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Ambient DESI and LESA-MS Analysis of Proteins Adsorbed to a Biomaterial Surface Using In-Situ Surface Tryptic Digestion

The detection and identification of proteins adsorbed onto biomaterial surfaces under ambient conditions has significant experimental advantages but has proven to be difficult to achieve with conventional measuring technologies. In this study, we present an adaptation of desorption electrospray ioni...

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Detalles Bibliográficos
Autores principales: Rao, Wei, Celiz, Adam D., Scurr, David J., Alexander, Morgan R., Barrett, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3837234/
https://www.ncbi.nlm.nih.gov/pubmed/24048891
http://dx.doi.org/10.1007/s13361-013-0737-3
Descripción
Sumario:The detection and identification of proteins adsorbed onto biomaterial surfaces under ambient conditions has significant experimental advantages but has proven to be difficult to achieve with conventional measuring technologies. In this study, we present an adaptation of desorption electrospray ionization (DESI) and liquid extraction surface analysis (LESA) mass spectrometry (MS) coupled with in-situ surface tryptic digestion to identify protein species from a biomaterial surface. Cytochrome c, myoglobin, and BSA in a combination of single and mixture spots were printed in an array format onto Permanox slides, followed by in-situ surface digestion and detection via MS. Automated tandem MS performed on surface peptides was able to identify the proteins via MASCOT. Limits of detection were determined for DESI-MS and a comparison of DESI and LESA-MS peptide spectra characteristics and sensitivity was made. DESI-MS images of the arrays were produced and analyzed with imaging multivariate analysis to automatically separate peptide peaks for each of the proteins within a mixture into distinct components. This is the first time that DESI and LESA-MS have been used for the in-situ detection of surface digested proteins on biomaterial surfaces and presents a promising proof of concept for the use of ambient MS in the rapid and automated analysis of surface proteins. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13361-013-0737-3) contains supplementary material, which is available to authorized users.