Journal: NPJ Systems Biology and Applications

Article Title: A deep learning framework for quantitative analysis of actin microridges

doi: 10.1038/s41540-023-00276-7

Figure Lengend Snippet: a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a Gaussian fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.

Article Snippet: Gaussian curvature ( https://www.mathworks.com/matlabcentral/fileexchange/11168-surface-curvature ) was modified to compute the Gauss gradient with σ = 1.2 μm using ( https://www.mathworks.com/matlabcentral/fileexchange/8060-gradient-using-first-order-derivative-of-gaussian ) to extract the first and second derivatives at each point in the image.

Techniques: Sequencing