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Górska AM, Santos-García I, Eiriz I, Brüning T, Nyman T, Pahnke J. Evaluation of cerebrospinal fluid (CSF) and interstitial fluid (ISF) mouse proteomes for the validation and description of Alzheimer's disease biomarkers. J Neurosci Methods 2024; 411:110239. [PMID: 39102902 DOI: 10.1016/j.jneumeth.2024.110239] [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: 03/26/2024] [Revised: 07/22/2024] [Accepted: 07/26/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND Mass spectrometry (MS)-based cerebrospinal fluid (CSF) proteomics is an important method for discovering biomarkers of neurodegenerative diseases. CSF serves as a reservoir for interstitial fluid (ISF), and extensive communication between the two fluid compartments helps to remove waste products from the brain. NEW METHOD We performed proteomic analyses of both CSF and ISF fluid compartments using intracerebral microdialysis to validate and detect novel biomarkers of Alzheimer's disease (AD) in APPtg and C57Bl/6J control mice. RESULTS We identified up to 625 proteins in ISF and 4483 proteins in CSF samples. By comparing the biofluid profiles of APPtg and C57Bl/6J mice, we detected 37 and 108 significantly up- and downregulated candidates, respectively. In ISF, 7 highly regulated proteins, such as Gfap, Aldh1l1, Gstm1, and Txn, have already been implicated in AD progression, whereas in CSF, 9 out of 14 highly regulated proteins, such as Apba2, Syt12, Pgs1 and Vsnl1, have also been validated to be involved in AD pathogenesis. In addition, we also detected new interesting regulated proteins related to the control of synapses and neurotransmission (Kcna2, Cacng3, and Clcn6) whose roles as AD biomarkers should be further investigated. COMPARISON WITH EXISTING METHODS This newly established combined protocol provides better insight into the mutual communication between ISF and CSF as an analysis of tissue or CSF compartments alone. CONCLUSIONS The use of multiple fluid compartments, ISF and CSF, for the detection of their biological communication enables better detection of new promising AD biomarkers.
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Affiliation(s)
- Anna Maria Górska
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Clinical Medicine (KlinMed), Medical Faculty, University of Oslo (UiO) and Section of Neuropathology Research, Department of Pathology, Clinics for Laboratory Medicine (KLM), Oslo University Hospital (OUS), Sognsvannsveien 20, Oslo NO-0372, Norway.
| | - Irene Santos-García
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Clinical Medicine (KlinMed), Medical Faculty, University of Oslo (UiO) and Section of Neuropathology Research, Department of Pathology, Clinics for Laboratory Medicine (KLM), Oslo University Hospital (OUS), Sognsvannsveien 20, Oslo NO-0372, Norway.
| | - Ivan Eiriz
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Clinical Medicine (KlinMed), Medical Faculty, University of Oslo (UiO) and Section of Neuropathology Research, Department of Pathology, Clinics for Laboratory Medicine (KLM), Oslo University Hospital (OUS), Sognsvannsveien 20, Oslo NO-0372, Norway.
| | - Thomas Brüning
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Clinical Medicine (KlinMed), Medical Faculty, University of Oslo (UiO) and Section of Neuropathology Research, Department of Pathology, Clinics for Laboratory Medicine (KLM), Oslo University Hospital (OUS), Sognsvannsveien 20, Oslo NO-0372, Norway.
| | - Tuula Nyman
- Proteomics Core Facility, Department of Immunology, Oslo University Hospital (OUS) and University of Oslo (UiO), Faculty of Medicine, Sognsvannsveien 20, Oslo NO-0372, Norway.
| | - Jens Pahnke
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Clinical Medicine (KlinMed), Medical Faculty, University of Oslo (UiO) and Section of Neuropathology Research, Department of Pathology, Clinics for Laboratory Medicine (KLM), Oslo University Hospital (OUS), Sognsvannsveien 20, Oslo NO-0372, Norway; Institute of Nutritional Medicine (INUM) and Lübeck Institute of Dermatology (LIED), University of Lübeck (UzL) and University Medical Center Schleswig-Holstein (UKSH), Ratzeburger Allee 160, Lübeck D-23538, Germany; Department of Pharmacology, Faculty of Medicine and Life Sciences, University of Latvia, Jelgavas iela 3, Rīga LV-1004, Latvia; School of Neurobiology, Biochemistry and Biophysics, The Georg S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv IL-6997801, Israel.
