Quantitative plant proteomics

Abstract

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.

Cite this paper

@article{bindschedler2010quantitative,
  title = {{Quantitative plant proteomics}},
  author = {Bindschedler, Laurence V. and Cramer, Rainer},
  journal = {PROTEOMICS},
  volume = {11},
  number = {4},
  pages = {756--775},
  year = {2010},
  doi = {10.1002/pmic.201000426}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers