Single-cell analysis reveals innate immunity dynamics in ankylosing spondylitis.
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ABSTRACT:
SUBMITTER: Liu J
PROVIDER: S-EPMC7982614 | biostudies-literature | 2021 Mar
REPOSITORIES: biostudies-literature
Publications
Single-cell analysis reveals innate immunity dynamics in ankylosing spondylitis.
Liu Jing J Tang Yulong Y Huang Yan Y Gao Jian J Jiang Shuai S Liu Qingmei Q Ma Yanyun Y Qian Xiaolin X Qian Feng F Reveille John D JD He Dongyi D Zou Hejian H Jin Li L Zhu Qi Q Pu Weilin W Wang Jiucun J
Clinical and translational medicine 20210301 3
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Project description:A detailed understanding of the gene-regulatory network in ankylosing spondylitis (AS) is vital for elucidating the mechanisms of AS pathogenesis. Assaying transposase-accessible chromatin in single cell sequencing (scATAC-seq) is a suitable method for revealing such networks. Thus, scATAC-seq was applied to define the landscape of active regulatory DNA in AS. As a result, there was a significant change in the percent of CD8+ T cells in PBMCs, and 37 differentially accessible transcription factor (TF) motifs were identified. T cells, monocytes-1 and dendritic cells were found to be crucial for the IL-17 signaling pathway and TNF signaling pathway, since they had 73 potential target genes regulated by 8 TF motifs with decreased accessibility in AS. Moreover, natural killer cells were involved in AS by increasing the accessibility to TF motifs TEAD1 and JUN to induce cytokine-cytokine receptor interactions. In addition, CD4+ T cells and CD8+ T cells may be vital for altering host immune functions through increasing the accessibility of TF motifs NR1H4 and OLIG (OLIGI and OLIG2), respectively. These results explain clear gene regulatory variation in PBMCs from AS patients, providing a foundational framework for the study of personal regulomes and delivering insights into epigenetic therapy.
| S-EPMC7655814 | biostudies-literature
Project description:To uncover the complex immune mechanisms driving inflammation in ankylosing spondylitis and lay the groundwork for identifying new therapeutic targets and innovative approaches, we conducted 10× single-cell sequencing on bone marrow cell samples collected from the vertebrae of three AS patients and three non-AS patients. Using single-cell sequencing data, we analysed the expression of differentially expressed genes (DEGs) by comparing AS patients with non-AS patients. Key genes among the related DEGs were identified through protein-protein interaction networks and hub gene screening and further validated using immunohistochemistry. We performed clustering and annotation of the single-cell sequencing data and externally validated the findings using the GSE232131 single-cell dataset. By integrating transcriptome data, we assessed the differential expression of immune cells in AS. Finally, we explored the interactions between immune cells in AS through cell communication analysis. The upregulated gene CD74 was identified as a hub gene in T cells in AS. Further research revealed the important relationship between T cells and NK cells in the fundamental processes of AS. Additionally, we found that the macrophage migration inhibitory factor signalling pathway is prominently expressed in the interactions among various cell types in AS.
| S-EPMC11648971 | biostudies-literature
Project description:Ankylosing spondylitis (AS) is an autoimmune disease with unknown aetiology. To unravel the mechanisms mediating AS pathogenesis, we profiled peripheral blood mononuclear cells (PBMCs) from AS patients and healthy subjects using 10X single-cell RNA sequencing. The frequencies of immune cell subsets were evaluated by flow cytometry. NK cells were purified from PBMCs using isolation kit and were examined for gene expression by RT-qPCR. Plasma levels of cytolytic molecules were examined by enzyme-linked immunosorbent assay. Compared to healthy controls, AS patients showed a significant decrease in total NK cells as well as CD56dim NK subset, whereas CD56bright NK cells were increased. Additionally, impaired expression of cytotoxic genes in NK cells of AS patients was observed by bioinformatics algorithm and verified by RT-qPCR and flow cytometry. Consistent with changes in transcriptomics, we found decreased plasma levels of granzymes, but not granulysin, in AS patients. Furthermore, Pearson correlation analysis revealed a negative correlation between plasma GZMB levels and disease activity (r = -0.5275, p = 0.0358). No correlation was observed between plasma cytolytic molecules and biochemical indexes (ESR and CRP). Our findings uncover altered NK cell subsets and cytotoxic profiles in peripheral circulation of AS patients at single-cell resolution.
| S-EPMC8831943 | biostudies-literature
Project description:Ankylosing spondylitis (AS) is an immune-mediated inflammatory disorder that primarily affects the axial skeleton, especially the sacroiliac joints and spine. This results in chronic back pain and, in extreme cases, ankylosis of the spine. Despite its debilitating effects, the pathogenesis of AS remains to be further elucidated. This study used single cell CITE-seq technology to analyze peripheral blood mononuclear cells (PBMCs) in AS and in healthy controls. We identified a number of molecular features associated with AS. CD52 was found to be overexpressed in both RNA and surface protein expression across several cell types in patients with AS. CD16+ monocytes overexpressed TNFSF10 and IL-18Rα in AS, while CD8+ TEM cells and natural killer cells overexpressed genes linked with cytotoxicity, including GZMH, GZMB, and NKG7. Tregs underexpressed CD39 in AS, suggesting reduced functionality. We identified an overrepresented NK cell subset in AS that overexpressed CD16, CD161, and CD38, as well as cytotoxic genes and pathways. Finally, we developed machine learning models derived from CITE-seq data for the classification of AS and achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of > 0.95. In summary, CITE-seq identification of AS-associated genes and surface proteins in specific cell subsets informs our understanding of pathogenesis and potential new therapeutic targets, while providing new approaches for diagnosis via machine learning.
| S-EPMC9135966 | biostudies-literature
Project description:BackgroundAnkylosing spondylitis (AS) is a chronic inflammatory rheumatic disease affecting the axial skeleton and peripheral joints. The etiology of this disease remains poorly understood, but interactions between genetic and environmental factors have been implicated. The present study identified differentially expressed proteins in the synovial fluid (SF) of AS patients to elucidate the underlying cause of AS.MethodsA cohort of 40 SF samples from 10 AS and 10 each of rheumatoid arthritis (RA), gout, and osteoarthritis (OA) patients were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify differentially expressed proteins specific to AS. The label-free LC-MS/MS results were verified by western blotting.ResultsWe identified 8 proteins that were > 1.5-fold upregulated in the SF of AS patients compared to that of the disease control groups, including HP, MMP1, MMP3, serum amyloid P-component (APCS), complement factor H-related protein 5 (CFHR5), mannose-binding lectin 2 (MBL2), complement component C9 (C9), and complement C4-A (C4A). CFHR5 and C9 were previously found in serum from AS patients, while APCS was previously found in SF as well as in serum. However, the present study has identified C4A, and MBL2 as potential AS biomarkers for the first time. The expression levels of MMP3, C9, and CFHR5 were verified in AS SF using western blotting.ConclusionWe performed quantitative comparative proteomic analysis using by LC-MS/MS of the SF from four disease states: RA, gout, and OA. This systematic comparison revealed novel differentially expressed proteins in AS SF, as well as two previously reported candidate biomarkers. We further verified the expression of MMP3, C9 and CFHR5 by western blot. These proteins may serve as diagnostic or prognostic biomarkers in patients with AS, and may thus improve the clinical outcomes of this serious disease.
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Project description:Single-cell transcriptome of >55,000 cells multiplexed into 4 channels obtained from peripheral blood and synovial fluid of two patients with HLA-B27+ ankylosing spondylitis,.
2021-10-18 | E-MTAB-10948 | biostudies-arrayexpress
Project description:A detailed understanding of the gene-regulatory network in ankylosing spondylitis (AS) is vital for elucidating the mechanisms of AS pathogenesis. Assaying transposase-accessible chromatin in single cell sequencing (scATAC-seq) is a suitable method for revealing such networks. Thus, scATAC-seq was applied to define the landscape of active regulatory DNA in AS. As a result, there was a significant change in the percent of CD8+ T cells in PBMCs, and 37 differentially accessible transcription factor (TF) motifs were identified. T cells, monocytes-1 and dendritic cells were found to be crucial for the IL-17 signaling pathway and TNF signaling pathway, since they had 73 potential target genes regulated by 8 TF motifs with decreased accessibility in AS. Moreover, natural killer cells were involved in AS by increasing the accessibility to TF motifs TEAD1 and JUN to induce cytokine-cytokine receptor interactions. In addition, CD4+ T cells and CD8+ T cells may be vital for altering host immune functions through increasing the accessibility of TF motifs NR1H4 and OLIG (OLIGI and OLIG2), respectively. These results explain clear gene regulatory variation in PBMCs from AS patients, providing a foundational framework for the study of personal regulomes and delivering insights into epigenetic therapy.
2020-11-18 | GSE157595 | GEO
Project description:ObjectiveGenetic studies on ankylosing spondylitis (AS) have identified more than 100 pathogenic genes. Building a bridge between these genes and biologically targeted therapies is the current research hotspot.MethodsWe integrated single-cell assaying transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) to explore the key genes and related mechanisms associated with AS pathogenesis.ResultsWe identified 18 cell types in peripheral mononuclear cells from patients with AS and normal controls and summarized the cell-type-specific abnormal genes by scRNA-seq. Interestingly, we found that the pathogenic gene NFKB involved in AS progression originated from CD8+ T cells. Moreover, we observed an abnormal tumor TNF pathway mediated by abnormal expression of TNF, NFKB, FOS, JUN, and JUNB, and scATAC-seq results confirmed the abnormal accessible binding sites of transcriptional factors FOS, JUN, and JUNB. The final magnetic bead sorting and quantitative real-time PCR(RT-qPCR) confirmed that NFKB, FOS, JUN, and JUNB in CD8+ T cells differed in the AS group.ConclusionsOur results revealed a possible mechanism by which NFKB abnormally regulates FOS, JUN, and JUNB and drives AS progression, providing a novel perspective from a single cell point of view in AS.
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Project description:Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.
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