Analysis of four-way two-dimensional liquid chromatography-diode array data: application to metabolomics

Area of Science:

• Analytical Chemistry
• Plant Science
• Biochemistry

Background:

• Two-dimensional liquid chromatography (2D-LC) offers superior separation for complex mixtures like proteomic and metabolomic samples.
• Analyzing the large datasets (millions of data points) from 2D-LC with diode-array detection (DAD) requires advanced data processing methods.

Purpose of the Study:

• To develop and adapt chemometric methods for analyzing large 2D-LC-DAD datasets.
• To investigate indole compounds, particularly those related to indole-3-acetic acid biosynthesis, in maize seedlings.

Main Methods:

• Application of Window Target Testing Factor Analysis (WTTFA) and Parallel Factor Analysis - Alternating Least Squares (PARAFAC-ALS).
• Analysis of 2D-LC-DAD chromatograms from indolic standards, mutant, and wild-type maize seedling extracts.
• Utilizing multivariate curve resolution with flexible constraints to handle spectrally rank-deficient data.

Main Results:

• WTTFA successfully identified the presence or absence of 26 indolic standards in maize samples.
• PARAFAC-ALS resolved 95 peaks, with 45 common to mutant and wild-type samples, and unique peaks identified in each.
• Several indole acetic acid conjugates were quantified in maize samples at levels of 0.3-2 microg/g.

Conclusions:

• Chemometric methods are effective for analyzing complex 2D-LC-DAD data in plant metabolomics.
• The developed methods enable the identification and quantification of specific metabolites in maize.
• This study demonstrates the quantitative potential of multivariate curve resolution for plant metabolite profiling.