Characterising extracellular vesicles from individual low volume cerebrospinal fluid samples, isolated by SmartSEC

Extracellular vesicles (EVs) are suggested to have a role in the progression of neurodegeneration, and are able to transmit pathological proteins from one cell to another. One of the biofluids from which EVs can be isolated is cerebrospinal fluid (CSF). However, so far, few studies have been performed on small volumes of CSF. Since pooling of patient samples possibly leads to the loss of essential individual patient information, and CSF samples are precious, it is important to have efficient techniques for the isolation of EVs from smaller volumes. In this study, the SmartSEC HT isolation kit from System Biosciences has been evaluated for this purpose. The SmartSEC HT isolation kit was used for isolation of EVs from 500 μL starting volumes of CSF, resulting in two possible EV fractions of 500 μL. Both fractions were characterised and compared to one another using a whole range of characterisation techniques. Results indicated the presence of EVs in both fractions, albeit fraction 1 showed more reproducible results over the different characterisation methods. For example, CMG (CellMask Green membrane stain) fluorescence nanotracking analysis (NTA), ExoView, and the particles/μg ratio demonstrated a clear difference between fraction 1 and 2, where fraction 1 came out as the one where most EVs were eluted with the least contamination. In the other methods, this difference was less noticeable. We successfully performed complementary characterisation tests using only 500 μL of CSF starting volume, and, conclude that fraction 1 consisted of sufficiently pure EVs for further biomarker studies. This means that future EV extractions may be based upon smaller CSF quantities, such as from individual patients. In that way, patient samples do not have to be pooled and individual patient information can be included in forthcoming studies, potentially linking EV content, size and distribution to individualised neurological diagnoses.

Jaar van publicatie:2022

Trefwoorden:A3 Journal article

Toegankelijkheid:Open