Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data

CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data

Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/ .

Multiparametric Flow Cytometry for Determination of Viability, Caspase 3 and 7 Activity, and Lipid Peroxidation Adduct (4-Hydroxynonenal) in Equine Spermatozoa

Flow cytometry is a powerful tool for the analysis of cell samples formed of multipopulations, such as spermatozoa. In recent years, multiparametric cytometers have evolved, allowing the study of different cellular characteristics, such as protein expression, DNA analysis, or mitochondrial activity. Whether using traditional fluorescent dyes or fluorophore-conjugated antibodies, each cell or cellular component is individually stained, the sample is analyzed at high velocities, and then is displayed and interpreted in a dot-plot. We hereby describe the procedure to perform a multiparametric flow cytometry analysis in equine spermatozoa using three sources of excitation and polychromatic flow cytometry for the detection of 4HNE, a lipid peroxidation adduct (by anti-4HNE antibody), apoptotic markers (by caspases 3 and 7 activity), and live/dead spermatozoa (by ethidium-homodimer) excluding the debris with Hoechst 33342 staining and gating. This multiparametric analysis allows the simultaneous detection of different spermatic parameters, providing useful information for the characterization of a seminal sample and fertility estimation. © 2023 Wiley Periodicals LLC. Basic Protocol: Determination of viability, caspase 3 and 7 activity, and 4-hydroxynonenal in equine spermatozoa by flow cytometry.

Analysis of Antigen-Specific T and B Cells for Monitoring Immune Protection Against SARS-CoV-2

Immunological memory is the basis of protection against most pathogens. Long-living memory T and B cells able to respond to specific stimuli, as well as persistent antibodies in plasma and in other body fluids, are crucial for determining the efficacy of vaccination and for protecting from a second infection by a previously encountered pathogen. Antigen-specific cells are represented at a very low frequency in the blood, and indeed, they can be considered "rare events" present in the memory T-cell pool. Therefore, such events should be analyzed with careful attention. In the last 20 years, different methods, mostly based upon flow cytometry, have been developed to identify such rare antigen-specific cells, and the COVID-19 pandemic has given a dramatic impetus to characterize the immune response against the virus. In this regard, we know that the identification, enumeration, and characterization of SARS-CoV-2-specific T and B cells following infection and/or vaccination require i) the use of specific peptides and adequate co-stimuli, ii) the use of appropriate inhibitors to avoid nonspecific activation, iii) the setting of appropriate timing for stimulation, and iv) the choice of adequate markers and reagents to identify antigen-specific cells. Optimization of these procedures allows not only determination of the magnitude of SARS-CoV-2-specific responses but also a comparison of the effects of different combinations of vaccines or determination of the response provided by so-called "hybrid immunity," resulting from a combination of natural immunity and vaccine-generated immunity. Here, we present two methods that are largely used to monitor the response magnitude and phenotype of SARS-CoV-2-specific T and B cells by polychromatic flow cytometry, along with some tips that can be useful for the quantification of these rare events. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Identification of antigen-specific T cells Basic Protocol 2: Identification of antigen-specific B cells.

Stem-like T cells and niches: Implications in human health and disease

Recently, accumulating evidence has elucidated the important role of T cells with stem-like characteristics in long-term maintenance of T cell responses and better patient outcomes after immunotherapy. The fate of TSL cells has been correlated with many physiological and pathological human processes. In this review, we described present advances demonstrating that stem-like T (TSL) cells are central players in human health and disease. We interpreted the evolutionary characteristics, mechanism and functions of TSL cells. Moreover, we discuss the import role of distinct niches and how they affect the stemness of TSL cells. Furthermore, we also outlined currently available strategies to generate TSL cells and associated affecting factors. Moreover, we summarized implication of TSL cells in therapies in two areas: stemness enhancement for vaccines, ICB, and adoptive T cell therapies, and stemness disruption for autoimmune disorders.

Flow cytometry: past and future

Flow cytometry is a single-cell technology that measures scatter and fluorescence to establish a set of unique cellular properties. Flow cytometry is used in many areas of science, in particular biotechnology and medicine, but also in industrial applications. Flow cytometry can identify multiple phenotypic subsets from a mixture, select a single cell and even isolate that cell by a process called cell sorting. The field is currently undergoing dramatic changes. We are moving rapidly from the polychromic flow cytometry that has been the go-to technology for 45 years to spectral flow cytometry, which is now the most significant change in nearly half a century of flow cytometry. With change comes opportunity. Even spectral flow cytometry will morph into second-generation spectral flow cytometry within 5 years. New, exciting features will open up molecular diagnostics and physiology to flow cytometry.

Making the most of high-dimensional cytometry data

High-dimensional cytometry represents an exciting new era of immunology research, enabling the discovery of new cells and prediction of patient responses to therapy. A plethora of analysis and visualization tools and programs are now available for both new and experienced users; however, the transition from low- to high-dimensional cytometry requires a change in the way users think about experimental design and data analysis. Data from high-dimensional cytometry experiments are often underutilized, because of both the size of the data and the number of possible combinations of markers, as well as to a lack of understanding of the processes required to generate meaningful data. In this article, we explain the concepts behind designing high-dimensional cytometry experiments and provide considerations for new and experienced users to design and carry out high-dimensional experiments to maximize quality data collection.

Transformation of multicolour flow cytometry data with OTflow prevents misleading multivariate analysis results and incorrect immunological conclusions

The rapid evolution of the flow cytometry field, currently allowing the measurement of 30-50 parameters per cell, has led to a marked increase in deep multivariate information. Manual gating is insufficient to extract all this information. Therefore, multivariate analysis (MVA) methods have been developed to extract information and efficiently analyze the high-density multicolour flow cytometry (MFC) data. To aid interpretation, MFC data are often logarithmically transformed before MVA. We studied the consequences of different transformations of flow cytometry data in datasets containing negative intensities caused by background subtractions and spreading error, as logarithmic transformation of negative data is impossible. Transformations such as logicle or hyperbolic arcsine transformations allow linearity around zero, whereas higher (positive and negative) intensities are logarithmically transformed. To define the linear range, a parameter (or cofactor) must be chosen. We show how the chosen transformation parameter has great impact on the MVA results. In some cases, peak splitting is observed, producing two distributions around zero in an actual homogeneous population. This may be misinterpreted as the presence of multiple cell populations. Moreover, when performing arbitrary transformation before MVA analysis, biologically relevant and statistically significant information might be missed. We present a new algorithm, Optimal Transformation for flow cytometry data (OTflow), which uses various statistical methods to optimally choose the parameter of the transformation and prevent artifacts such as peak splitting. Arbitrary or unconsidered transformation can lead to wrong conclusions for the MVA cluster methods, dimensionality reduction methods, and classification methods. We recommend transformation of flow cytometry data by using OTflow-defined parameters estimated per channel, in order to prevent peak splitting and other artifacts in the data.

The importance of advanced cytometry in defining new immune cell types and functions relevant for the immunopathogenesis of HIV infection

: In the last years, novel, exciting immunological findings of interest for HIV research and treatment were identified thanks to different cytometric approaches. The analysis of the phenotypes and functionality of cells belonging to the immune system could clarify their role in the immunopathogenesis of HIV infection, and to elaborate key concepts, relevant in the treatment of this disease. Important discoveries have been made concerning cells that are important for protective immunity like lymphocytes that display polyfunctionality, resident memory T cells, innate lymphoid cells, to mention a few. The complex phenotype of myeloid-derived suppressor cells has been investigated, and relevant changes have been reported during chronic and primary HIV infection, in correlation with changes in CD4 T-cell number, T-cell activation, and with advanced disease stage. The search for markers of HIV persistence present in latently infected cells, namely those molecules that are important for a functional or sterilizing cure, evidenced the role of follicular helper T cells, and opened a discussion on the meaning and use of different surface molecules not only in identifying such cells, but also in designing new strategies. Finally, advanced technologies based upon the simultaneous detection of HIV-RNA and proteins at the single cell level, as well as those based upon spectral cytometry or mass cytometry are now finding new actors and depicting a new scenario in the immunopathogenesis of the infection, that will allow to better design innovative therapies based upon novel drugs and vaccines.

Increased estrogen to androgen ratio enhances immunoglobulin levels and impairs B cell function in male mice

Sex steroids, such as estrogens and androgens, are important regulators of the humoral immune response. Studies in female mice have demonstrated that alteration of circulating estrogen concentration regulates antibody-mediated immunity. As males have normally little endogenous estrogen, we hypothesized that in males high estrogens and low androgens affect the immune system and enhance the allergic inflammatory response. Here, we studied transgenic male mice expressing human aromatase (AROM+). These animals have a high circulating estrogen to androgen ratio (E/A), causing female traits such as gynecomastia. We found that AROM+ male mice had significantly higher plasma immunoglobulin levels, particularly IgE. Flow cytometry analyses of splenocytes revealed changes in mature/immature B cell ratio together with a transcriptional upregulation of the Igh locus. Furthermore, higher proliferation rate and increased IgE synthesis after IgE class-switching was found. Subsequently, we utilized an ovalbumin airway challenge model to test the allergic response in AROM+ male mice. In line with above observations, an increase in IgE levels was measured, albeit no impact on immune cell infiltration into the lungs was detected. Together, our findings suggest that high circulating E/A in males significantly alters B cell function without any significant enhancement in allergic inflammation.

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.

Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions

BACKGROUND

The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation.

METHODS

Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting.

RESULTS

Cell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy).

CONCLUSIONS

These new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.

A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses

The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities.

Manifestations of allostatic load in residents of radiation contaminated areas aged 18–24 years

We studied the features of allostatic load (AL) in 100 students aged 18–24 years old who, from birth to adulthood, lived in the territories assigned to the IV radiation zone after the Chornobyl accident (density of soil contamination by isotopes 137Cs 3.7–18.5∙104 Bq/m2) and underwent prolonged exposure to small doses of ionizing radiation. The examined students did not have any clinical signs of the immune-neuroendocrine system dysfunction. 50 people had signs of vegetative-vascular dystonia syndrome (VVD), 48 had signs of moderate hyperthyroidism and 21 had signs of moderate hypothyroidism. During the examination session, as a factor of additional psycho-emotional load, in 66 of the examined the immunoregulatory index CD4+/CD8+ went below the lower limit of the homeostatic norm, in 62 of the examined low density lipoprotein cholesterol (LDL-C) exceeded the upper level. The relative risk (RR) and attributable risk (AR) of the participation of potential secondary factors of allostatic load formation in CD4+/CD8+ immunoregulatory index going below the lower limit were calculated. The presence of statistically significant relative risk of participation in the formation of suppression of the index CD4+/CD8+: the state of hyperthyroidism, state of hypothyroidism, vegetative-vascular dystonia syndrome, higher than normal LDL-C. When the examined students combined the signs of hyperthyroidism, vegetative-vascular dystonia syndrome and higher level of LDL-C; with combination of signs of hypothyroidism, vegetative-vascular dystonia syndrome and higher level of LDL-C. The attributable risk in all cases exceeded 0.10, which confirmed the importance of some of these factors and their complexes in the formation of the effect of reduced immunoregulatory index. The CD4+/CD8+ index can be considered an important biomarker of AL and premature age-related changes in the immune system in residents of radiation-contaminated areas. The risk of AL formation in the case of occurrence of a complex of mediated secondary biomarkers (vegetative-vascular dystonia syndrome, thyroid dysfunction, hypercholesterolemia) is higher compared to their individual significance.

Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification

Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.

Background fluorescence and spreading error are major contributors of variability in high-dimensional flow cytometry data visualization by t-distributed stochastic neighboring embedding

Multidimensional single-cell analysis requires approaches to visualize complex data in intuitive 2D graphs. In this regard, t-distributed stochastic neighboring embedding (tSNE) is the most popular algorithm for single-cell RNA sequencing and cytometry by time-of-flight (CyTOF), but its application to polychromatic flow cytometry, including the recently developed 30-parameter platform, is still under investigation. We identified differential distribution of background values between samples, generated by either background calculation or spreading error (SE), as a major source of variability in polychromatic flow cytometry data representation by tSNE, ultimately resulting in the identification of erroneous heterogeneity among cell populations. Biexponential transformation of raw data and limiting SE during panel development dramatically improved data visualization. These aspects must be taken into consideration when using computational approaches as discovery tools in large sets of samples from independent experiments or immunomonitoring in clinical trials.

Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm

Multicolor Flow Cytometry (MFC)-based gating allows the selection of cellular (pheno)types based on their unique marker expression. Current manual gating practice is highly subjective and may remove relevant information to preclude discovery of cell populations with specific co-expression of multiple markers. Only multivariate approaches can extract such aspects of cell variability from multi-dimensional MFC data. We describe the novel method ECLIPSE (Elimination of Cells Lying in Patterns Similar to Endogeneity) to identify and characterize aberrant cells present in individuals out of homeostasis. ECLIPSE combines dimensionality reduction by Simultaneous Component Analysis with Kernel Density Estimates. A Difference between Densities (DbD) is used to eliminate cells in responder samples that overlap in marker expression with cells of controls. Thereby, subsequent data analyses focus on the immune response-specific cells, leading to more informative and focused models. To prove the power of ECLIPSE, we applied the method to study two distinct datasets: the in vivo neutrophil response induced by systemic endotoxin challenge and in studying the heterogeneous immune-response of asthmatics. ECLIPSE described the well-characterized common response in the LPS challenge insightfully, while identifying slight differences between responders. Also, ECLIPSE enabled characterization of the immune response associated to asthma, where the co-expressions between all markers were used to stratify patients according to disease-specific cell profiles.

Single Cell and Population Level Analysis of HCA Data

High Content Analysis instrumentation has undergone tremendous hardware advances in recent years. It is now possible to obtain images of hundreds of thousands to millions of individual objects, across multiple wells, channels, and plates, in a reasonable amount of time. In addition, it is possible to extract dozens, or hundreds, of features per object using commonly available software tools. Analyzing this data provides new challenges to the scientists. The magnitude of these numbers is reminiscent of flow cytometer, where practitioners have long been taking what effectively amounted to very low resolution, multi-parametric measurements from individual cells for many decades. Flow cytometrists have developed a wide range of tools to effectively analyze and interpret these types of data. This chapter will review the techniques used in flow cytometry and show how they can easily and effectively be applied to High Content Analysis.

T memory stem cells in health and disease

T memory stem (T_SCM) cells are a rare subset of memory lymphocytes endowed with the stem cell-like ability to self-renew and the multipotent capacity to reconstitute the entire spectrum of memory and effector T cell subsets. Cumulative evidence in mice, nonhuman primates and humans indicates that T_SCM cells are minimally differentiated cells at the apex of the hierarchical system of memory T lymphocytes. Here we describe emerging findings demonstrating that T_SCM cells, owing to their extreme longevity and robust potential for immune reconstitution, are central players in many physiological and pathological human processes. We also discuss how T_SCM cell stemness could be leveraged therapeutically to enhance the efficacy of vaccines and adoptive T cell therapies for cancer and infectious diseases or, conversely, how it could be disrupted to treat T_SCM cell driven and sustained diseases, such as autoimmunity, adult T cell leukemia and HIV-1.

Novel data analysis method for multicolour flow cytometry links variability of multiple markers on single cells to a clinical phenotype

Multicolour Flow Cytometry (MFC) produces multidimensional analytical data on the quantitative expression of multiple markers on single cells. This data contains invaluable biomedical information on (1) the marker expressions per cell, (2) the variation in such expression across cells, (3) the variability of cell marker expression across samples that (4) may vary systematically between cells collected from donors and patients. Current conventional and even advanced data analysis methods for MFC data explore only a subset of these levels. The Discriminant Analysis of MultiAspect CYtometry (DAMACY) we present here provides a comprehensive view on health and disease responses by integrating all four levels. We validate DAMACY by using three distinct datasets: in vivo response of neutrophils evoked by systemic endotoxin challenge, the clonal response of leukocytes in bone marrow of acute myeloid leukaemia (AML) patients, and the complex immune response in blood of asthmatics. DAMACY provided good accuracy 91–100% in the discrimination between health and disease, on par with literature values. Additionally, the method provides figures that give insight into the marker expression and cell variability for more in-depth interpretation, that can benefit both physicians and biomedical researchers to better diagnose and monitor diseases that are reflected by changes in blood leukocytes.

HIV-DNA content in different CD4+ T-cell subsets correlates with CD4+ cell : CD8+ cell ratio or length of efficient treatment

OBJECTIVES

HIV establishes a latent infection at different degrees within naïve (TN) or central (TCM) and effector memory (TEM) CD4 T cell. Studying patients in whom HIV production was suppressed by combined antiretroviral therapy, our main aim was to find which factors are related or can influence intracellular viral reservoir in different CD4 T-cell subsets.

METHODS

We enrolled 32 HIV patients successfully treated for more than 2 years, with a CD4 T-cell count more than 500 cells/μl and plasma viremia undetectable from at least 1 year. Proviral HIV-DNA, the amount of cells expressing signal-joint T-cell receptor rearrangement excision circles and telomere length were quantified by droplet digital PCR in highly purified, sorted CD4 T-cell subsets; plasma IL-7 and IL-15 were measured by ELISA.

RESULTS

HIV-DNA was significantly lower in TN cells compared with TCM or to TEM. Conversely, TN cells contained more signal-joint T-cell receptor rearrangement excision circles compared with TCM or to TEM; no appreciable changes were observed in telomere length. HIV-DNA content was significantly higher in TN and TCM cells, but not in TEM, from patients with shorter time of treatment, or in those with lower CD4 : CD8 ratio.

CONCLUSION

Length of treatment or recovery of CD4 : CD8 ratio significantly influences viral reservoir in both TN and TCM. Measuring HIV-DNA in purified lymphocyte populations allows a better monitoring of HIV reservoir and could be useful for designing future eradication strategies.

A theorem proving approach for automatically synthesizing visualizations of flow cytometry data

Background

Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset.

Results

This paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections.

Conclusions

We describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data.

Basic science and pathogenesis of ageing with HIV: potential mechanisms and biomarkers

: The increased prevalence of age-related comorbidities and mortality is worrisome in ageing HIV-infected patients. Here, we aim to analyse the different ageing mechanisms with regard to HIV infection. Ageing results from the time-dependent accumulation of random cellular damage. Epigenetic modifications and mitochondrial DNA haplogroups modulate ageing. In antiretroviral treatment-controlled patients, epigenetic clock appears to be advanced, and some haplogroups are associated with HIV infection severity. Telomere shortening is enhanced in HIV-infected patients because of HIV and some nucleoside analogue reverse transcriptase inhibitors. Mitochondria-related oxidative stress and mitochondrial DNA mutations are increased during ageing and also by some nucleoside analogue reverse transcriptase inhibitors. Overall, increased inflammation or 'inflammageing' is a major driver of ageing and could result from cell senescence with secreted proinflammatory mediators, altered gut microbiota, and coinfections. In HIV-infected patients, the level of inflammation and innate immunity activation is enhanced and related to most comorbidities and to mortality. This status could result, in addition to age, from the virus itself or viral protein released from reservoirs, from HIV-enhanced gut permeability and dysbiosis, from antiretroviral treatment, from frequent cytomegalovirus and hepatitis C virus coinfections, and also from personal and environmental factors, as central fat accumulation or smoking. Adaptive immune activation and immunosenescence are associated with comorbidities and mortality in the general population but are less predictive in HIV-infected patients. Biomarkers to evaluate ageing in HIV-infected patients are required. Numerous systemic or cellular inflammatory, immune activation, oxidative stress, or senescence markers can be tested in serum or peripheral blood mononuclear cells. The novel European Study to Establish Biomarkers of Human Ageing MARK-AGE algorithm, evaluating the biological age, is currently assessed in HIV-infected patients and reveals an advanced biological age. Some enhanced inflammatory or innate immune activation markers are interesting but still not validated for the patient's follow-up. To be able to assess patients' biological age is an important objective to improve their healthspan.

Aging of the immune system: Focus on inflammation and vaccination

Major advances in preventing, delaying, or curing individual pathologies are responsible for an increasingly long life span in the developed parts of our planet, and indeed reaching eight to nine decades of life is nowadays extremely frequent. However, medical and sanitary advances have not prevented or delayed the underlying cause of the disparate pathologies occurring in the elderly: aging itself. The identification of the basis of the aging processes that drives the multiple pathologies and loss of function typical of older individuals is a major challenge in current aging research. Among the possible causes, an impairment of the immune system plays a major role, and indeed numerous studies have described immunological changes which occur with age. Far from the intention of being exhaustive, this review will focus on recent advances and views on the role that modifications of cell signalling and remodelling of the immune response play during human aging and longevity, paying particular attention to phenomena which are linked to the so called inflammaging process, such as dysregulation of innate immunity, altered T-cell or B-cell maturation and differentiation, as well as to the implications of immune aging for vaccination strategies in the elderly.

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

Generation of clinical-grade CD19-specific CAR-modified CD8+ memory stem cells for the treatment of human B-cell malignancies

Long-lived, self-renewing, multipotent T memory stem cells (TSCM) can trigger profound and sustained tumor regression but their rareness poses a major hurdle to their clinical application. Presently, clinically compliant procedures to generate relevant numbers of this T-cell population are undefined. Here, we provide a strategy for deriving large numbers of clinical-grade tumor-redirected TSCM starting from naive precursors. CD8(+)CD62L(+)CD45RA(+) naive T cells enriched by streptamer-based serial-positive selection were activated by CD3/CD28 engagement in the presence of interleukin-7 (IL-7), IL-21, and the glycogen synthase-3β inhibitor TWS119, and genetically engineered to express a CD19-specific chimeric antigen receptor (CD19-CAR). These conditions enabled the generation of CD19-CAR-modified CD8(+) TSCM that were phenotypically, functionally, and transcriptomically equivalent to their naturally occurring counterpart. Compared with CD8(+) T cells generated with clinical protocols currently under investigation, CD19-CAR-modified CD8(+) TSCM exhibited enhanced metabolic fitness and mediated robust, long-lasting antitumor responses against systemic acute lymphoblastic leukemia xenografts. This clinical-grade platform provides the basis for a phase 1 trial evaluating the activity of CD19-CAR-modified CD8(+) TSCM in patients with B-cell malignancies refractory to prior allogeneic hematopoietic stem cell transplantation.

Adenosine A1 receptors contribute to immune regulation after neonatal hypoxic ischemic brain injury

Neonatal brain hypoxic ischemia (HI) often results in long-term motor and cognitive impairments. Post-ischemic inflammation greatly effects outcome and adenosine receptor signaling modulates both HI and immune cell function. Here, we investigated the influence of adenosine A1 receptor deficiency (A1R(-/-)) on key immune cell populations in a neonatal brain HI model. Ten-day-old mice were subjected to HI. Functional outcome was assessed by open locomotion and beam walking test and infarction size evaluated. Flow cytometry was performed on brain-infiltrating cells, and semi-automated analysis of flow cytometric data was applied. A1R(-/-) mice displayed larger infarctions (+33%, p < 0.05) and performed worse in beam walking tests (44% more mistakes, p < 0.05) than wild-type (WT) mice. Myeloid cell activation after injury was enhanced in A1R(-/-) versus WT brains. Activated B lymphocytes expressing IL-10 infiltrated the brain after HI in WT, but were less activated and did not increase in relative frequency in A1R(-/-). Also, A1R(-/-) B lymphocytes expressed less IL-10 than their WT counterparts, the A1R antagonist DPCPX decreased IL-10 expression whereas the A1R agonist CPA increased it. CD4(+) T lymphocytes including FoxP3(+) T regulatory cells, were unaffected by genotype, whereas CD8(+) T lymphocyte responses were smaller in A1R(-/-) mice. Using PCA to characterize the immune profile, we could discriminate the A1R(-/-) and WT genotypes as well as sham operated from HI-subjected animals. We conclude that A1R signaling modulates IL-10 expression by immune cells, influences the activation of these cells in vivo, and affects outcome after HI.

Human bone marrow contains a subset of quiescent early memory CD8(+) T cells characterized by high CD127 expression and efflux capacity

Even today it is still not completely understood how CD8(+) T-cell memory is maintained long term. Since bone marrow (BM) is a niche for immunological memory, we sought to identify long-lasting early memory CD8(+) T cells in this compartment. To achieve this, we looked for CD8(+) T cells that are able to efflux Rhodamine 123, a typical property of stem cells. Indeed, we identified a distinct subset of CD8(+) T cells in BM, with the capacity to efflux and high CD127 expression. These CD127(hi) effluxers are conventional CD8(+) T cells exhibiting a broad TCR-Vβ repertoire and are generated in response to viral peptides in vitro. CD127(hi) effluxer CD8(+) T cells have an early memory phenotype defined by preferential TNF-α production and a Bcl-2(hi) , KLRG-1(low) profile. This population has long telomeres and shows constitutively low frequencies of Ki-67 expression ex vivo, but has a high proliferative and differentiation capacity in vitro. However, IL-15 downmodulates CD127 in CD127(hi) effluxer CD8(+) T cells in vitro. Consequently, the CD127(low) effluxer subset may comprise cells recently exposed to IL-15. Taken together, CD127(hi) effluxer CD8(+) T cells represent a novel population of early memory T cells resident in BM with properties required for long-lived memory.

WITHDRAWN: Systems Biology and Immune Aging

The Publisher regrets that this article is an accidental duplication of anarticle that has already been published, http://dx.doi.org/10.1016/j.imlet.2014.09.009. The duplicate article has therefore been withdrawn.

Integrating and mining diverse data in human immunological studies

Bioanalysts and immunologists can interrogate the immune system with a variety of high-throughput technologies such as gene expression, multiplex bead arrays and flow cytometry. Conceptually, these assays support systems immunology studies, in which phenomena can be measured and correlated across biological compartments. First, however, the resulting high-dimensional data must be combined in a consistent fashion that supports analysis of the data as an integrated whole. Next, analytical methods must be applied to the hundreds or thousands of readouts. We recommend the use of a four-part analytical pipeline, consisting of data integration, hypothesis generation, prediction and hypothesis testing, and validation. We describe a variety of established methods appropriate for these integrated datasets, and highlight their application to human immunological studies. Our goal is to provide bioanalysts, immunologists and data analysts with a valuable perspective with which to approach the multiassay high-dimensional datasets generated by contemporary immunological studies.

Functional Signatures of Human CD4 and CD8 T Cell Responses to Mycobacterium tuberculosis

With 1.4 million deaths and 8.7 million new cases in 2011, tuberculosis (TB) remains a global health care problem and together with HIV and Malaria represents one of the three infectious diseases world-wide. Control of the global TB epidemic has been impaired by the lack of an effective vaccine, by the emergence of drug-resistant forms of Mycobacterium tuberculosis (Mtb) and by the lack of sensitive and rapid diagnostics. It is estimated, by epidemiological reports, that one third of the world’s population is latently infected with Mtb, but the majority of infected individuals develop long-lived protective immunity, which controls and contains Mtb in a T cell-dependent manner. Development of TB disease results from interactions among the environment, the host, and the pathogen, and known risk factors include HIV co-infection, immunodeficiency, diabetes mellitus, overcrowding, malnutrition, and general poverty; therefore, an effective T cell response determines whether the infection resolves or develops into clinically evident disease. Consequently, there is great interest in determining which T cells subsets mediate anti-mycobacterial immunity, delineating their effector functions. On the other hand, many aspects remain unsolved in understanding why some individuals are protected from Mtb infection while others go on to develop disease. Several studies have demonstrated that CD4⁺ T cells are involved in protection against Mtb, as supported by the evidence that CD4⁺ T cell depletion is responsible for Mtb reactivation in HIV-infected individuals. There are many subsets of CD4⁺ T cells, such as T-helper 1 (Th1), Th2, Th17, and regulatory T cells (Tregs), and all these subsets co-operate or interfere with each other to control infection; the dominant subset may differ between active and latent Mtb infection cases. Mtb-specific-CD4⁺ Th1 cell response is considered to have a protective role for the ability to produce cytokines such as IFN-γ or TNF-α that contribute to the recruitment and activation of innate immune cells, like monocytes and granulocytes. Thus, while other antigen (Ag)-specific T cells such as CD8⁺ T cells, natural killer (NK) cells, γδ T cells, and CD1-restricted T cells can also produce IFN-γ during Mtb infection, they cannot compensate for the lack of CD4⁺ T cells. The detection of Ag-specific cytokine production by intracellular cytokine staining (ICS) and the use of flow cytometry techniques are a common routine that supports the studies aimed at focusing the role of the immune system in infectious diseases. Flow cytometry permits to evaluate simultaneously the presence of different cytokines that can delineate different subsets of cells as having “multifunctional/polyfunctional” profile. It has been proposed that polyfunctional T cells, are associated with protective immunity toward Mtb, in particular it has been highlighted that the number of Mtb-specific T cells producing a combination of IFN-γ, IL-2, and/or TNF-α may be correlated with the mycobacterial load, while other studies have associated the presence of this particular functional profile as marker of TB disease activity. Although the role of CD8 T cells in TB is less clear than CD4 T cells, they are generally considered to contribute to optimal immunity and protection. CD8 T cells possess a number of anti-microbial effector mechanisms that are less prominent or absent in CD4 Th1 and Th17 T cells. The interest in studying CD8 T cells that are either MHC-class Ia or MHC-class Ib-restricted, has gained more attention. These studies include the role of HLA-E-restricted cells, lung mucosal-associated invariant T-cells (MAIT), and CD1-restricted cells. Nevertheless, the knowledge about the role of CD8⁺ T cells in Mtb infection is relatively new and recent studies have delineated that CD8 T cells, which display a functional profile termed “multifunctional,” can be a better marker of protection in TB than CD4⁺ T cells. Their effector mechanisms could contribute to control Mtb infection, as upon activation, CD8 T cells release cytokines or cytotoxic molecules, which cause apoptosis of target cells. Taken together, the balance of the immune response in the control of infection and possibly bacterial eradication is important in understanding whether the host immune response will be appropriate in contrasting the infection or not, and, consequently, the inability of the immune response, will determine the dissemination and the transmission of bacilli to new subjects. In conclusion, the recent highlights on the role of different functional signatures of T cell subsets in the immune response toward Mtb infection will be discerned in this review, in order to summarize what is known about the immune response in human TB. In particular, we will discuss the role of CD4 and CD8 T cells in contrasting the advance of the intracellular pathogen in already infected people or the progression to active disease in subjects with latent infection. All the information will be aimed at increasing the knowledge of this complex disease in order to improve diagnosis, prognosis, drug treatment, and vaccination.

T-cell activation discriminates subclasses of symptomatic primary humoral immunodeficiency diseases in adults

Background

Symptomatic Primary Humoral Immunodeficiency Diseases (PHID) constitute a highly heterogeneous group of diseases characterized by a shared hypogammaglobulinemia, resulting in increased risk of recurrent or severe infections. Associations have been described with a variety of immunological abnormalities involving B and T-cell differentiation, T-cell activation and innate immunity. However, PHID discrimination remains based on B-lymphocyte abnormalities and other components of the immune system have not been sufficiently taken into account. We carried out unsupervised and supervised methods for classification in a cohort of 81 symptomatic PHID patients to evaluate the relative importance of 23 immunological parameters and to select relevant markers that may be useful for diagnosis and prognosis.

Results

We identified five groups of patients, among which the percentage of PHID complications varied substantially. Combining the set of markers involved in PHID supported the existence of two distinct mechanisms associated with complications. Switched memory B-cell attrition and CD8+ HLA-DR + activated T-cell increase were the prominent abnormalities observed in PHID complications. Furthermore, in a subgroup of 57 patients with common variable immunodeficiency, the classification that added CD8+ HLA-DR + to the consensual EUROclass classification was better than the EUROclass model in predicting complications.

Conclusion

These results highlight the importance of T-cell activation that may improve discrimination of PHID patients in specific subgroups and help to identify patients with different clinical outcomes.

The past, present, and future of immune repertoire biology - the rise of next-generation repertoire analysis

T and B cell repertoires are collections of lymphocytes, each characterized by its antigen-specific receptor. We review here classical technologies and analysis strategies developed to assess immunoglobulin (IG) and T cell receptor (TR) repertoire diversity, and describe recent advances in the field. First, we describe the broad range of available methodological tools developed in the past decades, each of which answering different questions and showing complementarity for progressive identification of the level of repertoire alterations: global overview of the diversity by flow cytometry, IG repertoire descriptions at the protein level for the identification of IG reactivities, IG/TR CDR3 spectratyping strategies, and related molecular quantification or dynamics of T/B cell differentiation. Additionally, we introduce the recent technological advances in molecular biology tools allowing deeper analysis of IG/TR diversity by next-generation sequencing (NGS), offering systematic and comprehensive sequencing of IG/TR transcripts in a short amount of time. NGS provides several angles of analysis such as clonotype frequency, CDR3 diversity, CDR3 sequence analysis, V allele identification with a quantitative dimension, therefore requiring high-throughput analysis tools development. In this line, we discuss the recent efforts made for nomenclature standardization and ontology development. We then present the variety of available statistical analysis and modeling approaches developed with regards to the various levels of diversity analysis, and reveal the increasing sophistication of those modeling approaches. To conclude, we provide some examples of recent mathematical modeling strategies and perspectives that illustrate the active rise of a "next-generation" of repertoire analysis.

The who's who of T-cell differentiation: human memory T-cell subsets

Following antigen encounter and subsequent resolution of the immune response, a single naïve T cell is able to generate multiple subsets of memory T cells with different phenotypic and functional properties and gene expression profiles. Single-cell technologies, first and foremost flow cytometry, have revealed the complex heterogeneity of the memory T-cell compartment and its organization into subsets. However, a consensus has still to be reached, both at the semantic (nomenclature) and phenotypic level, regarding the identification of these subsets. Here, we review recent developments in the characterization of the heterogeneity of the memory T-cell compartment, and propose a unified classification of both human and nonhuman primate T cells on the basis of phenotypic traits and in vivo properties. Given that vaccine studies and adoptive cell transfer immunotherapy protocols are influenced by these recent findings, it is important to use uniform methods for identifying and discussing functionally distinct subsets of T cells.

