A quantitative method for analyzing glycome profiles of plant cell walls

• Published in: Carbohydrate research Vol. 448; no. C; pp. 128 - 135
• Main Authors: Pattathil, Sivakumar, Ingwers, Miles W., Aubrey, Doug P., Li, Zenglu, Dahlen, Joseph
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 07.08.2017; Elsevier
• Subjects: BASIC BIOLOGICAL SCIENCES; Cell Wall - metabolism; Cell walls; Glycomics - methods; Moisture stress; Monoclonal antibodies; Pectin; Pinus taeda; Plant Cells - metabolism; Plant Roots - metabolism; Plant Stems - metabolism; Xylan; Xyloglucans; Cell walls; Moisture stress; Monoclonal antibodies; Pinus taeda; Xylan; Pectin; Xyloglucans
• ISSN: 0008-6215; 1873-426X
• DOI: 10.1016/j.carres.2017.06.009

Glycome profiling allows for the characterization of plant cell wall ultrastructure via sequential extractions and subsequent detection of specific epitopes with a suite of glycan-specific monoclonal antibodies (mAbs). The data are often viewed as the amount of materials recovered and coinciding colored heatmaps of mAb binding are generated. Interpretation of these data can be considered qualitative in nature as it depends on detecting subtle visual differences in antibody binding strength. Here, we report a mixed model-based quantitative approach for glycome profile analyses, which accounts for the amount of materials recovered and displays the normalized values in revised heatmaps and statistical heatmaps depicting significant differences. The utility of this methodology was demonstrated on a previously published dataset investigating the effects of moisture stress on the roots and needles of Pinus taeda. An annotated R script for the quantitative methodology is included to allow future studies to utilize the same approach. [Display omitted] •A quantitative methodology was developed to better analyze plant cell wall glycome profiles.•The quantitative methodology proposed herein allows for better estimation of epitope abundance.•The approach resulted in more in-depth findings when applied to a pre-existing dataset.