A High-Throughput Workflow for FFPE Tissue Proteomics.
J Am Soc Mass Spectrom
; 34(7): 1225-1229, 2023 Jul 05.
Article
in En
| MEDLINE
| ID: mdl-37267530
ABSTRACT
Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffin-embedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC-MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteomics
/
High-Throughput Screening Assays
Limits:
Humans
Language:
En
Journal:
J Am Soc Mass Spectrom
Year:
2023
Document type:
Article
Affiliation country: