Virtual Issue: Technological Innovations
Bibliographic record
Abstract
The mission of Molecular and Cellular Proteomics since its creation in 2002 has been to “foster the development and applications of proteomics in both basic and translational research.” Our mission statement mandates that Research Articles “report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life” and that “manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action.” This governing principle still forms the basis of a Research Article's acceptance in the journal. However, the Editors have also long realized that these achievements in proteomics discoveries are only possible in the context of advances in enabling technologies. As such, the journal has always welcomed—but has recently reemphasized its interest in—important “new computational methods and technological advancements that will enable future discoveries.” In contrast to standard MCP Research Articles, “manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data.” Since our Editorial of January 2018 titled “Your technological advances belong here,” we have seen an increase in the submission of excellent experimental and computational technology papers. Those that we published have in turn garnered high interest from the community, both in terms of page access, Mendeley saves, online attention and even (despite their young age) citations. This virtual issue celebrates some of the Technological Innovation manuscripts published in MCP since January 2018. We have selected several papers that represent the trends in improving the sensitivity in sample preparation, notably by coupling mass spectrometric analysis to laser microdissection (1Zhu Y. Dou M. Piehowski P.D. Liang Y. Wang F. Chu R.K. Chrisler W.B. Smith J.N. Schwarz K.C. Shen Y. Shukla A.K. Moore R.J. Smith R.D. Qian W.J. Kelly R.T. Spatially resolved proteome mapping of laser capture microdissected tissue with automated sample transfer to nanodroplets.Mol. Cell. Proteomics. 2018; 17: 1864-1874Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar) or Fluorescence Activated Cell Sorting (2Myers S.A. Rhoads A. Cocco A.R. Peckner R. Haber A.L. Schweitzer L.D. Krug K. Mani D.R. Clauser K.R. Rozenblatt-Rosen O. Hacohen N. Regev A. Carr S.A. Streamlined protocol for deep proteomic profiling of fac-sorted cells and its application to freshly isolated murine immune cells.Mol. Cell. Proteomics. 2019; 18: 995-1009Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar), by developing streamlined and cost effective isobaric labeling protocols for multiplexing (3Zecha 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 (64) Google Scholar), and by developing new protein capture strategies for global proteome profiling (4Batth T.S. Tollenaere M.A.X. Ruther P. Gonzalez-Franquesa A. Prabhakar B.S. Bekker-Jensen S. Deshmukh A.S. Olsen J.V. Protein aggregation capture on microparticles enables multipurpose proteomics sample preparation.Mol. Cell. Proteomics. 2019; 18: 1027-1035Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar). Some of the selected papers focus on sample analysis for specific applications, notably by overcoming common contaminants in phosphopeptide enrichment (5Potel C.M. Lin M.H. Heck A.J.R. Lemeer S. Defeating Major Contaminants in Fe(3+)- Immobilized metal ion affinity chromatography (IMAC) phosphopeptide enrichment.Mol. Cell. Proteomics. 2018; 17: 1028-1034Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar), systematically investigating post-translationally modified spectra (6Zolg D.P. Wilhelm M. Schmidt T. Medard G. Zerweck J. Knaute T. Wenschuh H. Reimer U. Schnatbaum K. Kuster B. ProteomeTools: Systematic Characterization of 21 post-translational protein modifications by liquid chromatography tandem mass spectrometry (LC-MS/MS) using synthetic peptides.Mol. Cell. Proteomics. 2018; 17: 1850-1863Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar), expanding the acetyl proteome in plants (7Liu S. Yu F. Yang Z. Wang T. Xiong H. Chang C. Yu W. Li N. Establishment of dimethyl labeling-based quantitative acetylproteomics in arabidopsis.Mol. Cell. Proteomics. 2018; 17: 1010-1027Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar) and enabling proximity dependent biotinylation across multiple cell types (8Samavarchi-Tehrani P. Abdouni H. Samson R. Gingras A.C. A versatile lentiviral delivery toolkit for proximity-dependent biotinylation in diverse cell types.Mol. Cell. Proteomics. 