Name: quantifying fibrillar collagen organization with curvelet transform-based tools
Uploaded: 2020-11-11T14:00:00
Description: Watch this Scientific Journal Video about Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools at JoVE.com

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.

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...

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...

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&#8212;an optimal, multiscale, directional image representation method&#8212;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 &#8220;CT-FIRE&#8221;18,21 and &#8220;CurveAlign&#8221;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&#160;ROI analysis. As an application example, the automatic boundary creation module can be used to register hematoxylin and eosin (H&#38;E) bright field images with second harmonic generation (SHG) images and generate the image mask of tumor boundaries from the registered H&#38;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.

<table>
	<tbody>
		<tr>
			<td>CT-FIRE</td>
			<td>Univerity of Wisconsin-Madison</td>
			<td>N/A</td>
			<td>open source software available from https://eliceirilab.org/software/ctfire/</td>
		</tr>
		<tr>
			<td>CurveAlign</td>
			<td>University of Wisconsin-Madison</td>
			<td>N/A</td>
			<td>open source software available from https://eliceirilab.org/software/curvealign/</td>
		</tr>
	</tbody>
</table>

quantifying fibrillar collagen organization with curvelet transform-based tools

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 &#8220;CT-FIRE&#8221; and &#8220;CurveAlign&#8221; 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.

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...

Watch this Scientific Journal Video about Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools at JoVE.com

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

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.

Fibrillar collagens are prominent extracellular matrix (ECM) components, and their topology changes have been shown to be associated with the progression ...

This tool helps to quantify changes in fibrillar collagen organization in the extracellular matrix related to many diseases on a global region of interest and individual fiber basis. This tool uses the curvelet transform, an optimal multi-scale directional image representation method to remove image noises and to enhance fiber edges. Comprehensive and user-friendly modules enable an efficient quantification.For individual fiber extraction, follow the protocol to open the CT-FIRE and click open files to select one or more images of interest. In the parameters panel, click update and adjust the background threshold and the nucleation searching radius. When all of the parameters have been set, click run.After the analysis is done, click any element in the output table to visualize the results and check the ctFIREout subfolder for the output files. To analyze a region of interest, click open files to load one or more images and select the image to be annotated. In the run options panel, select region of interest manager in the dropdown menu and click run to launch the region of interest manager module.Next, click the draw region of interest menu D dropdown menu and draw the regions of interest. If full image CT-FIRE analysis has been conducted and the results are saved in the default directory, click one or more regions of interest in the region of interest list and click CT-FIRE region of interest analyzer to launch the post region of interest analysis module. If full image CT-FIRE analysis has not been conducted, click one or more of the regions of interest in the region of interest list and click apply CT-FIRE on ROI to directly apply the CT-FIRE analysis to the selected regions of interest.For advanced post-processing, check the out. adv checkbox and click post-processing to launch the advanced post-processing graphic interface. In the analysis module, click select file and visualize fibers to allow the fiber number to be entered based on the labels in the original fibers tab.Click confirm update to move to the thresholding operation and check the thresholding box to enable the threshold settings. Select one of the four thresholding options from the dropdown menu and enter the desired thresholds in the thresholds panel for one or more fiber properties. Then click threshold now to apply the above thresholding conditions and check the prompt figure with the name ending with metrics visualization to see the selected fibers overlaid on the original images with the customized color maps.When the threshold has been set, click save fibers to save the selected fiber information and click generate stats and click OK to generate the summary statistics. To combine results from multiple images, check the batch mode and select multiple images to be analyzed before generating the summary statistics as demonstrated. To extract CT-based fiber features, follow the protocol to open the CurveAlign software and click get images in the main graphic user interface and select one or more images or image stacks from the prompt window.Enter the fraction of coeffs to keep value, the fraction of the largest coefficients of CT that will be used in the fiber analysis, and click run. Repeat to set a desirable threshold. When the process is complete, some summary statistics for each image will be displayed in an output table and all of the output files will be automatically saved in a subfolder named CA_out in the directory of the original images.To extract CT-FIRE based fiber features, click the fiber analysis method dropdown menu and select CT-FIRE fibers to use the fiber center point and fiber angle to represent the fiber. For relative alignment analysis of the tumor boundary, select the TIFF boundary condition. Open the CurveAlign software and click get images in the main graphic user interface and select one or more images or image stacks from the prompt window.In the primary parameters panel, enter the distance from the closest boundary to only evaluate the fibers within this distance range. In the output options panel, check the boundary association box to allow visualization of the point on the boundary that is associated with a fiber, fiber segment, or curvelet and click run, then click the output table to visualize the results. If a full image CurveAlign analysis has been conducted and the results are saved in the default directory, click one or more regions of interest in the region of interest list and click CurveAlign region of interest analyzer to run a post region of interest analysis.If a full image CurveAlign analysis has not been conducted, click one or more regions of interest in the region of interest list and click apply CurveAlign on region of interest to directly apply the CurveAlign analysis to the selected regions of interest. In this image, an overlay image of a tissue microarray core can be observed. In the magnification, the fibers parallel and perpendicular to the tumor boundary within the region of interest can be observed.The CurveAlign output overlay image shows the tumor boundaries and the representative fiber locations and orientations. This method can be used to quantify not only collagen fibers, but also other line-like structures such as elastin and can potentially facilitate early diagnosis and prognostic analysis of cancer.

quantifying-fibrillar-collagen-organization-with-curvelet-transform

Research

JoVE Journal

Bioengineering

Preparation of Complaint Matrices for Quantifying Cellular Contraction

The regulation of cellular adhesion to the extracellular matrix (ECM) is essential for cell migration and ECM remodeling. Focal adhesions are macromolecular assemblies that couple the contractile F-actin cytoskeleton to the ECM. This connection allows for the transmission of intracellular mechanical forces across the cell membrane to the underlying substrate. Recent work has shown the mechanical properties of the ECM regulate focal adhesion and F-actin morphology as well as numerous physiological processes, including cell differentiation, division, proliferation and migration. Thus, the use of cell culture substrates has become an increasingly prevalent method to precisely control and modulate ECM mechanical properties.
To quantify traction forces at focal adhesions in an adherent cell, compliant substrates are used in conjunction with high-resolution imaging and computational techniques in a method termed traction force microscopy (TFM). This technique relies on measurements of the local magnitude and direction of substrate deformations induced by cellular contraction. In combination with high-resolution fluorescence microscopy of fluorescently tagged proteins, it is possible to correlate cytoskeletal organization and remodeling with traction forces.
Here we present a detailed experimental protocol for the preparation of two-dimensional, compliant matrices for the purpose of creating a cell culture substrate with a well-characterized, tunable mechanical stiffness, which is suitable for measuring cellular contraction. These protocols include the fabrication of polyacrylamide hydrogels, coating of ECM proteins on such gels, plating cells on gels, and high-resolution confocal microscopy using a perfusion chamber. Additionally, we provide a representative sample of data demonstrating location and magnitude of cellular forces using cited TFM protocols.

In this video, we demonstrate the experimental techniques used to fabricate compliant, extracellular matrix (ECM) coated substrates suitable for cell culture, and which are amenable to traction force microscopy and observing effects of ECM stiffness on cell behavior.

The regulation of cellular adhesion to the extracellular matrix (ECM) is essential for cell migration and ECM remodeling.  Focal adhesions are ...

Quantifying the Mechanical Properties of the Endothelial Glycocalyx with Atomic Force Microscopy

Our understanding of the interaction of leukocytes and the vessel wall during leukocyte capture is limited by an incomplete understanding of the mechanical properties of the endothelial surface layer. It is known that adhesion molecules on leukocytes are distributed non-uniformly relative to surface topography 3, that topography limits adhesive bond formation with other surfaces 9, and that physiological contact forces (&asymp; 5.0 &minus; 10.0 pN per microvillus) can compress the microvilli to as little as a third of their resting length, increasing the accessibility of molecules to the opposing surface 3, 7. We consider the endothelium as a two-layered structure, the relatively rigid cell body, plus the glycocalyx, a soft protective sugar coating on the luminal surface 6. It has been shown that the glycocalyx can act as a barrier to reduce adhesion of leukocytes to the endothelial surface 4. In this report we begin to address the deformability of endothelial surfaces to understand how the endothelial mechanical stiffness might affect bond formation. Endothelial cells grown in static culture do not express a robust glycocalyx, but cells grown under physiological flow conditions begin to approximate the glycocalyx observed in vivo 2. The modulus of the endothelial cell body has been measured using atomic force microscopy (AFM) to be approximately 5 to 20 kPa 5. The thickness and structure of the glycocalyx have been studied using electron microscopy 8, and the modulus of the glycocalyx has been approximated using indirect methods, but to our knowledge, there have been no published reports of a direct measurement of the glycocalyx modulus in living cells. In this study, we present indentation experiments made with a novel AFM probe on cells that have been cultured in conditions to maximize their glycocalyx expression to make direct measurements of the modulus and thickness of the endothelial glycocalyx.

