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PXD012703

PXD012703 is an original dataset announced via ProteomeXchange.

Dataset Summary
TitleTMT labeling for the masses: A robust and cost-efficient, in-solution labeling strategy
DescriptionIsobaric stable isotope labeling using, for example, tandem mass tags (TMTs) is increasingly being applied for large-scale proteomic studies. Experiments focusing on proteoform analysis in drug time course or perturbation studies or in large patient cohorts greatly benefit from the reproducible quantification of single peptides across samples. However, such studies often require labeling of hundreds of micrograms of peptides such that the cost for labeling reagents represents a major contribution to the overall cost of an experiment. Here, we describe and evaluate a robust and cost-effective protocol for TMT labeling that reduces the quantity of required labeling reagent by a factor of eight and achieves complete labeling. Under- and over-labeling of peptides derived from complex digests of tissues and cell lines were systematically evaluated using peptide quantities of between 12.5 and 800�?g and TMT-to-peptide ratios (wt/wt) ranging from 8:1 to 1:2 at different TMT and peptide concentrations. When reaction volumes were reduced to maintain TMT and peptide concentrations of at least 10�mM and 2�g/L, respectively, TMT-to-peptide ratios as low as 1:1 (wt/wt) resulted in labeling efficiencies of >�99�% and excellent intra- and inter-laboratory reproducibility. The utility of the optimized protocol was further demonstrated in a deep-scale proteome and phosphoproteome analysis of patient-derived xenograft tumor tissue benchmarked against the labeling procedure recommended by the TMT vendor. Finally, we discuss the impact of labeling reaction parameters for N-hydroxysuccinimide ester-based chemistry and provide guidance on adopting efficient labeling protocols for different peptide quantities.
HostingRepositoryPRIDE
AnnounceDate2024-10-22
AnnouncementXMLSubmission_2024-10-22_04:52:50.888.xml
DigitalObjectIdentifier
ReviewLevelPeer-reviewed dataset
DatasetOriginOriginal dataset
RepositorySupportUnsupported dataset by repository
PrimarySubmitterJana Zecha
SpeciesList scientific name: Mus musculus (Mouse); NCBI TaxID: 10090; scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList(R)-5-oxo-1; 2-pyrrolidone-5-carboxylic acid (Gln); monohydroxylated residue; acetylated residue; iodoacetamide derivatized residue; deamidated residue
InstrumentOrbitrap Fusion Lumos; Q Exactive HF-X; Q Exactive Plus
Dataset History
RevisionDatetimeStatusChangeLog Entry
02019-02-14 02:06:58ID requested
12019-04-12 12:13:31announced
22024-10-22 04:52:59announced2024-10-22: Updated project metadata.
Publication List
10.1074/mcp.tir119.001385;
Zecha J, Satpathy S, Kanashova T, Avanessian SC, Kane MH, Clauser KR, Mertins P, Carr SA, Kuster B, TMT Labeling for the Masses: A Robust and Cost-efficient, In-solution Labeling Approach. Mol Cell Proteomics, 18(7):1468-1478(2019) [pubmed]
Keyword List
ProteomeXchange project tag: deep learning, benchmarking, machine learning, immunopeptidomics
curator keyword: Technical, Biomedical
submitter keyword: stable isotope labeling,Tandem Mass Tags, NHS ester chemistry, labeling efficiency
Contact List
Bernhard Kuster
contact affiliationTUM
contact emailkuster@tum.de
lab head
Jana Zecha
contact affiliationTechnical University of Munich, Freising, Germany
contact emailjana.zecha@tum.de
dataset submitter
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