Search

Summary: High-dimensional flow cytometry is the gold standard to study the human immune system in large cohorts. However, large sample sizes increase inter-experimental variation because of technical and experimental inaccuracies introduced by batch variability. Our high-throughput sample processing pipeline in combination with 28-color flow cytometry focuses on increased throughput (192 samples/experiment) and high reproducibility. We implemented quality control checkpoints to reduce technical and experimental variation. Finally, we integrated FlowSOM clustering to facilitate automated data analysis and demonstrate the reproducibility of our pipeline in a study with 3,357 samples. We reveal age-associated immune dynamics in 2,300 individuals, signified by decreasing T and B cell subsets with age. In addition, by combining genetic analyses, our approach revealed unique immune signatures associated with a single nucleotide polymorphism (SNP) that abrogates CD45 isoform splicing. In summary, we provide a versatile and reliable high-throughput, flow cytometry-based pipeline for immune discovery and exploration in large cohorts. Motivation: Flow cytometry-based immunophenotyping studies are crucial in human immunology research. However, large sample sizes cause non-biological data variation, which impacts precision. We developed a sample processing and analysis pipeline for high-dimensional flow cytometry that focuses on sample throughput and incorporates stringent instrument standardization, staining protocols, quality controls, and unsupervised data analysis. These measures mitigate batch effect and experimental errors and increase data precision. With our pipeline we measured 3,357 samples in 19 experiments and obtained minimal non-biological variation, showcasing its usability for large immunophenotyping studies.

Background Malignant peritoneal mesothelioma (MPM) is an aggressive malignancy with a poor prognosis. Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) improves survival outcomes, but recurrence rates remain high. Dendritic cell-based immunotherapy (DCBI) showed promising results in patients with pleural mesothelioma. The primary aim of this trial was to determine feasibility of adjuvant DCBI after CRS-HIPEC.Methods This open-label, single-center, phase II clinical trial, performed in the Erasmus MC Cancer Institute Rotterdam, the Netherlands, included patients with epithelioid MPM. 4–6 weeks before CRS-HIPEC leukapheresis was performed. 8–10 weeks after surgery, DCBI was administered three times biweekly. Feasibility was defined as administration of at least three adjuvant vaccinations in 75% of patients. Comprehensive immune cell profiling was performed on peripheral blood samples prior to and during treatment.Results All patients who received CRS-HIPEC (n=16) were successfully treated with adjuvant DCBI. No severe toxicity related to DCBI was observed. Median progression-free survival (PFS) was 12 months (IQR 5–23) and median overall survival was not reached. DCBI was associated with increased proliferation of circulating natural killer cells and CD4+ T-helper (Th) cells. Co-stimulatory molecules, including ICOS, HLA-DR, and CD28 were upregulated predominantly on memory or proliferating Th-cells and minimally on CD8+ cytotoxic T-lymphocytes (CTLs) after treatment. However, an increase in CD8+ terminally differentiated effector memory (Temra) cells positively correlated with PFS, whereas co-expression of ICOS and Ki67 on CTLs trended towards a positive correlation.Conclusions Adjuvant DCBI after CRS-HIPEC in patients with MPM was feasible and safe, and showed promising survival outcomes. DCBI had an immune modulatory effect on lymphoid cells and induced memory T-cell activation. Moreover, an increase of CD8+ Temra cells was more pronounced in patients with longer PFS. These data provide rationale for future combination treatment strategies.Trial registration number NTR7060; Dutch Trial Register (NTR).

