MS1-Level Proteome Quantification Platform Allowing Maximally Increased Multiplexity for SILAC and In Vitro Chemical Labeling Yeon Choi, ⊥ Kyowon Jeong, ⊥ Sanghee Shin, ⊥ Joon Won Lee, ⊥ Young-suk Lee, Sangtae Kim, Sun Ah Kim, Jaehun Jung, Kwang Pyo Kim, V. Narry Kim,* and Jong-Seo Kim* Cite This: Anal. Chem. 2020, 92, 4980-4989 Read Online ACCESS Metrics & More Article Recommendations * sı Supporting Information ABSTRACT: Quantitative proteomic platforms based on precursor intensity in mass spectrometry (MS1-level) uniquely support in vivo metabolic labeling with superior quantification accuracy but suffer from limited multiplexity (≤3-plex) and frequent missing quantities. Here we present a new MS1-level quantification platform that allows maximal multiplexing with high quantification accuracy and precision for the given labeling scheme. The platform currently comprises 6-plex in vivo SILAC or in vitro diethylation labeling with a dedicated algorithm and is also expandable to higher multiplexity (e.g., nine-plex for SILAC). For complex samples with broad dynamic ranges such as total cell lysates, our platform performs highly accurately and free of missing quantities. Furthermore, we successfully applied our method to measure protein synthesis rate under heat shock response in human cells by 6-plex pulsed SILAC experiments, demonstrating the unique biological merits of our in vivo platform to disclose translational regulations for cellular response to stress. A mong proteome quantification strategies based on liquid chromatography tandem mass spectrometry (LC-MS/ MS), the precursor intensity (MS1)-based approach is a highly accurate and reliable method that allows in vivo metabolic labeling such as stable isotope labeling with amino acids in cell culture (SILAC). 1,2 However, the multiplexity of existing MS1- level quantification methods (e.g., triple SILAC) 3,4 does not meet the increasing demand for highly multiplexed exper- imental design. 5 High multiplexity can be achieved by the use of deuterium, which allows for the largest number of heavy isotope incorporation on labels, and narrow mass spacing (≤2 Da) between labeling channels (Figure S1A). However, deuterated labels can cause inconsistent retention time (termed “RT shift”), and the narrow mass spacing results in severe isotope cluster overlapping between channel signals. 6-8 These complications substantially hinder the exact determi- nation of interchannel quantity ratios, as illustrated in Figure S1B-E. Thus, despite several attempts 9,10 to address the issues, MS1-level quantification is largely limited to a multiplexity of only three, keeping the conventional terms for labeling scheme (≥4 Da of mass spacing and minimum use of deuteriums). 11,12 In addition to limited multiplexity, MS1-level quantification methods suffer from frequent missing quantities, 13,14 especially when the peptides are low abundant. Existing computational tools often fail to detect signals from such peptides. Signal extraction failure leads to missing quantities that are hard to recover by general imputation methods. Here, we introduce a MS1-level proteome quantification platform comprising metabolic (in vivo)/chemical (in vitro) isotopic labeling and a dedicated algorithm called Epic Protein Integrative Quantification (EPIQ), enabling maximally in- creased multiplexity for any isotopic labeling scheme. To increase the multiplexity to the maximum level for a given chemical structure of labels, we allowed heavily differential deuteration and narrow mass spacing (2 Da) for both metabolic and chemical isotopic labeling. The necessarily accompanied complications such as retention time shift and isotopic overlapping are computationally addressed by EPIQ, achieving accurate quantification with few missing quantities. Through the benchmark tests using HeLa lysates labeled with metabolic SILAC-6plex labeling or chemical dimethyla- tion (DM)-5plex 9 /diethylation 7,12,15 (DE)-6plex labelings designed under the aforementioned labeling terms, we show that EPIQ exclusively achieves protein quantification over existing tools. Furthermore, we demonstrate that EPIQ together with our 6-plex labeling schemes enables sensitive Received: November 12, 2019 Accepted: March 11, 2020 Published: March 13, 2020 Article pubs.acs.org/ac © 2020 American Chemical Society 4980 https://dx.doi.org/10.1021/acs.analchem.9b05148 Anal. Chem. 2020, 92, 4980-4989 Downloaded via KYUNG HEE UNIV on January 18, 2023 at 00:26:13 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.