coupled time-of-flight mass spectrometry for multiplex proteomic analysis of more than 40 markers per cell. The ability to characterize the transcriptome is critical for the understanding of disease pathophysiology, medical diagnostics, and drug discovery. Current techniques allowing the in situ detection of transcripts in single cells are limited to a small number of simultaneous targets and are generally tedious and labor-intensive. In this report, we present the development of a multiplex method for targeted RNA detection by combining the mass cytometry and RNAscope® platforms. This novel assay, called Metal In Situ Hybridization (MISH), includes the hybridization of RNA-specific target probes followed by signal amplification achieved through a cascade of hybridization events, ending with the binding of amplifier-specific detector probes. The detector probes are tagged with isotopically pure metal atoms used for detection by mass cytometry. Proof-of-principle experiments show the simultaneous detection of three mRNA targets in Jurkat cells in suspension cell assay mode. The localization of transcripts was also investigated using the imaging mass cytometry platform in Jurkat and KG-1a cells. In addition, we optimized the antibody staining procedure to allow the co-detection of mRNA and cell surface markers. Our data demonstrate that MISH can be used to complement protein detection by mass cytometry as well as to investigate gene transcription and translation in single cells. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

Comparative immune-profiling of innate responses in humans and non-human primates is important to understand the pathogenesis of infectious and chronic inflammatory diseases as well as for the preclinical development of vaccines and immune therapies. However, direct comparisons of the two species are rare and were never performed using mass cytometry. Here, whole-blood-derived leukocytes from healthy humans and cynomolgus macaques were analyzed with mass cytometry. Two similar panels of around 30 monoclonal antibodies targeting human markers associated with innate myeloid cells to stain fixed human and macaque leukocytes were constructed. To compare the circulating innate cells from the two primate species, an analysis pipeline combining a clustering analysis by the Spanning-tree Progression Analysis of Density-normalized Events (SPADE) algorithm with a two-step hierarchical clustering of cells nodes and markers was used. Identical SPADE settings were applied to both datasets, except for the 20 clustering markers which slightly differed. A correlation analysis designed to compare the phenotypes of human and macaque cell nodes and based on 16 markers, including 15 shared clustering markers and CD19 for humans or CD20 for macaques, revealed similarities and differences between staining patterns. This study unique by the number of individuals (26 humans and 5 macaques) and the use of mass cytometry certainly contributes to better assess the advantages and limits of the use of non-human primates in preclinical research. © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

A great part of our knowledge on mammalian immunology has been established in laboratory settings. The use of inbred mouse strains enabled controlled studies of immune cell and molecule functions in defined settings. These studies were usually performed in specific-pathogen free (SPF) environments providing standardized conditions. In contrast, mammalians including humans living in their natural habitat are continuously facing pathogen encounters throughout their life. The influences of environmental conditions on the signatures of the immune system and on experimental outcomes are yet not well defined. Thus, the transferability of results obtained in current experimental systems to the physiological human situation has always been a matter of debate. Studies elucidating the diversity of "wild immunology" imprintings in detail and comparing it with those of "clean" lab mice are sparse. Here, we applied multidimensional mass cytometry to dissect phenotypic and functional differences between distinct groups of laboratory and pet shop mice as a source for "wild mice". For this purpose, we developed a 31-antibody panel for murine leukocyte subsets identification and a 35-antibody panel assessing various cytokines. Established murine leukocyte populations were easily identified and diverse immune signatures indicative of numerous pathogen encounters were classified particularly in pet shop mice and to a lesser extent in quarantine and non-SPF mice as compared to SPF mice. In addition, unsupervised analysis identified distinct clusters that associated strongly with the degree of pathogenic priming, including increased frequencies of activated NK cells and antigen-experienced B- and T-cell subsets. Our study unravels the complexity of immune signatures altered under physiological pathogen challenges and highlights the importance of carefully adapting laboratory settings for immunological studies in mice, including drug and therapy testing. © 2016 International Society for Advancement of Cytometry., (© 2016 International Society for Advancement of Cytometry.)

Mass cytometry has pioneered >40-parameter single-cell analyses that allow for the characterization of complex cellular networks at unprecedented depth. Up to 135 parameters can be simultaneously detected, but limited availability of metal tags suitable for labeling of specific probes prevents optimal exploitation of the analytical capacity of mass cytometers. To this end, we here establish the application of elemental silver nanoparticles (AgNP) of different size for reporting cell surface antigens on human leukocytes in mass cytometry assays. The mass channels at 107 Da and 109 Da are uniquely occupied by silver isotopes and do not interfere with other mass cytometry reagents. Streptavidin-coated AgNP (SA-AgNP) facilitated distinct and specific detection of various antigens, such as CD8, CD244 and CD294 on peripheral blood leukocytes pre-incubated with respective biotinylated primary antibodies. Signal intensities elicited by 40 nm-sized AgNP allowed specific detection of the low abundance antigen CD25 on both, peripheral blood regulatory T cells and CD25 lo CD127 + CD4 + T cells, enabling their distinct clustering in viSNE plots. SA-AgNP were of high elemental purity, showed minor background binding to cells in immunoassays, and were compatible with previously established staining protocols for PBMC and leukocytes, facilitating their use in complex mass cytometry panels. Considering the synthesis of AgNP from isotopically purified silver, the usage of AgNP extends the analytical capacity of mass cytometry panels by one, prospectively two, additional parameters, suitable for the detection of cellular targets of low abundance. © 2016 International Society for Advancement of Cytometry., (© 2016 International Society for Advancement of Cytometry.)