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

The CD38+HLA-DR+ T cells with activation and exhaustion characteristics as predictors of severity and mortality in COVID-19 patients

Background

The COVID-19 pandemic remains a global health challenge. Severe cases often respond poorly to standard treatments, highlighting the necessity for novel therapeutic targets and early predictive biomarkers.

Methods

We utilized flow cytometry to analyze peripheral immune cells from healthy, bacterial pneumonia patients, and COVID-19 patients. The expansion of activated T cells (CD38+HLA-DR+), monocytes, and myeloid-derived suppressor cells (MDSCs) were detected and correlated with clinical outcomes to evaluate prognostic potential. The single-cell RNA sequencing (scRNA-seq) was applied to characterize the critical cell subset associated with prognosis and elucidate its phenotype in COVID-19.

Results

We revealed a significant increase in CD38+HLA-DR+ T cells in non-survivor COVID-19 patients, establishing them as an independent risk factor for 28-day mortality. The scRNA-seq analysis identified the CD38+HLA-DR+ T cell as a terminally differentiated, Treg-like subset exhibiting both activation and exhaustion characteristics. This subset presented the highest IL-6 and IL-10 mRNA levels among all T-cell subsets. Further functional analysis demonstrated its enhanced major histocompatibility complex class II (MHC-II) cross-signaling and correspondingly enriched cytoskeletal rearrangement processes. In addition, there was dysregulated NAD+ metabolism in CD38+HLA-DR+ T cells via scRNA-seq, accompanied by elevated adenosine and decreased NAD+ levels in serums from COVID-19 patients.

Conclusions

We identified the selective expansion of CD38+HLA-DR+ T cells as a novel prognostic indicator for COVID-19 outcomes. These cells' unique activated-exhausted phenotype, along with their impact on NAD+ metabolism, provides new insights into COVID-19 immunopathogenesis.

Single-cell transcriptional responses of T cells during microsporidia infection

T cells have been reported to play critical roles in preventing of microsporidia dissemination. However, there roles and functions of each subset remain unclear. Here in the study, we performed a thorough analysis of murine splenic T-cell response analysis via single-cell RNA sequencing during microsporidia E. cuniculi infection. We demonstrated that Type I T helper (Th1) cells, T follicular helper (Tfh) cells, effector CD8 + T cells and proliferating CD8 + T cells were activated and expanded after infection. Activated Th1 cells and Tfh cells presented significantly upregulated gene expression of Ifng and Il21, respectively. A subcluster of Th1 cells with high Csf1 expression was detected after infection. Subsets of activated CD8 + T cells were markedly enriched with high expression of cytotoxic-function related genes such as Gzma and Gzmb, whereas some active CD8 T cells were enriched with proliferation-function related genes Mki67 and Stmn1. Other subsets of T cells including NK T cells, Myb+ T cells, γδ T cells and Cxcr6+ T cells, were also analyzed in this study yet no expansion was observed. In summary, our findings provide in-depth and comprehensive insights into T-cell responses during microsporidia infection, which will be valuable for further investigations.

Deciphering Host-Virus Interactions and Advancing Therapeutics for Chronic Viral Infection

Chronic viral infections like HIV, HBV, and HCV establish persistent interactions with the host immune system, resulting in immune evasion and long-term immune dysfunction. These viruses use a range of strategies to limit host defenses, such as downregulating MHC class I, disrupting interferon signaling, altering apoptosis pathways, and suppressing cytotoxic T-cell activity. Key viral proteins, including HIV Nef, HBV X protein, and HCV NS5A, interfere with antigen presentation and JAK/STAT signaling, thereby reducing antiviral immune responses. Chronic infections induce immune exhaustion due to persistent antigen exposure, which leads to the expression of inhibitory receptors like PD-1 and CTLA-4 on T cells. Viral epigenetic changes, such as N6-methyladenosine modifications and histone deacetylation, enhance immune evasion by modulating gene expression in infected cells. Viruses further manipulate host cytokine networks by promoting an immunosuppressive environment through IL-10 and TGF-β secretion, which suppress inflammatory responses and inhibit T-cell activation. This review examines the molecular/cellular mechanisms that enable chronic viruses to escape host immunity, focusing on antigenic variation, cytokine disruption, and control of apoptotic pathways. It also addresses how host genetic factors, such as HLA polymorphisms, influence disease progression. Lastly, we discuss host-targeted therapies, including immune checkpoint inhibitors, cytokine treatments, and CRISPR.

