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

Serotonin transporter inhibits antitumor immunity through regulating the intratumoral serotonin axis

Identifying additional immune checkpoints hindering antitumor T cell responses is key to the development of next-generation cancer immunotherapies. Here, we report the induction of serotonin transporter (SERT), a regulator of serotonin levels and physiological functions in the brain and peripheral tissues, in tumor-infiltrating CD8 T cells. Inhibition of SERT using selective serotonin reuptake inhibitors (SSRIs), the most widely prescribed antidepressants, significantly suppressed tumor growth and enhanced T cell antitumor immunity in various mouse syngeneic and human xenograft tumor models. Importantly, SSRI treatment exhibited significant therapeutic synergy with programmed cell death protein 1 (PD-1) blockade, and clinical data correlation studies negatively associated intratumoral SERT expression with patient survival in a range of cancers. Mechanistically, SERT functions as a negative-feedback regulator inhibiting CD8 T cell reactivities by depleting intratumoral T cell-autocrine serotonin. These findings highlight the significance of the intratumoral serotonin axis and identify SERT as an immune checkpoint, positioning SSRIs as promising candidates for cancer immunotherapy.

Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure

Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display metabolic and transcriptional diversity along with recalcitrance to antibiotics and host immune defenses. Here, we present an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure. BaSSSh-seq captures extensive transcriptional heterogeneity during biofilm compared to planktonic growth. We quantify and visualize transcriptional regulatory networks across heterogeneous biofilm subpopulations and identify gene sets that are associated with a trajectory from planktonic to biofilm growth. BaSSSh-seq also detects alterations in biofilm metabolism, stress response, and virulence induced by distinct immune cell populations. This work facilitates the exploration of biofilm dynamics at single-cell resolution, unlocking the potential for identifying biofilm adaptations to environmental signals and immune pressure.

Dichotomous outcomes of TNFR1 and TNFR2 signaling in NK cell-mediated immune responses during inflammation

Natural killer (NK) cell function is regulated by a balance of activating and inhibitory signals. Tumor necrosis factor (TNF) is an inflammatory cytokine ubiquitous across homeostasis and disease, yet its role in regulation of NK cells remains unclear. Here, we find upregulation of the immune checkpoint protein, T cell immunoglobulin and mucin domain 3 (Tim3), is a biomarker of TNF signaling in NK cells during Salmonella Typhimurium infection. In mice with conditional deficiency of either TNF receptor 1 (TNFR1) or TNF receptor 2 (TNFR2) in NK cells, we find TNFR1 limits bacterial clearance whereas TNFR2 promotes it. Mechanistically, via single cell RNA sequencing we find that both TNFR1 and TNFR2 induce the upregulation of Tim3, while TNFR1 accelerates NK cell death but TNFR2 promotes NK cell accumulation and effector function. Our study thus highlights the complex interplay of TNF-based regulation of NK cells by the two TNF receptors during inflammation.

SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction

The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.

Microbiome and infectious disease: diagnostics to therapeutics

Over 300 years of research on the microbial world has revealed their importance in human health and disease. This review explores the impact and potential of microbial-based detection methods and therapeutic interventions, integrating research of early microbiologists, current findings, and future perspectives.

Single-cell RNA sequencing in tuberculosis: Application and future perspectives

Tuberculosis (TB) has one of the highest mortality rates among infectious diseases worldwide. The immune response in the host after infection is proposed to contribute significantly to the progression of TB, but the specific mechanisms involved remain to be elucidated. Single-cell RNA sequencing (scRNA-seq) provides unbiased transcriptome sequencing of large quantities of individual cells, thereby defining biological comprehension of cellular heterogeneity and dynamic transcriptome state of cell populations in the field of immunology and is therefore increasingly applied to lung disease research. Here, we first briefly introduce the concept of scRNA-seq, followed by a summarization on the application of scRNA-seq to TB. Furthermore, we underscore the potential of scRNA-seq for clinical biomarker exploration, host-directed therapy, and precision therapy research in TB and discuss the bottlenecks that need to be overcome for the broad application of scRNA-seq to TB-related research.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure

Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display vast metabolic and transcriptional diversity along with high recalcitrance to antibiotics and host immune defenses. Investigating the complex heterogeneity within biofilm has been hindered by the lack of a sensitive and high-throughput method to assess stochastic transcriptional activity and regulation between bacterial subpopulations, which requires single-cell resolution. We have developed an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure. We validated the ability of BaSSSh-seq to capture extensive transcriptional heterogeneity during biofilm compared to planktonic growth. Application of new computational tools revealed transcriptional regulatory networks across the heterogeneous biofilm subpopulations and identification of gene sets that were associated with a trajectory from planktonic to biofilm growth. BaSSSh-seq also detected alterations in biofilm metabolism, stress response, and virulence that were tailored to distinct immune cell populations. This work provides an innovative platform to explore biofilm dynamics at single-cell resolution, unlocking the potential for identifying biofilm adaptations to environmental signals and immune pressure.

Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell transcriptional studies

Combining data from experiments on multispecies studies provides invaluable contributions to the understanding of basic disease mechanisms and pathophysiology of pathogens crossing species boundaries. The task of multispecies gene expression analysis, however, is often challenging given annotation inconsistencies and in cases of small sample sizes due to bias caused by batch effects. In this work we aim to demonstrate that an alternative approach to standard differential expression analysis in single cell RNA-sequencing (scRNA-seq) based on effect size profiles is suitable for the fusion of data from small samples and multiple organisms. The analysis pipeline is based on effect size metric profiles of samples in specific cell clusters. The effect size substitutes standard differentiation analyses based on p-values and profiles identified based on these effect size metrics serve as a tool to link cell type clusters between the studied organisms. The algorithms were tested on published scRNA-seq data sets derived from several species and subsequently validated on own data from human and bovine peripheral blood mononuclear cells stimulated with Mycobacterium tuberculosis. Correlation of the effect size profiles between clusters allowed for the linkage of human and bovine cell types. Moreover, effect size ratios were used to identify differentially regulated genes in control and stimulated samples. The genes identified through effect size profiling were confirmed experimentally using qPCR. We demonstrate that in situations where batch effects dominate cell type variation in single cell small sample size multispecies studies, effect size profiling is a valid alternative to traditional statistical inference techniques.

An advanced comprehensive muti-cell-type-specific model for predicting anti-PD-1 therapeutic effect in melanoma

Rationale: Immune checkpoint inhibitors targeting the programmed cell death (PD)-1/PD-L1 pathway have promise in patients with advanced melanoma. However, drug resistance usually results in limited patient benefits. Recent single-cell RNA sequencing studies have elucidated that MM patients display distinctive transcriptional features of tumor cells, immune cells and interstitial cells, including loss of antigen presentation function of tumor cells, exhaustion of CD8+T and extracellular matrix secreted by fibroblasts to prevents immune infiltration, which leads to a poor response to immune checkpoint inhibitors (ICIs). However, cell subgroups beneficial to anti-tumor immunity and the model developed by them remain to be further identified. Methods: In this clinical study of neoadjuvant therapy with anti-PD-1 in advanced melanoma, tumor tissues were collected before and after treatment for single-nucleus sequencing, and the results were verified using multicolor immunofluorescence staining and public datasets. Results: This study describes four cell subgroups which are closely associated with the effectiveness of anti-PD-1 treatment. It also describes a cell-cell communication network, in which the interaction of the four cell subgroups contributes to anti-tumor immunity. Furthermore, we discuss a newly developed predictive model based on these four subgroups that holds significant potential for assessing the efficacy of anti-PD-1 treatment. Conclusions: These findings elucidate the primary mechanism of anti-PD-1 resistance and offer guidance for clinical drug administration for melanoma.

Nanozyme-Coated Bacteria Hitchhike on CD11b+ Immune Cells to Boost Tumor Radioimmunotherapy

Bacterial-based delivery strategies have recently emerged as a unique research direction in the field of drug delivery. However, bacterial vectors are quickly phagocytosed by immune cells after entering the bloodstream. Taking advantage of this phenomenon, herein, this work seeks to harness the potential of immune cells to delivery micron-sized bacterial vectors, and find that inactivated bacterial can accumulate at tumor-site after intravenous injection through CD11b+ cells hitchhiking. To this end, this work then designs a gold-platinum bimetallic nanozyme coated bacterial vector (Au-Pt@VNP20009, APV). Utilizing strong tumor inflammatory response induced by low dose X-rays, this work further heightens the ability of CD11b+ immune cells to assist APV hitchhiking for tumor-targeted delivery, which can significantly relieve tumor hypoxia and immunosuppression, and inhibit tumor growth and metastasis. This work elucidates the potential mechanisms of bacterial vector targeted delivery, opening up new horizons for bacterial vector delivery strategies and clinical tumor radioimmunotherapy.

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.

Decoding the immune landscape following hip fracture in elderly patients: unveiling temporal dynamics through single-cell RNA sequencing

BACKGROUND

Hip fractures in the elderly have significant consequences, stemming from the initial trauma and subsequent surgeries. Hidden blood loss and stress due to concealed injury sites could impact the whole osteoimmune microenvironment. This study employs scRNA-seq technique to map immune profiles in elderly hip fracture patients from post-trauma to the recovery period, investigating the dynamic changes of immune inflammation regulation subgroups.

METHODS

We collected peripheral blood samples from four elderly hip fracture patients (two males and two females, all > 75 years of age) at three different time points (24 h post-trauma, 24 h post-operation, and day 7 post-operation) and applied scRNA-seq technique to analyze the cellular heterogeneity and identify differentially expressed genes in peripheral blood individual immune cells from elderly hip fracture patients.

RESULTS

In this study, we analyzed the composition and gene expression profiles of peripheral blood mononuclear cells (PBMCs) from elderly hip fracture patients by scRNA-seq and further identified new CD14 monocyte subpopulations based on marker genes and transcriptional profiles. Distinct gene expression changes were observed in various cell subpopulations at different time points. C-Mono2 monocyte mitochondria-related genes were up-regulated and interferon-related and chemokine-related genes were down-regulated within 24 h post-operation. Further analysis of gene expression profiles at day 7 post-operation showed that C-Mono2 monocytes showed downregulation of inflammation-related genes and osteoblast differentiation-related genes. However, the expression of these genes in cytotoxic T cells, Treg cells, and B cell subsets exhibited a contrasting trend. GZMK+CD8+ cytotoxic T cells showed downregulation of chemokine-related genes, and Treg cells showed upregulation of genes related to the JAK/STAT signaling pathway. Furthermore, we examined interactions among diverse immune cell subsets, pinpointing specific ligand-receptor pairs. These findings imply cross-talk and communication between various cell types in the post-traumatic immune response.

CONCLUSIONS

Our study elucidates the notable alterations in immune cell subpopulations during different stages of hip fracture in elderly patients, both in terms of proportions and differential gene expressions. These changes provide significant clinical implications for tissue repair, infection prevention, and fracture healing in clinic.

