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Fröhlich K, Fahrner M, Brombacher E, Seredynska A, Maldacker M, Kreutz C, Schmidt A, Schilling O. Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics. Mol Cell Proteomics 2024; 23:100800. [PMID: 38880244 PMCID: PMC11380018 DOI: 10.1016/j.mcpro.2024.100800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/08/2024] [Accepted: 06/13/2024] [Indexed: 06/18/2024] Open
Abstract
Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.
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Affiliation(s)
- Klemens Fröhlich
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
| | - Eva Brombacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany; Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Adrianna Seredynska
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Maximilian Maldacker
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.
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Hackett WE, Chang D, Carvalho L, Zaia J. RAMZIS: a bioinformatic toolkit for rigorous assessment of the alterations to glycoprotein composition that occur during biological processes. BIOINFORMATICS ADVANCES 2024; 4:vbae012. [PMID: 38384861 PMCID: PMC10879752 DOI: 10.1093/bioadv/vbae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/15/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024]
Abstract
Motivation Glycosylation elaborates the structures and functions of glycoproteins; glycoproteins are common post-translationally modified proteins and are heterogeneous and non-deterministically synthesized as an evolutionarily driven mechanism that elaborates the functions of glycosylated gene products. Glycoproteins, accounting for approximately half of all proteins, require specialized proteomics data analysis methods due to micro- and macro-heterogeneities as a given glycosite can be divided into several glycosylated forms, each of which must be quantified. Sampling of heterogeneous glycopeptides is limited by mass spectrometer speed and sensitivity, resulting in missing values. In conjunction with the low sample size inherent to glycoproteomics, a specialized toolset is needed to determine if observed changes in glycopeptide abundances are biologically significant or due to data quality limitations. Results We developed an R package, Relative Assessment of m/z Identifications by Similarity (RAMZIS), that uses similarity metrics to guide researchers to a more rigorous interpretation of glycoproteomics data. RAMZIS uses a permutation test to generate contextual similarity, which assesses the quality of mass spectral data and outputs a graphical demonstration of the likelihood of finding biologically significant differences in glycosylation abundance datasets. Investigators can assess dataset quality, holistically differentiate glycosites, and identify which glycopeptides are responsible for glycosylation pattern change. RAMZIS is validated by theoretical cases and a proof-of-concept application. RAMZIS enables comparison between datasets too stochastic, small, or sparse for interpolation while acknowledging these issues in its assessment. Using this tool, researchers will be able to rigorously define the role of glycosylation and the changes that occur during biological processes. Availability and implementation https://github.com/WillHackett22/RAMZIS.
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Affiliation(s)
| | - Deborah Chang
- Department of Biochemistry, Boston University, Boston, MA 02215, United States
| | - Luis Carvalho
- Bioinformatics Program, Boston University, Boston, MA 02215, United States
- Department of Mathematics, Boston University, Boston, MA 02215, United States
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, MA 02215, United States
- Department of Biochemistry, Boston University, Boston, MA 02215, United States
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