Raman spectroscopy for estimating collagen content in tumor extracellular matrix
Elena D. Antipova1, Igor D. Romanishkin2, Tatiana A. Savelieva1,2, Anastasia V. Ryabova1,2;
1 NRNU MEPhI, Moscow, Russia
2 Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
Abstract
In recent years, more and more attention has been paid to the role of extracellular matrix (ECM) components in the processes of oncogenesis and metastasis. Dense ECM is a significant barrier limiting the penetration of antitumor agents and reducing the effectiveness of therapy. As such, the visualization of ECM density is of great interest.
ECM is a dynamic three-dimensional network of macromolecules: proteins/glycoproteins, such as collagens, elastin, laminins, proteoglycans and glycosaminoglycans constitute a well-organized functional network [1].
This study proposes using Raman scattering spectroscopy to non-invasively estimate the density of ECM. Raman scattering (RS) was excited by a Ramulaser-785 laser (StellarNet, USA) at 785 nm and 150 mW, and observed using a Raman-HR-TEC-785 fiber-optic spectrometer (StellarNet, USA).
The obtained spectra were processed using MATLAB (MathWorks, R2020) software package. All spectra in the series were averaged and had the baseline subtracted. The spectra were then smoothed using Savitsky-Golay filter in the 800—1700 cm−1 range, and background fluorescence signal subtracted using airPLS algorithm [2]. The content of individual biocomponents was assessed by the integral intensity ration in their respective spectral ranges.
Biotissue (gelatin, Collagen Type I, albumin, human red blood cell solution) and tumors (FGF-23 human mesenchymal tumor, human glioblastoma and meningioma) were analyzed for collagen, lipid, hemoglobin and protein content.
The study was supported by a grant from Russian Science Foundation No. 25-12-00436.
1.Karamanos NK, Theocharis AD, Piperigkou Z, et al. A guide to the composition and functions of the extracellular matrix. FEBS J. 2021 Dec;288(24):6850-6912. doi: 10.1111/febs.15776.
2.Z.-M. Zhang, S. Chen, and Y.-Z. Liang, Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst 135 (5), 1138-1146 (2010).
Speaker
Antipova Elena
NRNU MEPhI, Moscow, Russia
Russia
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