Orthogonality of Two-Dimensional Separations Based on Conditional Entropy

Pourhaghighi, Mohammad Reza; Karzand, Mina; Girault, Hubert H.

doi:10.1021/ac2017772

Cited by 35 publications

(42 citation statements)

References 19 publications

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“…This work uncovers a problem with the previously derived 2D OM from information theory known as %O [8]. We will show that this OM is not symmetrical with respect to the input data, and we will derive a more universal equation able to be employed in any number of dimensions.…”

Section: Introductionmentioning

confidence: 79%

Orthogonality measurements for multidimensional chromatography in three and higher dimensional separations

Schure¹,

Davis

2017

Journal of Chromatography A

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Orthogonality metrics (OMs) for three and higher dimensional separations are proposed as extensions of previously developed OMs, which were used to evaluate the zone utilization of two-dimensional (2D) separations. These OMs include correlation coefficients, dimensionality, information theory metrics and convex-hull metrics. In a number of these cases, lower dimensional subspace metrics exist and can be readily calculated. The metrics are used to interpret previously generated experimental data. The experimental datasets are derived from Gilar’s peptide data, now modified to be three dimensional (3D), and a comprehensive 3D chromatogram from Moore and Jorgenson. The Moore and Jorgenson chromatogram, which has 25 identifiable 3D volume elements or peaks, displayed good orthogonality values over all dimensions. However, OMs based on discretization of the 3D space changed substantially with changes in binning parameters. This example highlights the importance in higher dimensions of having an abundant number of retention times as data points, especially for methods that use discretization. The Gilar data, which in a previous study produced 21 2D datasets by the pairing of 7 one-dimensional separations, was reinterpreted to produce 35 3D datasets. These datasets show a number of interesting properties, one of which is that geometric and harmonic means of lower dimensional subspace (i.e., 2D) OMs correlate well with the higher dimensional (i.e., 3D) OMs. The space utilization of the Gilar 3D datasets was ranked using OMs, with the retention times of the datasets having the largest and smallest OMs presented as graphs. A discussion concerning the orthogonality of higher dimensional techniques is given with emphasis on molecular diversity in chromatographic separations. In the information theory work, an inconsistency is found in previous studies of orthogonality using the 2D metric often identified as %O. A new choice of metric is proposed, extended to higher dimensions, characterized by mixes of ordered and random retention times, and applied to the experimental datasets. In 2D, the new metric always equals or exceeds the original one. However, results from both the original and new methods are given.

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Section: Introductionmentioning

confidence: 79%

Orthogonality measurements for multidimensional chromatography in three and higher dimensional separations

Schure¹,

Davis

2017

Journal of Chromatography A

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“…The OM's include the dimensionality [35], the correlation coefficients of Kendall, Spearmen and Pearson [36] expressed as 1-r 2 [37], the relative hull area [26], Gilar's surface coverage metric [38] and the % orthogonality metric [39] using both Sturges [26,40] and square root of N discretization [26]. These results are shown in Table 2.…”

Section: Orthogonality Evaluationmentioning

confidence: 99%

Comprehensive two-dimensional liquid chromatography–tandem mass spectrometry for the simultaneous determination of wine polyphenols and target contaminants

Donato

Rigano²,

Cacciola

et al. 2016

Journal of Chromatography A

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“…In coupling with capillary zone electrophoresis (CZE), the 2D separation can provide orthogonality comparable with reverse phase chromatography coupled to strong cation exchange chromatography (RP/SCX) [12]. However, a typical OGE-MS proteome strategy is a tedious process requiring the collection of fractionated proteins, adjusting the system pH of each fraction into preferable condition for trypsin digestion, offline overnight proteolysis, and peptide fractionation [11,13,14].…”

Section: Introductionmentioning

confidence: 99%

Porous silica enhanced proteolysis during Off-Gel separation for efficient protein identification

Gan

Qiao

et al. 2015

Talanta

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a b s t r a c tAn advanced approach is developed in this work for simultaneous on-line separation and digestion of proteins by combining the Off-Gel isoelectric focusing (IEF) and enzymatic nanoreactor enhanced proteolysis. The nanoreactor was prepared by preloading trypsin in amino-functionalized macroporous silica, and then directly added into Off-Gel wells. With the nanoreactor loaded Off-Gel device, effective digestion of proteins happened during IEF electrophoresis to generate directly fractionated tryptic peptides, which not only accelerated the experimental flow but also avoided sample loss, leading to a more comprehensive protein identification from complex biological samples. A successful identification of 3592 proteins was achieved from Hela cell line by using the approach followed with LC-MS/MS analysis, while only 1877 proteins were identified from the same sample when using standard in-solution proteolysis followed with Off-Gel electrophoresis and then LC-MS/MS analysis. Therefore, we have demonstrated that the approach can greatly simplify high-throughput proteomics and significantly improve protein identification.

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Orthogonality of Two-Dimensional Separations Based on Conditional Entropy

Cited by 35 publications

References 19 publications

Orthogonality measurements for multidimensional chromatography in three and higher dimensional separations

Orthogonality measurements for multidimensional chromatography in three and higher dimensional separations

Comprehensive two-dimensional liquid chromatography–tandem mass spectrometry for the simultaneous determination of wine polyphenols and target contaminants

Porous silica enhanced proteolysis during Off-Gel separation for efficient protein identification

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