A FIJI Macro for quantifying pattern in extracellular matrix

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

doi:10.1101/867507

Cited by 11 publications

(15 citation statements)

References 32 publications

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“…3D image stacks were analyzed with Fiji/ImageJ. Collagen IV and nidogen deposition patterns were quantified with the TWOMBLI Fiji macro ( 76 ). We used three metrics: (i) the proportion of high-density matrix (% HDM), which indicates the proportion of pixels in a given image that corresponds to immunofluorescence signal within the podocyte matrix; (ii) area of the HDM; and (iii) matrix fiber thickness, which is derived from the area measurement and the length of fibers.…”

Section: Methodsmentioning

confidence: 99%

A basement membrane discovery pipeline uncovers network complexity, regulators, and human disease associations

et al. 2022

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Basement membranes (BMs) are ubiquitous extracellular matrices whose composition remains elusive, limiting our understanding of BM regulation and function. By developing a bioinformatic and in vivo discovery pipeline, we define a network of 222 human proteins and their animal orthologs localized to BMs. Network analysis and screening in C. elegans and zebrafish uncovered BM regulators, including ADAMTS, ROBO, and TGFβ. More than 100 BM network genes associate with human phenotypes, and by screening 63,039 genomes from families with rare disorders, we found loss-of-function variants in LAMA5 , MPZL2 , and MATN2 and show that they regulate BM composition and function. This cross-disciplinary study establishes the immense complexity of BMs and their impact on in human health.

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Section: Methodsmentioning

confidence: 99%

A basement membrane discovery pipeline uncovers network complexity, regulators, and human disease associations

et al. 2022

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“…Automated quantification approaches are promising to improve assessment accuracy of prognostic variables in clinical pathologic practice, and also expand research possibilities by enabling the measurement of larger areas of interest and greater numbers of samples than with current, manually intensive imaging technologies. Some most frequently cited or recently emerged open-source tools include Fiji plugins of OrientationJ [66], Ridge Detection [83], FibrilTool [84], TWOMBLI [85], MATLAB-based CytoSpectre [68], CurveAlign [77,86,87] and CT-FIRE [75,77], and Python-based PyFibre [79]. Users are recommended to follow the tutorials or protocols to test and choose a tool that best meets their needs.…”

Section: Methodsmentioning

confidence: 99%

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer

et al. 2021

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Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows.

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“…Anomalies in fiber length (Fig 5) and pore isotropy maps (Fig 6) were detected at a few intensity thresholds (e.g. 70,80,90) and (10,12,14), respectively (Fig 5, Fig 6). Thus, using our approach, differences in fiber length and pore orientation isotropy could be localized in regions formed at different intensity thresholds.…”

Section: Resultsmentioning

confidence: 96%

A spatial statistical framework for the parametric study of fiber networks: application to fibronectin deposition by normal and activated fibroblasts

Grapa

Efthymiou

Obberghen-Schilling

et al. 2022

Preprint

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Changes in the spatial landscape of the extracellular matrix (ECM) in health and disease significantly impact the surrounding tissues. Quantifying the spatial variations in the fibrillar architecture of major ECM proteins could enable a profound understanding of the link between tissue structure and function. We propose a method to capture relevant ECM features using graph networks for fiber representation in normal and tumor-like states of 4 alternatively spliced isoforms of fibronectin (FN) associated with embryonic development and disease. Then, we construct graph-derived statistical parametric maps, to study the differences across variants in normal and tumor-like architectures. This novel statistical analysis approach, inspired from the analysis of functional magnetic resonance imaging (fMRI) images, provides an appropriate framework for measuring and detecting local variations of meaningful matrix parameters. We show that parametric maps representing fiber length and pore orientation isotropy can be studied within the proposed framework to differentiate among various tissue states. Such tools can potentially lead to a better understanding of dynamic matrix networks within the tumor microenvironment and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention.

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A FIJI Macro for quantifying pattern in extracellular matrix

Cited by 11 publications

References 32 publications

A basement membrane discovery pipeline uncovers network complexity, regulators, and human disease associations

A basement membrane discovery pipeline uncovers network complexity, regulators, and human disease associations

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer

A spatial statistical framework for the parametric study of fiber networks: application to fibronectin deposition by normal and activated fibroblasts

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