MassIVE MSV000088302

Partial Public PXD029531

Increasing the throughput of sensitive proteomics by plexDIA

Description

Mass-spectrometry methods enable high-throughput proteomics with large input samples, but the depth and throughput of protein analysis remain limited for smaller samples. We aimed to increase throughput for analyzing limited samples while achieving high proteome coverage and quantitative accuracy. Thus, we developed a general experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing protein coverage or quantitative accuracy. Specifically, 3-plex nonisobaric labeling of sub-microgram samples increases the number of quantitative protein ratios by about 3-fold, enabling the quantification of over 25,000 protein data points per hour of active gradient on a first-generation Q Exactive instrument. Furthermore, plexDIA increases the consistency of protein detection and quantification across samples and reduces missing data by over 2-fold. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content. The high sensitivity and accuracy of plexDIA detected many classical cell cycle proteins and discovered new ones. These results establish a general framework for increasing the throughput of highly sensitive and quantitative protein analysis. [doi:10.25345/C5PZ71] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: plexDIA ; DIA ; mTRAQ ; Cell cycle ; high-throughput sensitive proteomics ; multiplexing ; nonisobaric labels ; sub-microgram samples

Contact

Principal Investigators:
(in alphabetical order)
Nikolai Slavov, Northeastern University, USA
Submitting User: jderks
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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 peptide sequences (including modified variants or peptidoforms) 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.