Rapid and automated lipid profiling by nuclear magnetic resonance spectroscopy using neural networks
Rapid and automated lipid profiling by nuclear magnetic resonance spectroscopy using neural networks
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DOI：
10.1002/nbm.5010
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发表时间：
2023-08-02
影响因子：
2.9
通讯作者：
Tipirneni-Sajja，Aaryani
中科院分区：
文献类型：
--
作者：
Johnson，Hayden;Puppa，Melissa;Tipirneni-Sajja，Aaryani
核磁共振（NMR）光谱是定量代谢组学的有力工具;然而，从NMR数据中定量代谢物通常是一个缓慢而繁琐的过程，需要用户输入和专业知识。在这项研究中，我们提出了一种神经网络方法，用于快速，自动化的脂质识别和定量的NMR数据。多层感知器（MLP）网络的开发与NMR光谱作为输入和脂质浓度作为输出。通过使用标准品的线性组合和模拟类似实验的修改（线加宽、噪声、峰位移、基线位移）和常见干扰信号（水、四甲基硅烷、提取溶剂），生成了三个大型合成数据集，每个数据集包含来自参比标准品原始30次扫描的55，000个光谱，并用于训练MLP，以稳健预测脂质浓度。MLPS的性能首先在各种合成数据集上进行了验证，以评估将不同的修改对其准确性的影响。然后根据从复杂脂质混合物实验获得的数据评价MLP。与真实浓度相比，MLP衍生的脂质浓度显示出高相关性，并且实验混合物中大多数定量脂质代谢物的斜率接近1。使用最准确、最稳健的MLP分析大鼠代谢组学研究中亲脂性肝提取物中的脂质。通过双因素ANOVA分析饮食和性别差异的MLP脂质结果与常规NMR定量方法获得的结果相似。总之，这项研究证明了神经网络方法在提高NMR脂质分析速度和自动化方面的潜力和可行性，这种方法可以很容易地定制为学术界或工业界的其他定量，有针对性的光谱分析。
Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for quantitative metabolomics; however, quantification of metabolites from NMR data is often a slow and tedious process requiring user input and expertise. In this study, we propose a neural network approach for rapid, automated lipid identification and quantification from NMR data. Multilayered perceptron (MLP) networks were developed with NMR spectra as the input and lipid concentrations as output. Three large synthetic datasets were generated, each with 55,000 spectra from an original 30 scans of reference standards, by using linear combinations of standards and simulating experimental‐like modifications (line broadening, noise, peak shifts, baseline shifts) and common interference signals (water, tetramethylsilane, extraction solvent), and were used to train MLPs for robust prediction of lipid concentrations. The performances of MLPS were first validated on various synthetic datasets to assess the effect of incorporating different modifications on their accuracy. The MLPs were then evaluated on experimentally acquired data from complex lipid mixtures. The MLP‐derived lipid concentrations showed high correlations and slopes close to unity for most of the quantified lipid metabolites in experimental mixtures compared with ground‐truth concentrations. The most accurate, robust MLP was used to profile lipids in lipophilic hepatic extracts from a rat metabolomics study. The MLP lipid results analyzed by two‐way ANOVA for dietary and sex differences were similar to those obtained with a conventional NMR quantification method. In conclusion, this study demonstrates the potential and feasibility of a neural network approach for improving speed and automation in NMR lipid profiling and this approach can be easily tailored to other quantitative, targeted spectroscopic analyses in academia or industry.