CHAPTER 6. Overview and Implementation of Mass Spectrometry-Based Label-Free Quantitative Proteomics

2014 ◽  
pp. 129-153
Author(s):  
Erik J. Soderblom ◽  
J. Will Thompson ◽  
M. Arthur Moseley
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Label Free

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 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.

scholarly journals Current Status and Advances in Quantitative Proteomic Mass Spectrometry

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Valerie C. Wasinger ◽  
Ming Zeng ◽  
Yunki Yau
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Biomarker Discovery ◽  
Current Status ◽  
Label Free ◽  
Stable Isotope Labelling ◽  
Complex Samples ◽  
Bioinformatic Tools ◽  
Critical Overview ◽  
Proteins And Peptides

The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.

scholarly journals Large Scale Profiling of Protein Isoforms Using Label-Free Quantitative Proteomics Revealed the Regulation of Nonsense-Mediated Decay in Moso Bamboo (Phyllostachys edulis)

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 744 ◽  
Author(s):  
Xiaolan Yu ◽  
Yongsheng Wang ◽  
Markus V. Kohnen ◽  
Mingxin Piao ◽  
Min Tu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Large Scale ◽  
Gene Annotation ◽  
Function Analysis ◽  
Moso Bamboo ◽  
Protein Isoforms ◽  
Label Free ◽  
Total Proteins ◽  
Protein Coding

Moso bamboo is an important forest species with a variety of ecological, economic, and cultural values. However, the gene annotation information of moso bamboo is only based on the transcriptome sequencing, lacking the evidence of proteome. The lignification and fiber in moso bamboo leads to a difficulty in the extraction of protein using conventional methods, which seriously hinders research on the proteomics of moso bamboo. The purpose of this study is to establish efficient methods for extracting the total proteins from moso bamboo for following mass spectrometry-based quantitative proteome identification. Here, we have successfully established a set of efficient methods for extracting total proteins of moso bamboo followed by mass spectrometry-based label-free quantitative proteome identification, which further improved the protein annotation of moso bamboo genes. In this study, 10,376 predicted coding genes were confirmed by quantitative proteomics, accounting for 35.8% of all annotated protein-coding genes. Proteome analysis also revealed the protein-coding potential of 1015 predicted long noncoding RNA (lncRNA), accounting for 51.03% of annotated lncRNAs. Thus, mass spectrometry-based proteomics provides a reliable method for gene annotation. Especially, quantitative proteomics revealed the translation patterns of proteins in moso bamboo. In addition, the 3284 transcript isoforms from 2663 genes identified by Pacific BioSciences (PacBio) single-molecule real-time long-read isoform sequencing (Iso-Seq) was confirmed on the protein level by mass spectrometry. Furthermore, domain analysis of mass spectrometry-identified proteins encoded in the same genomic locus revealed variations in domain composition pointing towards a functional diversification of protein isoform. Finally, we found that part transcripts targeted by nonsense-mediated mRNA decay (NMD) could also be translated into proteins. In summary, proteomic analysis in this study improves the proteomics-assisted genome annotation of moso bamboo and is valuable to the large-scale research of functional genomics in moso bamboo. In summary, this study provided a theoretical basis and technical support for directional gene function analysis at the proteomics level in moso bamboo.

scholarly journals Mass Spectrometry-based Label-free Quantitative Proteomics To Study the Effect of 3PO Drug at Cellular Level

2019 ◽  
Vol 10 (4) ◽  
pp. 577-583 ◽  
Author(s):  
Sarath Babu Nukala ◽  
Giovanna Baron ◽  
Giancarlo Aldini ◽  
Marina Carini ◽  
Alfonsina D’Amato
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Cellular Level ◽  
Label Free

scholarly journals DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis

2020 ◽  
Vol 19 (6) ◽  
pp. 1047-1057 ◽  
Author(s):  
Yafeng Zhu ◽  
Lukas M. Orre ◽  
Yan Zhou Tran ◽  
Georgios Mermelekas ◽  
Henrik J. Johansson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Analysis ◽  
Protein Expression ◽  
Statistical Methods ◽  
Expression Analysis ◽  
Quantitative Proteomics ◽  
Data Sets ◽  
Label Free ◽  
Differential Protein Expression ◽  
Differential Protein

Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.

scholarly journals Mass spectrometry-based, label-free quantitative proteomics of round spermatids in mice

2014 ◽  
Vol 10 (4) ◽  
pp. 2009-2024 ◽  
Author(s):  
HAILONG WANG ◽  
YAN LI ◽  
LIJUAN YANG ◽  
BAOFENG YU ◽  
PING YAN ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quantitative Proteomics ◽  
Label Free
Sign in / Sign up

Export Citation Format

Share Document