Dataset Information

Quantitative proteomics of CUL2-deficienct hMPCs

ABSTRACT: Data Independent Acquisition (DIA) quantitative proteomics of CUL2+/+ and CUL2-/- hMPCs.

ORGANISM(S): Homo Sapiens

SUBMITTER: Weiqi Zhang

PROVIDER: PXD042924 | iProX | Tue Jun 13 00:00:00 BST 2023

REPOSITORIES: iProX

ACCESS DATA

Similar Datasets

Project description:Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis. </br></br> Serum metabolome of 40 age-related macular degeneration patients and 20 control samples was analyzed using untargeted mass spectrometry. We used data dependent acquisition data to build a MS/MS spectral assay library for more than 1,000 compounds and performed targeted extraction of MS2 ion chromatograms from data independent acquisition analysis.

Project description:Multiple primary cancers (MPC) refer to the occurrence of two or more independent primary malignant tumors in the same patient, either simultaneously or metachronously. Their clinical diagnosis and differential diagnosis are challenging, treatment strategies are complex, and the incidence has been increasing in recent years. However, there is still a lack of potential biomarkers that can be used for early identification and prognosis assessment, which has become a key bottleneck in current research. This study employed Oxford Nanopore Technologies (ONT) long-read transcriptome sequencing and data-independent acquisition (DIA) proteomics to perform integrated multi-omics analysis of Whole blood samples from patients with only primary lung cancer (OPLC), lung cancer with MPC, and healthy controls (HC), systematically characterizing the molecular features of MPC at both transcript and protein levels. The results showed that MPC patients exhibited significantly increased transcript complexity, with the numbers of differentially expressed genes (DEGs), differentially expressed transcripts (DETs), and differential transcript usage (DTU) events being substantially higher than those in OPLC and HC groups. Multiple genes displayed rich isoform diversity. Functional enrichment analysis indicated that MPC-specific molecules were significantly enriched in immune- and inflammation-related pathways such as NF-κB, NOD-like receptor, Toll-like receptor, and TNF signaling. By integrating gene, transcript, and protein expression profiles, several core molecules (e.g., ATP6AP2, APMAP, CIAO2A, and ITGB2) with consistent expression across multiple regulatory levels were identified, all showing significant and consistent alterations in MPC. This study reveals the molecular characteristics of transcript isoform complexity in the peripheral blood of MPC patients through long-read sequencing combined with proteomics, providing important theoretical insights for understanding the mechanisms of MPC and identifying potential targets.

Dataset Information

Quantitative proteomics of CUL2-deficienct hMPCs

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets