Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease

Loss of circulating CD8α+ NK cells during human Mycobacterium tuberculosis infection

Natural Killer (NK) cells can recognize and kill Mtb-infected cells in vitro, however their role after natural human exposure has not been well-studied. To identify Mtb-responsive NK cell populations, we analyzed the peripheral blood of healthy household contacts of active Tuberculosis (TB) cases and source community donors in an endemic region of Port-au-Prince, Haiti by flow cytometry. We observed higher CD8α expression on NK cells in putative resistors (IGRA-contacts) with a progressive loss of these circulating cells during household-associated latent infection and disease. In vitro assays and CITE-seq analysis of CD8α+ NK cells demonstrated enhanced maturity, cytotoxic gene expression, and response to cytokine stimulation relative to CD8α- NK cells. CD8α+ NK cells also displayed dynamic surface expression dependent on MHC I in contrast to conventional CD8+ T cells. Together, these results support a specialized role for CD8α+ NK cell populations during Mtb infection correlating with disease resistance.

High-parameter phenotypic characterization reveals a subset of human Th17 cells that preferentially produce IL-17 against M. tuberculosis antigen

Background

Interleukin-17-producing CD4 T cells contribute to the control of Mycobacterium tuberculosis (Mtb) infection in humans; whether infection with human immunodeficiency virus (HIV) disproportionately affects distinct Th17-cell subsets that respond to Mtb is incompletely defined.

Methods

We performed high-definition characterization of circulating Mtb-specific Th17 cells by spectral flow cytometry in people with latent TB and treated HIV (HIV-ART). We also measured kynurenine pathway activity by liquid chromatography-mass spectrometry (LC/MS) on plasma and tested the hypothesis that tryptophan catabolism influences Th17-cell frequencies in this context.

Results

We identified two subsets of Th17 cells: subset 1 defined as CD4+Vα7.2-CD161+CD26+and subset 2 defined as CD4+Vα7.2-CCR6+CXCR3-cells of which subset 1 was significantly reduced in latent tuberculosis infection (LTBI) with HIV-ART, yet Mtb-responsive IL-17-producing CD4 T cells were preserved; we found that IL-17-producing CD4 T cells dominate the response to Mtb antigen but not cytomegalovirus (CMV) antigen or staphylococcal enterotoxin B (SEB), and tryptophan catabolism negatively correlates with both subset 1 and subset 2 Th17-cell frequencies.

Conclusions

We found differential effects of ART-suppressed HIV on distinct subsets of Th17 cells, that IL-17-producing CD4 T cells dominate responses to Mtb but not CMV antigen or SEB, and that kynurenine pathway activity is associated with decreases of circulating Th17 cells that may contribute to tuberculosis immunity.

Age-associated CD4+ T cells with B cell-promoting functions are regulated by ZEB2 in autoimmunity

Aging is a significant risk factor for autoimmunity, and many autoimmune diseases tend to onset during adulthood. We conducted an extensive analysis of CD4+ T cell subsets from 354 patients with autoimmune disease and healthy controls via flow cytometry and bulk RNA sequencing. As a result, we identified a distinct CXCR3midCD4+ effector memory T cell subset that expands with age, which we designated "age-associated T helper (THA) cells." THA cells exhibited both a cytotoxic phenotype and B cell helper functions, and these features were regulated by the transcription factor ZEB2. Consistent with the highly skewed T cell receptor usage of THA cells, gene expression in THA cells from patients with systemic lupus erythematosus reflected disease activity and was affected by treatment with a calcineurin inhibitor. Moreover, analysis of single-cell RNA sequencing data revealed that THA cells infiltrate damaged organs in patients with autoimmune diseases. Together, our characterization of THA cells may facilitate improved understanding of the relationship between aging and autoimmune diseases.

High-parameter phenotypic characterization reveals a subset of human Th17 cells that preferentially produce IL17 against M. tuberculosis antigen

Background

Interleukin 17 producing CD4 T cells contribute to the control of Mycobacterium tuberculosis (Mtb) infection in humans; whether infection with Human Immunodeficiency Virus (HIV) disproportionately affects distinct Th17 cell subsets that respond to Mtb is incompletely defined.

Methods

We performed high-definition characterization of circulating Mtb-specific Th17 cells by spectral flow cytometry in people with latent TB and treated HIV (HIV-ART). We also measured kynurenine pathway activity by LC/MS on plasma and tested the hypothesis that tryptophan catabolism influences Th17 cell frequencies in this context.

Results

We identified two subsets of Th17 cells: subset 1 defined as CD4+Vα7.2-CD161+CD26+ and subset 2 defined as CD4+Vα7.2-CCR6+CXCR3- cells of which subset 1 was significantly reduced in LTBI with HIV-ART, yet Mtb-responsive IL17-producing CD4 T cells were preserved; we found that IL17-producing CD4 T cells dominate the response to Mtb antigen but not CMV antigen or staphylococcal enterotoxin B (SEB); and tryptophan catabolism negatively correlates with both subset 1 and subset 2 Th17 cell frequencies.

Conclusions

We found differential effects of ART-suppressed HIV on distinct subsets of Th17 cells, that IL17-producing CD4 T cells dominate responses to Mtb but not CMV antigen or SEB, and that kynurenine pathway activity is associated with decreases of circulating Th17 cells that may contribute to tuberculosis immunity.

Rare Variable M. tuberculosis Antigens induce predominant Th17 responses in human infection

CD4 T cells are essential for immunity to M. tuberculosis (Mtb), and emerging evidence indicates that IL-17-producing Th17 cells contribute to immunity to Mtb. While identifying protective T cell effector functions is important for TB vaccine design, T cell antigen specificity is also likely to be important. To identify antigens that induce protective immunity, we reasoned that as in other pathogens, effective immune recognition drives sequence diversity in individual Mtb antigens. We previously identified Mtb genes under evolutionary diversifying selection pressure whose products we term Rare Variable Mtb Antigens (RVMA). Here, in two distinct human cohorts with recent exposure to TB, we found that RVMA preferentially induce CD4 T cells that express RoRγt and produce IL-17, in contrast to 'classical' Mtb antigens that induce T cells that produce IFNγ. Our results suggest that RVMA can be valuable antigens in vaccines for those already infected with Mtb to amplify existing antigen-specific Th17 responses to prevent TB disease.

Multimodal and spatially resolved profiling identifies distinct patterns of T cell infiltration in nodal B cell lymphoma entities

The redirection of T cells has emerged as an attractive therapeutic principle in B cell non-Hodgkin lymphoma (B-NHL). However, a detailed characterization of lymphoma-infiltrating T cells across B-NHL entities is missing. Here we present an in-depth T cell reference map of nodal B-NHL, based on cellular indexing of transcriptomes and epitopes, T cell receptor sequencing, flow cytometry and multiplexed immunofluorescence applied to 101 lymph nodes from patients with diffuse large B cell, mantle cell, follicular or marginal zone lymphoma, and from healthy controls. This multimodal resource revealed quantitative and spatial aberrations of the T cell microenvironment across and within B-NHL entities. Quantitative differences in PD1+ TCF7- cytotoxic T cells, T follicular helper cells or IKZF3+ regulatory T cells were linked to their clonal expansion. The abundance of PD1+ TCF7- cytotoxic T cells was associated with poor survival. Our study portrays lymphoma-infiltrating T cells with unprecedented comprehensiveness and provides a unique resource for the investigation of lymphoma biology and prognosis.

Single-Cell Sequencing Reveals Functional Alterations in Tuberculosis

Despite its importance, the functional heterogeneity surrounding the dynamics of interactions between mycobacterium tuberculosis and human immune cells in determining host immune strength and tuberculosis (TB) outcomes, remains far from understood. This work now describes the development of a new technological platform to elucidate the immune function differences in individuals with TB, integrating single-cell RNA sequencing and cell surface antibody sequencing to provide both genomic and phenotypic information from the same samples. Single-cell analysis of 23 990 peripheral blood mononuclear cells from a new cohort of primary TB patients and healthy controls enables to not only show four distinct immune phenotypes (TB, myeloid, and natural killer (NK) cells), but also determine the dynamic changes in cell population abundance, gene expression, developmental trajectory, transcriptomic regulation, and cell-cell signaling. In doing so, TB-related changes in immune cell functions demonstrate that the immune response is mediated through host T cells, myeloid cells, and NK cells, with TB patients showing decreased naive, cytotoxicity, and memory functions of T cells, rather than their immunoregulatory function. The platform also has the potential to identify new targets for immunotherapeutic treatment strategies to restore T cells from dysfunctional or exhausted states.

