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Dynamic fingerprinting of sub-cellular nanostructures by image mean square displacement analysis.

ABSTRACT: Here we provide demonstration that image mean square displacement (iMSD) analysis is a fast and robust platform to address living matter dynamic organization at the level of sub-cellular nanostructures (e.g. endocytic vesicles, early/late endosomes, lysosomes), with no a-priori knowledge of the system, and no need to extract single trajectories. From each iMSD, a unique triplet of average parameters (namely: diffusivity, anomalous coefficient, size) are extracted and represented in a 3D parametric space, where clustering of single-cell points readily defines the structure "dynamic fingerprint", at the whole-cell-population level. We demonstrate that different sub-cellular structures segregate into separate regions of the parametric space. The potency of this approach is further proved through application to two exemplary, still controversial, cases: i) the intracellular trafficking of lysosomes, comprising both free diffusion and directed motion along cytoskeletal components, and ii) the evolving dynamic properties of macropinosomes, passing from early to late stages of intracellular trafficking. We strongly believe this strategy may represent a flexible, multiplexed platform to address the dynamic properties of living matter at the sub-cellular level, both in the physiological and pathological state.

SUBMITTER: Digiacomo L

PROVIDER: S-EPMC5665924 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Fluorescence microscopy and spectroscopy techniques are commonly used to investigate complex and interacting biological systems (e.g. proteins and nanoparticles in living cells), since these techniques can explore intracellular dynamics with high time resolution at the nanoscale. Here we extended one of the Image Correlation Spectroscopy (ICS) methods, i.e. the image Mean Square Displacement, in order to study 2-dimensional diffusive and flow motion in confined systems, whose driving speed is uniformly distributed in a variable angular range. Although these conditions are not deeply investigated in the current literature, they can be commonly found in the intracellular trafficking of nanocarriers, which diffuse in the cytoplasm and/or may move along the cytoskeleton in different directions. The proposed approach could reveal the underlying system's symmetry using methods derived from fluorescence correlation concepts and could recover dynamic and geometric features which are commonly done by single particle analyses. Furthermore, it improves the characterization of low-speed flow motions, when compared to SpatioTemporal Image Correlation Spectroscopy (STICS). Although we present a specific example (lipoplexes in living cells), the emphasis is in the discussion of the method, its basic assumptions and its validation on numeric simulations.Recent advances in nanoparticle-based drug and gene delivery systems have pointed out the interactions at cellular and subcellular levels as key-factors for the efficiency of the adopted biomaterials. Such biochemical and biophysical interactions drive and affect the intracellular dynamics, that is commonly characterized by means of fluorescence microscopy and spectroscopy techniques. Here we present a novel Image Correlation Spectroscopy (ICS) method as a promising tool to capture the intracellular behavior of nanoparticles with high resolution and low background's sensitivity. This study overcomes some of the approximations adopted so far, by decoupling the flow terms of the investigated dynamics and thus recovering ensemble's information from specific single particle behaviors. Finally, relevant implications for nanoparticle-based drug delivery are shown.

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Dynamic fingerprinting of sub-cellular nanostructures by image mean square displacement analysis.

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