PhenoGraph and viSNE facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data

CelltypeR: A flow cytometry pipeline to characterize single cells from brain organoids

Motivated by the cellular heterogeneity in complex tissues, particularly in brain and induced pluripotent stem cell (iPSC)-derived brain models, we developed a complete workflow to reproducibly characterize cell types in complex tissues. Our approach combines a flow cytometry (FC) antibody panel with our computational pipeline CelltypeR, enabling dataset aligning, unsupervised clustering optimization, cell type annotating, and statistical comparisons. Applied to human iPSC derived midbrain organoids, it successfully identified the major brain cell types. We performed fluorescence-activated cell sorting of CelltypeR-defined astrocytes, radial glia, and neurons, exploring transcriptional states by single-cell RNA sequencing. Among the sorted neurons, we identified subgroups of dopamine neurons: one reminiscent of substantia nigra cells most vulnerable in Parkinson's disease. Finally, we used our workflow to track cell types across a time course of organoid differentiation. Overall, our adaptable analysis framework provides a generalizable method for reproducibly identifying cell types across FC datasets in complex tissues.

Role of circulating T follicular helper subsets following Ty21a immunization and oral challenge with wild type S. Typhi in humans

Despite decades of intense research, our understanding of the correlates of protection against Salmonella Typhi (S. Typhi) infection and disease remains incomplete. T follicular helper cells (TFH), an important link between cellular and humoral immunity, play an important role in the development and production of high affinity antibodies. While traditional TFH cells reside in germinal centers, circulating TFH (cTFH) (a memory subset of TFH) are present in blood. We used specimens from a typhoid controlled human infection model whereby participants were immunized with Ty21a live attenuated S. Typhi vaccine and then challenged with virulent S. Typhi. Some participants developed typhoid disease (TD) and some did not (NoTD), which allowed us to assess the association of cTFH subsets in the development and prevention of typhoid disease. Of note, the frequencies of cTFH were higher in NoTD than in TD participants, particularly 7 days after challenge. Furthermore, the frequencies of cTFH2 and cTFH17, but not cTFH1 subsets were higher in NoTD than TD participants. However, we observed that ex-vivo expression of activation and homing markers were higher in TD than in NoTD participants, particularly after challenge. Moreover, cTFH subsets produced higher levels of S. Typhi-specific responses (cytokines/chemokines) in both the immunization and challenge phases. Interestingly, unsupervised analysis revealed unique clusters with distinct signatures for each cTFH subset that may play a role in either the development or prevention of typhoid disease. Importantly, we observed associations between frequencies of defined cTFH subsets and anti-S. Typhi antibodies. Taken together, our results suggest that circulating TFH2 and TFH17 subsets might play an important role in the development or prevention of typhoid disease. The contribution of these clusters was found to be distinct in the immunization and/or challenge phases. These results have important implications for vaccines aimed at inducing long-lived protective T cell and antibody responses.

High-dimensional mass cytometry identified circulating natural killer T-cell subsets associated with protection from cytomegalovirus infection in kidney transplant recipients

Cytomegalovirus (CMV) infection is associated with poor kidney transplant outcomes. While innate and adaptive immune cells have been implicated in its prevention, an in-depth characterization of the in vivo kinetics of multiple cell subsets and their role in protecting against CMV infection has not been achieved. Here, we performed high-dimensional immune phenotyping by mass cytometry, and functional assays, on 112 serially collected samples from CMV seropositive kidney transplant recipients. Advanced unsupervised deep learning analysis was used to assess immune cell populations that significantly correlated with prevention against CMV infection and anti-viral immune function. Prior to infection, kidney transplant recipients who developed CMV infection showed significantly lower CMV-specific cell-mediated immune (CMI) frequencies than those that did not. A broad diversity of circulating cell subsets within innate and adaptive immune compartments were associated with CMV infection or protective CMV-specific CMI. While percentages of CMV (tetramer-stained)-specific T cells associated with high CMI responses and clinical protection, circulating CD3+CD8midCD56+ NK-T cells overall strongly associated with low CMI and subsequent infection. However, three NK-T cell subsets sharing the CD11b surface marker associated with CMV protection and correlated with strong anti-viral CMI frequencies in vitro. These data were validated in two external independent cohorts of kidney transplant recipients. Thus, we newly describe the kinetics of a novel NK-T cell subset that may have a protective role in post-transplantation CMV infection. Our findings pave the way to more mechanistic studies aimed at understanding the function of these cells in protection against CMV infection.

