DOE Great Lakes Bioenergy Research Center, The Wisconsin Energy Institute, 1552 University Avenue, Madison, WI, 53726, USA.
Biotechnol Biofuels. 2013 Apr 26;6(1):45. doi: 10.1186/1754-6834-6-45.
Interest in the detailed lignin and polysaccharide composition of plant cell walls has surged within the past decade partly as a result of biotechnology research aimed at converting biomass to biofuels. High-resolution, solution-state 2D 1H-13C HSQC NMR spectroscopy has proven to be an effective tool for rapid and reproducible fingerprinting of the numerous polysaccharides and lignin components in unfractionated plant cell wall materials, and is therefore a powerful tool for cell wall profiling based on our ability to simultaneously identify and comparatively quantify numerous components within spectra generated in a relatively short time. However, assigning peaks in new spectra, integrating them to provide relative component distributions, and producing color-assigned spectra, are all current bottlenecks to the routine use of such NMR profiling methods.
We have assembled a high-throughput software platform for plant cell wall profiling that uses spectral deconvolution by Fast Maximum Likelihood Reconstruction (FMLR) to construct a mathematical model of the signals present in a set of related NMR spectra. Combined with a simple region of interest (ROI) table that maps spectral regions to NMR chemical shift assignments of chemical entities, the reconstructions can provide rapid and reproducible fingerprinting of numerous polysaccharide and lignin components in unfractionated cell wall material, including derivation of lignin monomer unit (S:G:H) ratios or the so-called SGH profile. Evidence is presented that ROI-based amplitudes derived from FMLR provide a robust feature set for subsequent multivariate analysis. The utility of this approach is demonstrated on a large transgenic study of Arabidopsis requiring concerted analysis of 91 ROIs (including both assigned and unassigned regions) in the lignin and polysaccharide regions of almost 100 related 2D 1H-13C HSQC spectra.
We show that when a suitable number of replicates are obtained per sample group, the correlated patterns of enriched and depleted cell wall components can be reliably and objectively detected even prior to multivariate analysis. The analysis methodology has been implemented in a publicly-available, cross-platform (Windows/Mac/Linux), web-enabled software application that enables researchers to view and publish detailed annotated spectra in addition to summary reports in simple spreadsheet data formats. The analysis methodology is not limited to studies of plant cell walls but is amenable to any NMR study where ROI segmentation techniques generate meaningful results.Please see Research Article: http://www.biotechnologyforbiofuels.com/content/6/1/46/.
在过去十年中，由于生物技术研究旨在将生物质转化为生物燃料，人们对植物细胞壁的详细木质素和多糖组成产生了浓厚的兴趣。高分辨率、溶液状态的 2D 1H-13C HSQC NMR 光谱已被证明是一种快速、可重复的方法，用于快速鉴定未分级植物细胞壁材料中的众多多糖和木质素成分，并因此成为基于我们同时识别和比较定量的能力的细胞壁分析的有力工具在相对较短的时间内生成的光谱中的众多组件。然而，在新光谱中分配峰、对其进行积分以提供相对成分分布以及生成彩色分配光谱，都是常规使用此类 NMR 分析方法的当前瓶颈。
我们已经组装了一个高通量的植物细胞壁分析软件平台，该平台使用快速最大似然重建（FMLR）的光谱去卷积来构建一组相关 NMR 光谱中存在信号的数学模型。与简单的感兴趣区域（ROI）表相结合，该表将光谱区域映射到化学实体的 NMR 化学位移分配，重建可以快速、可重复地鉴定未分级细胞壁材料中的众多多糖和木质素成分，包括推导木质素单体单元（S：G：H）比或所谓的 SGH 谱。有证据表明，基于 ROI 的 FMLR 衍生幅度为后续多元分析提供了一个稳健的特征集。该方法在对拟南芥的大型转基因研究中的实用性得到了证明，该研究需要协同分析近 100 个相关 2D 1H-13C HSQC 光谱中木质素和多糖区域的 91 个 ROI（包括分配和未分配区域）。
我们表明，当每个样品组获得足够数量的重复时，即使在进行多元分析之前，也可以可靠且客观地检测到富含和耗尽的细胞壁成分的相关模式。该分析方法已在一个公开的、跨平台（Windows/Mac/Linux）、支持网络的软件应用程序中实现，该程序允许研究人员除了以简单的电子表格数据格式发布摘要报告外，还可以查看和发布详细注释的光谱。该分析方法不仅限于植物细胞壁研究，而且适用于任何产生有意义结果的 ROI 分割技术的 NMR 研究。请参阅研究文章：http://www.biotechnologyforbiofuels.com/content/6/1/46/。