Unveiling the key mechanisms of FOLR2+ macrophage-mediated antitumor immunity in breast cancer using integrated single-cell RNA sequencing and bulk RNA sequencing

Breast cancer (BRCA) is a common malignant tumor, and its immune microenvironment plays a crucial role in disease progression. In this research, we utilized single-cell RNA sequencing and bulk RNA sequencing technologies, combined with in vivo and in vitro experiments, to thoroughly investigate the immunological functions and mechanisms of FOLR2+ macrophages in BRCA. Our findings demonstrate a significant enhancement in the interaction between FOLR2+ macrophages and CD8+ T cells within the tumor tissues of BRCA patients. FOLR2 is closely associated with T cell infiltration in the tumor microenvironment of BRCA patients, particularly with CD8+ T cells. By secreting CXCL9 and engaging with CXCR3, FOLR2+ macrophages can activate the functionality of CD8+ T cells, thereby promoting cancer cell apoptosis. Further animal experiments confirm that FOLR2+ macrophages activate CD8+ T cells through the CXCL9-CXCR3 axis, exhibiting an antitumor immunity effect in BRCA. FOLR2+ macrophages play a crucial role in antitumor immunity in BRCA through the CXCL9-CXCR3 axis.

Recognizing SARS-CoV-2 infection of nasopharyngeal tissue at the single-cell level by machine learning method

SARS-CoV-2 has posed serious global health challenges not only because of the high degree of virus transmissibility but also due to its severe effects on the respiratory system, such as inducing changes in multiple organs through the ACE2 receptor. This virus makes changes to gene expression at the single-cell level and thus to cellular functions and immune responses in a variety of cell types. Previous studies have not been able to resolve these mechanisms fully, and so our study tries to bridge knowledge gaps about the cellular responses under conditions of infection. We performed single-cell RNA-sequencing of nasopharyngeal swabs from COVID-19 patients and healthy controls. We assembled a dataset of 32,588 cells for 58 subjects for analysis. The data were sorted into eight cell types: ciliated, basal, deuterosomal, goblet, myeloid, secretory, squamous, and T cells. Using machine learning, including nine feature ranking algorithms and two classification algorithms, we classified the infection status of single cells and analyzed gene expression to pinpoint critical markers of SARS-CoV-2 infection. Our findings show distinct gene expression profiles between infected and uninfected cells across diverse cell types, with key indicators such as FKBP4, IFITM1, SLC35E1, CD200R1, MT-ATP6, KRT13, RBM15, and FTH1 illuminating unique immune responses and potential pathways for viral spread and immune evasion. The machine learning methods effectively differentiated between infected and non-infected cells, shedding light on the cellular heterogeneity of SARS-CoV-2 infection. The findings will improve our knowledge of the cellular dynamics of SARS-CoV-2.

Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases

Neurological diseases are a major cause of the global burden of disease. Although the mechanisms of the occurrence and development of neurological diseases are not fully clear, most of them are associated with cells mediating neuroinflammation. Yet medications and other therapeutic options to improve treatment are still very limited. Single-cell RNA sequencing (scRNA-seq), as a delightfully potent breakthrough technology, not only identifies various cell types and response states but also uncovers cell-specific gene expression changes, gene regulatory networks, intercellular communication, and cellular movement trajectories, among others, in different cell types. In this review, we describe the technology of scRNA-seq in detail and discuss and summarize the application of scRNA-seq in exploring neurological diseases, elaborating the corresponding specific mechanisms of the diseases as well as providing a reliable basis for new therapeutic approaches. Finally, we affirm that scRNA-seq promotes the development of the neuroscience field and enables us to have a deeper cellular understanding of neurological diseases in the future, which provides strong support for the treatment of neurological diseases and the improvement of patients' prognosis.

Protocol to achieve high-resolution single-cell transcriptomics of cardiomyocytes in multiple species

Single-cell RNA sequencing (scRNA-seq) remains state-of-the-art for transcriptomic cell-mapping. Here, we provide a protocol to generate high-resolution scRNA-seq of rare cardiomyocyte populations (e.g., regenerating/dividing, etc.) from mouse and zebrafish hearts as well as induced pluripotent stem cells, collected in time to achieve detailed transcriptomic insight. We describe the serial steps of viability staining, methanol fixation, storage, and cell sorting to preserve RNA integrity suited for scRNA-seq as well as the quality assessment of the data as shown by examples. For complete details on the use and execution of this protocol, please refer to Bak et al.1.

