The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF
Open accessAbstract
Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry-based proteomics because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation-serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility (IM) separated quadrupole time-of-flight mass spectrometer. This requires alignment of the IM separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared with DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and IM plane. This is implemented in the freely available py_diAID (Python package for DIA with an automated isolation design) package. In combination with in-depth project-specific proteomics libraries and the Evosep liquid chromatography system, we reproducibly identified over 7700 proteins in a human cancer cell line in 44 min with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 min liquid chromatography gradients), we consistently quantified more than 6000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor in triplicate 21 min runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803
Self-citationThe PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences
Perez-Riverol, Y., Bai, J., Bandla, C., García-Seisdedos, D., Hewapathirana, S., Kamatchinathan, S., Kundu, D. J., Prakash, A., Frericks-Zipper, A., Eisenacher, M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1038 · Cited by 6,125
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180
Self-citationA Statistical Model for Identifying Proteins by Tandem Mass Spectrometry
Nesvizhskii, A. I., Keller, A., Kolker, E., & Aebersold, R. (2003) · Analytical Chemistry · doi:10.1021/ac0341261 · Cited by 4,809
Goffeau, A., Barrell, B. G., Bussey, H., Davis, R. W., Dujon, B., Feldmann, H., Galibert, F., Hoheisel, J. D., Jacq, C., Johnston, M., et al. (1996) · Science · doi:10.1126/science.274.5287.546 · Cited by 4,263
Global, In Vivo, and Site-Specific Phosphorylation Dynamics in Signaling Networks
Olsen, J. V., Blagoev, B., Gnad, F., Macek, B., Kumar, C., Mortensen, P., & Mann, M. (2006) · Cell · doi:10.1016/j.cell.2006.09.026 · Cited by 3,499
Self-citationDIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S., & Ralser, M. (2020) · Nature Methods · doi:10.1038/s41592-019-0638-x · Cited by 3,343
Gillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424
MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D., & Nesvizhskii, A. I. (2017) · Nature Methods · doi:10.1038/nmeth.4256 · Cited by 2,402
Cited by
Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome
Rosenberger, F. A., Thielert, M., Strauss, M. T., Schweizer, L., Ammar, C., Mädler, S. C., Metousis, A., Skowronek, P., Wahle, M., Madden, K., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02007-6 · Cited by 176
Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer
(2024) · doi:10.1038/s41467-024-51274-0 · Cited by 83
Skowronek, P., Wallmann, G., Wahle, M., Willems, S., & Mann, M. (2025) · Nature Protocols · doi:10.1038/s41596-024-01104-w · Cited by 43
Self-citationKjærgaard, J., Lindqvist, C. B., Quesada, J. P., Jessen, S., Schlabs, F., Ehrlich, A. M., Yonamine, C. Y., García-Ureña, M., Schmalbruch, J. H., Small, L., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102163 · Cited by 11
Protein surface chemistry encodes an adaptive tolerance to desiccation
Romero-Pérez, P. S., Moran, H. M., Cordone, D. P., Horani, A., Truong, A., Manriquez-Sandoval, E., Ramirez, J. F., Martinez, A., Gollub, E., Hunter, K., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101407 · Cited by 9
Metadata
- Authors
- Skowronek, P., Thielert, M., Voytik, E., Tanzer, M. C., Hansen, F. M., Willems, S., Karayel, O., Brunner, A.-D., Meier, F., & Mann, M.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 21
- Issue
- 9
- Pages
- 100279
- ISSN
- 1535-9476 (ISSN-L)
- OpenAlex
- W4290052552
- Cited by
- 281
- References
- 70
- FWCI
- 16.08
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF. https://app.cochat.ai/papers/10.1016/j.mcpro.2022.100279
- The paper
- Skowronek, P., Thielert, M., Voytik, E., Tanzer, M. C., Hansen, F. M., Willems, S., Karayel, O., Brunner, A.-D., Meier, F., & Mann, M. (2022). Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF. Molecular & Cellular Proteomics. https://doi.org/10.1016/j.mcpro.2022.100279
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
