Single-cell transcriptomics of blood reveals a natural killer cell subset depletion in tuberculosis

Single-cell transcriptomics of blood identified IFIT1+ neutrophil subcluster expansion in NTM-PD patients

OBJECTIVE

Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is caused by an imbalance between pathogens and impaired host immune responses. Mycobacterium avium complex (MAC) and Mycobacterium abscessus (MAB) are the two major pathogens that cause NTM-PD. In this study, we sought to dissect the transcriptomes of peripheral blood immune cells at the single-cell resolution in NTM-PD patients and explore potential clinical markers for NTM-PD diagnosis and treatment.

METHODS

Peripheral blood samples were collected from six NTM-PD patients, including three MAB-PD patients, three MAC-PD patients, and two healthy controls. We employed single-cell RNA sequencing (scRNA-seq) to define the transcriptomic landscape at a single-cell resolution. A comprehensive scRNA-seq analysis was performed, and flow cytometry was conducted to validate the results of scRNA-seq.

RESULTS

A total of 27,898 cells were analyzed. Nine T-cells, six mononuclear phagocytes (MPs), and four neutrophil subclusters were defined. During NTM infection, naïve T-cells were reduced, and effector T-cells increased. High cytotoxic activities were shown in T-cells of NTM-PD patients. The proportion of inflammatory and activated MPs subclusters was enriched in NTM-PD patients. Among neutrophil subclusters, an IFIT1+ neutrophil subcluster was expanded in NTM-PD compared to healthy controls. This suggests that IFIT1+ neutrophil subcluster might play an important role in host defense against NTM. Functional enrichment analysis of this subcluster suggested that it is related to interferon response. Cell-cell interaction analysis revealed enhanced CXCL8-CXCR1/2 interactions between the IFIT1+ neutrophil subcluster and NK cells, NKT cells, classical mononuclear phagocytes subcluster 1 (classical Mo1), classical mononuclear phagocytes subcluster 2 (classical Mo2) in NTM-PD patients compared to healthy controls.

CONCLUSIONS

Our data revealed disease-specific immune cell subclusters and provided potential new targets of NTM-PD. Specific expansion of IFIT1+ neutrophil subclusters and the CXCL8-CXCR1/2 axis may be involved in the pathogenesis of NTM-PD. These insights may have implications for the diagnosis and treatment of NTM-PD.

Expression of HLA-DR and KLRG1 enhances the cytotoxic potential and cytokine secretion capacity of CD3+ T cells in tuberculosis patients

BACKGROUND

Human T cells play an important role in immunity against tuberculosis (TB) infection. Activating receptor HLA-DR and inhibitory receptor KLRG1 are critical regulators of T cell function during viral infection and tumorigenesis, but they have been less studied in TB infection.

METHODS

In this study, we explored the relationship between CD3+ T cell expression of HLA-DR and KLRG1 receptors and function against TB infection. Flow cytometry was conducted to assess the immunomodulatory effects of HLA-DR and KLRG1 receptors on CD3+ T cells in patients with different TB infection status.

RESULTS

We found activating receptors HLA-DR, NKG2C, CD57 and NKP46, and inhibitory receptors KLRG1 and KIR on CD3+ T cells in different TB infection status showed different distribution patterns; the cytotoxic potential and cytokine secretion capacity of CD3+ T cells after Mtb-specific antigen stimulation were significantly enhanced in TB infection groups. Further studies revealed HLA-DR+ T and KLRG1+ T cells expressed higher activating and inhibitory receptors than the negative population. In addition, the expression of cytotoxic potential and cytokine secretion capacity of HLA-DR+ T and KLRG1+ T cells was significantly higher than that of HLA-DR- T and KLRG1- T cells.

CONCLUSIONS

Expression of HLA-DR and KLRG1 enhances the cytotoxic potential and cytokine secretion capacity of CD3+ T cells in TB patients, suggesting CD3+ T cells expressing HLA-DR and KLRG1 are important effector cell phenotypes involved in the host anti-TB infection. HLA-DR and KLRG1 expressed by CD3+ T cells may be potential predictive markers of TB disease progression and clinical immune assessment.

A prediction model for prognosis of nephrotic syndrome with tuberculosis in intensive care unit patients: a nomogram based on the MIMIC-IV v2.2 database

Background

Currently, a scarcity of prognostic research exists that concentrates on patients with nephrotic syndrome (NS) who also have tuberculosis. The purpose of this study was to assess the in-hospital mortality status of NS patients with tuberculosis, identify crucial risk factors, and create a sturdy prognostic prediction model that can improve disease evaluation and guide clinical decision-making.

Methods

We utilized the Medical Information Mart for Intensive Care IV version 2.2 (MIMIC-IV v2.2) database to include 1,063 patients with NS complicated by TB infection. Confounding factors included demographics, vital signs, laboratory indicators, and comorbidities. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and the diagnostic experiment the receiver operating characteristic (ROC) curve analyses were used to select determinant variables. A nomogram was established by using a logistic regression model. The performance of the nomogram was tested and validated using the concordance index (C-index) of the ROC curve, calibration curves, internal cross-validation, and clinical decision curve analysis.

Results

The cumulative in-hospital mortality rate for patients with NS and TB was 18.7%. A nomogram was created to predict in-hospital mortality, utilizing Alb, Bun, INR, HR, Abp, Resp., Glu, CVD, Sepsis-3, and AKI stage 7 days. The area under the curve of the receiver operating characteristic evaluation was 0.847 (0.812-0.881), with a calibration curve slope of 1.00 (0.83-1.17) and a mean absolute error of 0.013. The cross-validated C-index was 0.860. The decision curves indicated that the patients benefited from this model when the risk threshold was 0.1 and 0.81.

Conclusion

Our clinical prediction model nomogram demonstrated a good predictive ability for in-hospital mortality among patients with NS combined with TB. Therefore, it can aid clinicians in assessing the condition, judging prognosis, and making clinical decisions for such patients.

Construction of Immune-Related Diagnostic Model for Latent Tuberculosis Infection and Active Tuberculosis

Background

Tuberculosis (TB) is one of the most infectious diseases caused by Mycobacterium tuberculosis (M. tb), and the diagnosis of active tuberculosis (TB) and latent TB infection (LTBI) remains challenging.

Methods

Gene expression files were downloaded from the GEO database to identify the differentially expressed genes (DEGs). The ssGSEA algorithm was applied to assess the immunological characteristics of patients with LTBI and TB. Weighted gene co-expression network analysis, protein-protein interaction network, and the cytoHubba plug-in of Cytoscape were used to identify the real hub genes. Finally, a diagnostic model was constructed using real hub genes and validated using a validation set.

Results

Macrophages and natural killer cells were identified as important immune cells strongly associated with TB. In total, 726 mRNAs were identified as DEGs. MX1, STAT1, IFIH1, DDX58, and IRF7 were identified as real hub immune-related genes. The diagnostic model generated by the five real hub genes could distinguish active TB from healthy controls or patients with LTBI.

Conclusion

Our study may provide implications for the diagnosis and drug development of M. tb infections.

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.

Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases

CD4+ T cells are key mediators of various autoimmune diseases; however, their role in disease progression remains unclear due to cellular heterogeneity. Here, we evaluated CD4+ T cell subpopulations using decomposition-based transcriptome characterization and canonical clustering strategies. This approach identified 12 independent gene programs governing whole CD4+ T cell heterogeneity, which can explain the ambiguity of canonical clustering. In addition, we performed a meta-analysis using public single-cell datasets of over 1.8 million peripheral CD4+ T cells from 953 individuals by projecting cells onto the reference and cataloging cell frequency and qualitative alterations of the populations in 20 diseases. The analyses revealed that the 12 transcriptional programs were useful in characterizing each autoimmune disease and predicting its clinical status. Moreover, genetic variants associated with autoimmune diseases showed disease-specific enrichment within the 12 gene programs. The results collectively provide a landscape of single-cell transcriptomes of CD4+ T cell subpopulations involved in autoimmune disease.

Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data Reveals Memory-like NK Cell Subset Associated with Mycobacterium tuberculosis Latency

Natural killer (NK) cells are innate-like lymphocytes that belong to the family of type-1 innate lymphoid cells and rapidly respond to virus-infected and tumor cells. In this study, we have combined scRNA-seq data and bulk RNA-seq data to define the phenotypic and molecular characteristics of peripheral blood NK cells. While the role of NK cells in immune surveillance against virus infections and tumors has been well established, their contribution to protective responses to other intracellular microorganisms, such as Mycobacterium tuberculosis (Mtb), is still poorly understood. In this study, we have combined scRNA-seq data and bulk RNA-seq data to illuminate the molecular characteristics of circulating NK cells in patients with active tuberculosis (TB) disease and subjects with latent Mtb infection (LTBI) and compared these characteristics with those of healthy donors (HDs) and patients with non-TB other pulmonary infectious diseases (ODs). We show here that the NK cell cluster was significantly increased in LTBI subjects, as compared to patients with active TB or other non-TB pulmonary diseases and HD, and this was mostly attributable to the expansion of an NK cell population expressing KLRC2, CD52, CCL5 and HLA-DRB1, which most likely corresponds to memory-like NK2.1 cells. These data were validated by flow cytometry analysis in a small cohort of samples, showing that LTBI subjects have a significant expansion of NK cells characterized by the prevalence of memory-like CD52+ NKG2C+ NK cells. Altogether, our results provide some new information on the role of NK cells in protective immune responses to Mtb.

Microbiome Alteration in Lung Tissues of Tuberculosis Patients Revealed by Metagenomic Next-Generation Sequencing and Immune-Related Transcriptional Profile Identified by Transcriptome Sequencing

This study explored alterations in the respiratory microbiome and transcriptome after Mycobacterium tuberculosis infection in tuberculosis (TB) patients. Metagenomic next-generation sequencing (mNGS) was adopted to reveal the microbiome in lung tissues from 110 TB and 25 nontuberculous (NonTB) patients. Transcriptome sequencing was performed in TB tissues (n = 3), tissues adjacent to TB (ParaTB, n = 3), and NonTB tissues (n = 3) to analyze differentially expressed genes (DEGs) and functional pathways. The microbial β diversity (p = 0.01325) in TB patients differed from that in the NonTB group, with 17 microbial species distinctively distributed. Eighty-three co-up-regulated DEGs were identified in the TB versus NonTB and the TB versus ParaTB comparison groups, and six were associated with immune response to Mtb. These DEGs were significantly enriched in the signaling pathways such as immune response, NF-κB, and B cell receptor. Data in the lung tissue microbiome and transcriptome in TB patients offer a sufficient understanding of the pathogenesis of TB.

High-dimensional single-cell proteomics analysis of esophageal squamous cell carcinoma reveals dynamic alterations of the tumor immune microenvironment after neoadjuvant therapy

BACKGROUND

Dynamic alterations of the tumor immune microenvironment in esophageal squamous cell carcinoma (ESCC) after different neoadjuvant therapies were understudied.

METHODS

We used mass cytometry with a 42-antibody panel for 6 adjacent normal esophageal mucosa and 26 tumor samples (treatment-naïve, n=12; postneoadjuvant, n=14) from patients with ESCC. Single-cell RNA sequencing of previous studies and bulk RNA sequencing from The Cancer Genome Atlas were analyzed, flow cytometry, immunohistochemistry, and immunofluorescence analyses were performed.

RESULTS

Poor tumor regression was observed in the neoadjuvant chemotherapy group. Radiotherapy-based regimens enhanced CD8+ T cells but diminished regulatory T cells and promoted the ratio of effector memory to central memory T cells. Immune checkpoint blockade augmented NK cell activation and cytotoxicity by increasing the frequency of CD16+ NK cells. We discovered a novel CCR4+CCR6+ macrophage subset that correlated with the enrichment of corresponding chemokines (CCL3/CCL5/CCL17/CCL20/CCL22). We established a CCR4/CCR6 chemokine-based model that stratified ESCC patients with differential overall survival and responsiveness to neoadjuvant chemoradiotherapy combined with immunotherapy, which was validated in two independent cohorts of esophageal cancer with neoadjuvant treatment.

CONCLUSIONS

This work reveals that neoadjuvant therapy significantly regulates the cellular composition of the tumor immune microenvironment in ESCC and proposes a potential model of CCR4/CCR6 system to predict the benefits from neoadjuvant chemoradiotherapy combined with immunotherapy.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

Identification and validation of ubiquitination-related signature and subgroups in immune microenvironment of tuberculosis

BACKGROUND

Mycobacterium tuberculosis (Mtb) is the bacterial pathogen responsible for causing tuberculosis (TB), a severe public health concern that results in numerous deaths worldwide. Ubiquitination (Ub) is an essential physiological process that aids in maintaining homeostasis and contributes to the development of TB. Therefore, the main objective of our study was to investigate the potential role of Ub-related genes in TB.

METHODS

Our research entailed utilizing single sample gene set enrichment analysis (ssGSEA) in combination with several machine learning techniques to discern the Ub-related signature of TB and identify potential diagnostic markers that distinguish TB from healthy controls (HC).

RESULTS

In summary, we used the ssGSEA algorithm to determine the score of Ub families (E1, E2, E3, DUB, UBD, and ULD). Notably, the score of E1, E3, and UBD were lower in TB patients than in HC individuals, and we identified 96 Ub-related differentially expressed genes (UbDEGs). Employing machine learning algorithms, we identified 11 Ub-related hub genes and defined two distinct Ub-related subclusters. Notably, through GSVA and functional analysis, it was determined that these subclusters were implicated in numerous immune-related processes. We further investigated these Ub-related hub genes in four TB-related diseases and found that TRIM68 exhibited higher correlations with various immune cells in different conditions, indicating that it may play a crucial role in the immune process of these diseases.

CONCLUSION

The observed enrichment of Ub-related gene expression in TB patients emphasizes the potential involvement of ubiquitination in the progression of TB. These significant findings establish a basis for future investigations to elucidate the molecular mechanisms associated with TB, select suitable diagnostic biomarkers, and design innovative therapeutic interventions for combating this fatal infectious disease.

Single-cell analysis of the miRNA activities in tuberculous meningitis (TBM) model mice injected with the BCG vaccine

BACKGROUND

Our previous study revealed the transcriptome atlas of specific cell types in tuberculous meningitis (TBM) model mice injected with the BCG vaccine via scRNA sequencing. However, the activities of miRNAs in TBM at single-cell resolution remain to be explored.

METHOD

Cell type-specific miRNA activities were investigated by using motif enrichment analyses (miReact) on the transcriptome data of 15 cell types. The target mRNAs of miRNAs were predicted and subjected to enrichment analysis. Furthermore, miRNAs and their target mRNAs with opposite expression trends were chosen to construct functional networks. Besides, qRT-PCR and RNA scope were performed to verify the expression level of representative miRNA.

