Evidence map›Paper›PMID 34410317›Full record

ArticleBrain : a journal of neurology2022

Imaging protein aggregates in the serum and cerebrospinal fluid in Parkinson's disease.

Evgeniia Lobanova, Daniel Whiten, Francesco S Ruggeri, Christopher G Taylor, Antonina Kouli, Zengjie Xia, Derya Emin, Yu P Zhang, Jeff Y L Lam, Caroline H Williams-Gray and 1 more

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Brain : a journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
4.2field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 1 institution in 2 countries.

Evgeniia LobanovaDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Daniel WhitenDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Francesco S RuggeriDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.ORCID 0000-0002-1232-1907
Christopher G TaylorDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.ORCID 0000-0001-8449-8704
Antonina KouliDepartment of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0PY, UK.ORCID 0000-0001-6553-6154
Zengjie XiaDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.ORCID 0000-0002-5977-9691
Derya EminDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Yu P ZhangDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Jeff Y L LamDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
Caroline H Williams-GrayDepartment of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0PY, UK.
David KlenermanDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK.
University of Cambridge · GB

Funding

Department of HealthEuropean Research Council 669237Medical Research Council MR/R007446/1
6 · The paper itself

Abstract

Aggregation of α-synuclein plays a key role in the development of Parkinson's disease. Soluble aggregates are present not only within human brain but also the CSF and blood. Characterizing the aggregates present in these biofluids may provide insights into disease mechanisms and also have potential for aiding diagnosis. We used two optical single-molecule imaging methods called aptamer DNA-PAINT and single-aggregate confocal fluorescence, together with high-resolution atomic force microscopy for specific detection and characterization of individual aggregates with intermolecular β-sheet structure, present in the CSF and serum of 15 early stage Parkinson's disease patients compared to 10 healthy age-matched controls. We found aggregates ranging in size from 20 nm to 200 nm, in both CSF and serum. There was a difference in aggregate size distribution between Parkinson's disease and control groups with a significantly increased number of larger aggregates (longer than 150 nm) in the serum of patients with Parkinson's disease. To determine the chemical composition of the aggregates, we performed aptamer DNA-PAINT on serum following α-synuclein and amyloid-β immunodepletion in an independent cohort of 11 patients with early stage Parkinson's disease and 10 control subjects. β-Sheet aggregates in the serum of Parkinson's disease patients were found to consist of, on average, 50% α-synuclein and 50% amyloid-β in contrast to 30% α-synuclein and 70% amyloid-β in control serum [the differences in the proportion of these aggregates were statistically significant between diseased and control groups (P = 1.7 × 10-5 for each species)]. The ratio of the number of β-sheet α-synuclein aggregates to β-sheet amyloid-β aggregates in serum extracted using our super-resolution method discriminated Parkinson's disease cases from controls with an accuracy of 98.2% (AUC = 98.2%, P = 4.3 × 10-5). Our data suggest that studying the protein aggregates present in serum can provide information about the disruption of protein homeostasis occurring in Parkinson's disease and warrants further investigation as a potential biomarker of disease.

Indexed as

alpha-SynucleinParkinson DiseaseAmyloid beta-PeptidesBiomarkersBrainHumansProtein Aggregatesalpha-SynucleinAmyloid beta-PeptidesBiomarkersProtein Aggregatesaggregatesamyloid-βearly-disease serum biomarkersParkinson’s diseasesuper-resolution imagingα-synuclein aggregates

Identifiers

PMID34410317
PMCPMC9014748
OpenAlexW3195557027

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.