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Bioingeniería

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published: November 11, 2020
doi:
10.3791/61931
,
1Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging,University of Wisconsin-Madison, 2Departments of Medical Physics and Biomedical Engineering,University of Wisconsin-Madison, 3Morgridge Institute for Research, Madison, Wisconsin, 4test,test

Summary

Here, we present a protocol to use a curvelet transform-based, open-source MATLAB software tool for quantifying fibrillar collagen organization in the extracellular matrix of both normal and diseased tissues. This tool can be applied to images with collagen fibers or other types of line-like structures.

Abstract

Fibrillar collagens are prominent extracellular matrix (ECM) components, and their topology changes have been shown to be associated with the progression of a wide range of diseases including breast, ovarian, kidney, and pancreatic cancers. Freely available fiber quantification software tools are mainly focused on the calculation of fiber alignment or orientation, and they are subject to limitations such as the requirement of manual steps, inaccuracy in detection of the fiber edge in noisy background, or lack of localized feature characterization. The collagen fiber quantitation tool described in this protocol is characterized by using an optimal multiscale image representation enabled by curvelet transform (CT). This algorithmic approach allows for the removal of noise from fibrillar collagen images and the enhancement of fiber edges to provide location and orientation information directly from a fiber, rather than using the indirect pixel-wise or window-wise information obtained from other tools. This CT-based framework contains two separate, but linked, packages named “CT-FIRE” and “CurveAlign” that can quantify fiber organization on a global, region of interest (ROI), or individual fiber basis. This quantification framework has been developed for more than ten years and has now evolved into a comprehensive and user-driven collagen quantification platform. Using this platform, one can measure up to about thirty fiber features including individual fiber properties such as length, angle, width, and straightness, as well as bulk measurements such as density and alignment. Additionally, the user can measure fiber angle relative to manually or automatically segmented boundaries. This platform also provides several additional modules including ones for ROI analysis, automatic boundary creation, and post-processing. Using this platform does not require prior experience of programming or image processing, and it can handle large datasets including hundreds or thousands of images, enabling efficient quantification of collagen fiber organization for biological or biomedical applications.

Introduction

Fibrillar collagens are prominent, structural ECM components. Their organization changes impact tissue function and are likely associated with the progression of many diseases ranging from osteogenesis imperfecta1, cardiac dysfunction2, and wound healing3 to different types of cancer including breast4,5,6, ovarian7,8, kidney9, and pancreatic cancers10. Many established imaging modalities can be used to visualize fibrillar collagen such as second harmonic generation microscopy11, stains or dyes in conjunction with bright field or fluorescence microscopy or polarized light microscopy12, liquid crystal-based polarization microscopy (LC-PolScope)13, and electron microscopy14. As the importance of fibrillar collagen organization has become clearer, and the use of these methods has increased, the need for improved collagen fiber analysis approaches has also grown.

There have been many efforts to develop computational methods for automated measurement of fibrillar collagen. Freely available software tools are mainly focused on the calculation of fiber alignment or orientation by adopting either first derivative or structure tensor for pixels15,16, or Fourier transform-based spectrum analysis for image tiles17. All these tools are subject to limitations such as the requirement of manual steps, inaccuracy in detection of the fiber edge in noisy background, or lack of localized feature characterization.

Compared to other freely available open-source free software tools, the methods described in this protocol use CT—an optimal, multiscale, directional image representation method—to remove noise from fibrillar collagen images and enhance or track fiber edges. Information about location and orientation can be provided directly from a fiber rather than by using the indirect pixel-wise or window-wise information to infer the metrics of fiber organization. This CT-based framework18,19,20,21 can quantify fiber organization on a global, ROI, or fiber basis, mainly via two separate, but linked, packages named “CT-FIRE”18,21 and “CurveAlign”19,21. As far as the implementation of the software is concerned, in CT-FIRE, CT coefficients on multiple scales can be used to reconstruct an image that enhances edges and reduces noise. Then, an individual fiber extraction algorithm is applied to the CT-reconstructed image to track fibers for finding their representative center points, extending fiber branches from the center points, and linking fiber branches to form a fiber network. In CurveAlign, CT coefficients on a user-specified scale can be used to track local fiber orientation, where the orientation and locations of curvelets are extracted and grouped to estimate the fiber orientation at the corresponding locations. This resulting quantification framework has been developed for more than ten years and has evolved greatly in many aspects such as functionality, user interface, and modularity. For instance, this tool can visualize local fiber orientation and allows the user to measure up to thirty fiber features including individual fiber properties such as length, angle, width, and straightness, as well as bulk measurements such as density and alignment. Additionally, the user can measure fiber angle relative to manually or automatically segmented boundaries, which, for example, plays an important role in image-based biomarker development in breast cancer22 and pancreatic cancer studies10. This platform provides several feature modules including ones for ROI analysis, automatic boundary creation, and post-processing. The ROI module can be used to annotate different shapes of ROI and conduct corresponding ROI analysis. As an application example, the automatic boundary creation module can be used to register hematoxylin and eosin (H&E) bright field images with second harmonic generation (SHG) images and generate the image mask of tumor boundaries from the registered H&E images. The post-processing module can help facilitate the processing and integration of output data files from individual images for possible statistical analysis.

