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Abstract
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 T1-T2 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.
Highlights
Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype
We report that the supervised models were at least on par or outperformed the average trained human being in performing the deep image analysis of molecular fingerprint of red blood cells (RBCs)
Three peaks (R-peak, S-peak and T-peak) with (T2 = 141 ms, T1 = 562 ms), (T2 = 4.47 ms, T1 = 335 ms) and (T2 = 1.12 ms, T1 = 188 ms) respectively were observed from the T1-T2 correlational spectroscopy performed on the water-proton nuclei (1H) of the RBCs (Fig. 3a)
Summary
Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. In high-field NMR, biochemical information is typically detected and encoded in the frequency domain (“chemical shift”), in which the spectral resolution scale with respect to the external magnetic field This reduces its portability and limit its downstream application in a large scale manner. By exploiting the recent development of fast and highly efficient multidimensional inverse Laplace decomposition algorithm[7,30], unique two-dimensional signature of various hemoglobin (Hb) derivatives with respect to its magnetic resonance relaxation reservoirs in oxygenated (oxy-Hb), deoxygenated (deoxy-Hb) and oxidized (oxidized Hb) states were observed for the first time (to the best of our knowledge) and its phenotypic expression in various pathological states (e.g., blood oxidation, hemoglobinopathies) are reported in this work. We report that the supervised models (e.g., neural network) were at least on par or outperformed the average trained human being in performing the deep image analysis of molecular fingerprint of red blood cells (RBCs)
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