MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Kohler, Devon; Tsai, Tsung-Heng; Verschueren, Erik; Huang, Ting; Hinkle, Trent; Phu, Lilian; Choi, Meena; Vitek, Olga

doi:10.1016/j.mcpro.2022.100477

Cited by 14 publications

(19 citation statements)

References 41 publications

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“…In the second analysis approach, we employed statistical analysis of phosphorylated peptides using the MSstatsPTM package, developed by Kohler et al This tool also adjusts to changes at the protein level, which highlights PTM changes that are more likely due to biological regulations rather than only expression (Figure S4). In total, 8 peptides were found to be increased in Npc1-null tissues, and 4 peptides were found to be decreased.…”

Section: Resultsmentioning

confidence: 99%

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Comparative Hippocampal Proteome and Phosphoproteome in a Niemann–Pick, Type C1 Mouse Model Reveal Insights into Disease Mechanisms

Nguyen,

Mohanty,

Yan

et al. 2023

J. Proteome Res.

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Niemann−Pick disease, type C (NPC) is a neurodegenerative, lysosomal storage disorder in individuals carrying two mutated copies of either the NPC1 or NPC2 gene. Consequently, impaired cholesterol recycling and an array of downstream events occur. Interestingly, in NPC, the hippocampus displays lysosomal lipid storage but does not succumb to progressive neurodegeneration as significantly as other brain regions. Since defining the neurodegeneration mechanisms in this disease is still an active area of research, we use mass spectrometry to analyze the overall proteome and phosphorylation pattern changes in the hippocampal region of a murine model of NPC. Using 3 week old mice representing an early disease time point, we observed changes in the expression of 47 proteins, many of which are consistent with the previous literature. New to this study, changes in members of the SNARE complex, including STX7, VTI1B, and VAMP7, were identified. Furthermore, we identified that phosphorylation of T286 on CaMKIIα and S1303 on NR2B increased in mutant animals, even at the late stage of the disease. These phosphosites are crucial to learning and memory and can trigger neuronal death by altering protein−protein interactions.

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Section: Resultsmentioning

confidence: 99%

“…Peptide–spectrum matches were then exported, and statistical analysis was repeated using the MSstatsTMT and MSstatsPTM packages. , MSstatsTMT was used to assess the statistical changes, based on the median of biological replicates, of the overall proteome in our data set. Phosphorylated peptides were evaluated using PD and MSstatsPTM.…”

Section: Methodsmentioning

confidence: 99%

Comparative Hippocampal Proteome and Phosphoproteome in a Niemann–Pick, Type C1 Mouse Model Reveal Insights into Disease Mechanisms

Nguyen,

Mohanty,

Yan

et al. 2023

J. Proteome Res.

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“…The MSstats family is applicable to a wide variety of data acquisitions, including DDA, DIA, SRM, and parallel reaction monitoring (PRM) experiments ( MSstats ), as well as DDA acquisitions labeled with tandem mass tags (TMTs) ( MSstatsTMT ). The packages help answer question regarding relative changes in the abundance of peptides and proteins ( MSstats / MSstatsTMT ), or relative changes in post-translational modifications (PTMs) ( MSstatsPTM ) . − …”

Section: Introductionmentioning

confidence: 99%

“…Additionally, MSstatsTMT includes optional functionality to adjust model variance through Empirical Bayes Moderation, and MSstatsPTM includes statistical methods to remove confounding between changes in abundance of the PTM and changes in abundance of the unmodified protein . The MSstats packages have been proven to effectively analyze the output of many different experimental designs, e.g., within the MassIVE.quant platform …”

Section: Introductionmentioning

confidence: 99%

“…8 Additionally, MSstatsTMT includes optional functionality to adjust model variance through Empirical Bayes Moderation, 16 and MSstatsPTM includes statistical methods to remove confounding between changes in abundance of the PTM and changes in abundance of the unmodified protein. 7 The MSstats packages have been proven to effectively analyze the output of many different experimental designs, e.g., within the MassIVE.quant platform. 17 While MSstats is an effective tool for quantitative proteomic analysis, so far the methods have only been implemented as coding packages in R, requiring users to have coding skills in order to utilize them effectively.…”

Section: ■ Introductionmentioning

confidence: 99%

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MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

et al. 2023

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Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/ MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).

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A Causal Model of Ion Interference Enables Assessment and Correction of Ratio Compression in Multiplex Proteomics

Madern,

Reiter,

Stanek

et al. 2024

Molecular & Cellular Proteomics

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MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Cited by 14 publications

References 41 publications

Comparative Hippocampal Proteome and Phosphoproteome in a Niemann–Pick, Type C1 Mouse Model Reveal Insights into Disease Mechanisms

Comparative Hippocampal Proteome and Phosphoproteome in a Niemann–Pick, Type C1 Mouse Model Reveal Insights into Disease Mechanisms

MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments

A Causal Model of Ion Interference Enables Assessment and Correction of Ratio Compression in Multiplex Proteomics

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