Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.

Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer.

Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.

Microtubules regulate GEF-H1 in response to extracellular matrix stiffness.

Breast epithelial cells sense the stiffness of the extracellular matrix through Rho-mediated contractility. In turn, matrix stiffness regulates RhoA activity. However, the upstream signaling mechanisms are poorly defined. Here we demonstrate that the Rho exchange factor GEF-H1 mediates RhoA activation in response to extracellular matrix stiffness. We demonstrate the novel finding that microtubule stability is diminished by a stiff three-dimensional (3D) extracellular matrix, which leads to the activation of GEF-H1. Surprisingly, activation of the mitogen-activated protein kinase kinase/extracellular signal-regulated kinase pathway did not contribute to stiffness-induced GEF-H1 activation. Loss of GEF-H1 decreases cell contraction of and invasion through 3D matrices. These data support a model in which matrix stiffness regulates RhoA through microtubule destabilization and the subsequent release and activation of GEF-H1.

A nondenatured, noncrosslinked collagen matrix to deliver stem cells to the heart.

AIMS: Stem cell transplantation holds promise as a therapeutic approach for the repair of damaged myocardial tissue. One challenge of this approach is efficient delivery and long-term retention of the stem cells. Although several synthetic and natural biomaterials have been developed for this purpose, the ideal formulation has yet to be identified. MATERIALS & METHODS: Here we investigate the utility of a nondenatured, noncrosslinked, commercially available natural biomaterial (TissueMend(®) [TEI Biosciences, Boston, MA, USA]) for delivery of human mesenchymal stem cells (MSCs) to the murine heart. RESULTS: We found that MSCs attached, proliferated and migrated within and out of the TissueMend matrix in vitro. Human MSCs delivered to damaged murine myocardium via the matrix (2.3 × 10(4) ± 0.8 × 10(4) CD73(+) cells/matrix) were maintained in vivo for 3 weeks and underwent at least three population doublings during that period (21.9 × 10(4) ± 14.4 × 10(4) CD73(+) cells/matrix). In addition, collagen within the TissueMend matrix could be remodeled by MSCs in vivo, resulting in a significant decrease in the coefficient of alignment of fibers (0.12 ± 0.12) compared with the matrix alone (0.28 ± 0.07), and the MSCs were capable of migrating out of the matrix and into the host tissue. CONCLUSION: Thus, TissueMend matrix offers a commercially available, biocompatible and malleable vehicle for the delivery and retention of stem cells to the heart.

Aligned collagen is a prognostic signature for survival in human breast carcinoma.

Evidence for the potent influence of stromal organization and function on invasion and metastasis of breast tumors is ever growing. We have performed a rigorous examination of the relationship of a tumor-associated collagen signature-3 (TACS-3) to the long-term survival rate of human patients. TACS-3 is characterized by bundles of straightened and aligned collagen fibers that are oriented perpendicular to the tumor boundary. An evaluation of TACS-3 was performed in biopsied tissue sections from 196 patients by second harmonic generation imaging of the backscattered signal generated by collagen. Univariate analysis of a Cox proportional hazard model demonstrated that the presence of TACS-3 was associated with poor disease-specific and disease-free survival, resulting in hazard ratios between 3.0 and 3.9. Furthermore, TACS-3 was confirmed to be an independent prognostic indicator regardless of tumor grade and size, estrogen or progesterone receptor status, human epidermal growth factor receptor-2 status, node status, and tumor subtype. Interestingly, TACS-3 was positively correlated to expression of stromal syndecan-1, a receptor for several extracellular matrix proteins including collagens. Because of the strong statistical evidence for poor survival in patients with TACS, and because the assessment can be performed in routine histopathological samples imaged via second harmonic generation or using picrosirius, we propose that quantifying collagen alignment is a viable, novel paradigm for the prediction of human breast cancer survival.