Early transcriptional responses to human enteric fever challenge

Enteric fever, caused by oral infection with typhoidal Salmonella serovars, presents as a non-specific febrile illness preceded by an incubation period of 5 days or more. The enteric fever human challenge model provides a unique opportunity to investigate the innate immune response during this incubation period, and how this response is altered by vaccination with the Vi polysaccharide or conjugate vaccine. We find that on the same day as ingestion of typhoidal Salmonella, there is already evidence of an immune response, with 199 genes upregulated in the peripheral blood transcriptome 12 hours post-challenge (false discovery rate <0.05). Gene sets relating to neutrophils, monocytes, and innate immunity were over-represented (false discovery rate <0.05). Estimating cell proportions from gene expression data suggested a possible increase in activated monocytes 12 hours post-challenge (P = 0.036, paired Wilcoxon signed-rank test). Furthermore, plasma TNF-α rose following exposure (P = 0.011, paired Wilcoxon signed-rank test). There were no significant differences in gene expression (false discovery rate <0.05) in the 12 hours response between those who did and did not subsequently develop clinical or blood culture confirmed enteric fever or between vaccination groups. Together, these results demonstrate early perturbation of the peripheral blood transcriptome after enteric fever challenge and provide initial insight into early mechanisms of protection.

Research progress of single-cell sequencing in tuberculosis

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

Single-Cell Transcriptomics of Mtb/HIV Co-Infection

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

Single-cell RNA sequencing of peripheral blood reveals immune cell dysfunction in premature ovarian insufficiency

Background

Premature ovarian insufficiency (POI) is one of the most common causes of female infertility and the etiology is highly heterogeneous. Most cases are idiopathic and the pathogenesis remains unclear. Previous studies proved that the immune system plays a crucial role in POI. However, the precise role of immune system remains unclear. This study aimed to analyze the characteristics of peripheral blood mononuclear cells (PBMC) from patients with POI by single-cell RNA sequencing (scRNA-seq) and to explore the potential involvement of immune response in idiopathic POI.

Methods

PBMC was collected from three normal subjects and three patients with POI. PBMC was subjected to scRNA-seq to identify cell clusters and differently expressed genes (DEGs). Enrichment analysis and cell-cell communication analysis were performed to explore the most active biological function in the immune cells of patients with POI.

Results

In total, 22 cell clusters and 10 cell types were identified in the two groups. Compared with normal subjects, the percentage of classical monocytes and NK cells was decreased, the abundance of plasma B cells was increased, and CD4/CD8 ratio was significantly higher in POI. Furthermore, upregulation of IGKC, IFITM1, CD69, JUND and downregulation of LYZ, GNLY, VCAN, and S100A9 were identified, which were enriched in NK cell-mediated cytotoxicity, antigen processing and presentation, and IL-17 signaling pathway. Among them, IGHM and LYZ were respectively the most significantly upregulated and downregulated genes among all cell clusters of POI. The strength of cell-cell communication differed between the healthy subjects and patients with POI, and multiple signaling pathways were assessed. The TNF pathway was found to be unique in POI with classical monocytes being the major target and source of TNF signaling.

Conclusions

Dysfunction of cellular immunity is related to idiopathic POI. Monocytes, NK cells, and B cells, and their enriched differential genes may play a role in the development of idiopathic POI. These findings provide novel mechanistic insight for understanding the pathogenesis of POI.

An E. coli display method for characterization of peptide-sensor kinase interactions

Bacteria use two-component system (TCS) signaling pathways to sense and respond to peptides involved in host defense, quorum sensing and inter-bacterial warfare. However, little is known about the broad peptide-sensing capabilities of TCSs. In this study, we developed an Escherichia coli display method to characterize the effects of human antimicrobial peptides (AMPs) on the pathogenesis-regulating TCS PhoPQ of Salmonella Typhimurium with much higher throughput than previously possible. We found that PhoPQ senses AMPs with diverse sequences, structures and biological functions. We further combined thousands of displayed AMP variants with machine learning to identify peptide sub-domains and biophysical features linked to PhoPQ activation. Most of the newfound AMP activators induce PhoPQ in S. Typhimurium, suggesting possible roles in virulence regulation. Finally, we present evidence that PhoPQ peptide-sensing specificity has evolved across commensal and pathogenic bacteria. Our method enables new insights into the specificities, mechanisms and evolutionary dynamics of TCS-mediated peptide sensing in bacteria.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Human variation in population-wide gene expression data predicts gene perturbation phenotype

Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.

Genomics and pathotypes of the many faces of Escherichia coli

Escherichia coli is the most researched microbial organism in the world. Its varied impact on human health, consisting of commensalism, gastrointestinal disease, or extraintestinal pathologies, has generated a separation of the species into at least eleven pathotypes (also known as pathovars). These are broadly split into two groups, intestinal pathogenic E. coli (InPEC) and extraintestinal pathogenic E. coli (ExPEC). However, components of E. coli's infinite open accessory genome are horizontally transferred with substantial frequency, creating pathogenic hybrid strains that defy a clear pathotype designation. Here, we take a birds-eye view of the E. coli species, characterizing it from historical, clinical, and genetic perspectives. We examine the wide spectrum of human disease caused by E. coli, the genome content of the bacterium, and its propensity to acquire, exchange, and maintain antibiotic resistance genes and virulence traits. Our portrayal of the species also discusses elements that have shaped its overall population structure and summarizes the current state of vaccine development targeted at the most frequent E. coli pathovars. In our conclusions, we advocate streamlining efforts for clinical reporting of ExPEC, and emphasize the pathogenic potential that exists throughout the entire species.

The tempo and mode of gene regulatory programs during bacterial infection

Innate immune recognition of bacterial pathogens is a key determinant of the ensuing systemic response, and host or pathogen heterogeneity in this early interaction can impact the course of infection. To gain insight into host response heterogeneity, we investigate macrophage inflammatory dynamics using primary human macrophages infected with Group B Streptococcus. Transcriptomic analysis reveals discrete cellular states within responding macrophages, one of which consists of four sub-states, reflecting inflammatory activation. Infection with six additional bacterial species-Staphylococcus aureus, Listeria monocytogenes, Enterococcus faecalis, Yersinia pseudotuberculosis, Shigella flexneri, and Salmonella enterica-recapitulates these states, though at different frequencies. We show that modulating the duration of infection and the presence of a toxin impacts inflammatory trajectory dynamics. We provide evidence for this trajectory in infected macrophages in an in vivo model of Staphylococcus aureus infection. Our cell-state analysis defines a framework for understanding inflammatory activation dynamics in response to bacterial infection.

