Mag-Net: Rapid enrichment of membrane-bound particles enables high coverage quantitative analysis of the plasma proteome
ABSTRACT Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ∼1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a “liquid biopsy,” while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer’s disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson’s disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 04. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wu, Christine C. [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.06.10.544439 |
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| 520 | |a ABSTRACT Membrane-bound particles in plasma are composed of exosomes, microvesicles, and apoptotic bodies and represent ∼1-2% of the total protein composition. Proteomic interrogation of this subset of plasma proteins augments the representation of tissue-specific proteins, representing a “liquid biopsy,” while enabling the detection of proteins that would otherwise be beyond the dynamic range of liquid chromatography-tandem mass spectrometry of unfractionated plasma. We have developed an enrichment strategy (Mag-Net) using hyper-porous strong-anion exchange magnetic microparticles to sieve membrane-bound particles from plasma. The Mag-Net method is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to a quantitative data-independent mass spectrometry analytical strategy, we demonstrate that we can collect results for >37,000 peptides from >4,000 plasma proteins with high precision. Using this analytical pipeline on a small cohort of patients with neurodegenerative disease and healthy age-matched controls, we discovered 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer’s disease dementia (ADD) from those without ADD. Our method also discovered 310 proteins that were different between Parkinson’s disease and those with either ADD or healthy cognitively normal individuals. Using machine learning we were able to distinguish between ADD and not ADD with a mean ROC AUC = 0.98 ± 0.06. | ||
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| 700 | 1 | |a Park, Jea |e verfasserin |4 aut | |
| 700 | 1 | |a Plubell, Deanna |e verfasserin |4 aut | |
| 700 | 1 | |a Sanders, Justin A. |e verfasserin |4 aut | |
| 700 | 1 | |a Naicker, Previn |e verfasserin |4 aut | |
| 700 | 1 | |a Govender, Ireshyn |e verfasserin |4 aut | |
| 700 | 1 | |a Buthelezi, Sindisiwe |e verfasserin |4 aut | |
| 700 | 1 | |a Stoychev, Stoyan |e verfasserin |4 aut | |
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| 700 | 1 | |a Huang, Eric |e verfasserin |4 aut | |
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| 700 | 1 | |a Riffle, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Hoofnagle, Andrew N. |e verfasserin |4 aut | |
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| 700 | 1 | |a Montine, Thomas J. |e verfasserin |0 (orcid)0000-0002-1346-2728 |4 aut | |
| 700 | 1 | |a MacCoss, Michael J. |e verfasserin |0 (orcid)0000-0003-1853-0256 |4 aut | |
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