Closed-form density-based framework for automatic detection of cellular morphology changes

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 109; no. 22; pp. 8382 - 8387
• Main Authors: Duong, Tarn, Goud, Bruno, Schauer, Kristine
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 29.05.2012; National Acad Sciences
• Subjects: Algorithms; Asymptotic methods; automatic detection; Biological Sciences; Cell Line; Cells; Comparative analysis; Cytoskeleton; Cytoskeleton - drug effects; Cytoskeleton - metabolism; Density estimation; Estimators; fluorescence microscopy; Green Fluorescent Proteins - genetics; Green Fluorescent Proteins - metabolism; Humans; Image analysis; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Intracellular Space - drug effects; Intracellular Space - metabolism; mathematics and statistics; Microscopy, Fluorescence - methods; Microtubules; Microtubules - drug effects; Microtubules - metabolism; Models, Biological; Molecular Dynamics Simulation; Multivariate Analysis; Multivesicular Bodies - drug effects; Multivesicular Bodies - metabolism; Nocodazole - pharmacology; Null hypothesis; P values; Physical Sciences; Proteins; Reproducibility of Results; Scatter plots; scientists; Statistical variance; Statistics; Tetraspanin 30 - genetics; Tetraspanin 30 - metabolism; Transfection; Tubulin Modulators - pharmacology
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1117796109

A primary method for studying cellular function is to examine cell morphology after a given manipulation. Fluorescent markers attached to proteins/intracellular structures of interest in conjunction with 3D fluorescent microscopy are frequently exploited for functional analysis. Despite the central role of morphology comparisons in cell biological approaches, few statistical tools are available that allow biological scientists without a high level of statistical training to quantify the similarity or difference of fluorescent images containing multifactorial information. We transform intracellular structures into kernels and develop a multivariate two-sample test that is nonparametric and asymptotically normal to directly and quantitatively compare cellular morphologies. The asymptotic normality bypasses the computationally intensive calculations used by the usual resampling techniques to compute the P-value. Because all parameters required for the statistical test are estimated directly from the data, it does not require any subjective decisions. Thus, we provide a black-box method for unbiased, automated comparison of cell morphology. We validate the performance of our test statistic for finite synthetic samples and experimental data. Employing our test for the comparison of the morphology of intracellular multivesicular bodies, we detect changes in their distribution after disruption of the cellular microtubule cytoskeleton with high statistical significance in fixed samples and live cell analysis. These results demonstrate that density-based comparison of multivariate image information is a powerful tool for automated detection of cell morphology changes. Moreover, the underlying mathematics of our test statistic is a general technique, which can be applied in situations where two data samples are compared.