Immune cell type 'fingerprints' at the basis of outcome diversity of human infection

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

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.

Chicken Peripheral Blood Mononuclear Cells Response to Avian Leukosis Virus Subgroup J Infection Assessed by Single-Cell RNA Sequencing

Chicken peripheral blood mononuclear cells (PBMCs) exhibit wide-ranging cell types, but current understanding of their subclasses, immune cell classification, and function is limited and incomplete. Here we performed single-cell RNA sequencing (scRNA-seq) of PBMCs in Avian leukosis virus subgroup J (ALV-J) infected and control chickens at 21 days post infection (DPI) to determine chicken PBMCs subsets and their specific molecular and cellular characteristics. Eight cell populations and their potential marker genes were identified in PBMCs. T cell populations had the strongest response to (ALV-J) infection, based on the detection of the largest number of differentially expressed genes (DEGs), and could be further grouped into four subsets: activated CD4⁺ T cells, Th1-like cells, Th2-like cells, and cytotoxic CD8⁺ T cells. Furthermore, pseudotime analysis results suggested that chicken CD4⁺ T cells could potentially differentiate into Th1-like and Th2-like cells. Moreover, ALV-J infection activated CD4⁺ T cell was probably inclined to differentiate into Th1-like cells. Compared to the control PBMCs, ALV-J infection also had an obvious impact on PBMCs composition. B cells showed inconspicuous response and their numbers decreased in PBMCs from ALV-J infected chicken. Proportions of cytotoxic Th1-like cells and CD8⁺ T cells increased in the T cell population of PBMCs from ALV-J infected chicken, which were potentially key mitigating effectors against ALV-J infection. More importantly, our results provide a rich resource of gene expression profiles of chicken PBMCs subsets for a systems-level understanding of their function in homeostatic condition as well as in response to viral infection.

The Application of Single-Cell RNA Sequencing in Vaccinology

Single-cell RNA sequencing allows highly detailed profiling of cellular immune responses from limited-volume samples, advancing prospects of a new era of systems immunology. The power of single-cell RNA sequencing offers various opportunities to decipher the immune response to infectious diseases and vaccines. Here, we describe the potential uses of single-cell RNA sequencing methods in prophylactic vaccine development, concentrating on infectious diseases including COVID-19. Using examples from several diseases, we review how single-cell RNA sequencing has been used to evaluate the immunological response to different vaccine platforms and regimens. By highlighting published and unpublished single-cell RNA sequencing studies relevant to vaccinology, we discuss some general considerations how the field could be enriched with the widespread adoption of this technology.

Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Advances in single cell RNA-sequencing (scRNA-seq) allow probing of these immune interactions, such as cell-type compositions, which are then interpreted by deconvolution algorithms using bulk RNA-seq measurements. However, not all aspects of immune surveillance are represented by current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we develop a deconvolution algorithm for inferring cell-type specific infection responses from bulk measurements. We apply our dynamic deconvolution algorithm to a cohort of healthy individuals challenged ex vivo with Salmonella, and to three cohorts of tuberculosis patients during different stages of disease. We reveal cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and human infection outcomes.

Complex interactions between different host immune cell types can determine the outcome of pathogen infections. Here, Avraham and colleagues present a deconvolution algorithm that uses single-cell RNA and bulk RNA sequencing measurements of pathogen-infected cells to predict disease risk outcomes.

The Role of the Host in Driving Phenotypic Heterogeneity in Salmonella

The complex infection environment within hosts exerts unique stresses across tissues and cell types, selecting for phenotypic heterogeneity in bacterial populations. Pathogens maintain variability during infection as a strategy to cope with fluctuating host immune conditions, leading to diversification of virulence phenotypes. Recent improvements in single-cell analyses have revealed that distinct bacterial subpopulations contribute unique colonization and growth strategies across infection sites. We discuss several examples of host-driven phenotypic heterogeneity in Salmonella populations throughout the course of infection, highlighting how variation in gene expression, growth rate, immune evasion, and metabolic activity contribute to overall bacterial success at the population level. We additionally focus our discussion on the implications of diversity within bacterial communities for antimicrobial efficacy.