MassIVE MSV000098369

Imported Reanalysis Dataset Public PXD024893

Online-2D nanoLC-MS for crude serum proteome profiling: assessing sample preparation impact on proteome composition

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Description

Although current LC-MS technology permits scientists to efficiently screen clinical samples in translational research, e.g. steroids, biogenic amines and even plasma or serum proteomes, in a daily routine, maintaining the balance between throughput and analytical depth is still a limiting factor. A typical approach to enhance the proteome depth is employing offline 2-dimensional (2D) fractionation techniques before nanoLC-MS/MS analysis. These additional sample preparation steps usually require extensive sample manipulation, which could result in sample alteration and sample loss. The consequent results variability increase the risk of false discovery, regardless of time-intensive sample preparation workload. Here we present and compare 1D-nanoLC-MS with an automated online-2D high-pH RP x low pH RP separation method for deep proteome profiling using a nanoLC system coupled to a high-resolution accurate-mass mass spectrometer. The proof-of-principle study permitted the identification of ca. 500 proteins with ~10,000 peptides in 15 enzymatically digested crude serum samples collected from healthy donors in 3 laboratories across Europe. The developed method identified 60% more peptides in comparison with conventional 1D nanoLC-MS/SM analysis with ca. 4 times lower throughput while retaining the quantitative information. Serum sample preparation artifacts were revealed by applying unsupervised classification techniques, and, therefore, must be taken into account while planning multicentric biomarker discovery and validation studies. Overall, this novel method reduces sample complexity and boosts the number of peptide and protein identifications without the need for extra sample handling procedures for samples equivalent to less than 1 µL of blood, which expands the space for potential biomarker discovery by looking deeper into the composition of biofluids. [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Peptide fractionation ; Blood ; Nanolc-ms ; 1d nanolc-ms ; Proteome profiling ; Crude serum ; High-resolution accurate mass-spectrometry ; Online-2d nanolc-ms ; DatasetType:Proteomics

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Principal Investigators:
(in alphabetical order) 		Alexander Boychenko, Thermo Fisher Scientific, N/A
Submitting User: ccms
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Number of distinct conditions across all analyses (original submission and reanalyses) associated with this dataset.

Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

Distinct replicate labels are counted across all files submitted in the "Metadata" category having a "BioReplicate" or "Replicate" column in this dataset.

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Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

The technical replicate count is defined as the maximum number of times any one distinct combination of condition and biological replicate was analyzed across all files submitted in the "Metadata" category. In the case of fractionated experiments, only the first fraction is considered.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.