RESULTS

The tSNE dimensionality reduction presented 15 cell types in TBM model mice, in which microglia and endothelial cells accounted for the majority. Target mRNAs of each cell type were predicted for verification or network construction. The immune and inflammation-related miRNA-mRNA networks of macrophages and microglia, oxidative phosphorylation-related miRNA-mRNA networks of neurons, ion and protein transport-related networks of epididymal cells, and angiogenesis-related miRNA-mRNA networks of VSMCs were constructed. The miRNA activity analysis revealed that miR-21a-3p activity was increased in microglia, macrophages, neurons and epididymal cells. The result of qRT-PCR and RNA scope indicate that miR-21a-3p was significantly higher-expressed in TBM brain tissue compared with normal brain tissue.

CONCLUSION

In our study, an in-depth exploration of the mRNA expression and miRNA activity of macrophages, microglia, epididymal cells, neurons and vascular smooth muscle cells during TBM progression was conducted using scRNA-Seq, which provided novel insights into the immune cell engagement in TBM patients.

Single-cell RNA-sequencing reveals heterogeneity and intercellular crosstalk in human tuberculosis lung

Lung inflammation indicated by 18F-labeled fluorodeoxyglucose (FDG) in patients with tuberculosis is associated with disease severity and relapse risk upon treatment completion. We revealed the heterogeneity and intercellular crosstalk in lung tissues with 18F-FDG avidity and adjacent uninvolved tissues from 6 tuberculosis patients by single-cell RNA-sequencing. Tuberculous lungs had an influx of regulatory T cells (Treg), exhausted CD8 T cells, immunosuppressive myeloid cells, conventional DC, plasmacytoid DC, and neutrophils. Immune cells in inflamed lungs showed general up-regulation of ATP synthesis and interferon-mediated signaling. Immunosuppressive myeloid and Treg cells strongly displayed transcriptions of genes related to tuberculosis disease progression. Intensive crosstalk between IL4I1-expressing myeloid cells and Treg cells involving chemokines, costimulatory molecules, and immune checkpoints, some of which are specific in 18F-FDG-avid lungs, were found. Our analysis provides insights into the transcriptomic heterogeneity and cellular crosstalk in pulmonary tuberculosis and guides unveiling cellular and molecular targets for tuberculosis therapy.

Early innate cell interactions with Mycobacterium tuberculosis in protection and pathology of tuberculosis

Tuberculosis (TB) remains a significant global health challenge, claiming the lives of up to 1.5 million individuals annually. TB is caused by the human pathogen Mycobacterium tuberculosis (Mtb), which primarily infects innate immune cells in the lungs. These immune cells play a critical role in the host defense against Mtb infection, influencing the inflammatory environment in the lungs, and facilitating the development of adaptive immunity. However, Mtb exploits and manipulates innate immune cells, using them as favorable niche for replication. Unfortunately, our understanding of the early interactions between Mtb and innate effector cells remains limited. This review underscores the interactions between Mtb and various innate immune cells, such as macrophages, dendritic cells, granulocytes, NK cells, innate lymphocytes-iNKT and ILCs. In addition, the contribution of alveolar epithelial cell and endothelial cells that constitutes the mucosal barrier in TB immunity will be discussed. Gaining insights into the early cellular basis of immune reactions to Mtb infection is crucial for our understanding of Mtb resistance and disease tolerance mechanisms. We argue that a better understanding of the early host-pathogen interactions could inform on future vaccination approaches and devise intervention strategies.

Preferential differential gene expression within the WC1.1+ γδ T cell compartment in cattle naturally infected with Mycobacterium bovis

Bovine tuberculosis (bTB), caused by infection with Mycobacterium bovis, continues to cause significant issues for the global agriculture industry as well as for human health. An incomplete understanding of the host immune response contributes to the challenges of control and eradication of this zoonotic disease. In this study, high-throughput bulk RNA sequencing (RNA-seq) was used to characterise differential gene expression in γδ T cells - a subgroup of T cells that bridge innate and adaptive immunity and have known anti-mycobacterial response mechanisms. γδ T cell subsets are classified based on expression of a pathogen-recognition receptor known as Workshop Cluster 1 (WC1) and we hypothesised that bTB disease may alter the phenotype and function of specific γδ T cell subsets. Peripheral blood was collected from naturally M. bovis-infected (positive for single intradermal comparative tuberculin test (SICTT) and IFN-γ ELISA) and age- and sex-matched, non-infected control Holstein-Friesian cattle. γδ T subsets were isolated using fluorescence activated cell sorting (n = 10-12 per group) and high-quality RNA extracted from each purified lymphocyte subset (WC1.1+, WC1.2+, WC1- and γδ-) was used to generate transcriptomes using bulk RNA-seq (n = 6 per group, representing a total of 48 RNA-seq libraries). Relatively low numbers of differentially expressed genes (DEGs) were observed between most cell subsets; however, 189 genes were significantly differentially expressed in the M. bovis-infected compared to the control groups for the WC1.1+ γδ T cell compartment (absolute log2 FC ≥ 1.5 and FDR P adj. ≤ 0.1). The majority of these DEGs (168) were significantly increased in expression in cells from the bTB+ cattle and included genes encoding transcription factors (TBX21 and EOMES), chemokine receptors (CCR5 and CCR7), granzymes (GZMA, GZMM, and GZMH) and multiple killer cell immunoglobulin-like receptor (KIR) proteins indicating cytotoxic functions. Biological pathway overrepresentation analysis revealed enrichment of genes with multiple immune functions including cell activation, proliferation, chemotaxis, and cytotoxicity of lymphocytes. In conclusion, γδ T cells have important inflammatory and regulatory functions in cattle, and we provide evidence for preferential differential activation of the WC1.1+ specific subset in cattle naturally infected with M. bovis.

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.

Natural Killer Repertoire Restoration in TB/HIV Co-Infected Individuals Experienced an Immune Reconstitution Syndrome (CAMELIA Trial, ANRS 12153)

IRIS is a common complication in HIV-infected patients treated for tuberculosis (TB) and cART. Our aim was to evaluate NK cell reconstitution in HIV-infected patients with TB-IRIS compared to those without IRIS. 147 HIV-infected patients with TB from the CAMELIA trial were enrolled. HIV+TB+ patients were followed for 32 weeks. The NK cell repertoire was assessed in whole blood at different time points. As CAMELIA has two arms (early and late cART initiation), we analysed them separately. At enrolment, individuals had low CD4 cell counts (27 cells/mm3) and high plasma viral loads (5.76 and 5.50 log/mL for IRIS and non-IRIS individuals, respectively). Thirty-seven people developed IRIS (in the early and late arms). In the early and late arms, we observed similar proportions of total NK and NK cell subsets in TB-IRIS and non-IRIS individuals during follow-up, except for the CD56dimCD16pos (both arms) and CD56dimCD16neg (late arm only) subsets, which were higher in TB-IRIS and non-IRIS individuals, respectively, after cART. Regarding the repertoire and markers of NK cells, significant differences (lower expression of NKp30, NKG2A (CD159a), NKG2D (CD314) were observed in TB-IRIS compared to non-IRIS individuals after the start of cART. In the late arm, some changes (increased expression of CD69, NKG2C, CD158i) were observed in TB-IRIS compared to non-IRIS individuals, but only before cART initiation (during TB treatment). KIR expression by NK cells (CD158a and CD158i) was similar in both groups. CD69 expression by NK cells decreased in all groups. Expression of the NCR repertoire (NKp30, NKp44, NKp46) has similar kinetics in TB-IRIS subjects compared to non-IRIS subjects regardless of the arm analysed. NK cell reconstitution appeared to be better in TB-IRIS subjects. Although NK cell reconstitution is impaired in HIV infection after cART, as previously reported, it does not appear to be affected by the development of IRIS in HIV and TB-infected individuals.

