Proteomics and plant biology: contributions to date and a look towards the next decade

Introduction This review presents the view of the author, that is opinionable and even speculative, on the field of proteomics, its application to plant biology knowledge, and translation to biotechnology. Written in a more academic than scientific style, it is based on past original and review articles by the author ' s group, and those published by leading scientists in the last two years. Areas covered Starting with a general definition and references to historical milestones, it covers sections devoted to the different platforms employed, the plant biology discourse in the protein language, challenges and future prospects, ending with the author opinion. Expert opinion In 25 years, five proteomics platform generations have appeared. We are now moving from proteomics to Systems Biology. While feasible with model organisms, proteomics of orphan species remains challenging. Proteomics, even in its simplest approach, sheds light on plant biological processes, central dogma, and molecular bases of phenotypes of interest, and it can be translated to areas such as food traceability and allergen detection. Proteomics should be validated and optimized to each experimental system, objectives, and hypothesis. It has limitations, artifacts, and biases. We should not blindly accept proteomics data and just create a list of proteins, networks, and avoid speculative biological interpretations. From the hundred to thousand proteins identified and quantified, it is important to obtain a focus and validate some of them, otherwise it is merely. We are starting to have the protein pieces, so let, from now, build the proteomics and biological puzzle.

Automated summary

This review presents the author's opinion on proteomics, its application to plant biology, and translation to biotechnology. Proteomics sheds light on plant biological processes and can be applied to areas such as food traceability and allergen detection. Researchers should validate and optimize proteomics data, avoiding blindly accepting results.