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. 2017 Nov 1;7:14836. doi: 10.1038/s41598-017-13865-4

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Schematic representation of the iMSD-based dynamic fingerprint analysis. (A) A stack of images of fluorescently-labelled intracellular structures is acquired by time-lapse confocal microscopy. (B) Spatiotemporal correlation function is derived from image analysis by the iMSD algorithm (see Materials and Methods for equations). (C) Gaussian fitting of correlation functions allows to extract the iMSD plot, which in turn depicts the average diffusion law of the structure of interest (exemplary cases are reported: super-diffusion, dotted red line; isotropic diffusion, dashed red line; sub-diffusion, solid red line). (D) Three relevant parameters are chosen from the fitting equation (see Materials and Methods) to quantitatively describe the average dynamic properties of the structure of interest, namely: the short-range diffusion coefficient (D_m), the anomalous diffusion coefficient (α), and the y-axis intercept of the iMSD plot, indicating the average size of the diffusing structures. These three parameters are organized in a 3D plot, used to identify the ‘dynamic fingerprint’ of the diffusing structure.