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.

Classification and characterisation of extracellular vesicles-related tuberculosis subgroups and immune cell profiles

Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism of Mycobacterium tuberculosis (Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs-related differentially expressed genes (DEGs) were identified, and 17 EVs-related DEGs were up-regulated and three DEGs were down-regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs-related gene signature was identified and two EVs-related subclusters were defined. The single-cell RNA sequence (scRNA-seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs-related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high-risk group had significantly enriched immune-related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs-related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.

sccomp: Robust differential composition and variability analysis for single-cell data

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

Transcriptional signatures measured in whole blood correlate with protection against tuberculosis in inbred and outbred mice

Although BCG has been used for almost 100 years to immunize against Mycobacterium tuberculosis, TB remains a global public health threat. Numerous clinical trials are underway studying novel vaccine candidates and strategies to improve or replace BCG, but vaccine development still lacks a well-defined set of immune correlates to predict vaccine-induced protection against tuberculosis. This study aimed to address this gap by examining transcriptional responses to BCG vaccination in C57BL/6 inbred mice, coupled with protection studies using Diversity Outbred mice. We evaluated relative gene expression in blood obtained from vaccinated mice, because blood is easily accessible, and data can be translated to human studies. We first determined that the average peak time after vaccination is 14 days for gene expression of a small subset of immune-related genes in inbred mice. We then performed global transcriptomic analyses using whole blood samples obtained two weeks after mice were vaccinated with BCG. Using comparative bioinformatic analyses and qRT-PCR validation, we developed a working correlate panel of 18 genes that were highly correlated with administration of BCG but not heat-killed BCG. We then tested this gene panel using BCG-vaccinated Diversity Outbred mice and revealed associations between the expression of a subset of genes and disease outcomes after aerosol challenge with M. tuberculosis. These data therefore demonstrate that blood-based transcriptional immune correlates measured within a few weeks after vaccination can be derived to predict protection against M. tuberculosis, even in outbred populations.

Identification of circulating monocytes as producers of tuberculosis disease biomarker C1q

Tuberculosis (TB) is a prevalent disease causing an estimated 1.6 million deaths and 10.6 million new cases annually. Discriminating TB disease from differential diagnoses can be complex, particularly in the field. Increased levels of complement component C1q in serum have been identified as a specific and accessible biomarker for TB disease but the source of C1q in circulation has not been identified. Here, data and samples previously collected from human cohorts, a clinical trial and a non-human primate study were used to identify cells producing C1q in circulation. Cell subset frequencies were correlated with serum C1q levels and combined with single cell RNA sequencing and flow cytometry analyses. This identified monocytes as C1q producers in circulation, with a pronounced expression of C1q in classical and intermediate monocytes and variable expression in non-classical monocytes.

Macrophages in immunoregulation and therapeutics

Macrophages exist in various tissues, several body cavities, and around mucosal surfaces and are a vital part of the innate immune system for host defense against many pathogens and cancers. Macrophages possess binary M1/M2 macrophage polarization settings, which perform a central role in an array of immune tasks via intrinsic signal cascades and, therefore, must be precisely regulated. Many crucial questions about macrophage signaling and immune modulation are yet to be uncovered. In addition, the clinical importance of tumor-associated macrophages is becoming more widely recognized as significant progress has been made in understanding their biology. Moreover, they are an integral part of the tumor microenvironment, playing a part in the regulation of a wide variety of processes including angiogenesis, extracellular matrix transformation, cancer cell proliferation, metastasis, immunosuppression, and resistance to chemotherapeutic and checkpoint blockade immunotherapies. Herein, we discuss immune regulation in macrophage polarization and signaling, mechanical stresses and modulation, metabolic signaling pathways, mitochondrial and transcriptional, and epigenetic regulation. Furthermore, we have broadly extended the understanding of macrophages in extracellular traps and the essential roles of autophagy and aging in regulating macrophage functions. Moreover, we discussed recent advances in macrophages-mediated immune regulation of autoimmune diseases and tumorigenesis. Lastly, we discussed targeted macrophage therapy to portray prospective targets for therapeutic strategies in health and diseases.

Single-cell transcriptomics reveals that tumor-infiltrating natural killer cells are activated by localized ablative immunotherapy and share anti-tumor signatures induced by immune checkpoint inhibitors

Rationale

Natural killer (NK) cells provide protective anti-cancer immunity. However, the cancer therapy induced activation gene signatures and pathways in NK cells remain unclear.

Methods

We applied a novel localized ablative immunotherapy (LAIT) by synergizing photothermal therapy (PTT) with intra-tumor delivering of the immunostimulant N-dihydrogalactochitosan (GC), to treat breast cancer using a mammary tumor virus-polyoma middle tumor-antigen (MMTV-PyMT) mouse model. We performed single-cell RNA sequencing (scRNAseq) analysis to unveil the cellular heterogeneity and compare the transcriptional alterations induced by PTT, GC, and LAIT in NK cells within the tumor microenvironment (TME).

Results

ScRNAseq showed that NK subtypes, including cycling, activated, interferon-stimulated, and cytotoxic NK cells. Trajectory analysis revealed a route toward activation and cytotoxicity following pseudotime progression. Both GC and LAIT elevated gene expression associated with NK cell activation, cytolytic effectors, activating receptors, IFN pathway components, and cytokines/chemokines in NK subtypes. Single-cell transcriptomics analysis using immune checkpoint inhibitor (ICI)-treated animal and human samples revealed that ICI-induced NK activation and cytotoxicity across several cancer types. Furthermore, ICI-induced NK gene signatures were also induced by LAIT treatment. We also discovered that several types of cancer patients had significantly longer overall survival when they had higher expression of genes in NK cells that were also specifically upregulated by LAIT.

Conclusion

Our findings show for the first time that LAIT activates cytotoxicity in NK cells and the upregulated genes positively correlate with beneficial clinical outcomes for cancer patients. More importantly, our results further establish the correlation between the effects of LAIT and ICI on NK cells, hence expanding our understanding of mechanism of LAIT in remodeling TME and shedding light on the potentials of NK cell activation and anti-tumor cytotoxic functions in clinical applications.

Systemic immune dysregulation in severe tuberculosis patients revealed by a single-cell transcriptome atlas

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) infection, is currently the deadliest infectious disease in human that can evolve to severe forms. A comprehensive immune landscape for Mtb infection is critical for achieving TB cure, especially for severe TB patients. We performed single-cell RNA transcriptome and T-cell/B-cell receptor (TCR/BCR) sequencing of 213,358 cells from 27 samples, including 6 healthy donors and 21 active TB patients with varying severity (6 mild, 6 moderate and 9 severe cases). Two published profiles of latent TB infection were integrated for the analysis. We observed an obviously elevated proportion of inflammatory immune cells (e.g., monocytes), as well as a markedly decreased abundance of various lymphocytes (e.g., NK and γδT cells) in severe patients, revealing that lymphopenia might be a prominent feature of severe disease. Further analyses indicated that significant activation of cell apoptosis pathways, including perforin/granzyme-, TNF-, FAS- and XAF1-induced apoptosis, as well as cell migration pathways might confer this reduction. The immune landscape in severe patients was characterized by widespread immune exhaustion in Th1, CD8⁺T and NK cells as well as high cytotoxic state in CD8⁺T and NK cells. We also discovered that myeloid cells in severe TB patients may involve in the immune paralysis. Systemic upregulation of S100A12 and TNFSF13B, mainly by monocytes in the peripheral blood, may contribute to the inflammatory cytokine storms in severe patients. Our data offered a rich resource for understanding of TB immunopathogenesis and designing effective therapeutic strategies for TB, especially for severe patients.

