Rajavel Archana, Kumar Jayasree, Essakipillai Narayanan, Anbazhagan Ramajayam, Panneerselvam Rajapandiyan, Ramakrishnan Jayashree, Venkataraman Viswanathan, Natesan Sella Raja
Membrane Protein Interaction Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603 203, Tamil Nadu, India.
Raman Research Laboratory (RARE Lab), Department of Chemistry, SRM University-AP, Andhra Pradesh, Amaravati 522502, India.
ACS Omega. 2025 Mar 24;10(16):16874-16883. doi: 10.1021/acsomega.5c00838. eCollection 2025 Apr 29.
Duchenne muscular dystrophy (DMD) is a neuromuscular disease that affects males in the pediatric age group. Currently, there is no painless, cost-effective prognostic method available to monitor DMD progression. The main hypothesis of this study was that the biochemical composition of extracellular vesicles (EVs) isolated from the urine of DMD patients can be distinctly differentiated from that of healthy controls using surface-enhanced Raman Spectroscopy (SERS) combined with machine learning models. This differentiation is expected to provide a noninvasive, rapid, and accurate diagnostic tool for the early detection, staging, and monitoring of DMD by identifying the molecular signatures captured by SERS and leveraging the analytical power of machine learning algorithms. We collected fasting morning urine samples from 52 DMD patients and 17 healthy controls and isolated EVs using a Total Exosome Isolation kit. The SERS substrates are prepared using silver nanoparticles, which were employed to capture the molecular fingerprints of the EVs with uniformity and reproducibility, achieving relative standard deviation values of 7.3% and 8.9%. We observed alterations in phenylalanine and α-helical proteins in patients with DMD compared to controls. These spectral data were analyzed using PCA, Support Vector Machines, and k-Nearest Neighbor (KNN) algorithms to identify distinct patterns and stage DMD based on biochemical composition. Our integrated approach demonstrated 60% sensitivity and 100% specificity in distinguishing DMD patients from healthy controls, highlighting the potential of SERS and KNN for noninvasive, accurate, and rapid diagnosis of DMD. This method offers a promising avenue for early detection and personalized treatment strategies, ultimately improving patient outcomes and quality of life.
杜氏肌营养不良症（DMD）是一种影响儿童年龄组男性的神经肌肉疾病。目前，尚无无痛、经济高效的预后方法可用于监测DMD的进展。本研究的主要假设是，使用表面增强拉曼光谱（SERS）结合机器学习模型，可以将从DMD患者尿液中分离出的细胞外囊泡（EVs）的生化组成与健康对照者的明显区分开来。通过识别SERS捕获的分子特征并利用机器学习算法的分析能力，这种区分有望为DMD的早期检测、分期和监测提供一种非侵入性、快速且准确的诊断工具。我们收集了52例DMD患者和17名健康对照者的空腹晨尿样本，并使用总外泌体分离试剂盒分离出EVs。SERS底物使用银纳米颗粒制备，用于均匀且可重复地捕获EVs的分子指纹，相对标准偏差值分别为7.3%和8.9%。与对照组相比，我们观察到DMD患者苯丙氨酸和α-螺旋蛋白的变化。使用主成分分析（PCA）、支持向量机和k近邻（KNN）算法对这些光谱数据进行分析，以识别基于生化组成的不同模式并对DMD进行分期。我们的综合方法在区分DMD患者和健康对照者方面显示出60%的灵敏度和100%的特异性，突出了SERS和KNN在DMD非侵入性、准确和快速诊断方面的潜力。该方法为早期检测和个性化治疗策略提供了一条有前景的途径，最终改善患者的预后和生活质量。
