Introduction to science behind FastEV
Extracellular vesicles and complexity of plasma as a biomarker source
Extracellular vesicles (EVs) execute multifaceted functions in health and disease. These secretory nano-sized vesicles carry and protect diverse cargo including variable RNAs, DNA, proteins, lipids and metabolites. This cargo is suitable for multiomics analyses. As it may also contain molecular tags revealing the cellular or tissue origins, EVs offer a rich and traceable source for biomarkers. Another benefit is that EVs can be obtained with minimally invasive or non-invasive methods e.g. from plasma, making repeated sampling possible. All factors combined, EVs offer great potential for diagnostics.
Heterogeneity of EVs - EVs and their subclasses
The complexity of EVs futher increases, when we consider the most typical sample type in biomarker discovery, plasma. Plasma contains a mixture of EVs derived from different tissues and cellular origins. The available EV populations in circulation is a result of EV secretion and uptake impacted by a great number of spatio-temporal and physiological factors.
Other circulating cell free carriers of important molecular cargo
In addition to heterogeneity of EVs, it is important to acknowledge that plasma contains other components, including, but not limited to:
- proteins (complexes, aggregates)
- cell-free RNA, especially in ribonucleoproteins (e.g. Ago2/miR) and HDL
- cell-free DNA
- mitochondria (free and in EVs)
- possibly: bacteria, their OMVs and viruses
- platelets, if the preanalytics have not been executed well
Interactions on EVs
Ideally, biomarker discovery could benefit of this added layer of complexity.
EV isolation– impact on biomarker discovery
Preanalytical factors, including the chosen method of EV isolation, have a major impact on the results of biomarker discovery. For plasma, anticoagulants, centrifugation steps, containers, temperatures, transport, freezing, storage and thawing procedures/times may affect the available population of biomarkers carriers. For EV isolation, no standard methods exist, thus novel methods are being developed in response to laborious workflows, low throughput or yields, impure or otherwise unsuitable out-puts. For example, traditional ultracentrifugation workflows – closest to gold standard – are laborious, low or medium throughput and prone to operator/equipment induced variation. The result is that outputs from different EV isolations vary in terms of yield, purity, concentration, EV population, molecular cargo and biomarker candidates.
For biomarker discovery, high throughput is essential. However, only a few new EV methods address this problem. Instead, most methods focus on improving the purity of the EV output. In the light of possible biologically meaningful interactions between different biomarker cargo carriers, it needs to be asked when is purity essential and when is it unnecessary?
This consideration concerns the preferred inclusion/exclusion of corona of the circulating EVs, molecular cargo in vs outside of EVs and typical cargo of other carriers, such as that of lipoproteins. Theoretically, when applying highly purifying methods, the cargo inside the minuscule EV containers is highlighted. Stripping EVs of surface associated molecules is a relatively common procedure, e.g. applying proteinase K and nuclease treatments before RNA/DNA isolations to destroy “non-EV” nucleic acids. This may strip the EVs of their natural cargo and interactome on surface. On the other end come methods such as precipitation with PEG, that produce outputs containing EV and abundant contaminations. While some of the contaminations may interfere with the downstream analytics, the isolates may retain the EV surface biomarkerome better than the highly stripped samples.
The difficult question is, which cargo is more important and when? Even the newest high throughput EV isolation methods produce only one kind of outputs (per method), with more or less corona, interactome and contaminations, thus they bring one combination of molecular information carriers on the analytical plate.
In summary, it is very difficult to predict where best biomarkers are carried and how they can be caught in each individual case – testing different methods one by one takes a lot of time and resources. The idea with the FastEV biomarker discovery platform is to prowide a shortcut to the best method avoiding the maze of complexity, uncertainty and laborious time-consuming testing.
How FastEV can help to identify the best workflow for detecting biomarkers
FastEV covers a wide range of chemical conditions to fractionate information carriers within plasma. It produces an array of different outputs in terms of EV purity (presence of corona and other biomolecular information carriers), EV yields and molecular contents, such as small RNA subtypes. The outputs are amenable to a wide selection of molecular content analysis, including smallRNAseq, RNAseq, EVArray, Lectin array and Nano flow cytometry.
FastEV can be applied in high-throughput 96-well plate format.
Therefore, FastEV can be applied to quickly test what is the best condition to separate cases from controls or to enrich preknown targets – thereby, it greatly cuts down the cost, time and resources required to find the optimal method for each specific case. The optimal FastEV condition can be used easily for thousands of samples allowing high impact biomarker discovery studies.
Barreiro et al., Comparison of urinary extracellular vesicle isolation methods for transcriptomic biomarker research in diabetic kidney disease, J Extracell Vesicles. 2020 Dec; 10(2): e12038, Published online 2021 Jan 7.
Buzás et al. Molecular interactions at the surface of extracellular vesicles. Semin Immunopathol 40, 453–464 (2018).
Galvanin et al., Diversity and heterogeneity of extracellular RNA in human plasma. Biochimie. 2019 Sep;164:22-36.
Geekiyanage et al., Extracellular microRNAs in human circulation are associated with miRISC complexes that are accessible to anti-AGO2 antibody and can bind target mimic oligonucleotides, Proceedings of the National Academy of Sciences Sep 2020, 117 (39) 24213-24223;
Jeppesen et al.,. Reassessment of Exosome Composition. Cell. 2019 Apr 4;177(2):428-445.e18.
Munir et al., Exosomes in Food: Health Benefits and Clinical Relevance in Diseases, December 2019, Advances in Nutrition, 11.
Nevalainen et al., Composition of the whole transcriptome in the human plasma: Cellular source and modification by aging. Exp Gerontol. 2021 Jan;143:111119.
Palviainen et al., 2020, Extracellular vesicles from human plasma and serum are carriers of extravesicular cargo-Implications for biomarker discovery, PLoS One , vol. 15 , no. 8 , 0236439 . (a)
Palviainen et al., Cancer Alters the Metabolic Fingerprint of Extracellular Vesicles. Cancers 2020, 12, 3292. (b)
Puhka et al., Metabolomic Profiling of Extracellular Vesicles and Alternative Normalization Methods Reveal Enriched Metabolites and Strategies to Study Prostate Cancer-Related Changes. Theranostics 2017; 7(16):3824-3841.
Rodosthenous et al., Profiling Extracellular Long RNA Transcriptome in Human Plasma and Extracellular Vesicles for Biomarker Discovery. iScience. 2020 Jun 26;23(6):101182.
Sadik, Noah et al. “Extracellular RNAs: A New Awareness of Old Perspectives.” Methods in molecular biology (Clifton, N.J.) vol. 1740 (2018): 1-15. Savelyeva et al. “Variety of RNAs in Peripheral Blood Cells, Plasma, and Plasma Fractions.” BioMed research international vol. 2017 (2017): 7404912.
Simonsen, What Are We Looking At? Extracellular Vesicles, Lipoproteins, or Both? Circ Res. 2017 Sep 29;121(8):920-922.
Sódar et al., Low-density lipoprotein mimics blood plasma-derived exosomes and microvesicles during isolation and detection. Sci Rep. 2016 Apr 18;6:24316.
Théry et al., Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles. 2018 Nov 23;7(1):1535750.
Valadi et al., Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 9, 654–659 (2007).
Valkonen et al., Lipid mediators in platelet concentrate and extracellular vesicles: Molecular mechanisms from membrane glycerophospholipids to bioactive molecules, Biochimica and Biophysica Acta. 2019, 1864, 8, s. 1168-1182, Molecular and Cell Biology of Lipids.
Veziroglu and Mias. Characterizing Extracellular Vesicles and Their Diverse RNA Contents. Front Genet. 2020;11:700. Published 2020 Jul 17.
Vickers et al. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol 13, 423–433 (2011).
Yang et al. Progress, opportunity, and perspective on exosome isolation – efforts for efficient exosome-based theranostics. Theranostics. 2020;10(8):3684-3707. Published 2020 Feb 19.
Yáñez-Mó, Siljander et al., Biological properties of extracellular vesicles and their physiological functions, J Extracell Vesicles. 2015 May 14;4:27066.
Zhou et al. Application of exosomes as liquid biopsy in clinical diagnosis. Sig Transduct Target Ther 5, 144 (2020).