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Dube CT, Gilbert HTJ, Rabbitte N, Baird P, Patel S, Herrera JA, Baricevic-Jones I, Unwin RD, Chan D, Gnanalingham K, Hoyland JA, Richardson SM. Proteomic profiling of human plasma and intervertebral disc tissue reveals matrisomal, but not plasma, biomarkers of disc degeneration. Arthritis Res Ther 2025; 27:28. [PMID: 39930483 PMCID: PMC11809052 DOI: 10.1186/s13075-025-03489-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2024] [Accepted: 01/26/2025] [Indexed: 02/14/2025] Open
Abstract
BACKGROUND Intervertebral disc (IVD) degeneration is a common cause of low back pain, and the most symptomatic patients with neural compression need surgical intervention to relieve symptoms. Current techniques used to diagnose IVD degeneration, such as magnetic resonance imaging (MRI), do not detect changes in the tissue extracellular matrix (ECM) as degeneration progresses. Improved techniques, such as a combination of tissue and blood biomarkers, are needed to monitor the progression of IVD degeneration for more effective treatment plans. METHODS To identify tissue and blood biomarkers associated with degeneration progression, we histologically graded 35 adult human degenerate IVD tissues and matched plasma from the individuals into two groups: mild degenerate and severe degenerate. Mass spectrometry was utilised to characterise proteomic differences in tissue and plasma between the two groups. Top differentially distributed proteins were further validated using immunohistochemistry and qRT-PCR. Additionally, correlational analyses were conducted to define similarities and differences between tissue and plasma protein changes in individuals with mild and severe IVD degeneration. RESULTS Our data revealed that the abundance of 31 proteins was significantly increased in severe degenerated IVD tissues compared to mild. Functional analyses showed that more than 40% of these proteins were matrisome-related, indicating differences in ECM protein composition between severe and mild degenerate IVD tissues. We confirmed adipocyte enhancer-binding protein 1 (AEBP1) as one of the most significantly enriched core matrisome genes and proteins as degeneration progressed. Compared to others, AEBP1 protein levels best distinguished between mild and severe degenerated IVD tissues with an area under the curve score of 0.768 (95% CI: 0.60-0.93). However, we found that protein changes from associated plasma exhibited a weak relationship with histological grading and AEBP1 tissue levels. Given that systemic plasma changes are complex, a larger sample cohort may be required to identify patterns in blood relating to IVD degeneration progression. CONCLUSIONS In this study, we have identified AEBP1 as a tissue marker for monitoring the severity of disc degeneration in humans. Further work to link alterations in tissue AEBP1 levels to changes in blood-related proteins will be beneficial for detailed monitoring of IVD degeneration thereby enabling more personalised treatment approaches.
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Affiliation(s)
- Christabel Thembela Dube
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
- Manchester Cell-Matrix Centre, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Hamish T J Gilbert
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
- Guy Hilton Research Centre, School of Life Sciences, Keele University, Stoke-on-Trent, ST4 7QB, UK
| | - Niamh Rabbitte
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
| | - Pauline Baird
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
- Manchester Cell-Matrix Centre, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Sonal Patel
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
- Manchester Cell-Matrix Centre, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Jeremy A Herrera
- Manchester Cell-Matrix Centre, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK
| | - Ivona Baricevic-Jones
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
| | - Richard D Unwin
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
| | - Danny Chan
- School of Biomedical Sciences, Faculty of Medicine Building, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong SAR, China
| | - Kanna Gnanalingham
- Department of Neurosurgery, Manchester Academy of Health Science Centre, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Stott Lane, Salford, M6 8HD, UK
| | - Judith A Hoyland
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK
| | - Stephen M Richardson
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK.
- Manchester Cell-Matrix Centre, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
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Mondelo-Macía P, García-González J, León-Mateos L, Abalo A, Bravo S, Chantada Vazquez MDP, Muinelo-Romay L, López-López R, Díaz-Peña R, Dávila-Ibáñez AB. Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer. Mol Cell Proteomics 2024; 23:100834. [PMID: 39216661 PMCID: PMC11474190 DOI: 10.1016/j.mcpro.2024.100834] [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: 05/26/2023] [Revised: 08/17/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.
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Affiliation(s)
- Patricia Mondelo-Macía
- Liquid Biopsy Analysis Unit, Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain
| | - Jorge García-González
- Department of Medical Oncology, Complexo Hospitalario Universitario de Santiago de Compostela (SERGAS), Santiago de Compostela, Spain; Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; CIBERONC, Centro de Investigación Biomédica en Red Cáncer, Madrid, Spain
| | - Luis León-Mateos
- Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Department of Medical Oncology, Complexo Hospitalario Universitario de Santiago de Compostela (SERGAS), Santiago de Compostela, Spain; Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; CIBERONC, Centro de Investigación Biomédica en Red Cáncer, Madrid, Spain
| | - Alicia Abalo
- Liquid Biopsy Analysis Unit, Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain
| | - Susana Bravo
- Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain
| | - María Del Pilar Chantada Vazquez
- Proteomic Unit, Instituto de Investigaciones Sanitarias-IDIS, Complejo Hospitalario Universitario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain
| | - Laura Muinelo-Romay
- Liquid Biopsy Analysis Unit, Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; CIBERONC, Centro de Investigación Biomédica en Red Cáncer, Madrid, Spain
| | - Rafael López-López
- Liquid Biopsy Analysis Unit, Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Galician Precision Oncology Research Group (ONCOGAL), Medicine and Dentistry School, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Spain; Department of Medical Oncology, Complexo Hospitalario Universitario de Santiago de Compostela (SERGAS), Santiago de Compostela, Spain; Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; CIBERONC, Centro de Investigación Biomédica en Red Cáncer, Madrid, Spain; Roche-Chus Joint Unit, Translational Medical Oncology Group, Oncomet, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Roberto Díaz-Peña
- Fundación Pública Galega de Medicina Xenómica, SERGAS; Grupo de Medicina Xenomica-USC, Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile
| | - Ana B Dávila-Ibáñez
- Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; CIBERONC, Centro de Investigación Biomédica en Red Cáncer, Madrid, Spain; Roche-Chus Joint Unit, Translational Medical Oncology Group, Oncomet, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain.
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3
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Nygren D, Torisson G, Happonen L, Mellhammar L, Linder A, Elf J, Yan H, Welinder C, Holm K. Proteomic Characterization of Plasma in Lemierre's Syndrome. Thromb Haemost 2024; 124:432-440. [PMID: 37857346 PMCID: PMC11038868 DOI: 10.1055/a-2195-3927] [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: 05/05/2023] [Accepted: 10/18/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND The underlying mechanisms of thrombosis in Lemierre's syndrome and other septic thrombophlebitis are incompletely understood. Therefore, in this case control study we aimed to generate hypotheses on its pathogenesis by studying the plasma proteome in patients with these conditions. METHODS All patients with Lemierre's syndrome in the Skåne Region, Sweden, were enrolled prospectively during 2017 to 2021 as cases. Age-matched patients with other severe infections were enrolled as controls. Patient plasma samples were analyzed using label-free data-independent acquisition liquid chromatography tandem mass spectrometry. Differentially expressed proteins in Lemierre's syndrome versus other severe infections were highlighted. Functions of differentially expressed proteins were defined based on a literature search focused on previous associations with thrombosis. RESULTS Eight patients with Lemierre's syndrome and 15 with other severe infections were compared. Here, 20/449 identified proteins were differentially expressed between the groups. Of these, 14/20 had functions previously associated with thrombosis. Twelve of 14 had a suggested prothrombotic effect in Lemierre's syndrome, whereas 2/14 had a suggested antithrombotic effect. CONCLUSION Proteins involved in several thrombogenic pathways were differentially expressed in Lemierre's syndrome compared to other severe infections. Among identified proteins, several were associated with endothelial damage, platelet activation, and degranulation, and warrant further targeted studies.
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Affiliation(s)
- David Nygren
- Division of Infection Medicine, Lund University, Lund, Sweden
- Department of Infectious Diseases, Skåne University Hospital, Lund/Malmö, Sweden
| | - Gustav Torisson
- Department of Infectious Diseases, Skåne University Hospital, Lund/Malmö, Sweden
- Department of Translational Medicine, Clinical Infection Medicine, Lund University, Malmö, Sweden
| | - Lotta Happonen
- Division of Infection Medicine, Lund University, Lund, Sweden
| | - Lisa Mellhammar
- Division of Infection Medicine, Lund University, Lund, Sweden
- Department of Infectious Diseases, Skåne University Hospital, Lund/Malmö, Sweden
| | - Adam Linder
- Division of Infection Medicine, Lund University, Lund, Sweden
- Department of Infectious Diseases, Skåne University Hospital, Lund/Malmö, Sweden
| | - Johan Elf
- Center of Thrombosis and Haemostasis, Skåne University Hospital, Malmö, Sweden
| | - Hong Yan
- The Swedish National Infrastructure for Biological Mass Spectrometry (BioMS), Lund University, Lund, Sweden
| | - Charlotte Welinder
- The Swedish National Infrastructure for Biological Mass Spectrometry (BioMS), Lund University, Lund, Sweden
| | - Karin Holm
- Division of Infection Medicine, Lund University, Lund, Sweden
- Department of Infectious Diseases, Skåne University Hospital, Lund/Malmö, Sweden
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Burns AR, Wiedrick J, Feryn A, Maes M, Midha MK, Baxter DH, Morrone SR, Prokop TJ, Kapil C, Hoopmann MR, Kusebauch U, Deutsch EW, Rappaport N, Watanabe K, Moritz RL, Miller RA, Lapidus JA, Orwoll ES. Proteomic changes induced by longevity-promoting interventions in mice. GeroScience 2024; 46:1543-1560. [PMID: 37653270 PMCID: PMC10828338 DOI: 10.1007/s11357-023-00917-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/20/2023] [Indexed: 09/02/2023] Open
Abstract
Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase mouse lifespan. This included two genetic mutations, a growth hormone receptor knockout (GHRKO mice) and a mutation in the Pit-1 locus (Snell dwarf mice), four drug treatments (rapamycin, acarbose, canagliflozin, and 17α-estradiol), and caloric restriction. Each of the interventions studied induced variable changes in the concentrations of proteins across liver, kidney, and gastrocnemius muscle tissue samples, with the strongest responses in the liver and limited concordance in protein responses across tissues. To the extent that these interventions promote longevity through common biological mechanisms, we anticipated that proteins associated with longevity could be identified by characterizing shared responses across all or multiple interventions. Many of the proteome alterations induced by each intervention were distinct, potentially implicating a variety of biological pathways as being related to lifespan extension. While we found no protein that was affected similarly by every intervention, we identified a set of proteins that responded to multiple interventions. These proteins were functionally diverse but tended to be involved in peroxisomal oxidation and metabolism of fatty acids. These results provide candidate proteins and biological mechanisms related to enhancing longevity that can inform research on therapeutic approaches to promote healthy aging.
