Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy

Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. Developments in genotyping and phenotyping, such as proteomics and lipidomics, are beginning to address these limitations. In this work, we developed a new methodology for rapid, label-free molecular phenotyping of biological fluids (e.g., blood) by exploiting the recent advances in fast and highly efficient multidimensional inverse Laplace decomposition technique. We demonstrated that using two-dimensional T 1 -T 2 correlational spectroscopy on a single drop of blood (<5 μL), a highly time- and patient-specific ‘molecular fingerprint’ can be obtained in minutes. Machine learning techniques were introduced to transform the NMR correlational map into user-friendly information for point-of-care disease diagnostic and monitoring. The clinical utilities of this technique were demonstrated through the direct analysis of human whole blood in various physiological (e.g., oxygenated/deoxygenated states) and pathological (e.g., blood oxidation, hemoglobinopathies) conditions. Weng Kung Peng et al. present a novel scheme using two-dimensional NMR spectroscopy for rapid and label-free testing of biological fluids (e.g., red blood cells) at point-of-care. They demonstrate its clinical utility (e.g., with haemoglobin disorders) and report a unique and specific ‘molecular fingerprint’ through direct analysis of a single drop of blood (<5 µl). Machine learning is used to facilitate simpler decision making.