Using immune clusters for classifying Mycobacterium tuberculosis infection

BACKGROUND

The differential diagnosis between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) is still a challenge worldwide.

METHODS

Immune indicators involved in innate, humoral, and cellular immune cells, as well as antigen-specific cells were simultaneously assessed in patients with ATB and LTBI.

RESULTS

Of 54 immune indicators, no indicator could distinguish ATB from LTBI, likely due to an obvious heterogeneity of immune indicators noticed in ATB patients. Cluster analysis of ATB patients identified three immune clusters with different severity. Cluster 1 (42.1 %) was a ''Treg/Th1/Tfh unbalance type" cluster, whereas cluster 2 (42.1 %) was an "effector type'' cluster, and cluster 3 was a ''inhibition type'' cluster (15.8 %) which showed the highest severity. A prediction model based on immune indicators was established and showed potential in classifying Mycobacterium tuberculosis infection.

CONCLUSIONS

We depicted the immune landscape of patients with ATB and LTBI. Three immune subtypes were identified in ATB patients with different severity.

The genetic basis of autoimmunity seen through the lens of T cell functional traits

Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?

Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. In an exploratory analysis of PBMC datasets, we find that some droplets that were originally called 'empty' due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a 'spongelet' which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.

A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Research progress of single-cell sequencing in tuberculosis

Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.

Single-Cell Transcriptomics of Mtb/HIV Co-Infection

Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) co-infection continues to pose a significant healthcare burden. HIV co-infection during TB predisposes the host to the reactivation of latent TB infection (LTBI), worsening disease conditions and mortality. There is a lack of biomarkers of LTBI reactivation and/or immune-related transcriptional signatures to distinguish active TB from LTBI and predict TB reactivation upon HIV co-infection. Characterizing individual cells using next-generation sequencing-based technologies has facilitated novel biological discoveries about infectious diseases, including TB and HIV pathogenesis. Compared to the more conventional sequencing techniques that provide a bulk assessment, single-cell RNA sequencing (scRNA-seq) can reveal complex and new cell types and identify more high-resolution cellular heterogeneity. This review will summarize the progress made in defining the immune atlas of TB and HIV infections using scRNA-seq, including host-pathogen interactions, heterogeneity in HIV pathogenesis, and the animal models employed to model disease. This review will also address the tools needed to bridge the gap between disease outcomes in single infection vs. co-infection. Finally, it will elaborate on the translational benefits of single-cell sequencing in TB/HIV diagnosis in humans.

Intricacies of TGF-β signaling in Treg and Th17 cell biology

Balanced immunity is pivotal for health and homeostasis. CD4+ helper T (Th) cells are central to the balance between immune tolerance and immune rejection. Th cells adopt distinct functions to maintain tolerance and clear pathogens. Dysregulation of Th cell function often leads to maladies, including autoimmunity, inflammatory disease, cancer, and infection. Regulatory T (Treg) and Th17 cells are critical Th cell types involved in immune tolerance, homeostasis, pathogenicity, and pathogen clearance. It is therefore critical to understand how Treg and Th17 cells are regulated in health and disease. Cytokines are instrumental in directing Treg and Th17 cell function. The evolutionarily conserved TGF-β (transforming growth factor-β) cytokine superfamily is of particular interest because it is central to the biology of both Treg cells that are predominantly immunosuppressive and Th17 cells that can be proinflammatory, pathogenic, and immune regulatory. How TGF-β superfamily members and their intricate signaling pathways regulate Treg and Th17 cell function is a question that has been intensely investigated for two decades. Here, we introduce the fundamental biology of TGF-β superfamily signaling, Treg cells, and Th17 cells and discuss in detail how the TGF-β superfamily contributes to Treg and Th17 cell biology through complex yet ordered and cooperative signaling networks.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

CD30 co-stimulation drives differentiation of protective T cells during Mycobacterium tuberculosis infection

Control of Mycobacterium tuberculosis (Mtb) infection requires generation of T cells that migrate to granulomas, complex immune structures surrounding sites of bacterial replication. Here we compared the gene expression profiles of T cells in pulmonary granulomas, bronchoalveolar lavage, and blood of Mtb-infected rhesus macaques to identify granuloma-enriched T cell genes. TNFRSF8/CD30 was among the top genes upregulated in both CD4 and CD8 T cells from granulomas. In mice, CD30 expression on CD4 T cells is required for survival of Mtb infection, and there is no major role for CD30 in protection by other cell types. Transcriptomic comparison of WT and CD30-/- CD4 T cells from the lungs of Mtb-infected mixed bone marrow chimeric mice showed that CD30 directly promotes CD4 T cell differentiation and the expression of multiple effector molecules. These results demonstrate that the CD30 co-stimulatory axis is highly upregulated on granuloma T cells and is critical for protective T cell responses against Mtb infection.