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Affiliation(s)
- Adam R Burns
- Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA.
| | - Jack Wiedrick
- Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA
| | - Alicia Feryn
- Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA
| | - Michal Maes
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | | | | | - Charu Kapil
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | | | | | | | | | - Richard A Miller
- Department of Pathology and Geriatrics Center, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Jodi A Lapidus
- School of Public Health, Oregon Health & Science University-Portland State University, Portland, OR, USA
| | - Eric S Orwoll
- Department of Endocrinology, Oregon Health & Science University, Portland, OR, USA
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5
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Turner N, Abeysinghe P, Flay H, Meier S, Sadowski P, Mitchell MD. SWATH-MS Analysis of Blood Plasma and Circulating Small Extracellular Vesicles Enables Detection of Putative Protein Biomarkers of Fertility in Young and Aged Dairy Cows. J Proteome Res 2023; 22:3580-3595. [PMID: 37830897 DOI: 10.1021/acs.jproteome.3c00406] [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] [Indexed: 10/14/2023]
Abstract
The development of biomarkers of fertility could provide benefits for the genetic improvement of dairy cows. Circulating small extracellular vesicles (sEVs) show promise as diagnostic or prognostic markers since their cargo reflects the metabolic state of the cell of origin; thus, they mirror the physiological status of the host. Here, we employed data-independent acquisition mass spectrometry to survey the plasma and plasma sEV proteomes of two different cohorts of Young (Peripubertal; n = 30) and Aged (Primiparous; n = 20) dairy cows (Bos taurus) of high- and low-genetic merit of fertility and known pregnancy outcomes (ProteomeXchange data set identifier PXD042891). We established predictive models of fertility status with an area under the curve of 0.97 (sEV; p value = 3.302e-07) and 0.95 (plasma; p value = 6.405e-08). Biomarker candidates unique to high-fertility Young cattle had a sensitivity of 0.77 and specificity of 0.67 (*p = 0.0287). Low-fertility biomarker candidates uniquely identified in sEVs from Young and Aged cattle had a sensitivity and specificity of 0.69 and 1.0, respectively (***p = 0.0005). Our bioinformatics pipeline enabled quantification of plasma and circulating sEV proteins associated with fertility phenotype. Further investigations are warranted to validate this research in a larger population, which may lead to improved classification of fertility status in cattle.
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Affiliation(s)
- Natalie Turner
- Centre for Children's Health Research (CCHR), Queensland University of Technology (QUT), 62 Graham Street, South Brisbane, Queensland 4101, Australia
| | - Pevindu Abeysinghe
- Centre for Children's Health Research (CCHR), Queensland University of Technology (QUT), 62 Graham Street, South Brisbane, Queensland 4101, Australia
| | - Holly Flay
- DairyNZ Limited, Private Bag 3221, Hamilton 3240, New Zealand
| | - Susanne Meier
- DairyNZ Limited, Private Bag 3221, Hamilton 3240, New Zealand
| | - Pawel Sadowski
- Central Analytical Research Facility (CARF), QUT, Gardens Point Campus, 2 George Street, Brisbane City, Queensland 4000, Australia
| | - Murray D Mitchell
- Centre for Children's Health Research (CCHR), Queensland University of Technology (QUT), 62 Graham Street, South Brisbane, Queensland 4101, Australia
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Wang J, Yu W, D'Anna R, Przybyla A, Wilson M, Sung M, Bullen J, Hurt E, D'Angelo G, Sidders B, Lai Z, Zhong W. Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics. Mol Cell Proteomics 2023; 22:100626. [PMID: 37517589 PMCID: PMC10494184 DOI: 10.1016/j.mcpro.2023.100626] [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: 01/23/2023] [Revised: 07/23/2023] [Accepted: 07/25/2023] [Indexed: 08/01/2023] Open
Abstract
The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.
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Affiliation(s)
- Jixin Wang
- Oncology Data Science, AstraZeneca, Gaithersburg, Maryland, USA
| | - Wen Yu
- Data Science and AI, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Rachel D'Anna
- Oncology Data Science, AstraZeneca, Gaithersburg, Maryland, USA
| | | | - Matt Wilson
- Early TDE Discovery, AstraZeneca, Cambridge, UK
| | | | - John Bullen
- Early TTD Discovery, AstraZeneca, Cambridge, UK
| | - Elaine Hurt
- Early TTD Discovery, AstraZeneca, Cambridge, UK
| | - Gina D'Angelo
- Late Oncology Statistics, Oncology R&D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Ben Sidders
- Oncology Data Science, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Zhongwu Lai
- Oncology Data Science, Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA
| | - Wenyan Zhong
- Oncology Data Science, Oncology R&D, AstraZeneca, New York, New York, USA.
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Ramírez Medina CR, Ali I, Baricevic-Jones I, Odudu A, Saleem MA, Whetton AD, Kalra PA, Geifman N. Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry. Clin Proteomics 2023; 20:19. [PMID: 37076799 PMCID: PMC10116780 DOI: 10.1186/s12014-023-09405-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/14/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Halting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking. METHODS Plasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses. RESULTS The resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched. CONCLUSIONS The in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.
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Affiliation(s)
- Carlos R Ramírez Medina
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
| | - Ibrahim Ali
- Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Ivona Baricevic-Jones
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Aghogho Odudu
- Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK
| | - Moin A Saleem
- Bristol Renal and Children's Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anthony D Whetton
- Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Philip A Kalra
- Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Nophar Geifman
- School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
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8
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Wolski WE, Nanni P, Grossmann J, d'Errico M, Schlapbach R, Panse C. prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis. J Proteome Res 2023; 22:1092-1104. [PMID: 36939687 PMCID: PMC10088014 DOI: 10.1021/acs.jproteome.2c00441] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.
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Affiliation(s)
- Witold E Wolski
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Paolo Nanni
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Jonas Grossmann
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Maria d'Errico
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Ralph Schlapbach
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Christian Panse
- Functional Genomics Center Zurich (FGCZ)-University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.,Swiss Institute of Bioinformatics (SIB) Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland
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9
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A Novel Blood Proteomic Signature for Prostate Cancer. Cancers (Basel) 2023; 15:cancers15041051. [PMID: 36831393 PMCID: PMC9954127 DOI: 10.3390/cancers15041051] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484.
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10
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Machipisa T, Chishala C, Shaboodien G, Zühlke LJ, Muhamed B, Pandie S, de Vries J, Laing N, Joachim A, Daniels R, Ntsekhe M, Hugo-Hamman CT, Gitura B, Ogendo S, Lwabi P, Okello E, Damasceno A, Novela C, Mocumbi AO, Madeira G, Musuku J, Mtaja A, ElSayed A, Alhassan HH, Bode-Thomas F, Yilgwan C, Amusa G, Nkereuwem E, Mulder N, Ramesar R, Lesosky M, Cordell HJ, Chong M, Keavney B, Paré G, Engel ME. Rationale, Design, and the Baseline Characteristics of the RHDGen (The Genetics of Rheumatic Heart Disease) Network Study†. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e003641. [PMID: 36548480 PMCID: PMC9946164 DOI: 10.1161/circgen.121.003641] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 08/30/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND The genetics of rheumatic heart disease (RHDGen) Network was developed to assist the discovery and validation of genetic variations and biomarkers of risk for rheumatic heart disease (RHD) in continental Africans, as a part of the global fight to control and eradicate rheumatic fever/RHD. Thus, we describe the rationale and design of the RHDGen study, comprising participants from 8 African countries. METHODS RHDGen screened potential participants using echocardiography, thereafter enrolling RHD cases and ethnically-matched controls for whom case characteristics were documented. Biological samples were collected for conducting genetic analyses, including a discovery case-control genome-wide association study (GWAS) and a replication trio family study. Additional biological samples were also collected, and processed, for the measurement of biomarker analytes and the biomarker analyses are underway. RESULTS Participants were enrolled into RHDGen between December 2012 and March 2018. For GWAS, 2548 RHD cases and 2261 controls (3301 women [69%]; mean age [SD], 37 [16.3] years) were available. RHD cases were predominantly Black (66%), Admixed (24%), and other ethnicities (10%). Among RHD cases, 34% were asymptomatic, 26% had prior valve surgery, and 23% had atrial fibrillation. The trio family replication arm included 116 RHD trio probands and 232 parents. CONCLUSIONS RHDGen presents a rare opportunity to identify relevant patterns of genetic factors and biomarkers in Africans that may be associated with differential RHD risk. Furthermore, the RHDGen Network provides a platform for further work on fully elucidating the causes and mechanisms associated with RHD susceptibility and development.
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Affiliation(s)
- Tafadzwa Machipisa
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa (T.M., G.S., L.J.Z., B.M., M.E.E.)
- Population Health Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
| | - Chishala Chishala
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
- Division of Cardiology, University of KwaZulu-Natal, Msunduzi, KwaZulu-Natal (C.C.)
| | - Gasnat Shaboodien
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa (T.M., G.S., L.J.Z., B.M., M.E.E.)
| | - Liesl J. Zühlke
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa (T.M., G.S., L.J.Z., B.M., M.E.E.)
- Division of Pediatric Cardiology, Department of Pediatrics and Child Health, Red Cross War Memorial Children’s Hospital, Cape Town, South Africa (L.J.Z.)
- South African Medical Research Council, Extramural Research and Internal Portfolio, Cape Town, South Africa (L.J.Z.)
| | - Babu Muhamed
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa (T.M., G.S., L.J.Z., B.M., M.E.E.)
- Population Health Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
| | - Shahiemah Pandie
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
| | - Jantina de Vries
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
| | - Nakita Laing
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
| | - Alexia Joachim
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
| | - Rezeen Daniels
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
| | - Mpiko Ntsekhe
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
| | - Christopher T. Hugo-Hamman
- Rheumatic Heart Disease Clinic, Windhoek Central Hospital, Ministry of Health and Social Services, Windhoek, Republic of Namibia (C.T.H.-H.)
| | - Bernard Gitura
- Cardiology Department of Medicine, Kenyatta National Hospital, University of Nairobi, Nairobi, Kenya (B.G.)
| | - Stephen Ogendo
- Uganda Heart Inst, Departments of Adult and Pediatric Cardiology, Kampala, Uganda (S.O.)
| | - Peter Lwabi
- School of Medicine, Maseno Univ, Kenya (P.L., E.O.)
| | - Emmy Okello
- School of Medicine, Maseno Univ, Kenya (P.L., E.O.)
| | - Albertino Damasceno
- Faculty of Medicine, Eduardo Mondlane Univ/Nucleo de Investigaçao, Departamento de Medicina, Hospital Central de Maputo, Maputo, Mozambique (A.D., C.N.)
| | - Celia Novela
- Faculty of Medicine, Eduardo Mondlane Univ/Nucleo de Investigaçao, Departamento de Medicina, Hospital Central de Maputo, Maputo, Mozambique (A.D., C.N.)
| | - Ana O. Mocumbi
- Instituto Nacional de Saúde Ministério da Saúde, Mozambique (A.O.M.)
| | | | - John Musuku
- University Teaching Hospital, Children’s Hospital, University of Zambia, Lusaka, Zambia (J.M., A.M.)
| | - Agnes Mtaja
- University Teaching Hospital, Children’s Hospital, University of Zambia, Lusaka, Zambia (J.M., A.M.)
| | - Ahmed ElSayed
- Department of Cardiothoracic Surgery, Alshaab Teaching Hospital, Alazhari Health Research Centre, Alzaiem Alazhari University, Khartoum, Sudan (A.E., H.H.M.A.)
| | - Huda H.M. Alhassan
- Department of Cardiothoracic Surgery, Alshaab Teaching Hospital, Alazhari Health Research Centre, Alzaiem Alazhari University, Khartoum, Sudan (A.E., H.H.M.A.)
| | - Fidelia Bode-Thomas
- Deptartments of Pediatrics and Medicine, Jos University Teaching Hospital and University of Jos, Jos, Plateau State, Nigeria (F.B.-T., C.Y., G.A., E.N.)
| | - Christopher Yilgwan
- Deptartments of Pediatrics and Medicine, Jos University Teaching Hospital and University of Jos, Jos, Plateau State, Nigeria (F.B.-T., C.Y., G.A., E.N.)
| | - Ganiyu Amusa
- Deptartments of Pediatrics and Medicine, Jos University Teaching Hospital and University of Jos, Jos, Plateau State, Nigeria (F.B.-T., C.Y., G.A., E.N.)
| | - Esin Nkereuwem
- Deptartments of Pediatrics and Medicine, Jos University Teaching Hospital and University of Jos, Jos, Plateau State, Nigeria (F.B.-T., C.Y., G.A., E.N.)
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences (N.M.), University of Cape Town, Cape Town, South Africa
| | - Raj Ramesar
- Department of Pathology (R.R.), University of Cape Town, Cape Town, South Africa
| | - Maia Lesosky
- Division of Epidemiology and Biostatistics, School of Public Health and Family Medicine (M.L.), University of Cape Town, Cape Town, South Africa
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, International Centre for Life, Newcastle upon Tyne, UK (H.J.C.)
| | - Michael Chong
- Population Health Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
| | - Bernard Keavney
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, UK (B.K.)
- Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, UK (B.K.)
| | - Guillaume Paré
- Population Health Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada (T.M., B.M., M.C., G.P.)
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada (G.P.)
| | - Mark E. Engel
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa (T.M., C.C., G.S., L.J.Z., B.M., S.P.; J.d.V., N.L., A.J., R.D., M.N., M.E.E.)
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa (T.M., G.S., L.J.Z., B.M., M.E.E.)
