Dataset Information

27-Plex Tandem Mass Tag Mass Spectrometry for Profiling Brain Proteome in Alzheimer's Disease.

ABSTRACT: Multiplexed isobaric labeling methods, such as tandem mass tags (TMT), remarkably improve the throughput of quantitative mass spectrometry. Here, we present a 27-plex TMT method coupled with two-dimensional liquid chromatography (LC/LC) for extensive peptide fractionation and high-resolution tandem mass spectrometry (MS/MS) for peptide quantification and then apply the method to profile the complex human brain proteome of Alzheimer's disease (AD). The 27-plex method combines multiplexed capacities of the 11-plex and the 16-plex TMT, as the peptides labeled by the two TMT sets display different mass and hydrophobicity, which can be well separated in LC-MS/MS. We first systematically optimized the protocol for the newly developed 16-plex TMT, including labeling reaction, desalting, and MS conditions, and then directly compared the 11-plex and 16-plex methods by analyzing the same human AD samples. Both methods yielded similar proteome coverage, analyzing >100 000 peptides in >10 000 human proteins. Furthermore, the 11-plex and 16-plex samples were mixed for a 27-plex assay, resulting in more than 8000 protein measurements within the same MS time. The 27-plex results are highly consistent with those of the individual 11-plex and 16-plex TMT analyses. We also used these proteomics data sets to compare the AD brain with the nondementia controls, discovering major AD-related proteins and revealing numerous novel protein alterations enriched in the pathways of amyloidosis, immunity, mitochondrial, and synaptic functions. Overall, our data strongly demonstrate that this new 27-plex strategy is highly feasible for routine large-scale proteomic analysis.

SUBMITTER: Wang Z

PROVIDER: S-EPMC8176402 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundDespite the widespread application of new drug-eluting stents, a considerable portion of patients experience in-stent restenosis (ISR). To date, the pathophysiologic mechanisms of ISR remain poorly understood.MethodsIn this study, we collected plasma samples from ISR patients (n = 29) and non-ISR patients (n = 36) after drug-eluting stent implantation, as well as from healthy controls (HCs) (n = 32). Our goal was to investigate differences in plasma protein profiles using tandem mass tag (TMT) labeling coupled with liquid chromatography and tandem mass spectrometry. The proteomic data were validated by enzyme-linked immunosorbent assay (ELISA). Bioinformatic analyses were conducted to analyze potential pathways and protein-protein interaction (PPI) involved in ISR.ResultsA total of 1,696 proteins were identified, of which 278 differed in protein abundance between non-ISR and HCs, 497 between ISR and HCs, and 387 between ISR and non-ISR, respectively. Bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and PPI, further demonstrated that differentially abundant proteins between ISR and non-ISR are involved in several crucial biological processes and signaling pathways, such as focal adhesion, platelet activation, Rap1 signaling, regulation of actin cytoskeleton, and cholesterol metabolism. Among the identified differentially abundant proteins in ISR, 170 were increased in abundance relative to both non-ISR patients and HCs. Some of these proteins were identified to have critical functions for atherosclerosis development and might be involved in ISR pathology. Among these proteins, 3 proteins with increased abundance including fetuin-B, apolipoprotein C-III (APOC3), and cholesteryl ester transfer protein (CETP) were confirmed by ELISA.ConclusionsThis is the first study provided a comprehensive proteomic profile to understand ISR pathology, which may help identify early diagnostic biomarkers and therapeutic targets.

Dataset Information

27-Plex Tandem Mass Tag Mass Spectrometry for Profiling Brain Proteome in Alzheimer's Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets