A review on mass spectrometry-based quantitative proteomics: Targeted and data independent acquisition

2017 ◽  
Vol 964 ◽  
pp. 7-23 ◽  
Author(s):  
Veronika Vidova ◽  
Zdenek Spacil
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Data Independent Acquisition

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 Sensitive Quantitative Proteomics of Human Hematopoietic Stem and Progenitor Cells by Data-independent Acquisition Mass Spectrometry

2019 ◽  
Vol 18 (7) ◽  
pp. 1454-1467 ◽  
Author(s):  
Sabine Amon ◽  
Fabienne Meier-Abt ◽  
Ludovic C. Gillet ◽  
Slavica Dimitrieva ◽  
Alexandre P. A. Theocharides ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Progenitor Cells ◽  
Quantitative Proteomics ◽  
Hematopoietic Stem ◽  
Data Independent Acquisition ◽  
Stem And Progenitor Cells

scholarly journals Matrix-matched calibration curves for assessing analytical figures of merit in quantitative proteomics

2019 ◽  
Author(s):  
Lindsay K Pino ◽  
Han-Yin Yang ◽  
William Stafford Noble ◽  
Brian C Searle ◽  
Andrew N Hoofnagle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Sample Preparation ◽  
Quantitative Proteomics ◽  
Protein Abundance ◽  
Complex Samples ◽  
Figures Of Merit ◽  
Data Independent Acquisition ◽  
Proteomics Experiment ◽  
Series Of Experiments ◽  
Analytical Figures Of Merit

AbstractMass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.

scholarly journals mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

2015 ◽  
Vol 129 ◽  
pp. 108-120 ◽  
Author(s):  
Guoshou Teo ◽  
Sinae Kim ◽  
Chih-Chiang Tsou ◽  
Ben Collins ◽  
Anne-Claude Gingras ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Analysis ◽  
Quantitative Proteomics ◽  
Proteomics Data ◽  
Data Independent Acquisition

scholarly journals Narrow Precursor Mass Range for DIA-MS Enhances Protein Identification and Quantification

2021 ◽  
Author(s):  
Huoming Zhang ◽  
Dalila Bensaddek
Keyword(s):  
Mass Spectrometry ◽  
Total Protein ◽  
Quantitative Proteomics ◽  
Protein Identification ◽  
Mass Range ◽  
Systematic Evaluation ◽  
Data Independent Acquisition ◽  
Subsequent Application ◽  
Accurate Quantification ◽  
Identification And Quantification

Data independent acquisition - mass spectrometry (DIA-MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA-MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on Arabidopsis sample with spike-in of known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400-1200 m/z. When combining several DIA-MS analyses with narrow precursor ranges (i.e., 400-650, 650-900 and 900-1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 59.4% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the 6-protein mix spike-in. In cDIA only 5 out of the 6-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.

scholarly journals Narrow Precursor Mass Range for DIA–MS Enhances Protein Identification and Quantification in Arabidopsis

Life ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 982
Author(s):  
Huoming Zhang ◽  
Dalila Bensaddek
Keyword(s):  
Mass Spectrometry ◽  
Total Protein ◽  
Quantitative Proteomics ◽  
Protein Identification ◽  
Mass Range ◽  
Systematic Evaluation ◽  
Data Independent Acquisition ◽  
Subsequent Application ◽  
Accurate Quantification ◽  
Identification And Quantification

Data independent acquisition–mass spectrometry (DIA–MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA–MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on an Arabidopsis sample with spike-in known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400–1200 m/z. When combining several DIA–MS analyses with narrow precursor ranges (i.e., 400–650, 650–900 and 900–1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 54.7% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the six-protein mix spike-in. In cDIA only five out of the six-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.

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
Sign in / Sign up

Export Citation Format

Share Document