Quantitative Chemoproteomic Profiling with Data-Independent Acquisition-Based Mass Spectrometry

2022 ◽  
Author(s):  
Fan Yang ◽  
Guogeng Jia ◽  
Jiuzhou Guo ◽  
Yuan Liu ◽  
Chu Wang
Keyword(s):  
Mass Spectrometry ◽  
Data Independent Acquisition

scholarly journals Proteomic insights into synaptic signaling in the brain: the past, present and future

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yalan Xu ◽  
Xiuyue Song ◽  
Dong Wang ◽  
Yin Wang ◽  
Peifeng Li ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Complexes ◽  
Synaptic Signaling ◽  
Brain Functions ◽  
The Past ◽  
Data Independent Acquisition ◽  
Technological Advances ◽  
Neural Communication ◽  
Chemical Synapses ◽  
The Brain

AbstractChemical synapses in the brain connect neurons to form neural circuits, providing the structural and functional bases for neural communication. Disrupted synaptic signaling is closely related to a variety of neurological and psychiatric disorders. In the past two decades, proteomics has blossomed as a versatile tool in biological and biomedical research, rendering a wealth of information toward decoding the molecular machinery of life. There is enormous interest in employing proteomic approaches for the study of synapses, and substantial progress has been made. Here, we review the findings of proteomic studies of chemical synapses in the brain, with special attention paid to the key players in synaptic signaling, i.e., the synaptic protein complexes and their post-translational modifications. Looking toward the future, we discuss the technological advances in proteomics such as data-independent acquisition mass spectrometry (DIA-MS), cross-linking in combination with mass spectrometry (CXMS), and proximity proteomics, along with their potential to untangle the mystery of how the brain functions at the molecular level. Last but not least, we introduce the newly developed synaptomic methods. These methods and their successful applications marked the beginnings of the synaptomics era.

scholarly journals Novel circulating protein biomarkers for thyroid cancer determined through data-independent acquisition mass spectrometry

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9507
Author(s):  
Dandan Li ◽  
Jie Wu ◽  
Zhongjuan Liu ◽  
Ling Qiu ◽  
Yimin Zhang
Keyword(s):  
Mass Spectrometry ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Factor H ◽  
Complement Factor ◽  
Protein Biomarkers ◽  
Serum Samples ◽  
Data Independent Acquisition ◽  
Median Serum ◽  
And Control

Background Distinguishing between different types of thyroid cancers (TC) remains challenging in clinical laboratories. As different tumor types require different clinical interventions, it is necessary to establish new methods for accurate diagnosis of TC. Methods Proteomic analysis of the human serum was performed through data-independent acquisition mass spectrometry for 29 patients with TC (stages I–IV): 13 cases of papillary TC (PTC), 10 cases of medullary TC (MTC), and six cases follicular TC (FTC). In addition, 15 patients with benign thyroid nodules (TNs) and 10 healthy controls (HCs) were included in this study. Subsequently, 17 differentially expressed proteins were identified in 291 patients with TC, including 247 with PTC, 38 with MTC, and six with FTC, and 69 patients with benign TNs and 176 with HC, using enzyme-linked immunosorbent assays. Results In total, 517 proteins were detected in the serum samples using an Orbitrap Q-Exactive-plus mass spectrometer. The amyloid beta A4 protein, apolipoprotein A-IV, gelsolin, contactin-1, gamma-glutamyl hydrolase, and complement factor H-related protein 1 (CFHR1) were selected for further analysis. The median serum CFHR1 levels were significantly higher in the MTC and FTC groups than in the PTC and control groups (P < 0.001). CFHR1 exhibited higher diagnostic performance in distinguishing patients with MTC from those with PTC (P < 0.001), with a sensitivity of 100.0%, specificity of 85.08%, area under the curve of 0.93, and detection cut-off of 0.92 ng/mL. Conclusion CFHR1 may serve as a novel biomarker to distinguish PTC from MTC with high sensitivity and specificity.

scholarly journals Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics

2018 ◽  
Vol 15 (5) ◽  
pp. 371-378 ◽  
Author(s):  
Ryan Peckner ◽  
Samuel A Myers ◽  
Alvaro Sebastian Vaca Jacome ◽  
Jarrett D Egertson ◽  
Jennifer G Abelin ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Independent Acquisition ◽  
Targeted Analysis

scholarly journals Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments

2020 ◽  
Vol 19 (6) ◽  
pp. 944-959 ◽  
Author(s):  
Tsung-Heng Tsai ◽  
Meena Choi ◽  
Balazs Banfai ◽  
Yansheng Liu ◽  
Brendan X. MacLean ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Profile ◽  
Protein Quantification ◽  
Selected Reaction Monitoring ◽  
Estimation Accuracy ◽  
Spectral Features ◽  
Data Set ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Manual Curation

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

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