Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Standard

Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods. / Moore, John P.; Gao, Yu; Zietsman, Anscha J.J.; Fangel, Jonatan U.; Trygg, Johan; Willats, William G.T.; Vivier, Melané A.

The Plant Cell Wall. ed. / Zoë A. Popper. Humana Press, 2020. p. 327-337 (Methods in Molecular Biology, Vol. 2149).

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Moore, JP, Gao, Y, Zietsman, AJJ, Fangel, JU, Trygg, J, Willats, WGT & Vivier, MA 2020, Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods. in ZAP (ed.), The Plant Cell Wall. Humana Press, Methods in Molecular Biology, vol. 2149, pp. 327-337. https://doi.org/10.1007/978-1-0716-0621-6_18

APA

Moore, J. P., Gao, Y., Zietsman, A. J. J., Fangel, J. U., Trygg, J., Willats, W. G. T., & Vivier, M. A. (2020). Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods. In Z. A. P. (Ed.), The Plant Cell Wall (pp. 327-337). Humana Press. Methods in Molecular Biology Vol. 2149 https://doi.org/10.1007/978-1-0716-0621-6_18

Vancouver

Moore JP, Gao Y, Zietsman AJJ, Fangel JU, Trygg J, Willats WGT et al. Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods. In ZAP, editor, The Plant Cell Wall. Humana Press. 2020. p. 327-337. (Methods in Molecular Biology, Vol. 2149). https://doi.org/10.1007/978-1-0716-0621-6_18

Author

Moore, John P. ; Gao, Yu ; Zietsman, Anscha J.J. ; Fangel, Jonatan U. ; Trygg, Johan ; Willats, William G.T. ; Vivier, Melané A. / Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods. The Plant Cell Wall. editor / Zoë A. Popper. Humana Press, 2020. pp. 327-337 (Methods in Molecular Biology, Vol. 2149).

Bibtex

@inbook{19b38f55e0b74bfd8c01bff04cee2564,
title = "Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods",
abstract = "Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant–pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography–mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).",
keywords = "Cell wall profiling, CoMPP, FT-IR, GC-MS, Multivariate data analysis",
author = "Moore, {John P.} and Yu Gao and Zietsman, {Anscha J.J.} and Fangel, {Jonatan U.} and Johan Trygg and Willats, {William G.T.} and Vivier, {Melan{\'e} A.}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2020",
doi = "10.1007/978-1-0716-0621-6_18",
language = "English",
isbn = "978-1-0716-0619-3",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "327--337",
editor = "{Zo{\"e} A. Popper}",
booktitle = "The Plant Cell Wall",
address = "United States",

}

RIS

TY - CHAP

T1 - Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods

AU - Moore, John P.

AU - Gao, Yu

AU - Zietsman, Anscha J.J.

AU - Fangel, Jonatan U.

AU - Trygg, Johan

AU - Willats, William G.T.

AU - Vivier, Melané A.

N1 - Publisher Copyright: © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

PY - 2020

Y1 - 2020

N2 - Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant–pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography–mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).

AB - Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant–pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography–mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).

KW - Cell wall profiling

KW - CoMPP

KW - FT-IR

KW - GC-MS

KW - Multivariate data analysis

U2 - 10.1007/978-1-0716-0621-6_18

DO - 10.1007/978-1-0716-0621-6_18

M3 - Book chapter

C2 - 32617943

AN - SCOPUS:85087473292

SN - 978-1-0716-0619-3

T3 - Methods in Molecular Biology

SP - 327

EP - 337

BT - The Plant Cell Wall

A2 - null, Zoë A. Popper

PB - Humana Press

ER -

ID: 270665637