Application of information-theoretic measures to quantitative analysis of immunofluorescent microscope imaging

The goal of this contribution is to apply model-based information-theoretic measures to the quantification of relative differences between immunofluorescent signals. Several models for approximating the empirical fluorescence intensity distributions are considered, namely Gaussian, Gamma, Beta, and kernel densities. As a distance measure the Hellinger distance and the Kullback–Leibler divergence are considered. For the Gaussian, Gamma, and Beta models the closed-form expressions for evaluating the distance as a function of the model parameters are obtained. The advantages of the proposed quantification framework as compared to simple mean-based approaches are analyzed with numerical simulations. Two biological experiments are also considered. The first is the functional analysis of the p8 subunit of the TFIIH complex responsible for a rare hereditary multi-system disorder—trichothiodystrophy group A (TTD-A). In the second experiment the proposed methods are applied to assess the UV-induced DNA lesion repair rate. A good agreement between our in vivo results and those obtained with an alternative in vitro measurement is established. We believe that the computational simplicity and the effectiveness of the proposed quantification procedure will make it very attractive for different analysis tasks in functional proteomics, as well as in high-content screening.