Measuring proteins in H2O using 2D-IR spectroscopy: pre-processing steps and applications toward a protein library

The ability of two-dimensional infrared (2D-IR) spectroscopy to measure the amide I band of proteins in H2O rather than D2O-based solvents by evading the interfering water signals has enabled in vivo studies of proteins under physiological conditions and in biofluids. Future exploitation of 2D-IR in analytical settings, from diagnostics to protein screening, will, however, require comparisons between multiple datasets, necessitating control of data collection protocols to minimize measurement-to-measurement inconsistencies. Inspired by analytical spectroscopy applications in other disciplines, we describe a workflow for pre-processing 2D-IR data that aims to simplify spectral cross-comparisons. Our approach exploits the thermal water signal that is collected simultaneously with, but is temporally separated from the amide I response to guide custom baseline correction and spectral normalization strategies before combining them with Principal Component noise reduction tools. Case studies show that application of elements of the pre-processing workflow to previously published data enables improvements in quantification accuracy and detection limits. We subsequently apply the complete workflow in a new pilot study, testing the ability of a prototype library of 2D-IR spectra to quantify the four major protein constituents of blood serum in a single, label-free measurement. These advances show progress toward the robust data handling strategies that will be necessary for future applications of 2D-IR to pharmaceutical or biomedical problems. (c) 2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

The ability of 2D-IR spectroscopy to measure protein in physiological conditions and biofluids has prompted the development of a workflow for pre-processing data that simplifies spectral cross-comparisons. This workflow includes baseline correction, spectral normalization, and noise reduction techniques. Application of this workflow to previously published data and a new pilot study demonstrates improved quantification accuracy and detection limits.