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Novel Insights Into Quantitative Proteomics from an Innovative Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy Nicolas Sénécaut, Gelio Alves, Hendrik Weisser, Laurent Lignières, Samuel Terrier, Lilian Yang-Crosson, Pierre Poulain, Gaëlle Lelandais, Yi-Kuo Yu, Jean-Michel Camadro To cite this version: Nicolas Sénécaut, Gelio Alves, Hendrik Weisser, Laurent Lignières, Samuel Terrier, et al.. Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy. Journal of Proteome Research, American Chemical Society, 2021, 20 (3), pp.1476-1487. 10.1021/acs.jproteome.0c00478. hal-03266586 HAL Id: hal-03266586 https://hal.archives-ouvertes.fr/hal-03266586 Submitted on 21 Jun 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives| 4.0 International License pubs.acs.org/jpr Article Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy Nicolas Sénécaut, Gelio Alves, Hendrik Weisser, Laurent Lignieres,̀ Samuel Terrier, Lilian Yang-Crosson, Pierre Poulain, Gaellë Lelandais, Yi-Kuo Yu, and Jean-Michel Camadro* Cite This: J. Proteome Res. 2021, 20, 1476−1487 Read Online ACCESS Metrics & More Article Recommendations *sı Supporting Information ABSTRACT: Simple light isotope metabolic labeling (SLIM labeling) is an innovative method to quantify variations in the proteomebasedonanoriginalin vivo labeling strategy. Heterotrophic cells grown in U-[12C] as the sole source of carbon synthesize U-[12C]-amino acids, which are incorporated into proteins, giving rise to U-[12C]-proteins. This results in a large increase in the intensity of the monoisotope ion of peptides and proteins, thus allowing higher identification scores and protein sequence coverage in mass spectrometry experiments. This method, initially developed for signal processing and quantification of the incorporation rate of 12C into peptides, was based on a multistep process that was difficult to implement for many laboratories. To overcome these limitations, we developed a new theoretical background to analyze bottom-up proteomics data using SLIM-labeling (bSLIM) and established simple procedures based on open-source software, using dedicated OpenMS modules, and embedded R scripts to process the bSLIM experimental data. These new tools allow computation of both the 12C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12C-labeled peptides in multiplexing experiments to determine the relative abundance of proteins extracted under different biological conditions. They also make it possible to consider incomplete 12C labeling, such as that observed in cells with nutritional requirements for nonlabeled amino acids. These tools were validated on an experimental dataset produced using various yeast strains of Saccharomyces cerevisiae and growth conditions. The workflows are built on the implementation of appropriate calculation modules in a KNIME working environment. These new integrated tools provide a convenient framework for Downloaded via 78.198.29.234 on June 21, 2021 at 19:54:12 (UTC). the wider use of the SLIM-labeling strategy. KEYWORDS: In vivo metabolic labeling, light carbon isotope, 12C, quantitative proteomics, data processing workflow, OpenMS, KNIME, yeast See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles. ■ INTRODUCTION several advantages for MS-based proteomics developments. The We recently developed a new method to quantify protein half- isotope cluster for peptides remains strictly bound to the natural lives in vivo based on an original labeling strategy, simple light monoisotopic ion, leading to a large increase in the intensity of isotope metabolic labeling (SLIM labeling),1 derived from the the monoisotopic ion of peptides and proteins. This allows for − pioneering work of the group of Marshall.2 4 Briefly, we grew better precision in the monoisotopic mass measurement, with fi 1 yeast cells on a synthetic medium containing glucose as the sole higher identi cation scores and protein sequence coverage in source of carbon (referred to as the normal carbon condition mass spectrometry-based experiments. This method, initially 12 13 developed for signal processing and quantifying the incorpo- (NC): 98.93% C, 1.07% C) and shifted the cells to the same 12 medium containing U-[12C]-glucose as the sole source of carbon ration rate of C into peptides, was based on a multistep process (referred to as the 12C condition: 100% 12C). This allowed us to follow the incorporation of the 12C-amino acids into proteins Received: June 29, 2020 rapidly synthesized by the cells over time. SLIM labeling is, with Published: February 11, 2021 the whole-organism heavy water labeling