piv analysis matlab plugin pivlab  (MathWorks Inc)

Journal: The Journal of Cell Biology

Article Title: Design principles for selective polarization of PAR proteins by cortical flows

doi: 10.1083/jcb.202209111

Figure Lengend Snippet: Extracting advection and diffusion parameters from single particle trajectories. (A) Schematic for decomposing trajectories into advective and diffusive components. Particle displacements are projected onto axes x and y defined as parallel and perpendicular to the local flow vector, respectively. In principle, these should take the form of a Gaussian. Drift is defined by the shift of the mean population displacement (<Δ x >, <Δ y >), along the relevant axis, where we expect <Δ y > ∼ 0. Advection velocity ( υ x ) is then given by mean displacement <Δ x >, divided by the time lag, τ, with the cortex coupling coefficient given by ratio of advection velocity ( υ x ) to local flow velocity c c = υ x / ν . (B) Schematic for extraction of particle motion (PAR-3 clusters shown) and local flow field. Two-channel image series were captured for cortical NMY-2 and the molecule of interest using a HILO imaging regime. The resulting image series were subject to either a Python-based particle tracking scheme or particle image velocimetry (PIVLab, Matlab). Particle displacements for a given τ were then projected onto the relevant x- and y-axes defined by the local flow vector. Note, positive movement on the x-axis generally reflects motion toward the anterior. (C) Distribution of displacements in x and y for simulations of varying D for τ = 0.5 s. (D) Reliability of detection of drift as a function of D . Significance of difference between <Δ x > and <Δ y > calculated using 1,000 random displacements (τ = 0.5 s). Results shown for 20 independent simulations. Student’s t test, unpaired, two-tailed. (E) Mean cc ∼ 1.0 is obtained for all D , though error increases with D . Each point indicates cc measured from 1,000 random displacements from a single simulated dataset in D, with mean indicated. (F and G) Example of the distribution of displacements for NMY-2 (F) and HMR-1 (G) for single embryos (τ = 5 s, n = 1,000 randomly selected steps). In both cases, there is a characteristic drift component along the flow axis (x). Displacements parallel (red) and orthogonal (blue) to the local flow axis are shown. (H and I) Fit values for advection velocity for displacements parallel ( υ x , red) and orthogonal ( υ y , blue) to the flow axis (H) and coupling coefficients (I) shown for NMY-2 and HMR-1, as well as beads immobilized to the exterior of the eggshell. Lines in H connect paired data points from single embryos. Mean values for individual embryos shown in H and I, along with mean of all embryos in I.

Article Snippet: The local flow field of the acto-myosin cortex was measured by applying particle image velocimetry (PIV) to the NMY-2 image channel using the PIVlab MATLAB plugin ( ).

Techniques: Diffusion-based Assay, Single Particle, Plasmid Preparation, Extraction, Imaging, Two Tailed Test

Journal: The Journal of Cell Biology

Article Title: FMNL2 regulates dynamics of fascin in filopodia

doi: 10.1083/jcb.201906111

Figure Lengend Snippet: Single-molecule analysis of fascin behavior in filopodia. (A) HeLa cell protrusion and retraction (percentage and velocity) was measured with the open source ImageJ plugin ADAPT. Left image sequence shows protrusion and retraction velocity along the cell border. Scale bar, 10 μm. (B) mEOS2-fascin was photoconverted in the cell center (perinuclear region), and diffusion of mEOS2-fascin was monitored over time. Shown is a representative time-lapse series with preconversion on the left, followed by pseudocolored ratios of converted/nonconverted intensities. Scale bar = 5 µm. (C) Relative intensity values (% of maximum for each ROI) of photoconverted mEOS2-fascin fluorescence measured for the photoconversion area, unconverted lamellipodia and filopodia over time. Peak intensity times are highlighted with dashed lines. Data from one representative experiment are shown. Fascin moved at ∼3 µm/s from the center toward the periphery. (D) Simulation of monoexponential (red) and biexponential (orange) decay curve fitting for fascin movement out of filopodia. For biexponential decay curves, fast moving fascin was estimated at ∼20%. Simulated data (color) were overlaid with real experimental data (black). (E) Kymographs of small region FRAP experiments (as in ) were analyzed for fascin speed (base to tip movement vs. tip-base movement). Student’s t test; **, P ≤ 0.01. (F) Single-molecule track on one fascin molecule along the filopodia shaft (borders in red) with binding and unbinding behavior. (G) Simulation of single-molecule movement. Cropped tracks show simulated single-molecule tracks along hypothetical filopodium (borders in red). Log10 step-size distribution for a bimodal single molecule behavior. Simulation of step size distribution in X (along filopodia shaft) and Y (perpendicular to filopodia shaft) demonstrates localization precision of the method used, with an error peak ∼0 ± 1 pixel (arrow) and real diffusive motion left and right to the peak (arrowheads). (H) Experimental data for one filopodium showing comparative statistics as in G. Number of tracked molecules was limited as visualized in cropped tracks. Log10 stepsize distribution shows bimodal distribution, and step size distribution graph validates model used (with error peak around XY = 0 and free diffusive motion identified around the peak). As tracks were limited for some filopodia, data from separated filopodia were aggregated and fitted to reduce the variation and extract reliable numbers for diffusion and bound/unbound fractions. (I) Unbound fraction (left) and diffusion constant (right) values for WT and fascin-S39A single molecules extracted from individual filopodia (WT n = 15, S39A n = 18) of three independent experiments. Student’s t test; *, P ≤ 0.05; ns, not significant.

