Fast quantitative time lapse displacement imaging of endothelial cell invasion

Abstract

In order to unravel rapid mechano-chemical feedback mechanisms in sprouting angiogenesis, we combine selective plane illumination microscopy (SPIM) and tailored image registration algorithms -further referred to as SPIM-based displacement microscopy -with an in vitro model of angiogenesis. SPIM successfully tackles the problem of imaging large volumes while upholding the spatial resolution required for the analysis of matrix displacements at a subcellular level. Applied to in vitro angiogenic sprouts, this unique methodological combination relates subcellular activity -minute to second time scale growing and retracting of protrusions -of a multicellular systems to the surrounding matrix deformations with an exceptional temporal resolution of 1 minute for a stack with multiple sprouts simultaneously or every 4 seconds for a single sprout, which is 20 times faster than with a conventional confocal setup. Our study reveals collective but non-synchronised, non-continuous activity of adjacent sprouting cells along with correlations between matrix deformations and protrusion dynamics. OPEN ACCESS Citation: Steuwe C, Vaeyens M-M, Jorge-Peñas A, Cokelaere C, Hofkens J, Roeffaers MBJ, et al. (2020) Fast quantitative time lapse displacement imaging of endothelial cell invasion. PLoS ONE 15 (1): e0227286. https://doi.org/10.

The role of cellular traction forces in deciphering nuclear mechanics

“…Using this technique, raw input images can be computed using a graphical user interface. The built-in free-form deformation algorithm and physical-based nonlinear inverse method render this technique suitable for computing cellular traction force in 2D, 3D, and time-lapse 4D in single-and multiple-cell systems [157][158][159][160].…”

Luminescence lifetime imaging of three-dimensional biological objects

“…Organoids and other advanced 'engineered tissue' 3D models permit real-time, quantitative monitoring of genome organization (Lu et al, 2008;Hoppe et al, 2008;Wollman et al, 2020), self-organization of cellular processes (Lu et al, 2008;Hoppe et al, 2008;Wollman et al, 2020), metabolism, intracellular trafficking (Skruzny et al, 2020) and organelle-organelle interactions, as well as cancer cell migration, invasion and metastasis (Alexander et al, 2013;Steuwe et al, 2020), enabling an integrated understanding of the molecular biology of the cell in health and disease. Furthermore, metabolic FLIM and PLIM imaging are valuable tools in developmental biology and research into assisted reproductive technologies (Ma et al, 2019;Sosnik et al, 2016;Stringari et al, 2011Stringari et al, , 2012, and hold promise for improving tissue engineering, recreating stem cell niches and tumor microenvironments and developing advanced tissues-on-a-chip systems (Okkelman et al, 2020b;Ozturk et al, 2020;Floudas et al, 2020;Barron et al, 2020).…”

TFMLAB: a MATLAB toolbox for 4D traction force microscopy

“…Using the GUI set_dispCalc the user can choose between different optimizers, distance metrics and can tune the spacing between the control points of the B-spline curves to adapt it to experimental conditions of each sample. The main function then calls timeSeriesDispCalc, which uses the Elastix toolbox for efficient image registration as previously described [29], [30], [40]. The cell segmentations S k can be used as masks to ignore beads engulfed by the cells.…”