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.

Combined protein and transcript single-cell RNA sequencing in human peripheral blood mononuclear cells

BACKGROUND

Cryopreserved peripheral blood mononuclear cells (PBMCs) are frequently collected and provide disease- and treatment-relevant data in clinical studies. Here, we developed combined protein (40 antibodies) and transcript single-cell (sc)RNA sequencing (scRNA-seq) in PBMCs.

RESULTS

Among 31 participants in the Women's Interagency HIV Study (WIHS), we sequenced 41,611 cells. Using Boolean gating followed by Seurat UMAPs (tool for visualizing high-dimensional data) and Louvain clustering, we identified 50 subsets among CD4+ T, CD8+ T, B, NK cells, and monocytes. This resolution was superior to flow cytometry, mass cytometry, or scRNA-seq without antibodies. Combined protein and transcript scRNA-seq allowed for the assessment of disease-related changes in transcriptomes and cell type proportions. As a proof-of-concept, we showed such differences between healthy and matched individuals living with HIV with and without cardiovascular disease.

CONCLUSIONS

In conclusion, combined protein and transcript scRNA sequencing is a suitable and powerful method for clinical investigations using PBMCs.

Microbiome-derived antimicrobial peptides offer therapeutic solutions for the treatment of Pseudomonas aeruginosa infections

Microbiomes are rife for biotechnological exploitation, particularly the rumen microbiome, due to their complexicity and diversity. In this study, antimicrobial peptides (AMPs) from the rumen microbiome (Lynronne 1, 2, 3 and P15s) were assessed for their therapeutic potential against seven clinical strains of Pseudomonas aeruginosa. All AMPs exhibited antimicrobial activity against all strains, with minimum inhibitory concentrations (MICs) ranging from 4–512 µg/mL. Time-kill kinetics of all AMPs at 3× MIC values against strains PAO1 and LES431 showed complete kill within 10 min to 4 h, although P15s was not bactericidal against PAO1. All AMPs significantly inhibited biofilm formation by strains PAO1 and LES431, and induction of resistance assays showed no decrease in activity against these strains. AMP cytotoxicity against human lung cells was also minimal. In terms of mechanism of action, the AMPs showed affinity towards PAO1 and LES431 bacterial membrane lipids, efficiently permeabilising the P. aeruginosa membrane. Transcriptome and metabolome analysis revealed increased catalytic activity at the cell membrane and promotion of β-oxidation of fatty acids. Finally, tests performed with the Galleria mellonella infection model showed that Lynronne 1 and 2 were efficacious in vivo, with a 100% survival rate following treatment at 32 mg/kg and 128 mg/kg, respectively. This study illustrates the therapeutic potential of microbiome-derived AMPs against P. aeruginosa infections.

In silico identification of two peptides with antibacterial activity against multidrug-resistant Staphylococcus aureus

Here we report two antimicrobial peptides (AMPs), HG2 and HG4 identified from a rumen microbiome metagenomic dataset, with activity against multidrug-resistant (MDR) bacteria, especially methicillin-resistant Staphylococcus aureus (MRSA) strains, a major hospital and community-acquired pathogen. We employed the classifier model design to analyse, visualise, and interpret AMP activities. This approach allowed in silico discrimination of promising lead AMP candidates for experimental evaluation. The lead AMPs, HG2 and HG4, are fast-acting and show anti-biofilm and anti-inflammatory activities in vitro and demonstrated little toxicity to human primary cell lines. The peptides were effective in vivo within a Galleria mellonella model of MRSA USA300 infection. In terms of mechanism of action, HG2 and HG4 appear to interact with the cytoplasmic membrane of target cells and may inhibit other cellular processes, whilst preferentially binding to bacterial lipids over human cell lipids. Therefore, these AMPs may offer additional therapeutic templates for MDR bacterial infections.

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.

Transcriptome-Based Molecular Networks Uncovered Interplay Between Druggable Genes of CD8+ T Cells and Changes in Immune Cell Landscape in Patients With Pulmonary Tuberculosis

BACKGROUND

Tuberculosis (TB) is a major infectious disease, where incomplete information about host genetics and immune responses is hindering the development of transformative therapies. This study characterized the immune cell landscape and blood transcriptomic profile of patients with pulmonary TB (PTB) to identify the potential therapeutic biomarkers.

METHODS

The blood transcriptome profile of patients with PTB and controls were used for fractionating immune cell populations with the CIBERSORT algorithm and then to identify differentially expressed genes (DEGs) with R/Bioconductor packages. Later, systems biology investigations (such as semantic similarity, gene correlation, and graph theory parameters) were implemented to prioritize druggable genes contributing to the immune cell alterations in patients with TB. Finally, real time-PCR (RT-PCR) was used to confirm gene expression levels.

RESULTS

Patients with PTB had higher levels of four immune subpopulations like CD8⁺ T cells (P = 1.9 × 10^-8), natural killer (NK) cells resting (P = 6.3 × 10^-5), monocytes (P = 6.4 × 10^-6), and neutrophils (P = 1.6 × 10^-7). The functional enrichment of 624 DEGs identified in the blood transcriptome of patients with PTB revealed major dysregulation of T cell-related ontologies and pathways (q ≤ 0.05). Of the 96 DEGs shared between transcriptome and immune cell types, 39 overlapped with TB meta-profiling genetic signatures, and their semantic similarity analysis with the remaining 57 genes, yielded 45 new candidate TB markers. This study identified 9 CD8⁺ T cell-associated genes (ITK, CD2, CD6, CD247, ZAP70, CD3D, SH2D1A, CD3E, and IL7R) as potential therapeutic targets of PTB by combining computational druggability and co-expression (r² ≥ |0.7|) approaches.

