A FIJI macro for quantifying pattern in extracellular matrix

Wershof, Esther; Park, Danielle; Barry, David J.; Jenkins, Robert P.; Rullan, Antonio; Wilkins, Anna; Schlegelmilch, Karin; Roxanis, Ioannis; Anderson, Kurt I.; Bates, Paul A.; Sahai, Erik

doi:10.26508/lsa.202000880

Cited by 97 publications

(72 citation statements)

References 32 publications

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“…The performance of the algorithm presented in this work was compared with OrientationJ (Rezakhaniha et al, 2012) and TWOMBLI (Wershof et al, 2021), by using two 10-image samples of fibronectin-stained cell-derived matrices that were either isotropic or anisotropic. To access the alignment vector field for OrientationJ, a Fiji macro was designed to batch run the OrientationJ Vector Field plugin.…”

Section: Comparison With Other Available Alignment Toolsmentioning

confidence: 99%

“…Tools based on representation/transformation of the image, such as the Fiji plugins OrientationJ (Rezakhaniha et al, 2012) and FibrilTool (Boudaoud et al, 2014) or the CytoSpectre suite (Kartasalo et al, 2015), supply overall fiber alignment information. Hybrid tools, such as TWOMBLI, which exploit a combination of approaches (i.e., fiber segmentation followed by matrix representation of the image) have also been recently made available (Wershof et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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

Marcotti

Freitas

Troughton

et al. 2021

Front. Comput. Sci.

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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.

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Section: Comparison With Other Available Alignment Toolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Marcotti

Freitas

Troughton

et al. 2021

Front. Comput. Sci.

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“…From deep-learning models to neural networks, improvements in ECM image analysis furthers our ability to explore and characterize the fundamental structures of tissues and organs [ 192 , 193 ]. The development of open-source tools and plugins to accurately track structural changes of the ECM are welcome and needed as we attempt to establish patterns regarding the analysis of such features [ 38 , 194 , 195 ]. Now, more than ever, software and hardware development are advancing hand in hand.…”

Section: Discussionmentioning

confidence: 99%

Optical Microscopy and the Extracellular Matrix Structure: A Review

Poole

Mostaço-Guidolin

2021

Cells

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Biological tissues are not uniquely composed of cells. A substantial part of their volume is extracellular space, which is primarily filled by an intricate network of macromolecules constituting the extracellular matrix (ECM). The ECM serves as the scaffolding for tissues and organs throughout the body, playing an essential role in their structural and functional integrity. Understanding the intimate interaction between the cells and their structural microenvironment is central to our understanding of the factors driving the formation of normal versus remodelled tissue, including the processes involved in chronic fibrotic diseases. The visualization of the ECM is a key factor to track such changes successfully. This review is focused on presenting several optical imaging microscopy modalities used to characterize different ECM components. In this review, we describe and provide examples of applications of a vast gamut of microscopy techniques, such as widefield fluorescence, total internal reflection fluorescence, laser scanning confocal microscopy, multipoint/slit confocal microscopy, two-photon excited fluorescence (TPEF), second and third harmonic generation (SHG, THG), coherent anti-Stokes Raman scattering (CARS), fluorescence lifetime imaging microscopy (FLIM), structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), ground-state depletion microscopy (GSD), and photoactivated localization microscopy (PALM/fPALM), as well as their main advantages, limitations.

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“…PSR fluorescent images (PSR-fluo) were generated with Zeiss LSM 780 CLSM confocal microscope (Carl Zeiss NTS GmbH, Oberkochen, Germany), λ ex 561 nm/λ em 566/670 nm at 40× magnification [ 38 ]. COL1A1, Elastin scans and PSR-fluo images were analyzed with TWOMBLI, an ImageJ/Fiji [ 39 ] plugin to quantify patterns in ECM [ 40 ]. Before analyzing the COL1A1 and Elastin, the Vector ® NovaRED™ color was isolated from the images using a color deconvolution plugin [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Architecture and Composition Dictate Viscoelastic Properties of Organ-Derived Extracellular Matrix Hydrogels

et al. 2021

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The proteins and polysaccharides of the extracellular matrix (ECM) provide architectural support as well as biochemical and biophysical instruction to cells. Decellularized, ECM hydrogels replicate in vivo functions. The ECM’s elasticity and water retention renders it viscoelastic. In this study, we compared the viscoelastic properties of ECM hydrogels derived from the skin, lung and (cardiac) left ventricle and mathematically modelled these data with a generalized Maxwell model. ECM hydrogels from the skin, lung and cardiac left ventricle (LV) were subjected to a stress relaxation test under uniaxial low-load compression at a 20%/s strain rate and the viscoelasticity determined. Stress relaxation data were modelled according to Maxwell. Physical data were compared with protein and sulfated GAGs composition and ultrastructure SEM. We show that the skin-ECM relaxed faster and had a lower elastic modulus than the lung-ECM and the LV-ECM. The skin-ECM had two Maxwell elements, the lung-ECM and the LV-ECM had three. The skin-ECM had a higher number of sulfated GAGs, and a highly porous surface, while both the LV-ECM and the lung-ECM had homogenous surfaces with localized porous regions. Our results show that the elasticity of ECM hydrogels, but also their viscoelastic relaxation and gelling behavior, was organ dependent. Part of these physical features correlated with their biochemical composition and ultrastructure.

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A FIJI macro for quantifying pattern in extracellular matrix

Cited by 97 publications

References 32 publications

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

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

Optical Microscopy and the Extracellular Matrix Structure: A Review

Architecture and Composition Dictate Viscoelastic Properties of Organ-Derived Extracellular Matrix Hydrogels

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