Time-lapse confocal imaging datasets to assess structural and dynamic properties of subcellular nanostructures

Abstract

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.

Insulin secretory granules labelled with phogrin-fluorescent proteins show alterations in size, mobility and responsiveness to glucose stimulation in living β-cells

“…The workflow from time-lapse imaging of ISGs to the derivation of their structural/dynamic properties has been carefully assessed and validated in a recently published work 24 and shown for a typical experiment on ISGs in Fig. 1.…”

“…Spatiotemporal fluorescence fluctuation spectroscopy allows quantitative measurement of average structural and dynamic properties for molecules 18–21 or sub-cellular organelles 22–24 . This live-cell-imaging approach does not require any preliminary assumptions or knowledge of the system.…”

“…A distinguishing feature of i MSD is that it yields dynamic information combined with the average size of the moving objects 18,22 . The dynamic properties of the same objects, such as their average diffusivity or mode of motion, are extracted by fitting the i MSD trace 22–24 .…”

Lysosome Dynamic Properties during Neuronal Stem Cell Differentiation Studied by Spatiotemporal Fluctuation Spectroscopy and Organelle Tracking

“…The iMSD processing of the acquired image stacks and the subsequent data analysis were carried out with custom scripts in MATLAB (MathWorks Inc., Natick, MA, USA), as described in detail in Refs. [8,13]. For the parameter α at short time scale, the fits had been done for the first seven points of the iMSD, and the results were considered valid only when the anomalous diffusion fit was better, with a confidence level above 95%, than a fit with a constant (F-test, with p-value of 0.05).…”

“…The dynamics of vesicles can be explored by different methods within fluorescence (or, in general, optical) microscopy; amongst these, we selected imaging-derived mean squared displacement (or iMSD) and single-particle tracking (SPT). The former is a method based on the spatiotemporal correlation analysis of fluorescence fluctuation; it allows fast and robust extraction of the average dynamic features of moving objects (from molecules to entire organelles) directly from stacks of images, with no need of calculating the single trajectories [12][13][14]. The latter, through localization, makes it possible to retrieve information on single molecules or vesicles [10,[15][16][17], which are averaged out by the iMSD method.…”