Single‐Molecule Two‐Color Coincidence Detection of Unlabeled alpha‐Synuclein Aggregates
Protein misfolding and aggregation into oligomeric and fibrillar structures is a common feature of many neurogenerative disorders. Single‐molecule techniques have enabled characterization of these lowly abundant, highly heterogeneous protein aggregates, previously inaccessible using ensemble averagi...
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Published in | Angewandte Chemie Vol. 135; no. 15; pp. e202216771 - n/a |
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Main Authors | , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Germany
Wiley Subscription Services, Inc
03.04.2023
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Protein misfolding and aggregation into oligomeric and fibrillar structures is a common feature of many neurogenerative disorders. Single‐molecule techniques have enabled characterization of these lowly abundant, highly heterogeneous protein aggregates, previously inaccessible using ensemble averaging techniques. However, they usually rely on the use of recombinantly‐expressed labeled protein, or on the addition of amyloid stains that are not protein‐specific. To circumvent these challenges, we have made use of a high affinity antibody labeled with orthogonal fluorophores combined with fast‐flow microfluidics and single‐molecule confocal microscopy to specifically detect α‐synuclein, the protein associated with Parkinson's disease. We used this approach to determine the number and size of α‐synuclein aggregates down to picomolar concentrations in biologically relevant samples.
Pathological protein aggregates in neurodegenerative disorders are difficult to characterise using current methods. We present a novel single‐molecule detection method to specifically detect and characterise α‐synuclein aggregates at picomolar concentrations. We demonstrate the ability to detect aggregates in biologically relevant samples. |
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Bibliography: | These authors contributed equally to this work. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0044-8249 1521-3757 |
DOI: | 10.1002/ange.202216771 |