Single-cell mass cytometry in immunological skin diseases

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

Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning

Microplastics (MPs) in natural waters are heterogeneously mixed with other natural particles including algal cells and suspended sediments. An easy-to-use and rapid method for directly measuring and distinguishing MPs from other naturally present colloids in the environment would expedite analytical workflows. Here, we established a database of MP scattering and fluorescence properties, either alone or in mixtures with natural particles, by stain-free flow cytometry. The resulting high-dimensional data were analyzed using machine learning approaches, either unsupervised (e.g., viSNE) or supervised (e.g., random forest algorithms). We assessed our approach in identifying and quantifying model MPs of diverse sizes, morphologies, and polymer compositions in various suspensions including phototrophic microorganisms, suspended biofilms, mineral particles, and sediment. We could precisely quantify MPs in microbial phototrophs and natural sediments with high organic carbon by both machine learning models (identification accuracies over 93%), although it was not possible to distinguish between different MP sizes or polymer compositions. By testing the resulting method in environmental samples through spiking MPs into freshwater samples, we further highlight the applicability of the method to be used as a rapid screening tool for MPs. Collectively, this workflow can be easily applied to a diverse set of samples to assess the presence of MPs in a time-efficient manner.

Automated prediction of acute promyelocytic leukemia from flow cytometry data using a graph neural network pipeline

OBJECTIVES

Our study aimed to develop a machine learning (ML) model to accurately classify acute promyelocytic leukemia (APL) from other types of acute myeloid leukemia (other AML) using multicolor flow cytometry (MFC) data. Multicolor flow cytometry is used to determine immunophenotypes that serve as disease signatures for diagnosis.

METHODS

We used a data set of MFC files from 27 patients with APL and 41 patients with other AML, including those with uncommon immunophenotypes. Our ML pipeline involved training a graph neural network (GNN) to output graph-level labels and identifying the most crucial MFC parameters and cells for predictions using an input perturbation method.

RESULTS

The top-performing GNN achieved 100% accuracy on the training/validation and test sets on classifying APL from other AML and used MFC parameters similarly to expert pathologists. Pipeline performance is amenable to use in a clinical decision support system, and our deep learning architecture readily enables prediction explanations.

CONCLUSIONS

Our ML pipeline shows robust performance on predicting APL and could be used to screen for APL using MFC data. It also allowed for intuitive interrogation of the model's predictions by clinicians.

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.

Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress

There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.

Machine learning optimized multiparameter radar plots for B-cell acute lymphoblastic leukemia minimal residual disease analysis

BACKGROUND

Flow cytometry is widely used for B-ALL minimal residual disease (MRD) analysis given its speed, availability, and sensitivity; however, distinguishing B-lymphoblasts from regenerative B-cells is not always straightforward. Radar plots, which project multiple markers onto a single plot, have been applied to other MRD analyses. Here we aimed to develop optimized radar plots for B-ALL MRD analysis.

METHODS

We compiled Children's Oncology Group (COG) flow data from 20 MRD-positive and 9 MRD-negative B-ALL cases (enriched for hematogones) to create labeled training and test data sets with equal numbers of B-lymphoblasts, hematogones, and mature B-cells. We used an automated approach to create hundreds of radar plots and ranked them based on the ability of support vector machine (SVM) models to separate blasts from normal B-cells in the training data set. Top-performing radar plots were compared with PCA, t-SNE, and UMAP plots, evaluated with the test data set, and integrated into clinical workflows.

RESULTS

SVM area under the ROC curve (AUC) for COG tube 1/2 radar plots improved from 0.949/0.921 to 0.989/0.968 after optimization. Performance was superior to PCA plots and comparable to UMAP, but with better generalizability to new data. When integrated into an MRD workflow, optimized radar plots distinguished B-lymphoblasts from other CD19-positive populations. MRD quantified by radar plots and serial gating were strongly correlated.

DISCUSSION

Radar plots were successfully optimized to discriminate between diverse B-lymphoblast populations and non-malignant CD19-positive populations in B-ALL MRD analysis. Our novel radar plot optimization strategy could be adapted to other MRD panels and clinical scenarios.