The mechanical characteristics of endothelial glycocalyx were measured by indentation using micron sized spheres on AFM cantilevers. Endothelial cells were cultured in a custom chamber under physiological flow conditions to induce glycocalyx expression. Data were analyzed using a thin film model to determine the glycocalyx thickness and modulus.

Our  understanding of the interaction of leukocytes and the vessel wall during  leukocyte capture is limited by an incomplete understanding of the ...

Observing and Quantifying Fibroblast-mediated Fibrin Gel Compaction

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global reorganization and realignment of the gel microstructure. This process proceeds in a complex manner that is dependent in part on the interplay between the location of the cells, the geometry of the gel, and the mechanical constraints on the gel. To better understand how these variables produce global fiber alignment patterns, we use time-lapse differential interference contrast (DIC) microscopy coupled with an environmentally controlled bioreactor to observe the compaction process between geometrically spaced explants (clusters of fibroblasts). The images are then analyzed with a custom image processing algorithm to obtain maps of the strain. The information obtained from this technique can be used to probe the mechanobiology of various cell-matrix interactions, which has important implications for understanding processes in wound healing, disease development, and tissue engineering applications.

Time-lapse microscopy and image processing techniques were used to observe and analyze fibroblast-mediated gel compaction and fibrin fiber realignment in an environmentally controlled bioreactor over a 48 hr&#160;period.

Cells embedded in collagen and fibrin gels attach and exert traction forces on the fibers of the gel. These forces can lead to local and global ...

Imaging Denatured Collagen Strands In vivo and Ex vivo via Photo-triggered Hybridization of Caged Collagen Mimetic Peptides

Collagen is a major structural component of the extracellular matrix that supports tissue formation and maintenance. Although collagen remodeling is an integral part of normal tissue renewal, excessive amount of remodeling activity is involved in tumors, arthritis, and many other pathological conditions. During collagen remodeling, the triple helical structure of collagen molecules is disrupted by proteases in the extracellular environment. In addition, collagens present in many histological tissue samples are partially denatured by the fixation and preservation processes. Therefore, these denatured collagen strands can serve as effective targets for biological imaging. We previously developed a caged collagen mimetic peptide (CMP) that can be photo-triggered to hybridize with denatured collagen strands by forming triple helical structure, which is unique to collagens. The overall goals of this procedure are i)&#160;to image denatured collagen strands resulting from normal remodeling activities in vivo, and ii) to visualize collagens in ex vivo tissue sections using the photo-triggered caged CMPs. To achieve effective hybridization and successful in vivo and ex vivo imaging, fluorescently labeled caged CMPs are either photo-activated immediately before intravenous injection, or are directly activated on tissue sections. Normal skeletal collagen remolding in nude mice and collagens in prefixed mouse cornea tissue sections are imaged in this procedure. The imaging method based on the CMP-collagen hybridization technology presented here could lead to deeper understanding of the tissue remodeling process, as well as allow development of new diagnostics for diseases associated with high collagen remodeling activity.

Imaging Denatured Collagen Strands In vivo and Ex vivo via Photo-triggered Hybridization of Caged Collagen Mimetic Peptides

This procedure demonstrates in vivo near IR fluorescence imaging of collagen remodeling activities in mice as well as ex vivo staining of collagens in tissue sections using caged collagen mimetic peptides that can be photo-triggered to hybridize with denatured collagen strands.

Collagen is a major structural component of the extracellular matrix that supports tissue formation and maintenance. Although collagen remodeling is an ...

