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.

Time-lapse optical microscopy datasets from living cells can potentially afford an enormous amount of quantitative information on the relevant structural and dynamic properties of sub-cellular organelles/structures, provided that both the spatial and temporal dimensions are properly sampled during the experiment. Here we provide exemplary live-cell, time-lapse confocal imaging datasets corresponding to three sub-cellular structures of the endo-lysosomal pathway, i.e. early endosomes, late endosomes and lysosomes, along with detailed guidelines to produce analogous experiments. Validation of the datasets is conducted by means of established analytical tools to extract the structural and dynamic properties at the sub-cellular scale, such as Single Particle Tracking (SPT) and imaging derived Mean Square Displacement (iMSD) analyses. In our aim, the present work would help other researchers in the field to reuse the provided datasets for their own scopes, and to combine their creative approaches/analyses to similar acquisitions.

We investigated lysosome dynamics during neuronal stem cell (NSC) differentiation by two quantitative and complementary biophysical methods based on fluorescence: imaging-derived mean square displacement (iMSD) and single-particle tracking (SPT). The former extracts the average dynamics and size of the whole population of moving lysosomes directly from imaging, with no need to calculate single trajectories; the latter resolves the finest heterogeneities and dynamic features at the single-lysosome level, which are lost in the iMSD analysis. In brief, iMSD analysis reveals that, from a structural point of view, lysosomes decrement in size during NSC differentiation, from 1 μm average diameter in the embryonic cells to approximately 500 nm diameter in the fully differentiated cells. Concomitantly, iMSD analysis highlights modification of key dynamic parameters, such as the average local organelle diffusivity and anomalous coefficient, which may parallel cytoskeleton remodeling during the differentiation process. From average to local, SPT allows mapping heterogeneous dynamic responses of single lysosomes in different districts of the cells. For instance, a dramatic decrease of lysosomal transport in the soma is followed by a rapid increase of transport in the projections at specific time points during neuronal differentiation, an observation compatible with the hypothesis that lysosomal active mobilization shifts from the soma to the newborn projections. Our combined results provide new insight into the lysosome size and dynamics regulation throughout NSC differentiation, supporting new functions proposed for this organelle.