Rapid and in‐depth proteomic profiling of small extracellular vesicles for ultralow samples
Abstract: The integration of robust single‐pot, solid‐phase‐enhanced sample preparation with powerful liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) is routinely used to define the extracellular vesicle (EV) proteome landscape and underlying biology. However, EV proteome studies are often limited by sample availability, requiring upscaling cell cultures or larger volumes of biofluids to generate sufficient materials. Here, we have refined data independent acquisition (DIA)‐based MS analysis of EV proteome by optimizing both protein enzymatic digestion and chromatography gradient length (ranging from 15 to 44 min). Our short 15 min gradient length can reproducibly quantify 1168 (from as little as 500 pg of EV peptides) to 3882 proteins groups (from 50 ng peptides), including robust quantification of 22 core EV marker proteins. Compared to data‐dependent acquisition, DIA achieved significantly greater EV proteome coverage and quantification of low abundant protein species. Moreover, we have achieved optimal magnetic bead‐based sample preparation tailored to low quantities of EVs (0.5 to 1 µg protein) to obtain sufficient peptides for MS quantification of 1908–2340 protein groups. We demonstrate the power and robustness of our pipeline in obtaining sufficient EV proteomes granularity of different cell sources to ascertain known EV biology. This underscores the capacity of our optimised workflow to capture precise and comprehensive proteome of EVs, especially from ultra‐low sample quantities (sub‐nanogram), an important challenge in the field where obtaining in‐depth proteome information is essential.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Rapid and in‐depth proteomic profiling of small extracellular vesicles for ultralow samples ; day:03 ; month:10 ; year:2023 ; extent:12
Proteomics ; (03.10.2023) (gesamt 12)
- Creator
- DOI
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10.1002/pmic.202300211
- URN
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urn:nbn:de:101:1-2023100315403916409122
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:01 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Cross, Jonathon
- Rai, Alin
- Fang, Haoyun
- Claridge, Bethany
- Greening, David
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