Helical Organization of Blood Coagulation Factor VIII on Lipid Nanotubes

Cryo-electron microscopy (Cryo-EM)1 is a powerful approach to investigate the functional structure of proteins and complexes in a hydrated state and membrane environment2.
Coagulation Factor VIII (FVIII)3 is a multi-domain blood plasma glycoprotein. Defect or deficiency of FVIII is the cause for Hemophilia type A&#160;-&#160;a severe bleeding disorder. Upon proteolytic activation, FVIII binds to the serine protease Factor IXa on the negatively charged platelet membrane, which is critical for normal blood clotting4. Despite the pivotal role FVIII plays in coagulation, structural information for its membrane-bound state is incomplete5. Recombinant FVIII concentrate is the most effective drug against Hemophilia type A and commercially available FVIII can be expressed as human or porcine, both forming functional complexes with human Factor IXa6,7.
In this study we present a combination of Cryo-electron microscopy (Cryo-EM), lipid nanotechnology and structure analysis applied to resolve the membrane-bound structure of two highly homologous FVIII forms: human and porcine. The methodology developed in our laboratory to helically organize the two functional recombinant FVIII forms on negatively charged lipid nanotubes (LNT) is described.&#160;The representative results demonstrate that our approach is sufficiently sensitive to define the differences in the helical organization between the two highly homologous in sequence (86% sequence identity) proteins. Detailed protocols for the helical organization, Cryo-EM and electron tomography (ET) data acquisition are given. The two-dimensional (2D) and three-dimensional (3D) structure analysis applied to obtain the 3D reconstructions of human and porcine FVIII-LNT is discussed. The presented human and porcine FVIII-LNT structures show the potential of the proposed methodology to calculate the functional, membrane-bound organization of blood coagulation Factor VIII at high resolution.

We present a combination of Cryo-electron microscopy, lipid nanotechnology, and structure analysis applied to resolve the membrane-bound structure of two highly homologous FVIII forms: human and porcine. The methodology developed in our laboratory&#160;to helically organize the two functional recombinant FVIII forms on negatively charged lipid nanotubes (LNT) is described.

Cryo-electron microscopy (Cryo-EM)1 is a powerful approach to investigate the functional structure of proteins and complexes in a hydrated state and ...

Electrospun Nanofiber Scaffolds with Gradations in Fiber Organization

The goal of this protocol is to report a simple method for generating nanofiber scaffolds with gradations in fiber organization and test their possible applications in controlling cell morphology/orientation. Nanofiber organization is controlled with a new fabrication apparatus that enables the gradual decrease of fiber organization in a scaffold. Changing the alignment of fibers is achieved through decreasing deposition time of random electrospun fibers on a uniaxially aligned fiber mat. By covering the collector with a moving barrier/mask, along the same axis as fiber deposition, the organizational structure is easily controlled. For tissue engineering purposes, adipose-derived stem cells can be seeded to these scaffolds. Stem cells undergo morphological changes as a result of their position on the varied organizational structure, and can potentially differentiate into different cell types depending on their locations. Additionally, the graded organization of fibers enhances the biomimicry of nanofiber scaffolds so they more closely resemble the natural orientations of collagen nanofibers at tendon-to-bone insertion site compared to traditional scaffolds. Through nanoencapsulation, the gradated fibers also afford the possibility to construct chemical gradients in fiber scaffolds, and thereby further strengthen their potential applications in fast screening of cell-materials interaction and interfacial tissue regeneration. This technique enables the production of continuous gradient scaffolds, but it also can potentially produce fibers in discrete steps by controlling the movement of the moving barrier/mask in a discrete fashion.

Here, we present a protocol to fabricate electrospun nanofiber scaffolds with gradated organization of fibers and explore their applications in regulating cell morphology/orientation. Gradients with regard to physical and chemical properties of the nanofiber scaffolds offer a wide variety of applications in the biomedical field.

The goal of this protocol is to report a simple method for generating nanofiber scaffolds with gradations in fiber organization and test their possible ...

Engineering 3D Cellularized Collagen Gels for Vascular Tissue Regeneration

Synthetic materials are known to initiate clinical complications such as inflammation, stenosis, and infections when implanted as vascular substitutes. Collagen has been extensively used for a wide range of biomedical applications and is considered a valid alternative to synthetic materials due to its inherent biocompatibility (i.e., low antigenicity, inflammation, and cytotoxic responses). However, the limited mechanical properties and the related low hand-ability of collagen gels have hampered their use as scaffold materials for vascular tissue engineering. Therefore, the rationale behind this work was first to engineer cellularized collagen gels into a tubular-shaped geometry and second to enhance smooth muscle cells driven reorganization of collagen matrix to obtain tissues stiff enough to be handled.
The strategy described here is based on the direct assembling of collagen and smooth muscle cells (construct) in a 3D cylindrical geometry with the use of a molding technique. This process requires a maturation period, during which the constructs are cultured in a bioreactor under static conditions (without applied external dynamic mechanical constraints) for 1 or 2 weeks. The &#8220;static bioreactor&#8221; provides a monitored and controlled sterile environment (pH, temperature, gas exchange, nutrient supply and waste removal) to the constructs. During culture period, thickness measurements were performed to evaluate the cells-driven remodeling of the collagen matrix, and glucose consumption and lactate production rates were measured to monitor the cells metabolic activity. Finally, mechanical and viscoelastic properties were assessed for the resulting tubular constructs. To this end, specific protocols and a focused know-how (manipulation, gripping, working in hydrated environment, and so on) were developed to characterize the engineered tissues.

