Automated Sample Preparation Workflow for Tandem Mass Tag-Based Proteomics.
J Am Soc Mass Spectrom
; 34(10): 2087-2092, 2023 Oct 04.
Article
en En
| MEDLINE
| ID: mdl-37657774
ABSTRACT
Although tandem mass tag (TMT)-based isobaric labeling has become a powerful approach for multiplexed protein quantitation, automating the workflow for this technique has not been easy to achieve for widespread adoption. This is because preparation of TMT-labeled peptide samples involves multiple steps ranging from protein extraction, denaturation, reduction, and alkylation to tryptic digestion, desalting, labeling, and cleanup, all of which require a high level of proficiency. The variability resulting from multiple processing steps is inherently problematic, especially with large-scale clinical studies that involve hundreds of samples where reproducibility is critical for quantitation. Here, we sought to compare the performance of a recently introduced platform, AccelerOme, for an automated proteomic workflow employing TMT labeling with the manual processing of samples. Cell pellets were prepared and subjected to a 16-plex experiment using an automated platform and a conventional manual protocol. Single-shot liquid chromatography with tandem mass spectrometry analysis revealed a higher number of proteins and peptides identified using the automated platform. Efficiency of tryptic digestion, alkylation, and TMT labeling were similar in both manual and automated processes. In addition, comparison of quantitation accuracy and precision showed similar performance in an automated workflow compared to manual sample preparation by an expert. Overall, we demonstrated that the automated platform performs at a level similar to a manual process performed by an expert for TMT-based proteomics. We anticipate that this automated workflow will increasingly replace manual pipelines and has the potential to be applied to large-scale TMT-based studies, providing robust results and high sample throughput.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas
/
Proteómica
Tipo de estudio:
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
J Am Soc Mass Spectrom
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos