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Spike-in benchmark study : Ubiquitination, Label-free experiments

ABSTRACT: Post-translational modifications (PTMs) play a crucial role in dynamically altering proteomes and are key regulators for a multitude of complex processes in eukaryotic cells. Affinity enrichment followed by quantitative Mass Spectrometry (MS) is currently the most successful approach to systematically identify PTMs and quantify their relative abundance with great depth and throughput. Relative changes in PTM site abundance, quantified by MS, are traditionally modeled with a two-sample t-test, comparing either the mean intensity or the modified to unmodified ratio of representative peptides. However, the interpretation of changes at a single modification site in a typical bottom-up proteomics workflow is complicated by sparse coverage and confounded by both changes in overall protein abundance and variability in enrichment efficiency. We're proposing an alternative statistical approach to model relative abundance changes for modification sites, which explicitly incorporates major sources of variability and confounding factors present in PTM experiments. Moreover, the general statistical framework underlying the proposed approach allows for natural extensions to complex experimental designs including multiple conditions and multiple batches. We plan to evaluate our proposed approach by comparing it to the results of a naive t-test using computer simulations and a custom-designed benchmark experiment.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Escherichia Coli (ncbitaxon:562) Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Olga Vitek Meena Choi

PROVIDER: MSV000088971 | MassIVE | Wed Mar 02 07:52:00 GMT 2022

REPOSITORIES: MassIVE

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Project description:In the era of open-modification search engines, more post-translational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis and ageing. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by the inherently low abundance of many PTMs and the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM, and at the peptidoform level. The latter is important for validating significantly found PTMs. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might the other PTM driving the perceived differential abundance. Our workflows can flag both Differential Peptidoform (PTM) Abundance (DPA) and Differential Peptidoform (PTM) Usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA), and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform (PTM) intensities, while for DPU, we correct for parent protein abundance by an intermediate normalisation step which calculates the log2-ratio of the peptidoform (PTM) intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it on datasets with known ground truth, as well as on biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art method, MSstatsPTM. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

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Spike-in benchmark study : Ubiquitination, Label-free experiments

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