ArticleAnalytical chemistry2020
Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry.
Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 37 citations in OpenAlex.
- Branched-Chain Amino and Keto Acids Reduce Hepatocyte Lipid Droplet Size and Number via Distinct Proteomic Pathways.Proteomics · 2026Article
- Deep Proteomic Analysis With Machine Learning Identifies Aqueous Humor Biomarkers of ADAMTSL4-associated Congenital Ectopia Lentis.Investigative ophthalmology & visual science · 2026Article
- Transcriptomic and proteomic profiling of piroplasmosis resistance in Yunnan humped cattle.BMC genomics · 2026Article
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- Intermethod Characterization of Commercially Available Extracellular Vesicles as Reference Materials.Biomolecules · 2025Article
- Identification of adrenocorticotropic hormone-specific therapeutic biomarkers in infantile epileptic spasm syndrome using data-independent acquisition mass spectrometry.Clinical proteomics · 2025Article
- Liquid Chromatographic and Mass Spectrometric Methods for Quantitative Proteomic Analysis from Single-Cell and Nanogram-Level Samples.Analytical chemistry · 2025Article
- Identifcation of candidate biomarkers for polyarteritis nodosa using data-independent acquisition mass spectrometry.American journal of translational research · 2025Article
- Challenges of MS-based small extracellular vesicles proteomics.Journal of extracellular vesicles · 2024Review
- Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform.Nature communications · 2023Article
- Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness.Cancer cell · 2023Article
- Optimized data-independent acquisition approach for proteomic analysis at single-cell level.Clinical proteomics · 2022Article
- Coupling suspension trapping-based sample preparation and data-independent acquisition mass spectrometry for sensitive exosomal proteomic analysis.Analytical and bioanalytical chemistry · 2022Article
- COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19.International journal of molecular sciences · 2022Review
- Proteomic characterization of primary and metastatic prostate cancer reveals reduced proteinase activity in aggressive tumors.Scientific reports · 2021Article
- Article
- Evaluation of the Sensitivity and Reproducibility of Targeted Proteomic Analysis Using Data-Independent Acquisition for Serum and Cerebrospinal Fluid Proteins.Journal of proteome research · 2021Article
- Polyphenol Supplementation Reverses Age-Related Changes in Microglial Signaling Cascades.International journal of molecular sciences · 2021Article
- Article
- Proteomic signatures of 16 major types of human cancer reveal universal and cancer-type-specific proteins for the identification of potential therapeutic targets.Journal of hematology & oncology · 2020Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.