{"doi":"10.1002/pmic.202100243","title":"29‐Plex tandem mass tag mass spectrometry enabling accurate quantification by interference correction","abstract":"Tandem mass tag (TMT) mass spectrometry is a mainstream isobaric chemical labeling strategy for profiling proteomes. Here we present a 29-plex TMT method to combine the 11-plex and 18-plex labeling strategies. The 29-plex method was examined with a pooled sample composed of 1×, 3×, and 10× Escherichia coli peptides with 100× human background peptides, which generated two E. coli datasets (TMT11 and TMT18), displaying the distorted ratios of 1.0:1.7:4.2 and 1.0:1.8:4.9, respectively. This ratio compression from the expected 1:3:10 ratios was caused by co-isolated TMT-labeled ions (i.e., noise). Interestingly, the mixture of two TMT sets produced MS/MS spectra with unique features for the noise detection: (i) in TMT11-labeled spectra, TMT18-specific reporter ions (e.g., 135N) were shown as the noise; (ii) in TMT18-labeled spectra, the TMT11/TMT18-shared reporter ions (e.g., 131C) typically exhibited higher intensities than TMT18-specific reporter ions, due to contaminated TMT11-labeled ions in these shared channels. We further estimated the noise levels contributed by both TMT11- and TMT18-labeled peptides, and corrected reporter ion intensities in every spectrum. Finally, the anticipated 1:3:10 ratios were largely restored. This strategy was also validated using another 29-plex sample with 1:5 ratios. Thus the 29-plex method expands the TMT throughput and enhances the quantitative accuracy.","journal":"PROTEOMICS","year":2022,"id":249965,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":27,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8964,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":498250,"name":"Suresh Poudel","orcid":null,"position":1,"is_corresponding":false},{"id":620520,"name":"David Vanderwall","orcid":"0000-0002-0206-0053","position":2,"is_corresponding":false},{"id":891306,"name":"Dong Geun Lee","orcid":"0000-0003-3668-0485","position":3,"is_corresponding":false},{"id":94638,"name":"Yuxin Li","orcid":"0000-0001-9739-9860","position":4,"is_corresponding":false},{"id":97162,"name":"Junmin Peng","orcid":"0000-0003-0472-7648","position":5,"is_corresponding":false},{"id":862449,"name":"Huan Sun","orcid":null,"position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T00:24:28.242323Z","pmid":"35723178","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}