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Raman spectroscopic analysis of high molecular weight proteins in solution – considerations for sample analysis and data pre-processing

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Abstract

This study explores the potential of Raman spectroscopy, coupled with multivariate regression techniques and a protein separation technique (ion exchange chromatography), to quantitatively monitor diagnostically relevant changes in high molecular weight proteins in liquid plasma. Measurement protocols to detect the imbalances in plasma proteins as an indicator of various diseases using Raman spectroscopy are optimised, such that strategic clinical applications for early stage disease diagnostics can be evaluated. In a simulated plasma protein mixture, concentrations of two proteins of identified diagnostic potential (albumin and fibrinogen) were systematically varied within physiologically relevant ranges. Scattering from the poorly soluble fibrinogen fraction is identified as a significant impediment to the accuracy of measurement of mixed proteins in solution, although careful consideration of pre-processing methods allows construction of an accurate multivariate regression prediction model for detecting subtle changes in the protein concentration. Furthermore, ion exchange chromatography is utilised to separate fibrinogen from the rest of the proteins and mild sonication is used to improve the dispersion and therefore quality of the prediction. The proposed approach can be expeditiously employed for early detection of pathological disorders associated with high or low plasma/serum proteins.

Graphical abstract: Raman spectroscopic analysis of high molecular weight proteins in solution – considerations for sample analysis and data pre-processing

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Publication details

The article was received on 03 Sep 2018, accepted on 24 Sep 2018 and first published on 08 Oct 2018


Article type: Paper
DOI: 10.1039/C8AN01701H
Citation: Analyst, 2018, Advance Article
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    Raman spectroscopic analysis of high molecular weight proteins in solution – considerations for sample analysis and data pre-processing

    D. R. Parachalil, B. Brankin, J. McIntyre and H. J. Byrne, Analyst, 2018, Advance Article , DOI: 10.1039/C8AN01701H

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