CytoML for cross-platform cytometry data sharing

MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.

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

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

CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data

BACKGROUND

With the increase of the dimensionality in flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream analyses. This part can in turn be split into a number of elementary steps: signal compensation or unmixing, scale transformation, debris, doublets and dead cells removal, batch effect correction, etc. However, assembling and assessing the pre-processing part can be challenging for a number of reasons. First, each of the involved elementary steps can be implemented using various methods and R packages. Second, the order of the steps can have an impact on the downstream analysis results. Finally, each method typically comes with its specific, non standardized diagnostic and visualizations, making objective comparison difficult for the end user.

RESULTS

Here, we present CytoPipeline and CytoPipelineGUI, two R packages to build, compare and assess pre-processing pipelines for flow cytometry data. To exemplify these new tools, we present the steps involved in designing a pre-processing pipeline on a real life dataset and demonstrate different visual assessment use cases. We also set up a benchmarking comparing two pre-processing pipelines differing by their quality control methods, and show how the package visualization utilities can provide crucial user insight into the obtained benchmark metrics.

CONCLUSION

CytoPipeline and CytoPipelineGUI are two Bioconductor R packages that help building, visualizing and assessing pre-processing pipelines for flow cytometry data. They increase productivity during pipeline development and testing, and complement benchmarking tools, by providing user intuitive insight into benchmarking results.

MetaGate: Interactive Analysis of High-Dimensional Cytometry Data with Meta Data Integration

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.

Nuanced role for dendritic cell intrinsic IRE1 RNase in the regulation of antitumor adaptive immunity

In cancer, activation of the IRE1/XBP1s axis of the unfolded protein response (UPR) promotes immunosuppression and tumor growth, by acting in cancer cells and tumor infiltrating immune cells. However, the role of IRE1/XBP1s in dendritic cells (DCs) in tumors, particularly in conventional type 1 DCs (cDC1s) which are cellular targets in immunotherapy, has not been fully elucidated. Here, we studied the role of IRE1/XBP1s in subcutaneous B16/B78 melanoma and MC38 tumors by generating loss-of-function models of IRE1 and/or XBP1s in DCs or in cDC1s. Data show that concomitant deletion of the RNase domain of IRE1 and XBP1s in DCs and cDC1s does not influence the kinetics of B16/B78 and MC38 tumor growth or the effector profile of tumor infiltrating T cells. A modest effect is observed in mice bearing single deletion of XBP1s in DCs, which showed slight acceleration of melanoma tumor growth and dysfunctional T cell responses, however, this effect was not recapitulated in animals lacking XBP1 only in cDC1s. Thus, evidence presented here argues against a general pro-tumorigenic role of the IRE1/XBP1s pathway in tumor associated DC subsets.

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

Vaccination induces HIV broadly neutralizing antibody precursors in humans

Broadly neutralizing antibodies (bnAbs) can protect against HIV infection but have not been induced by human vaccination. A key barrier to bnAb induction is vaccine priming of rare bnAb-precursor B cells. In a randomized, double-blind, placebo-controlled phase 1 clinical trial, the HIV vaccine-priming candidate eOD-GT8 60mer adjuvanted with AS01B had a favorable safety profile and induced VRC01-class bnAb precursors in 97% of vaccine recipients with median frequencies reaching 0.1% among immunoglobulin G B cells in blood. bnAb precursors shared properties with bnAbs and gained somatic hypermutation and affinity with the boost. The results establish clinical proof of concept for germline-targeting vaccine priming, support development of boosting regimens to induce bnAbs, and encourage application of the germline-targeting strategy to other targets in HIV and other pathogens.

My data choices: Pilot evaluation of patient-controlled medical record sharing technology

Patients desire greater control over sharing their digital health data. Consent2Share (C2S) is an open-source consent tool offered by SAMHA and the VA to support granular data sharing (GDS) options that align with patient preferences and data privacy regulations. The need to validate this tool exists. We pilot tested C2S with 199 English and Spanish-speaking patients with behavioral health conditions (BHCs) and patient guardians. Data were analyzed using mixed methodology. All participants desired granular control over the sharing of their health data. Most participants (87%) were highly interested in using a tool that offered granular options for executing data sharing decisions, with over half (55%) indicated that being able to specify the data type, data recipient, and data use purpose made them more willing to share their medical records. Majority (83%) indicated that the supported data type sharing categories satisfied their data-sharing privacy preferences. Majority (87%) also reported that knowing the purpose of data use made them more comfortable in sharing. Some participants (28%) accessed the education materials provided on data type sharing options. Patients want granular choices when sharing medical records. Consent2Share and its supported data type sharing categories are adequate to capture patients’ data sharing preferences. Further development is needed before deployment in clinical environments.