Deep phenotyping of T cell populations under long-term treatment of tacrolimus and rapamycin in patients receiving renal transplantations by mass cytometry

Tacrolimus (FK506) and rapamycin (RAPA) are widely used to maintain long-term immunosuppression after organ transplantation. However, the impact of accumulative drug administration on the recipients' immune systems remains unclear. We investigated the impact of 3-year FK506 or RAPA treatment after renal transplantation on the human immune systems. A discovery cohort of 30 patients was first recruited, and we discovered two distinctive T lineage suppressive regulatory patterns induced by chronic treatment of FK506 and RAPA. The increased percentage of senescent CD8+ CD57+ T lineages and less responsive T cell receptor (TCR) pathway in the FK506 group indicate better graft acceptance. Meanwhile, percentages of regulatory T cells (Tregs) and expression of CTLA-4 were both up to two-fold higher in the RAPA group, suggesting the inconsistent reactivation potential of the FK506 and RAPA groups when an anti-tumour or anti-infection immune response is concerned. Additionally, up-regulation of phosphorylated signaling proteins in T lineages after in vitro CD3/CD28 stimulation suggested more sensitive TCR-signaling pathways reserved in the RAPA group. An independent validation cohort of 100 renal transplantation patients was further investigated for the hypothesis that long-term RAPA administration mitigates the development of tumours and infections during long-term intake of immunosuppressants. Our results indicate that RAPA administration indeed results in less clinical oncogenesis and infection. The deep phenotyping of T-cell lineages, as educated by the long-term treatment of different immunosuppressants, provides new evidence for personalized precision medicine after renal transplantations.

CyTOF-Enabled Analysis Identifies Class-Switched B Cells as the Main Lymphocyte Subset Associated With Disease Relapse in Children With Idiopathic Nephrotic Syndrome

B cell depleting therapies permit immunosuppressive drug withdrawal and maintain remission in patients with frequently relapsing nephrotic syndrome (FRNS) or steroid–dependent nephrotic syndrome (SDNS), but lack of biomarkers for treatment failure. Post-depletion immune cell reconstitution may identify relapsing patients, but previous characterizations suffered from methodological limitations of flow cytometry. Time-of-flight mass cytometry (CyTOF) is a comprehensive analytic modality that simultaneously quantifies over 40 cellular markers. Herein, we report CyTOF-enabled immune cell comparisons over a 12-month period from 30 children with SDNS receiving B cell depleting therapy who either relapsed (n = 17) or remained stable (n = 13). Anti-CD20 treatment depleted all B cells subsets and CD20 depleting agent choice (rituximab vs ofatumumab) did not affect B cell subset recovery. Despite equal total numbers of B cells, 5 subsets of B cells were significantly higher in relapsing individuals; all identified subsets of B cells were class-switched. T cell subsets (including T follicular helper cells and regulatory T cells) and other major immune compartments were largely unaffected by B cell depletion, and similar between relapsing and stable children. In conclusion, CyTOF analysis of immune cells from anti-CD20 antibody treated patients identifies class-switched B cells as the main subset whose expansion associates with disease relapse. Our findings set the basis for future studies exploring how identified subsets can be used to monitor treatment response and improve our understanding of the pathogenesis of the disease.

ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data

High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in high-dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

OMIP 071: A 31-Parameter Flow Cytometry Panel for In-Depth Immunophenotyping of Human T-Cell Subsets Using Surface Markers

Dissecting the functional diversity of T cells is critical in elucidating mechanisms and in developing therapies for various diseases. Here, we designed a 31-parameter (29-color) panel to enable the characterization of T-cell subsets and immunophenotyping of the human peripheral blood and lymph nodes using cell surface staining. In addition to adaptive T-cell markers, TCR Vα24-Jα18, TCR γδ, TCR Vɑ7.2, and CD161 were included to identify iNKT, γδ T, and MAIT cells, respectively, which are innate-like T cells. C-X-C chemokine receptors (CXCR3, CXCR4, CXCR5, CXCR6) and C-C motif chemokine receptors (CCR4, CCR6, CCR7) were included to enable the identification of Th cell subsets (Th1, Th2, Th17), Tfh cell subsets (Tfh1, Tfh2, Tfh17), and Th cells with specific homing capacities. Furthermore, in this panel, we also used markers for assessing cell differentiation (CD45RO, CD7), activation (CD57, CD95, HLA-DR) and the expression of some cosignaling molecules (PD-1, NKG2D, CD28). Particularly, CD69 and CD103 were included for the further analysis of tissue resident memory T (Trm) cells. This panel would enable the in-depth immunophenotyping of human T-cell subsets, and may be applied in the monitoring, prognosis, and mechanistic studies of various immune-related diseases.

Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?

PURPOSE OF REVIEW

This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies.

RECENT FINDINGS

In recent years, computational analysis methods have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary.

SUMMARY

Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies.

RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging

Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artifacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsy samples, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance.

Chang et al. develop an analytical method called RESTORE to control for variations due to technical artifacts in multiplexed imaging. They test their method on a CycIF stained tissue microarray dataset and biopsies processed at different times. Their method can improve the applicability of imaging techniques in diagnostics and inference using unbiased clustering methods.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data

Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization. SIGNIFICANCE: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data.

Circulating B Cells With Memory and Antibody-Secreting Phenotypes Are Detectable in Pediatric Kidney Transplant Recipients Before the Development of Antibody-Mediated Rejection

Development of anti-human leukocyte antigen donor-specific antibodies (DSAs) is associated with antibody-mediated rejection (AMR) and reduced allograft survival in kidney transplant recipients. Whether changes in circulating lymphocytes anticipate DSA or AMR development is unclear.

METHODS

We used time-of-flight mass cytometry to analyze prospectively collected peripheral blood mononuclear cells (PBMC) from pediatric kidney transplant recipients who developed DSA (DSA-positive recipients [DSAPOS], n = 10). PBMC were obtained at 2 months posttransplant, 3 months before DSA development, and at DSA detection. PBMC collected at the same time points posttransplant from recipients who did not develop DSA (DSA-negative recipients [DSANEG], n = 11) were used as controls.

RESULTS

DSAPOS and DSANEG recipients had similar baseline characteristics and comparable frequencies of total B and T cells. Within DSAPOS recipients, there was no difference in DSA levels (mean fluorescence intensity [MFI]: 13 687 ± 4159 vs 11 375 ± 1894 in DSAPOSAMR-positive recipients (AMRPOS) vs DSAPOSAMR-negative recipients (AMRNEG), respectively; P = 0.630), C1q binding (5 DSAPOSAMRPOS [100%] vs 4 DSAPOSAMRNEG [80%]; P = 1.000), or C3d binding (3 DSAPOSAMRPOS [60%] vs 1 DSAPOSAMRNEG [20%]; P = 0.520) between patients who developed AMR and those who did not. However, DSAPOS patients who developed AMR (n = 5; 18.0 ± 3.6 mo post-DSA detection) had increased B cells with antibody-secreting (IgD-CD27+CD38+; P = 0.002) and memory (IgD-CD27+CD38-; P = 0.003) phenotypes compared with DSANEG and DSAPOSAMRNEG recipients at DSA detection.

CONCLUSIONS

Despite the small sample size, our comprehensive phenotypic analyses show that circulating B cells with memory and antibody-secreting phenotypes are present at DSA onset, >1 year before biopsy-proven AMR in pediatric kidney transplant recipients.

Single-Cell Analysis of Human Mononuclear Phagocytes Reveals Subset-Defining Markers and Identifies Circulating Inflammatory Dendritic Cells

Human mononuclear phagocytes comprise phenotypically and functionally overlapping subsets of dendritic cells (DCs) and monocytes, but the extent of their heterogeneity and distinct markers for subset identification remains elusive. By integrating high-dimensional single-cell protein and RNA expression data, we identified distinct markers to delineate monocytes from conventional DC2 (cDC2s). Using CD88 and CD89 for monocytes and HLA-DQ and FcεRIα for cDC2s allowed for their specific identification in blood and tissues. We also showed that cDC2s could be subdivided into phenotypically and functionally distinct subsets based on CD5, CD163, and CD14 expression, including a distinct subset of circulating inflammatory CD5^-CD163⁺CD14⁺ cells related to previously defined DC3s. These inflammatory DC3s were expanded in systemic lupus erythematosus patients and correlated with disease activity. These findings further unravel the heterogeneity of DC subpopulations in health and disease and may pave the way for the identification of specific DC subset-targeting therapies.