Landscape of Exhausted T Cells in Tuberculosis Revealed by Single-Cell Sequencing

Tuberculosis, a contagious bacterial infection caused by Mycobacterium tuberculosis, is a substantial global health problem, impacting millions of lives annually. Exhausted T-cell signatures are critical for predicting clinical responses to tuberculosis infection. To obtain a panoramic transcriptional profile of T cells, we performed single-cell RNA-sequencing analysis of CD4+ T and CD8+ T cells isolated from peripheral blood mononuclear cells of healthy individuals and patients with tuberculosis. We identified seven subsets in CD8+ T cells and eight subsets in CD4+ T cells and elucidated the transcriptomic landscape changes and characteristics of each subset. We further investigated the cell-to-cell relationship of each subgroup of the two cell types. Different signature genes and pathways of exhausted CD4+ and CD8+ T cells were examined. We identified 12 genes with potential associations of T-cell exhaustion after tuberculosis infection. We also identified five genes as potential exhaustion marker genes. The CD8-EX3 subcluster in CD8+ T-exhausted cells was identified as an exhaustion-specific subcluster. The identified gene module further clarified the key factors influencing CD8+ T cell exhaustion. These data provide new insights into T-cell signatures in tuberculosis-exhausted populations. IMPORTANCE Identifying the changes in immune cells in response to infection can provide a better understanding of the effects of Mycobacterium tuberculosis on the host immune system. We performed single-cell RNA-sequencing analysis of CD4+ T and CD8+ T cells isolated from peripheral blood mononuclear cells of healthy individuals and patients with tuberculosis to reveal the cellular characteristics. Different signature genes and pathways of exhausted CD4+ and CD8+ T cells were examined. These will facilitate a more comprehensive understanding of the onset and underlying mechanism of T-cell exhaustion during active Mtb infection.

Single-cell analysis reveals changes in BCG vaccine-injected mice modeling tuberculous meningitis brain infection

Tuberculous meningitis (TBM) is the most severe and deadly manifestation of tuberculosis. Neurological complications are observed in up to 50% of patients affected. Here, attenuated Mycobacterium bovis are injected into the cerebellum of mice, and histopathological images and cultured colonies confirm successful brain infection. Then, whole-brain tissue is dissected for 10X Genomics single-cell sequencing, and we acquire 15 cell types. Transcriptional changes of inflammation processes are found in multiple cell types. Specifically, Stat1 and IRF1 are shown to mediate inflammation in macrophages and microglia. For neurons, decreased oxidative phosphorylation activity in neurons is observed, which corresponds to TBM clinical symptoms of neurodegeneration. Finally, ependymal cells present prominent transcriptional changes, and decreased FERM domain containing 4A (Frmd4a) may contribute to TBM clinical symptoms of hydrocephalus and neurodegeneration. This study shows a single-cell transcriptome of M. bovis infection in mice and improves the understanding of brain infection and neurological complications in TBM.

Whole blood transcriptome identifies interferon-regulated genes as key drivers in thrombotic primary antiphospholipid syndrome

OBJECTIVE

Pathogenesis of antiphospholipid syndrome (APS) isn't fully elucidated. We aimed to identify gene signatures characterizing thrombotic primary APS (thrPAPS) and subgroups at high risk for worse outcomes.

METHODS

We performed whole blood next-generation RNA-sequencing in 62 patients with thrPAPS and 29 age-/sex-matched healthy controls (HCs), followed by differential gene expression analysis (DGEA) and enrichment analysis. We trained models on transcriptomics data using machine learning.

RESULTS

DGEA of 12.306 genes revealed 34 deregulated genes in thrPAPS versus HCs; 33 were upregulated by at least 2-fold, and 14/33 were type I and II interferon-regulated genes (IRGs) as determined by interferome database. Machine learning applied to deregulated genes returned 79% accuracy to discriminate thrPAPS from HCs, which increased to 82% when only the most informative IRGs were analyzed. Comparison of thrPAPS subgroups versus HCs showed an increased presence of IRGs among upregulated genes in venous thrombosis (21/23, 91%), triple-antiphospholipid antibody (aPL) positive (30/50, 60%), and recurrent thrombosis (19/42, 45%) subgroups. Enrichment analysis of upregulated genes in triple-aPL positive patients revealed terms related to 'type I interferon signaling pathway' and 'innate immune response'. DGEA among thrPAPS subgroups revealed upregulated genes, including IRGs, in patients with venous versus arterial thrombosis (n = 11, 9 IRGs), triple-aPL versus non-triple aPL (n = 10, 9 IRGs), and recurrent versus non-recurrent thrombosis (n = 10, 3 IRGs).

CONCLUSION

Upregulated IRGs may better discriminate thrPAPS from HCs than all deregulated genes in peripheral blood. Taken together with DGEA data, IRGs are highly expressed in thrPAPS and high-risk subgroups of triple-aPL and recurrent thrombosis, with potential treatment implications.

Single-cell transcriptomics reveals immune infiltrate in sepsis

Immune cells and immune microenvironment play important in the evolution of sepsis. This study aimed to explore hub genes related to the abundance of immune cell infiltration in sepsis. The GEOquery package is used to download and organize data from the GEO database. A total of 61 differentially expressed genes (DEGs) between sepsis samples and normal samples were obtained through the 'limma' package. T cells, natural killer (NK) cells, monocytes, megakaryocytes, dendritic cells (DCs), and B cells formed six distinct clusters on the t-distributed stochastic neighbor embedding (t-SNE) plot generated using the Seurat R package. Gene set enrichment analysis (GSEA) enrichment analysis showed that sepsis samples and normal samples were related to Neutrophil Degranulation, Modulators of Tcr Signaling and T Cell Activation, IL 17 Pathway, T Cell Receptor Signaling Pathway, Ctl Pathway, Immunoregulatory Interactions Between a Lymphoid and A Non-Lymphoid Cell. GO analysis and KEGG analysis of immune-related genes showed that the intersection genes were mainly associated with Immune-related signaling pathways. Seven hub genes (CD28, CD3D, CD2, CD4, IL7R, LCK, and CD3E) were screened using Maximal Clique Centrality, Maximum neighborhood component, and Density of Maximum Neighborhood Component algorithms. The lower expression of the six hub genes (CD28, CD3D, CD4, IL7R, LCK, and CD3E) was observed in sepsis samples. We observed the significant difference of several immune cell between sepsis samples and control samples. Finally, we carried out in vivo animal experiments, including Western blotting, flow cytometry, Elisa, and qPCR assays to detect the concentration and the expression of several immune factors.

