Noise reduction and quantification of fiber orientations in greyscale images

Fourier transform-based method for quantifying the three-dimensional orientation distribution of fibrous units

Several materials and tissues are characterized by a microstructure composed of fibrous units embedded in a ground matrix. In this paper, a novel three-dimensional (3D) Fourier transform-based method for quantifying the distribution of fiber orientations is presented. The method allows for an accurate identification of individual fiber families, their in-plane and out-of-plane dispersion, and showed fast computation times. We validated the method using artificially generated 3D images, in terms of fiber dispersion by considering the error between the standard deviation of the reconstructed and the prescribed distributions of the artificial fibers. In addition, we considered the measured mean orientation angles of the fibers and validated the robustness using a measure of fiber density. Finally, the method is employed to reconstruct a full 3D view of the distribution of collagen fiber orientations based on in vitro second harmonic generation microscopy of collagen fibers in human and mouse skin. The dispersion parameters of the reconstructed fiber network can be used to inform mechanical models of soft fiber-reinforced materials and biological tissues that account for non-symmetrical fiber dispersion.

Development of a facile method to compute collagen network pathological anisotropy using AFM imaging

Type I collagen, a fundamental extracellular matrix (ECM) component, is pivotal in maintaining tissue integrity and strength. It is also the most prevalent fibrous biopolymer within the ECM, ubiquitous in mammalian organisms. This structural protein provides essential mechanical stability and resilience to various tissues, including tendons, ligaments, skin, bone, and dentin. Collagen has been structurally investigated for several decades, and variation to its ultrastructure by histology has been associated with several pathological conditions. The current study addresses a critical challenge in the field of collagen research by providing a novel method for studying collagen fibril morphology at the nanoscale. It offers a computational approach to quantifying collagen properties, enabling a deeper understanding of how collagen type I can be affected by pathological conditions. The application of Fast Fourier Transform (FFT) coupled with Atomic Force Microscope (AFM) imaging distinguishes not only healthy and diseased skin but also holds potential for automated diagnosis of connective tissue disorders (CTDs), contributing to both clinical diagnostics and fundamental research in this area. Here we studied the changes in the structural parameters of collagen fibrils in Ehlers Danlos Syndrome (EDS). We have used skin extracted from genetically mutant mice that exhibit EDS phenotype as our model system (Col1a1Jrt/+ mice). The collagen fibrils were analyzed by AFM based descriptive-structural parameters, coupled with a 2D Fast Fourier Transform(2D-FFT) approach that automated the analysis of AFM images. In addition, each sample was characterized based on its FFT and power spectral density. Our qualitative data showed morphological differences in collagen fibril clarity (clearness of the collagen fibril edge with their neighbouring fibri), D-banding, orientation, and linearity. We have also demonstrated that FFT could be a new tool for distinguishing healthy from tissues with CTDs by measuring the disorganization of fibrils in the matrix. We have also employed FFT to reveal the orientations of the collagen fibrils, providing clinically relevant phenotypic information on their organization and anisotropy. The result of this study can be used to develop a new automated tool for better diagnosis of CTDs.

A method to analyze the influence of mechanical strain on dermal collagen morphologies

Collagen fibers and their orientation play a major role in the mechanical behavior of soft biological tissue such as skin. Here, we present a proof-of-principle study correlating mechanical properties with collagen fiber network morphologies. A dedicated multiphoton stretching device allows for mechanical deformations in combination with a simultaneous analysis of its collagen fiber network by second harmonic generation imaging (SHG). The recently introduced Fiber Image Network Evaluation (FINE) algorithm is used to obtain detailed information about the morphology with regard to fiber families in collagen network images. To demonstrate the potential of our method, we investigate an isotropic and an anisotropic ex-vivo dorsal pig skin sample under quasi-static cyclic stretching and relaxation sequences. Families of collagen fibers are found to form a partially aligned collagen network under strain. We find that the relative force uptake is accomplished in two steps. Firstly, fibers align within their fiber families and, secondly, fiber families orient in the direction of force. The maximum alignment of the collagen fiber network is found to be determined by the largest strain. Isotropic and anisotropic samples reveal a different micro structural behavior under repeated deformation leading to a similar force uptake after two stretching cycles. Our method correlates mechanical properties with morphologies in collagen fiber networks.

X-ray-Based Techniques to Study the Nano-Bio Interface

X-ray-based analytics are routinely applied in many fields, including physics, chemistry, materials science, and engineering. The full potential of such techniques in the life sciences and medicine, however, has not yet been fully exploited. We highlight current and upcoming advances in this direction. We describe different X-ray-based methodologies (including those performed at synchrotron light sources and X-ray free-electron lasers) and their potentials for application to investigate the nano-bio interface. The discussion is predominantly guided by asking how such methods could better help to understand and to improve nanoparticle-based drug delivery, though the concepts also apply to nano-bio interactions in general. We discuss current limitations and how they might be overcome, particularly for future use in vivo.

Electrical and network properties of flexible silver-nanowire composite electrodes under mechanical strain

Flexible and conductive silver-nanowire photopolymer composites are fabricated and studied under mechanical strain. The initial resistances of the unstretched flexible composites are between 0.27 Ω mm-1 and 1.2 Ω mm-1 for silver-nanowire concentrations between 120 μg cm-2 and 40 μg cm-2. Stretching of the samples leads to an increased resistance by a factor of between 72 for 120 μg cm-2 and 343 for 40 μg cm-2 at elongations of 23%. In order to correlate network morphology and electrical properties, micrographs are recorded during stretching. The Fiber Image Network Evaluation (FINE) algorithm determines morphological silver-nanowire network properties under stretching. For unstretched and stretched samples, an isotropic nanowire network is found with only small changes in fiber orientation. Monte-Carlo simulations on 2D percolation networks of 1D conductive wires and the corresponding network resistance due to tunneling of electrons at nanowire junctions confirm that the elastic polymer matrix under strain exhibits forces in agreement with Hooke's law. By variation of a critical force distribution the resistance curves are accurately reproduced. This results in a model that is dominated by quantum-mechanical tunneling at nanowire junctions explaining the electrical behavior and the sensitivity of nanowire-composites with different filler concentrations under mechanical strain.

General method for classification of fiber families in fiber-reinforced materials: application to in-vivo human skin images

Fiber structures play a major role for the function of fiber-reinforced materials such as biological tissue. An objective classification of the fiber orientations into fiber families is crucial to understand its mechanical properties. We introduce the Fiber Image Network Evaluation Algorithm (FINE algorithm) to classify and quantify the number of fiber families in scientific images. Each fiber family is characterized by an amplitude, a mean orientation, and a dispersion. A new alignment index giving the averaged fraction of aligned fibers is defined. The FINE algorithm is validated by realistic grayscale Monte-Carlo fiber images. We apply the algorithm to an in-vivo depth scan of second harmonic generation images of dermal collagen in human skin. The derived alignment index exhibits a crossover at a critical depth where two fiber families with a perpendicular orientation around the main tension line arise. This strongly suggests the presence of a transition from the papillary to the reticular dermis. Hence, the FINE algorithm provides a valuable tool for a reliable classification and a meaningful interpretation of in-vivo collagen fiber networks and general fiber reinforced materials.