Methods for Quantifying Fibrillar Collagen Alignment

Abstract

Recent evidence has implicated collagen, particularly fibrillar collagen, in a number of diseases ranging from osteogenesis imperfecta and asthma to breast and ovarian cancer. A key property of collagen that has been correlated with disease has been the alignment of collagen fibers. Collagen can be visualized using a variety of imaging techniques including second-harmonic generation (SHG) microscopy, polarized light microscopy, and staining with dyes or antibodies. However, there exists a great need to easily and robustly quantify images from these modalities for individual fibers in specified regions of interest and with respect to relevant boundaries. Most currently available computational tools rely on calculation of pixel-wise orientation or global window-wise orientation that do not directly calculate or give visible fiber-wise information and do not provide relative orientation against boundaries. We describe and detail how to use a freely available, open-source MATLAB software framework that includes two separate but linked packages "CurveAlign" and "CT-FIRE" that can address this need by either directly extracting individual fibers using an improved fiber tracking algorithm or directly finding optimal representation of fiber edges using the curvelet transform. This curvelet-based framework allows the user to measure fiber alignment on a global, region of interest, and fiber basis. Additionally, users can measure fiber angle relative to manually or automatically segmented boundaries. This tool does not require prior experience of programming or image processing and can handle multiple files, enabling efficient quantification of collagen organization from biological datasets.

Valve leaflet‐inspired elastomeric scaffolds with tunable and anisotropic mechanical properties

“…Confocal images were obtained using an inverted confocal laser scanning microscope (Olympus Fluoview 1000, Tokyo, Japan) under 10× objectives. The images were quantified for its actin orientation using the CurveAlign Software (https://loci.wisc.edu/software/curvealign) as previously described …”

A FIJI Macro for quantifying pattern in extracellular matrix

“…These parameters are saved so that in future runs, the user can skip directly to the processing stage. In processing (steps [11][12][13][14], all of the images are analysed in batch using the parameters generated in the preprocessing stage. An input image of 1MB in size takes approximately 30 seconds to process on a computer with 2.2 GHz Intel Core i7 and 16GB of memory.…”

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods

“…Moreover, curvelets can directly provide an optimal sparse representation of the collagen image and thus can track individual fibers and fiber branches either they are straight or curvy. We have put significant efforts on the development of two open-source CT-based collagen quantification tools: CurveAlign (Schneider et al, 2013;Bredfeldt et al, 2014a;Liu et al, 2017) and CT-FIRE (fiber extraction) (Bredfeldt et al, 2014b). These MATLAB (MathWorks, Natick, MA) tools were designed to meet user-defined features in terms of accuracy, speed, and compatibility.…”

“…We have previously reported the general framework and basic concept of both CT-FIRE (Bredfeldt et al, 2014b) and CurveAlign (Bredfeldt et al, 2014a) as well as the protocol of using CurveAlign for fibrillar collagen alignment quantification (Liu et al, 2017). In this paper, we present an introduction to the curvelets fiber representation with detailed description and validation that has not been previously published as well as some latest developments in both tools.…”