Literature DB >> 32924290

Measurement of cartilage sub-component distributions through the surface by Raman spectroscopy-based multivariate analysis.

Daniel Mason1, Sangeeta Murugkar2, Andrew D Speirs1.   

Abstract

Articular cartilage posesses unique material properties due to a complex depth-dependent composition of sub-components. Raman spectroscopy has proven valuable in quantifying this composition through cartilage cross-sections. However, cross-sectioning requires tissue destruction and is not practical in situ. In this work, Raman spectroscopy-based multivariate curve resolution (MCR) was employed in porcine cartilage samples (n = 12) to measure collagen, glycosaminoglycan, and water distributions through the surface for the first time; these were compared against cross-section standards. Through the surface Raman measurements proved reliable in predicting composition distribution up to a depth of approximately 0.5 mm. A fructose-based optical clearing agent (OCA) was also used in an attempt to further improve depth of resolution of this measurement method. However, it did not; mainly due to a high-spectral overlap with the Raman spectra of main cartilage sub-components. This measurement technique potentially could be used in situ, to better understand the etiology of joint diseases such as osteoarthritis (OA).
© 2020 Wiley-VCH GmbH.

Entities:  

Keywords:  Raman spectroscopy; articular cartilage; multivariate analysis; optical clearing

Mesh:

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Year:  2020        PMID: 32924290     DOI: 10.1002/jbio.202000289

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  2 in total

1.  Raman spectroscopy-based water measurements identify the origin of MRI T2 signal in human articular cartilage zones and predict histopathologic score.

Authors:  Mustafa Unal; Robert L Wilson; Corey P Neu; Ozan Akkus
Journal:  J Biophotonics       Date:  2021-11-02       Impact factor: 3.207

Review 2.  Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review.

Authors:  Chen Yu; Bing Zhao; Yan Li; Hengchang Zang; Lian Li
Journal:  Int J Mol Sci       Date:  2021-05-15       Impact factor: 5.923

  2 in total

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