Gorbet Gary E, Pearson Joseph Z, Demeler Aysha K, Cölfen Helmut, Demeler Borries
Department of Biochemistry, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Physical Chemistry, Department of Chemistry, University of Konstanz, Konstanz, Germany.
Methods Enzymol. 2015;562:27-47. doi: 10.1016/bs.mie.2015.04.013. Epub 2015 Aug 21.
We describe important advances in methodologies for the analysis of multiwavelength data. In contrast to the Beckman-Coulter XL-A/I ultraviolet-visible light detector, multiwavelength detection is able to simultaneously collect sedimentation data for a large wavelength range in a single experiment. The additional dimension increases the data density by orders of magnitude, posing new challenges for data analysis and management. The additional data not only improve the statistics of the measurement but also provide new information for spectral characterization, which complements the hydrodynamic information. New data analysis and management approaches were integrated into the UltraScan software to address these challenges. In this chapter, we describe the enhancements and benefits realized by multiwavelength analysis and compare the results to those obtained from the traditional single-wavelength detector. We illustrate the advances offered by the new instruments by comparing results from mixtures that contain different ratios of protein and DNA samples, representing analytes with distinct spectral and hydrodynamic properties. For the first time, we demonstrate that the spectral dimension not only adds valuable detail, but when spectral properties are known, individual components with distinct spectral properties measured in a mixture by the multiwavelength system can be clearly separated and decomposed into traditional datasets for each of the spectrally distinct components, even when their sedimentation coefficients are virtually identical.
我们描述了多波长数据分析方法的重要进展。与贝克曼库尔特XL - A/I紫外 - 可见光检测器不同，多波长检测能够在单个实验中同时收集大波长范围内的沉降数据。这一额外维度将数据密度提高了几个数量级，给数据分析和管理带来了新挑战。这些额外的数据不仅改善了测量的统计结果，还为光谱表征提供了新信息，补充了流体动力学信息。新的数据分析和管理方法被集成到UltraScan软件中以应对这些挑战。在本章中，我们描述了多波长分析所实现的增强功能和优势，并将结果与传统单波长检测器获得的结果进行比较。我们通过比较含有不同比例蛋白质和DNA样品混合物的结果来说明新仪器带来的进展，这些混合物代表了具有不同光谱和流体动力学性质的分析物。首次，我们证明光谱维度不仅增加了有价值的细节，而且当光谱性质已知时，即使各组分沉降系数几乎相同，多波长系统在混合物中测量的具有不同光谱性质的单个组分也能被清晰分离，并分解为每个光谱不同组分的传统数据集。
