Proteomic comparison between non-purified cerebrospinal fluid and cerebrospinal fluid-derived extracellular vesicles from patients with Alzheimer's, Parkinson's and Lewy body dementia.
Yael HirschbergNatalia Valle-TamayoOriol Dols-IcardoSebastiaan EngelborghsBart BuelensRoosmarijn E. VandenbrouckeYannick VermeirenKurt BoonenInge MertensPublished in: Journal of extracellular vesicles (2023)
Dementia is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases. The most common neurodegenerative disorders are Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). With this study, we took an in-depth look at the proteome of the (non-purified) cerebrospinal fluid (CSF) and the CSF-derived extracellular vesicles (EVs) of AD, PD, PD-MCI (Parkinson's disease with mild cognitive impairment), PDD and DLB patients analysed by label-free mass spectrometry. This has led to the discovery of differentially expressed proteins that may be helpful for differential diagnosis. We observed a greater number of differentially expressed proteins in CSF-derived EV samples (N = 276) compared to non-purified CSF (N = 169), with minimal overlap between both datasets. This finding suggests that CSF-derived EV samples may be more suitable for the discovery phase of a biomarker study, due to the removal of more abundant proteins, resulting in a narrower dynamic range. As disease-specific markers, we selected a total of 39 biomarker candidates identified in non-purified CSF, and 37 biomarker candidates across the different diseases under investigation in the CSF-derived EV data. After further exploration and validation of these proteins, they can be used to further differentiate between the included dementias and may offer new avenues for research into more disease-specific pharmacological therapeutics.
Keyphrases
- mild cognitive impairment
- cerebrospinal fluid
- cognitive decline
- mass spectrometry
- label free
- end stage renal disease
- cognitive impairment
- ejection fraction
- chronic kidney disease
- machine learning
- newly diagnosed
- peritoneal dialysis
- high throughput
- rna seq
- deep learning
- electronic health record
- capillary electrophoresis
- patient reported outcomes