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pmid:28195392

Relative protein quantification and accessible biology in lung tumor proteomes from four LC-MS/MS discovery platforms.

Paul A Stewart · Bin Fang · Robbert J C Slebos · Guolin Zhang · Adam L Borne · Katherine Fellows · Jamie K Teer · Y Ann Chen · Eric Welsh · Steven A Eschrich · Eric B Haura · John M Koomen

Proteomics · 2017 doi:10.1002/pmic.201600300

Paper

complete

Experiment

partial

Data

partial

Analysis

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Execution

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Relative protein quantification and accessible biology in lung tumor proteomes from four LC-MS/MS discovery platforms.
doi
10.1002/pmic.201600300
pmid
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Paul A Stewart
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Bin Fang
[2]
Robbert J C Slebos
[3]
Guolin Zhang
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Adam L Borne
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Katherine Fellows
[6]
Jamie K Teer
[7]
Y Ann Chen
[8]
Eric Welsh
[9]
Steven A Eschrich
[10]
Eric B Haura
[11]
John M Koomen
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Proteomics
year
2017
abstract
Discovery proteomics experiments include many options for sample preparation and MS data acquisition, which are capable of creating datasets for quantifying thousands of proteins. To define a strategy that would produce a dataset with sufficient content while optimizing required resources, we compared (1) single-sample LC-MS/MS with data-dependent acquisition to single-sample LC-MS/MS with data-independent acquisition and (2) peptide fractionation with label-free (LF) quantification to peptide fractionation with relative quantification of chemically labeled peptides (sixplex tandem mass tags (TMT)). These strategies were applied to the same set of four frozen lung squamous cell carcinomas and four adjacent tissues, and the overall outcomes of each experiment were assessed. We identified 6656 unique protein groups with LF, 5535 using TMT, 3409 proteins from single-sample analysis with data-independent acquisition, and 2219 proteins from single-sample analysis with data-dependent acquisition. Pathway analysis indicated the number of proteins per pathway was proportional to the total protein identifications from each method, suggesting limited biological bias between experiments. The results suggest the use of single-sample experiments as a rapid tissue assessment tool and digestion quality control or as a technique to maximize output from limited samples and use of TMT or LF quantification as methods for larger amounts of tumor tissue with the selection being driven mainly by instrument time limitations. Data are available via ProteomeXchange with identifiers PXD004682, PXD004683, PXD004684, and PXD005733.
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Across the same eight lung squamous cell carcinoma and adjacent-tissue samples, label-free quantification identified the most proteins (6,656 groups), followed by TMT (5,535), single-sample DIA (3,409), and single-sample DDA (2,219), with pathway coverage largely proportional to total identifications.
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