Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows

• Published in: Journal of proteome research Vol. 18; no. 3; pp. 1299 - 1306
• Main Authors: Erickson, Brian K, Mintseris, Julian, Schweppe, Devin K, Navarrete-Perea, José, Erickson, Alison R, Nusinow, David P, Paulo, Joao A, Gygi, Steven P
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.03.2019
• Subjects: Algorithms; Databases, Factual; enzymes; Humans; peptides; Peptides - chemistry; Peptides - isolation & purification; proteome; Proteome - chemistry; Proteome - isolation & purification; proteomics; Proteomics - methods; Tandem Mass Spectrometry; Workflow; IAPI; proteomics; real-time; multiplexing; SPS-MS3; iTRAQ; online search; TMT; isobaric tag
• ISSN: 1535-3893; 1535-3907; 1535-3907
• DOI: 10.1021/acs.jproteome.8b00899

Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS3 was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS3 method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS3 approach provides dramatic performance improvements for quantitative multiplexed experiments.