Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China.
Suzhou Advanced Research Institute, University of Science and Technology of China, Suzhou, Jiangsu 215123, China.
Anal Chem. 2022 Oct 18;94(41):14232-14241. doi: 10.1021/acs.analchem.2c02518. Epub 2022 Oct 6.
Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise. Here, we present a hybrid principal component analysis (PCA) denoising method, where a small number (∼30 spectra) of high signal-to-noise ratio (SNR) training data construct an effective principal component subspace into which low SNR test data are projected. Simulations and experiments prove the method outperforms traditional denoising methods such as the wavelet transform or traditional PCA. On experimental liposome samples, denoising accelerated data acquisition from 90 to 3 s, with an overall 4.5-fold improvement in particle throughput. The denoised data retained the ability to accurately determine complex morphochemical parameters such as lamellarity of individual nanoliposomes, as confirmed by comparison with cryo-EM imaging. We therefore show that hybrid PCA denoising is an efficient and effective tool for denoising spectral data sets with limited chemical variability and that the RR-NTA technique offers an ideal path for studying the multidimensional heterogeneity of nanoliposomes and other micro/nanoscale bioparticles.
激光镊子拉曼光谱能够对单个生物纳米粒子（如载药纳米脂质体）进行多重、定量的化学和形态分析，但它需要对每个粒子进行分钟级的采集时间，导致在典型的小数据集缺乏统计能力。长采集时间不仅是测量时间的瓶颈，也是分析通量的瓶颈，因为必须将粒子浓度（因此通量）保持在足够低的水平，以避免群集测量。改善这种情况的唯一有效方法是减少曝光时间，这会增加噪声。在这里，我们提出了一种混合主成分分析（PCA）去噪方法，其中少量（约 30 个光谱）具有高信噪比（SNR）的训练数据构建了一个有效的主成分子空间，将低 SNR 测试数据投影到该子空间中。模拟和实验证明，该方法优于传统的去噪方法，如小波变换或传统 PCA。在实验脂质体样本上，去噪将数据采集速度从 90 秒加速到 3 秒，整体颗粒通量提高了 4.5 倍。去噪数据保留了准确确定复杂形态化学参数的能力，例如单个纳米脂质体的层状，这通过与冷冻电镜成像的比较得到了证实。因此，我们表明，混合 PCA 去噪是一种有效的工具，用于对具有有限化学变异性的光谱数据集进行去噪，而 RR-NTA 技术为研究纳米脂质体和其他微/纳米级生物粒子的多维异质性提供了理想的途径。
