Devitt George, Hanrahan Niall, Ramírez Moreno Miguel, Mudher Amrit, Mahajan Sumeet
School of Biological Sciences, University of Southampton, Highfield Campus, SO17 1BJ Southampton, U.K.
School of Chemistry and Chemical Engineering, University of Southampton, Highfield Campus, SO17 1BJ Southampton, U.K.
Anal Chem. 2025 Jun 17;97(23):12189-12197. doi: 10.1021/acs.analchem.5c00776. Epub 2025 Jun 3.
We report the development and application of a novel spectral barcoding approach that exploits our multiexcitation (MX) Raman spectroscopy-based methodology for improved label-free detection and classification of complex biological samples. To develop our improved MX-Raman methodology, we utilized post-mortem brain tissue from several neurodegenerative diseases (NDDs) that have considerable clinical overlap. For improving our methodology we used three sources of spectral information arising from distinct physical phenomena to assess which was most important for NDD classification. Spectral measurements utilized combinations of data from multiple, distinct excitation laser wavelengths and polarization states to differentially probe molecular vibrations and autofluorescence signals. We demonstrate that the more informative MX-Raman (532 nm-785 nm) spectra are classified with 96.7% accuracy on average, compared to conventional single-excitation Raman spectroscopy that resulted in 78.5% accuracy (532 nm) or 85.6% accuracy (785 nm) using linear discriminant analysis (LDA) on 5 NDD classes. By combining information from distinct laser polarizations we observed a nonsignificant increase in classification accuracy without the need of a second laser (785 nm-785 nm polarized), whereas combining Raman spectra with autofluorescence signals did not increase classification accuracy. Finally, by filtering out spectral features that were redundant for classification or not descriptive of disease class, we engineered spectral barcodes consisting of a minimal subset of highly disease-specific MX-Raman features that improved the unsupervised and cross-validated clustering of MX-Raman spectra. The results demonstrate that increasing spectral information content using our optical MX-Raman methodology enables enhanced identification and distinction of complex biological samples but only when that information is independent and descriptive of class. The future translation of such technology to biofluids could support diagnosis and stratification of patients living with dementia and potentially other clinical conditions such as cancer and infectious disease.
我们报告了一种新型光谱条形码方法的开发与应用，该方法利用我们基于多激发（MX）拉曼光谱的方法，以改进对复杂生物样品的无标记检测和分类。为了开发我们改进的MX拉曼方法，我们使用了来自几种具有相当大临床重叠的神经退行性疾病（NDD）的死后脑组织。为了改进我们的方法，我们使用了来自不同物理现象的三种光谱信息源，以评估哪一种对NDD分类最为重要。光谱测量利用了来自多个不同激发激光波长和偏振态的数据组合，以差异探测分子振动和自发荧光信号。我们证明，与传统单激发拉曼光谱相比，信息量更大的MX拉曼（532 nm - 785 nm）光谱平均分类准确率为96.7%，而传统单激发拉曼光谱在对5种NDD类别进行线性判别分析（LDA）时，准确率分别为78.5%（532 nm）或85.6%（785 nm）。通过组合来自不同激光偏振的信息，我们观察到分类准确率有不显著的提高，而无需第二个激光（785 nm - 785 nm偏振），而将拉曼光谱与自发荧光信号相结合并没有提高分类准确率。最后，通过滤除对分类冗余或不能描述疾病类别的光谱特征，我们设计了由高度疾病特异性的MX拉曼特征的最小子集组成的光谱条形码，从而改进了MX拉曼光谱的无监督和交叉验证聚类。结果表明，使用我们的光学MX拉曼方法增加光谱信息含量能够增强对复杂生物样品的识别和区分，但前提是该信息是独立的且能描述类别。这种技术未来向生物流体的转化可能支持对痴呆症患者以及潜在的其他临床病症（如癌症和传染病）的诊断和分层。