Rigor and Reproducibility of Cytometry Practices for Immuno-Oncology: A multifaceted challenge

The rapid advancement of immunotherapy strategies has created a need for technologies that can reliably and reproducibly identify rare populations, detect subtle changes in modulatory signals, and assess antigenic expression patterns that are time-sensitive. Accomplishing these tasks requires careful planning and the employment of tools that provide greater sensitivity and specificity without demanding extensive time. Flow Cytometry has earned its place as a preferred analysis platform. This technology offers a flexible path to the interrogation of protein expression patterns and detection of functional properties in cell populations of interest. Mass Cytometry is a newcomer technology that has generated significant interest in the field. By incorporating mass spectrometry analysis to the traditional principles of flow cytometry, this innovative tool promises to significantly expand the ability to detect multiple proteins on a single cell. The use of these technologies in a manner that is consistent and reproducible through multiple sample sets demands careful attention to experiment design, reagent selection, and instrumentation. Whether applying flow or mass cytometry, reaching successful, reliable results involves many factors. Sample preparation, antibody titrations, and appropriate controls are major biological considerations that impact cytometric analysis. Additionally, instrument voltages, lasers, and run quality assessments are essential for ensuring comparability and reproducibility between analyses. In this article, we aim to discuss the critical aspects that impact flow cytometry, and will touch on important considerations for mass cytometry as well. Focusing on their relevance to immunotherapy studies, we will address the importance of appropriate sample processing and will discuss how selection of suitable panels, controls, and antibodies must follow a carefully designed plan. We will also comment on how educated use of instrumentation plays a significant role in the reliability and reproducibility of results.Through this work, we hope to contribute to the effort toward establishing higher standards for rigor and reproducibility of cytometry practices by researchers, operators, and general cytometry users employing cytometry-based assays in their work. © 2019 International Society for Advancement of Cytometry.

T-cell exhaustion correlates with improved outcomes in kidney transplant recipients

Continuous antigen stimulation during chronic infection or malignancy can promote functional T cell silencing, a phenomenon called T cell exhaustion. The prevalence and impact of T cell exhaustion following organ transplantation, another immune stimulus with persistently high antigen load, are unknown. Here, we characterized serially collected peripheral blood mononuclear cells from 26 kidney transplant recipients using time-of-flight mass cytometry (CyTOF) to define distinct subsets of circulating exhausted T cells and their relationship to induction therapy and allograft function. We observed an increase in specific subsets of CD4⁺ and CD8⁺ exhausted T cells from pre-transplant to 6-months post-transplant, with greater increases in participants given anti-thymocyte globulin induction than in participants who received no induction or non-depleting induction. The percentages of exhausted T cells at 6 months correlated inversely with adenosine triphosphate (ATP) production (a surrogate of T cell function) and with allograft interstitial fibrosis. Guided by the CyTOF data, we delineated a PD-1⁺CD57^- phenotype for CD4⁺ and CD8⁺ exhausted T cells, and confirmed that these cells have limited capacity for cytokine secretion and ATP production. In an independent cohort of 50 kidney transplant recipients, we confirmed the predicted increase of PD-1⁺CD57^- exhausted T cells after lymphocyte-depleting induction therapy and its direct correlation with better allograft function. Our findings suggest that monitoring T cell exhaustion can be useful for post-transplant risk assessment and support the need to develop and test strategies aimed at augmenting T cell exhaustion following kidney transplantation.

Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM)

Multiplexed single-cell experimental techniques like mass cytometry measure 40 or more features and enable deep characterization of well-known and novel cell populations. However, traditional data analysis techniques rely extensively on human experts or prior knowledge, and novel machine learning algorithms may generate unexpected population groupings. Marker enrichment modeling (MEM) creates quantitative identity labels based on features enriched in a population relative to a reference. While developed for cell type analysis, MEM labels can be generated for a wide range of multidimensional data types, and MEM works effectively with output from expert analysis and diverse machine learning algorithms. MEM is implemented as an R package and includes three steps: (1) calculation of MEM values that quantify each feature's relative enrichment in the population, (2) reporting of MEM labels as a heatmap or as a text label, and (3) quantification of MEM label similarity between populations. The protocols here show MEM analysis using datasets from immunology and oncology. These MEM implementations provide a way to characterize population identity and novelty in the context of computational and expert analyses. © 2018 by John Wiley & Sons, Inc.