Single Cell Transcriptome and Surface Epitope Analysis of Ankylosing Spondylitis Facilitates Disease Classification by Machine Learning

Abstract

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.

Monocyte subtype expression patterns in septic patients with diabetes are distinct from patterns observed in obese patients

“…Moreover, the evaluation of distinct comorbidities and complications, sites of infection, types of pathogens, antibiotic therapy, hypoglycemic agents, and comprehensive treatment strategies might help to understand individual variations of a given patient and subgroups. More importantly, more detailed monocyte classifications could be performed using single-cell RNA sequencing approaches ( 55 ). A most important limitation of the current study is the low number of patients in defined subgroups such as non-diabetic, obese patients.…”

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

“…Riel et al used computed tomography (CT) to construct an early diagnosis model using machine learning methods [ 47 ]. Samuel et al used single-cell transcriptome and surface epitope analysis of AS to classify diseases using machine learning methods [ 48 ]. Liang et al used LASSO regression to find that the severity of the platelet-to-lymphocyte ratio was related to the severity of AS, which is helpful for diagnosing and treating physicians [ 8 ].…”

Sex differences in musculoskeletal injury and disease risks across the lifespan: Are there unique subsets of females at higher risk than males for these conditions at distinct stages of the life cycle?

“…The term spondyloarthritis covers a heterogeneous set of conditions that can involve a variety of tissue ( Sharip and Kunz, 2020 ) including joints and the spine, specifically the sacroiliac joints ( Brown et al, 2020 ; Xiong et al, 2022 ). These inflammatory immune-mediated conditions can affect spinal tissues especially support tissues such as ligaments and their entheses of the axial skeleton ( Alber et al, 2022 ; Li et al, 2022 ). Long considered mainly a disease of men, particularly ankylosing spondylitis, has more recently been reported to also affect a subset of females ( Masi, 1992 ; Wright et al, 2020 ; Cunha et al, 2022 ; Marzo-Ortega et al, 2022 ; Stovall et al, 2022 ).…”

“…The onset and progression of the axial spondyloarthritis spectrum of diseases clearly has a genetic basis ( Brown et al, 2020 ), with association with HLA B27 ( Masi, 1992 ; Diaconu et al, 2022 ). Recent attempts to identify biomarkers of disease subtypes has progressed ( Akhtari et al, 2020 ; Alber et al, 2022 ), as well as insights into inflammatory mechanisms ( Lee et al, 2022b ). In addition, recent information has revealed that the gut microbiome of patients is different from a healthy control population, but no sex differences were noted ( Wang et al, 2022 ).…”