CONCLUSION

The changes in immune cell proportion and the downregulation of T cell-related genes may provide new insights in developing therapeutic compounds against chronic TB.

Blood transcriptome profiling identifies 2 candidate endotypes of atopic dermatitis

BACKGROUND

Few studies have analyzed the blood transcriptome in atopic dermatitis (AD).

OBJECTIVE

We explored blood transcriptomic features of moderate to severe AD.

METHODS

Blood messenger RNA sequencing on 60 adults from the TREATgermany registry including 49 patients before and after dupilumab treatment, as well as from an independent cohort of 31 patients and 43 controls was performed. Patient clustering, differential expression, correlation and coexpression network analysis, and unsupervised learning were conducted.

RESULTS

AD patients showed pronounced inflammatory expression signatures with increased myeloid and IL-5-related patterns, and clearly segregated into 2 distinct clusters, with striking differences in particular for transcripts involved in eosinophil signaling. The eosinophil-high endotype showed a more pronounced global dysregulation, a positive correlation between disease activity and signatures related to IL-5 signaling, and strong correlations with several target proteins of antibodies or small molecules under development for AD. In contrast, the eosinophil-low endotype showed little transcriptomic dysregulation and no association between disease activity and gene expression. Clinical improvement with receipt of dupilumab was accompanied by a decrease of innate immune responses and an increase of lymphocyte signatures including B-cell activation and natural killer cell composition and/or function. The proportion of super responders was higher in the eosinophil-low endotype (32% vs 11%). Continued downregulation of IL18RAP, IFNG, and granzyme A in the eosinophil-high endotype suggests a residual disturbance of natural killer cell function despite clinical improvement.

CONCLUSION

AD can be stratified into eosinophilic and noneosinophilic endotypes; such stratification may be useful when assessing stratified trial designs and treatment strategies.

The immune-suppressive landscape in lepromatous leprosy revealed by single-cell RNA sequencing

Lepromatous leprosy (L-LEP), caused by the massive proliferation of Mycobacterium leprae primarily in macrophages, is an ideal disease model for investigating the molecular mechanism of intracellular bacteria evading or modulating host immune response. Here, we performed single-cell RNA sequencing of both skin biopsies and peripheral blood mononuclear cells (PBMCs) of L-LEP patients and healthy controls. In L-LEP lesions, we revealed remarkable upregulation of APOE expression that showed a negative correlation with the major histocompatibility complex II gene HLA-DQB2 and MIF, which encodes a pro-inflammatory and anti-microbial cytokine, in the subset of macrophages exhibiting a high expression level of LIPA. The exhaustion of CD8⁺ T cells featured by the high expression of TIGIT and LAG3 in L-LEP lesions was demonstrated. Moreover, remarkable enhancement of inhibitory immune receptors mediated crosstalk between skin immune cells was observed in L-LEP lesions. For PBMCs, a high expression level of APOE in the HLA-DR^highFBP1^high monocyte subset and the expansion of regulatory T cells were found to be associated with L-LEP. These findings revealed the primary suppressive landscape in the L-LEP patients, providing potential targets for the intervention of intracellular bacteria caused persistent infections.

Dissecting Human Blood Immune Cells Response to Intracellular Infection Using Single-Cell RNA Sequencing

Complex interactions between diverse host immune cells can determine the outcome of pathogen infections. Advances in single-cell RNA sequencing (scRNA-seq) allow detection of the transcriptional patterns of different immune cells at steady state and after infection. To reveal the complex interactions of the human immune system in response to diverse intracellular pathogens, we developed a protocol for scRNA-seq of ex vivo infected human peripheral blood mononuclear cells (PBMCs). We demonstrate here infection with Salmonella enterica serovar Typhimurium, but this protocol can be used for any other pathogen of interest, and expand our knowledge of human host-pathogen biology.

Interaction between human leukocyte antigen (HLA-C) and killer cell Ig-like receptors (KIR2DL) inhibits the cytotoxicity of natural killer cells in patients with hepatoblastoma

BACKGROUND

Hepatoblastoma (HB) is the most common liver malignancy in childhood with poor prognosis and lack of effective therapeutic targets. Single-cell transcriptome sequencing technology has been widely used in the study of malignant tumors, which can understand the tumor microenvironment and tumor heterogeneity.

MATERIALS AND METHODS

Two children with HB and a healthy child were selected as the research subjects. Peripheral blood and tumor tissue were collected for single-cell transcriptome sequencing, and the sequencing data were compared and analyzed to describe the differences in the immune microenvironment between children with HB and normal children.

RESULTS

There were significant differences in the number and gene expression levels of natural killer cells (NK cells) between children with HB and normal children. More natural killer cells were seen in children with HB compared to normal control. KIR2DL were highly expressed in children with HB.

CONCLUSION

Single-cell transcriptome sequencing of peripheral blood mononuclear cells (PBMC) and tumor tissue from children with HB revealed that KIR2DL was significantly up-regulated in NK cells from children with HB. HLA-C molecules on the surface of tumor cells interact with inhibitory receptor KIR2DL on the surface of NK cells, inhibiting the cytotoxicity of NK cells, resulting in immune escape of tumors. Inhibitors of related immune checkpoints to block the interaction between HLA-C and KIR2DL and enhance the cytotoxicity of NK cells, which may be a new strategy for HB treatment.

