Department of Physics, Ludwig Maximilian University of Munich, Garching, Germany.
Max Planck Institute of Quantum Optics, Garching, Germany.
Nat Commun. 2021 Mar 8;12(1):1511. doi: 10.1038/s41467-021-21668-5.
Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.
健康状态的转变反映在生物流体分子组成的特征变化中。同时检测这些变化,跨越广泛的分子种类,可以有助于检测异常生理状况。通过红外振动光谱对生物流体进行指纹识别就具有这种能力。这种方法是否真的能够用于健康监测,关键取决于个体的红外分子指纹(IMFs)随时间推移的稳定性如何。本文报告了一项概念验证研究,旨在解决这个问题。我们使用傅里叶变换红外光谱法对 31 名健康、无症状的个体的血清和血浆样本进行了指纹识别,这些个体在 7 周的时间内被采样多达 13 次,然后在 6 个月后再次采样。这些测量是直接在液体血清和血浆样本上进行的,因此具有时间和成本效益高的工作流程,以及高度的可重复性。结果表明,IMFs 在临床上相关的时间尺度内具有高度的稳定性。单次测量产生了多种个体特异性光谱标记,可以检测和跟踪个体随时间变化的分子表型。这种个体生化指纹的先前未知的时间稳定性为基于血液的红外光谱指纹识别作为一种基于多组学的健康监测模式的未来应用奠定了基础。