A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images

Abstract

Measuring the organization of the cellular cytoskeleton and the surrounding extracellular matrix (ECM) is currently of wide interest as changes in both local and global alignment can highlight alterations in cellular functions and material properties of the extracellular environment. Different approaches have been developed to quantify these structures, typically based on fiber segmentation or on matrix representation and transformation of the image, each with its own advantages and disadvantages. Here we present AFT − Alignment by Fourier Transform, a workflow to quantify the alignment of fibrillar features in microscopy images exploiting 2D Fast Fourier Transforms (FFT). Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. These comparisons reveal that AFT has a number of advantages due to its grid-based FFT approach. 1) Flexibility in defining the window and neighborhood sizes allows for performing a parameter search to determine an optimal length scale to carry out alignment metrics. This approach can thus easily accommodate different image resolutions and biological systems. 2) The length scale of decay in alignment can be extracted by comparing neighborhood sizes, revealing the overall distance that features remain anisotropic. 3) The approach is ambivalent to the signal source, thus making it applicable for a wide range of imaging modalities and is dependent on fewer input parameters than segmentation methods. 4) Finally, compared to segmentation methods, this algorithm is computationally inexpensive, as high-resolution images can be evaluated in less than a second on a standard desktop computer. This makes it feasible to screen numerous experimental perturbations or examine large images over long length scales. Implementation is made available in both MATLAB and Python for wider accessibility, with example datasets for single images and batch processing. Additionally, we include an approach to automatically search parameters for optimum window and neighborhood sizes, as well as to measure the decay in alignment over progressively increasing length scales.

GliaMorph: A modular image analysis toolkit to quantify Müller glial cell morphology

“…We observed a downward migration of nuclei, increased MG height, and increased overall complexity (as indicated by skeleton distributions), particularly in the IPL. We then examined the alignment of structures in the image ( i.e., horizontal vs vertical) that can be described as image order [35] ( Fig. 6B,C ), which showed a significant difference from 60-to-96 hpf (p=0.0049; Fig.…”

“…8M ; p=0.0072) data. To further understand this, we performed directionality analysis based on Fourier Transformation as previously described [35] ( Fig. 8N ).…”

“…To obtain information on MG alignment, AFT, a previously published open-source tool for evaluating the alignment of fibrillar structures across different length scales, was used [35]. This tool allows for local evaluation of alignment by exploiting the representation of small tiles of the image in the Fourier space.…”

A systematic review and comparison of automated tools for quantification of fibrous networks

“…We tested nine tools that could quantify the alignment of the fibrin network (Supplementary Table II): OrientationJ 27 , CurveAlign 28 , GTFiber 29 , CT-FIRE 24 , Cytospectre 30 , Directionality, FOA tool 31 , FiberFit 32 , and Alignment by Fourier Transform (AFT) 33 . Most of the methods measured global alignment (within the whole image), while OrientationJ, GTFiber, and AFT used local alignment measurements (within the local neighborhood).…”

Enoxaparin Increases D6 Receptor Expression and Restores Cytoskeleton Organization in Trophoblast Cells from Preeclampsia

“…If the image in the real space contains aligned and organized features, the corresponding 2D FFT shows an elongated (elliptical) shape in the frequency domain. Conversely, an isotropic signal results in a rounder shape [28]. Measuring the eccentricity of the ellipse in the frequency domain can thus provide a good indicator of cytoskeleton organization.…”