ArticleAnalytical chemistry2023
Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion.
Article in Analytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
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- Integrative proteomics and metabolomics advance early cancer detection and targeted therapy with emerging technologies and clinical applications.Discover oncology · 2026Review
- Female reproductive tract-on-a-chip for selecting sperm with ultra-low DNA fragmentation index.Microsystems & nanoengineering · 2026Article
- Unveiling low-abundance ACE inhibitory peptides in not-from-concentrate orange juice processed by high-pressure processing and pasteurization.NPJ science of food · 2025Article
- Relevance of proteomics and metabolomics approaches to overview the tumorigenesis and better management of cancer.3 Biotech · 2025Review
- HemizygousThe application of clinical genetics · 2025Article
- PATL2 mutations affect human oocyte maternal mRNA homeostasis and protein interactions in cell cycle regulation.Cell & bioscience · 2024Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
Abstract
Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and
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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.