Biomolecular Medicine, Sir Alexander Fleming Building, Division of Surgery, Oncology, Reproductive Biology, and Anesthetics, Faculty of Medicine, Imperial College London, SW7 2AZ, United Kingdom.
Anal Chem. 2009 Aug 15;81(16):6581-9. doi: 10.1021/ac901240j.
We present a new approach for analysis, information recovery, and display of biological (1)H nuclear magnetic resonance (NMR) spectral data, cluster analysis statistical spectroscopy (CLASSY), which profiles qualitative and quantitative changes in biofluid metabolic composition by utilizing a novel local-global correlation clustering scheme to identify structurally related spectral peaks and arrange metabolites by similarity of temporal dynamic variation. Underlying spectral data sets are presented in a novel graphical format to represent high-dimensionality biochemical information conveying both statistical metabolite relationships and their responses to experimental perturbation simultaneously in a high-throughput and intuitive manner. The method is exemplified using multiple 600 MHz (1)H NMR spectra of rat (n = 40) urine samples collected over 160 h following the development of experimental pancreatitis induced by L-arginine (ARG) and a wider range of model toxins including acetaminophen, galactosamine, and 2-bromoethanamine. The CLASSY approach deconvolutes complex biofluid mixture spectra into quantitative fold-change metabolic trajectories and clusters metabolites by commonalities of coexpression patterns. We demonstrate that the developing pathological processes cause coordinated changes in the levels of many compounds which share similar pathway connectivities. Variability in individual responses to toxin exposure is also readily detected and visualized allowing the assessment of interanimal variability. As an untargeted, unsupervised approach, CLASSY provides significant advantages in biological information recovery in terms of increased throughput, interpretability, and robustness and has wide potential metabonomic/metabolomic applications in clinical, toxicological, and nutritional studies of biofluids as well as in studies of cellular biochemistry, microbial fermentation monitoring, and functional genomics.
我们提出了一种新的方法，用于分析、信息恢复和显示生物（1）H 核磁共振（NMR）光谱数据，即聚类分析统计光谱学（CLASSY），它通过利用新颖的局部-全局相关聚类方案来分析生物流体代谢成分的定性和定量变化，从而识别结构相关的光谱峰，并根据时间动态变化的相似性排列代谢物。基础光谱数据集以新颖的图形格式呈现，以表示高维生物化学信息，同时传达统计代谢物关系及其对实验扰动的响应，具有高通量和直观的特点。该方法使用 L-精氨酸（ARG）诱导实验性胰腺炎后 160 小时内收集的 40 只大鼠（n = 40）尿液的多个 600 MHz（1）H NMR 光谱进行了示例，还包括更广泛的模型毒素，包括对乙酰氨基酚、半乳糖胺和 2-溴乙胺。CLASSY 方法将复杂的生物流体混合物光谱分解为定量折叠变化代谢轨迹，并通过共表达模式的共性对代谢物进行聚类。我们证明，发展中的病理过程导致许多化合物的水平发生协调变化，这些化合物具有相似的途径连接性。对毒素暴露的个体反应的变异性也很容易被检测和可视化，从而可以评估动物间的变异性。作为一种非靶向、无监督的方法，CLASSY 在生物信息恢复方面具有显著的优势，包括提高了通量、可解释性和稳健性，并且在生物流体的临床、毒理学和营养研究以及细胞生物化学、微生物发酵监测和功能基因组学研究中具有广泛的代谢组学/代谢组学应用潜力。
