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Automated computational techniques are essential for the quantitative analysis of cellular dynamics using time-lapse light microscopy.
In recent years, numerous novel techniques using QPM have been developed to enable a stable and quantitative measurement for long-term cellular dynamics using low-coherent illumination [ 7, 8] and diffraction phase microscopy [ 9].
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In parallel with the analysis of cellular morphology, we studied the effect of TUBB3 silencing on the mt dynamics using live cell imaging of A549 cells stably expressing GFP– α-tubulin.
The ideal technology would provide cellular maps of binding and interaction data at diffraction-limited resolution and over a wide range of dynamics using image sequences of migrating cells.
Cellular dynamics were assessed using electric cell-substrate impedance sensing (Applied Biophysics, Troy, USA).
We determined that the Virtual Cell modeling environment [ 36] can be used to study cellular dynamics associated with disease states, particularly spinocerebellar ataxias with altered IP3R1 abundance or sensitivity or both.
We note that this simple model describes the stem cell dynamics with a degree of detail appropriate to our data, and has equivalent scaling behavior to the more complex model previously used to understand cellular dynamics at the crypt base (Ritsma et al., 2014).
One way of monitoring the cellular dynamics of living neurons in mouse tissue is to use hippocampal slices of 5- to 7-day-old mice [ 97, 98].
The methodology described has the potential to be used for the study of the cellular dynamics of a range of signalling processes.
We have recently investigated what type of cellular dynamics is most likely to generate multiple cell types, by using computer simulations of interacting cells with on/off switching expression dynamics.
Recent studies using C. elegans have provided insights into the cellular dynamics surrounding activation of the UPR by other ALS-related proteins, including FUS and TDP-43 (Vaccaro et al., 2012; Vaccaro et al., 2013).
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