Abstract
Measuring the organisation 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 fibre 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 neighbourhood 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 neighbourhood 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 neighbourhood sizes, as well as to measure the decay in alignment over progressively increasing length scales.
Highlights
Measuring the anisotropy of features in biological images is of increasing interest as the degree of alignment can inform on both the underlying cellular behaviors and material properties of the sample
If the image contains aligned features in the real space, the corresponding fast Fourier transform (FFT) in the frequency domain will be asymmetrically skewed, with the direction of skew orthogonal to the original feature orientation (Figure 1A,B). This process can be repeated on each successive window across the image, resulting in a vector field that represents the local alignment in the image
3.1.1 Local Alignment Square windows of n x n pixels are chosen from the original real space image and a 2D FFT is performed
Summary
Measuring the anisotropy of features in biological images is of increasing interest as the degree of alignment can inform on both the underlying cellular behaviors and material properties of the sample. Subcellular cytoskeletal networks can spontaneously organize in response to the stresses of their environment (Gupta et al, 2015, 2019) or changes in biochemical signalling (Ridley and Hall, 1992). The extracellular matrix (ECM) has an inherent capacity to align in different tissues or pathologies, such as cancer (Ouellette et al, 2021) or tissue fibrosis (Park et al, 2020; Mascharak et al, 2021), which is thought to alter the ECM network mechanical properties. These examples illustrate the variety of environments where alignment of features reveals important biological properties. It is necessary to develop approaches to efficiently quantify anisotropy of features across a range of length scales, from subcellular organization to tissue level alignment
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