Condensing Raman spectrum for single-cell phenotype analysis.
Condensing Raman spectrum for single-cell phenotype analysis.
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用于单细胞表型分析的凝聚拉曼光谱
DOI：
10.1186/1471-2105-16-s18-s15
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发表时间：
2015
影响因子：
3
通讯作者：
Ning K
中科院分区：
文献类型：
--
作者：
Sun S;Wang X;Gao X;Ren L;Su X;Bu D;Ning K
背景近年来，高通量和非侵入性拉曼光谱技术已经成熟，成为按物种（甚至在复杂的混合群体中）识别单个细胞的有效方法。拉曼分析是实现这一目标的一种有吸引力的光学显微方法。为了充分利用拉曼谱分析进行单细胞分析，需要对拉曼光谱有广泛的了解，以回答诸如哪些过滤方法对拉曼光谱的预处理有效、哪些菌株可以通过拉曼光谱区分以及哪些特征最适合作为单细胞的基于拉曼的生物标志物等问题。结果在这项工作中，我们提出了一种称为 rDisc 的方法，将原始拉曼光谱离散化为少数（通常少于 20 个）代表性峰（拉曼位移）。该方法在去除噪声、压缩原始频谱方面具有优势。特别是，设计了有效的信号处理程序来消除噪声，利用小波变换去噪、基线校正和信号归一化。在离散化过程中，选择代表性峰以显着减小拉曼数据大小。更重要的是，选择的峰适合作为区分物种和其他细胞特征的关键生物标记。此外，我们发现离散光谱的分类性能与具有超过 1000 个拉曼位移的全光谱相当。总体而言，离散谱大约需要全谱的5倍的存储空间，并且处理速度要快得多。这使得 rDisc 明显优于其他单细胞分类方法。
BackgroundIn recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc.ResultsIn this work, we have proposed an approach called rDisc to discretize the original Raman spectrum into only a few (usually less than 20) representative peaks (Raman shifts). The approach has advantages in removing noises, and condensing the original spectrum. In particular, effective signal processing procedures were designed to eliminate noise, utilising wavelet transform denoising, baseline correction, and signal normalization. In the discretizing process, representative peaks were selected to signicantly decrease the Raman data size. More importantly, the selected peaks are chosen as suitable to serve as key biological markers to differentiate species and other cellular features. Additionally, the classication performance of discretized spectra was found to be comparable to full spectrum having more than 1000 Raman shifts. Overall, the discretized spectrum needs about 5storage space of a full spectrum and the processing speed is considerably faster. This makes rDisc clearly superior to other methods for single-cell classication.