Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Fröhlich, Klemens; Brombacher, Eva; Fahrner, Matthias; Vogele, Daniel; Kook, Lucas; Pinter, Niko; Bronsert, Peter; Timme-Bronsert, Sylvia; Schmidt, Alexander; Baerenfaller, Katja; Kreutz, Clemens; Schilling, Oliver

doi:10.1038/s41467-022-30094-0

Cited by 65 publications

(84 citation statements)

References 65 publications

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“…To benchmark the MSFragger-DIA algorithm and evaluate the actual false discovery rates of the entire workflow, we used the dataset from Frohlich et al [56], consisting of four experiments: “Lymphnode”, “1-25”, “1-12”, and “1-06”. The samples used in the first experiment contained peptides from H. sapiens only.…”

Section: Resultsmentioning

confidence: 99%

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One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Teo

Kong

et al. 2022

Preprint

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Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. We have integrated MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data dependent acquisition (DDA), or both data types combined. We compared MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, and in silico library-based DIA-NN and MaxDIA. We demonstrated the fast and sensitive performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

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Section: Resultsmentioning

confidence: 99%

“…The dataset published by Frohlich et al [56] was used to estimate the actual FDR. The Spectronaut (version 15) directDIA results were used as provided by the authors.…”

Section: Methodsmentioning

confidence: 99%

One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Teo

Kong

et al. 2022

Preprint

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“…We focused our benchmark on comparing the statistical modeling methods using three different scores, while we fixed the pre-processing steps. However, there are other equally or even more important parameters of a protein quantification pipeline (Fröhlich et al 2022). One of them is the normalization of the intensities within the samples to remove systematic differences (Pursiheimo et al 2015).…”

Section: Resultsmentioning

confidence: 99%

prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

Wolski

Nanni

Grossmann

et al. 2022

Preprint

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Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. Nevertheless, there is a need for a flexible and easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures. The prolfqua package can model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. It integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The application programmer interface strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis easy and exciting. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard dataset. Finally, we show that the implemented methods allow sensitive and specific differential expression analysis. The prolfqua R package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT licence, and runs on all platforms supported by the R free software environment for statistical computing and graphics.

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“…To corroborate the applicability of the TMTpro labelling approach for quantitative proteomics with our instrumental setting, we performed a TMTpro-16plex benchmark analysis. Interspecies titration series are a commonly used approach to corroborate the applicability of quantitative proteomics strategies [ 42 – 45 ]. Therefore, we made use of this multi-species approach and analysed a benchmark dataset consisting of 16 TMT-labelled samples representing HEK- E.coli peptide mixtures of four defined ratios (Sup.…”

Section: Resultsmentioning

confidence: 99%

“…However, it should be noted that the described benchmark dataset only tested protein quantification for fold changes bigger than 2.7 while fold changes in biological TMT-datasets could be considerably smaller. The focus on elevated quantitative alterations in the benchmark dataset is in line with common practice in the field of proteomics [ 42 – 45 , 49 ].…”

Section: Resultsmentioning

confidence: 99%

Proteome alterations during clonal isolation of established human pancreatic cancer cell lines

Bernhard

Feilen

Rogg

et al. 2022

Cell. Mol. Life Sci.

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Clonal isolation is an integral step of numerous workflows in genome editing and cell engineering. It comprises the isolation of a single progenitor cell from a defined cell line population with subsequent expansion to obtain a monoclonal cell population. This process is associated with transient loss of cell–cell contacts and absence of a multicellular microenvironment. Previous studies have revealed transcriptomic changes upon clonal isolation with cell line specific extent. Since transcriptome alterations are only partially reflected on the proteome level, we sought to investigate the impact of clonal isolation on the cellular proteome to a depth of > 6000 proteins in three established pancreatic cancer cell lines. We show that clonal isolation does have an impact on the cellular proteome, however, with cell line specific extent, affecting different biological processes, and also depending on the isolation method. We demonstrate a different impact of clonal isolation on mesenchymal- and epithelial-derived cell lines mainly affecting cell proliferation, metabolism, cell adhesion and cellular stress. The results bear relevance to the field of genomic editing and cell engineering and highlight the need to consider the impact of clonal isolation when interpreting data stemming from experiments that include this step.

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Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Cited by 65 publications

References 65 publications

One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis

Proteome alterations during clonal isolation of established human pancreatic cancer cell lines

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