Single-cell analysis of allergic diseases.

Abstract

The immune system is a highly interactive networking of more than 30 actively migrating cells with resident tissue cells, which makes its decisions based on input from all organs and tissues, infections, normal flora bacteria, and many or even any environmental agents. Their gene expression profiles change under several life situations, such as disease, environmental factors, age, gender, and circadian rhythm. Recent discoveries have enabled several modern immunology and cell biology tools for the in-depth investigation of these cells and their molecular interactions. Single-cell RNA sequencing can profile the transcriptome of individual cells, thus enabling the unbiased characterization of mixed populations of cells to identify novel cellular populations. The method has gradually evolved within three decades. Brady et al. developed the methodology for complementary DNA (cDNA) libraries for the first time in 1993.1 Single cells need to be reverse-transcribed to cDNA followed by cycles of polymerase chain reaction amplification. Compared to microarray analysis, RNA-sequencing analysis expanded the spectrum of detected genes with high accuracy and effectively increased the proportion of full-length cDNA. Thus, single-cell transcriptomics analysis opened a new era in the biological and clinical research field. Discovery of novel biomarkers for prevention, prediction, or therapy response in many diseases and better understanding the physiopathology are two major research aims in medicine and biology.2 Recent advancements in single-cell isolation and barcoding technologies have enabled us to know the individual profiles of cells at the single-cell level (transcriptomics, genomics, epigenomics, and proteomics).3 Various novel experimental protocols for single-cell multiomics technologies have been reported (Box 1, summarized in ref.3). For example, CITE-seq and REAP-seq can measure mRNA together with targeted cell surface proteins using oligonucleotide-labeled antibodies. This editorial portrays the recent findings that have been reported using single-cell analysis to investigate individual cells isolated from allergic diseases. 1. Genome: To investigate the correlation between genomic alterations, such as mutation, and their linked transcriptomic changes in single cells G&T-seq, TARGET-seq 2. Proteome: To explore the link between proteome profiling, such as surface markers, and transcriptome CITE-seq, REAP-seq, PEA/STA, PLAYR, RAID 3. Epigenome: To demonstrate the links between epigenome, such as methylation and open chromatin sites, and transcriptome scM&T-seq, scTrio-seq, scNMT-seq, sci-CAR, SNARE-seq Genome and transcriptome (G&T), cellular indexing of transcriptomes and epitopes (CITE), RNA expression and protein (REAP), proximity extension assay/specific RNA target amplification (PEA/STA), proximity ligation assay for RNA (PLAYR), RNA and immunodetection (RAID), single-cell (sc), methylome and transcriptome (M&T), triple omics (Trio), nucleosome, methylation and transcription (NMT), combinatorial indexing chromatin accessibility and mRNA expression sequencing (sci-CAR), single-nucleus chromatin accessibility and mRNA (SNARE). In this issue, Zhou and coworkers demonstrated that IL19+IGFL+ keratinocyte subpopulations are increased in atopic dermatitis (AD) lesions by using single-cell RNA sequencing in AD patients. In addition, the expression of T helper (Th) 17-associated genes (S100A12, PI3, DEFB4A) and histamine receptors (HRH1 and HRH2) was upregulated in this subpopulation of keratinocyte in AD lesions. This cluster may be involved in the magnification of Th2 and Th17 inflammatory responses and mediation of skin pruritus. Additionally, another notable article is published in the same issue in the same context. Zhang et al. applied single-cell RNA and T-cell receptor sequencing on immune cells from skin biopsies and matched peripheral blood mononuclear cells (PBMC) samples of AD and psoriasis.4 Connecting data on skin and circulating immune cell subsets demonstrate that there are clear differences between AD and psoriasis. It is important to note that they showed the clonal expression of AD-specific Th2/Th22 and psoriasis-specific Th17/Tc17 clusters in lesional skin. In contrast, especially CD8+ effector memory T cells showed the clonal expansion in the PBMC, but the clonality of the disease-specific cell cluster was not associated with the