期刊
ACS OMEGA
卷 6, 期 19, 页码 12660-12666出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.1c00776
关键词
-
资金
- NIH [U24 DK112349, U01 CA214116, U01 CA227544]
- Department of Energy [DE-AC05-76RL0 1830]
Isobaric labeling using TMT reagents allows for sample multiplexing in LC-MS/MS, but achieving robust quantification requires careful control of labeling reactions. Factors leading to poor reactions are not fully understood, but controlling sample pH and implementing multiple quality assessment methods can lead to successful TMT labeling for large-scale quantitative studies.
Isobaric labeling via tandem mass tag (TMT) reagents enables sample multiplexing prior to LC-MS/MS, facilitating high-throughput large-scale quantitative proteomics. Consistent and efficient labeling reactions are essential to achieve robust quantification; therefore, embedded in our clinical proteomic protocol is a quality control (QC) sample that contains a small aliquot from each sample within a TMT set, referred to as Mixing QC. This Mixing QC enables the detection of TMT labeling issues by LC-MS/MS before combining the full samples to allow for salvaging of poor TMT labeling reactions. While TMT labeling is a valuable tool, factors leading to poor reactions are not fully studied. We observed that relabeling does not necessarily rescue TMT reactions and that peptide samples sometimes remained acidic after resuspending in 50 mM HEPES buffer (pH 8.5), which coincided with low labeling efficiency (LE) and relatively low median reporter ion intensities (MRIIs). To obtain a more resilient TMT labeling procedure, we investigated LE, reporter ion missingness, the ratio of mean TMT set MRII to individual channel MRII, and the distribution of log 2 reporter ion ratios of Mixing QC samples. We discovered that sample pH is a critical factor in LE, and increasing the buffer concentration in poorly labeled samples before relabeling resulted in the successful rescue of TMT labeling reactions. Moreover, resuspending peptides in 500 mM HEPES buffer for TMT labeling resulted in consistently higher LE and lower missing data. By better controlling the sample pH for labeling and implementing multiple methods for assessing labeling quality before combining samples, we demonstrate that robust TMT labeling for large-scale quantitative studies is achievable.
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