Ramanujan V Krishnan, Biener-Ramanujan Eva, Armmer Kinton, Centonze Victoria E, Herman Brian A
Department of Cellular and Structural Biology, University of Texas Health Science Center, San Antonio, TX 78229, USA.
Cytometry A. 2006 Aug 1;69(8):912-9. doi: 10.1002/cyto.a.20297.
Spectral Imaging Microscopy is gaining attention in biological research. Most of the commercial systems in vogue employ linear spectral un-mixing algorithms and/or spectral profile matching algorithms to extract the component spectral information from the measured specimen spectra. The need to accurately deconvolve multiple spectra with minimal cross-contamination is always accompanied by an increase in system complexity and cost.
We describe here a variant of the spectral waveform cross-correlation analysis (SWCCA) method where the master reference spectral library is constructed by composite spectra with varying ratios of component spectra, unlike the conventional spectral library where pure spectra form the components. We demonstrate that this spectral kinetics ratiometric approach gives realistic estimates of fluorophore distribution in living cells with a better spectral correlation as compared with pure component spectral libraries.
Biological applications demonstrated in this article include acceptor photobleaching FRET, caspase activity during cell death and mitochondrial membrane polarization kinetics during substrate metabolism.
Beyond the representative applications presented in this article, we think the proposed approach can be valuable in dynamic studies of a variety of other cellular processes such as pH oscillations, photobleaching and quenching kinetics. Besides giving better spectral correlation and real-time monitoring of biophysical processes in living cells, this method can serve as an economical solution for high-throughput spectral classification requirements.
光谱成像显微镜在生物学研究中越来越受到关注。大多数流行的商业系统采用线性光谱解混算法和/或光谱轮廓匹配算法，从测量的样本光谱中提取成分光谱信息。要在最小交叉污染的情况下准确地对多个光谱进行去卷积，总是伴随着系统复杂性和成本的增加。
我们在此描述一种光谱波形互相关分析（SWCCA）方法的变体，其中主参考光谱库由具有不同成分光谱比例的复合光谱构建，这与由纯光谱构成成分的传统光谱库不同。我们证明，与纯成分光谱库相比，这种光谱动力学比率方法能够更真实地估计活细胞中荧光团的分布，并且具有更好的光谱相关性。
本文展示的生物学应用包括受体光漂白荧光共振能量转移、细胞死亡过程中的半胱天冬酶活性以及底物代谢过程中的线粒体膜极化动力学。
除了本文介绍的代表性应用外，我们认为所提出的方法在诸如pH振荡、光漂白和猝灭动力学等各种其他细胞过程的动态研究中可能具有价值。除了能提供更好的光谱相关性和对活细胞生物物理过程进行实时监测外，该方法还可作为满足高通量光谱分类要求的一种经济解决方案。
