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MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics

Isaksson, Marc LU ; Karlsson, Christofer LU ; Laurell, Thomas LU ; Kirkeby, Agnete LU and Heusel, Moritz LU (2022) In Journal of Proteome Research 21(2). p.535-546
Abstract
Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the... (More)
Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library and analysis approach. Predicted-vs-observed comparisons enabled optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups, and optimization of the library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded experiment of species-mixed proteins and quantitative ratio-validation confirmed gains of up to 13% on peptide and 8% on protein level at equivalent FDR control and validation criteria. MSLibrarian is made available as an open-source R software package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian. (Less)
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
data-independent acquisition, spectral predictions, proteomics, deep-learning, R-software
in
Journal of Proteome Research
volume
21
issue
2
pages
12 pages
publisher
The American Chemical Society (ACS)
external identifiers
  • scopus:85123873142
  • pmid:35042333
ISSN
1535-3893
DOI
10.1021/acs.jproteome.1c00796
language
English
LU publication?
yes
id
fe3286d5-b0de-4259-aa1f-55a3ef06fe45
date added to LUP
2022-02-15 11:13:47
date last changed
2024-05-17 13:49:29
@article{fe3286d5-b0de-4259-aa1f-55a3ef06fe45,
  abstract     = {{Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library and analysis approach. Predicted-vs-observed comparisons enabled optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups, and optimization of the library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded experiment of species-mixed proteins and quantitative ratio-validation confirmed gains of up to 13% on peptide and 8% on protein level at equivalent FDR control and validation criteria. MSLibrarian is made available as an open-source R software package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian.}},
  author       = {{Isaksson, Marc and Karlsson, Christofer and Laurell, Thomas and Kirkeby, Agnete and Heusel, Moritz}},
  issn         = {{1535-3893}},
  keywords     = {{data-independent acquisition; spectral predictions; proteomics; deep-learning; R-software}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{2}},
  pages        = {{535--546}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Journal of Proteome Research}},
  title        = {{MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics}},
  url          = {{http://dx.doi.org/10.1021/acs.jproteome.1c00796}},
  doi          = {{10.1021/acs.jproteome.1c00796}},
  volume       = {{21}},
  year         = {{2022}},
}