Computational Image Modeling for Characterization and Analysis of Intracellular Cargo Transport
Active intracellular cargo transport is essential to survival and function of eukaryotic cells. How this process is controlled spatially and temporally so that the right cargo is delivered to the right destination at the right time remains poorly understood. To address this question, it is essential to characterize and analyze the molecular machinery and spatiotemporal behavior of intracellular transport. To this end, we developed related computational image models. Specifically, to study the molecular machinery of intracellular transport, we developed anisotropic spatial density kernels for reconstruction and segmentation of related super-resolution STORM (stochastic optical reconstruction microscopy) images. To study the spatiotemporal behavior of intracellular transport, we developed hidden Markov models and principal component analysis for representation and analysis of movement of individual transported cargoes. We validated and benchmarked the image models using simulated and actual experimental images. The models and related computational analysis methods developed in this study are general and can be used for studying molecular machinery and spatiotemporal dynamics of other cellular processes.
Keywordsimage modeling intracellular transport spatiotemporal dynamics super-resolution imaging STORM imaging spatial density estimation hidden Markov model principal component analysis
Unable to display preview. Download preview PDF.
- 6.Qiu, M., Yang, G.: Nanometer resolution tracking and modeling of bidirectional axonal cargo transport. In: Proc. IEEE Int. Symp. Biomedical Imaging (ISBI), Barcelona, Spain, pp. 992–995 (2012)Google Scholar
- 8.Jolliffe, I.T.: Principal Component Analysis. Springer (2002)Google Scholar
- 9.Scott, D.W.: Multivariate Density Estimation. John Wiley & Sons (1992)Google Scholar
- 11.Chen, K.C.J., Yu, Y., Li, R., Lee, H.-C., Yang, G., Kovacevic, J.: Adaptive active-mask image segmentation for quantitative characterization of mitochondrial morphology. In: 2012 19th IEEE Int. Conf. Image Processing (ICIP), pp. 2033–2036 (2012)Google Scholar