Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes

Abstract

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.

Rapid Intracellular Detection and Analysis of Lipid Droplets’ Morpho-Chemical Composition by Phase-Guided Raman Sampling

“…The collected LD spectra contain a spectral background (primarily glass), which was removed before spectral fitting using asymmetric least-squares 42 (as shown in Figure S3). While the raw lipid droplet spectra already have acceptable SNR, the large database of spectra allowed us to gainfully apply our recently developed hybrid-PCA denoising method 43 to further improve the SNR (one example can be found in Figure S4). Excellent spectral fitting was achieved using the non-negative least-squares method (NNLS) and a library of spectra consisting of the four most representative major fatty acids in the cell, namely, methyl palmitate, methyl oleate, methyl arachidonic acid, and methyl linoleate (examples shown in the Supporting Information, Figure S5).…”

Professional identity of grid workers based on SPSS 22.0

“…The key idea of PCA is to find a direction to minimize the sum of data reconstruction errors in the least squares sense [19]. However, it is well known that the least squares technique is not robust due to the presence of edge noise, and traditional PCA methods are not robust to outliers.…”

Detection Method of Cracks in Expressway Asphalt Pavement Based on Digital Image Processing Technology

“…In the field of image processing, a standard denoising criterion considers that the information with large changes is the target, the information with small changes is noise, and the size of changes is described by variance. The idea of the PCA method is to project high-dimensional data into a low-dimensional subspace, use new variables with fewer dimensions and mutual independence to reflect most of the information provided by the original variables, and then solve the problem by analyzing the new variables [11,12]. PCA has many different calculation methods.…”

“…Among them, N is the total number of pixels in the region. In actual processing, using a 21 × 21 size window scans the acquired image and calculates the intensity value of each point according to Formulas (12) and (13) as O and the quantity influence factor n. According to the prior knowledge obtained from a large number of experiments, the best threshold suitable for fracture extraction is O < 30, n > 0.8N. When the calculated O and n meet the requirements, the value of O can be assigned to the current point f (x, y).…”