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Quantification of lung surfactant lipid (dipalmitoylphosphatidylcholine/sphingomyelin) ratio in binary liposomes using Raman spectroscopy

Veluthandath, Aneesh Vincent; Ahmed, Waseem; Madsen, Jens; Clark, Howard W; Postle, Anthony D; Wilkinson, James S; Murugan, Ganapathy Senthil; (2023) Quantification of lung surfactant lipid (dipalmitoylphosphatidylcholine/sphingomyelin) ratio in binary liposomes using Raman spectroscopy. Journal of Raman Spectroscopy 10.1002/jrs.6631. (In press). Green open access

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Abstract

Early diagnosis of neonatal respiratory distress syndrome (nRDS) is important in reducing the mortality of preterm babies. Knowledge of the ratio of two components of lung surfactant, dipalmitoylphosphatidylcholine (DPPC), and sphingomyelin (SM) can be used as biomarkers of lung maturity and inform treatment. Raman spectroscopy is a powerful tool to analyze vibrational spectra of organic molecules which requires only limited sample preparation steps and, unlike IR spectroscopy, is not masked by water absorption. In this paper, we explore the potential of using Raman spectroscopy as a tool to estimate the ratio of DPPC and SM from aqueous vesicles of binary mixture of DPPC and SM. We demonstrate that the ratio of DPPC and SM can be estimated by estimating the ratio of intensity of CO stretch of DPPC and CC stretch of SM as well as CO stretch of DPPC and amide I of SM. Further, we employ a partial least squares regression (PLSR) model to automate the estimation and demonstrate that PLSR method can predict the DPPC and SM ratio with an R2 value of 0.968.

Type: Article
Title: Quantification of lung surfactant lipid (dipalmitoylphosphatidylcholine/sphingomyelin) ratio in binary liposomes using Raman spectroscopy
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/jrs.6631
Publisher version: http://dx.doi.org/10.1002/jrs.6631
Language: English
Additional information: 2023 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, https://creativecommons.org/licenses/by/4.0/, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Lipid vesicles, liposome, machine learning, neonatal respiratory distress syndrome, phospholipids, Raman spectroscopy
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Neonatology
URI: https://discovery.ucl.ac.uk/id/eprint/10184815
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