scholarly journals Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis

2019 ◽  
Vol 20 (23) ◽  
pp. 5932 ◽  
Author(s):  
Yusuke Kawashima ◽  
Eiichiro Watanabe ◽  
Taichi Umeyama ◽  
Daisuke Nakajima ◽  
Masahira Hattori ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Proteomic Analysis ◽  
Protein Quantification ◽  
Single Shot ◽  
Human Embryonic Kidney Cells ◽  
Practical Applications ◽  
Data Independent Acquisition ◽  
Highly Sensitive ◽  
Specific Pathogen ◽  
Sequence Database Search

Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.

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.

scholarly journals Initial Guidelines for Manuscripts Employing Data-independent Acquisition Mass Spectrometry for Proteomic Analysis

2019 ◽  
Vol 18 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Robert J. Chalkley ◽  
Michael J. MacCoss ◽  
Jacob D. Jaffe ◽  
Hannes L. Röst
Keyword(s):  
Mass Spectrometry ◽  
Proteomic Analysis ◽  
Data Independent Acquisition

scholarly journals Ultra-streamlined single cell proteomics by all-in-one chip and data-independent-acquisition mass spectrometry

2021 ◽  
Author(s):  
Hsiung-Lin Tu ◽  
Sofani Gebreyesus ◽  
Asad Ali Siyal ◽  
Reta Birhanu Kitata ◽  
Eric Sheng-Wen Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Single Cell ◽  
Missing Values ◽  
Protein Quantification ◽  
Single Cell Level ◽  
Cell Level ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
General Utility ◽  
Ultrahigh Sensitivity

Abstract Single cell proteomics provides the ultimate resolution to reveal cellular phenotypic heterogeneity and functional network underlying biological processes. Here, we present an ultra-streamlined workflow combining an integrated proteomic chip (iProChip) and data-independent-acquisition (DIA) mass spectrometry for sensitive microproteomics analysis down to single cell level. The iProChip offers multiplexed and automated all-in-one station from cell isolation/counting/imaging to complete proteomic processing within a single device. By mapping to project-specific spectra libraries, the iProChip-DIA enables profiling of 1160 protein groups from triplicate analysis of a single mammalian cell. Furthermore, the applicability of iProChip-DIA was demonstrated using both adherent and non-adherent malignant cells, which reveals 5 orders of proteome coverage, highly consistent ~100-fold protein quantification (1-100 cells) and high reproducibility with low missing values (<16%). With the demonstrated all-in-one cell characterization, ultrahigh sensitivity, robustness, and versatility to add other functionalities, the iProChip-DIA is anticipated to offer general utility to realize advanced proteomics applications at single cell level.

scholarly journals Ultra-streamlined single cell proteomics by all-in-one chip and data-independent-acquisition mass spectrometry

2021 ◽  
Author(s):  
Hsiung-Lin Tu ◽  
Sofani Gebreyesus ◽  
Asad Ali Siyal ◽  
Reta Birhanu Kitata ◽  
Eric Sheng-Wen Chen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Single Cell ◽  
Missing Values ◽  
Protein Quantification ◽  
Single Cell Level ◽  
Cell Level ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
General Utility ◽  
Ultrahigh Sensitivity

Abstract Single cell proteomics provides the ultimate resolution to reveal cellular phenotypic heterogeneity and functional network underlying biological processes. Here, we present an ultra-streamlined workflow combining an integrated proteomic chip (iProChip) and data-independent-acquisition (DIA) mass spectrometry for sensitive microproteomics analysis down to single cell level. The iProChip offers multiplexed and automated all-in-one station from cell isolation/counting/imaging to complete proteomic processing within a single device. By mapping to project-specific spectra libraries, the iProChip-DIA enables profiling of 1160 protein groups from triplicate analysis of a single mammalian cell. Furthermore, the applicability of iProChip-DIA was demonstrated using both adherent and non-adherent malignant cells, which reveals 5 orders of proteome coverage, highly consistent ~100-fold protein quantification (1-100 cells) and high reproducibility with low missing values (<16%). With the demonstrated all-in-one cell characterization, ultrahigh sensitivity, robustness, and versatility to add other functionalities, the iProChip-DIA is anticipated to offer general utility to realize advanced proteomics applications at single cell level.

scholarly journals BoxCarmax: a high-selectivity data-independent acquisition mass spectrometry method for the analysis of protein turnover and complex samples

2020 ◽  
Author(s):  
Barbora Salovska ◽  
Wenxue Li ◽  
Yi Di ◽  
Yansheng Liu
Keyword(s):  
Mass Spectrometry ◽  
Protein Turnover ◽  
High Selectivity ◽  
Isotope Labeling ◽  
Cultured Cells ◽  
Protein Quantification ◽  
Serum Starvation ◽  
Complex Samples ◽  
Data Independent Acquisition ◽  
Ms Analysis

ABSTRACTThe data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labelled peptide pairs. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1- and MS2-levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax modestly increased the identification rate for label-free and labeled samples but significantly improved the quantitative accuracy in SILAC and pSILAC samples. We further applied BoxCarmax in studying the protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

scholarly journals Label-free mass spectrometry-based protein quantification technologies in proteomic analysis

2008 ◽  
Vol 7 (5) ◽  
pp. 329-339 ◽  
Author(s):  
M. Wang ◽  
J. You ◽  
K. G. Bemis ◽  
T. J. Tegeler ◽  
D. P. G. Brown
Keyword(s):  
Mass Spectrometry ◽  
Proteomic Analysis ◽  
Protein Quantification ◽  
Label Free

scholarly journals Deep ubiquitination site profiling by single-shot data-independent acquisition mass spectrometry

2020 ◽  
Author(s):  
Martin Steger ◽  
Phillip Ihmor ◽  
Mattias Backman ◽  
Stefan Müller ◽  
Henrik Daub
Keyword(s):  
Mass Spectrometry ◽  
High Precision ◽  
Protein Extraction ◽  
Single Shot ◽  
Spectral Library ◽  
Sample Analysis ◽  
Precision Data ◽  
Data Independent Acquisition ◽  
Ubiquitination Site ◽  
High Precision Data

We report a highly optimized proteomics method for in-depth ubiquitination profiling, which combines efficient protein extraction and data-independent acquisition mass spectrometry (DIA-MS). Employing DIA for both spectral library generation and single-shot sample analysis, we quantify up to 70,000 ubiquitinated peptides per MS run with high precision, data completeness and throughput. Our approach resolves the dynamics of ubiquitination and protein degradation with an unprecedented analytical depth.

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