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

Single-cell sequencing: Current applications in various tuberculosis specimen types

Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.

An overview of next generation sequencing strategies and genomics tools used for tuberculosis research

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.

Lasting differential gene expression of circulating CD8 T cells in chronic HCV infection with cirrhosis identifies a role for Hedgehog signaling in cellular hyperfunction

Background

The impact of chronic hepatic infection on antigen non-specific immune cells in circulation remains poorly understood. We reported lasting global hyperfunction of peripheral CD8 T cells in HCV-infected individuals with cirrhosis. Whether gene expression patterns in bulk CD8 T cells are associated with the severity of liver fibrosis in HCV infection is not known.

Methods

RNA sequencing of blood CD8 T cells from treatment naïve, HCV-infected individuals with minimal (Metavir F0-1 ≤ 7.0 kPa) or advanced fibrosis or cirrhosis (F4 ≥ 12.5 kPa), before and after direct-acting antiviral therapy, was performed. CD8 T cell function was assessed by flow cytometry.

Results

In CD8 T cells from pre-DAA patients with advanced compared to minimal fibrosis, Gene Ontology analysis and Gene Set Enrichment Analysis identified differential gene expression related to cellular function and metabolism, including upregulated Hedgehog (Hh) signaling, IFN-α, -γ, TGF-β response genes, apoptosis, apical surface pathways, phospholipase signaling, phosphatidyl-choline/inositol activity, and second-messenger-mediated signaling. In contrast, genes in pathways associated with nuclear processes, RNA transport, cytoskeletal dynamics, cMyc/E2F regulation, oxidative phosphorylation, and mTOR signaling, were reduced. Hh signaling pathway was the top featured gene set upregulated in cirrhotics, wherein hallmark genes GLI1 and PTCH1 ranked highly. Inhibition of Smo-dependent Hh signaling ablated the expression of IFN-γ and perforin in stimulated CD8 T cells from chronic HCV-infected patients with advanced compared to minimal fibrosis. CD8 T cell gene expression profiles post-DAA remained clustered with pre-DAA profiles and disparately between advanced and minimal fibrosis, suggesting a persistent perturbation of gene expression long after viral clearance.

Conclusions

This analysis of bulk CD8 T cell gene expression in chronic HCV infection suggests considerable reprogramming of the CD8 T cell pool in the cirrhotic state. Increased Hh signaling in cirrhosis may contribute to generalized CD8 T cell hyperfunction observed in chronic HCV infection. Understanding the lasting nature of immune cell dysfunction may help mitigate remaining clinical challenges after HCV clearance and more generally, improve long term outcomes for individuals with severe liver disease.

Integrated single-cell transcriptome and T cell receptor profiling reveals defects of T cell exhaustion in pulmonary tuberculosis

Tuberculosis-affected lungs with chronic inflammation harbor abundant immunosuppressive immune cells but the nature of such inflammation is unclear. Dysfunction in T cell exhaustion, while implicated in chronic inflammatory diseases, remains unexplored in tuberculosis. Given that immunotherapy targeting exhaustion checkpoints exacerbates tuberculosis, we speculate that T cell exhaustion is dysfunctional in tuberculosis. Using integrated single-cell RNA sequencing and T cell receptor profiling we reported defects in exhaustion responses within inflamed tuberculosis-affected lungs. Tuberculosis lungs demonstrated significantly reduced levels of exhausted CD8+ T cells and exhibited diminished expression of exhaustion-related transcripts among clonally expanded CD4+ and CD8+ T cells. Additionally, clonal expansion of CD4+ and CD8+ T cells bearing T cell receptors specific for CMV was observed. Expanded CD8+ T cells expressed the cytolytic marker GZMK. Hence, inflamed tuberculosis-affected lungs displayed dysfunction in T cell exhaustion. Our findings likely hold implications for understanding the reactivation of tuberculosis observed in patients undergoing immunotherapy targeting the exhaustion checkpoint.

Cell-Mediated Immune Response Against Mycobacterium tuberculosis and Its Potential Therapeutic Impact

Cell-mediated immune response is critical for Mycobacterium tuberculosis (M.tb) control. Understanding of pathophysiology and role played by different cell mediators is essential for vaccine development and better management of patients with M.tb. A complex array of cytokines and chemokines are involved in the immune response against M.tb; however, their relative contribution in protection remains to be further explored. The purpose of this review is to summarize the current understanding regarding the cytokine and chemokine profiles in M.tb infection in order to assist research in the field to pursue new direction in prevention and control. We have also summarized recent findings on vaccine trials that have been developed and or are under trials that are targeting these molecules.

Single-cell transcriptome reveals highly complement activated microglia cells in association with pediatric tuberculous meningitis

Background

Tuberculous meningitis (TBM) is a devastating form of tuberculosis (TB) causing high mortality and disability. TBM arises due to immune dysregulation, but the underlying immune mechanisms are unclear.

Methods

We performed single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) and cerebrospinal fluid (CSF) cells isolated from children (n=6) with TBM using 10 xGenomics platform. We used unsupervised clustering of cells and cluster visualization based on the gene expression profiles, and validated the protein and cytokines by ELISA analysis.

Results

We revealed for the first time 33 monocyte populations across the CSF cells and PBMCs of children with TBM. Within these populations, we saw that CD4_C04 cells with Th17 and Th1 phenotypes and Macro_C01 cells with a microglia phenotype, were enriched in the CSF. Lineage tracking analysis of monocyte populations revealed myeloid cell populations, as well as subsets of CD4 and CD8 T-cell populations with distinct effector functions. Importantly, we discovered that complement-activated microglial Macro_C01 cells are associated with a neuroinflammatory response that leads to persistent meningitis. Consistently, we saw an increase in complement protein (C1Q), inflammatory markers (CRP) and inflammatory factor (TNF-α and IL-6) in CSF cells but not blood. Finally, we inferred that Macro_C01 cells recruit CD4_C04 cells through CXCL16/CXCR6.

Discussion

We proposed that the microglial Macro_C01 subset activates complement and interacts with the CD4_C04 cell subset to amplify inflammatory signals, which could potentially contribute to augment inflammatory signals, resulting in hyperinflammation and an immune response elicited by Mtb-infected tissues.

Unraveling T cell exhaustion in the immune microenvironment of osteosarcoma via single-cell RNA transcriptome

Osteosarcoma (OS) represents a profoundly invasive malignancy of the skeletal system. T cell exhaustion (Tex) is known to facilitate immunosuppression and tumor progression, but its role in OS remains unclear. In this study, single-cell RNA sequencing data was employed to identify exhausted T cells within the tumor immune microenvironment (TIME) of OS. We found that exhausted T cells exhibited substantial infiltration in OS samples. Pseudotime trajectory analysis revealed a progressive increase in the expression of various Tex marker genes, including PDCD1, CTLA4, LAG3, ENTPD1, and HAVCR2 in OS. GSVA showed that apoptosis, fatty acid metabolism, xenobiotic metabolism, and the interferon pathway were significantly activated in exhausted T cells in OS. Subsequently, a prognostic model was constructed using two Tex-specific genes, MYC and FCGR2B, which exhibited exceptional prognostic accuracy in two independent cohorts. Drug sensitivity analysis revealed that OS patients with a low Tex risk were responsive to Dasatinib and Pazopanib. Finally, immunohistochemistry verified that MYC and FCGR2B were significantly upregulated in OS tissues compared with adjacent tissues. This study investigates the role of Tex within the TIME of OS, and offers novel insights into the mechanisms underlying disease progression as well as the potential treatment strategies for OS.

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.

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.