This quantification platform does not require prior experience of programming or image processing and can handle large datasets including hundreds or thousands of images, enabling efficient quantification of collagen organization for biological or biomedical applications. It has been widely used in different research fields by many researchers all over the world, including ourselves. There are four main publications on CT-FIRE and CurveAlign18,19,20,21, out of which the first three have been cited 272 times (as of 2020-05-04 according to Google Scholar). A review of the publications that cited this platform (CT-FIRE or CurveAlign) indicates that there are approximately 110 journal papers that directly used it for their analysis, in which approximately 35 publications were collaborative with our group, and the others (~ 75) were written by other groups. For instance, this platform was used for the following studies: breast cancer22,23,24, pancreatic cancer10,25, kidney cancer9,26, wound healing3,27,28,29,30, ovarian cancer8,31,7, uterosacral ligament32, hypophosphatemic dentin33, basal cell carcinoma34, hypoxic sarcoma35, cartilage tissue36, cardiac dysfunction37, neurons38, glioblastoma39, lymphatic contractions40, fibrous cacffolds41, gastric cancer42, microtubule43, and bladder fibrosis44. Figure 1 demonstrates the cancer imaging application of CurveAlign to find the tumor-associated collagen signatures of breast cancer19 from the SHG image. Figure 2 describes a typical schematic workflow of this platform. Although these tools have been reviewed technically18,19,21, and a regular protocol20 for alignment analysis with CurveAlign is also available, a visual protocol that demonstrates all the essential features could be useful. A visualized protocol, as presented here, will facilitate the learning process of using this platform as well as more efficiently address concerns and questions that users might have.

Protocol

NOTE: This protocol describes the use of CT-FIRE and CurveAlign for collagen quantification. These two tools have complementary, but different, main goals and are linked together to some extent. CT-FIRE can be launched from the CurveAlign interface to conduct most operations except for advanced post-processing and ROI analysis. For a full operation of CT-FIRE, it should be launched separately. 1. Image collection and image requirement NOTE: The tool can process any im…

Representative Results

These methods have been successfully applied in numerous studies. Some typical applications include: 1) Conklin et al.22 used CurveAlign to calculate tumor-associated collagen signatures, and found that collagen fibers were more frequently aligned perpendicularly to the duct perimeter in ductal carcinoma in situ (DCIS) lesions; 2) Drifka et al.10 used the CT-FIRE mode in CurveAlign to quantify the stromal collagen alignment for pancreatic ductal adenocarcinoma and normal/ch…

Discussion

This protocol describes the use of CT-FIRE and CurveAlign for fibrillar collagen quantification and can be applied to any image with collagen fibers or other line-like or fiber-like elongated structures suitable for analysis by CT-FIRE or CurveAlign. For example, elastin or elastic fibers could be processed in a similar way on this platform. We have tested both tools on computationally generated synthetic fibers21. Depending on the application, users should choose the analysis mode that is most ap…

Divulgaciones

The authors have nothing to disclose.

Acknowledgements

We thank many contributors and users to CT-FIRE and CurveAlign over the years, including Dr. Rob Nowak, Dr. Carolyn Pehlke, Dr. Jeremy Bredfeldt, Guneet Mehta, Andrew Leicht, Dr. Adib Keikhosravi, Dr. Matt Conklin, Dr. Jayne Squirrell, Dr. Paolo Provenzano, Dr. Brenda Ogle, Dr. Patricia Keely, Dr. Joseph Szulczewski, Dr. Suzanne Ponik and additional technical contributions from Swati Anand and Curtis Rueden. This work was supported by funding from Semiconductor Research Corporation, Morgridge Institute for Research, and NIH grants R01CA199996, R01CA181385 and U54CA210190 to K.W.E.

Materials

CT-FIRE Univerity of Wisconsin-Madison N/A open source software available from https://eliceirilab.org/software/ctfire/
CurveAlign University of Wisconsin-Madison N/A open source software available from https://eliceirilab.org/software/curvealign/
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Referencias

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Tags

Fibrillar CollagenCollagen OrganizationExtracellular MatrixCurvelet TransformCT-FIREImage AnalysisFiber ExtractionRegion Of InterestPost-processing
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Liu, Y., Eliceiri, K. W. Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools. J. Vis. Exp. (165), e61931, doi:10.3791/61931 (2020).
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