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2
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Ma Q, Sun J, Liu Q, Fu J, Wen Y, Zhang F, Wu Y, Zhang X, Gong L, Zhang W. Identification of a biomarker to predict doxorubicin/cisplatin chemotherapy efficacy in osteosarcoma patients using primary, recurrent and metastatic specimens. Transl Oncol 2024; 49:102098. [PMID: 39153366 PMCID: PMC11381801 DOI: 10.1016/j.tranon.2024.102098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/23/2024] [Accepted: 08/11/2024] [Indexed: 08/19/2024] Open
Abstract
BACKGROUND Doxorubicin and cisplatin are both first-line chemotherapeutics for osteosarcoma (OS) treatment. However, the efficacy of doxorubicin/cisplatin chemotherapy varies considerably. Thus, identifying an efficient diagnostic biomarker to distinguish patients with good and poor responses to doxorubicin/cisplatin chemotherapy is of paramount importance. METHODS To predict the efficacy of doxorubicin/cisplatin chemotherapy, we analyzed the differentially expressed proteins in 37 resected OS samples, which were categorized into the primary group (PG), the recurrent group (RG) and the metastatic group (MG). The characteristics of the enriched differentially expressed proteins were assessed via GO and KEGG analyses. Protein‒protein interactions were identified to determine the relationships among the differentially expressed proteins. Receiver operating characteristic (ROC) curve analyses were performed to explore the clinical significance of the differentially expressed proteins. Parallel reaction monitoring (PRM) was used to validate the candidate proteins. Immunohistochemical (IHC) staining was performed to confirm the expression of cathepsin (CTSG) in patients with good and poor response to doxorubicin/cisplatin. RESULTS A total of 9458 proteins were identified and quantified, among which 143 and 208 exhibited significant changes (|log2FC|>1, p < 0.05) in the RG and MG compared with the PG, respectively. GO and KEGG enrichment led to the identification of neutrophil extracellular traps (NETs). ROC curve analyses revealed 74 and 86 proteins with areas under the curve greater than 0.7 in the RG and MG, respectively. PRM validation revealed the statistical significance of CTSG, which is involved in NET formation, at the protein level in both the RG and MG. IHC staining of another cohort revealed that CTSG was prominently upregulated in the poor response group after treatment with doxorubicin/cisplatin. CONCLUSION CTSG and its associated NETs are potential biomarkers with which the efficacy of doxorubicin/cisplatin chemotherapy could be predicted in OS patients.
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Affiliation(s)
- Qiong Ma
- Department of Pathology, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China; Orthopedic Oncology Institute, Department of Orthopedic Surgery, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Jin Sun
- Orthopedic Oncology Institute, Department of Orthopedic Surgery, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Qiao Liu
- Department of Pathology, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Jin Fu
- Department of Pathology, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Yanhua Wen
- Orthopedic Oncology Institute, Department of Orthopedic Surgery, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Fuqin Zhang
- Department of Pathology, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Yonghong Wu
- Orthopedic Oncology Institute, Department of Orthopedic Surgery, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Xiaoyu Zhang
- Orthopedic Oncology Institute, Department of Orthopedic Surgery, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China
| | - Li Gong
- Department of Pathology, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China.
| | - Wei Zhang
- Department of Pathology, Tangdu Hospital, Air Force Medical University, 569 Xinsi Road, Xi'an 710038, China.
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Rajczewski AT, Blakeley-Ruiz JA, Meyer A, Vintila S, McIlvin MR, Van Den Bossche T, Searle BC, Griffin TJ, Saito MA, Kleiner M, Jagtap PD. Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.18.613707. [PMID: 39345414 PMCID: PMC11430069 DOI: 10.1101/2024.09.18.613707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.
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Kabatnik S, Post F, Drici L, Bartels AS, Strauss MT, Zheng X, Madsen GI, Mund A, Rosenberger FA, Moreira J, Mann M. Spatial characterization and stratification of colorectal adenomas by deep visual proteomics. iScience 2024; 27:110620. [PMID: 39252972 PMCID: PMC11381895 DOI: 10.1016/j.isci.2024.110620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/13/2024] [Accepted: 07/26/2024] [Indexed: 09/11/2024] Open
Abstract
Colorectal adenomas (CRAs) are potential precursor lesions to adenocarcinomas, currently classified by morphological features. We aimed to establish a molecular feature-based risk allocation framework toward improved patient stratification. Deep visual proteomics (DVP) is an approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here, we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine male patients, immunohistologically stained for caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP identified DMBT1, MARCKS, and CD99 as protein markers linked to recurrence, suggesting their potential for risk assessment. It also detected a metabolic shift to anaerobic glycolysis in cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.