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11
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Kong W, Hui HWH, Peng H, Goh WWB. Dealing with missing values in proteomics data. Proteomics 2022; 22:e2200092. [PMID: 36349819 DOI: 10.1002/pmic.202200092] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/15/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022]
Abstract
Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.
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Affiliation(s)
- Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Harvard Wai Hann Hui
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Hui Peng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.,Centre for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore
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12
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Fröhlich K, Brombacher E, Fahrner M, Vogele D, Kook L, Pinter N, Bronsert P, Timme-Bronsert S, Schmidt A, Bärenfaller K, Kreutz C, Schilling O. Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nat Commun 2022; 13:2622. [PMID: 35551187 PMCID: PMC9098472 DOI: 10.1038/s41467-022-30094-0] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 04/14/2022] [Indexed: 12/25/2022] Open
Abstract
Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best. Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.
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Affiliation(s)
- Klemens Fröhlich
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany
| | - Eva Brombacher
- Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany.,Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg im Breisgau, Germany
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Vogele
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
| | - Lucas Kook
- Epidemiology, Biostatistics & Prevention Institute, University of Zurich, Zurich, Switzerland.,Institute for Data Analysis and Process Design, Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Niko Pinter
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
| | - Sylvia Timme-Bronsert
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Katja Bärenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, and Swiss Institute of Bioinformatics (SIB), Wolfgang, Switzerland
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg im Breisgau, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany. .,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany. .,BIOSS Centre for Biological Signaling Studies, University of Freiburg, Freiburg im Breisgau, Germany.
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13
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Iqbal Z, Fachim HA, Gibson JM, Baricevic-Jones I, Campbell AE, Geary B, Donn RP, Hamarashid D, Syed A, Whetton AD, Soran H, Heald AH. Changes in the Proteome Profile of People Achieving Remission of Type 2 Diabetes after Bariatric Surgery. J Clin Med 2021; 10:3659. [PMID: 34441954 PMCID: PMC8396849 DOI: 10.3390/jcm10163659] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 02/07/2023] Open
Abstract
Bariatric surgery (BS) results in metabolic pathway recalibration. We have identified potential biomarkers in plasma of people achieving type 2 diabetes mellitus (T2DM) remission after BS. Longitudinal analysis was performed on plasma from 10 individuals following Roux-en-Y gastric bypass (n = 7) or sleeve gastrectomy (n = 3). Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was done on samples taken at 4 months before (baseline) and 6 and 12 months after BS. Four hundred sixty-seven proteins were quantified by SWATH-MS. Principal component analysis resolved samples from distinct time points after selection of key discriminatory proteins: 25 proteins were differentially expressed between baseline and 6 months post-surgery; 39 proteins between baseline and 12 months. Eight proteins (SHBG, TF, PRG4, APOA4, LRG1, HSPA4, EPHX2 and PGLYRP) were significantly different to baseline at both 6 and 12 months post-surgery. The panel of proteins identified as consistently different included peptides related to insulin sensitivity (SHBG increase), systemic inflammation (TF and HSPA4-both decreased) and lipid metabolism (APOA4 decreased). We found significant changes in the proteome for eight proteins at 6- and 12-months post-BS, and several of these are key components in metabolic and inflammatory pathways. These may represent potential biomarkers of remission of T2DM.
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Affiliation(s)
- Zohaib Iqbal
- The School of Medicine and Manchester Academic Health Sciences Centre, Manchester University, Manchester M13 9PL, UK; (Z.I.); (J.M.G.); (R.P.D.); (H.S.)
- Department of Endocrinology, Diabetes and Metabolism, Salford Royal Foundation Trust, Salford M6 8HD, UK; (D.H.); (A.S.)
| | - Helene A. Fachim
- The School of Medicine and Manchester Academic Health Sciences Centre, Manchester University, Manchester M13 9PL, UK; (Z.I.); (J.M.G.); (R.P.D.); (H.S.)
- Department of Endocrinology, Diabetes and Metabolism, Salford Royal Foundation Trust, Salford M6 8HD, UK; (D.H.); (A.S.)
| | - J. Martin Gibson
- The School of Medicine and Manchester Academic Health Sciences Centre, Manchester University, Manchester M13 9PL, UK; (Z.I.); (J.M.G.); (R.P.D.); (H.S.)
- Department of Endocrinology, Diabetes and Metabolism, Salford Royal Foundation Trust, Salford M6 8HD, UK; (D.H.); (A.S.)
| | - Ivona Baricevic-Jones
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (I.B.-J.); (A.E.C.); (B.G.); (A.D.W.)
| | - Amy E. Campbell
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (I.B.-J.); (A.E.C.); (B.G.); (A.D.W.)
| | - Bethany Geary
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (I.B.-J.); (A.E.C.); (B.G.); (A.D.W.)
| | - Rachelle P. Donn
- The School of Medicine and Manchester Academic Health Sciences Centre, Manchester University, Manchester M13 9PL, UK; (Z.I.); (J.M.G.); (R.P.D.); (H.S.)
| | - Dashne Hamarashid
- Department of Endocrinology, Diabetes and Metabolism, Salford Royal Foundation Trust, Salford M6 8HD, UK; (D.H.); (A.S.)
| | - Akheel Syed
- Department of Endocrinology, Diabetes and Metabolism, Salford Royal Foundation Trust, Salford M6 8HD, UK; (D.H.); (A.S.)
| | - Anthony D. Whetton
- Stoller Biomarker Discovery Centre, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (I.B.-J.); (A.E.C.); (B.G.); (A.D.W.)
- Manchester National Institute for Health Research Biomedical Research Centre, Manchester M13 9WL, UK
| | - Handrean Soran
- The School of Medicine and Manchester Academic Health Sciences Centre, Manchester University, Manchester M13 9PL, UK; (Z.I.); (J.M.G.); (R.P.D.); (H.S.)
| | - Adrian H. Heald
- The School of Medicine and Manchester Academic Health Sciences Centre, Manchester University, Manchester M13 9PL, UK; (Z.I.); (J.M.G.); (R.P.D.); (H.S.)
- Department of Endocrinology, Diabetes and Metabolism, Salford Royal Foundation Trust, Salford M6 8HD, UK; (D.H.); (A.S.)
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14
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Egert J, Brombacher E, Warscheid B, Kreutz C. DIMA: Data-Driven Selection of an Imputation Algorithm. J Proteome Res 2021; 20:3489-3496. [PMID: 34062065 DOI: 10.1021/acs.jproteome.1c00119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Imputation is a prominent strategy when dealing with missing values (MVs) in proteomics data analysis pipelines. However, it is difficult to assess the performance of different imputation methods and varies strongly depending on data characteristics. To overcome this issue, we present the concept of a data-driven selection of an imputation algorithm (DIMA). The performance and broad applicability of DIMA are demonstrated on 142 quantitative proteomics data sets from the PRoteomics IDEntifications (PRIDE) database and on simulated data consisting of 5-50% MVs with different proportions of missing not at random and missing completely at random values. DIMA reliably suggests a high-performing imputation algorithm, which is always among the three best algorithms and results in a root mean square error difference (ΔRMSE) ≤ 10% in 80% of the cases. DIMA implementation is available in MATLAB at github.com/kreutz-lab/OmicsData and in R at github.com/kreutz-lab/DIMAR.
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Affiliation(s)
- Janine Egert
- Institute of Medical Biometry and Statistics (IMBI), Institute of Medicine and Medical Center Freiburg, 79104 Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signalling Studies (CIBSS), Albert-Ludwigs-Universität Freiburg, 79104 Freiburg, Germany
| | - Eva Brombacher
- Institute of Medical Biometry and Statistics (IMBI), Institute of Medicine and Medical Center Freiburg, 79104 Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signalling Studies (CIBSS), Albert-Ludwigs-Universität Freiburg, 79104 Freiburg, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-Universität Freiburg, 79104 Freiburg, Germany.,Faculty of Biology, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany
| | - Bettina Warscheid
- Biochemistry and Functional Proteomics, Institute of Biology II, Faculty of Biology, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany.,Signalling Research Centres BIOSS and CIBSS, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics (IMBI), Institute of Medicine and Medical Center Freiburg, 79104 Freiburg im Breisgau, Germany.,Signalling Research Centres BIOSS and CIBSS, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany.,Center for Data Analysis and Modeling (FDM), Albert-Ludwigs-Universität Freiburg, 79104 Freiburg im Breisgau, Germany
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15
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Quiroz-Baez R, Hernández-Ortega K, Martínez-Martínez E. Insights Into the Proteomic Profiling of Extracellular Vesicles for the Identification of Early Biomarkers of Neurodegeneration. Front Neurol 2020; 11:580030. [PMID: 33362690 PMCID: PMC7759525 DOI: 10.3389/fneur.2020.580030] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 11/11/2020] [Indexed: 12/11/2022] Open
Abstract
Extracellular vesicles (EVs) are involved in the development and progression of neurodegenerative diseases, including Alzheimer's and Parkinson's disease. Moreover, EVs have the capacity to modify the physiology of neuronal circuits by transferring proteins, RNA, lipids, and metabolites. The proteomic characterization of EVs (exosomes and microvesicles) from preclinical models and patient samples has the potential to reveal new proteins and molecular networks that affect the normal physiology prior to the appearance of traditional biomarkers of neurodegeneration. Noteworthy, many of the genetic risks associated to the development of Alzheimer's and Parkinson's disease affect the crosstalk between mitochondria, endosomes, and lysosomes. Recent research has focused on determining the role of endolysosomal trafficking in the onset of neurodegenerative diseases. Proteomic studies indicate an alteration of biogenesis and molecular content of EVs as a result of endolysosomal and autophagic dysfunction. In this review, we discuss the status of EV proteomic characterization and their usefulness in discovering new biomarkers for the differential diagnosis of neurodegenerative diseases. Despite the challenges related to the failure to follow a standard isolation protocol and their implementation for a clinical setting, the analysis of EV proteomes has revealed the presence of key proteins with post-translational modifications that can be measured in peripheral fluids.