Delving deeper into the mechanisms fundamental to HIV-associated immunopathology using single-cell sequencing techniques: A scoping review of current literature

Human immunodeficiency virus (HIV) infection has evolved into an established global pandemic over the past four decades; however, despite massive research investment globally, the precise underlying mechanisms which are fundamental to HIV-related pathogenesis remain unclear. Single cell ribonucleic acid (RNA) sequencing methods are increasingly being used for the identification of specific cell-type transcriptional changes in HIV infection. In this scoping review, we have considered information extracted from fourteen published HIV-associated single-cell RNA sequencing-related studies, hoping to throw light on the underlying mechanisms of HIV infection and pathogenesis, and to explore potential candidate biomarkers for HIV disease progression and antiviral treatment. Generally, HIV positive individuals tend to manifest disturbances of frequency of multiple cellular types, and specifically exhibit diminished levels of CD4+ T-cells and enriched numbers of CD8+ T-cells. Cell-specific transcriptional changes tend to be linked to cell permissiveness, hyperacute or acute HIV infection, viremia, and cell productivity. The transcriptomes of CD4+ T-cell and CD8+ T-cell subpopulations are also observed to change in HIV-positive diabetic individuals, spontaneous HIV controllers, individuals with high levels of HIV viremia, and those in an acute phase of HIV infection. The transcriptional changes seen in B cells, natural killer (NK) cells, and myeloid dendritic cells (mDCs) of HIV-infected individuals demonstrate that the humoral immune response, antiviral response, and immune response regulation, respectively, are all altered following HIV infection. Antiretroviral therapy (ART) plays a crucial role in achieving immune reconstitution, in improving immunological disruption, and in mitigating immune system imbalances in HIV-infected individuals, while not fully restoring inherent cellular transcription to levels seen in HIV-negative individuals. The preceding observations not only illustrate compelling advances in the understanding of HIV-associated immunopathogenesis, but also identify specific cell-type transcriptional changes that may serve as potential biomarkers for HIV disease monitoring and therapeutic targeting.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

Single-cell analysis of VACV infection reveals pathogen-driven timing of early and late phases and host-limited dynamics of virus production

The extent and origin of variation in the replication dynamics of complex DNA viruses is not well-defined. Here, we investigate the vaccinia virus (VACV) infection cycle at the single-cell level, quantifying the temporal dynamics of early and post(dna)-replicative phase gene expression across thousands of infections. We found that viral factors determine the initiation time of these phases, and this is influenced by the multiplicity of infection (MOI). In contrast, virus production dynamics are largely constrained by the host cell. Additionally, between-cell variability in infection start time and virus production rate were strongly influenced by MOI, providing evidence for cooperativity between infecting virions. Blocking programmed cell death by pan-caspase inhibition increased infection frequency but not virus production at the population level due to a concurrent attenuation of per-cell virus yield, suggesting a dual role for caspase signaling in VACV infection. Our findings provide key insights into the pivotal factors influencing heterogeneity in the infection cycle of a large DNA virus at the single-cell level.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Intestinal cell diversity and treatment responses in a parasitic nematode at single cell resolution

BACKGROUND

Parasitic nematodes, significant pathogens for humans, animals, and plants, depend on diverse organ systems for intra-host survival. Understanding the cellular diversity and molecular variations underlying these functions holds promise for developing novel therapeutics, with specific emphasis on the neuromuscular system's functional diversity. The nematode intestine, crucial for anthelmintic therapies, exhibits diverse cellular phenotypes, and unraveling this diversity at the single-cell level is essential for advancing knowledge in anthelmintic research across various organ systems.

RESULTS

Here, using novel single-cell transcriptomics datasets, we delineate cellular diversity within the intestine of adult female Ascaris suum, a parasitic nematode species that infects animals and people. Gene transcripts expressed in individual nuclei of untreated intestinal cells resolved three phenotypic clusters, while lower stringency resolved additional subclusters and more potential diversity. Clusters 1 and 3 phenotypes displayed variable congruence with scRNA phenotypes of C. elegans intestinal cells, whereas the A. suum cluster 2 phenotype was markedly unique. Distinct functional pathway enrichment characterized each A. suum intestinal cell cluster. Cluster 2 was distinctly enriched for Clade III-associated genes, suggesting it evolved within clade III nematodes. Clusters also demonstrated differential transcriptional responsiveness to nematode intestinal toxic treatments, with Cluster 2 displaying the least responses to short-term intra-pseudocoelomic nematode intestinal toxin treatments.

