PXD047857 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | NEAT PLASMA PROTEOMICS: GETTING THE BEST OUT OF THE WORST |
Description | Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using Strap and analysed on Evosep and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searched using DIANN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhances protein identification in neat plasma samples. Globally, out optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used. |
HostingRepository | PRIDE |
AnnounceDate | 2024-08-14 |
AnnouncementXML | Submission_2024-08-14_06:54:52.690.xml |
DigitalObjectIdentifier | |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Unsupported dataset by repository |
PrimarySubmitter | Chiara guerrera |
SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
ModificationList | monohydroxylated residue; iodoacetamide derivatized residue |
Instrument | timsTOF Pro |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2023-12-15 07:40:49 | ID requested | |
⏵ 1 | 2024-08-14 06:54:53 | announced | |
Publication List
10.1186/s12014-024-09477-6; |
Metatla I, Roger K, Chhuon C, Ceccacci S, Chapelle M, Pierre-Olivier Schmit, Demichev V, Guerrera IC, Neat plasma proteomics: getting the best out of the worst. Clin Proteomics, 21(1):22(2024) [pubmed] |
Keyword List
submitter keyword: Plasma, DIA-NN, DIA, Extracellular Vesicles, timsTOF Pro |
Contact List
Ida Chiara Guerrera |
contact affiliation | Head of the Necker Proteomics Faculty of Medecine, University Paris Cité SFR Necker INSERM US24 160 rue de Vaugirard | 75015 Paris Tél. 01 40 61 54 67 |
contact email | chiara.guerrera@inserm.fr |
lab head | |
Chiara guerrera |
contact affiliation | Necker proteomics, INSERM |
contact email | chiara.guerrera@inserm.fr |
dataset submitter | |
Full Dataset Link List
Dataset FTP location
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PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD047857
- Label: PRIDE project
- Name: NEAT PLASMA PROTEOMICS: GETTING THE BEST OUT OF THE WORST