BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics

2022 ◽  
Author(s):  
Devang Mehta ◽  
Sabine Scandola ◽  
R. Glen Uhrig
Keyword(s):  
Quantitative Proteomics ◽  
Depth Range ◽  
Label Free ◽  
Data Independent Acquisition ◽  
Free Data

scholarly journals NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

2020 ◽  
Vol 48 (14) ◽  
pp. e83-e83 ◽  
Author(s):  
Shisheng Wang ◽  
Wenxue Li ◽  
Liqiang Hu ◽  
Jingqiu Cheng ◽  
Hao Yang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Missing Values ◽  
Protein Complexes ◽  
Label Free ◽  
Missing Value ◽  
Imputation Methods ◽  
Data Independent Acquisition ◽  
Low Performance ◽  
User Friendly

Abstract Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

scholarly journals Differential urine proteome analysis of a ventilator-induced lung injury rat model by label-free quantitative and parallel reaction monitoring proteomics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weiwei Qin ◽  
Xiao Zhang ◽  
Lingnan Chen ◽  
Qiujie Li ◽  
Benwang Zhang ◽  
...  
Keyword(s):  
Lung Injury ◽  
Quantitative Proteomics ◽  
Urinary Biomarkers ◽  
Reaction Monitoring ◽  
Label Free ◽  
Parallel Reaction ◽  
Ventilator Induced Lung Injury ◽  
Parallel Reaction Monitoring ◽  
Free Data ◽  
Differential Proteins

AbstractUrine is a promising resource for biomarker research. Therefore, the purpose of this study was to investigate potential urinary biomarkers to monitor the disease activity of ventilator-induced lung injury (VILI). In the discovery phase, a label-free data-dependent acquisition (DDA) quantitative proteomics method was used to profile the urinary proteomes of VILI rats. For further validation, the differential proteins were verified by parallel reaction monitoring (PRM)-targeted quantitative proteomics. In total, 727 high-confidence proteins were identified with at least 1 unique peptide (FDR ≤ 1%). Compared to the control group, 110 proteins (65 upregulated, 45 downregulated) were significantly changed in the VILI group (1.5-fold change, P < 0.05). The canonical pathways and protein–protein interaction analyses revealed that the differentially expressed proteins were enriched in multiple functions, including oxidative stress and inflammatory responses. Finally, thirteen proteins were identified as candidate biomarkers for VILI by PRM validation. Among these PRM-validated proteins, AMPN, MEP1B, LYSC1, DPP4 and CYC were previously reported as lung-associated disease biomarkers. SLC31, MEP1A, S15A2, NHRF1, XPP2, GGT1, HEXA, and ATPB were newly discovered in this study. Our results suggest that the urinary proteome might reflect the pathophysiological changes associated with VILI. These differential proteins are potential urinary biomarkers for the activity of VILI.

Analysis of B-type natriuretic peptide impurities using label-free data-independent acquisition mass spectrometry technology

2021 ◽  
Vol 59 (1) ◽  
pp. 217-226
Author(s):  
Peng Xiao ◽  
Fan Zhang ◽  
Xinxue Wang ◽  
Dewei Song ◽  
Hongmei Li
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Natriuretic Peptide ◽  
Measurement Errors ◽  
Internal Standard ◽  
Raw Material ◽  
Label Free ◽  
Data Independent Acquisition ◽  
Free Data ◽  
Mass Spectrometry Technology

AbstractObjectivesSynthetic B-type natriuretic peptide (BNP) is employed in most clinical testing platforms as a raw material of calibrator. Characterization of impurities with structures similar (BNPstrimp compounds) to that of BNP is a reasonable way to decrease clinical measurement errors and improve drug safety.MethodsA novel quantitative method targeted towards BNPstrimp compounds was developed. First, the peptide samples were separated and identified using ultra-performance liquid chromatography, coupled with high-resolution mass spectrometry (MS). To evaluate biological activity further, BNPstrimp immunoaffinity was investigated using western blot (WB) assays. Second, a quantitative label-free data-independent acquisition (DIA) MS approach was developed, and the internal standard peptide (ISP) was hydrolyzed. Absolute quantification was performed using an isotope dilution MS (ID-MS) approach. Third, method precision was investigated using the C-peptide reference material.ResultsSeventeen BNPstrimp compounds were identified in synthetic BNP, and 10 of them were successfully sequenced. The immunoassay results indicated that deaminated, oxidized, and isomerized BNPstrimp compounds exhibited weaker immunoaffinity than intact BNP1-32. The mass fraction of the synthetic solid ISP1-16, quantified by ID-MS, was 853.5 (±17.8) mg/g. Validation results indicated that the developed method was effective and accurate for the quantitation of the well-separated BNP impurities.ConclusionsThe developed approach was easy to perform, and it was suitable for the parallel quantification of low-abundance BNPstrimp compounds when they performed a good separation in liquid chromatography. The quantitative results were comparable and traceable. This approach is a promising tool for BNP product quality and safety assessment.

scholarly journals The Differential Urine Proteome Analysis of a Ventilator-induced Lung Injury Rat Model by Label-free Quantitative and Parallel Reaction Monitoring Proteomics

2021 ◽  
Author(s):  
Weiwei Qin ◽  
Xiao Zhang ◽  
Lingnan Chen ◽  
Qiujie Li ◽  
Benwang Zhang ◽  
...  
Keyword(s):  
Lung Injury ◽  
Quantitative Proteomics ◽  
Urinary Biomarkers ◽  
Reaction Monitoring ◽  
Label Free ◽  
Parallel Reaction ◽  
Ventilator Induced Lung Injury ◽  
Parallel Reaction Monitoring ◽  
Free Data ◽  
Differential Proteins

Abstract Background: Urine is a promising resource for biomarker research. Therefore, the purpose of this study was to investigate potential urinary biomarkers to monitor the disease activity of ventilator-induced lung injury (VILI). Methods: In the discovery phase, a label-free data-dependent acquisition (DDA) quantitative proteomics method was used to profile the urinary proteomes of VILI rats. For further validation, the differential proteins were verified by parallel reaction monitoring (PRM)-targeted quantitative proteomics.Results: In all, 727 high-confidence proteins were identified with at least 1 unique peptide (FDR ≤1%). Compared to the control group, 110 proteins (65 upregulated, 45 downregulated) were significantly changed in the VILI group (1.5-fold change, P<0.05). The canonical pathways and protein-protein interaction analyses revealed that the differentially expressed proteins were enriched in multiple functions, including oxidative stress and inflammatory responses. Finally, thirteen proteins were identified as candidate biomarkers for VILI by PRM validation. Among these PRM-validated proteins, AMPN, MEP1B, LYSC1, DPP4 and CYC were previously reported as lung-associated disease biomarkers. SLC31, MEP1A, S15A2, NHRF1, XPP2, GGT1, HEXA, and ATPB were newly discovered in this study. Conclusions: Our results suggest that the urinary proteome might reflect the pathophysiological changes associated with VILI. These differential proteins are potential urinary biomarkers for the activity of VILI.

scholarly journals iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics

2020 ◽  
Vol 36 (8) ◽  
pp. 2611-2613 ◽  
Author(s):  
Thang V Pham ◽  
Alex A Henneman ◽  
Connie R Jimenez
Keyword(s):  
Open Source ◽  
State Of The Art ◽  
R Package ◽  
Protein Quantification ◽  
Supplementary Information ◽  
Label Free ◽  
Software Suite ◽  
Data Independent Acquisition ◽  
Free Data ◽  
Acquisition Mode

Abstract Summary We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation. Availability and implementation The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Direct data-independent acquisition (direct DIA) enables substantially improved label-free quantitative proteomics in Arabidopsis

2020 ◽  
Author(s):  
Devang Mehta ◽  
Sabine Scandola ◽  
R. Glen Uhrig
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Cost Effective ◽  
Shotgun Proteomics ◽  
Label Free ◽  
Data Independent Acquisition ◽  
Quantitative Accuracy ◽  
Ratio Distortion ◽  
Direct Data ◽  
Critical Resource

AbstractThe last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. This has included the use of both labelled and label-free quantitative liquid-chromatography mass spectrometry (LC-MS) strategies in model1,2 and non-model plants3. While chemical labelling-based workflows (e.g. iTRAQ and TMT) are generally considered to possess high quantitative accuracy, they nonetheless suffer from ratio distortion and sample interference issues4,5, while being less cost-effective and offering less throughput than label-free approaches. Consequently, label free quantification (LFQ) has been widely used in comparative quantitative experiments profiling the native6 and post-translationally modified (PTM-ome)7,8 proteomes of plants. However, LFQ shotgun proteomics studies in plants have so far, almost universally, used data-dependent acquisition (DDA) for tandem MS (MS/MS) analysis. Here, we systematically compare and benchmark a state-of-the-art DDA LFQ workflow for plants against a new direct data-independent acquisition (direct DIA) method9. Our study demonstrates several advantages of direct DIA and establishes it as the method of choice for quantitative proteomics on plant tissue. We also applied direct DIA to perform a quantitative proteomic comparison of dark and light grown Arabidopsis cell cultures, providing a critical resource for future plant interactome studies using this well-established biochemistry platform.

Development of Algorithms for Mass Spectrometry-based Label-free Quantitative Proteomics*

2011 ◽  
Vol 38 (6) ◽  
pp. 506-518 ◽  
Author(s):  
Wei ZHANG ◽  
Ji-Yang ZHANG ◽  
Hui LIU ◽  
Han-Chang SUN ◽  
Chang-Ming XU ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Label Free
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