State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.
Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P. R. China.
ACS Sens. 2024 Nov 22;9(11):5999-6010. doi: 10.1021/acssensors.4c01873. Epub 2024 Oct 17.
Label-free surface-enhanced Raman spectroscopy (SERS) is capable of capturing rich compositional information from complex biosamples by providing vibrational spectra that are crucial for biosample identification. However, increasing complexity and subtle variations in biological media can diminish the discrimination accuracy of traditional SERS excited by a single laser wavelength. Herein, we introduce a multiwavelength SERS approach combined with machine learning (ML)-based classification to improve the discrimination accuracy of human urine specimens for bladder cancer (BCa) diagnosis. This strategy leverages the excitation-wavelength-dependent SERS spectral profiles of complex matrices, which are mainly attributed to wavelength-related vibrational changes in individual analytes and differences in the variation ratios of SERS intensity across different wavelengths among various analytes. By capturing SERS fingerprints under multiple excitation wavelengths, we can acquire more comprehensive and unique chemical information on complex samples. Further experimental examinations with clinical urine specimens, supported by ML algorithms, demonstrate the effectiveness of this multiwavelength strategy and improve the diagnostic accuracy of BCa and staging of its invasion with SERS spectra from increasing numbers of wavelengths. The multiwavelength SERS holds promise as a convenient, cost-effective, and broadly applicable technique for the precise identification of complex matrices and diagnosis of diseases based on body fluids.
无标记表面增强拉曼光谱（SERS）能够通过提供对生物样本识别至关重要的振动光谱，从复杂的生物样本中捕获丰富的成分信息。然而，生物介质复杂性的增加和细微变化会降低传统 SERS 的区分准确性，因为传统 SERS 是由单一激光波长激发的。在此，我们介绍了一种多波长 SERS 方法，结合基于机器学习（ML）的分类，以提高用于膀胱癌（BCa）诊断的人尿标本的区分准确性。该策略利用复杂基质中与激发波长相关的 SERS 光谱特性，这主要归因于单个分析物的与波长相关的振动变化以及不同分析物之间不同波长的 SERS 强度变化比的差异。通过在多个激发波长下捕获 SERS 指纹，我们可以获取更全面、独特的复杂样本的化学信息。通过对临床尿液样本的进一步实验检查，并辅以 ML 算法，证明了这种多波长策略的有效性，并通过增加波长数量的 SERS 光谱提高了 BCa 的诊断准确性和侵袭分期。多波长 SERS 有望成为一种方便、经济高效且广泛适用于基于体液的复杂基质的精确识别和疾病诊断的技术。