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Affiliation(s)
- Ricardo Quiroz-Baez
- Departamento de Investigación Básica, Dirección de Investigación, Instituto Nacional de Geriatría, Ciudad de México, Mexico
| | - Karina Hernández-Ortega
- Departamento de Genética del Desarrollo y Fisiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Eduardo Martínez-Martínez
- Laboratory of Cell Communication & Extracellular Vesicles, Division of Basic Science, Instituto Nacional de Medicina Genómica, Ciudad de México, Mexico
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16
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Weghorst F, Mirzakhanyan Y, Samimi K, Dhillon M, Barzik M, Cunningham LL, Gershon PD, Cramer KS. Caspase-3 Cleaves Extracellular Vesicle Proteins During Auditory Brainstem Development. Front Cell Neurosci 2020; 14:573345. [PMID: 33281555 PMCID: PMC7689216 DOI: 10.3389/fncel.2020.573345] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/05/2020] [Indexed: 12/26/2022] Open
Abstract
Sound localization requires extremely precise development of auditory brainstem circuits, the molecular mechanisms of which are largely unknown. We previously demonstrated a novel requirement for non-apoptotic activity of the protease caspase-3 in chick auditory brainstem development. Here, we used mass spectrometry to identify proteolytic substrates of caspase-3 during chick auditory brainstem development. These auditory brainstem caspase-3 substrates were enriched for proteins previously shown to be cleaved by caspase-3, especially in non-apoptotic contexts. Functional annotation analysis revealed that our caspase-3 substrates were also enriched for proteins associated with several protein categories, including proteins found in extracellular vesicles (EVs), membrane-bound nanoparticles that function in intercellular communication. The proteome of EVs isolated from the auditory brainstem was highly enriched for our caspase-3 substrates. Additionally, we identified two caspase-3 substrates with known functions in axon guidance, namely Neural Cell Adhesion Molecule (NCAM) and Neuronal-glial Cell Adhesion Molecule (Ng-CAM), that were found in auditory brainstem EVs and expressed in the auditory pathway alongside cleaved caspase-3. Taken together, these data suggest a novel developmental mechanism whereby caspase-3 influences auditory brainstem circuit formation through the proteolytic cleavage of extracellular vesicle (EV) proteins.
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Affiliation(s)
- Forrest Weghorst
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States
| | - Yeva Mirzakhanyan
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
| | - Kian Samimi
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States
| | - Mehron Dhillon
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States
| | - Melanie Barzik
- Section on Sensory Cell Biology, NIDCD, NIH, Bethesda, MD, United States
| | - Lisa L. Cunningham
- Section on Sensory Cell Biology, NIDCD, NIH, Bethesda, MD, United States
| | - Paul D. Gershon
- Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
| | - Karina S. Cramer
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States
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17
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Cai X, Ge W, Yi X, Sun R, Zhu J, Lu C, Sun P, Zhu T, Ruan G, Yuan C, Liang S, Lyu M, Huang S, Zhu Y, Guo T. PulseDIA: Data-Independent Acquisition Mass Spectrometry Using Multi-Injection Pulsed Gas-Phase Fractionation. J Proteome Res 2020; 20:279-288. [PMID: 32975123 DOI: 10.1021/acs.jproteome.0c00381] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The performance of data-independent acquisition (DIA) mass spectrometry (MS) depends on the separation efficiency of peptide precursors. In Orbitrap-based mass spectrometers, separation efficiency of peptide precursors is limited by the relatively slow scanning rate compared to time of flight (TOF)-based MS. Here, we present PulseDIA, a multi-injection gas-phase fractionation (GPF) strategy for enhanced DIA-MS. This is achieved by equally dividing the conventional DIA analysis covering the entire mass range into multiple injections for DIA analyses with complementary windows. Using mouse liver digests, the PulseDIA method identified up to 50% more peptides and 29% more protein groups than that by conventional DIA with the same length of effective gradient time. Compared to conventional multi-injection GPF, PusleDIA exhibited higher flexibility and identified up to 18% more peptides and 8% more protein groups using two injections. The gain of peptides per effective time unit was the highest in PulseDIA compared to conventional DIA and GPF. We further applied the PulseDIA method to profile the proteome of 18 human tissue samples (benign and malignant) from nine cholangiocarcinoma (CCA) patients. PulseDIA identified 7796 protein groups in these CCA samples, with a 14% increase of protein group identification compared to the conventional DIA method. The missing value for protein matrix dropped by 7% using PulseDIA compared to DIA. A total of 681 significantly altered proteins were detected in CCA samples using PulseDIA, including several dysregulated proteins, which were absent in the conventional DIA analysis. Taken together, we present PulseDIA as an enhanced DIA-MS method with improved sensitivity and reproducibility.
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Affiliation(s)
- Xue Cai
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Weigang Ge
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Xiao Yi
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Rui Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Ping Sun
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Tiansheng Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Guan Ruan
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Chunhui Yuan
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Shuang Liang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Mengge Lyu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Shiang Huang
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Yi Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Tiannan Guo
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
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