The T cell receptor sequence influences the likelihood of T cell memory formation

T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.

History of tuberculosis disease is associated with genetic regulatory variation in Peruvians

A quarter of humanity is estimated to be latently infected with Mycobacterium tuberculosis (Mtb) with a 5-10% risk of developing tuberculosis (TB) disease. Variability in responses to Mtb infection could be due to host or pathogen heterogeneity. Here, we focused on host genetic variation in a Peruvian population and its associations with gene regulation in monocyte-derived macrophages and dendritic cells (DCs). We recruited former household contacts of TB patients who previously progressed to TB (cases, n=63) or did not progress to TB (controls, n=63). Transcriptomic profiling of monocyte-derived dendritic cells (DCs) and macrophages measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified 330 and 257 eQTL genes in DCs and macrophages (False Discovery Rate (FDR) < 0.05), respectively. Five genes in DCs showed interaction between eQTL variants and TB progression status. The top eQTL interaction for a protein-coding gene was with FAH, the gene encoding fumarylacetoacetate hydrolase, which mediates the last step in mammalian tyrosine catabolism. FAH expression was associated with genetic regulatory variation in cases but not controls. Using public transcriptomic and epigenomic data of Mtb-infected monocyte-derived dendritic cells, we found that Mtb infection results in FAH downregulation and DNA methylation changes in the locus. Overall, this study demonstrates effects of genetic variation on gene expression levels that are dependent on history of infectious disease and highlights a candidate pathogenic mechanism through pathogen-response genes. Furthermore, our results point to tyrosine metabolism and related candidate TB progression pathways for further investigation.

Attenuated Mycobacterium tuberculosis vaccine protection in a low-dose murine challenge model

Bacillus Calmette-Guérin (BCG) remains the only approved tuberculosis (TB) vaccine despite limited efficacy. Preclinical studies of next-generation TB vaccines typically use a murine aerosol model with a supraphysiologic challenge dose. Here, we show that the protective efficacy of a live attenuated Mycobacterium tuberculosis (Mtb) vaccine ΔLprG markedly exceeds that of BCG in a low-dose murine aerosol challenge model. BCG reduced bacterial loads but did not prevent establishment or dissemination of infection in this model. In contrast, ΔLprG prevented detectable infection in 61% of mice and resulted in anatomic containment of 100% breakthrough infections to a single lung. Protection was partially abrogated in a repeated low-dose challenge model, which showed serum IL-17A, IL-6, CXCL2, CCL2, IFN-γ, and CXCL1 as correlates of protection. These data demonstrate that ΔLprG provides increased protection compared to BCG, including reduced detectable infection and anatomic containment, in a low-dose murine challenge model.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Th17.1 cell driven sarcoidosis-like inflammation after anti-BCMA CAR T cells in multiple myeloma

Pseudo-progression and flare-up phenomena constitute a novel diagnostic challenge in the follow-up of patients treated with immune-oncology drugs. We present a case study on pulmonary flare-up after Idecabtagen Vicleucel (Ide-cel), a BCMA targeting CAR T-cell therapy, and used single-cell RNA-seq (scRNA-seq) to identify a Th17.1 driven autoimmune mechanism as the biological underpinning of this phenomenon. By integrating datasets of various lung pathological conditions, we revealed transcriptomic similarities between post CAR T pulmonary lesions and sarcoidosis. Furthermore, we explored a noninvasive PET based diagnostic approach and showed that tracers binding to CXCR4 complement FDG PET imaging in this setting, allowing discrimination between immune-mediated changes and true relapse after CAR T-cell treatment. In conclusion, our study highlights a Th17.1 driven autoimmune phenomenon after CAR T, which may be misinterpreted as disease relapse, and that imaging with multiple PET tracers and scRNA-seq could help in this diagnostic dilemma.

Single-cell profiling identifies T cell subsets associated with control of tuberculosis dissemination

To identify T cell subsets associated with control of tuberculosis, single-cell transcriptome and T cell receptor sequencing were performed on total T cells from patients with tuberculosis and healthy controls. Fourteen distinct subsets of T cells were identified by unbiased UMAP clustering. A GZMK-expressing CD8⁺ cytotoxic T cell cluster and a SOX4-expressing CD4⁺ central memory T cell cluster were depleted, while a MKI67-expressing proliferating CD3⁺ T cell cluster was expanded in patients with tuberculosis compared with healthy controls. The ratio of Granzyme K-expressing CD8⁺CD161^-Ki-67^- and CD8⁺Ki-67⁺ T cell subsets was significantly reduced and inversely correlated with the extent of TB lesions in patients with TB. In contrast, ratio of Granzyme B-expressing CD8⁺Ki-67⁺ and CD4⁺CD161⁺Ki-67^- T cells and Granzyme A-expressing CD4⁺CD161⁺Ki-67^- T cells were correlated with the extent of TB lesions. It is concluded that granzyme K-expressing CD8⁺ T cell subsets might contribute to protection against tuberculosis dissemination.

Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. Using an exploratory analysis of PBMC datasets, we find that some droplets that were originally called "empty" due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a "spongelet" which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.

Chemokine positioning determines mutually exclusive roles for their receptors in extravasation of pathogenic human T cells

Pro-inflammatory T cells co-express multiple chemokine receptors, but the distinct functions of individual receptors on these cells are largely unknown. Human Th17 cells uniformly express the chemokine receptor CCR6, and we discovered that the subgroup of CD4+CCR6+ cells that co-express CCR2 possess a pathogenic Th17 signature, can produce inflammatory cytokines independent of TCR activation, and are unusually efficient at transendothelial migration (TEM). The ligand for CCR6, CCL20, was capable of binding to activated endothelial cells (ECs) and inducing firm arrest of CCR6+CCR2+ cells under conditions of flow - but CCR6 could not mediate TEM. By contrast, CCL2 and other ligands for CCR2, despite being secreted from both luminal and basal sides of ECs, failed to bind to the EC surfaces - and CCR2 could not mediate arrest. Nonetheless, CCR2 was required for TEM. To understand if CCR2's inability to mediate arrest was due solely to an absence of EC-bound ligands, we generated a CCL2-CXCL9 chimeric chemokine that could bind to the EC surface. Although display of CCL2 on the ECs did indeed lead to CCR2-mediated arrest of CCR6+CCR2+ cells, activating CCR2 with surface-bound CCL2 blocked TEM. We conclude that mediating arrest and TEM are mutually exclusive activities of chemokine receptors and/or their ligands that depend, respectively, on chemokines that bind to the EC luminal surfaces versus non-binding chemokines that form transendothelial gradients under conditions of flow. Our findings provide fundamental insights into mechanisms of lymphocyte extravasation and may lead to novel strategies to block or enhance their migration into tissue.

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

The impact of single-cell genomics on the field of mycobacterial infection

Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.

Single-cell eQTL models reveal dynamic T cell state dependence of disease loci

Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states-for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation-are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.

The Road to Elimination: Current State of Schistosomiasis Research and Progress Towards the End Game

The new WHO Roadmap for Neglected Tropical Diseases targets the global elimination of schistosomiasis as a public health problem. To date, control strategies have focused on effective diagnostics, mass drug administration, complementary and integrative public health interventions. Non-mammalian intermediate hosts and other vertebrates promote transmission of schistosomiasis and have been utilized as experimental model systems. Experimental animal models that recapitulate schistosomiasis immunology, disease progression, and pathology observed in humans are important in testing and validation of control interventions. We discuss the pivotal value of these models in contributing to elimination of schistosomiasis. Treatment of schistosomiasis relies heavily on mass drug administration of praziquantel whose efficacy is comprised due to re-infections and experimental systems have revealed the inability to kill juvenile schistosomes. In terms of diagnosis, nonhuman primate models have demonstrated the low sensitivity of the gold standard Kato Katz smear technique. Antibody assays are valuable tools for evaluating efficacy of candidate vaccines, and sera from graded infection experiments are useful for evaluating diagnostic sensitivity of different targets. Lastly, the presence of Schistosomes can compromise the efficacy of vaccines to other infectious diseases and its elimination will benefit control programs of the other diseases. As the focus moves towards schistosomiasis elimination, it will be critical to integrate treatment, diagnostics, novel research tools such as sequencing, improved understanding of disease pathogenesis and utilization of experimental models to assist with evaluating performance of new approaches.

Mycobacterium tuberculosis impedes CD40-dependent notch signaling to restrict Th17 polarization during infection

Early Th₁₇ responses are necessary to provide protection against Mycobacterium tuberculosis (Mtb). Mtb impedes Th₁₇ polarization by restricting CD40 co-stimulatory pathway on dendritic cells (DCs). We previously demonstrated that engaging CD40 on DCs increased Th₁₇ responses. However, the molecular mechanisms that contributed to Th₁₇ polarization were unknown. Here, we identify the Notch ligand DLL4 as necessary for Th₁₇ polarization and demonstrate that Mtb limits DLL4 on DCs to prevent optimal Th₁₇ responses. Although Mtb infection induced only low levels of DLL4, engaging CD40 on DCs increased DLL4 expression. Antibody blockade of DLL4 on DCs reduced Th₁₇ polarization in vitro and in vivo. In addition, we show that the Mtb Hip1 protease attenuates DLL4 expression on lung DCs by impeding CD40 signaling. Overall, our results demonstrate that Mtb impedes CD40-dependent DLL4 expression to restrict Th₁₇ responses and identify the CD40-DLL4 pathways as targets for developing new Th₁₇-inducing vaccines and adjuvants for tuberculosis.

•

Mtb restricts Th₁₇ responses by impairing CD40 signaling on dendritic cells

•

Engaging CD40 on DCs increases Notch ligand Dll4 transcript and surface expression

•

DLL4 is necessary for polarizing Th₁₇ and multifunctional T cells in the lungs of mice

•

Mtb impairs CD40/DLL4 pathway through the Hip1 serine protease immune evasion protein

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization, but the sparsity of the measurements and integrating multiple binding maps represent substantial challenges. Here we introduce scCUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce scChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states, and identify extensive and cell type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis

While Tuberculosis (TB) infection remains a serious challenge worldwide, big data and “omic” approaches have greatly contributed to the understanding of the disease. Transcriptomics have been used to tackle a wide variety of queries including diagnosis, treatment evolution, latency and reactivation, novel target discovery, vaccine response or biomarkers of protection. Although a powerful tool, the elevated cost and difficulties in data interpretation may hinder transcriptomics complete potential. Technology evolution and collaborative efforts among multidisciplinary groups might be key in its exploitation. Here, we discuss the main fields explored in TB using transcriptomics, and identify the challenges that need to be addressed for a real implementation in TB diagnosis, prevention and therapy.

Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

Repertoire analyses reveal T cell antigen receptor sequence features that influence T cell fate

T cells acquire a regulatory phenotype when their T cell receptors (TCRs) experience an intermediate-to-high affinity interaction with a self-peptide presented via the major histocompatibility complex (MHC). Using TCRβ sequences from flow-sorted human cells, we identified TCR features that promote regulatory T cell (T_reg) fate. From these results, we developed a scoring system to quantify TCR-intrinsic regulatory potential (TiRP). When applied to the tumor microenvironment, TiRP scoring helped to explain why only some T cell clones maintained the T_conv phenotype through expansion. To elucidate drivers of these predictive TCR features, we then examined the two elements of the T_reg TCR ligand separately: the self-peptide, and the human MHC II molecule. These analyses revealed that hydrophobicity in the third complementarity determining region (CDR3β) of the TCR promotes reactivity to self-peptides, while TCR variable gene (TRBV gene) usage shapes the TCR’s general propensity for human MHC II-restricted activation.

Effects of BCG vaccination on donor unrestricted T cells in two prospective cohort studies

BACKGROUND

Non-protein antigen classes can be presented to T cells by near-monomorphic antigen-presenting molecules such as CD1, MR1, and butyrophilin 3A1. Such T cells, referred to as donor unrestricted T (DURT) cells, typically express stereotypic T cell receptors. The near-unrestricted nature of DURT cell antigen recognition is of particular interest for vaccine development, and we sought to define the roles of DURT cells, including MR1-restricted MAIT cells, CD1b-restricted glucose monomycolate (GMM)-specific T cells, CD1d-restricted NKT cells, and γδ T cells, in vaccination against Mycobacterium tuberculosis.

METHODS

We compared and characterized DURT cells following primary bacille Calmette-Guerin (BCG) vaccination in a cohort of vaccinated and unvaccinated infants, as well as before and after BCG-revaccination in adults.

FINDINGS

BCG (re)vaccination did not modulate peripheral blood frequencies, T cell activation or memory profiles of MAIT cells, CD1b-restricted GMM-specific and germline-encoded mycolyl-reactive (GEM) cells or CD1d-restricted NKT cells. By contrast, primary BCG vaccination was associated with increased frequencies of γδ T cells as well as a novel subset of CD26⁺CD161⁺TRAV1-2^- IFN-γ-expressing CD4⁺ T cells in infants.

INTERPRETATION

Our findings, that most DURT cell populations were not modulated by BCG, do not preclude a role of BCG in modulating other qualitative aspects of DURT cells. More studies are required to understand the full potential of DURT cells in new TB vaccine strategies.

FUNDING

Aeras, the National Institutes of Health, and the Bill and Melinda Gates Foundation.

Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing

Tuberculosis (TB), is an infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis), and presents with high morbidity and mortality. Alveolar macrophages play an important role in TB pathogenesis although there is heterogeneity and functional plasticity. This study aimed to show the characteristics of alveolar macrophages from bronchioalveolar lavage fluid (BALF) in active TB patients. Single-cell RNA sequencing (scRNA-seq) was performed on BALF cells from three patients with active TB and additional scRNA-seq data from three healthy adults were established as controls. Transcriptional profiles were analyzed and compared by differential geneexpression and functional enrichment analysis. We applied pseudo-temporal trajectory analysis to investigate correlations and heterogeneity within alveolar macrophage subclusters. Alveolar macrophages from active TB patients at the single-cell resolution are described. We found that TB patients have higher cellular percentages in five macrophage subclusters. Alveolar macrophage subclusters with increased percentages were involved in inflammatory signaling pathways as well as the basic macrophage functions. The TB-increased alveolar macrophage subclusters might be derived from M1-like polarization state, before switching to an M2-like polarization state with the development ofM. tuberculosis infection. Cell-cell communications of alveolar macrophages also increased and enhanced in active TB patients. Overall, our study demonstrated the characteristics of alveolar macrophages from BALF in active TB patients by using scRNA-seq.

CD4+CCR6+ T cells dominate the BCG-induced transcriptional signature

BACKGROUND

The century-old Mycobacterium bovis Bacillus Calmette-Guerin (BCG) remains the only licensed vaccine against tuberculosis (TB). Despite this, there is still a lot to learn about the immune response induced by BCG, both in terms of phenotype and specificity.

METHODS

We investigated immune responses in adult individuals pre and 8 months post BCG vaccination. We specifically determined changes in gene expression, cell subset composition, DNA methylome, and the TCR repertoire induced in PBMCs and CD4 memory T cells associated with antigen stimulation by either BCG or a Mycobacterium tuberculosis (Mtb)-derived peptide pool.

FINDINGS

Following BCG vaccination, we observed increased frequencies of CCR6+ CD4 T cells, which includes both Th1* (CXCR3+CCR6+) and Th17 subsets, and mucosal associated invariant T cells (MAITs). A large number of immune response genes and pathways were upregulated post BCG vaccination with similar patterns observed in both PBMCs and memory CD4 T cells, thus suggesting a substantial role for CD4 T cells in the cellular response to BCG. These upregulated genes and associated pathways were also reflected in the DNA methylome. We described both qualitative and quantitative changes in the BCG-specific TCR repertoire post vaccination, and importantly found evidence for similar TCR repertoires across different subjects.

INTERPRETATION

The immune signatures defined herein can be used to track and further characterize immune responses induced by BCG, and can serve as reference for benchmarking novel vaccination strategies.

Maximizing statistical power to detect differentially abundant cell states with scPOST

To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.

Efficient and precise single-cell reference atlas mapping with Symphony

Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony ( https://github.com/immunogenomics/symphony ), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.