The induction of peripheral trained immunity in the pancreas incites anti-tumor activity to control pancreatic cancer progression

Despite the remarkable success of immunotherapy in many types of cancer, pancreatic ductal adenocarcinoma has yet to benefit. Innate immune cells are critical to anti-tumor immunosurveillance and recent studies have revealed that these populations possess a form of memory, termed trained innate immunity, which occurs through transcriptomic, epigenetic, and metabolic reprograming. Here we demonstrate that yeast-derived particulate β-glucan, an inducer of trained immunity, traffics to the pancreas, which causes a CCR2-dependent influx of monocytes/macrophages to the pancreas that display features of trained immunity. These cells can be activated upon exposure to tumor cells and tumor-derived factors, and show enhanced cytotoxicity against pancreatic tumor cells. In orthotopic models of pancreatic ductal adenocarcinoma, β-glucan treated mice show significantly reduced tumor burden and prolonged survival, which is further enhanced when combined with immunotherapy. These findings characterize the dynamic mechanisms and localization of peripheral trained immunity and identify an application of trained immunity to cancer.

Mitogen-activated protein kinase signaling in childhood asthma development and environment-mediated protection

BACKGROUND

While childhood asthma prevalence is rising in Westernized countries, farm children are protected. The mitogen-activated protein kinase (MAPK) pathway with its negative regulator dual-specificity phosphatase-1 (DUSP1) is presumably associated with asthma development.

OBJECTIVES

We aimed to investigate the role of MAPK signaling in childhood asthma and its environment-mediated protection, including a representative selection of 232 out of 1062 children from two cross-sectional cohorts and one birth cohort study.

METHODS

Peripheral blood mononuclear cells (PBMC) from asthmatic and healthy children were cultured upon stimulation with farm-dust extracts or lipopolysaccharide. In subgroups, gene expression was analyzed by qPCR (PBMCs, cord blood) and NanoString technology (dendritic cells). Protein expression of phosphorylated MAPKs was measured by mass cytometry. Histone acetylation was investigated by chromatin immunoprecipitation.

RESULTS

Asthmatic children expressed significantly less DUSP1 (p = .006) with reduced acetylation at histone H4 (p = .012) compared with healthy controls. Farm-dust stimulation upregulated DUSP1 expression reaching healthy levels and downregulated inflammatory MAPKs on gene and protein levels (PBMCs; p ≤ .01). Single-cell protein analysis revealed downregulated pMAPKs upon farm-dust stimulation in B cells, NK cells, monocytes, and T-cell subpopulations.

CONCLUSION

Lower DUSP1 baseline levels in asthmatic children and anti-inflammatory regulation of MAPK in several immune cell types by farm-dust stimulation indicate a regulatory function for DUSP1 for future therapy contributing to anti-inflammatory characteristics of farming environments.

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

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer-reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state-of-the-art handbook for basic and clinical researchers.

Longitudinal immune dynamics of mild COVID-19 define signatures of recovery and persistence

SARS-CoV-2 has infected over 200 million and caused more than 4 million deaths to date. Most individuals (>80%) have mild symptoms and recover in the outpatient setting, but detailed studies of immune responses have focused primarily on moderate to severe COVID-19. We deeply profiled the longitudinal immune response in individuals with mild COVID-19 beginning with early time points post-infection (1-15 days) and proceeding through convalescence to >100 days after symptom onset. We correlated data from single cell analyses of peripheral blood cells, serum proteomics, virus-specific cellular and humoral immune responses, and clinical metadata. Acute infection was characterized by vigorous coordinated innate and adaptive immune activation that differed in character by age (young vs. old). We then characterized signals associated with recovery and convalescence to define and validate a new signature of inflammatory cytokines, gene expression, and chromatin accessibility that persists in individuals with post-acute sequelae of SARS-CoV-2 infection (PASC).

Analyzing high-dimensional cytometry data using FlowSOM

The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1-3 h to complete, though quality issues can increase this time considerably.

Data processing workflow for large-scale immune monitoring studies by mass cytometry

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 used to generate the data. 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.

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating interleukin (IL)-15. CD8+ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17-23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19.

No evidence for basigin/CD147 as a direct SARS-CoV-2 spike binding receptor

The spike protein of SARS-CoV-2 is known to enable viral invasion into human cells through direct binding to host receptors including ACE2. An alternate entry receptor for the virus was recently proposed to be basigin/CD147. These early studies have already prompted a clinical trial and multiple published hypotheses speculating on the role of this host receptor in viral infection and pathogenesis. Here, we report that we are unable to find evidence supporting the role of basigin as a putative spike binding receptor. Recombinant forms of the SARS-CoV-2 spike do not interact with basigin expressed on the surface of human cells, and by using specialized assays tailored to detect receptor interactions as weak or weaker than the proposed basigin-spike binding, we report no evidence for a direct interaction between the viral spike protein to either of the two common isoforms of basigin. Finally, removing basigin from the surface of human lung epithelial cells by CRISPR/Cas9 results in no change in their susceptibility to SARS-CoV-2 infection. Given the pressing need for clarity on which viral targets may lead to promising therapeutics, we present these findings to allow more informed decisions about the translational relevance of this putative mechanism in the race to understand and treat COVID-19.

