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.

中文翻译：

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使用光谱加权的无串扰多色 RICS

光栅图像互相关光谱 (ccRICS) 可用于通过分析两色共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，将多色实施限制为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 (RSICS) 来消除这种限制，它采用基于光谱信息的统计滤波在空间相关分析期间定量分离荧光团的信号。我们通过测试不同级别的光谱重叠或不同的相对信号强度如何影响相关函数并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 来解析发射光谱非常相似的染料，并在活细胞中进行 RSICS，以同时分析携带三种不同荧光蛋白标记（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每个荧光团仅用一个检测通道记录的数据，突出了该方法对使用任何类型的多色检测获得的数据的普遍适用性。总之，RSICS 能够对用不同荧光团标记的多个物种的浓度、迁移率和相互作用进行无伪影定量分析。它可以在商用激光扫描显微镜上进行，并且该算法可以很容易地扩展到其他图像相关方法。因此，