Improved Sample Preparation Method for Protein and Peptide Identification from Human Hair.

SP3 method protein spectrum reproducibility trace detection variant peptide discovery

Journal

Journal of proteome research
ISSN: 1535-3907
Titre abrégé: J Proteome Res
Pays: United States
ID NLM: 101128775

Informations de publication

Date de publication:
27 Nov 2023
Historique:
medline: 27 11 2023
pubmed: 27 11 2023
entrez: 27 11 2023
Statut: aheadofprint

Résumé

A fast and sensitive direct extraction (DE) method developed in our group can efficiently extract proteins in 30 min from a 5 cm-long hair strand. Previously, we coupled DE to downstream analysis using gel electrophoresis followed by in-gel digestion, which can be time-consuming. In searching for a better alternative, we found that a combination of DE with a bead-based method (SP3) can lead to significant improvements in protein discovery in human hair. Since SP3 is designed for general applications, we optimized it to process hair proteins following DE and compared it to several other in-solution digestion methods. Of particular concern are genetically variant peptides (GVPs), which can be used for human identification in forensic analysis. Here, we demonstrated improved GVP discovery with the DE and SP3 workflow, which was 3 times faster than the previous in-gel digestion method and required significantly less instrument time depending on the number of gel slices processed. Additionally, it led to an increased number of identified proteins and GVPs. Among the tested in-solution digestion methods, DE combined with SP3 showed the highest sequence coverage, with higher abundances of the identified peptides. This provides a significantly enhanced means for identifying proteins and GVPs in human hair.

Identifiants

pubmed: 38009783
doi: 10.1021/acs.jproteome.3c00627
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Zheng Zhang (Z)

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.

William E Wallace (WE)

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.

Guanghui Wang (G)

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.

Meghan C Burke (MC)

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.

Yi Liu (Y)

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.

Sergey L Sheetlin (SL)

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.

Stephen E Stein (SE)

Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.

Classifications MeSH