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Lan Y, Guo W, Chen W, Chen M, Li S. Resistin as a potential diagnostic biomarker for sepsis: insights from DIA and ELISA analyses. Clin Proteomics 2024; 21:46. [PMID: 38951753 PMCID: PMC11218185 DOI: 10.1186/s12014-024-09498-1] [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: 04/15/2024] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
PURPOSE The primary objective of this investigation is to systematically screen and identify differentially expressed proteins (DEPs) within the plasma of individuals afflicted with sepsis. This endeavor employs both Data-Independent Acquisition (DIA) and enzyme-linked immunosorbent assay (ELISA) methodologies. The overarching goal is to furnish accessible and precise serum biomarkers conducive to the diagnostic discernment of sepsis. METHOD The study encompasses 53 sepsis patients admitted to the Affiliated Hospital of Southwest Medical University between January 2019 and December 2020, alongside a control cohort consisting of 16 individuals devoid of sepsis pathology. Subsequently, a subset comprising 10 randomly selected subjects from the control group and 22 from the sepsis group undergoes quantitative proteomic analysis via DIA. The acquired data undergoes Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analyses, facilitating the construction of a Protein-Protein Interaction (PPI) network to discern potential markers. Validation of core proteins is then accomplished through ELISA. Comparative analysis between the normal and sepsis groups ensues, characterized by Receiver Operating Characteristic (ROC) curve construction to evaluate diagnostic efficacy. RESULT A total of 187 DEPs were identified through bioinformatic methodologies. Examination reveals their predominant involvement in biological processes such as wound healing, coagulation, and blood coagulation. Functional pathway analysis further elucidates their engagement in the complement pathway and malaria. Resistin emerges as a candidate plasma biomarker, subsequently validated through ELISA. Notably, the protein exhibits significantly elevated levels in the serum of sepsis patients compared to the normal control group. ROC curve analysis underscores the robust diagnostic capacity of these biomarkers for sepsis. CONCLUSION Data-Independent Acquisition (DIA) and Enzyme-Linked Immunosorbent Assay (ELISA) show increased Resistin levels in sepsis patients, suggesting diagnostic potential, warranting further research.
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Affiliation(s)
- Youyu Lan
- Department of Rheumatology and Immunology, the Affiliated Hospital of Southwest Medical University, 25 Taiping Street, Jiangyang District, Luzhou, 646000, Sichuan, China
| | - Wentao Guo
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China
| | - Wenhao Chen
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China
| | - Muhu Chen
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China.
| | - Shaolan Li
- Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Jiangyang District, Luzhou City, 646000, Sichuan Province, China.
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2
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Danaeifar M, Najafi A. Artificial Intelligence and Computational Biology in Gene Therapy: A Review. Biochem Genet 2024:10.1007/s10528-024-10799-1. [PMID: 38635012 DOI: 10.1007/s10528-024-10799-1] [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: 08/16/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.
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Affiliation(s)
- Mohsen Danaeifar
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran
| | - Ali Najafi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Science, P.O. Box 19395-5487, Tehran, Iran.
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3
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Yang Y, Fang Q. Prediction of glycopeptide fragment mass spectra by deep learning. Nat Commun 2024; 15:2448. [PMID: 38503734 PMCID: PMC10951270 DOI: 10.1038/s41467-024-46771-1] [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: 09/13/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
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Affiliation(s)
- Yi Yang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
| | - Qun Fang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
- Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [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: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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5
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Wevers D, Ramautar R, Clark C, Hankemeier T, Ali A. Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics. Electrophoresis 2023; 44:2000-2024. [PMID: 37667867 DOI: 10.1002/elps.202300105] [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: 05/11/2023] [Revised: 08/08/2023] [Accepted: 08/19/2023] [Indexed: 09/06/2023]
Abstract
Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.
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Affiliation(s)
- Dirk Wevers
- Wageningen University and Research, Wageningen, The Netherlands
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Rawi Ramautar
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Charlie Clark
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Thomas Hankemeier
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Ahmed Ali
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
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Sun Z, Ning Z, Cheng K, Duan H, Wu Q, Mayne J, Figeys D. MetaPep: A core peptide database for faster human gut metaproteomics database searches. Comput Struct Biotechnol J 2023; 21:4228-4237. [PMID: 37692080 PMCID: PMC10491838 DOI: 10.1016/j.csbj.2023.08.025] [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: 06/14/2023] [Revised: 08/18/2023] [Accepted: 08/25/2023] [Indexed: 09/12/2023] Open
Abstract
Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. Peptide identification is an important step in metaproteomics research, with sequence database search (SDS) and spectral library search (SLS) as the two main methods to identify peptides. However, the large search space in metaproteomics studies causes significant challenges for both identification methods. Moreover, with the development of mass spectrometry, it is now feasible to perform metaproteomic projects involving 100-1000 individual microbiomes. These large-scale projects create a conundrum for searching large databases. In this study, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut metaproteomics. MetaPep has a large collection of peptides and spectra that have been identified in published human gut metaproteomics datasets. MetaPep database can be used as an important resource in the current stage of human gut metaproteomics research. This study showed the possibility of applying a core peptide database as a generic metaproteomics workflow. MetaPep could also be an important resource for future human gut metaproteomics research, such as DIA (data-independent acquisition) analysis.