Future Applications of Metagenomic Next-Generation Sequencing for Infectious Diseases Diagnostics

Metagenomic next-generation sequencing (mNGS) has the theoretical capacity to detect any microbe present in a host. mNGS also has the potential to infer a pathogen's phenotypic characteristics, including the ability to colonize humans, cause disease, and resist treatment. Concurrent host nucleic acid sequencing can assess the infected individual's physiological state, including characterization and appropriateness of the immune response. When the pathogen cannot be identified, host RNA sequencing may help infer the organism's nature. While the full promise of mNGS remains far from realization, the potential ability to identify all microbes in a complex clinical sample, assess each organism's virulence and antibiotic susceptibility traits, and simultaneously characterize the host's response to infection provide opportunities for mNGS to supplant existing technologies and become the primary method of infectious diseases diagnostics.

Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network

Background

Although great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear. Single-cell sequencing technology provides a new perspective for researchers to explore the pathogens of tumors to further help make treatment and prognosis decisions for patients with tumors.

Methods

In this study, we proposed an algorithm framework to explore the molecular mechanisms of glioma by integrating single-cell gene expression profiles and gene regulatory relations. First, since there were great differences among malignant cells from different glioma samples, we analyzed the expression status of malignant cells for each sample, and then tumor consensus genes were identified by constructing and analyzing cell-specific networks. Second, to comprehensively analyze the characteristics of glioma, we integrated transcriptional regulatory relationships and consensus genes to construct a tumor-specific regulatory network. Third, we performed a hybrid clustering analysis to identify glioma cell types. Finally, candidate tumor gene biomarkers were identified based on cell types and known glioma-related genes.

Results

We got six identified cell types using the method we proposed and for these cell types, we performed functional and biological pathway enrichment analyses. The candidate tumor gene biomarkers were analyzed through survival analysis and verified using literature from PubMed.

Conclusions

The results showed that these candidate tumor gene biomarkers were closely related to glioma and could provide clues for the diagnosis and prognosis of patients with glioma. In addition, we found that four of the candidate tumor gene biomarkers (NDUFS5, NDUFA1, NDUFA13, and NDUFB8) belong to the NADH ubiquinone oxidoreductase subunit gene family, so we inferred that this gene family may be strongly related to glioma.

Understanding the pathogenesis of infectious diseases by single-cell RNA sequencing

Infections are highly orchestrated and dynamic processes, which involve both pathogen and host. Transcriptional profiling at the single-cell level enables the analysis of cell diversity, heterogeneity of the immune response, and detailed molecular mechanisms underlying infectious diseases caused by bacteria, viruses, fungi, and parasites. Herein, we highlight recent remarkable advances in single-cell RNA sequencing (scRNA-seq) technologies and their applications in the investigation of host-pathogen interactions, current challenges and potential prospects for disease treatment are discussed as well. We propose that with the aid of scRNA-seq, the mechanism of infectious diseases will be further revealed thus inspiring the development of novel interventions and therapies.

New Insights on the Early Interaction Between Typhoid and Non-typhoid Salmonella Serovars and the Host Cells

Salmonella enterica is a common source of food and water-borne infections, causing a wide range of clinical ailments in both human and animal hosts. Immunity to Salmonella involves an interplay between different immune responses, which are rapidly initiated to control bacterial burden. However, Salmonella has developed several strategies to evade and modulate the host immune responses. In this sense, the main knowledge about the pathogenicity of this bacterium has been obtained by the study of mouse models with non-typhoidal serovars. However, this knowledge is not representative of all the pathologies caused by non-typhoidal serovars in the human. Here we review the most important features of typhoidal and non-typhoidal serovars and the diseases they cause in the human host, describing the virulence mechanisms used by these pathogens that have been identified in different models of infection.

Visual Genomics Analysis Studio as a Tool to Analyze Multiomic Data

Type B adverse drug reactions (ADRs) are iatrogenic immune-mediated syndromes with mechanistic etiologies that remain incompletely understood. Some of the most severe ADRs, including delayed drug hypersensitivity reactions, are T-cell mediated, restricted by specific human leukocyte antigen risk alleles and sometimes by public or oligoclonal T-cell receptors (TCRs), central to the immunopathogenesis of tissue-damaging response. However, the specific cellular signatures of effector, regulatory, and accessory immune populations that mediate disease, define reaction phenotype, and determine severity have not been defined. Recent development of single-cell platforms bringing together advances in genomics and immunology provides the tools to simultaneously examine the full transcriptome, TCRs, and surface protein markers of highly heterogeneous immune cell populations at the site of the pathological response at a single-cell level. However, the requirement for advanced bioinformatics expertise and computational hardware and software has often limited the ability of investigators with the understanding of diseases and biological models to exploit these new approaches. Here we describe the features and use of a state-of-the-art, fully integrated application for analysis and visualization of multiomic single-cell data called Visual Genomics Analysis Studio (VGAS). This unique user-friendly, Windows-based graphical user interface is specifically designed to enable investigators to interrogate their own data. While VGAS also includes tools for sequence alignment and identification of associations with host or organism genetic polymorphisms, in this review we focus on its application for analysis of single-cell TCR-RNA-Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE)-seq, enabling holistic cellular characterization by unbiased transcriptome and select surface proteome. Critically, VGAS does not require user-directed coding or access to high-performance computers, instead incorporating performance-optimized hidden code to provide application-based fast and intuitive tools for data analyses and production of high-resolution publication-ready graphics on standard specification laptops. Specifically, it allows analyses of comprehensive single-cell TCR sequencing (scTCR-seq) data, detailing (i) functional pairings of α-β heterodimer TCRs, (ii) one-click histograms to display entropy and gene rearrangements, and (iii) Circos and Sankey plots to visualize clonality and dominance. For unbiased single-cell RNA sequencing (scRNA-seq) analyses, users extract cell transcriptome signatures according to global structure via principal component analysis, t-distributed stochastic neighborhood embedding, or uniform manifold approximation and projection plots, with overlay of scTCR-seq enabling identification and selection of the immunodominant TCR-expressing populations. Further integration with similar sequence-based detection of surface protein markers using oligo-labeled antibodies (CITE-seq) provides comparative understanding of surface protein expression, with differential gene or protein analyses visualized using volcano plot or heatmap functions. These data can be compared to reference cell atlases or suitable controls to reveal discrete disease-specific subsets, from epithelial to tissue-resident memory T-cells, and activation status, from senescence through exhaustion, with more finite transcript expression displayed as violin and box plots. Importantly, guided tutorial videos are available, as are regular application updates based on the latest advances in bioinformatics and user feedback.

Utilizing single-cell RNA sequencing for analyzing the characteristics of PBMC in patients with Kawasaki disease

BACKGROUND

Kawasaki disease (KD) is the main cause of acquired heart disease in children and can lead to coronary artery lesions. This present study was designed to analyze the characteristics of KD peripheral blood mononuclear cells (PBMC) through single-cell RNA sequencing (scRNA-seq) and to explore the potential molecular mechanism of KD.

METHODS

PBMC was collected from one healthy child and one KD patient, and was used to single-cell RNA sequencing for cell clusters identification and differently expressed gene (DEG) determination. GO function enrichment analysis of DEG in B cell and T cells were performed to explore the most active biological function in KD immune cells.

RESULTS

Twelve cell clusters can be identified in two samples. Compared with healthy child, naive CD8+ T cell, T helper cell and B cell in KD child were decreased, mainly immune-related T cells, and natural killer T (NKT) cell were increased. Cell activation, lymphocyte activation and regulation of immune system process were 3 GO function shared by all four types of T cells and B cell.

CONCLUSIONS

Immune cell disorder appears in the KD patient at single cell level by scRNA-seq.

Platypus: an open-access software for integrating lymphocyte single-cell immune repertoires with transcriptomes

High-throughput single-cell sequencing (scSeq) technologies are revolutionizing the ability to molecularly profile B and T lymphocytes by offering the opportunity to simultaneously obtain information on adaptive immune receptor repertoires (VDJ repertoires) and transcriptomes. An integrated quantification of immune repertoire parameters, such as germline gene usage, clonal expansion, somatic hypermutation and transcriptional states opens up new possibilities for the high-resolution analysis of lymphocytes and the inference of antigen-specificity. While multiple tools now exist to investigate gene expression profiles from scSeq of transcriptomes, there is a lack of software dedicated to single-cell immune repertoires. Here, we present Platypus, an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. Platypus provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. To showcase the capabilities of Platypus, we use it to analyze and visualize single-cell immune repertoires and transcriptomes from B and T cells from convalescent COVID-19 patients, revealing unique insight into the repertoire features and transcriptional profiles of clonally expanded lymphocytes. Platypus will expedite progress by facilitating the analysis of single-cell immune repertoire and transcriptome sequencing.

Pseudocell Tracer-A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination

Single cell RNA sequencing (scRNAseq) can be used to infer a temporal ordering of cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNAseq data into a low-dimensional manifold that maps the transcriptional states a cell can occupy. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results revealed an ordering of key transcription factors regulating CSR to the IgG1 isotype, including the concomitant expression of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors suggest a poised IL-4 signaling state that preceeds CSR to the IgG1 isotype.

High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery

High throughput single cell multi-omics platforms, such as mass cytometry (cytometry by time-of-flight; CyTOF), high dimensional imaging (>6 marker; Hyperion, MIBIscope, CODEX, MACSima) and the recently evolved genomic cytometry (Citeseq or REAPseq) have enabled unprecedented insights into many biological and clinical questions, such as hematopoiesis, transplantation, cancer, and autoimmunity. In synergy with constantly adapting new single-cell analysis approaches and subsequent accumulating big data collections from these platforms, whole atlases of cell types and cellular and sub-cellular interaction networks are created. These atlases build an ideal scientific discovery environment for reference and data mining approaches, which often times reveals new cellular disease networks. In this review we will discuss how combinations and fusions of different -omic workflows on a single cell level can be used to examine cellular phenotypes, immune effector functions, and even dynamic changes, such as metabolomic state of different cells in a sample or even in a defined tissue location. We will touch on how pre-print platforms help in optimization and reproducibility of workflows, as well as community outreach. We will also shortly discuss how leveraging single cell multi-omic approaches can be used to accelerate cellular biomarker discovery during clinical trials to predict response to therapy, follow responsive cell types, and define novel druggable target pathways. Single cell proteome approaches already have changed how we explore cellular mechanism in disease and during therapy. Current challenges in the field are how we share these disruptive technologies to the scientific communities while still including new approaches, such as genomic cytometry and single cell metabolomics.

Single-Cell Sequencing Applications in the Inner Ear

Genomics studies face specific challenges in the inner ear due to the multiple types and limited amounts of inner ear cells that are arranged in a very delicate structure. However, advances in single-cell sequencing (SCS) technology have made it possible to analyze gene expression variations across different cell types as well as within specific cell groups that were previously considered to be homogeneous. In this review, we summarize recent advances in inner ear research brought about by the use of SCS that have delineated tissue heterogeneity, identified unknown cell subtypes, discovered novel cell markers, and revealed dynamic signaling pathways during development. SCS opens up new avenues for inner ear research, and the potential of the technology is only beginning to be explored.