methodology,5,6 one of the few methods that allows an integral labeling of the cellular components, without bias in functional group requirement or amino-acid composition. The SLIM-labeling strategy provides © 2021 The Authors. Published by American Chemical Society https://dx.doi.org/10.1021/acs.jproteome.0c00478 1476 J. Proteome Res. 2021, 20, 1476−1487 Journal of Proteome Research pubs.acs.org/jpr Article that was difficult to implement for many laboratories. It involved to the 13C, 2H, 15N, 17O, and 33S terms of the distribution (1) extracting the intensities of isotopologues using the (elements containing one extra neutron) commercial software Progenesis QI for metabolomics, (2) aligning the “Features” files with the peptide identification files fi Ai−+11 A (Mascot les), (3) calculating the ratio of the monoisotope M1 =××∑ iPX() P ( X )× intensity to the intensity of all isotopologues (called R ), (4) X=C,H,N,O,S iso ixyvwu= ,,, , Ä Å establishing the equation of the theoretical Riso values for each Å 7 Å peptide sequence, calculated using the MIDAs application as a Å 12 Å function of C enrichment, after determining the elemental Å Ai composition of each peptide and therefore the exact number of ∏ Å PY() ixyvwu= ,,, , ÇÅ 12 É carbons it contains, and (5) calculating the abundance of Cin YX≠=C,H,N,O,S Ñ (2) fi Ñ each experimental peptide by tting its experimental Riso to the Ñ closest theoretical R . The data were then filtered to eliminate Although formal expressionsÑ for higher-order isotopologues iso 13 Ñ 12 have been produced, the presentÑ method uses only the M0 and outliers based on monoisotopic intensity, peptide size, and C Ñ composition derived from the adjustment. Protein abundance M1 intensities. The developedÖÑ expressions of M0 and M1 are was calculated on the basis of the abundance of their three most provided in Supporting File S1. 14 12 13 intense peptides (Top-3). The absence of an automated Under standard conditions, P( C) = 0.9893; P( C) = 1 2 14 processing tool contributed to limiting the widespread diffusion 0.0107; P( H) = 0.999885; P( H) = 0.000115; P( N) = 15 16 17 of the method. 0.99632; P( N) = 0.00368; P( O) = 0.99757; P( O) = 18 32 33 To overcome these limitations, we established the basis of a 0.00038; P( O) = 0.00205; P( S) = 0.9493; P( S) = 0.0076; 34 36 new theoretical background to process the experimental data in P( S) = 0.0429; P( S) = 0.0002. fi SLIM-labeling experiments and developed the appropriate The quanti cation of bSLIM labeling allows determination of 12 procedures based on open-source resources, using, in particular, the amount of C present in every peptide in experiments in dedicated OpenMS modules8 for peptide identification in which the carbon isotope composition is manipulated to favor 12 ′ 12 conjunction with a modified version of FeatureFinderIdentifi- C incorporation. This amount is expressed as P ( C) and the cation.9 This solution provides a Gaussian fit of the intensities of residual amount of 13CasP′(13C). every isotopologue chromatographic trace (mass trace) for For every peptide, it is possible to have experimental access to exp exp peptides with a validated identification, allowing extraction of at least M0 and M1 . Equations 1 and 2 provide the theoretical the relevant information. The only experimental data required values of M0 and M1, with the sum of the intensity (probability) fi for high-quality quanti cation are the abundance of M0 and M1, of all of the isotopologues normalized to 1. the monoisotopic ion, and the +1 isotopologue of the peptides It is therefore possible to set the equivalence identified with high confidence. Computation of the 12C exp exp exp M0 M1 M1 M1 abundance of peptides and the molar fraction of peptides from M0 = n exp and M1 = n exp => = exp 12 ∑ ∑ the NC and C conditions in multiplexing experiments is 0 Mi 0 Mi M0 M0 performed by implementing appropriate calculation modules in Equations 1 and 2 are rearranged to set a KNIME working environment.10,11 We also present the theoretical basis for establishing appropriate filters to obtain Mexp P′(C)13 P(H)2 P(N)15 1 =×x +×y +×v high-quality processed data from experimental datasets. These M exp P′(C)12 P(H)1 P(N)14 new integrated tools provide a convenient framework that 0 17 33 enables wider use of the bSLIM-labeling strategy. P(O) P(S) +×w +×u 32 Theoretical Basis of bSLIM-Labeling Data Processing P(O)16 P(S) (3) fi The isotope cluster of every peptide Pept is de ned as a series of Let us define B as the sum of the terms that remain identical isotopologues (M0, M1, M2, ..
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