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Benchmarking of DDA and plexDIA analysis with SILAC-labelled Hela cell samples

ABSTRACT: SILAC labelled Hela cell lysates were mixed in defined ratios and analysed using both DDA and DIA methods, to serve as a benchmark for the novel plexDIA features of DIA-NN.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Hela Cell

DISEASE(S): Cervix Carcinoma

SUBMITTER: Toman Bortecen

LAB HEAD: Jeroen Krijgsveld

PROVIDER: PXD039580 | Pride | 2023-10-19

REPOSITORIES: Pride

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Benchmarking plexDIA analysis with SILAC-labelled Hela cell samples

Project description:SILAC labelled Hela cell lysates were mixed in defined ratios and analysed using both DDA and DIA methods, to serve as a benchmark for the novel plexDIA features of DIA-NN.

2023-10-19 | PXD039578 | Pride

semi-automated enrichment of AHA-containing newly-synthesized proteins with magnetic alkyne agarose beads

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2023-10-19 | PXD036886 | Pride

Applying plexDIA analysis to newly synthesized proteome samples generated using a semi-automated enrichment protocol

Project description:Samples generated with a newly development semi-automated enrichment protocol for newly synthesized proteins, using different amounts of protein input, were analysed using data-independent acquisition (DIA) and processed with the plexDIA software features of DIA-NN (https://www.nature.com/articles/s41587-022-01389-w).

2023-10-19 | PXD043817 | Pride

Newly synthesized proteome analysis of interferon gamma treated Hela cells

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Project description:Liquid chromatography coupled to tandem mass spectrometry has become the main method for high-throughput identification and quantification of peptides and the inferred proteins. Discovery proteomics commonly employs data-dependent acquisition in combination with spectrum-centric analysis. The accumulation of data generated from thousands of samples by this method has approached saturation coverage of different proteomes. Recently, as a result of technological advances, methods based on data acquisition strategies compatible with peptide-centric scoring have also reached similar proteome coverage in individual runs, and scalability. This is exemplified by SWATH-MS, which combines data-independent acquisition (DIA) with targeted data extraction of groups of transitions uniquely detecting a peptide. As the data matrices generated by these experiments continue to grow with respect to both the number of peptides identified per sample and the number of samples analyzed per study, challenges for error rate control have emerged. Here, we discuss the adaptation of statistical concepts developed for discovery proteomics based on spectrum-centric scoring to large-scale DIA experiments analyzed with peptide-centric scoring strategies, and provide some guidance on their application. We propose that, in order to increase the quality and reproducibility of published proteomic results, well-established confidence criteria should be reported at each level as we progress from spectral evidence to identified or detected peptides and inferred proteins. These confidence criteria should equally be applied to proteomic analyses based on spectrum- and peptide-centric scoring strategies.

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Project description:Raw files, spectral library, DIA-NN and Dinosaur outputs for "Strategies for increasing the depth and throughput of protein analysis by plexDIA"

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