Hi, my name is Michele,
and today I will be sharing considerations for working with extracellular vesicle RNA,
including technical sources of variability,
types of analysis,
and how these relate to the development of EV applications.
Broadly, there are three key sources of variability that impact EV-RNA research.
These are: the choice of EV isolation method; the method used to extract RNA from the purified EV preparation,
and the type of EV-RNA analysis performed.
To study and characterise RNA content exclusively associated with EVs,
EVs must be reliably separated from other RNA-binding non-EV components that might be present in the sample.
EV separation methods rely on different principles and provide a spectrum of purity,
making purity an important consideration when selecting an EV isolation method for EV-RNA analysis.
Yield is another important aspect,
and EV preparations typically must be concentrated so that the resulting quantities yield enough RNA for analysis.
EV purity and yield are closely related parameters.
Without an appropriate method to remove non-EV components with RNA-binding capacity,
the yield of EV-RNA is artificially overestimated,
with a clear compromise on EV purity.
And, EV-RNA is particularly sensitive to temperature-driven degradation.
Therefore, when storing unprocessed or purified EV samples,
it’s important to do everything you can to help maintain native EV-RNA composition.
Storage protocol studies have demonstrated the protection of RNA cargo when EV samples are stored at negative 80 degrees Celsius and when freeze-thaw cycles are minimised.
Some biofluids are more widely studied than others regarding the presence and abundance of RNA-binding non-EV structures.
Plasma, of course contains high- and low-density Lipoproteins, which can bind to microRNAs.
Meanwhile, cell culture often requires supplementation with Fetal Bovine Serum for optimal growth,
which contains significant amounts of bovine-derived EVs,
EV-RNA and non-EV-RNA.
Ribonucleoproteins are another non-EV component to be aware of which,
by definition, contain RNA as part of their complexes.
Importantly, luminal EV RNA is protected from RNase degradation,
unlike other non-EV RNA-binding structures or RNA loosely associated to the exterior of EVs.
Therefore, to minimise non-EV RNA contamination and help with the exclusive analysis of EV-RNA,
RNase treatment is commonly applied to purified EV preparations before proceeding with RNA extraction.
The most common EV-RNA extraction method is a combination of solid phase extraction with organic RNA extraction.
Organic RNA extraction separates an organic phase containing proteins and DNA from an aqueous phase containing RNA.
And the solid phase relies on the affinity of RNA to a solid support, typically using a filter in a spin column.
Some kits have protocols to extract total RNA or small RNA from EVs.
Or, protocols may be tailored to enable the preferential extraction of small RNA separated from larger RNA greater than 200 nucleotides.
No kit is perfect at extracting the full spectrum of RNA sizes,
Therefore, awareness of biases towards specific RNA sizes in each protocol is encouraged.
As the amount of RNA found in EVs is very small,
it is common to use an RNA carrier in the extraction procedure,
usually an unrelated nucleic acid that helps with the binding and recovery of the RNA of interest.
A drawback to consider is the resulting presence of carrier RNA mixed with EV-RNA of interest,
which may have potential unknown effects in functional studies.
The quantification of the extracted EV-RNA is the first and most basic RNA analysis.
RNA concentration can be estimated using principles of spectrophotometry,
fluorometry, and conventional or automated electrophoresis.
Each approach has its own strengths and limitations and works within a specific range of RNA concentration.
Assay selection depends on how relevant the limitations are to the endpoint analysis.
Other assays can be performed to characterise RNA to different degrees.
Broad RNA characterisation is commonly achieved by profiling the sizes of the EV-RNA species in a sample.
For example,
automated electrophoresis plots the time at which tagged RNA fragments are eluted,
where smaller RNA fragments elute earlier and larger RNA fragments elute later.
Like in the figure shown here,
the sample of cellular RNA represented by the pink line shows a high relative abundance of large RNA fragments,
with size matching ribosomal RNAs and a low abundance of small RNA fragments.
In contrast, the EV-RNA sample represented in grey shows the presence of mostly small RNAs.
These assays will not distinguish or identify even smaller RNA fragments,
like microRNAs,
in the same assay that detects large RNAs.
To assess microRNAs, therefore, another assay will be needed with adequate resolution in the microRNA size range.
In-depth and sensitive RNA analysis requires advanced technologies such as reverse transcriptase quantitative PCR,
microarrays and RNA-sequencing.
These tools provide detailed profiling on the presence and abundance of specific RNA sequences.
Most of these assays are therefore restricted to known targets or sequences and involve multiple steps around comprehensive optimisation and normalisation.
As an example,
this figure shows RT-qPCR results demonstrating the presence and abundance of known micro and messenger RNA targets
in a sample of plasma-derived EVs isolated using qEV columns,
with EV-RNA extracted using the qEV RNA extraction kit.
EV-RNA research has gained popularity,
largely due to its huge potential in medical applications like biomarker discovery and nanomedicine.
Although major advances have been made in the EV-RNA biomarker discovery field, challenges remain.
There is a need for large databases of reference EV-RNA profiles in different biofluids of healthy subjects to act as comprehensive controls.
There is also a recognised need for standardised sample processing and EV-RNA isolation methods,
as there are significant discrepancies in the results obtained using different methods.
And there is a need to decrease variability in RNA studies and focus on specific EV subtypes and their unique RNA profiles.
Depending on the context, the development of EV-based therapeutics may require progress in a range of areas.
For example, process development for large volume EV separation,
and strategies for enriching and loading functional RNA into EVs.
Other areas of development include strategies for enhancing cell targeting and uptake,
and the selection of an appropriate vehicle EV if native EV-RNA is not used.
In this example,
electroporation was used to exogenously load microRNAs into EVs,
and specific electroporation settings were identified which enhanced loading efficiency.
Consecutive experiments assessing apoptosis of cancer cells treated with the engineered EVs
corroborated the anti-cancer effect of these microRNAs loaded into EVs.
And finally, given the huge potential of EV-RNA in diagnostic and therapeutic applications,
understanding the limitations of protocols and analyses is highly important to obtaining accurate conclusions.
For more information on EV-RNA we invite you to download the app note linked below.
Thanks for watching.