Schulze H Georg, Rangan Shreyas, Vardaki Martha Z, Blades Michael W, Turner Robin F B, Piret James M
Independent, Monte do Tojal, Hortinhas, Terena, Portugal.
Michael Smith Laboratories, The University of British Columbia, Vancouver, British Columbia, Canada.
Appl Spectrosc. 2025 Sep;79(9):1303-1312. doi: 10.1177/00037028241311296. Epub 2025 Feb 2.
To better interpret the Raman spectra from mammalian cells, it is often desirable to reduce their complexity by decomposing them into the spectral contributions from individual macromolecules or types of macromolecules. Diverse methods exist for demixing complex spectra, each with different benefits and drawbacks. However, some methods require a library of component spectra that might not be available, while others are hampered by noise and peak congestion that includes many proximal overlapping peaks. Through rapid fitting of individual peaks in every spectrum of a Raman hyperspectral data set, we have obtained individual peak parameters from which we determined the trends for all the peak amplitudes. We then grouped similar trends with -means clustering. Then we used the peak parameters of all the peaks in a given cluster to reconstruct a spectrum representative of that cluster. This method produced spectra that were less distorted by unrelated overlapping peaks or noise, were less congested than those in the hyperspectral set, and thereby improved peak identification and macromolecule recognition. We have demonstrated the application of the method with Raman spectra from a perchlorate-polystyrene model system and extended it to complex spectra from methanol-fixed mammalian cells. We were able to recover independent spectra of perchlorate and polystyrene in the model system and spectra pertaining to individual macromolecular types (proteins, nucleic acids, lipids) from the mammalian cell data. We discuss how imperfections in spectral preprocessing and peak fitting can adversely affect the results. In summary, we have provided a proof-of-concept for a novel mixture resolution method with different attributes than extant ones.
为了更好地解读哺乳动物细胞的拉曼光谱，通常希望通过将其分解为单个大分子或大分子类型的光谱贡献来降低其复杂性。存在多种用于解混复杂光谱的方法，每种方法都有不同的优缺点。然而，一些方法需要一个可能无法获得的组分光谱库，而其他方法则受到噪声和峰拥挤的阻碍，其中包括许多近端重叠峰。通过对拉曼高光谱数据集的每个光谱中的单个峰进行快速拟合，我们获得了单个峰参数，从中确定了所有峰振幅的趋势。然后，我们使用K均值聚类对相似趋势进行分组。然后，我们使用给定聚类中所有峰的峰参数来重建代表该聚类的光谱。该方法产生的光谱受无关重叠峰或噪声的扭曲较小，比高光谱集中的光谱拥挤程度更低，从而改善了峰识别和大分子识别。我们已经证明了该方法在高氯酸盐 - 聚苯乙烯模型系统的拉曼光谱中的应用，并将其扩展到甲醇固定的哺乳动物细胞的复杂光谱。我们能够在模型系统中恢复高氯酸盐和聚苯乙烯的独立光谱，以及从哺乳动物细胞数据中恢复与单个大分子类型（蛋白质、核酸、脂质）相关的光谱。我们讨论了光谱预处理和峰拟合中的缺陷如何对结果产生不利影响。总之，我们为一种具有与现有方法不同属性的新型混合物分辨率方法提供了概念验证。
