Data, Reagents, Assays and Merits of Proteomics for SARS-CoV-2 Research and Testing
2020; Elsevier BV; Volume: 19; Issue: 9 Linguagem: Inglês
10.1074/mcp.ra120.002164
ISSN1535-9484
AutoresJana Zecha, Chien‐Yun Lee, Florian Bayer, Chen Meng, Vincent Grass, Johannes Zerweck, Karsten Schnatbaum, Thomas Michler, Andreas Pichlmair, Christina Ludwig, Bernhard Küster,
Tópico(s)Biosensors and Analytical Detection
ResumoAs the COVID-19 pandemic continues to spread, thousands of scientists around the globe have changed research direction to understand better how the virus works and to find out how it may be tackled. The number of manuscripts on preprint servers is soaring and peer-reviewed publications using MS-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, the virus that causes COVID-19, this report presents deep-scale proteomes (10,000 proteins; >130,000 peptides) of common cell line models, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that characterize their protein expression profiles including viral entry factors such as ACE2 or TMPRSS2. Using the 9 kDa protein SRP9 and the breast cancer oncogene BRCA1 as examples, we show how the proteome expression data can be used to refine the annotation of protein-coding regions of the African green monkey and the Vero cell line genomes. Monitoring changes of the proteome on viral infection revealed widespread expression changes including transcriptional regulators, protease inhibitors, and proteins involved in innate immunity. Based on a library of 98 stable-isotope labeled synthetic peptides representing 11 SARS-CoV-2 proteins, we developed PRM (parallel reaction monitoring) assays for nano-flow and micro-flow LC–MS/MS. We assessed the merits of these PRM assays using supernatants of virus-infected Vero E6 cells and challenged the assays by analyzing two diagnostic cohorts of 24 (+30) SARS-CoV-2 positive and 28 (+9) negative cases. In light of the results obtained and including recent publications or manuscripts on preprint servers, we critically discuss the merits of MS-based proteomics for SARS-CoV-2 research and testing. As the COVID-19 pandemic continues to spread, thousands of scientists around the globe have changed research direction to understand better how the virus works and to find out how it may be tackled. The number of manuscripts on preprint servers is soaring and peer-reviewed publications using MS-based proteomics are beginning to emerge. To facilitate proteomic research on SARS-CoV-2, the virus that causes COVID-19, this report presents deep-scale proteomes (10,000 proteins; >130,000 peptides) of common cell line models, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that characterize their protein expression profiles including viral entry factors such as ACE2 or TMPRSS2. Using the 9 kDa protein SRP9 and the breast cancer oncogene BRCA1 as examples, we show how the proteome expression data can be used to refine the annotation of protein-coding regions of the African green monkey and the Vero cell line genomes. Monitoring changes of the proteome on viral infection revealed widespread expression changes including transcriptional regulators, protease inhibitors, and proteins involved in innate immunity. Based on a library of 98 stable-isotope labeled synthetic peptides representing 11 SARS-CoV-2 proteins, we developed PRM (parallel reaction monitoring) assays for nano-flow and micro-flow LC–MS/MS. We assessed the merits of these PRM assays using supernatants of virus-infected Vero E6 cells and challenged the assays by analyzing two diagnostic cohorts of 24 (+30) SARS-CoV-2 positive and 28 (+9) negative cases. In light of the results obtained and including recent publications or manuscripts on preprint servers, we