Classification of tuberculosis-related programmed cell death-related patient subgroups and associated immune cell profiling

Background

Tuberculosis (TB) is the deadliest communicable disease in the world with the exception of the ongoing COVID-19 pandemic. Programmed cell death (PCD) patterns play key roles in the development and progression of many disease states such that they may offer value as effective biomarkers or therapeutic targets that can aid in identifying and treating TB patients.

Materials and methods

The Gene Expression Omnibus (GEO) was used to gather TB-related datasets after which immune cell profiles in these data were analyzed to examine the potential TB-related loss of immune homeostasis. Profiling of differentially expressed PCD-related genes was performed, after which candidate hub PCD-associated genes were selected via a machine learning approach. TB patients were then stratified into two subsets based on the expression of PCD-related genes via consensus clustering. The potential roles of these PCD-associated genes in other TB-related diseases were further examined.

Results

In total, 14 PCD-related differentially expressed genes (DEGs) were identified and highly expressed in TB patient samples and significantly correlated with the abundance of many immune cell types. Machine learning algorithms enabled the selection of seven hub PCD-related genes that were used to establish PCD-associated patient subgroups, followed by the validation of these subgroups in independent datasets. These findings, together with GSVA results, indicated that immune-related pathways were significantly enriched in TB patients exhibiting high levels of PCD-related gene expression, whereas metabolic pathways were significantly enriched in the other patient group. Single cell RNA-seq (scRNA-seq) further highlighted significant differences in the immune status of these different TB patient samples. Furthermore, we used CMap to predict five potential drugs for TB-related diseases.

Conclusion

These results highlight clear enrichment of PCD-related gene expression in TB patients and suggest that this PCD activity is closely associated with immune cell abundance. This thus indicates that PCD may play a role in TB progression through the induction or dysregulation of an immune response. These findings provide a foundation for further research aimed at clarifying the molecular drivers of TB, the selection of appropriate diagnostic biomarkers, and the design of novel therapeutic interventions aimed at treating this deadly infectious disease.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

BACKGROUND

The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status.

METHODS

T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms.

RESULTS

A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively.

CONCLUSIONS

Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

Naturally selected CD7 CAR-T therapy without genetic editing demonstrates significant antitumour efficacy against relapsed and refractory acute myeloid leukaemia (R/R-AML)

Background

The survival rate for patients with relapsed and refractory acute myeloid leukaemia (R/R-AML) remains poor, and treatment is challenging. Chimeric antigen receptor T cells (CAR-T cells) have been widely used for haematologic malignancies. Current CAR-T therapies for acute myeloid leukaemia mostly target myeloid-lineage antigens, such as CD123 and CD33, which may be associated with potential haematopoietic toxicity. As a lineage-specific receptor, CD7 is expressed in acute myeloid leukaemia cells and T cells but is not expressed in myeloid cells. Therefore, the use of CD7 CAR-T cells for R/R-AML needs to be further explored.

Methods

In this report, immunohistochemistry and flow cytometry were used to analyse CD7 expression in clinical samples from R/R-AML patients and healthy donors (HDs). We designed naturally selected CD7 CAR-T cells to analyse various functions and in vitro antileukaemic efficacy based on flow cytometry, and xenograft models were used to validate in vivo tumour dynamics.

Results

We calculated the percentage of cells with CD7 expression in R/R-AML patients with minimal residual disease (MRD) (5/16, 31.25%) from our institution and assessed CD7 expression in myeloid and lymphoid lineage cells of R/R-AML patients, concluding that CD7 is expressed in T cells but not in myeloid cells. Subsequently, we designed and constructed naturally selected CD7 CAR-T cells (CD7 CAR). We did not perform CD7 antigen knockdown on CD7 CAR-T cells because CD7 molecule expression is naturally eliminated at Day 12 post transduction. We then evaluated the ability to target and kill CD7+ acute myeloid leukaemia cells in vitro and in vivo. Naturally selected CD7 CAR-T cells efficiently killed CD7+ acute myeloid leukaemia cells and CD7+ primary blasts of R/R-AML patients in vitro and significantly inhibited leukaemia cell growth in a xenograft mouse model.

Conclusion

Naturally selected CD7 CAR-T cells represent an effective treatment strategy for relapsed and refractory acute myeloid leukaemia patients in preclinical studies.

Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection

To determine the mechanisms that mediate resistance to Mycobacterium tuberculosis (M. tuberculosis) infection in household contacts (HHCs) of patients with tuberculosis (TB), we followed 452 latent TB infection-negative (LTBI-) HHCs for 2 years. Those who remained LTBI- throughout the study were identified as nonconverters. At baseline, nonconverters had a higher percentage of CD14+ and CD3-CD56+CD27+CCR7+ memory-like natural killer (NK) cells. Using a whole-transcriptome and metabolomic approach, we identified deoxycorticosterone acetate as a metabolite with elevated concentrations in the plasma of nonconverters, and further studies showed that this metabolite enhanced glycolytic ATP flux in macrophages and restricted M. tuberculosis growth by enhancing antimicrobial peptide production through the expression of the surface receptor sialic acid binding Ig-like lectin-14. Another metabolite, 4-hydroxypyridine, from the plasma of nonconverters significantly enhanced the expansion of memory-like NK cells. Our findings demonstrate that increased levels of specific metabolites can regulate innate resistance against M. tuberculosis infection in HHCs of patients with TB who never develop LTBI or active TB.

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis

Methods

Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test dataset (GSE152532). Gene expression-based immune cell infiltration and the relationship between such infiltration and hub gene expression were further assessed via a single-sample gene set enrichment analysis (ssGSEA).

Results

In total, 485 differentially expressed genes were analyzed, with the WGCNA approach yielding 8 coexpression models. Of these, the black module was the most closely correlated with ATB. In total, five hub genes (FBXO6, ATF3, GBP1, GBP4, and GBP5) were identified as potential biomarkers associated with LTBI progression to ATB based on a combination of differential expression and LASSO analyses. The area under the ROC curve values for these five genes ranged from 0.8 to 0.9 in the test dataset, and ssGSEA revealed the expression of these genes to be negatively correlated with lymphocyte activity but positively correlated with myeloid and inflammatory cell activity.

Conclusion

The five hub genes identified in this study may play a novel role in tuberculosis-related immunopathology and offer value as novel biomarkers differentiating LTBI from ATB.

Alzheimer's disease alters the transcriptomic profile of natural killer cells at single-cell resolution

Alzheimer's disease (AD) is the most common dementia without an effective cure at least partially due to incomplete understanding of the disease. Inflammation has emerged as a central player in the onset and progression of AD. As innate lymphoid cells, natural killer (NK) cells orchestrate the initiation and evolution of inflammatory responses. Yet, the transcriptomic features of NK cells in AD remain poorly understood. We assessed the diversity of NK cells using web-based single-cell RNA sequencing data of blood NK cells from patients with AD and control subjects and flow cytometry. We identified a contraction of NK cell compartment in AD, accompanied by a reduction of cytotoxicity. Unbiased clustering revealed four subsets of NK cells in AD, i.e., CD56^bright NK cells, CD56^dim effector NK cells, adaptive NK cells, and a unique NK cell subset that is expanded and characterized by upregulation of CX3CR1, TBX21, MYOM2, DUSP1, and ZFP36L2, and negatively correlated with cognitive function in AD patients. Pseudo-temporal analysis revealed that this unique NK cell subset was at a late stage of NK cell development and enriched with transcription factors TBX21, NFATC2, and SMAD3. Together, our study identified a distinct NK cell subset and its potential involvement in AD.

