Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing
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Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing. / Koenig, Claire; Martinez-Val, Ana; Franciosa, Giulia; Olsen, Jesper V.
In: Proteomics, Vol. 22, No. 19-20, 2100245, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing
AU - Koenig, Claire
AU - Martinez-Val, Ana
AU - Franciosa, Giulia
AU - Olsen, Jesper V.
N1 - Funding Information: Work at the NNF CPR is funded by a donation from the NNF (NNF14CC0001). This work was funded by the European Union's Horizon 2020 research and innovation program under grant agreement EPIC‐XS‐823839 and the Marie Sklodowska‐Curie grant agreement No. 861389. Publisher Copyright: © 2022 The Authors. Proteomics published by Wiley-VCH GmbH.
PY - 2022
Y1 - 2022
N2 - In large-scale quantitative mass spectrometry (MS)-based phosphoproteomics, isobaric labeling with tandem mass tags (TMTs) coupled with offline high-pH reversed-phase peptide chromatographic fractionation maximizes depth of coverage. To investigate to what extent limited sample amounts affect sensitivity and dynamic range of the analysis due to sample losses, we benchmarked TMT-based fractionation strategies against single-shot label-free quantification with spectral library-free data independent acquisition (LFQ-DIA), for different peptide input per sample. To systematically examine how peptide input amounts influence TMT-fractionation approaches in a phosphoproteomics workflow, we compared two different high-pH reversed-phase fractionation strategies, microflow (MF) and stage-tip fractionation (STF), while scaling the peptide input amount down from 12.5 to 1 μg per sample. Our results indicate that, for input amounts higher than 5 μg per sample, TMT labeling, followed by microflow fractionation (MF) and phospho-enrichment, achieves the deepest phosphoproteome coverage, even compared to single shot direct-DIA analysis. Conversely, STF of enriched phosphopeptides (STF) is optimal for lower amounts, below 5 μg/peptide per sample. As a result, we provide a decision tree to help phosphoproteomics users to choose the best workflow as a function of sample amount.
AB - In large-scale quantitative mass spectrometry (MS)-based phosphoproteomics, isobaric labeling with tandem mass tags (TMTs) coupled with offline high-pH reversed-phase peptide chromatographic fractionation maximizes depth of coverage. To investigate to what extent limited sample amounts affect sensitivity and dynamic range of the analysis due to sample losses, we benchmarked TMT-based fractionation strategies against single-shot label-free quantification with spectral library-free data independent acquisition (LFQ-DIA), for different peptide input per sample. To systematically examine how peptide input amounts influence TMT-fractionation approaches in a phosphoproteomics workflow, we compared two different high-pH reversed-phase fractionation strategies, microflow (MF) and stage-tip fractionation (STF), while scaling the peptide input amount down from 12.5 to 1 μg per sample. Our results indicate that, for input amounts higher than 5 μg per sample, TMT labeling, followed by microflow fractionation (MF) and phospho-enrichment, achieves the deepest phosphoproteome coverage, even compared to single shot direct-DIA analysis. Conversely, STF of enriched phosphopeptides (STF) is optimal for lower amounts, below 5 μg/peptide per sample. As a result, we provide a decision tree to help phosphoproteomics users to choose the best workflow as a function of sample amount.
KW - high-pH fractionation
KW - isobaric labeling
KW - phosphoproteomics
KW - scale-down
KW - tandem mass tags
U2 - 10.1002/pmic.202100245
DO - 10.1002/pmic.202100245
M3 - Journal article
C2 - 35713889
AN - SCOPUS:85133167809
VL - 22
JO - Proteomics
JF - Proteomics
SN - 1615-9853
IS - 19-20
M1 - 2100245
ER -
ID: 312770677