T cell activation but not polyfunctionality after primary HIV infection predicts control of viral load and length of the time without therapy

Objective

Immune changes occurring after primary HIV infection (PHI) have a pivotal relevance. Our objective was to characterize the polyfunctionality of immune response triggered by PHI, and to characterize immune activation and regulatory T cells, correlating such features to disease progression.

Patients and Methods

We followed 11 patients experiencing PHI for 4 years. By polychromatic flow cytometry, we studied every month, for the first 6 months, T lymphocyte polyfunctionality after cell stimulation with peptides derived from HIV-1 gag and nef. Tregs were identified by flow cytometry, and T cell activation studied by CD38 and HLA-DR expression.

Results

An increase of anti-gag and anti-nef CD8+ specific T cells was observed 3 months after PHI; however, truly polyfunctional T cells, also able to produce IL-2, were never found. No gross changes in Tregs were present. T lymphocyte activation was maximal 1 and 2 months after PHI, and significantly decreased in the following period. The level of activation two months after PHI was strictly correlated to the plasma viral load 1 year after infection, and significantly influenced the length of period without therapy. Indeed, 80% of patients with less than the median value of activated CD8+ (15.5%) or CD4+ (0.9%) T cells remained free of therapy for >46 months, while all patients over the median value had to start treatment within 26 months.

Conclusions

T cell activation after PHI, more than T cell polyfunctionality or Tregs, is a predictive marker for the control of viral load and for the time required to start treatment.

Use of direct gradient analysis to uncover biological hypotheses in 16s survey data and beyond

This study investigated the use of direct gradient analysis of bacterial 16S pyrosequencing surveys to identify relevant bacterial community signals in the midst of a "noisy" background, and to facilitate hypothesis-testing both within and beyond the realm of ecological surveys. The results, utilizing 3 different real world data sets, demonstrate the utility of adding direct gradient analysis to any analysis that draws conclusions from indirect methods such as Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA). Direct gradient analysis produces testable models, and can identify significant patterns in the midst of noisy data. Additionally, we demonstrate that direct gradient analysis can be used with other kinds of multivariate data sets, such as flow cytometric data, to identify differentially expressed populations. The results of this study demonstrate the utility of direct gradient analysis in microbial ecology and in other areas of research where large multivariate data sets are involved.

Advances in human B cell phenotypic profiling

To advance our understanding and treatment of disease, research immunologists have been called-upon to place more centralized emphasis on impactful human studies. Such endeavors will inevitably require large-scale study execution and data management regulation (“Big Biology”), necessitating standardized and reliable metrics of immune status and function. A well-known example setting this large-scale effort in-motion is identifying correlations between eventual disease outcome and T lymphocyte phenotype in large HIV-patient cohorts using multiparameter flow cytometry. However, infection, immunodeficiency, and autoimmunity are also characterized by correlative and functional contributions of B lymphocytes, which to-date have received much less attention in the human Big Biology enterprise. Here, we review progress in human B cell phenotyping, analysis, and bioinformatics tools that constitute valuable resources for the B cell research community to effectively join in this effort.

Toward new insights on the white blood cell differential by flow cytometry: a proof of concept study on the sepsis model

BACKGROUND

We sought to evaluate, on a model of sepsis, the clinical relevance of new parameters obtained on a white blood cell (WBC) differential by flow cytometry, implemented in the routine workflow of our hematology laboratory.

METHODS

A WBC with differential by flow cytometry was done on 459 patients at admission in intensive care unit. They were retrospectively categorized in having (i) infection or not or (ii) a high gravity score (severe sepsis or septic shock) or not. We analyzed by hierarchical clustering, in a multidimensional manner, 50 parameters provided by the flow cytometric platform in place of the standard seven parameters for a standard differential.

RESULTS

Our approach allows to discriminate on the basis of a WBC differential (i) infected patients at admission based on a 16 parameter signature, with a concordance rate of 72.7% and a specificity of 79.9% and (ii) patients with high gravity score (septic shock or severe sepsis) at admission with a signature of eight parameters, with a concordance rate of 74.7% and a specificity of 75.9%.

CONCLUSIONS

This study shows the clinical relevance of an extended WBC differential to obtain by a flow cytometer integrated into a routine hematology laboratory workflow. Development of such approach implicates the redefinition of the WBC differential by integrating new parameters.

Stable changes in CD4+ T lymphocyte miRNA expression after exposure to HIV-1

MicroRNAs (miRNAs) inhibit HIV-1 expression by either modulating host innate immunity or by directly interfering with viral mRNAs. We evaluated the expression of 377 miRNAs in CD4(+) T cells from HIV-1 élite long-term nonprogressors (éLTNPs), naive patients, and multiply exposed uninfected (MEU) patients, and we observed that the éLTNP patients clustered with naive patients, whereas all MEU subjects grouped together. The discriminatory power of miRNAs showed that 21 miRNAs significantly differentiated éLTNP from MEU patients and 23 miRNAs distinguished naive from MEU patients, whereas only 1 miRNA (miR-155) discriminated éLTNP from naive patients. We proposed that miRNA expression may discriminate between HIV-1-infected and -exposed but negative patients. Analysis of miRNAs expression after exposure of healthy CD4(+) T cells to gp120 in vitro confirmed our hypothesis that a miRNA profile could be the result not only of a productive infection but also of the exposure to HIV-1 products that leave a signature in immune cells. The comparison of normalized Dicer and Drosha expression in ex vivo and in vitro condition revealed that these enzymes did not affect the change of miRNA profiles, supporting the existence of a Dicer-independent biogenesis pathway.

