Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Strasse 9, 07745 Jena, Germany.
Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany.
Analyst. 2024 Aug 19;149(17):4395-4406. doi: 10.1039/d4an00648h.
Broadband Coherent Anti-Stokes Raman Scattering (BCARS) is a valuable spectroscopic imaging tool for visualizing cellular structures and lipid distributions in biomedical applications. However, the inevitable biological changes in the samples (cells/tissues/lipids) introduce spectral variations in BCARS data and make analysis challenging. In this work, we conducted a systematic study to estimate the biological variance in BCARS data of two commonly used cell lines (HEK293 and HepG2) in biomedical research. The BCARS data were acquired from two different experimental setups (Leibniz Institute of Photonics Technology (IPHT) in Jena and Politecnico di Milano (POLIMI) in Milano) to evaluate the reproducibility of results. Also, spontaneous Raman data were independently acquired at POLIMI to validate those results. First, Kramers-Kronig (KK) algorithm was utilized to retrieve Raman-like signals from the BCARS data, and a pre-processing pipeline was subsequently used to standardize the data. Principal component analysis - Linear discriminant analysis (PCA-LDA) was performed using two cross-validation (CV) methods: batch-out CV and 10-fold CV. Additionally, the analysis was repeated, considering different spectral regions of the data as input to the PCA-LDA. Finally, the classification accuracies of the two BCARS datasets were compared with the results of spontaneous Raman data. The results demonstrated that the CH band region (2770-3070 cm) and spectral data in the 1500-1800 cm region have significantly contributed to the classification. A maximum of 100% balanced accuracies were obtained for the 10-fold CV for both BCARS setups. However, in the case of batch-out CV, it is 92.4% for the IPHT dataset and 98.8% for the POLIMI dataset. This study offers a comprehensive overview for estimating biological variance in biomedical applications. The insights gained from this analysis hold promise for improving the reliability of BCARS measurements in biomedical applications, paving the way for more accurate and meaningful spectroscopic analyses in the study of biological systems.
宽带相干反斯托克斯拉曼散射（BCARS）是一种非常有价值的用于可视化生物医学应用中细胞结构和脂质分布的光谱成像工具。然而，样品（细胞/组织/脂质）中不可避免的生物变化会导致 BCARS 数据中的光谱变化，从而使得分析具有挑战性。在这项工作中，我们对生物医学研究中两种常用细胞系（HEK293 和 HepG2）的 BCARS 数据中的生物学变化进行了系统研究。BCARS 数据是从两个不同的实验设置（耶拿的莱布尼茨光子技术研究所（IPHT）和米兰的米兰理工大学（POLIMI））中获取的，以评估结果的重现性。此外，还在 POLIMI 独立获取了自发拉曼数据来验证这些结果。首先，利用 Kramers-Kronig（KK）算法从 BCARS 数据中提取出拉曼样信号，然后使用预处理管道对数据进行标准化。使用两种交叉验证（CV）方法（批处理外 CV 和 10 倍 CV）执行主成分分析-线性判别分析（PCA-LDA）。此外，还重复了分析，将数据的不同光谱区域作为 PCA-LDA 的输入。最后，将两个 BCARS 数据集的分类准确性与自发拉曼数据的结果进行了比较。结果表明，CH 带区域（2770-3070cm）和 1500-1800cm 区域的光谱数据对分类有显著贡献。对于两个 BCARS 设置，10 倍 CV 的最大平衡准确率均达到 100%。然而，在批处理外 CV 的情况下，IPHT 数据集为 92.4%，POLIMI 数据集为 98.8%。这项研究为估计生物医学应用中的生物学变化提供了全面的概述。从这种分析中获得的见解有望提高 BCARS 测量在生物医学应用中的可靠性，为生物系统的更准确和有意义的光谱分析铺平道路。