Validation of a hybrid approach to standardize immunophenotyping analysis in large population studies: The Health and Retirement Study

Traditional manual gating strategies are often time-intensive, place a high burden on the analyzer, and are susceptible to bias between analyzers. Several automated gating methods have shown to exceed performance of manual gating for a limited number of cell subsets. However, many of the automated algorithms still require significant manual interventions or have yet to demonstrate their utility in large datasets. Therefore, we developed an approach that utilizes a previously published automated algorithm (OpenCyto framework) with a manually created hierarchically cell gating template implemented, along with a custom developed visualization software (FlowAnnotator) to rapidly and efficiently analyze immunophenotyping data in large population studies. This approach allows pre-defining populations that can be analyzed solely by automated analysis and incorporating manual refinement for smaller downstream populations. We validated this method with traditional manual gating strategies for 24 subsets of T cells, B cells, NK cells, monocytes and dendritic cells in 931 participants from the Health and Retirement Study (HRS). Our results show a high degree of correlation (r ≥ 0.80) for 18 (78%) of the 24 cell subsets. For the remaining subsets, the correlation was low (<0.80) primarily because of the low numbers of events recorded in these subsets. The mean difference in the absolute counts between the hybrid method and manual gating strategy of these cell subsets showed results that were very similar to the traditional manual gating method. We describe a practical method for standardization of immunophenotyping methods in large scale population studies that provides a rapid, accurate and reproducible alternative to labor intensive manual gating strategies.

Multicenter flow cytometry proficiency testing of canine blood and lymph node samples

BACKGROUND

Flow cytometry (FC) is used increasingly in veterinary medicine for further characterization of hematolymphoid cells. Guidelines for optimizing assay performance and interpretation of results are limited, and concordance of results across laboratories is unknown.

OBJECTIVES

This study aimed to determine inter-investigator agreement on the interpretation of FC results from split samples analyzed in different laboratories using various protocols, cytometers, and software; and on the interpretation of archived FC standard (FCS) data files contributed by the different investigators.

METHODS

This was a multicenter observational cross-sectional study. Anticoagulated blood or lymph node aspirate samples from nine client-owned dogs were aliquoted and shipped to participating laboratories. Samples were analyzed with individual laboratory-developed protocols. In addition, FCS files from a set of separate samples from 11 client-owned dogs were analyzed by participating investigators. A person not associated with the study tabulated the results and interpretations. Agreement of interpretations was assessed with Fleiss' kappa statistic.

RESULTS

Prolonged transit times affected sample quality for some laboratories. Overall agreement among investigators regarding the FC sample interpretation was strong (κ = 0.86 ± 0.19, P < .001), and for specific categories, ranged from moderate to perfect. Agreement of the lymphoproliferation or other leukocyte sample category from the analysis of the FCS files was weak (κ = 0.58 ± 0.05, P < .001).

CONCLUSIONS

Lymphoproliferations were readily identified by FC, but identification of the categories of hematolymphoid neoplasia in fresh samples or archived files was variable. There is a need for a more standardized approach to maximize the enormous potential of FC in veterinary medicine.

MiSet RFC Standards: Defining a Universal Minimum Set of Standards Required for Reproducibility and Rigor in Research Flow Cytometry Experiments

Poor adherence to best practices, insufficient training, and pressure to produce data quickly may lead to publications of suboptimal biomedical research flow cytometry data, which contributes to the body of irreproducible research findings. In addition, documentation of compliance with best flow cytometry practices for submission, visualization, and publication of flow cytometry data is currently endorsed by very few scientific journals, which is particularly concerning as numerous peer-reviewed flow cytometry publications emphasize instrumentation, experimental design, and data analysis as important sources of variability. Guidelines and resources for adequate reporting, annotation and deposition of flow cytometry experiments are provided by MIFlowCyt and the FlowRepository database, and comprehensive expert recommendations covering principles and techniques of field-specific flow cytometry applications have been published. To facilitate the integration of quality-defining parameters into manuscript and grant submission and publication requirements across biomedical fields that rely on the use of flow-cytometry-based techniques, a single comprehensive yet easily and universally applicable document is needed. To produce such a list of gold-standard parameters that assess whether a research flow cytometry experiment has been planned, conducted, interpreted, and reported at the highest standard, a new initiative defining the minimum set of standards a robust and rigorous research flow experiment must fulfill (MiSet RFC Standards) was proposed at CYTO 2019. MiSet RFC Standards will integrate and simplify existing resources to provide a universal benchmark a flow cytometry experiment can easily be measured against. The goal of MiSET RFC Standards is its integration into peer-review and publication procedures through partnership with stakeholders, journals and publishers in biomedical and translational research. This article introduces the aims and anticipated timeline and discusses strategies for interdisciplinary consensus and implementation. A single-resource broadly applicable guideline will harmonize standards across different fields of biomedical research and lead to publication of more robust research findings. © 2019 International Society for Advancement of Cytometry.

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.