IgG glycosylation associates with risk of progression from latent to active tuberculosis.

OBJECTIVES: Glycosylation motifs shape antibody structure, stability and antigen affinity and play an important role in antibody localization and function. Serum IgG glycosylation profiles are significantly altered in infectious diseases, including tuberculosis (TB), but have not been studied in the context of progression from latent to active TB. METHODS: We performed a longitudinal study of paired bulk IgG glycosylation and transcriptomic profiling in blood from individuals with active TB (ATB) or latent TB infection (LTBI) before and after treatment. RESULTS: We identified that a combination of two IgG1 glycosylation traits were sufficient to distinguish ATB from LTBI with high specificity and sensitivity, prior to, and after treatment. Importantly, these two features positively correlated with previously defined cellular and RNA signatures of ATB risk in LTBI, namely monocyte to lymphocyte ratio and the expression of interferon (IFN)-associated gene signature of progression (IFN-risk signature) in blood prior to treatment. Additional glycosylation features at higher prevalence in LTBI individuals with high expression of the IFN-risk signature prior to treatment included fucosylation on IgG1, IgG2 and IgG3. CONCLUSIONS: Together, our results demonstrate that bulk IgG glycosylation features could be useful in stratifying the risk of LTBI reactivation and progression to ATB.

Antigen-specificity measurements are the key to understanding T cell responses.

Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.

Distinguishing cell-cell complexes from dual lineage cells using single-cell transcriptomics is not trivial.

In their recent correspondence, Jie et al. strongly defend that the DE cell population they discovered are always dual lineage co-expressing cells and not complexes of B cells and T cells, which we have previously described as frequently present in single-cell RNA sequencing data. Here, we respond to the specific arguments made in their correspondence. Specifically, we demonstrate that the presence of a gene signature in a given cell population is not enough to ascertain that it does not contain cell-cell complexes, or that it represents a biologically distinct cell type. We also show that the gene signature of DE cells contains several genes from the myeloid lineage, suggesting either that their DE cells are a triple-lineage co-expressing cell, or a three-component cell aggregate. Finally, we identify multiple transcriptomic features of DE cells that correspond to B cell-T cell complexes, namely the presence of lower average expression of B- and T-cell specific genes, and a higher number of detected genes per cell. Taken together, our results demonstrate that solely based on their scRNAseq profile, it is not possible to ascertain that DE cells are dual expressing cells and not cell-cell complexes.

Single-cell profiling reveals distinct subsets of CD14+ monocytes drive blood immune signatures of active tuberculosis.

Introduction: Previous studies suggest that monocytes are an important contributor to tuberculosis (TB)-specific immune signatures in blood. Methods: Here, we carried out comprehensive single-cell profiling of monocytes in paired blood samples of active TB (ATB) patients at diagnosis and mid-treatment, and healthy controls. Results: At diagnosis, ATB patients displayed increased monocyte-to-lymphocyte ratio, increased frequency of CD14+CD16- and intermediate CD14+CD16+ monocytes, and upregulation of interferon signaling genes that significantly overlapped with previously reported blood TB signatures in both CD14+ subsets. In this cohort, we identified additional transcriptomic and functional changes in intermediate CD14+CD16+ monocytes, such as the upregulation of inflammatory and MHC-II genes, and increased capacity to activate T cells, reflecting overall increased activation in this population. Single-cell transcriptomics revealed that distinct subsets of intermediate CD14+CD16+ monocytes were responsible for each gene signature, indicating significant functional heterogeneity within this population. Finally, we observed that changes in CD14+ monocytes were transient, as they were no longer observed in the same ATB patients mid-treatment, suggesting they are associated with disease resolution. Discussion: Together, our study demonstrates for the first time that both intermediate and classical monocytes individually contribute to blood immune signatures of ATB and identifies novel subsets and associated gene signatures that may hold disease relevance.

Host Immune-Metabolic Adaptations Upon Mycobacterial Infections and Associated Co-Morbidities.

Mycobacterial diseases are a major public health challenge. Their causative agents include, in order of impact, members of the Mycobacterium tuberculosis complex (causing tuberculosis), Mycobacterium leprae (causing leprosy), and non-tuberculous mycobacterial pathogens including Mycobacterium ulcerans. Macrophages are mycobacterial targets and they play an essential role in the host immune response to mycobacteria. This review aims to provide a comprehensive understanding of the immune-metabolic adaptations of the macrophage to mycobacterial infections. This metabolic rewiring involves changes in glycolysis and oxidative metabolism, as well as in the use of fatty acids and that of metals such as iron, zinc and copper. The macrophage metabolic adaptations result in changes in intracellular metabolites, which can post-translationally modify proteins including histones, with potential for shaping the epigenetic landscape. This review will also cover how critical tuberculosis co-morbidities such as smoking, diabetes and HIV infection shape host metabolic responses and impact disease outcome. Finally, we will explore how the immune-metabolic knowledge gained in the last decades can be harnessed towards the design of novel diagnostic and therapeutic tools, as well as vaccines.

Distinct blood transcriptomic signature of treatment in latent tuberculosis infected individuals at risk of developing active disease.

Although only a small fraction will ever develop the active form of tuberculosis (ATB) disease, chemoprophylaxis treatment in latent TB infected (LTBI) individuals is an effective strategy to control pathogen transmission. Characterizing immune responses in LTBI upon chemoprophylactic treatment is important to facilitate treatment monitoring, and thus improve TB control strategies. Here, we studied changes in the blood transcriptome in a cohort of 42 LTBI and 8 ATB participants who received anti-TB therapy. Based on the expression of previously published gene signatures of progression to ATB, we stratified the LTBI cohort in two groups and examined if individuals deemed to be at elevated risk of developing ATB before treatment (LTBI-Risk) differed from others (LTBI-Other). We found that LTBI-Risk and LTBI-Other groups were associated with two distinct transcriptomic treatment signatures, with the LTBI-Risk signature resembling that of treated ATB patients. Notably, overlapping genes between LTBI-Risk and ATB treatment signatures were associated with risk of progression to ATB and interferon (IFN) signaling, and were selectively downregulated upon treatment in the LTBI-Risk but not the LTBI-Other group. Our results suggest that transcriptomic reprogramming following treatment of LTBI is heterogeneous and can be used to distinguish LTBI-Risk individuals from the LTBI cohort at large.

HLA-DR Marks Recently Divided Antigen-Specific Effector CD4 T Cells in Active Tuberculosis Patients.

Upon Ag encounter, T cells can rapidly divide and form an effector population, which plays an important role in fighting acute infections. In humans, little is known about the molecular markers that distinguish such effector cells from other T cell populations. To address this, we investigated the molecular profile of T cells present in individuals with active tuberculosis (ATB), where we expect Ag encounter and expansion of effector cells to occur at higher frequency in contrast to Mycobacterium tuberculosis-sensitized healthy IGRA+ individuals. We found that the frequency of HLA-DR+ cells was increased in circulating CD4 T cells of ATB patients, and was dominantly expressed in M. tuberculosis Ag-specific CD4 T cells. We tested and confirmed that HLA-DR is a marker of recently divided CD4 T cells upon M. tuberculosis Ag exposure using an in vitro model examining the response of resting memory T cells from healthy IGRA+ to Ags. Thus, HLA-DR marks a CD4 T cell population that can be directly detected ex vivo in human peripheral blood, whose frequency is increased during ATB disease and contains recently divided Ag-specific effector T cells. These findings will facilitate the monitoring and study of disease-specific effector T cell responses in the context of ATB and other infections.

Classical CD4 T cells as the cornerstone of antimycobacterial immunity.

Tuberculosis is a significant health problem without an effective vaccine to combat it. A thorough understanding of the immune response and correlates of protection is needed to develop a more efficient vaccine. The immune response against Mycobacterium tuberculosis (Mtb) is complex and involves all aspects of the immune system, however, the optimal protective, non-pathogenic T cell response against Mtb is still elusive. This review will focus on discussing CD4 T cell immunity against mycobacteria and its importance in Mtb infection with a primary focus on human studies. We will in particular discuss the large heterogeneity of immune cell subsets that have been revealed by recent immunological investigations at an unprecedented level of detail. These studies have identified specific classical CD4 T cell subsets important for immune responses against Mtb in various states of infection. We further discuss the functional attributes that have been linked to the various subsets such as upregulation of activation markers and cytokine production. Another important topic to be considered is the antigenic targets of Mtb-specific immune responses, and how antigen reactivity is influenced by both disease state and environmental exposure(s). These are key points for both vaccines and immune diagnostics development. Ultimately, these factors are holistically considered in the definition and investigations of what are the correlates on protection and resolution of disease.

A population of CD4hiCD38hi T cells correlates with disease severity in patients with acute malaria.

OBJECTIVE: CD4+ T cells are critical mediators of immunity to Plasmodium spp. infection, but their characteristics during malarial episodes and immunopathology in naturally infected adults are poorly defined. Flow cytometric analysis of PBMCs from patients with either P. falciparum or P. knowlesi malaria revealed a pronounced population of CD4+ T cells co-expressing very high levels of CD4 and CD38 we have termed CD4hiCD38hi T cells. We set out to gain insight into the function of these novel cells. METHODS: CD4+ T cells from 18 patients with P. falciparum or P. knowlesi malaria were assessed by flow cytometry and sorted into populations of CD4hiCD38hi or CD4norm T cells. Gene expression in the sorted populations was assessed by qPCR and NanoString. RESULTS: CD4hiCD38hi T cells expressed high levels of CD4 mRNA and canonical type 1 regulatory T-cell (TR1) genes including IL10, IFNG, LAG3 and HAVCR2 (TIM3), and other genes with relevance to cell migration and immunomodulation. These cells increased in proportion to malaria disease severity and were absent after parasite clearance with antimalarials. CONCLUSION: In naturally infected adults with acute malaria, a prominent population of type 1 regulatory T cells arises that can be defined by high co-expression of CD4 and CD38 (CD4hiCD38hi) and that correlates with disease severity in patients with falciparum malaria. This study provides fundamental insights into T-cell biology, including the first evidence that CD4 expression is modulated at the mRNA level. These findings have important implications for understanding the balance between immunity and immunopathology during malaria.

Quantitative and Qualitative Perturbations of CD8+ MAITs in Healthy Mycobacterium tuberculosis-Infected Individuals.

CD8 T cells are considered important contributors to the immune response against Mycobacterium tuberculosis, yet limited information is currently known regarding their specific immune signature and phenotype. In this study, we applied a cell population transcriptomics strategy to define immune signatures of human latent tuberculosis infection (LTBI) in memory CD8 T cells. We found a 41-gene signature that discriminates between memory CD8 T cells from healthy LTBI subjects and uninfected controls. The gene signature was dominated by genes associated with mucosal-associated invariant T cells (MAITs) and reflected the lower frequency of MAITs observed in individuals with LTBI. There was no evidence for a conventional CD8 T cell-specific signature between the two cohorts. We, therefore, investigated MAITs in more detail based on Vα7.2 and CD161 expression and staining with an MHC-related protein 1 (MR1) tetramer. This revealed two distinct populations of CD8+Vα7.2+CD161+ MAITs: MR1 tetramer+ and MR1 tetramer-, which both had distinct gene expression compared with memory CD8 T cells. Transcriptomic analysis of LTBI versus noninfected individuals did not reveal significant differences for MR1 tetramer+ MAITs. However, gene expression of MR1 tetramer- MAITs showed large interindividual diversity and a tuberculosis-specific signature. This was further strengthened by a more diverse TCR-α and -ß repertoire of MR1 tetramer- cells as compared with MR1 tetramer+ Thus, circulating memory CD8 T cells in subjects with latent tuberculosis have a reduced number of conventional MR1 tetramer+ MAITs as well as a difference in phenotype in the rare population of MR1 tetramer- MAITs compared with uninfected controls.

The Challenge of Distinguishing Cell-Cell Complexes from Singlet Cells in Non-Imaging Flow Cytometry and Single-Cell Sorting.

Our recent work has highlighted that care needs to be taken when interpreting single cell data originating from flow cytometry acquisition or cell sorting: We found that doublets of T cells bound to other immune cells are often present in the live singlet gate of human peripheral blood samples acquired by flow cytometry. This hidden "contamination" generates atypical gene signatures of mixed cell lineage in what is assumed to be single cells, which can lead to data misinterpretation, such as the description of novel immune cell types. Here, based on the example of T cell-monocyte complexes, we identify experimental and data analysis strategies to help distinguishing between singlets and cell-cell complexes in non-imaging flow cytometry and single-cell sorting. We found robust molecular signatures in both T cell-monocyte and T cell-B cell complexes that can distinguish them from singlets at both protein and mRNA levels. Imaging flow cytometry with appropriate gating strategy (matching the one used in cell sorting) and direct microscopy imaging after cell sorting were the two methods of choice to detect the presence of cell-cell complexes in suspicious dual-expressing cells. We finally applied this knowledge to highlight the likely presence of T cell-B cell complexes in a recently published dataset describing a novel cell population with mixed T cell and B cell lineage properties. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Reporting and connecting cell type names and gating definitions through ontologies.

BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. 'T cells'), and the description of the marker pattern utilized (e.g. CD14-, CD3+). RESULTS: We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two. CONCLUSIONS: We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.

Circulating T cell-monocyte complexes are markers of immune perturbations.

Our results highlight for the first time that a significant proportion of cell doublets in flow cytometry, previously believed to be the result of technical artifacts and thus ignored in data acquisition and analysis, are the result of biological interaction between immune cells. In particular, we show that cell:cell doublets pairing a T cell and a monocyte can be directly isolated from human blood, and high resolution microscopy shows polarized distribution of LFA1/ICAM1 in many doublets, suggesting in vivo formation. Intriguingly, T cell-monocyte complex frequency and phenotype fluctuate with the onset of immune perturbations such as infection or immunization, reflecting expected polarization of immune responses. Overall these data suggest that cell doublets reflecting T cell-monocyte in vivo immune interactions can be detected in human blood and that the common approach in flow cytometry to avoid studying cell:cell complexes should be re-visited.

Host Transcriptomics as a Tool to Identify Diagnostic and Mechanistic Immune Signatures of Tuberculosis.

Tuberculosis (TB) is a major infectious disease worldwide, and is associated with several challenges for control and eradication. First, more accurate diagnostic tools that better represent the spectrum of infection states are required; in particular, identify the latent TB infected individuals with high risk of developing active TB. Second, we need to better understand, from a mechanistic point of view, why the immune system is unsuccessful in some cases for control and elimination of the pathogen. Host transcriptomics is a powerful approach to identify both diagnostic and mechanistic immune signatures of diseases. We have recently reported that optimal study design for these two purposes should be guided by different sets of criteria. Here, based on already published transcriptomics signatures of tuberculosis, we further develop these guidelines and identify additional factors to consider for obtaining diagnostic vs. mechanistic signatures in terms of cohorts, samples, data generation and analysis. Diagnostic studies should aim to identify small disease signatures with high discriminatory power across all affected populations, and against similar pathologies to TB. Specific focus should be made on improving the diagnosis of infected individuals at risk of developing active disease. Conversely, mechanistic studies should focus on tissues biopsies, immune relevant cell subsets, state of the art transcriptomic techniques and bioinformatics tools to understand the biological meaning of identified gene signatures that could facilitate therapeutic interventions. Finally, investigators should ensure their data are made publicly available along with complete annotations to facilitate metadata and cross-study analyses.

Transcriptomic Analysis of CD4+ T Cells Reveals Novel Immune Signatures of Latent Tuberculosis.

In the context of infectious diseases, cell population transcriptomics are useful to gain mechanistic insight into protective immune responses, which is not possible using traditional whole-blood approaches. In this study, we applied a cell population transcriptomics strategy to sorted memory CD4 T cells to define novel immune signatures of latent tuberculosis infection (LTBI) and gain insight into the phenotype of tuberculosis (TB)-specific CD4 T cells. We found a 74-gene signature that could discriminate between memory CD4 T cells from healthy latently Mycobacterium tuberculosis-infected subjects and noninfected controls. The gene signature presented a significant overlap with the gene signature of the Th1* (CCR6+CXCR3+CCR4-) subset of CD4 T cells, which contains the majority of TB-specific reactivity and is expanded in LTBI. In particular, three Th1* genes (ABCB1, c-KIT, and GPA33) were differentially expressed at the RNA and protein levels in memory CD4 T cells of LTBI subjects compared with controls. The 74-gene signature also highlighted novel phenotypic markers that further defined the CD4 T cell subset containing TB specificity. We found the majority of TB-specific epitope reactivity in the CD62L-GPA33- Th1* subset. Thus, by combining cell population transcriptomics and single-cell protein-profiling techniques, we identified a CD4 T cell immune signature of LTBI that provided novel insights into the phenotype of TB-specific CD4 T cells.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

Unique phenotypes and clonal expansions of human CD4 effector memory T cells re-expressing CD45RA.

The expression of CD45RA is generally associated with naive T cells. However, a subset of effector memory T cells re-expresses CD45RA (termed TEMRA) after antigenic stimulation with unknown molecular characteristics and functions. CD4 TEMRA cells have been implicated in protective immunity against pathogens such as dengue virus (DENV). Here we show that not only the frequency but also the phenotype of CD4 TEMRA cells are heterogeneous between individuals. These cells can be subdivided into two major subsets based on the expression of the adhesion G protein-coupled receptor GPR56, and GPR56+ TEMRA cells display a transcriptional and proteomic program with cytotoxic features that is distinct from effector memory T cells. Moreover, GPR56+ TEMRA cells have higher levels of clonal expansion and contain the majority of virus-specific TEMRA cells. Overall, this study reveals the heterogeneity of CD4 TEMRA cells and provides insights into T-cell responses against DENV and other viral pathogens.