High-Parameter Single-Cell Analysis

terraFlow, a high-parameter analysis tool, reveals T cell exhaustion and dysfunctional cytokine production in classical Hodgkin's lymphoma

Immune cells express an incredible variety of proteins; by measuring combinations of these, cell types influencing disease can be precisely identified. We developed terraFlow, a platform that defines cell subsets exhaustively by combinatorial protein expression. Using high-parameter checkpoint-focused and function-focused panels, we studied classical Hodgkin's lymphoma (cHL), where systemic T cells have not been investigated in detail. terraFlow revealed immune perturbations in patients, including elevated activated, exhausted, and interleukin (IL)-17+ phenotypes, along with diminished early, interferon (IFN)γ+, and tumor necrosis factor (TNF)+ T cells before treatment; many perturbations remained after treatment. terraFlow identified more disease-associated differences than other tools, often with better predictive power, and included a non-gating approach, eliminating time-consuming and subjective manual thresholds. It also reports a method to identify the smallest set of markers distinguishing study groups. Our results provide mechanistic support for past reports of immune deficiency in cHL and demonstrate the value of terraFlow in immunotherapy and biomarker studies.

Immunophenotyping with (phospho)protein profiling and fluorescent cell barcoding for single-cell signaling analysis and biomarker discovery

The microenvironment of hematologic cancers contributes to tumor cell survival and proliferation, as well as treatment resistance. Understanding tumor- and drug-induced changes to the immune cell composition and functionality is therefore critical for implementing optimal treatment strategies and for the development of novel cancer therapies. The liquid nature of peripheral blood makes this organ uniquely suited for single-cell studies by flow cytometry. (Phospho)protein profiles detected by flow cytometry analyses have been shown to correlate with ex vivo drug sensitivity and to predict treatment outcomes in hematologic cancers, demonstrating that this method is suitable for pre-clinical studies. Here, we present a flow cytometry protocol that combines multi-parameter immunophenotyping with single-cell (phospho)protein profiling. The protocol makes use of fluorescent cell barcoding, which means that multiple cell samples, either collected from different donors or exposed to different treatment conditions, can be combined and analyzed as one experiment. This reduces variability between samples, increases the throughput of the experiment, and lowers experimental costs. This protocol may serve as a guide for the use and further development of assays to study immunophenotype and cell signaling at single-cell resolution in normal and malignant cells. The read-outs may provide biological insight into cancer pathogenesis, identify novel drug targets, and ultimately serve as a biomarker to guide clinical decision-making.

Molecular cytometry for comprehensive immune profiling

Molecular cytometry refers to a group of high-parameter technologies for single-cell analysis that share the following traits: (1) combined (multimodal) measurement of protein and transcripts, (2) medium throughput (10-100K cells), and (3) the use of oligonucleotide-tagged antibodies to detect protein expression. The platform can measure over 100 proteins and either hundreds of targeted genes or the whole transcriptome, on a cell-by-cell basis. It is currently one of the most powerful technologies available for immune monitoring. Here, we describe the technology platform (which includes CITE-Seq, REAP-Seq, and AbSeq), provide guidance for its optimization, and discuss advantages and limitations. Finally, we provide some vignettes from studies that demonstrate the application and potential insight that can be gained from molecular cytometry studies.

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.

SpyMask enables combinatorial assembly of bispecific binders

Bispecific antibodies are a successful and expanding therapeutic class. Standard approaches to generate bispecifics are complicated by the need for disulfide reduction/oxidation or specialized formats. Here we present SpyMask, a modular approach to bispecifics using SpyTag/SpyCatcher spontaneous amidation. Two SpyTag-fused antigen-binding modules can be precisely conjugated onto DoubleCatcher, a tandem SpyCatcher where the second SpyCatcher is protease-activatable. We engineer a panel of structurally-distinct DoubleCatchers, from which binders project in different directions. We establish a generalized methodology for one-pot assembly and purification of bispecifics in 96-well plates. A panel of binders recognizing different HER2 epitopes were coupled to DoubleCatcher, revealing unexpected combinations with anti-proliferative or pro-proliferative activity on HER2-addicted cancer cells. Bispecific activity depended sensitively on both binder orientation and DoubleCatcher scaffold geometry. These findings support the need for straightforward assembly in different formats. SpyMask provides a scalable tool to discover synergy in bispecific activity, through modulating receptor organization and geometry.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison

High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Advances in application of single-cell RNA sequencing in cardiovascular research

Single-cell RNA sequencing (scRNA-seq) provides high-resolution information on transcriptomic changes at the single-cell level, which is of great significance for distinguishing cell subtypes, identifying stem cell differentiation processes, and identifying targets for disease treatment. In recent years, emerging single-cell RNA sequencing technologies have been used to make breakthroughs regarding decoding developmental trajectories, phenotypic transitions, and cellular interactions in the cardiovascular system, providing new insights into cardiovascular disease. This paper reviews the technical processes of single-cell RNA sequencing and the latest progress based on single-cell RNA sequencing in the field of cardiovascular system research, compares single-cell RNA sequencing with other single-cell technologies, and summarizes the extended applications and advantages and disadvantages of single-cell RNA sequencing. Finally, the prospects for applying single-cell RNA sequencing in the field of cardiovascular research are discussed.

Flow Cytometry Analysis of Mycobacteria and Mycobacteria-Infected Immune Cells

Flow cytometry enables the measurement of tens of features on individual cells from complex mixtures. Flow cytometry enables high-throughput quantification of cell size, gene and protein expression. In the case of studies of host-pathogen interactions, this tool provides a facile way of identifying cells that have been successfully infected by a pathogen. Several recent technological advances have greatly improved throughput and the number of features that can be simultaneously monitored by this technique. Here, we describe common workflows to study Mycobacterium tuberculosis heterogeneity and host-M. tuberculosis interactions using flow cytometry and related technologies.

AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data

Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry.

Clinical applications of monitoring immune status with 90 immune cell subsets in human whole blood by 10-color flow cytometry

INTRODUCTION

The immune system may involve and predict the different prognosis and therapy consequences. So, it's important to monitor and evaluate the immune status before and after treatments.

METHODS

Flow cytometry is the best technology to perform immune monitoring, because it can detect immune cells using small amount of sample in a short time. The whole blood is the ideal sample for immune status monitoring, since it includes almost all the immune cells and it's relatively easy to obtain and less invasive than bone marrow or lymph node.

RESULTS

Here we developed and validated a 10-color panel with only four tubes containing 29 antibodies to monitor 90 immune cell subsets in 2 ml whole blood samples. The major immune cell populations detected by our panel included T cell subsets (CD3⁺ total T, Th, Tc, Treg, CD8^hi , CD8^low , αβTCR, γδTCR, naïve, and memory T), T cell activation markers (CD25, CD69, and HLA-DR) and one immune checkpoint PD1, B cell subsets (B1, switched memory, non-switched, naïve B, and CD27^- IgD^- B cells), neutrophils, basophils, four monocytic cell subsets, dendritic cells (pDCs and mDCs), and four NK cell subsets. These panels of antibodies had been applied to monitor immune status (percentage and absolute number) in total 303 cases with various diseases, such as leukemia (AML, CML, MM, and ALL), lymphoma (B cells and NK/T cells), cancers (colon, lung, prostate, and breast), immune deficiencies, and autoimmune diseases.

CONCLUSION

We provided proof of feasibility for clinical monitoring immune status and guiding immunotherapy by multicolor flow cytometry testing.

CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data

BACKGROUND

The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge.

RESULTS

Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner.

CONCLUSIONS

CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.

A Burned-Out CD8+ T-cell Subset Expands in the Tumor Microenvironment and Curbs Cancer Immunotherapy

Specific mechanisms by which tumor-infiltrating lymphocytes (TIL) become dysfunctional remain poorly understood. Here, we employed a two-pronged approach using single-cell mass cytometry and tissue imaging technologies to dissect TILs from 25 patients with resectable and 35 patients with advanced non-small cell lung cancer (NSCLC). We identified a burned-out CD8⁺ TIL subset (Ebo) that specifically accumulated within the tumor microenvironment (TME) but not in adjacent nontumoral tissues. Ebo showed the highest expression of proliferation and activation markers but produced the lowest amount of IFNγ and were the most apoptotic CD8⁺ TIL subset. Using a humanized patient-derived tumor xenograft model, we demonstrated that Ebo expansion occurred within the TME in a PD-1/B7-H1 pathway-dependent manner. Ebo abundance in baseline tumor tissues was associated with resistance to anti-PD therapy in patients with NSCLC. Our study identifies a dysfunctional TIL subset, with distinct features from previously described exhausted T cells, and implies strategies to overcome immunotherapy resistance. SIGNIFICANCE: We identified a highly proliferative, overactivated, and apoptotic dysfunctional CD8⁺ tumor-infiltrating subpopulation that is functionally distinct from previously described exhausted T cells. This population is expanded in lung cancer tissues in a PD-1/B7-H1-dependent manner, and its abundance is associated with resistance to cancer immunotherapy, thus becoming a potential tissue biomarker.This article is highlighted in the In This Issue feature, p. 1601.

Flow Cytometry and Sorting in Arabidopsis

Flow cytometry and sorting represents a valuable and mature experimental platform for the analysis of cellular populations. Applications involving higher plants started to emerge around 40 years ago and are now widely employed both to provide unique information regarding basic and applied questions in the biosciences and to advance agricultural productivity in practical ways. Further development of this platform is being actively pursued, and this promises additional progress in our understanding of the interactions of cells within complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types held together by tough cell walls, and, second, because individual plant cells are generally larger than those of mammals.This chapter, which updates work last reviewed in 2014 [Galbraith DW (2014) Flow cytometry and sorting in Arabidopsis. In: Sanchez Serrano JJ, Salinas J (eds) Arabidopsis Protocols, 3rd ed. Methods in molecular biology, vol 1062. Humana Press, Totowa, pp 509-537], describes the application of techniques of flow cytometry and sorting to the model plant species Arabidopsis thaliana, in particular emphasizing (a) fluorescence labeling in vivo of specific cell types and of subcellular components, (b) analysis using both conventional cytometers and spectral analyzers, (c) fluorescence-activated sorting of protoplasts and nuclei, and (d) transcriptome analyses using sorted protoplasts and nuclei, focusing on population analyses at the level of single protoplasts and nuclei. Since this is an update, details of new experimental methods are emphasized.

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

A Comprehensive Workflow for Applying Single-Cell Clustering and Pseudotime Analysis to Flow Cytometry Data

The introduction of single-cell platforms inspired the development of high-dimensional single-cell analysis tools to comprehensively characterize the underlying cellular heterogeneity. Flow cytometry data are traditionally analyzed by (subjective) gating of subpopulations on two-dimensional plots. However, the increasing number of parameters measured by conventional and spectral flow cytometry reinforces the need to apply many of the recently developed tools for single-cell analysis on flow cytometry data, as well. However, the myriads of analysis options offered by the continuously released novel packages can be overwhelming to the immunologist with limited computational background. In this article, we explain the main concepts of such analyses and provide a detailed workflow to illustrate their implications and additional prerequisites when applied on flow cytometry data. Moreover, we provide readily applicable R code covering transformation, normalization, dimensionality reduction, clustering, and pseudotime analysis that can serve as a template for future analyses. We demonstrate the merit of our workflow by reanalyzing a public human dataset. Compared with standard gating, the results of our workflow provide new insights in cellular subsets, alternative classifications, and hypothetical trajectories. Taken together, we present a well-documented workflow, which utilizes existing high-dimensional single-cell analysis tools to reveal cellular heterogeneity and intercellular relationships in flow cytometry data.

Rainbow Kaposi's Sarcoma-Associated Herpesvirus Revealed Heterogenic Replication with Dynamic Gene Expression

Molecular mechanisms of Kaposi's sarcoma-associated herpesvirus (KSHV) reactivation have been studied primarily by measuring the total or average activity of an infected cell population, which often consists of a mixture of both nonresponding and reactivating cells that in turn contain KSHVs at various stages of replication. Studies on KSHV gene regulation at the individual cell level would allow us to better understand the basis for this heterogeneity, and new preventive measures could be developed based on findings from nonresponding cells exposed to reactivation stimuli. Here, we generated a recombinant reporter virus, which we named "Rainbow-KSHV," that encodes three fluorescence-tagged KSHV proteins (mBFP2-ORF6, mCardinal-ORF52, and mCherry-LANA). Rainbow-KSHV replicated similarly to a prototype reporter-KSHV, KSHVr.219, and wild-type BAC16 virus. Live imaging revealed unsynchronized initiation of reactivation and KSHV replication with diverse kinetics between individual cells. Cell fractionation revealed temporal gene regulation, in which early lytic gene expression was terminated in late protein-expressing cells. Finally, isolation of fluorescence-positive cells from nonresponders increased dynamic ranges of downstream experiments 10-fold. Thus, this study demonstrates a tool to examine heterogenic responses of KSHV reactivation for a deeper understanding of KSHV replication.IMPORTANCE Sensitivity and resolution of molecular analysis are often compromised by the use of techniques that measure the ensemble average of large cell populations. Having a research tool to nondestructively identify the KSHV replication stage in an infected cell would not only allow us to effectively isolate cells of interest from cell populations but also enable more precise sample selection for advanced single-cell analysis. We prepared a recombinant KSHV that can report on its replication stage in host cells by differential fluorescence emission. Consistent with previous host gene expression studies, our experiments reveal the highly heterogenic nature of KSHV replication/gene expression at individual cell levels. The utilization of a newly developed reporter-KSHV and initial characterization of KSHV replication in single cells are presented.

Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors

Novel treatments based upon the use of immune checkpoint inhibitors have an impressive efficacy in different types of cancer. Unfortunately, most patients do not derive benefit or lasting responses, and the reasons for the lack of therapeutic success are not known. Over the past two decades, a pressing need to deeply profile either the tumor microenvironment or cells responsible for the immune response has led investigators to integrate data obtained from traditional approaches with those obtained with new, more sophisticated, single-cell technologies, including high parameter flow cytometry, single-cell sequencing and high resolution imaging. The introduction and use of these technologies had, and still have a prominent impact in the field of cancer immunotherapy, allowing delving deeper into the molecular and cellular crosstalk between cancer and immune system, and fostering the identification of predictive biomarkers of response. In this review, besides the molecular and cellular cancer-immune system interactions, we are discussing how cutting-edge single-cell approaches are helping to point out the heterogeneity of immune cells in the tumor microenvironment and in blood.

EuroFlow Lymphoid Screening Tube (LST) data base for automated identification of blood lymphocyte subsets

In recent years the volume and complexity of flow cytometry data has increased substantially. This has led to a greater number of identifiable cell populations in a single measurement. Consequently, new gating strategies and new approaches for cell population definition are required. Here we describe how the EuroFlow Lymphoid Screening Tube (LST) reference data base for peripheral blood (PB) samples was designed, constructed and validated for automated gating of the distinct lymphoid (and myeloid) subsets in PB of patients with chronic lymphoproliferative disorders (CLPD). A total of 46 healthy/reactive PB samples which fulfilled pre-defined technical requirements, were used to construct the LST-PB reference data base. In addition, another set of 92 PB samples (corresponding to 10 healthy subjects, 51 B-cell CLPD and 31 T/NK-cell CLPD patients), were used to validate the automated gating and cell-population labeling tools with the Infinicyt software. An overall high performance of the LST-PB data base was observed with a median percentage of alarmed cellular events of 0.8% in 10 healthy donor samples and of 44.4% in CLPD data files containing 49.8% (range: 1.3-96%) tumor cells. The higher percent of alarmed cellular events in every CLPD sample was due to aberrant phenotypes (75.6% cases) and/or to abnormally increased cell counts (86.6% samples). All 18 (22%) data files that only displayed numerical alterations, corresponded to T/NK-cell CLPD cases which showed a lower incidence of aberrant phenotypes (41%) vs B-cell CLPD cases (100%). Comparison between automated vs expert-bases manual classification of normal (r² = 0.96) and tumor cell populations (rho = 0.99) showed a high degree of correlation. In summary, our results show that automated gating of cell populations based on the EuroFlow LST-PB data base provides an innovative, reliable and reproducible tool for fast and simplified identification of normal vs pathological B and T/NK lymphocytes in PB of CLPD patients.