In this work, we present a technique for the rapid fabrication of living vascular tissues by direct culturing of collagen, smooth muscle cells and endothelial cells. In addition, a new protocol for the mechanical characterization of engineered vascular tissues is described.

Synthetic materials are known to initiate clinical complications such as inflammation, stenosis, and infections when implanted as vascular substitutes. ...

A Novel Tenorrhaphy Suture Technique with Tissue Engineered Collagen Graft to Repair Large Tendon Defects

Surgical management of large tendon defects with tendon grafts is challenging, as there are a finite number of sites where donors can be readily identified and used. Currently, this gap is filled with tendon auto-, allo-, xeno-, or artificial grafts, but clinical methods to secure them are not necessarily translatable to animals because of the scale. In order to evaluate new biomaterials or study a tendon graft made up of collagen type 1, we have developed a modified suture technique to help maintain the engineered tendon in alignment with the tendon ends. Mechanical properties of these grafts are inferior to the native tendon. To incorporate engineered tendon into clinically relevant models of loaded repair, a strategy was adopted to offload the tissue engineered tendon graft and allow for the maturation and integration of the engineered tendon in vivo until a mechanically sound neo-tendon was formed. We describe this technique using incorporation of the collagen type 1 tissue engineered tendon construct.

In this paper, we present an in vitro and in situ protocol to repair a tendon gap of up to 1.5 cm by filling it with engineered collagen graft. This was performed by developing a modified suture technique to take the mechanical load until the graft matures into the host tissue.

Surgical management of large tendon defects with tendon grafts is challenging, as there are a finite number of sites where donors can be readily ...

Quantifying Intermembrane Distances with Serial Image Dilations

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for electrical conduction between cardiomyocytes. The current method for quantifying this space by manual segmentation is slow and has low spatial resolution.We developed an algorithm that uses serial image dilations of a binary outline to count the number of pixels between two opposing 2 dimensional edges.This algorithm requires fewer man hours and has a higher spatial resolution than the manual method while preserving the reproducibility of the manual process.In fact, experienced and novice investigators were able to recapitulate the results of a previous study with this new algorithm.The algorithm is limited by the human input needed to manually outline the perinexus and computational power mainly encumbered by a pre-existing pathfinding algorithm.However, the algorithm&#39;s high-throughput capabilities, high spatial resolution and reproducibility make it a versatile and robust measurement tool for use across a variety of applications requiring the measurement of the distance between any 2-dimensional (2D) edges.

The purpose of this algorithm is to continuously measure the distance between two 2-dimensional edges using serial image dilations and pathfinding. This algorithm can be applied to a variety of fields such as cardiac structural biology, vascular biology, and civil engineering.

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for ...

Biomimetic Replication of Root Surface Microstructure using Alteration of Soft Lithography

Biomimetics is the use of chemistry and material sciences to mimic biological systems, specifically biological structures, to better humankind. Recently, biomimetic surfaces mimicking the microstructure of leaf surface, were used to study the effects of leaf microstructure on leaf-environment interactions. However, no such tool exists for roots. We developed a tool allowing the synthetic mimicry of the root surface microstructure into an artificial surface. We relied on the soft lithography method, known for leaf surface microstructure replication, using a two-step process. The first step is the more challenging one as it involves the biological tissue. Here, we used a different polymer and curing strategy, relying on the strong, rigid, polyurethane, cured by UV for the root molding. This allowed us to achieve a reliable negative image of the root surface microstructure including the delicate, challenging features such as root hairs. We then used this negative image as a template to achieve the root surface microstructure replication using both the well-established polydimethyl siloxane (PDMS) as well as a cellulose derivative, ethyl cellulose, which represents a closer mimic of the root and which can also be degraded by cellulase enzymes secreted by microorganisms. This newly formed platform can be used to study the microstructural effects of the surface in root-microorganism interactions in a similar manner to what has previously been shown in leaves. Additionally, the system enables us to track the microorganism&#8217;s locations, relative to surface features, and in the future its activity, in the form of cellulase secretion.

Biomimetics has been previously used as a tool to study leaf-microorganism interactions. However, no such tool exists for roots. Here, we develop a protocol to form synthetic surfaces mimicking root surface microstructure for the study of root-environment interactions.

Biomimetics is the use of chemistry and material sciences to mimic biological systems, specifically biological structures, to better humankind. Recently, ...