Genetics and immunity in the era of single-cell genomics

Genetic Susceptibility to Fungal Infections

Reports of fungal infections have increased over the past decades, making them a major threat to human health. In this study, we review the effects of genetic defects on susceptibility to fungal diseases. To identify all relevant literature, we searched Google Scholar, PubMed, and Scopus and profiled studies published between 2008 and 2021. The results of several studies conducted on this subject have shown the significant effects of genetic variations such as hyper-IgE syndrome, Autoimmune polyendocrinopathy candidiasis ectodermal dystrophy syndrome, dectin-1 deficiency, CARD9 mutations, STAT1 mutations, and IL17 mutationson the host immune system's response, which has an important impact on susceptibility to fungal infections. The underlying immune system-related genetic profile affects the susceptibility of individuals to different fungal infections; therefore, this subject should be further studied for better treatment of fungal diseases.

Oxidative Stress and Epigenetics: miRNA Involvement in Rare Autoimmune Diseases

Autoimmune diseases (ADs) such as Sjögren’s syndrome, Kawasaki disease, and systemic sclerosis are characterized by chronic inflammation, oxidative stress, and autoantibodies, which cause joint tissue damage, vascular injury, fibrosis, and debilitation. Epigenetics participate in immune cell proliferation and differentiation, which regulates the development and function of the immune system, and ultimately interacts with other tissues. Indeed, overlapping of certain clinical features between ADs indicate that numerous immunologic-related mechanisms may directly participate in the onset and progression of these diseases. Despite the increasing number of studies that have attempted to elucidate the relationship between miRNAs and oxidative stress, autoimmune disorders and oxidative stress, and inflammation and miRNAs, an overall picture of the complex regulation of these three actors in the pathogenesis of ADs has yet to be formed. This review aims to shed light from a critical perspective on the key AD-related mechanisms by explaining the intricate regulatory ROS/miRNA/inflammation axis and the phenotypic features of these rare autoimmune diseases. The inflamma-miRs miR-155 and miR-146, and the redox-sensitive miR miR-223 have relevant roles in the inflammatory response and antioxidant system regulation of these diseases. ADs are characterized by clinical heterogeneity, which impedes early diagnosis and effective personalized treatment. Redox-sensitive miRNAs and inflamma-miRs can help improve personalized medicine in these complex and heterogeneous diseases.

SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq

SPLiT-seq provides a low-cost platform to generate single-cell data by labeling the cellular origin of RNA through four rounds of combinatorial barcoding. However, an automatic and rapid method for preprocessing and classifying single-cell sequencing (SCS) data from SPLiT-seq, which directly identified and labeled combinatorial barcoding reads and distinguished special cell sequencing data, is currently lacking. Here, we develop a high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq (SCSit), which can directly identify combinatorial barcodes and UMI of cell types and obtain more labeled reads, and remarkably enhance the retained data from SCS due to the exact alignment of insertion and deletion. Compared with the original method used in SPLiT-seq, the consistency of identified reads from SCSit increases to 97%, and mapped reads are twice than the original. Furthermore, the runtime of SCSit is less than 10% of the original. It can accurately and rapidly analyze SPLiT-seq raw data and obtain labeled reads, as well as effectively improve the single-cell data from SPLiT-seq platform. The data and source of SCSit are available on the GitHub website https://github.com/shang-qian/SCSit.

Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing

Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Multi-Dimensional Gene Regulation in Innate and Adaptive Lymphocytes: A View From Regulomes

The precise control of cytokine production by innate lymphoid cells (ILCs) and their T cell adaptive system counterparts is critical to mounting a proper host defense immune response without inducing collateral damage and autoimmunity. Unlike T cells that differentiate into functionally divergent subsets upon antigen recognition, ILCs are developmentally programmed to rapidly respond to environmental signals in a polarized manner, without the need of T cell receptor (TCR) signaling. The specification of cytokine production relies on dynamic regulation of cis-regulatory elements that involve multi-dimensional epigenetic mechanisms, including DNA methylation, transcription factor binding, histone modification and DNA-DNA interactions that form chromatin loops. How these different layers of gene regulation coordinate with each other to fine tune cytokine production, and whether ILCs and their T cell analogs utilize the same regulatory strategy, remain largely unknown. Herein, we review the molecular mechanisms that underlie cell identity and functionality of helper T cells and ILCs, focusing on networks of transcription factors and cis-regulatory elements. We discuss how higher-order chromatin architecture orchestrates these components to construct lineage- and state-specific regulomes that support ordered immunoregulation.

