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Defining tissue proteomes by systematic literature review.


ABSTRACT: Defining protein composition is a key step in understanding the function of both healthy and diseased biological systems. There is currently little consensus between existing published proteomes in tissues such as the aorta, cartilage and organs such as skin. Lack of agreement as to both the number and identity of proteins may be due to issues in protein extraction, sensitivity/specificity of detection and the use of disparate tissue/cell sources. Here, we developed a method combining bioinformatics and systematic review to screen >32M articles from the Web of Science for evidence of proteins in healthy human skin. The resulting Manchester Proteome ( www.manchesterproteome.manchester.ac.uk ) collates existing evidence which characterises 2,948 skin proteins, 437 unique to our database and 2011 evidenced by both mass spectrometry and immune-based techniques. This approach circumvents the limitations of individual proteomics studies and can be applied to other species, organs, cells or disease-states. Accurate tissue proteomes will aid development of engineered constructs and offer insight into disease treatments by highlighting differences in proteomic composition.

SUBMITTER: Hibbert SA 

PROVIDER: S-EPMC5765030 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Defining tissue proteomes by systematic literature review.

Hibbert Sarah A SA   Ozols Matiss M   Griffiths Christopher E M CEM   Watson Rachel E B REB   Bell Mike M   Sherratt Michael J MJ  

Scientific reports 20180111 1


Defining protein composition is a key step in understanding the function of both healthy and diseased biological systems. There is currently little consensus between existing published proteomes in tissues such as the aorta, cartilage and organs such as skin. Lack of agreement as to both the number and identity of proteins may be due to issues in protein extraction, sensitivity/specificity of detection and the use of disparate tissue/cell sources. Here, we developed a method combining bioinforma  ...[more]

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