critically discuss the merits of MS-based proteomics for SARS-CoV-2 research and testing. Mass spectrometry-based proteomics is continuing to make tremendous contributions to life science research and the latest "explosion" of activities around SARS-CoV-2 also motivates proteomic scientists to join efforts that aim at better understanding how this new virus works and how that may inform the development of effective treatments and vaccines. In this context, it is worth reflecting in which areas of the life sciences proteomics has historically been particularly successful. One of the first areas was cell biology. For example, the ability to identify and characterize protein complexes systematically has profoundly changed the way we think about the functional organization of cells. Proteomics then revolutionized the analysis of post-translational modifications to the extent that the vast majority of all PTMs known to date have been found by proteomic approaches. The rapid development of quantitative MS, with or without the use of stable isotopes, paved the way for large-scale cell perturbation studies ranging from growth factors to nutrients to knock-outs or drugs. This has significantly shaped our current understanding of the inner workings of a cell including protein expression regulation, biochemical fluxes and signaling networks to name a few. Today, proteomics is also becoming ever more important in pre-clinical drug discovery and much of the chemical biology field is powered by the ability to interrogate proteins and drugs on a proteome-wide scale. More recently, proteomics has also begun making inroads into structural biology, an area that is undergoing very rapid development and will undoubtedly make important contributions in the future. In comparison, and despite great efforts, progress in clinical proteomics has been slower as many additional challenges present when analyzing complex biology in heterogeneous human populations. That said, recent technological developments and their application suggest that clinical proteomics will become much more successful soon. In light of the above, it should come as no surprise that proteomics has also become highly successful in virology and a recent special issue of Molecular and Cellular Proteomics has highlighted many of the important achievements made in the area of infectious diseases (1Greco T.M. Cristea I.M. Proteomics Tracing the Footsteps of Infectious Disease.Mol. Cell. Proteomics. 2017; 16: S5-S14Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar). The recent COVID-19 outbreak has spurred a remarkable amount of research activities. At the time of writing, the preprint servers medRxiv and bioRxiv listed ∼5,500 manuscripts for SARS-CoV-2. More than 300 of these mention the term proteomics and a few have already entered the peer-reviewed scientific literature. One example for the latter is a protein interaction map constructed by using affinity-tagged viral proteins and MS that provides an initial overview of how SARS-CoV-2 proteins interact with host proteins (2Gordon D.E. Jang G.M. Bouhaddou M. Xu J. Obernier K. White K.M. O'Meara M.J. Rezelj V.V. Guo J.Z. Swaney D.L. Tummino T.A. Huettenhain R. Kaake R.M. Richards A.L. Tutuncuoglu B. Foussard H. Batra J. Haas K. Modak M. Kim M. Haas P. Polacco B.J. Braberg H. Fabius J.M. Eckhardt M. Soucheray M. Bennett M.J. Cakir M. McGregor M.J. Li Q. Meyer B. Roesch F. Vallet T. Mac Kain A. Miorin L. Moreno E. Naing Z.Z.C. Zhou Y. Peng S. Shi Y. Zhang Z. Shen W. Kirby I.T. Melnyk J.E. Chorba J.S. Lou K. Dai S.A. Barrio-Hernandez I. Memon D. Hernandez-Armenta C. Lyu J. Mathy C.J.P. Perica T. Pilla K.B. Ganesan S.J. Saltzberg D.J. Rakesh R. Liu X. Rosenthal S.B. Calviello L. Venkataramanan S. Liboy-Lugo J. Lin Y. Huang X.