Article Snippet: For bead displacement measurements in 3D collagen gels, beads were tracked, and time-resolved PIV was analyzed using the Matlab plugin PIVlab ( ).

Techniques: Sequencing, Diffusion-based Assay, Fluorescence, Binding Assay

Journal: The Journal of Cell Biology

Article Title: FMNL2 regulates dynamics of fascin in filopodia

doi: 10.1083/jcb.201906111

Figure Lengend Snippet: Fascin dynamics and actin binding are dependent on F-actin turnover, and fascin contributes to local force transmission. (A) Filopodia FRAP on confocal microscope for evaluation of the influence of cytoskeleton on fascin movement without (CTR, black) or with Kinesore (orange) or Jasplakinolide (blue); n = 9 cells for each; data were normalized to the mean value of control filopodia to allow comparison between the four independent experiments. (B) Comparison of FRAP recovery curves of GFP-fascin or GFP alone showed early influx retention of fascin, suggesting active transport or regulated entry and not passive diffusion. FRAP curves were compared by two-way ANOVA, and t 1/2 values, using Student’s t test; *, P ≤ 0.05. (C) Photoconversion of filopodia of mEOS2-fascin expressing HeLa cells that were left untreated or treated with Jasplakinolide. Representative time-lapse experiment is shown. Scale bar, 2 μm. (D) Mean relative intensity data of photoconversion experiment (as in B) for control and Jasplakinolide-treated cells. n = 7 cells from one of two independent experiments. (E) Fascin–actin interaction was evaluated over time using live FLIM-FRET in growing, shrinking, and stable filopodia. Graphs show FRET efficiency (calculated relative to GFP only lifetime) at the base and the tip of filopodia at the beginning (before) and at the end (after) of the respective process. Groups were compared using two-way ANOVA followed by a Bonferroni posttest, n = 14 for each condition and representative of three independent experiments; *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ns, not significant. (F) Soft and stiff 3D collagen matrix visualized by confocal reflection microscopy. Collagen fibers were bundled and stiffened using 200 mM ribose. Scale bar = 10 µm. (G) HeLa cells expressing GFP only (left) and GFP-fascin in soft 3D collagen matrix with embedded 200-nm beads. Images were captured using LLSM. Scale bar = 5 µm. (H) Analysis of beads only or cells with beads in 3D ECM using PIVlab identifies pulling and pushing forces exerted by the cell on the ECM. Matrix deformation is pseudocolored depending on matrix velocity. Vector arrows indicate the direction of matrix movement, and the size corresponds to the velocity magnitude. The cell body is excluded in the analysis (red mask). Scale bar, 5 μm. (I) Velocity data were separated into slow (1–1.5 µm/s, top panel) and fast (2–2.5 µm/s, bottom panel) ECM movement, and percentages are shown on bar graphs for GFP only and GFP-fascin–expressing cells in soft matrix (left) and GFP-fascin–expressing cells in soft and stiff matrix (right). n = 15 for each condition and representative of three independent experiments; *, P ≤ 0.05; **, P ≤ 0.01.

Article Snippet: For bead displacement measurements in 3D collagen gels, beads were tracked, and time-resolved PIV was analyzed using the Matlab plugin PIVlab ( ).

Techniques: Binding Assay, Transmission Assay, Microscopy, Control, Comparison, Diffusion-based Assay, Expressing, Plasmid Preparation