Evolutionary Genetics of Mycobacterium tuberculosis and HIV-1: "The Tortoise and the Hare"

The already enormous burden caused by Mycobacterium tuberculosis and Human Immunodeficiency Virus type 1 (HIV-1) alone is aggravated by co-infection. Despite obvious differences in the rate of evolution comparing these two human pathogens, genetic diversity plays an important role in the success of both. The extreme evolutionary dynamics of HIV-1 is in the basis of a robust capacity to evade immune responses, to generate drug-resistance and to diversify the population-level reservoir of M group viral subtypes. Compared to HIV-1 and other retroviruses, M. tuberculosis generates minute levels of genetic diversity within the host. However, emerging whole-genome sequencing data show that the M. tuberculosis complex contains at least nine human-adapted phylogenetic lineages. This level of genetic diversity results in differences in M. tuberculosis interactions with the host immune system, virulence and drug resistance propensity. In co-infected individuals, HIV-1 and M. tuberculosis are likely to co-colonize host cells. However, the evolutionary impact of the interaction between the host, the slowly evolving M. tuberculosis bacteria and the HIV-1 viral "mutant cloud" is poorly understood. These evolutionary dynamics, at the cellular niche of monocytes/macrophages, are also discussed and proposed as a relevant future research topic in the context of single-cell sequencing.

Elevated NK-cell transcriptional signature and dysbalance of resting and activated NK cells in atopic dermatitis

BACKGROUND

Altered quantities, activity, and composition of natural killer (NK) cells in blood as well as expression changes of genes involved in NK-cell function in skin lesions of patients with atopic dermatitis (AD) were recently reported.

OBJECTIVES

We sought to comprehensively analyze cutaneous NK-cell transcriptomic signatures in AD, and to examine changes under treatment.

METHODS

We analyzed NK-cell signatures in skin transcriptome data from 57 patients with moderate to severe AD and 31 healthy controls. In addition, changes after 12 weeks of systemic treatment (dupilumab n = 21, cyclosporine n = 8) were analyzed. Deconvolution of leucocyte fractions was conducted. Immunofluorescence staining of NK cells was performed on paraffin-embedded skin sections.

RESULTS

Immunofluorescence staining revealed a relatively high abundance of both NK cells and CD3⁺CD56⁺ cells in lesional as compared with nonlesional and healthy skin. Lesional and to a lesser extent nonlesional skin showed a strong upregulation of NK-cell markers together with a dysbalanced expression of inhibitory and activating receptors, which was not reverted under treatment. Digital cytometry showed a decrease in activated and an increase in resting NK cells in both lesional and nonlesional skin, which was reverted by both treatment with dupilumab and cyclosporine. The NK-cell transcriptomic signature remained upregulated after treatment, but there was a shift on the qualitative level, indicating a compositional change in NK-cell subsets toward CD56^bright NK cells.

CONCLUSIONS

Lesional AD skin shows a NK-cell dysregulation, which despite clinical improvement under systemic therapy was only partially reverted, and which may represent a yet underappreciated disease mechanism.

Comprehensive mapping of the human cytokine gene regulatory network

Proper cytokine gene expression is essential in development, homeostasis and immune responses. Studies on the transcriptional control of cytokine genes have mostly focused on highly researched transcription factors (TFs) and cytokines, resulting in an incomplete portrait of cytokine gene regulation. Here, we used enhanced yeast one-hybrid (eY1H) assays to derive a comprehensive network comprising 1380 interactions between 265 TFs and 108 cytokine gene promoters. Our eY1H-derived network greatly expands the known repertoire of TF–cytokine gene interactions and the set of TFs known to regulate cytokine genes. We found an enrichment of nuclear receptors and confirmed their role in cytokine regulation in primary macrophages. Additionally, we used the eY1H-derived network as a framework to identify pairs of TFs that can be targeted with commercially-available drugs to synergistically modulate cytokine production. Finally, we integrated the eY1H data with single cell RNA-seq and phenotypic datasets to identify novel TF–cytokine regulatory axes in immune diseases and immune cell lineage development. Overall, the eY1H data provides a rich resource to study cytokine regulation in a variety of physiological and disease contexts.

Single-cell RNA-Seq revealed profound immune alteration in the peripheral blood of patients with bacterial infection

OBJECTIVES

Bacterial infection remains one of the greatest threats to human health. However, how human hosts respond to bacterial infection has not been thoroughly understood. Better understanding of this response will improve human health.

METHODS

Here, we conducted an investigation on host response to bacterial infection using unperturbed clinical samples and single-cell RNA-Seq (scRNA-Seq) technology. To evaluate immune alteration upon bacterial infection in scRNA-Seq data of peripheral blood mononuclear cells (PBMCs), we developed a barcode analytical framework named PBMCode.

RESULTS

Using this PBMCode framework, we revealed profound immune alteration in peripheral blood under bacterial infection, including the emergence of natural killer T (NKT) cell cluster, reduction of B cell population, and considerable changes in T cells and monocytes. In addition, we also observed a large quantity of low-density neutrophils.

CONCLUSIONS

Our investigation on single cells provided unprecedented details in the alteration of both cell population and cell state under bacterial infection. These findings may be relevant to clinical decisions. The complexity of host response to bacterial infection revealed by scRNA-Seq deserves further attention in future studies.

Image-Based Live Cell Sorting

Technologies capable of cell separation based on cell images provide powerful tools enabling cell selection criteria that rely on spatially or temporally varying properties. Image-based cell sorting (IBCS) systems utilize microfluidic or microarray platforms, each having unique characteristics and applications. The advent of IBCS marks a new paradigm in which cell phenotype and behavior can be explored with high resolution and tied to cellular physiological and omics data, providing a deeper understanding of single-cell physiology and the creation of cell lines with unique properties. Cell sorting guided by high-content image information has far-reaching implications in biomedical research, clinical medicine, and pharmaceutical development.

Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives

The increasing application of single-cell RNA sequencing (scRNA-seq) technology in life science and biomedical research has significantly increased our understanding of the cellular heterogeneities in immunology, oncology and developmental biology. This review will summarize the development of various scRNA-seq technologies; primarily discussing the application of scRNA-seq on infectious diseases, and exploring the current development, challenges, and potential applications of scRNA-seq technology in the future.