Mohammadrahim Kazemzadeh, Miguel Martinez-Calderon, Robert Otupiri, Anastasiia Artuyants, MoiMoi Lowe, Xia Ning, Eduardo Reategui, Zachary D Schultz, Weiliang Xu, Cherie Blenkiron, Lawrence W Chamley, Neil G R Broderick, Colin L Hisey
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引用次数: 0
摘要
表面增强拉曼光谱(SERS)是一种功能强大的工具,能让人深入了解化学和生物样品中的分子内容。然而,解读来自复杂或动态数据集的拉曼光谱仍然具有挑战性,尤其是对于细胞外囊泡(EVs)等高度异构的生物样本。为了克服这一难题,我们开发了一种可调整、可解释的深度自动编码器,用于分析几种具有挑战性的拉曼光谱应用,包括合成数据集、化学混合物、化学研磨反应和 EVs 混合物。我们将结果与经典方法(PCA 和 UMAP)进行了比较,以证明所提技术的优越性能。我们的方法可以处理小型数据集,具有高度概括性,可以填补光谱数据集中的未知空白,甚至可以量化混合物中细胞系衍生 EV 与胎牛血清衍生 EV 的相对比率。这种简单而强大的方法将大大提高许多其他拉曼光谱应用的分析能力。
Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures.
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.
期刊介绍:
The journal''s scope encompasses fundamental research, technology development, biomedical studies and clinical applications. BOEx focuses on the leading edge topics in the field, including:
Tissue optics and spectroscopy
Novel microscopies
Optical coherence tomography
Diffuse and fluorescence tomography
Photoacoustic and multimodal imaging
Molecular imaging and therapies
Nanophotonic biosensing
Optical biophysics/photobiology
Microfluidic optical devices
Vision research.