-P. Liu Y. Wankowicz S.A. Bohn M. Safari M. Ugur F.S. Koh C. Savar N.S. Tran Q.D. Shengjuler D. Fletcher S.J. O'Neal M.C. Cai Y. Chang J.C.J. Broadhurst D.J. Klippsten S. Sharp P.P. Wenzell N.A. Kuzuoglu D. Wang H.-Y. Trenker R. Young J.M. Cavero D.A. Hiatt J. Roth T.L. Rathore U. Subramanian A. Noack J. Hubert M. Stroud R.M. Frankel A.D. Rosenberg O.S. Verba K.A. Agard D.A. Ott M. Emerman M. Jura N. von Zastrow M. Verdin E. Ashworth A. Schwartz O. d'Enfert C. Mukherjee S. Jacobson M. Malik H.S. Fujimori D.G. Ideker T. Craik C.S. Floor S.N. Fraser J.S. Gross J.D. Sali A. Roth B.L. Ruggero D. Taunton J. Kortemme T. Beltrao P. Vignuzzi M. García-Sastre A. Shokat K.M. Shoichet B.K. Krogan N.J. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.Nature. 2020; 10.1038/s41586-020-2286-9Crossref PubMed Scopus (2361) Google Scholar). Another group applied a pulse-labeling approach to monitor the modulation of the viral translatome and proteome on infection (3Bojkova D. Klann K. Koch B. Widera M. Krause D. Ciesek S. Cinatl J. Münch C. Proteomics of SARS-CoV-2-infected host cells reveals therapy targets.Nature. 2020; 10.1038/s41586-020-2332-7Crossref Scopus (552) Google Scholar), and two laboratories analyzed sera of COVID-19 cases by LC–MS/MS in the search for biomarkers (4Shen B. Yi X. Sun Y. Bi X. Du J. Zhang C. Quan S. Zhang F. Sun R. Qian L. Ge W. Liu W. Liang S. Chen H. Zhang Y. Li J. Xu J. He Z. Chen B. Wang J. Yan H. Zheng Y. Wang D. Zhu J. Kong Z. Kang Z. Liang X. Ding X. Ruan G. Xiang N. Cai X. Gao H. Li L. Li S. Xiao Q. Lu T. Zhu Y. Liu H. Chen H. Guo T. Proteomic and metabolomic characterization of COVID-19 patient sera.Cell. 2020; Abstract Full Text Full Text PDF Scopus (737) Google Scholar, 5Messner C.B. Demichev V. Wendisch D. Michalick L. White M. Freiwald A. Textoris-Taube K. Vernardis S.I. Egger A.-S. Kreidl M. Ludwig D. Kilian C. Agostini F. Zelezniak A. Thibeault C. Pfeiffer M. Hippenstiel S. Hocke A. von Kalle C. Campbell A. Hayward C. Porteous D.J. Marioni R.E. Langenberg C. Lilley K.S. Kuebler W.M. Mülleder M. Drosten C. Witzenrath M. Kurth F. Sander L.E. Ralser M. Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection.Cell Systems. 2020; Abstract Full Text Full Text PDF PubMed Scopus (260) Google Scholar). Several studies also present candidate drug targets and small molecules that show antiviral activity in vitro. This is of note as controlling the pandemic will require a multitude of measures including effective treatments for patients with severe course of disease using drugs that exist today. At the beginning of a new research activity, considerable time and effort is required for the molecular characterization of the biological model systems, generating research reagents, and setting up assays. Because the SARS-CoV-2 pandemic puts scientists under heavy time pressure, sharing such resources with the scientific community rapidly can facilitate progress provided that high standards of quality can be upheld. In this report, we contribute high-quality LC–MS/MS data on the proteomes of common cell line models for SARS-CoV-2 research, notably Vero E6, Calu-3, Caco-2, and ACE2-A549 that may be used as a protein expression resource or to build spectral libraries. The African green monkey and derived Vero cell lines often serve as in vitro and in vivo models for virus research and our analysis exemplifies how mass spectrometric data can be used to improve the annotation of protein-coding regions. Furthermore, we present data on how the virus modulates the proteome of infected cells. In addition, we provide a physical and spectral library of 98 stable isotope-labeled, synthetic peptides representing 11 viral proteins along with optimized PRM assays that were tested on two diagnostic cohorts of in total 91 COVID-19 suspected individuals. Based on our results and examples from the emerging literature, we critically project and discuss the merits of MS-based proteomics for SARS-CoV-2 research and testing. The rationale of the experimental design is described in more detail in the respective method and result sections and in the supplemental Methods. In brief, we first aimed to characterize the protein expression profiles of three model cell lines (African green monkey Vero E6 kidney cell line, human Caco-2 colon and Calu-3 lung-cancer cell lines) commonly used in virology studies. In addition, the human A459 lung cancer cell line stably transfected with ACE2, a peptidase reported to serve as entry point for SARS-CoV-2 into cells (6Hoffmann M. Kleine-Weber H. Schroeder S. Krüger N. Herrler T. Erichsen S. Schiergens T.S. Herrler G. Wu N.-H. Nitsche A. Müller M.A. Drosten C. Pöhlmann S. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor.Cell. 2020; 181: 271-280Abstract Full Text Full Text PDF PubMed Scopus (11332) Google Scholar) was included for deep proteome profiling. To this end, we performed deep proteome analyses measuring 48 basic reversed phase (RP) fractions for each cell line and generating high resolution and mass accuracy fragment spectra. For Vero E6 and ACE2-A459 cells, a workflow replicate was prepared by employing a faster, but lower resolution method for MS2 spectra acquisition. High resolution and mass accuracy MS2 spectra from Vero E6 cells and a database search including human protein sequences were used further to exemplify a proteomics-guided refinement of the expressed genome and identify genes or parts of genes that have been completely missed in the African green monkey genome annotation provided by Uniprot and/or RefSeq. Next, the response of Vero E6 cells 24 h after SARS-CoV-2 infection at 2 different multiplicities of infection (MOI) was investigated in cell culture triplicates to enable analyses of significant protein expression changes. In addition, obtained infectome data were compared with a recently published virus-host response study (3Bojkova D. Klann K. Koch B. Widera M. Krause D. Ciesek S. Cinatl J. Münch C. Proteomics of SARS-CoV-2-infected host cells reveals therapy targets.Nature. 2020; 10.1038/s41586-020-2332-7Crossref Scopus (552) Google Scholar) and a SARS-CoV-2 interactome study (2Gordon D.E. Jang G.M. Bouhaddou M. Xu J. Obernier K. White K.M. O'Meara M.J. Rezelj V.V. Guo J.Z. Swaney D.L. Tummino T.A. Huettenhain R. Kaake R.M. Richards A.L. Tutuncuoglu B. Foussard H. Batra J. Haas K. Modak M. Kim M. Haas P. Polacco B.J. Braberg H. Fabius J.M. Eckhardt M. Soucheray M. Bennett M.J. Cakir M. McGregor M.J. Li Q. Meyer B. Roesch F. Vallet T. Mac Kain A. Miorin L. Moreno E. Naing Z.Z.C. Zhou Y. Peng S. Shi Y. Zhang Z. Shen W. Kirby I.T. Melnyk J.E. Chorba J.S. Lou K. Dai S.A. Barrio-Hernandez I. Memon D. Hernandez-Armenta C. Lyu J. Mathy C.J.P. Perica T. Pilla K.B. Ganesan S.J. Saltzberg D.J. Rakesh R. Liu X. Rosenthal S.B. Calviello L. Venkataramanan S. Liboy-Lugo J. Lin Y. Huang X.-P. Liu Y. Wankowicz S.A. Bohn M. Safari M. Ugur F.S. Koh C. Savar N.S. Tran Q.D. Shengjuler D. Fletcher S.J. O'Neal M.C. Cai Y. Chang J.C.J. Broadhurst D.J. Klippsten S. Sharp P.P. Wenzell N.A. Kuzuoglu D. Wang H.-Y. Trenker R. Young J.M. Cavero D.A. Hiatt J. Roth T.L. Rathore U. Subramanian A. Noack J. Hubert M. Stroud R.M. Frankel A.D. Rosenberg O.S. Verba K.A. Agard D.A. Ott M. Emerman M. Jura N. von Zastrow M. Verdin E. Ashworth A. Schwartz O. d'Enfert C. Mukherjee S. Jacobson M. Malik H.S. Fujimori D.G. Ideker T. Craik C.S. Floor S.N. Fraser J.S. Gross J.D. Sali A. Roth B.L. Ruggero D. Taunton J. Kortemme T. Beltrao P. Vignuzzi M. García-Sastre A. Shokat K.M. Shoichet B.K. Krogan N.J. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.Nature. 2020; 10.1038/s41586-020-2286-9Crossref PubMed Scopus (2361) Google Scholar). Finally, using heavy synthetic peptide references, we generated a spectral library entailing fragment ion spectra and retention time information for 98 SARS-CoV-2 peptides. This was refined further to a PRM assay panel containing 23 peptides and applied to the detection of SARS-CoV-2 in two clinical cohorts. In total, 91 respiratory specimens, of which 37 were tested negative and 54 were tested positive for SARS-CoV-2 by RT-PCR (RT-PCR), were analyzed by nano- and micro-flow PRM using two different input quantities. All significance and enrichment analyses were corrected for multiple testing at 5% FDR. Further, instead of choosing a p-value cut-off, S0 was specified to adjust the significance cut-off of statistical analyses on the fold-change level in a data-driven way while accounting for differing variances across the range of measured values and groups. For two-sided t-tests, at least 2 valid quantifications per group were required, and equal variances were assumed for each group as well as normal distribution of log transformed protein intensities. To characterize correlations, Pearson correlation coefficients (R) were computed under the assumption of a linear relationship between two variables. Isotopically labeled SpikeTidesTM peptides covering 11 SARS-CoV-2 proteins were kindly provided by JPT Peptide Technologies (for details see supplemental Methods). All quantities per spike-in peptide specified in the following represent only rough estimates, as the isotopically labeled peptides were not purified and concentrations were not determined accurately. For retention time calibration, PROCAL retention time peptides from JPT Peptide Technologies (7Zolg D.P. Wilhelm M. Yu P. Knaute T. Zerweck J. Wenschuh H. Reimer U. Schnatbaum K. Kuster B. PROCAL: a set of 40 peptide standards for retention time indexing, column performance monitoring, and collision energy calibration.Proteomics. 2017; 17: 1700263Crossref Scopus (33) Google Scholar) and indexed retention time (iRT) peptides from Biognosys (8Escher C. Reiter L. MacLean B. Ossola R. Herzog F. Chilton J. MacCoss M.J. Rinner O. Using iRT, a normalized retention time for more targeted measurement of peptides.Proteomics. 2012; 12: 1111-1121Crossref PubMed Scopus (387) Google Scholar) were used. Western blots were performed according to standard procedures using 30 µg protein as input and antibodies against human ACE2 (R&D Systems, Cat# AF933, 1 µg/ml) and β-actin (Santa Cruz Biotechnology, sc-47778, 1:500). Details of cell culture conditions, the generation of a cell line expressing ACE2, and virus growth and virus titer and cell viability assays are specified in supplemental Methods. For investigation of the host cell response to the virus, 10e6 Vero E6 cells were infected with mock or SARS-CoV-2-MUN-IMB-1 strain at a MOI of 3 or 0.1, and triplicates of each condition were lysed 24 h post infection. Supernatant of infected Vero E6 cells was collected 48 h post infection using a MOI of 0.01 and spun twice at 1000 × g for 10 min All cells were lysed in SDS lysis buffer (2% SDS in 40 or 50 mm Tris/HCl pH 7.6), and virus-containing cell lysates and supernatant were heated at 95°C for 5 to 10 min before storage at −80°C. To hydrolyze DNA and reduce viscosity, cell lysates were heated at 95°C for 5 min and TFA was added to a final concentration of 1% (9Dagley L.F. Infusini G. Larsen R.H. Sandow J.J. Webb A.I. Universal solid-phase protein preparation (USP3) for bottom-up and top-down proteomics.J. Proteome Res. 2019; 18: 2915-2924Crossref PubMed Scopus (27) Google Scholar). Quenching was performed using 3 M Tris, pH 10 (final concentration of ∼195 mm, pH 7.8). Protein concentration was determined using the Pierce BCA Protein Assay Kit (Thermo Scientific). Proteins were cleaned up and digested using the SP3 method on an automated Bravo liquid handling system (Agilent) as previously described (10Hughes C.S. Moggridge S. Müller T. Sorensen P.H. Morin G.B. Krijgsveld J. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments.Nat. Protoc. 2019; 14: 68-85Crossref PubMed Scopus (391) Google Scholar) with minor modifications, details of which are specified in the supplemental Methods. In brief, 1 mg of a 1:1 mix of two types of carboxylate beads (cat# 45152105050250 and 65152105050250, GE Healthcare), 200 µg of protein digest (120 µg for Vero E6 measured with ion trap MS2 method) and a 1:50 trypsin-to-protein ratio for overnight digestion at 37 °C were used. Peptides were desalted using RP-S cartridges (5 μl bed volume, Agilent) and the standard peptide cleanup v2.0 protocol on the AssayMAP Bravo Platform (Agilent, wash solvent: 0.1% FA; elution solvent: 0.1% FA in 70% ACN). Triplicates of SARS-CoV-2 infected Vero E6 cells (30 µg of peptides per replicate) were labeled with 9 channels of TMT10plex reagent kit (Thermo Scientific, channel 127N was omitted) according to our previously published protocol (11Zecha J. Satpathy S. Kanashova T. Avanessian S.C. Kane M.H. Clauser K.R. Mertins P. Carr S.A. Kuster B. TMT labeling for the masses: a robust and cost-efficient, in-solution labeling approach.Mol. Cell. Proteomics. 2019; 18: 1468-1478Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar) with minor modifications as specified in the supplemental Methods. After vacuum drying, TMT-labeled peptides were dissolved in 0.1% FA and desalted by the AssayMAP Bravo Platform (Agilent) as described above. For off-line high pH reversed phase (RP) fractionation of label-free and TMT-labelled cell lines, a Dionex Ultra 3000 HPLC system equipped with a Waters XBridge BEH130 C18 column (3.5 μm 2.1 × 150 mm) was operated at a flow rate of 200 µl/min with a constant 10% of 25 mm ammonium bicarbonate (pH = 8.0) in the running solvents. Nonlabeled peptides (200 µg) were separated using a 57 min linear gradient from 4 to 32% ACN in ddH2O followed by a 3 min linear gradient up to 85% ACN. For TMT-labeled peptides, a 57 min linear gradient from 7 to 45% ACN in ddH2O followed by a 6 min linear gradient up to 80% ACN was employed. Forty-eight fractions were collected every half minute from minute 3 to 51 and pooled discontinuously into 48 fractions (fraction 1 + 49, fraction 2 + 50, and so on). Peptide fractions were frozen at −80°C and dried by vacuum centrifugation. Supernatant of SARS-CoV-2 infected Vero E6 cells, which contained 2e6 virions (infectious virus particles) per ml as measured by plaque assay, was used to evaluate SP3-based, in-gel and in-solution digestion in urea buffer for the detection of SARS-CoV-2 derived peptides (see supplemental Methods for details). Further, a dilution series was prepared from the virus supernatant sample in 8 steps (15, 5, 1.5, 0.5, 0.15, 0.05, 0.015, and 0.005 μg of total protein amount). Dilutions were used as input for the in-gel digestion workflow by mixing them 1:1 with 4× Novex NuPAGE LDS sample buffer (Invitrogen) containing 20 mm DTT. Samples were run 1 cm into a 4–12% Bis-Tris-protein gel using 1× MOPS SDS running buffer (Novex NuPAGE, Invitrogen). Reduction, alkylation, and overnight digestion of proteins (using 250 ng trypsin) were performed according to standard in-gel procedures. In parallel, gel bands loaded with sample buffer only were processed representing "blank" samples. The identical amount (∼15 fmol) of isotopically labeled SARS-CoV-2 peptide mix was added to all 9 samples. Subsequently, one-third of the sample was measured by nano-flow and two-third by micro-flow PRM targeting 23 and 21 SARS-CoV-2 peptides, respectively. Details on the collection of respiratory specimens and the determination of their virus load in genome equivalents (geq) via RT-PCR are given in the supplemental Methods. In this study, 91 specimens that were collected as part of the standard diagnostic testing and would normally be discarded were used. Approval to do so was granted by the ethics committee of the University Hospital "rechts der Isar" of the Technical University of Munich. Person identification was not recorded, and only SARS-CoV-2 proteins were investigated. For nano-flow PRM analysis of clinical cohort 1, 15 µl of residual material from testing of 52 diagnostic samples was mixed 3:1 with 4× Novex NuPAGE LDS sample buffer containing 40 mm DTT and used as input for in-gel digestion using 250 ng trypsin. Isotopically labeled SARS-CoV-2 peptide mix (∼5 fmol/injection), PROCAL retention time peptides and iRT peptides were spiked into all 52 clinical samples directly before measurement. For the micro-flow PRM measurements of cohort 1, in total 50 µl of each sample were mixed with 4× LDS sample buffer containing 40 mm DTT, added to two gel pockets, and combined after digestion using 500 ng trypsin per gel lane. For cohort 2, up to 300 µl of 39 nasopharyngeal swab samples were dried down and resuspended in 25 µl of 2× Novex NuPAGE LDS sample buffer containing 10 mm DTT before subjection to in-gel digestion using 1 µg of trypsin. Before micro-flow PRM measurement, heavy SARS-CoV-2 peptides (∼50 fmol/injection), PROCAL, and iRT peptides were spiked into all samples. For the nano-flow setup, all peptides corresponding to 5 µl of the original sample were used, whereas for micro-flow analyses a quantity corresponding to 46.4 µl of the original sample was injected into the MS (equivalent to the input amount for standard RT-PCR diagnostic analysis). As negative/blank controls, empty gel lanes were processed and analyzed in parallel with all clinical samples. All PRM assays were designed in accordance with the Tier 3 guidelines for targeted assay development (12Carr S.A. Abbatiello S.E. Ackermann B.L. Borchers C. Domon B. Deutsch E.W. Grant R.P. Hoofnagle A.N. Hüttenhain R. Koomen J.M. Liebler D.C. Liu T. MacLean B. Mani D.R. Mansfield E. Neubert H. Paulovich A.G. Reiter L. Vitek O. Aebersold R. Anderson L. Bethem R. Blonder J. Boja E. Botelho J. Boyne M. Bradshaw R.A. Burlingame A.L. Chan D. Keshishian H. Kuhn E. Kinsinger C. Lee J.S.H. Lee S.-W. Moritz R. Oses-Prieto J. Rifai N. Ritchie J. Rodriguez H. Srinivas P.R. Townsend R.R. Van Eyk J. Whiteley G. Wiita A. Weintraub S. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach.Mol. Cell. Proteomics. 2014; 13: 907-917Abstract Full Text Full Text PDF PubMed Scopus (406) Google Scholar, 13Abbatiello S. Ackermann B.L. Borchers C. Bradshaw R.A. Carr S.A. Chalkley R. Choi M. Deutsch E. Domon B. Hoofnagle A.N. Keshishian H. Kuhn E. Liebler D.C. MacCoss M. MacLean B. Mani D.R. Neubert H. Smith D. Vitek O. Zimmerman L. New guidelines for publication of manuscripts describing development and application of targeted mass spectrometry measurements of peptides and proteins.Mol. Cell. Proteomics. 