Membrane Protein Interaction Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603 203, Tamil Nadu, India.
Department of Computer Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603 203, Tamil Nadu, India.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Feb 5;326:125236. doi: 10.1016/j.saa.2024.125236. Epub 2024 Oct 1.
To identify and monitor the FTIR spectral signatures of plasma extracellular vesicles (EVs) from Duchenne Muscular Dystrophy (DMD) patients at different stages with Healthy controls using machine learning models.
Whole blood samples were collected from the DMD (n = 30) and Healthy controls (n = 12). EVs were extracted by the Total Exosome Isolation (TEI) Method and resuspended in 1XPBS. We characterize the morphology, size, particle count, and surface markers (CD9, Alix, and Flotillin) by HR-TEM, NTA, and Western Blot analysis. The mid-IR spectra were recorded from (4000-400 cm) by Bruker ALPHA II FTIR spectrometer model, which was equipped with an attenuated total reflection (ATR) module. Machine learning algorithms like Principal Component Analysis (PCA) and Random Forest (RF) for dimensionality reduction and classifying the two study groups based on the FTIR spectra. The model performance was evaluated by a confusion matrix and the sensitivity, specificity, and Receiver Operating Characteristic Curve (ROC) was calculated respectively.
Alterations in Amide I & II (1700-1470 cm) and lipid (3000-2800 cm) regions in FTIR spectra of DMD compared with healthy controls. The PCA-RF model classified correctly the two study groups in the range of 4000-400 cm with a sensitivity of 20 %, specificity of 87.50 %, accuracy of 71.43 %, precision of 33.33 %, and 5-fold cross-validation accuracy of 82 %. We analyzed the ten different spectral regions which showed statistically significant at P < 0.01 except the Ester Acyl Chain region.
Our proof-of-concept study demonstrated distinct infrared (IR) spectral signatures in plasma EVs derived from DMD. Consistent alterations in protein and lipid configurations were identified using a PCA-RF model, even with a small clinical dataset. This minimally invasive liquid biopsy method, combined with automated analysis, warrants further investigation for its potential in early diagnosis and monitoring of disease progression in DMD patients within clinical settings.
使用机器学习模型，从不同阶段的杜氏肌营养不良症 (DMD) 患者和健康对照者中识别和监测血浆细胞外囊泡 (EV) 的傅里叶变换红外 (FTIR) 光谱特征。
从 DMD（n=30）和健康对照者（n=12）采集全血样本。通过总外泌体分离（TEI）法提取 EV，并悬浮于 1XPBS 中。我们通过高分辨率透射电子显微镜（HR-TEM）、纳米颗粒跟踪分析（NTA）和 Western Blot 分析来表征形态、大小、颗粒计数和表面标志物（CD9、Alix 和 Flotillin）。使用 Bruker ALPHA II FTIR 光谱仪模型记录从中红外光谱（4000-400 cm），该模型配备衰减全反射（ATR）模块。使用主成分分析（PCA）和随机森林（RF）等机器学习算法进行降维和基于 FTIR 光谱对两个研究组进行分类。通过混淆矩阵评估模型性能，并分别计算敏感性、特异性和接收者操作特征曲线（ROC）。
与健康对照组相比，DMD 的 FTIR 光谱中酰胺 I 和 II（1700-1470 cm）和脂质（3000-2800 cm）区域发生改变。PCA-RF 模型在 4000-400 cm 范围内正确分类了两个研究组，其敏感性为 20%，特异性为 87.50%，准确性为 71.43%，精度为 33.33%，5 倍交叉验证准确性为 82%。我们分析了十个不同的光谱区域，除了酯酰链区域外，其余区域均在 P < 0.01 时有统计学意义。
我们的概念验证研究表明，来自 DMD 的血浆 EV 中存在明显的红外（IR）光谱特征。使用 PCA-RF 模型鉴定了蛋白质和脂质结构的一致改变，即使在临床数据集较小的情况下也是如此。这种微创的液体活检方法与自动化分析相结合，值得在临床环境中进一步研究其在 DMD 患者早期诊断和疾病进展监测中的潜力。