2018; 17: 2256-2269Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar), and permitting multiplex protein profiling with DNA barcoded antibodies (9Lee J. Geiss G.K. Demirkan G. Vellano C.P. Filanoski B. Lu Y. Ju Z. Yu S. Guo H. Bogatzki L.Y. Carter W. Meredith R.K. Krishnamurthy S. Ding Z. Beechem J.M. Mills G.B. Implementation of a multiplex and quantitative proteomics platform for assessing protein lysates using DNA-barcoded antibodies.Mol. Cell. Proteomics. 2018; 17: 1245-1258Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar). Papers improving liquid chromatography (10Kovalchuk S.I. Jensen O.N. Rogowska-Wrzesinska A. FlashPack: fast and simple preparation of ultrahigh-performance capillary columns for LC-MS.Mol. Cell. Proteomics. 2019; 18: 383-390Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar, 11Bache N. Geyer P.E. Bekker-Jensen D.B. Hoerning O. Falkenby L. Treit P.V. Doll S. Paron I. Muller J.B. Meier F. Olsen J.V. Vorm O. Mann M. A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics.Mol. Cell. Proteomics. 2018; 17: 2284-2296Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar), adapting ion mobility workflows (12Meier F. Brunner A.D. Koch S. Koch H. Lubeck M. Krause M. Goedecke N. Decker J. Kosinski T. Park M.A. Bache N. Hoerning O. Cox J. Rather O. Mann M. Online parallel accumulation-serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer.Mol. Cell. Proteomics. 2018; 17: 2534-2545Abstract Full Text Full Text PDF PubMed Scopus (146) Google Scholar, 13Pfammatter S. Bonneil E. McManus F.P. Prasad S. Bailey D.J. Belford M. Dunyach J.J. Thibault P. A novel differential ion mobility device expands the depth of proteome coverage and the sensitivity of multiplex proteomic measurements.Mol. Cell. Proteomics. 2018; 17: 2051-2067Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar), expanding Data Independent Acquisition (14Amon S. Meier-Abt F. Gillet L.C. Dimitrieva S. Theocharides A.P.A. Manz M.G. Aebersold R. Sensitive quantitative proteomics of human hematopoietic stem and progenitor cells by data-independent acquisition mass spectrometry.Mol. Cell. Proteomics. 2019; 18: 1454-1467Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar) or defining new real-time quality control measurements (15Stanfill B.A. Nakayasu E.S. Bramer L.M. Thompson A.M. Ansong C.K. Clauss T.R. Gritsenko M.A. Monroe M.E. Moore R.J. Orton D.J. Piehowski P.D. Schepmoes A.A. Smith R.D. Webb-Robertson B.M. Metz T.O. Group T.S. Quality control analysis in real-time (QC-ART): a tool for real-time quality control assessment of mass spectrometry-based proteomics data.Mol. Cell. Proteomics. 2018; 17: 1824-1836Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar) also exemplify the type of manuscripts that have been frequently accessed and/or cited. Lastly, a clear trend consistent with the need for new computational workflows, data sharing and the development of databases is that several computational or bioinformatics papers have been among the most read manuscripts at the journal since January 2018. Although several of these studies were published as part of our August 2019 Special Issue on Multi-Omics Data Integration (see the Editorial for this special issue (16Zhang B. Kuster B. Proteomics is not an island: multi-omics integration is the key to understanding biological systems.Mol. Cell. Proteomics. 2019; 18: S1-S4Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar)), we highlight here computational resources that facilitate comparison of data sets with the popular tool Skyline (17Sharma 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 (64) Google Scholar) or the analysis of phosphopeptides (18Krug K. Mertins P. Zhang B. Hornbeck P. Raju R. Ahmad R. Szucs M. Mundt F. Forestier D. Jane-Valbuena J. Keshishian H. Gillette M.A. Tamayo P. Mesirov J.P. Jaffe J.D. Carr S.A. Mani D.R. A curated resource for phosphosite-specific signature analysis.Mol. Cell. Proteomics. 2019; 18: 576-593Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar) and improve do novo sequencing (19Yang H. Li Y.C. Zhao M.Z. Wu F.L. Wang X. Xiao W.D. Wang Y.H. Zhang J.L. Wang F.Q. Xu F. Zeng W.F. Overall C.M. He S.M. Chi H. Xu P. Precision de novo peptide sequencing using mirror proteases of Ac-LysargiNase and trypsin for large-scale proteomics.Mol. Cell. Proteomics. 2019; 18: 773-785Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar). The author versions of these research papers and all other MCP content are always freely available at mcponline.org. We hope you enjoy this collection and continue submitting your Technological Innovation manuscripts to MCP!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".