Relative and Absolute Quantitation in Mass Spectrometry–Based Proteomics

2018 ◽  
Vol 11 (1) ◽  
pp. 49-77 ◽  
Author(s):  
J. Astor Ankney ◽  
Adil Muneer ◽  
Xian Chen
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrum ◽  
Quantitative Proteomics ◽  
Label Free ◽  
Absolute Quantitation ◽  
Chemical Labeling ◽  
Stable Isotope Label ◽  
Proteome Coverage ◽  
Isotope Label ◽  
Quantification Accuracy

Mass spectrometry–based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label–based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling–based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

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

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 Analytical Considerations of Stable Isotope Labelling in Lipidomics

Biomolecules ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 151 ◽  
Author(s):  
Alexander Triebl ◽  
Markus Wenk
Keyword(s):  
Mass Spectrometry ◽  
Stable Isotope ◽  
Clinical Settings ◽  
Stable Isotope Labelling ◽  
Stable Isotope Label ◽  
Isotope Labelling ◽  
Isotope Label ◽  
Lipid Biomarker ◽  
Staple Technique ◽  
Biomarker Research

Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage, and are now also being implicated to play important roles in a variety of diseases, with lipid biomarker research one of the most widespread applications of lipidomic techniques both in research and in clinical settings. Stable isotope labelling has become a staple technique in the analysis of small molecule metabolism and dynamics, as it is the only experimental setup by which biosynthesis, remodelling and degradation of biomolecules can be directly measured. Using state-of-the-art analytical technologies such as chromatography-coupled high resolution tandem mass spectrometry, the stable isotope label can be precisely localized and quantified within the biomolecules. The application of stable isotope labelling to lipidomics is however complicated by the diversity of lipids and the complexity of the necessary data analysis. This article discusses key experimental aspects of stable isotope labelling in the field of mass spectrometry-based lipidomics, summarizes current applications and provides an outlook on future developments and potential.

A label-free method based on MALDI-TOF mass spectrometry for the absolute quantitation of troponin T in mouse cardiac tissue

2008 ◽  
Vol 391 (5) ◽  
pp. 1969-1976 ◽  
Author(s):  
Mariano Bizzarri ◽  
Chiara Cavaliere ◽  
Patrizia Foglia ◽  
Chiara Guarino ◽  
Roberto Samperi ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Cardiac Tissue ◽  
Troponin T ◽  
Label Free ◽  
Absolute Quantitation ◽  
Maldi Tof Mass Spectrometry ◽  
Maldi Tof ◽  
The Absolute

scholarly journals DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

2019 ◽  
Author(s):  
Mark V. Ivanov ◽  
Julia A. Bubis ◽  
Vladimir Gorshkov ◽  
Irina A. Tarasova ◽  
Lev I. Levitsky ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Tandem Mass ◽  
Label Free ◽  
Hela Cell Line ◽  
Sequence Coverage ◽  
Proteome Coverage ◽  
Successful Attempt ◽  
Higher Sensitivity ◽  
Lengthy Analysis

AbstractProteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MS-based label-free quantitation approaches.

scholarly journals proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry

2020 ◽  
Author(s):  
Constantin Ahlmann-Eltze ◽  
Simon Anders
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Statistical Power ◽  
Missing Values ◽  
Ad Hoc ◽  
Linear Models ◽  
Statistical Tests ◽  
High Sensitivity ◽  
Small Sample ◽  
Label Free

Abstract Protein mass spectrometry with label-free quantification (LFQ) is widely used for quantitative proteomics studies. Nevertheless, well-principled statistical inference procedures are still lacking, and most practitioners adopt methods from transcriptomics. These, however, cannot properly treat the principal complication of label-free proteomics, namely many non-randomly missing values. We present proDA, a method to perform statistical tests for differential abundance of proteins. It models missing values in an intensity-dependent probabilistic manner. proDA is based on linear models and thus suitable for complex experimental designs, and boosts statistical power for small sample sizes by using variance moderation. We show that the currently widely used methods based on ad hoc imputation schemes can report excessive false positives, and that proDA not only overcomes this serious issue but also offers high sensitivity. Thus, proDA fills a crucial gap in the toolbox of quantitative proteomics.

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