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

IntroductionMultiparameter flow cytometry (FCM) immunophenotyping is an important tool in the diagnostic screening and classification of primary immunodeficiencies (PIDs). The EuroFlow Consortium recently developed the PID Orientation Tube (PIDOT) as a universal screening tool to identify lymphoid-PID in suspicious patients. Although PIDOT can identify different lymphoid-PIDs with high sensitivity, clinical validation in a broad spectrum of patients with suspicion of PID is missing. In this study, we investigated the diagnostic performance of PIDOT, as part of the EuroFlow diagnostic screening algorithm for lymphoid-PID, in a daily practice at a tertiary reference center for PID.MethodsPIDOT was tested in 887 consecutive patients suspicious of PID at the Ghent University Hospital, Belgium. Patients were classified into distinct subgroups of lymphoid-PID vs. non-PID disease controls (non-PID DCs), according to the IUIS and ESID criteria. For the clinical validation of PIDOT, comprehensive characterization of the lymphoid defects was performed, together with the identification of the most discriminative cell subsets to distinguish lymphoid-PID from non-PID DCs. Next, a decision-tree algorithm was designed to guide subsequent FCM analyses.ResultsThe mean number of lymphoid defects detected by PIDOT in blood was 2.87 times higher in lymphoid-PID patients vs. non-PID DCs (p < 0.001), resulting in an overall sensitivity and specificity of 87% and 62% to detect severe combined immunodeficiency (SCID), combined immunodeficiency with associated or syndromic features (CID), immune dysregulation disorder (ID), and common variable immunodeficiency (CVID). The most discriminative populations were total memory and switched memory B cells, total T cells, TCD4+cells, and naive TCD4+cells, together with serum immunoglobulin levels. Based on these findings, a decision-tree algorithm was designed to guide further FCM analyses, which resulted in an overall sensitivity and specificity for all lymphoid-PIDs of 86% and 82%, respectively.ConclusionAltogether, our findings confirm that PIDOT is a powerful tool for the diagnostic screening of lymphoid-PID, particularly to discriminate (S)CID, ID, and CVID patients from other patients suspicious of PID. The combination of PIDOT and serum immunoglobulin levels provides an efficient guide for further immunophenotypic FCM analyses, complementary to functional and genetic assays, for accurate PID diagnostics.

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

The role of innate T cell subsets in the pathogenesis of spondyloarthritis (SpA) is not well understood. Here, the authors examine the role of invariant natural killer T (iNKT) and γδ-T cells in SpA and show that disease-derived iNKT and γδ-T cells have unique and Th17-skewed phenotype and gene expression profiles within inflamed joints.

Invariant natural killer T (iNKT) cells rapidly enhance cytokine secretion and effector function following activation, but the underlying mechanism is still unclear. Here the authors show that an endoplasmic reticulum stress sensor, inositol-requiring enzyme 1α, activates the p38 kinase to stabilize cytokine mRNA for enhanced iNKT functions.

Common variable immunodeficiency (CVID) is one of the most frequently diagnosed primary antibody deficiencies (PADs), a group of disorders characterized by a decrease in one or more immunoglobulin (sub)classes and/or impaired antibody responses caused by inborn defects in B cells in the absence of other major immune defects. CVID patients suffer from recurrent infections and disease-related, non-infectious, complications such as autoimmune manifestations, lymphoproliferation, and malignancies. A timely diagnosis is essential for optimal follow-up and treatment. However, CVID is by definition a diagnosis of exclusion, thereby covering a heterogeneous patient population and making it difficult to establish a definite diagnosis. To aid the diagnosis of CVID patients, and distinguish them from other PADs, we developed an automated machine learning pipeline which performs automated diagnosis based on flow cytometric immunophenotyping. Using this pipeline, we analyzed the immunophenotypic profile in a pediatric and adult cohort of 28 patients with CVID, 23 patients with idiopathic primary hypogammaglobulinemia, 21 patients with IgG subclass deficiency, six patients with isolated IgA deficiency, one patient with isolated IgM deficiency, and 100 unrelated healthy controls. Flow cytometry analysis is traditionally done by manual identification of the cell populations of interest. Yet, this approach has severe limitations including subjectivity of the manual gating and bias toward known populations. To overcome these limitations, we here propose an automated computational flow cytometry pipeline that successfully distinguishes CVID phenotypes from other PADs and healthy controls. Compared to the traditional, manual analysis, our pipeline is fully automated, performing automated quality control and data pre-processing, automated population identification (gating) and deriving features from these populations to build a machine learning classifier to distinguish CVID from other PADs and healthy controls. This results in a more reproducible flow cytometry analysis, and improves the diagnosis compared to manual analysis: our pipelines achieve on average a balanced accuracy score of 0.93 (±0.07), whereas using the manually extracted populations, an averaged balanced accuracy score of 0.72 (±0.23) is achieved.