1
|
Fields L, Ma M, DeLaney K, Phetsanthad A, Li L. A crustacean neuropeptide spectral library for data-independent acquisition (DIA) mass spectrometry applications. Proteomics 2024; 24:e2300285. [PMID: 38171828 PMCID: PMC11219527 DOI: 10.1002/pmic.202300285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/06/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method of choice to achieve sensitive and accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and Cancer borealis as a model of choice for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student's t-test, P value < 0.05) in the number of identifications achieved from the TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications was found in the library search compared to the library-free search. This work highlights the necessity for the use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.
Collapse
Affiliation(s)
- Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Min Ma
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, United States
| | - Kellen DeLaney
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Ashley Phetsanthad
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, United States
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, United States
| |
Collapse
|
2
|
Scott AM, Karlsson C, Mohanty T, Hartman E, Vaara ST, Linder A, Malmström J, Malmström L. Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics. Commun Biol 2023; 6:628. [PMID: 37301900 PMCID: PMC10257694 DOI: 10.1038/s42003-023-04977-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics.
Collapse
Affiliation(s)
- Aaron M Scott
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
| | - Christofer Karlsson
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Tirthankar Mohanty
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Erik Hartman
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Suvi T Vaara
- Division of Anaesthesia and Intensive Care Medicine Department of Surgery, Intensive Care Units, Helsinki University Central Hospital, Box 340, 00029 HUS, Helsinki, Finland
| | - Adam Linder
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Lars Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
| |
Collapse
|
3
|
Searle BC, Shannon AE, Wilburn DB. Scribe: Next Generation Library Searching for DDA Experiments. J Proteome Res 2023; 22:482-490. [PMID: 36695531 DOI: 10.1021/acs.jproteome.2c00672] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.
Collapse
Affiliation(s)
- Brian C Searle
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States.,Proteome Software Inc., Portland, Oregon97219, United States
| | - Ariana E Shannon
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States
| | - Damien Beau Wilburn
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States
| |
Collapse
|
4
|
Cox J. Prediction of peptide mass spectral libraries with machine learning. Nat Biotechnol 2023; 41:33-43. [PMID: 36008611 DOI: 10.1038/s41587-022-01424-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/11/2022] [Indexed: 01/21/2023]
Abstract
The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.
Collapse
Affiliation(s)
- Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
| |
Collapse
|
5
|
Baker CP, Phair IR, Brenes AJ, Atrih A, Ryan DG, Bruderer R, Dinkova-Kostova AT, Lamont DJ, Arthur JSC, Howden AJ. DIA label-free proteomic analysis of murine bone-marrow-derived macrophages. STAR Protoc 2022; 3:101725. [PMID: 36166358 PMCID: PMC9519785 DOI: 10.1016/j.xpro.2022.101725] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/09/2022] [Accepted: 08/31/2022] [Indexed: 01/26/2023] Open
Abstract
Here, we describe an optimized protocol to analyze murine bone-marrow-derived macrophages using label-free data-independent acquisition (DIA) proteomics. We provide a complete step-by-step protocol describing sample preparation utilizing the S-Trap approach for on-column digestion and peptide purification. We then detail mass spectrometry data acquisition and approaches for data analysis. Single-shot DIA protocols achieve comparable proteomic depth with data-dependent MS approaches without the need for fractionation. This allows for better scaling for large sample numbers with high inter-experimental reproducibility. For complete details on the use and execution of this protocol, please refer to Ryan et al. (2022).
Collapse
Affiliation(s)
- Christa P. Baker
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - Iain R. Phair
- Division of Cellular and Systems Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, James Arrott Drive, Dundee DD1 9SY, Scotland
| | - Alejandro J. Brenes
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - Abdelmadjid Atrih
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - Dylan G. Ryan
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge CB2 0XY, Scotland
| | | | - Albena T. Dinkova-Kostova
- Division of Cellular and Systems Medicine, School of Medicine, University of Dundee, Ninewells Hospital and Medical School, James Arrott Drive, Dundee DD1 9SY, Scotland
| | - Douglas J. Lamont
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland
| | - J. Simon C. Arthur
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland,Corresponding author
| | - Andrew J.M. Howden
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, Scotland,Corresponding author
| |
Collapse
|
6
|
Illing PT, Ramarathinam SH, Purcell AW. New insights and approaches for analyses of immunopeptidomes. Curr Opin Immunol 2022; 77:102216. [PMID: 35716458 DOI: 10.1016/j.coi.2022.102216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/10/2022] [Indexed: 11/03/2022]
Abstract
Human leucocyte antigen (HLA) molecules play a key role in health and disease by presenting antigen to T-lymphocytes for immunosurveillance. Immunopeptidomics involves the study of the collection of peptides presented within the antigen-binding groove of HLA molecules. Identifying their nature and diversity is crucial to understanding immunosurveillance especially during infection or for the recognition and potential eradication of tumours. This review discusses recent advances in the isolation, identification, and quantitation of these peptide antigens. New informatics approaches and databases have shed light on the extent of peptide antigens derived from unconventional sources including peptides derived from transcripts associated with frame shifts, long noncoding RNA, incorrectly annotated untranslated regions, post-translational modifications, and proteasomal splicing. Several challenges remain in successful analysis of immunopeptides, yet recent developments point to unexplored biology waiting to be unravelled.
Collapse
Affiliation(s)
- Patricia T Illing
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.
| |
Collapse
|