Rheumatoid arthritis (RA) possess not only a substantial degree of clinical heterogeneity but is diagnosed on a diverse array of clinical criteria. The lack of a single marker predictive methodology means that the timely diagnosis and treatment of these patients proves challenging. With the advent of targeted therapies, it is becoming increasingly important to accurately diagnose RA at an early stage of disease in order to ensure effective and timely disease management which can minimise long term sequelae such as joint tissue damage. Raman spectroscopy has recently gained increasing clinical recognition as a non-invasive and label-free method for obtaining a complete biochemical fingerprint of the content of biological samples. This study explored the application of Raman spectroscopy coupled with multivariate data analysis, as an adjunct or alternative tool for the differential diagnosis of RA using peripheral blood mononuclear cells (PBMCs) and purified primary immune cell subsets. High performance partial least square discriminant analysis (PLSDA) classification models constructed in this study enabled identification of spectroscopic discrimination of RA patients and healthy controls without influence from confounding factors. Spectral fitting analysis identified potential spectral biomarkers such as Proteinase K and TNF-α that elucidate the spectral classification between healthy controls and RA patients. These results demonstrate the capability of Raman Spectroscopy in RA diagnosis.

类风湿关节炎（RA）不仅具有很大程度的临床异质性，而且可以根据多种临床标准进行诊断。缺乏单一标记物的预测方法意味着对这些患者的及时诊断和治疗证明具有挑战性。随着靶向疗法的出现，在疾病的早期阶段准确诊断RA以确保有效和及时的疾病管理（这可以使诸如关节组织损伤的长期后遗症最小化）变得越来越重要。拉曼光谱法最近获得了越来越多的临床认可，这是一种用于获取生物样本内容的完整生化指纹图谱的无创且无标签的方法。这项研究探索了拉曼光谱结合多元数据分析的应用，作为使用外周血单核细胞（PBMC）和纯化的原代免疫细胞亚群鉴别诊断RA的辅助或替代工具。这项研究中构建的高性能偏最小二乘判别分析（PLSDA）分类模型能够在不受混杂因素影响的情况下鉴定RA患者和健康对照的光谱学辨别力。光谱拟合分析确定了潜在的光谱生物标志物，例如蛋白酶K和TNF-α，阐明了健康对照与RA患者之间的光谱分类。这些结果证明了拉曼光谱在RA诊断中的能力。这项研究中构建的高性能偏最小二乘判别分析（PLSDA）分类模型能够在不受混杂因素影响的情况下鉴定RA患者和健康对照的光谱学辨别力。光谱拟合分析确定了潜在的光谱生物标志物，例如蛋白酶K和TNF-α，阐明了健康对照与RA患者之间的光谱分类。这些结果证明了拉曼光谱在RA诊断中的能力。这项研究中构建的高性能偏最小二乘判别分析（PLSDA）分类模型能够在不受混杂因素影响的情况下鉴定RA患者和健康对照的光谱学辨别力。光谱拟合分析确定了潜在的光谱生物标志物，例如蛋白酶K和TNF-α，阐明了健康对照与RA患者之间的光谱分类。这些结果证明了拉曼光谱在RA诊断中的能力。光谱拟合分析确定了潜在的光谱生物标志物，例如蛋白酶K和TNF-α，阐明了健康对照组和RA患者之间的光谱分类。这些结果证明了拉曼光谱在RA诊断中的能力。光谱拟合分析确定了潜在的光谱生物标志物，例如蛋白酶K和TNF-α，阐明了健康对照与RA患者之间的光谱分类。这些结果证明了拉曼光谱在RA诊断中的能力。