severity. Recently, Nakamizo and colleagues also investigated the immune cell profiles in psoriasis in comparison with AD. They identified IL1B/IL23A co-producing CD14+ dendritic cell subpopulation in psoriasis but not in AD.5 Using innate lymphoid cell (ILC)-enriched cell samples, Alkon et al. characterized the ILC populations in AD lesions and healthy skin. They showed the vast majority of ILCs in both healthy and lesional skin belong to CRTH2+ ILC2 subset and demonstrated the heterogeneous ILC2 by showing co-expression of various combinations of transcription factors GATA3, AHR, and RORC in the skin.6 Using multiomics profiling with single-cell RNA sequencing and multiplex proteomics, Bangert et al. identified disease-linked immune cell populations in resolved AD indicative of a persisting disease memory, facilitating a rapid response system of epidermal-dermal cross-talk between keratinocytes, dendritic cells, and T cells in AD patients who were treated with the IL-4Rα blocker dupilumab.7 These findings provide a comprehensive understanding of cellular and molecular pathogenesis in AD. Recent single-cell RNA sequencing studies have also unraveled the complex heterogeneity of cell types present in other allergic diseases. Wang et al. used RNA sequencing in their mouse model of asthma exacerbation and identified multicellular signaling pathways closely associated with asthma exacerbations. They reported that IL-13 produced by CD8+ memory T cells, ILC2, and basophils is key in dexamethasone-resistant asthma.8 Of note, it is possible to show the response to allergens in individual cell types. Using house dust mite (HDM) stimulated PBMC, Seumois et al. presented that IFNR-expressing Th subsets exist in asthmatics without HDM allergy and this subset may dampen the activation of Th cells. In addition, the authors identified that the number of IL-9–expressing HDM-reactive Th2 cells is increased in asthmatics with HDM allergy compared to nonasthmatics with HDM allergy.9 Iinuma et al. also used a combination of single-cell RNA sequence and repertoire sequencing in PBMC obtained both before and at 1 year after initiating sublingual immunotherapy (SLIT) in allergic rhinitis patients.10 The authors demonstrated that SLIT reduced the number of clonal functional Th2 cells, but increased the specific Th2 cell population that expresses musculin. They identified musculin as a potential biomarker of SLIT. Other group showed 4 months of SLIT increased ryegrass pollen-specific IgE and IgG4 in serum and induced Lol p 1-specific memory B-cell subset, which is characterized with the upregulated expression of beta 1 integrin ITGB1 (CD29), IGHE (IgE), IGHG4 (IgG4), FCER2 (CD23), and IL13RA1 by single-cell RNA-sequencing analyses.11 In the T cells, Stark et al. demonstrated that dog extracts induce robust airway hyperresponsiveness and promote Th17 cell responses, which was associated with a high neutrophilic infiltration of the airways in their mouse model of asthma. A mixed Th2/Th17 cell-mediated airway inflammation induced by dog allergen was clearly identified by single-cell RNA-Seq analysis of T helper cells in the airways.12 A recent study by Rochman et al. showed that allergic inflammation in human eosinophilic esophagitis blocks the epithelial differentiation program, leading to loss of KRT6hi differentiated epithelial cell subpopulation by applying single-cell RNA-sequencing analyses and pseudotime analysis.13 This change was associated with dysregulation of transcription factors in the most differentiated epithelial cells and affected NOTCH-related cell-to-cell communication. In another single-cell RNA-seq analyses in human EoE also demonstrate that IL-13 (but not IL-4) was found to be strongly associated with epithelial cell pathology and remodeling in human EoE.14 It is essential to analyze the multiomics data with bioinformatics support, experiments for functional confirmation, and comparative expression studies between affected tissues, non-affected tissue (such as lesional vs non lesional AD skin) of the same individual and circulation. Overall, single-cell transcriptomics is rapidly evolving in the field of allergic research. A better understanding of the gene expression profiles in allergic diseases using single-cell approaches provides disease hallmarks in the niche, which can potentially serve as novel targets for therapeutic interventions and biomarkers. The authors declare that there are no conflicts of interest.