Department of Chemistry, Center for Integrated Protein Science (CIPSM), Nanosystems Initiative München (NIM) and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität München, München, Germany.
Dynamic Bioimaging Lab, Hasselt University, Diepenbeek, Belgium; Advanced Optical Microscopy Centre, Biomedical research institute (BIOMED), Hasselt University, Diepenbeek, Belgium; Molecular Imaging and Photonics, Chemistry Department, KU Leuven, Heverlee, Belgium.
Methods. 2018 May 1;140-141:97-111. doi: 10.1016/j.ymeth.2018.01.022. Epub 2018 Feb 22.
Raster image cross-correlation spectroscopy (ccRICS) can be used to quantify the interaction affinities between diffusing molecules by analyzing the fluctuations between two-color confocal images. Spectral crosstalk compromises the quantitative analysis of ccRICS experiments, limiting multicolor implementations to dyes with well-separated emission spectra. Here, we remove this restriction by introducing raster spectral image correlation spectroscopy (RSICS), which employs statistical filtering based on spectral information to quantitatively separate signals of fluorophores during spatial correlation analysis. We investigate the performance of RSICS by testing how different levels of spectral overlap or different relative signal intensities affect the correlation function and analyze the influence of statistical filter quality. We apply RSICS in vitro to resolve dyes with very similar emission spectra, and carry out RSICS in live cells to simultaneously analyze the diffusion of molecules carrying three different fluorescent protein labels (eGFP, Venus and mCherry). Finally, we successfully apply statistical weighting to data that was recorded with only a single detection channel per fluorophore, highlighting the general applicability of this method to data acquired with any type of multicolor detection. In conclusion, RSICS enables artifact-free quantitative analysis of concentrations, mobility and interactions of multiple species labeled with different fluorophores. It can be performed on commercial laser scanning microscopes, and the algorithm can be easily extended to other image correlation methods. Thus, RSICS opens the door to quantitative multicolor fluctuation analyses of complex (bio-) molecular systems.
光栅图像互相关光谱学（ccRICS）可通过分析双色共聚焦图像之间的波动来量化扩散分子之间的相互作用亲和力。光谱串扰会影响 ccRICS 实验的定量分析，这限制了多色实验只能使用发射光谱分离良好的染料。在这里，我们通过引入光栅光谱图像相关光谱学（RSICS）来消除这种限制，RSICS 采用基于光谱信息的统计滤波来在空间相关分析过程中定量分离荧光团的信号。我们通过测试不同程度的光谱重叠或不同的相对信号强度如何影响相关函数以及分析统计滤波器质量的影响来研究 RSICS 的性能。我们在体外应用 RSICS 来解析具有非常相似发射光谱的染料，并在活细胞中进行 RSICS 分析，以同时分析携带三种不同荧光蛋白标签（eGFP、Venus 和 mCherry）的分子的扩散。最后，我们成功地将统计加权应用于仅每个荧光团使用单个检测通道记录的数据，突出了这种方法对任何类型的多色检测获取的数据的普遍适用性。总之，RSICS 能够实现对用不同荧光团标记的多种物质的浓度、迁移率和相互作用进行无伪影的定量分析。它可以在商用激光扫描显微镜上进行，并且算法可以轻松扩展到其他图像相关方法。因此，RSICS 为复杂（生物）分子系统的定量多色波动分析开辟了道路。
