Dataset Information

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

ABSTRACT: 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.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7186312 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

Liu Yuming Y Keikhosravi Adib A Pehlke Carolyn A CA Bredfeldt Jeremy S JS Dutson Matthew M Liu Haixiang H Mehta Guneet S GS Claus Robert R Patel Akhil J AJ Conklin Matthew W MW Inman David R DR Provenzano Paolo P PP Sifakis Eftychios E Patel Jignesh M JM Eliceiri Kevin W KW

Frontiers in bioengineering and biotechnology 20200421

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 ...[more]

PMID: 32373594

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Project description:ImportanceSpontaneous coronary artery dissection (SCAD) is an increasingly recognized nonatherosclerotic cause of acute myocardial infarction enriched among individuals with early-onset myocardial infarction but is of unclear etiology.ObjectiveTo assess which genes contribute to the development of SCAD.Design, setting, and participantsTo prioritize genes influencing risk for SCAD, whole-exome sequencing was performed among individuals with SCAD in the discovery and replication cohorts from a tertiary care hospital outpatient specialty clinic, and gene set enrichment analyses were also performed for disruptive coding variants. All patients were sequentially enrolled beginning July 2013. Aggregate prevalence of rare disruptive variants for prioritized gene sets was compared between individuals with SCAD with population-based controls comprising 46 468 UK Biobank participants with whole-exome sequencing. Complementary mice models were used for in vivo validation. Analysis took place between June 2020 and January 2021.Main outcomes and measuresThe frequency and identity of rare genetic variants in individuals with SCAD.ResultsOf 130 patients, 109 (83.8%) were female (26 of 32 [81.2%] in the discovery cohort and 83 of 98 [84.7%] in the replication cohort) with mean (SD) age at first SCAD event of 48.41 (8.76) years in the discovery cohort and 47.74 (10.09) years in the replication cohort. Across all patients with SCAD, rare disruptive variants were found within 10 collagen genes (COL3A1, COL5A1, COL4A1, COL6A1, COL5A2, COL12A1, COL4A5, COL1A1, COL1A2, and COL27A1) were 17-fold (P = 1.5 × 10-9) enriched among individuals with SCAD compared with a background of 2506 constrained genes expressed in coronary artery. Furthermore, compared with individuals from the UK Biobank, individuals with SCAD were 1.75-fold (P = .04) more likely to carry disruptive rare variants within fibrillar collagen genes. Complementary mice models haploinsufficient for Col3a1 or Col5a1, the 2 most common collagen gene variants identified in SCAD cases, demonstrated increased risk of arterial dissection and increased size of arterial diameters especially in female mice, with resulting changes in collagen fibril organization and diameter.Conclusions and relevanceUnbiased gene discovery in patients with SCAD with independent human and murine validation highlights the role of the extracellular matrix dysfunction in SCAD.

Project description:BackgroundGlioblastoma multiforme (GBM, WHO grade IV) is the most common and most malignant of astrocytic brain tumors, and is associated with rapid invasion into neighboring tissue. In other tumor types it is well established that such invasion involves a complex interaction between tumor cells and locally produced extracellular matrix. In GBMs, surprisingly little is known about the associated matrix components, in particular the fibrillar proteins such as collagens that are known to play a key role in the invasion of other tumor types.Methodology/principal findingsIn this study we have used both the Masson's trichrome staining and a high resolution multiple immunofluorescence labeling method to demonstrate that intratumoral fibrillar collagens are an integral part of the extracellular matrix in a subset of GBMs. Correlated with this collagen deposition we observed high level expression of the collagen-binding receptor Endo180 (CD280) in the tumor cells. Further, interrogation of multiple expression array datasets identified Endo180 as one of the most highly upregulated transcripts in grade IV GBMs compared to grade III gliomas. Using promoter analysis studies we show that this increased expression is, in part, mediated via TGF-beta signaling. Functionally, we demonstrate that Endo180 serves as the major collagen internalization receptor in GBM cell lines and provide the first evidence that this activity is critical for the invasion of GBM cells through fibrillar collagen matrices.Conclusions/significanceThis study demonstrates, for the first time, that fibrillar collagens are extensively deposited in GBMs and that the collagen internalization receptor Endo180 is both highly expressed in these tumors and that it serves to mediate the invasion of tumor cells through collagen-containing matrices. Together these data provide important insights into the mechanism of GBM invasion and identify Endo180 as a potential target to limit matrix turnover by glioma cells and thereby restrict tumor progression.

Dataset Information

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

Publications

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

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