Ultra-filtration of human serum for improved quantitative analysis of low molecular weight biomarkers using ATR-IR spectroscopy. Issue 8 (9th January 2017) - Bonnier, Franck; Blasco, Hélène; Wasselet, Clément; Brachet, Guillaume; Respaud, Renaud; Carvalho, Luis Felipe C. S.; Bertrand, Dominique; Baker, Matthew J.; Byrne, Hugh J.; Chourpa, Igor
Abstract : Monitoring of changes in the concentrations of the low molecular weight constituents enhanced by abundant proteins depletion. Abstract : Infrared spectroscopy is a reliable, rapid and cost effective characterisation technique, delivering a molecular finger print of the sample. It is expected that its sensitivity would enable detection of small chemical variations in biological samples associated with disease. ATR-IR is particularly suitable for liquid sample analysis and, although air drying is commonly performed before data collection, just a drop of human serum is enough for screening and early diagnosis. However, the dynamic range of constituent biochemical concentrations in the serum composition remains a limiting factor to the reliability of the technique. Using glucose as a model spike in human serum, it has been demonstrated in the present study that fractionating the serum prior to spectroscopic analysis can considerably improve the precision and accuracy of quantitative models based on the partial least squares regression algorithm. By depleting the abundant high molecular weight proteins, which otherwise dominate the spectral signatures collected, the ability to monitor changes in the concentrations of the low molecular weight constituents is enhanced. The Root Mean Square Error for the Validation set (RMSEV) has been improved by a factor of 5 following human serum processing with an average relative error in the predictive values below 1% being achieved. Moreover, the approach is easily transferable to different bodily fluids, which would support the development of more efficient and suitable clinical protocols for exploration of vibrational spectroscopy based ex vivo diagnostic tools.