The impact of single-cell RNA sequencing on understanding the functional organization of the immune system

Application of single-cell genomics technologies has revolutionized our approach to study the immune system. Unravelling the functional diversity of immune cells and their coordinated response is key to understanding immunity. Single-cell transcriptomics technologies provide high-dimensional assessment of the transcriptional states of immune cells and have been successfully applied to discover new immune cell types, reveal haematopoietic lineages, identify gene modules dictating immune responses and investigate lymphocyte antigen receptor diversity. In this review, we discuss the impact and applications of single-cell RNA sequencing technologies in immunology.

Integrative Approaches to Understanding the Pathogenic Role of Genetic Variation in Rheumatic Diseases

The use of high-throughput omics may help to understand the contribution of genetic variants to the pathogenesis of rheumatic diseases. We discuss the concept of missing heritability: that genetic variants do not explain the heritability of rheumatoid arthritis and related rheumatologic conditions. In addition to an overview of how integrative data analysis can lead to novel insights into mechanisms of rheumatic diseases, we describe statistical approaches to prioritizing genetic variants for future functional analyses. We illustrate how analyses of large datasets provide hope for improved approaches to the diagnosis, treatment, and prevention of rheumatic diseases.

New Directions for Natural Killer T Cells in the Immunotherapy of Cancer

Natural killer T (NKT) cells have been placed at the interface between innate and adaptive immunity by a long series of experiments that convincingly showed that beyond cytokine secretion and NK cell recruitment, NKT cells were coordinating dendritic cell and B cell maturation through direct membrane contacts and initiate productive responses. As such, NKT cells are the cellular adjuvant of many immune reactions and have functions that go much beyond what their name encapsulates. In addition, the initial discovery of the ligands of NKT cells is deeply linked to cancer biology and therapy. However, for a host of reasons, animal models in which agonists of NKT cells were used did not translate well to human cancers. A systematic reassessment of NKT cells role in tumorigenesis, especially spontaneous one, is now accessible using single cell analysis technologies both in mouse and man, and should be taken advantage of. Similarly, the migration, localization, phenotype of NKT cells following induced expansion after injection of an agonist can be examined at the single cell level. This technological revolution will help evaluate where and how NKT cells can be used in cancer.

Impact of sequencing depth and read length on single cell RNA sequencing data of T cells

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. Successful TCRαβ reconstruction was achieved for 6 datasets (81% − 100%) with at least 0.25 millions (PE) reads of length >50 bp, while it failed for datasets with <30 bp reads. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCRαβ and gene expression profiles from scRNA-seq data of T cells.

Genomic and transcriptomic approaches to study immunology in cyprinids: What is next?

Accelerated by the introduction of Next-Generation Sequencing (NGS), a number of genomes of cyprinid fish species have been drafted, leading to a highly valuable collective resource of comparative genome information on cyprinids (Cyprinidae). In addition, NGS-based transcriptome analyses of different developmental stages, organs, or cell types, increasingly contribute to the understanding of complex physiological processes, including immune responses. Cyprinids are a highly interesting family because they comprise one of the most-diversified families of teleosts and because of their variation in ploidy level, with diploid, triploid, tetraploid, hexaploid and sometimes even octoploid species. The wealth of data obtained from NGS technologies provides both challenges and opportunities for immunological research, which will be discussed here. Correct interpretation of ploidy effects on immune responses requires knowledge of the degree of functional divergence between duplicated genes, which can differ even between closely-related cyprinid fish species. We summarize NGS-based progress in analysing immune responses and discuss the importance of respecting the presence of (multiple) duplicated gene sequences when performing transcriptome analyses for detailed understanding of complex physiological processes. Progressively, advances in NGS technology are providing workable methods to further elucidate the implications of gene duplication events and functional divergence of duplicates genes and proteins involved in immune responses in cyprinids. We conclude with discussing how future applications of NGS technologies and analysis methods could enhance immunological research and understanding.

Leukocyte diversity in resolving and nonresolving mechanisms of cardiac remodeling

In response to myocardial infarction (MI), time-dependent leukocyte infiltration is critical to program the acute inflammatory response. Post-MI leukocyte density, residence time in the infarcted area, and exit from the infarcted injury predict resolving or nonresolving inflammation. Overactive or unresolved inflammation is the primary determinant in heart failure pathology post-MI. Here, our review describes supporting evidence that the acute inflammatory response also guides the generation of healing and regenerative mediators after cardiac damage. Time-dependent leukocyte density and diversity and the magnitude of myocardial injury is responsible for the resolving and nonresolving pathway in myocardial healing. Post MI, the diversity of leukocytes, such as neutrophils, macrophages, and lymphocytes, has been explored that regulate the clearance of deceased cardiomyocytes by using the classic and reparative pathways. Among the innovative factors and intermediates that have been recognized as essential in acute the self-healing and clearance mechanism, we highlight specialized proresolving mediators as the emerging factor for post-MI reparative mechanisms-translational leukocyte modifiers, such as aging, the source of leukocytes, and the milieu around the leukocytes. In the clinical setting, it is possible that leukocyte diversity is more prominent as a result of risk factors, such as obesity, diabetes, and hypertension. Pharmacologic agents are critical modifiers of leukocyte diversity in healing mechanisms that may impair or stimulate the clearance mechanism. Future research is needed, with a focused approach to understand the molecular targets, cellular effectors, and receptors. A clear understanding of resolving and nonresolving inflammation in myocardial healing will help to develop novel targets with major emphasis on the resolution of inflammation in heart failure pathology.-Tourki, B., Halade, G. Leukocyte diversity in resolving and nonresolving mechanisms of cardiac remodeling.

Computational approaches for interpreting scRNA-seq data

The recent developments in high‐throughput single‐cell RNA sequencing technology (scRNA‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we consider biological questions for which scRNA‐seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene‐level analyses are particularly informative areas of computational analysis.

JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets

Recent advances in single-cell RNA-sequencing (scRNA-seq) technology increase the understanding of immune differentiation and activation processes, as well as the heterogeneity of immune cell types. Although the number of available immune-related scRNA-seq datasets increases rapidly, their large size and various formats render them hard for the wider immunology community to use, and read-level data are practically inaccessible to the non-computational immunologist. To facilitate datasets reuse, we created the JingleBells repository for immune-related scRNA-seq datasets ready for analysis and visualization of reads at the single-cell level (http://jinglebells.bgu.ac.il/). To this end, we collected the raw data of publicly available immune-related scRNA-seq datasets, aligned the reads to the relevant genome, and saved aligned reads in a uniform format, annotated for cell of origin. We also added scripts and a step-by-step tutorial for visualizing each dataset at the single-cell level, through the commonly used Integrated Genome Viewer (www.broadinstitute.org/igv/). The uniform scRNA-seq format used in JingleBells can facilitate reuse of scRNA-seq data by computational biologists. It also enables immunologists who are interested in a specific gene to visualize the reads aligned to this gene to estimate cell-specific preferences for splicing, mutation load, or alleles. Thus JingleBells is a resource that will extend the usefulness of scRNA-seq datasets outside the programming aficionado realm.

Exploring viral infection using single-cell sequencing

Single-cell sequencing (SCS) has emerged as a valuable tool to study cellular heterogeneity in diverse fields, including virology. By studying the viral and cellular genome and/or transcriptome, the dynamics of viral infection can be investigated at single cell level. Most studies have explored the impact of cell-to-cell variation on the viral life cycle from the point of view of the virus, by analyzing viral sequences, and from the point of view of the cell, mainly by analyzing the cellular host transcriptome. In this review, we will focus on recent studies that use single-cell sequencing to explore viral diversity and cell variability in response to viral replication.