ArticleProceedings of the National Academy of Sciences of the United States of America2018
IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed, 87 citations in OpenAlex.
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- DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.Nature communications · 2023Article
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- Improvement of Workflows and Assay Reproducibility by The Introduction of "Assay-Ready" Culturing of MDCK Cells for Transport Studies.Pharmaceutical research · 2023Article
- Article
- High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation.Nature protocols · 2023Review
- Comparative Proteomics Analysis of Exosomes Identifies Key Pathways and Protein Markers Related to Breast Cancer Metastasis.International journal of molecular sciences · 2023Article
- Quantitative Proteomics of Human Retinal Pigment Epithelium Reveals Key Regulators for the Pathogenesis of Age-Related Macular Degeneration.International journal of molecular sciences · 2023Article
- MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer's Biomarker Data.Journal of proteome research · 2023Article
- In-depth mapping of protein localizations in whole tissue by micro-scaffold assisted spatial proteomics (MASP).Nature communications · 2022Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (
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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.