Phaedra, a Protocol-Driven System for Analysis and Validation of High-Content Imaging and Flow Cytometry

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.

Multiparameter flow cytometry and bioanalytics for B cell profiling in systemic lupus erythematosus

B lymphocyte involvement in systemic lupus erythematosus has been recognized for several decades, mainly in the context of autoantibody production. Both mouse and human studies reveal that different types of antibody responses, as well as antibody-independent effector functions can be ascribed to distinct subpopulations (subsets) of circulating B cells. Characterizing human B cell subsets can advance the field of autoimmunity even further by establishing B cell signatures associated with disease severity, progression, and response-to-treatment. For this purpose, we have developed specialized B cell reagent panels for multiparameter flow cytometry, and combine their use with advanced bioinformatics strategies that together will likely be advantageous for improving the characterization, prognosis, and for possibly improving treatment regimens of chronic inflammatory diseases such as lupus.

A human memory T cell subset with stem cell-like properties

Immunological memory is thought to depend on a stem cell-like, self-renewing population of lymphocytes capable of differentiating into effector cells in response to antigen re-exposure. Here we describe a long-lived human memory T cell population that has an enhanced capacity for self-renewal and a multipotent ability to derive central memory, effector memory and effector T cells. These cells, specific to multiple viral and self-tumor antigens, were found within a CD45RO(-), CCR7(+), CD45RA(+), CD62L(+), CD27(+), CD28(+) and IL-7Rα(+) T cell compartment characteristic of naive T cells. However, they expressed large amounts of CD95, IL-2Rβ, CXCR3, and LFA-1, and showed numerous functional attributes distinctive of memory cells. Compared with known memory populations, these lymphocytes had increased proliferative capacity and more efficiently reconstituted immunodeficient hosts, and they mediated superior antitumor responses in a humanized mouse model. The identification of a human stem cell-like memory T cell population is of direct relevance to the design of vaccines and T cell therapies.

CD4+ T-cell differentiation, regulatory T cells and gag-specific T lymphocytes are unaffected by CD4-guided treatment interruption and therapy resumption

OBJECTIVES

Despite limiting exposure to antiretroviral drugs, structured treatment interruptions can influence multiple aspects of T-cell immunity, particularly those regarding CD4(+) T lymphocytes. We evaluated the impact of CD4-guided treatment interruption (CD4-GTI) and treatment resumption on regulatory T cells (Tregs), T-lymphocyte activation, differentiation and polyfunctional gag-specific response.

METHODS

Patients were analyzed just prior to treatment interruption, at 2 and 6 months after treatment interruption, just prior to treatment resumption and at 2 and 6 months after treatment resumption. Thawed peripheral blood mononuclear cells were stained immediately for phenotype analysis or stimulated with HIV-gag peptides and analyzed by polychromatic flow cytometry.

RESULTS

Treatment interruption resulted in a CD4(+) cell count decrease and plasma viral load (pVL) increase, but did not preclude a good immune reconstitution and a complete suppression of pVL after treatment resumption. Treatment interruption did not influence CD4(+) T-cell differentiation and Treg subsets. During treatment interruption, gag-specific CD4(+) T cells were not lost, although the frequency of HIV-specific CD8(+) cells increased. Most gag-specific CD4(+) T cells were potentially cytotoxic (CD107a(+)) and were not influenced by pVL or by HAART. Most helper (CD154(+)) gag-specific CD4(+) T lymphocytes did not produce interferon-γ or interleukin-2.

CONCLUSION

CD4-GTI did not cause depletion of memory cells, Tregs or HIV-specific CD4(+) cells and, on the contrary, could induce HIV-specific responses. If guided by CD4(+) T-cell count, treatment interruption does not provoke irreversible immune damages.

CD4saurus Rex &HIVelociraptor vs. development of clinically useful immunological markers: a Jurassic tale of frozen evolution

One of the most neglected areas of everyday clinical practice for HIV physicians is unexpectedly represented by CD4 T cell counts when used as an aid to clinical decisions. All who care for HIV patients believe that CD4+ T cell counts are a reliable method to evaluate a patient immune status. There is however a fatalistic acceptance that besides its general usefulness, CD4+ T cell counts have relevant clincal and immunological limits. Shortcomings of CD4 counts appear in certain clinical scenarios including identification of immunological nonresponders, subsequent development of cancer on antiretroviral teatment, failure on tretment simplification. Historical and recently described parameters might be better suited to advise management of patients at certain times during their disease history. Immunogenotypic parameters and innate immune parameters that define progression as well as immune parameters associated with immune recovery are available and have not been introduced into validation processes in larger trials. The scientific and clinical community needs an effort in stimulating clinical evolution of immunological tests beyond "CD4saurus Rex" introducing new parameters in the clinical arena after appropriate validation

Classifying tissue samples from measurements on cells with within-class tissue sample heterogeneity

We consider here the problem of classifying a macro-level object based on measurements of embedded (micro-level) observations within each object, for example, classifying a patient based on measurements on a collection of a random number of their cells. Classification problems with this hierarchical, nested structure have not received the same statistical understanding as the general classification problem. Some heuristic approaches have been developed and a few authors have proposed formal statistical models. We focus on the problem where heterogeneity exists between the macro-level objects within a class. We propose a model-based statistical methodology that models the log-odds of the macro-level object belonging to a class using a latent-class variable model to account for this heterogeneity. The latent classes are estimated by clustering the macro-level object density estimates. We apply this method to the detection of patients with cervical neoplasia based on quantitative cytology measurements on cells in a Papanicolaou smear. Quantitative cytology is much cheaper and potentially can take less time than the current standard of care. The results show that the automated quantitative cytology using the proposed method is roughly equivalent to clinical cytopathology and shows significant improvement over a statistical model that does not account for the heterogeneity of the data.