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Affiliation(s)
- Zhongzhi Sun
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Zhibin Ning
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Kai Cheng
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Haonan Duan
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Qing Wu
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Janice Mayne
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Daniel Figeys
- School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
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7
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Chen M, Zhu P, Wan Q, Ruan X, Wu P, Hao Y, Zhang Z, Sun J, Nie W, Chen S. High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multidimensional Predictions. Anal Chem 2023; 95:7495-7502. [PMID: 37126374 DOI: 10.1021/acs.analchem.2c05414] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, we developed a versatile multifunctional deep learning model Deep4D based on self-attention that could predict the collisional cross section, retention time, fragment ion intensity, and charge state with high accuracies for both the unmodified and phosphorylated peptides and thus established the complete workflows for high-coverage 4D DIA proteomics and phosphoproteomics based on multidimensional predictions. A 4D predicted library containing ∼2 million peptides was established that could realize experimental library-free DIA analysis, and 33% more proteins were identified than using an experimental library of single-shot measurement in the example of HeLa cells. These results show the great values of the convenient high-coverage 4D DIA proteomics methods.
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Affiliation(s)
- Moran Chen
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Pujia Zhu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Qiongqiong Wan
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Xianqin Ruan
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Pengfei Wu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Yanhong Hao
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Zhourui Zhang
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Jian Sun
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Wenjing Nie
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Suming Chen
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
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8
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Cox J. Prediction of peptide mass spectral libraries with machine learning. Nat Biotechnol 2023; 41:33-43. [PMID: 36008611 DOI: 10.1038/s41587-022-01424-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/11/2022] [Indexed: 01/21/2023]
Abstract
The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.
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Affiliation(s)
- Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
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Wang C, Li Y, Li S, Chen M, Hu Y. Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis. Infect Drug Resist 2022; 15:5575-5587. [PMID: 36172619 PMCID: PMC9512028 DOI: 10.2147/idr.s380137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 09/10/2022] [Indexed: 12/23/2022] Open
Abstract
Purpose To screen biomarkers in the serum of patients with sepsis by proteomics combined with RNA sequencing technology, and to find new diagnostic and therapeutic targets for sepsis. Patients and Methods Blood samples of 22 sepsis patients (sepsis group) and 10 healthy volunteers (normal group) were collected from January 2019 to December 2020. Data-independent acquisition (DIA) method was employed for protein profiling, RNA sequencing was employed for gene sequencing. Subsequently, quality control and differential analysis (FC≥2; FDR<0.05) of DIA data and RNA sequencing data were performed. Then we identified expression trend-consistent divergence factors by nine-quadrant analysis; subsequent protein-protein interaction (PPI) and gene ontology (GO) functional enrichment analysis of intersection factors was performed, and meta-analysis of targets at transcriptome level was implemented using public datasets. Finally, five Peripheral blood mononuclear cell (PBMC) samples (NC=2; SIRS=1; SEPSIS =2) were collected, and cell localization analysis of core genes was performed by 10× single-cell RNA sequencing (scRNA-seq). Results Compared with the normal group, there were 4681 differentially expressed genes and 202 differentially expressed proteins in the sepsis group. Among them, 25 factors were expressed in both proteome and transcriptome, and the analysis of PPI and GO found that they were mainly involved in biological processes such as white blood cell and neutrophil response, inflammatory and immune response. Four core genes GSTO1, C1QA, RETN, and GRN were screened by meta-analysis, all of which were highly expressed in the sepsis group compared with the normal group (P<0.05); scRNA-seq showed the core genes were mainly localized in macrophage cell lines. Conclusion The core genes GSTO1, C1QA, RETN and GRN are mainly expressed in macrophages, widely involved in inflammation and immune responses, and are highly expressed in plasma in the sepsis, suggesting that they may become potential research targets for sepsis.
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Affiliation(s)
- Chenglin Wang
- Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China
| | - Yang Li
- Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China
| | - Shilin Li
- Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China
| | - Muhu Chen
- Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China
| | - Yingchun Hu
- Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China
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Yang Y, Qiao L. Data-independent acquisition proteomics methods for analyzing post-translational modifications. Proteomics 2022; 23:e2200046. [PMID: 36036492 DOI: 10.1002/pmic.202200046] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/20/2022] [Accepted: 08/23/2022] [Indexed: 11/06/2022]
Abstract
Protein post-translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate and high throughput identification and quantification of protein PTMs is a key task in proteomics research. Recent advancements in data-independent acquisition (DIA) mass spectrometry (MS) technology have achieved deep coverage and accurate quantification of proteins and PTMs. This review provides an overview of DIA data processing methods that cover three aspects of PTMs analysis, i.e., detection of PTMs, site localization, and characterization of complex modification moieties, such as glycosylation. In addition, a survey of deep learning methods that boost DIA-based PTMs analysis is presented, including in silico spectral library generation, as well as feature scoring and error rate control. The limitations and future directions of DIA methods for PTMs analysis are also discussed. Novel data analysis methods will take advantage of advanced MS instrumentation techniques to empower DIA MS for in-depth and accurate PTMs measurements. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yi Yang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
| | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
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Abstract
There are probably no biological samples that did more to spur interest in proteomics than serum and plasma. The belief was that comparing the proteomes of these samples obtained from healthy and disease-affected individuals would lead to biomarkers that could be used to diagnose conditions such as cancer. While the continuing development of mass spectrometers with greater sensitivity and resolution has been invaluable, the invention of strategic strategies to separate circulatory proteins has been just as critical. Novel and creative separation techniques were required because serum and plasma probably have the greatest dynamic range of protein concentration of any biological sample. The concentrations of circulating proteins can range over twelve orders of magnitude, making it a challenge to identify low-abundance proteins where the bulk of the useful biomarkers are believed to exist. The major goals of this article are to (i) provide an historical perspective on the rapid development of serum and plasma proteomics; (ii) describe various separation techniques that have made obtaining an in-depth view of the proteome of these biological samples possible; and (iii) describe applications where serum and plasma proteomics have been employed to discover potential biomarkers for pathological conditions.
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