CONCLUSIONS

This investigation presents advances in knowledge related to biological differences among major cell populations of adult A. suum intestinal cells. For the first time, diverse nematode intestinal cell populations were characterized, and associated biological markers of these cells were identified to support tracking of constituent cells under experimental conditions. These advances will promote better understanding of this and other parasitic nematodes of global importance, and will help to guide future anthelmintic treatments.

Advancements in technology for characterizing the tumor immune microenvironment

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

Temporal and cellular analysis of granuloma development in mycobacterial infected adult zebrafish

Because granulomas are a hallmark of tuberculosis pathogenesis, the study of the dynamic changes in their cellular composition and morphological character can facilitate our understanding of tuberculosis pathogenicity. Adult zebrafish infected with Mycobacterium marinum form granulomas that are similar to the granulomas in human patients with tuberculosis and therefore have been used to study host-mycobacterium interactions. Most studies of zebrafish granulomas, however, have focused on necrotic granulomas, while a systematic description of the different stages of granuloma formation in the zebrafish model is lacking. Here, we characterized the stages of granulomas in M. marinum-infected zebrafish, including early immune cell infiltration, nonnecrotizing granulomas, and necrotizing granulomas, using corresponding samples from patients with pulmonary tuberculosis as references. We combined hematoxylin and eosin staining and in situ hybridization to identify the different immune cell types and follow their spatial distribution in the different stages of granuloma development. The macrophages in zebrafish granulomas were shown to belong to distinct subtypes: epithelioid macrophages, foamy macrophages, and multinucleated giant cells. By defining the developmental stages of zebrafish granulomas and the spatial distribution of the different immune cells they contain, this work provides a reference for future studies of mycobacterial granulomas and their immune microenvironments.

Single-cell RNA sequencing to understand host-virus interactions

Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.

Heat Inactivation of Nipah Virus for Downstream Single-Cell RNA Sequencing Does Not Interfere with Sample Quality

Single-cell RNA sequencing (scRNA-seq) technologies are instrumental to improving our understanding of virus-host interactions in cell culture infection studies and complex biological systems because they allow separating the transcriptional signatures of infected versus non-infected bystander cells. A drawback of using biosafety level (BSL) 4 pathogens is that protocols are typically developed without consideration of virus inactivation during the procedure. To ensure complete inactivation of virus-containing samples for downstream analyses, an adaptation of the workflow is needed. Focusing on a commercially available microfluidic partitioning scRNA-seq platform to prepare samples for scRNA-seq, we tested various chemical and physical components of the platform for their ability to inactivate Nipah virus (NiV), a BSL-4 pathogen that belongs to the group of nonsegmented negative-sense RNA viruses. The only step of the standard protocol that led to NiV inactivation was a 5 min incubation at 85 °C. To comply with the more stringent biosafety requirements for BSL-4-derived samples, we included an additional heat step after cDNA synthesis. This step alone was sufficient to inactivate NiV-containing samples, adding to the necessary inactivation redundancy. Importantly, the additional heat step did not affect sample quality or downstream scRNA-seq results.

Cytometry in High-Containment Laboratories

The emergence of new pathogens continues to fuel the need for advanced high-containment laboratories across the globe. Here we explore challenges and opportunities for integration of cytometry, a central technology for cell analysis, within high-containment laboratories. We review current applications in infectious disease, vaccine research, and biosafety. Considerations specific to cytometry within high-containment laboratories, such as biosafety requirements, and sample containment strategies are also addressed. We further tour the landscape of emerging technologies, including combination of cytometry with other omics, the application of automation, and artificial intelligence. Finally, we propose a framework to fast track the immersion of advanced technologies into the high-containment research setting to improve global preparedness for new emerging diseases.

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.

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.

Identifying SARS-CoV-2 infected cells with scVDN

Introduction

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for understanding cellular heterogeneity and identifying cell types in virus-related research. However, direct identification of SARS-CoV-2-infected cells at the single-cell level remains challenging, hindering the understanding of viral pathogenesis and the development of effective treatments.

Methods

In this study, we propose a deep learning framework, the single-cell virus detection network (scVDN), to predict the infection status of single cells. The scVDN is trained on scRNA-seq data from multiple nasal swab samples obtained from several contributors with varying cell types. To objectively evaluate scVDN's performance, we establish a model evaluation framework suitable for real experimental data.

Results and Discussion

Our results demonstrate that scVDN outperforms four state-of-the-art machine learning models in identifying SARS-CoV-2-infected cells, even with extremely imbalanced labels in real data. Specifically, scVDN achieves a perfect AUC score of 1 in four cell types. Our findings have important implications for advancing virus research and improving public health by enabling the identification of virus-infected cells at the single-cell level, which is critical for diagnosing and treating viral infections. The scVDN framework can be applied to other single-cell virus-related studies, and we make all source code and datasets publicly available on GitHub at https://github.com/studentiz/scvdn.

Establishment of an Inactivation Method for Ebola Virus and SARS-CoV-2 Suitable for Downstream Sequencing of Low Cell Numbers

Technologies that facilitate the bulk sequencing of small numbers of cells as well as single-cell RNA sequencing (scRNA-seq) have aided greatly in the study of viruses as these analyses can be used to differentiate responses from infected versus bystander cells in complex systems, including in organoid or animal studies. While protocols for these analyses are typically developed with biosafety level 2 (BSL-2) considerations in mind, such analyses are equally useful for the study of viruses that require higher biosafety containment levels. Many of these workstreams, however, are not directly compatible with the more stringent biosafety regulations of BSL-3 and BSL-4 laboratories ensuring virus inactivation and must therefore be modified. Here we show that TCL buffer (Qiagen), which was developed for bulk sequencing of small numbers of cells and also facilitates scRNA-seq, inactivates both Ebola virus (EBOV) and SARS-CoV-2, BSL-4 and BSL-3 viruses, respectively. We show that additional heat treatment, necessary for the more stringent biosafety concerns for BSL-4-derived samples, was additionally sufficient to inactivate EBOV-containing samples. Critically, this heat treatment had minimal effects on extracted RNA quality and downstream sequencing results.

Complex Analysis of Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.

Development of Single-Cell Transcriptomics and Its Application in COVID-19

Over the last three years, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related health crisis has claimed over six million lives and caused USD 12 trillion losses to the global economy. SARS-CoV-2 continuously mutates and evolves with a high basic reproduction number (R0), resulting in a variety of clinical manifestations ranging from asymptomatic infection to acute respiratory distress syndrome (ARDS) and even death. To gain a better understanding of coronavirus disease 2019 (COVID-19), it is critical to investigate the components that cause various clinical manifestations. Single-cell sequencing has substantial advantages in terms of identifying differentially expressed genes among individual cells, which can provide a better understanding of the various physiological and pathological processes. This article reviewed the use of single-cell transcriptomics in COVID-19 research, examined the immune response disparities generated by SARS-CoV-2, and offered insights regarding how to improve COVID-19 diagnosis and treatment plans.

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

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

Single‐cell RNA sequencing in oral science: Current awareness and perspectives

The emergence of single‐cell RNA sequencing enables simultaneous sequencing of thousands of cells, making the analysis of cell population heterogeneity more efficient. In recent years, single‐cell RNA sequencing has been used in the investigation of heterogeneous cell populations, cellular developmental trajectories, stochastic gene transcriptional kinetics, and gene regulatory networks, providing strong support in life science research. However, the application of single‐cell RNA sequencing in the field of oral science has not been reviewed comprehensively yet. Therefore, this paper reviews the development and application of single‐cell RNA sequencing in oral science, including fields of tissue development, teeth and jaws diseases, maxillofacial tumors, infections, etc., providing reference and prospects for using single‐cell RNA sequencing in studying the oral diseases, tissue development, and regeneration.

Viral-Host Dependency Factors as Therapeutic Targets to Overcome Antiviral Drug-Resistance: A Focus on Innate Immune Modulation

The development of antiviral drugs, has provided enormous achievements in our recent history in the fight against viral infections. To date, most of the approved antiviral drugs target virus-encoded proteins to achieve direct antiviral activity. Nonetheless, the inherent idiosyncrasy of viral mutations during their replication cycle, enable many viruses to adapt to the new barriers, becoming resistant to therapies, therefore, representing an ever-present menace and prompting the scientific community towards the development of novel therapeutic strategies. Taking advantage of the increasing knowledge of virus-host cell interactions, the targeting of cellular factors or pathways essential for virus survival turns into an alternative strategy to intervene in almost every step of viral replication cycle. Since host factors are evolutionary conserved, viral evasion to host-directed therapies (HDT) would impose a higher genetic barrier to the emergence of resistant strains. Thus, targeting host factors has long been considered an alternative strategy to overcome viral resistance. Nevertheless, targeting host factors or pathways potentially hints undesired off targets effects, and therefore, a critical risk-benefit evaluation is required. The present review discusses the current state-of-the-art on the identification of viral host dependency factors (HDF) and the workflow required for the development of HDT as antivirals. Then, we focus on the feasibility of using a specific class of host factors, those involved in innate immune modulation, as broad-spectrum antiviral therapeutic strategies. Finally, a brief summary of major roadblocks derived from targeting host cellular proteins and putative future strategies to overcome its major limitations is proposed.

Fantastic AAV Gene Therapy Vectors and How to Find Them—Random Diversification, Rational Design and Machine Learning

Parvoviruses are a diverse family of small, non-enveloped DNA viruses that infect a wide variety of species, tissues and cell types. For over half a century, their intriguing biology and pathophysiology has fueled intensive research aimed at dissecting the underlying viral and cellular mechanisms. Concurrently, their broad host specificity (tropism) has motivated efforts to develop parvoviruses as gene delivery vectors for human cancer or gene therapy applications. While the sum of preclinical and clinical data consistently demonstrates the great potential of these vectors, these findings also illustrate the importance of enhancing and restricting in vivo transgene expression in desired cell types. To this end, major progress has been made especially with vectors based on Adeno-associated virus (AAV), whose capsid is highly amenable to bioengineering, repurposing and expansion of its natural tropism. Here, we provide an overview of the state-of-the-art approaches to create new AAV variants with higher specificity and efficiency of gene transfer in on-target cells. We first review traditional and novel directed evolution approaches, including high-throughput screening of AAV capsid libraries. Next, we discuss programmable receptor-mediated targeting with a focus on two recent technologies that utilize high-affinity binders. Finally, we highlight one of the latest stratagems for rational AAV vector characterization and optimization, namely, machine learning, which promises to facilitate and accelerate the identification of next-generation, safe and precise gene delivery vehicles.

Single-cell sequencing: expansion, integration and translation

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity

As the importance of cell heterogeneity has begun to be emphasized, single-cell sequencing approaches are rapidly adopted to study cell heterogeneity and cellular evolutionary relationships of various cells, including stem cell populations. The hematopoietic stem and progenitor cell (HSPC) compartment contains HSC hematopoietic stem cells (HSCs) and distinct hematopoietic cells with different abilities to self-renew. These cells perform their own functions to maintain different hematopoietic lineages. Undeniably, single-cell sequencing approaches, including single-cell RNA sequencing (scRNA-seq) technologies, empower more opportunities to study the heterogeneity of normal and pathological HSCs. In this review, we discuss how these scRNA-seq technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis, leading to exciting new findings in HSC biology.

Decrypting the complexity of the human malaria parasite biology through systems biology approaches

The human malaria parasite, Plasmodium falciparum, is a unicellular protozoan responsible for over half a million deaths annually. With a complex life cycle alternating between human and invertebrate hosts, this apicomplexan is notoriously adept at evading host immune responses and developing resistance to all clinically administered treatments. Advances in omics-based technologies, increased sensitivity of sequencing platforms and enhanced CRISPR based gene editing tools, have given researchers access to more in-depth and untapped information about this enigmatic micro-organism, a feat thought to be infeasible in the past decade. Here we discuss some of the most important scientific achievements made over the past few years with a focus on novel technologies and platforms that set the stage for subsequent discoveries. We also describe some of the systems-based methods applied to uncover gaps of knowledge left through single-omics applications with the hope that we will soon be able to overcome the spread of this life-threatening disease.

Therapeutic Effect of IL-38 on Experimental Autoimmune Uveitis: Reprogrammed Immune Cell Landscape and Reduced Th17 Cell Pathogenicity

Purpose

The purpose of this study was to elucidate the effects of interleukin (IL)-38 on experimental autoimmune uveitis (EAU) and its underlying mechanisms.

Methods

Mice with EAU were treated with IL-38, and the retinas and cervical draining lymph nodes (CDLNs) were analyzed by flow cytometry. Single-cell RNA sequencing (scRNA-seq) was conducted to analyze the immune cell profiles of CDLNs from normal, EAU, and IL-38-treated mice.

Results

Administration of IL-38 attenuated EAU symptoms and reduced the proportion of T helper 17 (Th17) and T helper 1 (Th1) cells in the retinas and CDLNs. In scRNA-seq analysis, IL-38 downregulated the IL-17 signaling pathway and reduced the expression of Th17 cell pathogenicity-related genes (Csf2 and Il23r), findings which were also confirmed by flow cytometry. In vitro, IL-38 reduced the granulocyte-macrophage colony-stimulating factor (GM-CSF) stimulation function of IL-23 and inhibited IL-23R expression in Th17 cells. Moreover, when co-cultured with Th17 cells, IL-38 prevented IL-23 production in antigen-presenting cells (APCs).

Conclusions

Our data demonstrate the therapeutic effect of IL-38 on EAU, and suggest that the effect of IL-38 may be caused by dampening of the GM-CSF/IL-23R/IL-23 feedback loop between Th17 cells and APCs.

Atomic-scale insights into allosteric inhibition and evolutional rescue mechanism of Streptococcus thermophilus Cas9 by the anti-CRISPR protein AcrIIA6

CRISPR-Cas systems are prokaryotic adaptive immunity against invading phages and plasmids. Phages have evolved diverse protein inhibitors of CRISPR-Cas systems, called anti-CRISPR (Acr) proteins, to neutralize this CRISPR machinery. In response, bacteria have co-evolved Cas variants to escape phage's anti-CRISPR strategies, called anti-anti-CRISPR systems. Here we explore the anti-CRISPR allosteric inhibition and anti-anti-CRISPR rescue mechanisms between Streptococcus thermophilus Cas9 (St1Cas9) and the anti-CRISPR protein AcrIIA6 at the atomic level, by generating mutants of key residues in St1Cas9. Extensive unbiased molecular dynamics simulations show that the functional motions of St1Cas9 in the presence of AcrIIA6 differ substantially from those of St1Cas9 alone. AcrIIA6 binding triggers a shift of St1Cas9 conformational ensemble towards a less catalytically competent state; this state significantly compromises protospacer adjacent motif (PAM) recognition and nuclease activity by altering interdependently conformational dynamics and allosteric signals among nuclease domains, PAM-interacting (PI) regions, and AcrIIA6 binding motifs. Via in vitro DNA cleavage assays, we further elucidate the rescue mechanism of efficiently escaping AcrIIA6 inhibition harboring St1Cas9 triple mutations (G993K/K1008M/K1010E) in the PI domain and identify the evolutionary landscape of such mutational escape within species. Our results provide mechanistic insights into Acr proteins as natural brakes for the CRISPR-Cas systems and a promising potential for the design of allosteric Acr peptidomimetics.

Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0-14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.

Single-cell RNA sequencing in cancer research

Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.

Analysis of the Immune Responses in the Ileum of Gnotobiotic Pigs Infected with the Recombinant GII.p12_GII.3 Human Norovirus by mRNA Sequencing

Norovirus genogroup II (NoV GII) induces acute gastrointestinal food-borne illness in humans. Because gnotobiotic pigs can be infected with human norovirus (HuNoV) GII, they are frequently used to analyze the associated pathogenic mechanisms and immune responses, which remain poorly understood. Recently, mRNA sequencing analysis (RNA-Seq) has been used to identify cellular responses to viruses. In this study, we investigated the host immune response and possible mechanisms involved in virus evasion in the ileum of gnotobiotic pigs infected with HuNoV by RNA-Seq. HuNoV was detected in the feces, blood, and tissues of the jejunum, ileum, colon, mesenteric lymph node, and spleen of pigs infected with HuNoV. In analysis of mRNA sequencing, expression of anti-viral protein genes such as OAS1, MX1, and MX2 were largely decreased, whereas type I IFN was increased in pigs infected with HuNoV. In addition, expression of TNF and associated anti-inflammatory cytokine genes such as IL10 was increased in HuNoV-infected pigs. Expression of genes related to natural killer (NK) cell cytotoxicity and CD8⁺ T cell exhaustion was increased, whereas that of MHC class I genes was decreased. Expression profiles of selected genes were further confirmed by qRT-PCR and Western blot. These results suggest that infection with HuNoV induces NK cell-mediated cytotoxicity but suppresses type I IFN- and CD8⁺ T cell-mediated antiviral responses.