2017; 16: 327-328Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar). In addition, isotopically labeled reference peptides were used for detection of SARS-CoV-2 derived peptides to achieve maximally confident identification. A more detailed description of different PRM assays can be found in the supplemental Methods section. In brief, peptides for ACE2 and TMPRSS2 were selected based on most intense peptides in data dependent acquisition (DDA) measurements of high pH RP fractions. For monkey proteins, peptides that are identical or correspond to a human peptide that has been identified in any of the human cell lines were additionally included. In total, 15/6/9/6 peptide sequences were targeted for human ACE2/TMPRSS2/monkey ACE2/TMPRSS2 proteins. Spectral libraries were built from experimental spectra of deep proteome measurements and predicted spectra using Skyline (version 20.1.1.83) (14MacLean B. Tomazela D.M. Shulman N. Chambers M. Finney G.L. Frewen B. Kern R. Tabb D.L. Liebler D.C. MacCoss M.J. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.Bioinformatics. 2010; 26: 966-968Crossref PubMed Scopus (3006) Google Scholar) and the Prosit 2019 algorithm (15Gessulat S. Schmidt T. Zolg D.P. Samaras P. Schnatbaum K. Zerweck J. Knaute T. Rechenberger J. Delanghe B. Huhmer A. Reimer U. Ehrlich H.-C. Aiche S. Kuster B. Wilhelm M. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.Nat. Methods. 2019; 16: 509-518Crossref PubMed Scopus (295) Google Scholar) and are available for download from Panorama Public (16Sharma V. Eckels J. Schilling B. Ludwig C. Jaffe J.D. MacCoss M.J. MacLean B. Panorama public: a public repository for quantitative data sets processed in Skyline.Mol. Cell. Proteomics. 2018; 17: 1239-1244Abstract Full Text Full Text PDF PubMed Scopus (113) Google Scholar) (https://panoramaweb.org/SARS-CoV-2.url). For peptides that have not been identified in DDA runs, retention time was predicted using Prosit. SARS-CoV-2 peptide selection started with the in silico tryptic digestion of the Uniprot derived SARS-CoV-2 proteome (UP000464024, 14 entries, last modified on 22nd of March 2020). In total, 113 peptides representing 11 proteins met our selection criteria. All peptides were synthesized as SpikeTidesTM in isotopically labeled form (JPT Peptide Technologies) and pooled into a single peptide mix. Spectral libraries of 98 confidently detected peptides (MaxQuant score > 50) containing high-quality reference spectra and retention time information were built from experimental spectra of synthetic peptides and predicted spectra using Skyline and the Prosit algorithm and are available for download from Panorama Public (16Sharma V. Eckels J. Schilling B. Ludwig C. Jaffe J.D. MacCoss M.J. MacLean B. Panorama public: a public repository for quantitative data sets processed in Skyline.Mol. Cell. Proteomics. 2018; 17: 1239-1244Abstract Full Text Full Text PDF PubMed Scopus (113) Google Scholar) (https://panoramaweb.org/SARS-CoV-2.url). The assay panel was further refined for the detection of SARS-CoV-2 in respiratory specimens using supernatant sample and based on uniqueness for SARS-CoV-2 and the highest endogenous PRM-MS2 signal using the top 6 fragment ions from the spectral library. Finally, we derived a panel of 23/21 optimal PRM assays for SARS-CoV-2 detection using nano-/micro-flow PRM and a 50/15-min linear gradient. For LC-ESI-MS/MS measurement of deep-scale proteomes, a Dionex UltiMate 3000 RSLCnano System equipped with a Vanquish pump module and coupled to a Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific) was operated under micro-flow conditions as we described recently (17Bian Y. Zheng R. Bayer F.P. Wong C. Chang Y.-C. Meng C. Zolg D.P. Reinecke M. Zecha J. Wiechmann S. Heinzlmeir S. Scherr J. Hemmer B. Baynham M. Gingras A.-C. Boychenko O. Kuster B. Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS.Nat. Commun. 2020; 11: 157Crossref PubMed Scopus (138) Google Scholar). Peptide fractions were dissolved in 1% FA containing 500 fmol of PROCAL peptides per injection, and t
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