ScRNA-seq expression of IFI27 and APOC2 identifies four alveolar macrophage superclusters in healthy BALF

Alveolar macrophages (AMs) reside on the luminal surface of the airways and alveoli, ensuring proper gas exchange by ingesting cellular debris and pathogens, and regulating inflammatory responses. Therefore, understanding the heterogeneity and diverse roles played by AMs, interstitial macrophages, and recruited monocytes is critical for treating airway diseases. We performed single-cell RNA sequencing on 113,213 bronchoalveolar lavage cells from four healthy and three uninflamed cystic fibrosis subjects and identified two MARCKS+LGMN+IMs, FOLR2+SELENOP+ and SPP1+PLA2G7+ IMs, monocyte subtypes, DC1, DC2, migDCs, plasmacytoid DCs, lymphocytes, epithelial cells, and four AM superclusters (families) based on the gene expression of IFI27 and APOC2 These four AM families have at least eight distinct functional members (subclusters) named after their differentially expressed gene(s): IGF1, CCL18, CXCL5, cholesterol, chemokine, metallothionein, interferon, and small-cluster AMs. Interestingly, the chemokine cluster further divides with each subcluster selectively expressing a unique combination of chemokines. One of the most striking observations, besides the heterogeneity, is the conservation of AM family members in relatively equal ratio across all AM superclusters and individuals. Transcriptional data and TotalSeq technology were used to investigate cell surface markers that distinguish resident AMs from recruited monocytes. Last, other AM datasets were projected onto our dataset. Similar AM superclusters and functional subclusters were observed, along with a significant increase in chemokine and IFN AM subclusters in individuals infected with COVID-19. Overall, functional specializations of the AM subclusters suggest that there are highly regulated AM niches with defined programming states, highlighting a clear division of labor.

Distinct responses of newly identified monocyte subsets to advanced gastrointestinal cancer and COVID-19

Monocytes are critical cells of the immune system but their role as effectors is relatively poorly understood, as they have long been considered only as precursors of tissue macrophages or dendritic cells. Moreover, it is known that this cell type is heterogeneous, but our understanding of this aspect is limited to the broad classification in classical/intermediate/non-classical monocytes, commonly based on their expression of only two markers, i.e. CD14 and CD16. We deeply dissected the heterogeneity of human circulating monocytes in healthy donors by transcriptomic analysis at single-cell level and identified 9 distinct monocyte populations characterized each by a profile suggestive of specialized functions. The classical monocyte subset in fact included five distinct populations, each enriched for transcriptomic gene sets related to either inflammatory, neutrophil-like, interferon-related, and platelet-related pathways. Non-classical monocytes included two distinct populations, one of which marked specifically by elevated expression levels of complement components. Intermediate monocytes were not further divided in our analysis and were characterized by high levels of human leukocyte antigen (HLA) genes. Finally, we identified one cluster included in both classical and non-classical monocytes, characterized by a strong cytotoxic signature. These findings provided the rationale to exploit the relevance of newly identified monocyte populations in disease evolution. A machine learning approach was developed and applied to two single-cell transcriptome public datasets, from gastrointestinal cancer and Coronavirus disease 2019 (COVID-19) patients. The dissection of these datasets through our classification revealed that patients with advanced cancers showed a selective increase in monocytes enriched in platelet-related pathways. Of note, the signature associated with this population correlated with worse prognosis in gastric cancer patients. Conversely, after immunotherapy, the most activated population was composed of interferon-related monocytes, consistent with an upregulation in interferon-related genes in responder patients compared to non-responders. In COVID-19 patients we confirmed a global activated phenotype of the entire monocyte compartment, but our classification revealed that only cytotoxic monocytes are expanded during the disease progression. Collectively, this study unravels an unexpected complexity among human circulating monocytes and highlights the existence of specialized populations differently engaged depending on the pathological context.

Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

Single-cell transcriptomics enables the definition of diverse human immune cell types across multiple tissues and disease contexts. Further deeper biological understanding requires comprehensive integration of multiple single-cell omics (transcriptomic, proteomic, and cell-receptor repertoire). To improve the identification of diverse cell types and the accuracy of cell-type classification in multi-omics single-cell datasets, we developed SuPERR, a novel analysis workflow to increase the resolution and accuracy of clustering and allow for the discovery of previously hidden cell subsets. In addition, SuPERR accurately removes cell doublets and prevents widespread cell-type misclassification by incorporating information from cell-surface proteins and immunoglobulin transcript counts. This approach uniquely improves the identification of heterogeneous cell types and states in the human immune system, including rare subsets of antibody-secreting cells in the bone marrow.

•

SuPERR removes heterotypic doublets and cell-type misclassifications in scRNA-seq

•

Sequential gating on cell-surface proteins resolves major cell lineages in scRNA-seq

•

Defining major cell lineages before clustering reduces cell-type misclassifications

•

Antibody counts from single-cell V(D)J matrix accurately identify plasma cells

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.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Comprehensive identification of immuno-related transcriptional signature for active pulmonary tuberculosis by integrated analysis of array and single cell RNA-seq

BACKGROUND

Tuberculosis (TB) continues to be a major cause of morbidity and mortality worldwide. However, the molecular mechanism underlying immune response to human infection with Mycobacterium tuberculosis (Mtb) remains unclear. Assessing changes in transcript abundance in blood between health and disease on a genome-wide scale affords a comprehensive view of the impact of Mtb infection on the host defense and a reliable way to identify novel TB biomarkers.

METHODS

We combined expression profiling by array and single cell RNA-sequencing (scRNA-seq) via 10X Genomics platform to better illustrate the immuno-related transcriptional signature of TB and explore potential diagnostic markers for differentiating TB from latent tuberculosis infection (LTBI) and healthy control (HC).

FINDINGS

Pathway analysis based on differential expressed genes (DEGs) revealed that immune transcriptional profiling could effectively differ TB with LTBI and HC. Following WGCNA and PPI network analysis based on DEGs, we screened out three key immuno-related hub genes (ADM, IFIT3 and SERPING1) highly associated with TB. Further validation found only ADM expression significantly increased in TB patients in both adult and children's datasets. By comparing the scRNA-seq datasets from TB, LTBI and HC, we observed a remarkable elevated expression level and proportion of ADM in TB Myeloid cells, further supporting that ADM expression changes could distinguish patients with TB from LTBI and HC. Besides, the hsa-miR-24-3p-NEAT1-ADM-CEBPB regulation pathway might be one of the critical networks regulating the pathogenesis of TB. Although further investigation in a larger cohort is warranted, we provide useful and novel insight to explore the potential candidate genes for TB diagnosis and intervention.

INTERPRETATION

We propose that the expression of ADM in peripheral blood could be used as a novel biomarker for differentiating TB with LTBI and HC.

Impact of single-cell RNA sequencing on understanding immune regulation

Single‐cell RNA sequencing (scRNA‐seq), one of the most powerful technologies, can describe the transcriptomic heterogeneity of single cells and reveal previously unreported cell types or states in complex tissues. With the rapid development of scRNA‐seq, it has renewed our view of cellular heterogeneity and its significance for deeply understanding cell development and function. There are a large number of studies applying scRNA‐seq to investigate the heterogeneity of immune cells and disease pathogenesis, focusing on differences among every individual cell, which have provided novel inspiration for disease therapy and biological processes. In this review, we describe the development of scRNA‐seq and its application in immune‐related physiological states, regulatory mechanisms and diseases. In addition, we further discuss the opportunities and challenges of scRNA‐seq in immune regulation.

Mycobacterium tuberculosis infection drives a type I IFN signature in lung lymphocytes

Mycobacterium tuberculosis (Mtb) infects 25% of the world's population and causes tuberculosis (TB), which is a leading cause of death globally. A clear understanding of the dynamics of immune response at the cellular level is crucial to design better strategies to control TB. We use the single-cell RNA sequencing approach on lung lymphocytes derived from healthy and Mtb-infected mice. Our results show the enrichment of the type I IFN signature among the lymphoid cell clusters, as well as heat shock responses in natural killer (NK) cells from Mtb-infected mice lungs. We identify Ly6A as a lymphoid cell activation marker and validate its upregulation in activated lymphoid cells following infection. The cross-analysis of the type I IFN signature in human TB-infected peripheral blood samples further validates our results. These findings contribute toward understanding and characterizing the transcriptional parameters at a single-cell depth in a highly relevant and reproducible mouse model of TB.

Immunopathogenic overlap between COVID-19 and tuberculosis identified from transcriptomic meta-analysis and human macrophage infection

Current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. To identify mechanisms of immunopathogenic interaction between COVID-19 and TB, we performed a systematic review and patient-level meta-analysis of COVID-19 transcriptomic signatures, spanning disease severity, from whole blood, PBMCs, and BALF. 35 eligible signatures were profiled on 1181 RNA-seq samples from 853 individuals across the spectrum of TB infection. Thirteen COVID-19 gene-signatures had significantly higher "COVID-19 risk scores" in active TB and latent TB progressors compared with non-progressors and uninfected controls (p<0·005), in three independent cohorts. Integrative single-cell-RNAseq analysis identified FCN1- and SPP1-expressing macrophages enriched in severe COVID-19 BALF and active TB blood. Gene ontology and protein-protein interaction networks identified 12-gene disease-exacerbation hot spots between COVID-19 and TB. Finally, we in vitro validated that SARS-CoV-2 infection is increased in human macrophages cultured in the inflammatory milieu of Mtb-infected macrophages, correlating with TMPRSS2, IFNA1, IFNB1, IFNG, TNF, and IL1B induction.

Viral coinfection promotes tuberculosis immunopathogenesis by type I IFN signaling-dependent impediment of Th1 cell pulmonary influx

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is often exacerbated upon coinfection, but the underlying immunological mechanisms remain unclear. Here, to elucidate these mechanisms, we use an Mtb and lymphocytic choriomeningitis virus coinfection model. Viral coinfection significantly suppresses Mtb-specific IFN-γ production, with elevated bacterial loads and hyperinflammation in the lungs. Type I IFN signaling blockade rescues the Mtb-specific IFN-γ response and ameliorates lung immunopathology. Single-cell sequencing, tissue immunofluorescence staining, and adoptive transfer experiments indicate that viral infection-induced type I IFN signaling could inhibit CXCL9/10 production in myeloid cells, ultimately impairing pulmonary migration of Mtb-specific CD4⁺ T cells. Thus, our study suggests that augmented and sustained type I IFNs by virus coinfection prior to the pulmonary localization of Mtb-specific Th1 cells exacerbates TB immunopathogenesis by impeding the Mtb-specific Th1 cell influx. Our study highlights a negative function of viral coinfection-induced type I IFN responses in delaying Mtb-specific Th1 responses in the lung.

Viral coinfection alongside mycobacterium tuberculosis (Mtb) infection may lead to immune complications or interference with immune responses. Here the authors show that in mice infected with Mtb and LCMV virus the specific T_H1 response to MTb is reduced through a type I IFN response to the infecting virus.

Microfluidic cell sorter sample preparation for genomic assays

Single-cell RNA-Sequencing has led to many novel discoveries such as the detection of rare cell populations, microbial populations, and cancer mutations. The quality of single-cell transcriptomics relies heavily on sample preparation and cell sorting techniques that best preserve RNA quality while removing dead cells or debris prior to cDNA generation and library preparation. Magnetic bead cell enrichment is a simple process of cleaning up a sample but can only separate on a single-criterion. Droplet-based cell sorters, on the other hand, allows for higher purity of sorted cells gated on several fluorescent and scatter properties. The downside of traditional droplet-based sorters is their operational complexity, accessibility, and potential stress on cells due to their high-pressure pumps. The WOLF^® Cell Sorter, and WOLF G2^®, developed by NanoCellect Biomedical, are novel microfluidic-based cell sorters that use gentle sorting technology compatible with several RNA-sequencing platforms. The experiments highlighted here demonstrate how microfluidic sorting can be successfully used to remove debris and unwanted cells prior to genomic sample preparation resulting in more data per cell and improved library complexity.

Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection

BACKGROUND

Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI.

METHODS

Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning.

RESULTS

A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively.

CONCLUSIONS

We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.

Effects of 25-hydroxy vitamin D on T lymphocyte subsets and sputum smear conversion during anti-tuberculosis treatment

Objectives This study was aimed to explore the effects of 25-hydroxy vitamin D [25(OH)D] on T lymphocyte subsets and sputum smear conversion during anti-tuberculosis (TB) treatment. Methods 120 newly diagnosed active pulmonary TB patients were collected and classified into vitamin D sufficiency group, vitamin D insufficiency group, and vitamin D deficiency group according to serum 25(OH)D levels. The clinical data and sputum smear conversion were collected, serum 25(OH)D and T lymphocyte subsets were also measured and compared. Results Our data showed that 25(OH)D levels reached the lowest point at 2 months of anti-TB treatment. Significant differences existed in the increase of CD4+ and CD8+ T cells based on vitamin D levels. Vitamin D sufficiency group had a significantly higher increase of CD4+ T cells during 6 months of anti-TB treatment and CD8+ T cells after 4 months of anti-TB treatment than the other groups. Vitamin D had no effect on the time to sputum smear conversion [vitamin D sufficiency group: adjusted hazard ratio (HR): 1.27 (95% CI: 0.78 - 2.06); vitamin D insufficiency group: adjusted HR: 1.05 (95% CI: 0.63 - 1.75)]. Conclusions Through null effects on sputum smear conversion, vitamin D may have a beneficial effect on the increase of CD4+ and CD8+ T cells during anti-TB treatment.

Radiotherapy orchestrates natural killer cell dependent antitumor immune responses through CXCL8

Radiotherapy is a mainstay cancer therapy whose antitumor effects partially depend on T cell responses. However, the role of Natural Killer (NK) cells in radiotherapy remains unclear. Here, using a reverse translational approach, we show a central role of NK cells in the radiation-induced immune response involving a CXCL8/IL-8-dependent mechanism. In a randomized controlled pancreatic cancer trial, CXCL8 increased under radiotherapy, and NK cell positively correlated with prolonged overall survival. Accordingly, NK cells preferentially infiltrated irradiated pancreatic tumors and exhibited CD56^dim-like cytotoxic transcriptomic states. In experimental models, NF-κB and mTOR orchestrated radiation-induced CXCL8 secretion from tumor cells with senescence features causing directional migration of CD56^dim NK cells, thus linking senescence-associated CXCL8 release to innate immune surveillance of human tumors. Moreover, combined high-dose radiotherapy and adoptive NK cell transfer improved tumor control over monotherapies in xenografted mice, suggesting NK cells combined with radiotherapy as a rational cancer treatment strategy.

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.