Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Abstract

Cells can crawl, self-heal, and tune their stiffness due to their remarkably dynamic cytoskeleton. As such, reconstituting networks of cytoskeletal biopolymers may lead to a host of active and adaptable materials. However, engineering such materials with precisely tuned properties requires measuring how the dynamics depend on the network composition and synthesis methods. Quantifying such dynamics is challenged by variations across the time, space, and formulation space of composite networks. The protocol here describes how the Fourier analysis technique, differential dynamic microscopy (DDM), can quantify the dynamics of biopolymer networks and is particularly well suited for studies of cytoskeleton networks. DDM works on time sequences of images acquired using a range of microscopy modalities, including laserscanning confocal, widefield fluorescence, and brightfield imaging. From such image sequences, one can extract characteristic decorrelation times of density fluctuations across a span of wave vectors. A user-friendly, open-source Python package to perform DDM analysis is also developed. With this package, one can measure the dynamics of labeled cytoskeleton components or of embedded tracer particles, as demonstrated here with data of intermediate filament (vimentin) networks and active actin-microtubule networks. Users with no prior programming or image processing experience will be able to perform DDM using this software package and associated documentation.

Enzymatic cleaving of entangled DNA rings drives scale-dependent rheological trajectories

“…Moreover all t(q) data roughly follow power-law scaling t(q) B q À2 , expected for diffusive dynamics (rather than subdiffusive or superdiffusive), and the diffusion coefficient is computed via D = t À1 q À2 . 35,70,72,73 Fig. 4C plots D versus t a , as determined from fits of t(q), revealing a strikingly similar trajectory as [hg 0 i(t a )] L and b(t a ) L , with an initial slowing of dynamics (expected for increased elasticity and steric constraints) until reaching a plateau at t a E 100 s for 0.5 U mg À1 .…”

“…We perform DDM on each ROI using the PyDDM package on Github. 72 Following the method of DDM originally described by Cerbino and Trappe, 70 we compute the Fourier transform of the differences between images separated by a given lag time, Dt, for lag times that range from 50 ms (the time between frames) to 10 s. From this DDM analysis, we obtain the image structure function, which we model as D(q, Dt) = A(q)[1 À f (q, Dt)] + B where A(q) is the amplitude, B is the background, and f (q, Dt) is the intermediate scattering function (ISF). The amplitude and background terms depend on the properties of the imaging system, structure of the sample being imaged, and the camera noise.…”

Autonomous assembly and disassembly of gliding molecular robots regulated by a DNA-based molecular controller

“…As a typical wavelength for the analysis, we used 6.1 and 46.7 μm for single filament and swarm, respectively. The program for the DDM analysis was the one provided in (61)(62)(63). The first four frames (including F = 1, which is the value at lag time = 0) of the obtained intermediate scattering function for each wave number were fitted with an exponential function.…”

Enzymatic cleaving of entangled DNA rings drives scale-dependent rheological trajectories

“…We perform DDM on each ROI using the PyDDM package on Github 53 . Following the method of DDM originally described by Cerbino and Trappe 51 , we compute the Fourier transform of the differences between images separated by a given lag time, 𝚫𝒕, for lag times that range from 50 ms (the time between frames) to 10 s. From this DDM analysis, we obtain the image structure function which we model as 𝑫(𝒒, 𝚫𝒕) = 𝑨(𝒒)[𝟏 − 𝒇(𝒒, 𝚫𝒕)] + 𝑩 where 𝑨(𝒒) is the amplitude, 𝑩 is the background, and 𝒇(𝒒, 𝚫𝒕) is the intermediate scattering function (ISF).…”