“…2 ). Here, the observed ratio is 5.00 ± 1.37, 1.97 ± 0.76, 1.05 ± 0.34, which is close to theoretical ratio as 5:2:1) [1] , suggesting accurate quantification as expected. Fig.…”
Section: Methods Detailssupporting
confidence: 85%
“… 3. Procedures [ 1 , 2 ] 3.1. Mobile phase A (MPA) is made up of 0.01% TFA, 0.585% acetic acid, 2.5% IPA, and 5% ACN in water.…”
“…2 ). Here, the observed ratio is 5.00 ± 1.37, 1.97 ± 0.76, 1.05 ± 0.34, which is close to theoretical ratio as 5:2:1) [1] , suggesting accurate quantification as expected. Fig.…”
Section: Methods Detailssupporting
confidence: 85%
“… 3. Procedures [ 1 , 2 ] 3.1. Mobile phase A (MPA) is made up of 0.01% TFA, 0.585% acetic acid, 2.5% IPA, and 5% ACN in water.…”
“…Thus, molecular weight-based fractionation methods such as gel-eluted liquid fraction entrapment electrophoresis (GELFrEE), , passively eluting proteins from polyacrylamide gels as intact species for MS (PEPPI-MS), , and size-exclusion chromatography (SEC) − may be considered for prefractionation so that single NCEs or more targeted SNCEs may be used for improved proteoform identification in a narrow MW range for a better balance between quantification sensitivity and identification confidence. As mentioned previously, the TMT labeling protocol was optimized for quantification of low molecular weight proteoforms; thus, sample preparation to observe larger proteins such as IgG (∼150 kDa) needs to be optimized in the future using MS-compatible detergent (e.g., Azo). ,, In this case, the optimization of NCE used for TMT-labeled larger intact proteoforms may also need to be optimized. In addition, some multidimensional separation approaches (e.g., high-pH/low-pH RPLC) ,− can also be implemented to reduce sample complexity for improved identification while maintaining high quantification sensitivity and accuracy.…”
Section: Discussionmentioning
confidence: 99%
“…Isobaric chemical tag-based quantification has been extended to top-down proteomics for the analysis of standard proteins or simple protein mixtures − but has remained limited with respect to complex samples due to the presence of protein precipitation and side products during the labeling. , To overcome these challenges, our group recently developed and optimized an intact protein-level TMT labeling platform for the quantification of low molecular weight proteoforms (<35 kDa) . Moreover, a systematic evaluation of protein labeling conditions was performed using top-down proteomics, and >90% labeling efficiency was achieved in complex samples. , Konrad et al also applied thiol-directed isobaric labeling (e.g., cysteine labeling) for the quantification of E. coli and combined E. coli /yeast cell lysate using top-down proteomics …”
Section: Introductionmentioning
confidence: 99%
“…17 Moreover, a systematic evaluation of protein labeling conditions was performed using top-down proteomics, and >90% labeling efficiency was achieved in complex samples. 17,18 Konrad et al also applied thiol-directed isobaric labeling (e.g., cysteine labeling) for the quantification of E. coli and combined E. coli/yeast cell lysate using top-down proteomics. 19 Protein quantification using isobaric chemical tag-based labeling relies on the efficient and accurate measurement of low-mass reporter ions generated in MS2 fragmentation; therefore, the selection and optimization of fragmentation techniques is essential to generate reporter ions and protein fragment ions simultaneously.…”
Isobaric chemical tag labeling (e.g., TMT) is a commonly
used approach
in quantitative proteomics, and quantification is enabled through
detection of low-mass reporter ions generated after MS2 fragmentation.
Recently, we have introduced and optimized an intact protein-level
TMT labeling platform that demonstrated >90% labeling efficiency
in
complex samples with top-down proteomics. Higher-energy collisional
dissociation (HCD) is commonly utilized for isobaric tag-labeled peptide
fragmentation because it produces accurate reporter ion intensities
and avoids loss of low mass ions. HCD energies have been optimized
for isobaric tag labeled-peptides but have not been systematically
evaluated for isobaric tag-labeled intact proteins. In this study,
we report a systematic evaluation of normalized HCD fragmentation
energies (NCEs) on TMT-labeled HeLa cell lysate using top-down proteomics.
Our results suggested that reporter ions often result in higher ion
intensities at higher NCEs. Optimal fragmentation of intact proteins
for identification, however, required relatively lower NCE. We further
demonstrated that a stepped NCE scheme with energies from 30% to 50%
resulted in optimal quantification and identification of TMT-labeled
HeLa proteins. These parameters resulted in an average reporter ion
intensity of ∼4E4 and average proteoform spectrum matches (PrSMs)
of >1000 per RPLC-MS/MS run with a 1% false discovery rate (FDR)
cutoff.
Top-down proteomics (TDP) identifies, quantifies, and characterises proteins at the intact proteoform level in complex biological samples to understand proteoform function and cellular mechanisms. However, analysing complex biological samples using TDP is still challenging due to high sample complexity and wide dynamic range. Highresolution separation methods are often applied prior to mass spectrometry (MS) analysis to decrease sample complexity and increase proteomics throughput. These separation methods, however, may not be efficient enough to characterise low abundance intact proteoforms in complex samples. As such, multidimensional separation techniques (combination of two or more separation methods with high orthogonality) have been developed and applied that demonstrate improved separation resolution and more comprehensive identification in TDP. A suite of multidimensional separation methods that couple various types of liquid chromatography (LC), capillary electrophoresis (CE), and/or gel electrophoresis-based separation approaches have been developed and applied in TDP to analyse complex biological samples. Here, we reviewed multidimensional separation strategies employed for TDP, summarised current applications, and discussed the gaps that may be addressed in the future.
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