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Affiliation(s)
- Sonja Kabatnik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Frederik Post
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Lylia Drici
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Annette Snejbjerg Bartels
- Precision Cancer Medicine Laboratory, Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maximilian T Strauss
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Xiang Zheng
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Gunvor I Madsen
- Department of Pathology, Odense University Hospital, Odense, Denmark
| | - Andreas Mund
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Florian A Rosenberger
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - José Moreira
- Precision Cancer Medicine Laboratory, Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
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5
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Lapcik P, Synkova K, Janacova L, Bouchalova P, Potesil D, Nenutil R, Bouchal P. A hybrid DDA/DIA-PASEF based assay library for a deep proteotyping of triple-negative breast cancer. Sci Data 2024; 11:794. [PMID: 39025866 PMCID: PMC11258311 DOI: 10.1038/s41597-024-03632-2] [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: 02/14/2024] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and deeper proteome coverage is needed for its molecular characterization. We present comprehensive library of targeted mass spectrometry assays specific for TNBC and demonstrate its applicability. Proteins were extracted from 105 TNBC tissues and digested. Aliquots were pooled, fractionated using hydrophilic chromatography and analyzed by LC-MS/MS in data-dependent acquisition (DDA) parallel accumulation-serial fragmentation (PASEF) mode on timsTOF Pro LC-MS system. 16 individual lysates were analyzed in data-independent acquisition (DIA)-PASEF mode. Hybrid library was generated in Spectronaut software and covers 244,464 precursors, 168,006 peptides and 11,564 protein groups (FDR = 1%). Application of our library for pilot quantitative analysis of 16 tissues increased identification numbers in Spectronaut 18.5 and DIA-NN 1.8.1 software compared to library-free setting, with Spectronaut achieving the best results represented by 190,310 precursors, 140,566 peptides, and 10,463 protein groups. In conclusion, we introduce assay library that offers the deepest coverage of TNBC proteome to date. The TNBC library is available via PRIDE repository (PXD047793).
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Grants
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- CZ.02.1.01/0.0/0.0/18_046/0015974 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2023033 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
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Affiliation(s)
- Petr Lapcik
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Klara Synkova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Lucia Janacova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Pavla Bouchalova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - David Potesil
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Rudolf Nenutil
- Department of Oncological Pathology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Pavel Bouchal
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
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6
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Kverneland AH, Harking F, Vej-Nielsen JM, Huusfeldt M, Bekker-Jensen DB, Svane IM, Bache N, Olsen JV. Fully Automated Workflow for Integrated Sample Digestion and Evotip Loading Enabling High-Throughput Clinical Proteomics. Mol Cell Proteomics 2024; 23:100790. [PMID: 38777088 PMCID: PMC11251069 DOI: 10.1016/j.mcpro.2024.100790] [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: 01/10/2024] [Revised: 05/02/2024] [Accepted: 05/19/2024] [Indexed: 05/25/2024] Open
Abstract
Protein identification and quantification is an important tool for biomarker discovery. With the increased sensitivity and speed of modern mass spectrometers, sample preparation remains a bottleneck for studying large cohorts. To address this issue, we prepared and evaluated a simple and efficient workflow on the Opentrons OT-2 robot that combines sample digestion, cleanup, and loading on Evotips in a fully automated manner, allowing the processing of up to 192 samples in 6 h. Analysis of 192 automated HeLa cell sample preparations consistently identified ∼8000 protein groups and ∼130,000 peptide precursors with an 11.5 min active liquid chromatography gradient with the Evosep One and narrow-window data-independent acquisition (nDIA) with the Orbitrap Astral mass spectrometer providing a throughput of 100 samples per day. Our results demonstrate a highly sensitive workflow yielding both reproducibility and stability at low sample inputs. The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts. Building on the digesting workflow, we incorporated an automated phosphopeptide enrichment step using magnetic titanium-immobilized metal ion affinity chromatography beads. This allows for a fully automated proteome and phosphoproteome sample preparation in a single step with high sensitivity. Using the integrated digestion and Evotip loading workflow, we evaluated the effects of cancer immune therapy on the plasma proteome in metastatic melanoma patients.
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Affiliation(s)
- Anders H Kverneland
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark; Department of Oncology, National Center of Cancer Immune Therapy, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Florian Harking
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | | | | | | | - Inge Marie Svane
- Department of Oncology, National Center of Cancer Immune Therapy, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | | | - Jesper V Olsen
- Faculty of Health Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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7
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Steigerwald S, Sinha A, Fort KL, Zeng WF, Niu L, Wichmann C, Kreutzmann A, Mourad D, Aizikov K, Grinfeld D, Makarov A, Mann M, Meier F. Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis. Mol Cell Proteomics 2024; 23:100713. [PMID: 38184013 PMCID: PMC10851225 DOI: 10.1016/j.mcpro.2024.100713] [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: 08/30/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 01/08/2024] Open
Abstract
Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.
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Affiliation(s)
- Sophia Steigerwald
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ankit Sinha
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Kyle L Fort
- Thermo Fisher Scientific (GmbH), Bremen, Germany
| | - Wen-Feng Zeng
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Lili Niu
- Department Clinical Proteomics, NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Christoph Wichmann
- Department Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | | | | | | | | | - Matthias Mann
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Department Clinical Proteomics, NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Florian Meier
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Functional Proteomics, Jena University Hospital, Jena, Germany.
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8
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Hay BN, Akinlaja MO, Baker TC, Houfani AA, Stacey RG, Foster LJ. Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities. Proteomics 2023; 23:e2200278. [PMID: 37144656 DOI: 10.1002/pmic.202200278] [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: 10/28/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 05/06/2023]
Abstract
Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.
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Affiliation(s)
- Brenna N Hay
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Mopelola O Akinlaja
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Teesha C Baker
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Aicha Asma Houfani
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - R Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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9
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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10
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Liu X, Salokas K, Keskitalo S, Martínez-Botía P, Varjosalo M. Analyzing Protein Interactions by MAC-Tag Approaches. Methods Mol Biol 2023; 2690:281-297. [PMID: 37450155 DOI: 10.1007/978-1-0716-3327-4_24] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Proteomics methods such as affinity purification (AP) and proximity-dependent labeling (PL) coupled with mass spectrometry (MS) are currently commonly utilized to define interaction landscapes. BioID is one of the PL approaches, and it employs the expression of bait proteins fused to a nonspecific biotin ligase (BirA*), to induce in vivo biotinylation of proximal proteins. We developed the multiple approaches combined (MAC)-tag workflow, which allows for both AP and BioID analysis with a single construct and with almost identical protein purification and MS identification procedures. MAC-tag is a well-established method and has been widely used. Recent developed PL tags such as BioID2 and UltraID are smaller versions of BirA* with faster labeling efficiency. We therefore incorporate these tags into our system to develop MAC2-tag (containing BioID2) and MAC3-tag (containing UltraID) to overcome potential limitations of the original MAC-tag system and broaden the spectrum of applications for MAC-tags. Here, we describe a detailed procedure for the MAC-tag system workflow including cell line generation for the MAC/MAC2/MAC3-tagged protein of interest (POI), sample preparation for AP and PL protein purification, and MS analysis.
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Affiliation(s)
- Xiaonan Liu
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Physiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
| | - Kari Salokas
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Salla Keskitalo
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | | | - Markku Varjosalo
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
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Skowronek P, Thielert M, Voytik E, Tanzer MC, Hansen FM, Willems S, Karayel Ö, Brunner AD, Meier F, Mann M. Rapid and in-depth coverage of the (phospho-)proteome with deep libraries and optimal window design for dia-PASEF. Mol Cell Proteomics 2022; 21:100279. [PMID: 35944843 PMCID: PMC9465115 DOI: 10.1016/j.mcpro.2022.100279] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/31/2022] [Accepted: 08/02/2022] [Indexed: 11/05/2022] Open
Abstract
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. Optimal dia-PASEF window design with py_diAID combined with deep libraries. Quantification of the HeLa cell proteome to a depth of >7700 in only 44 min. Ion mobility–resolved phosphoproteomics determines >35,000 class I phosphosites. py_diAID is freely available as GUI, CLI, and Python modules.
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