Chen Xiang, Tang Ping, Wan Jianhui, Zhang Weina, Zhong Liyun
Key Laboratory of Photonics Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China.
Guangdong Provincial Key Laboratory of Information Photonics Technology, Guangdong University of Technology, Guangzhou 510006, China.
Biomed Opt Express. 2025 Feb 28;16(3):1284-1298. doi: 10.1364/BOE.553461. eCollection 2025 Mar 1.
Raman spectroscopy, with its unique "molecular fingerprint" characteristics, is an essential tool for label-free, non-invasive biochemical analysis of cells. It provides precise information on cellular biochemical components, such as proteins, lipids, and nucleic acids by analyzing molecular vibrational modes. However, overlapping Raman spectral signals make spectral unmixing crucial for accurate quantification. Traditional unmixing methods face challenges: unsupervised algorithms yield poorly interpretable results, while supervised methods like BCA rely heavily on accurate reference spectra and are sensitive to environmental changes (e.g., pH, temperature, excitation wavelength), causing spectral distortion and reducing quantitative reliability. This study addresses these challenges by introducing a parameterized Voigt function into the linear spectral mixing model for element spectrum compensation, using iterative least-squares optimization for adaptive unmixing and quantitative analysis. Simulations show that the Voigt-compensated unmixing algorithm improves spectral fitting accuracy and robustness. Applied to Raman spectra from Hela cell apoptosis and iPSCs differentiation, the algorithm accurately tracks biochemical molecular changes, proving its applicability in cellular Raman spectral analysis and a precise, reliable, and versatile tool for quantitative biochemical analysis.
拉曼光谱凭借其独特的“分子指纹”特性，是用于对细胞进行无标记、非侵入性生化分析的重要工具。它通过分析分子振动模式，提供有关细胞生化成分（如蛋白质、脂质和核酸）的精确信息。然而，拉曼光谱信号的重叠使得光谱解混对于准确量化至关重要。传统的解混方法面临挑战：无监督算法产生的结果难以解释，而像BCA这样的监督方法严重依赖准确的参考光谱，并且对环境变化（如pH值、温度、激发波长）敏感，会导致光谱失真并降低定量可靠性。本研究通过将参数化的洛伦兹函数引入线性光谱混合模型进行元素光谱补偿，使用迭代最小二乘法优化进行自适应解混和定量分析来应对这些挑战。模拟表明，洛伦兹补偿解混算法提高了光谱拟合精度和鲁棒性。将该算法应用于来自Hela细胞凋亡和诱导多能干细胞分化的拉曼光谱，该算法准确跟踪生化分子变化，证明了其在细胞拉曼光谱分析中的适用性，是一种用于定量生化分析的精确、可靠且通用的工具。
