As the liquid biopsy field adopts new analytes beyond cfDNA only, one of the first asks we receive is for “an RNA tube.” Yet RNA is not a singular type of molecule. It’s a whole family of molecules. The species within that family differ in where they sit, how they are packaged, and how abundant they are, and those differences change what it takes to measure them. Each type of RNA can tell a different story about the state of a person’s health or disease, and any collection tubes and workflows need to be specific and validated for that particular RNA species.
RNA location shapes analytical challenges
Where RNA resides in the blood influences how it should be collected and analyzed. Each location, whether in the plasma compartment, within cells, or within extracellular vesicles, has its own challenges, both in preserving the RNA of interest and in keeping contaminating species at bay.
Plasma cell-free RNA
What is it? As the name suggests, plasma cell-free RNA (cfRNA) are RNA molecules that are present beyond the bounds of a cell’s membrane. These RNAs are released at very low levels either through secretion or passively during apoptosis or necrosis. cfRNA travels in several forms including inside vesicles, bound to proteins, and associated with lipoproteins. Longer messenger RNAs are carried predominantly in extracellular vesicles (Kim et al., 2023).
Assay vulnerabilities: Because cfRNA is already very dilute (Larson et al., 2021), it can easily succumb to two processes that make it even more difficult to detect. RNA is inherently susceptible to degradation by nucleases like RNases, which are abundant in blood plasma (Tsui et al., 2002). Secondly, as cells break down after the draw, they release cellular RNA. Any additional RNA input can quickly alter the apparent analyte concentration or affect whether it is detected at all.
Pre-analytical requirements: To avoid an excess of cellular RNA from entering the plasma, samples should be processed quickly after the draw. Where prompt processing is not possible, the alternative is a collection approach that slows the breakdown of blood cells that would otherwise release cellular RNA into the plasma.
Cellular RNA
What is it? Cellular RNA can be found primarily in white blood cells, the predominant nucleated cells in blood. Platelets and immature red blood cells also contain RNA, including an abundance of globin RNA. Compared to cfRNA, cellular RNA is much more abundant.
Assay vulnerabilities: For cellular RNA, the challenge is that the cells are still alive and responding to handling conditions (for example temperature and agitation). Gene expression patterns can change after collection, which means RNA levels during processing may not represent what they were at the draw time.
Pre-analytical requirements: Blood collection tubes designed for RNA take one of two approaches, and they differ in what happens to the cells at the moment of collection. One lyses the cells at the point of collection and stabilizes the RNA released, which captures the transcriptome close to where it was at the draw. The trade-off is that the sample becomes a whole blood lysate rather than an intact specimen from which plasma can later be separated. The other keeps cells intact and helps slow the changes using a fixation or stabilization step before cells are further processed. That leaves the plasma fraction available. It also leaves the cells intact for downstream workflows built to work with them. Which approach fits depends on whether the assay needs the plasma compartment as well.
Small RNA
What is it? Small RNAs are a family of RNA molecules less than 200 nucleotides long. This includes microRNAs, small interfering RNA, piwi-interacting RNA, and small nucleolar RNA. This broader category of RNA can be found in the cfRNA fraction, in the cellular fraction, and associated with EVs.
Assay vulnerabilities: Because of their short length and secondary structure, small RNAs have different recovery and stability profiles compared to longer transcripts. While any activity from RNases could destroy the entire target due to its size, small RNAs are largely protected by binding to proteins or packaging in vesicles. Additionally, hemolysis has been found to affect microRNA levels (Myklebust et al., 2019).
Pre-analytical requirements: For small RNAs in plasma, how the plasma is prepared matters as much as how the blood was collected. Residual platelets are a particular concern (Kim et al., 2022), since platelets carry abundant microRNA and incomplete removal shifts the measured profile. The centrifugation protocol and the delay before separation both feed into this.
A special note about extracellular vesicle-associated RNA
Extracellular vesicles (EVs) are membrane vesicles that are released by many cell types and can contain nucleic acids, proteins, and lipids from their cell of origin. There are many types of RNA that can be found in EVs including messenger RNAs (mRNAs), microRNAs, long noncoding RNAs, and circular RNAs. While EV-associated RNA are protected by a vesicle, their low abundance makes them even more vulnerable to additional inputs of RNA that appear after collection. Platelet activation after collection can also generate vesicles containing mRNAs, microRNAs, and other noncoding RNAs (Tao et al., 2017), shifting the apparent EV-associated RNA population (Kim et al., 2022). A collection approach that helps limit activation and breakdown across cell types is key here.
Beyond collection: why RNA extraction methods must fit the analyte
The extraction chemistry has to match the RNA you are after. A kit optimized for long transcripts will not necessarily recover short species efficiently, and a workflow built around one carrier fraction may not capture another. Some kits give you the choice: raising the alcohol concentration at the binding step shifts recovery toward small RNAs like miRNAs rather than longer transcripts (Hu et al., 2020). Reported RNA yields depend heavily on the extraction and quantitation method, which is part of why published figures vary so widely between studies. A workflow validated on one extraction protocol does not automatically transfer to another, so the extraction step deserves the same scrutiny as the collection step.
Most RNA assays do not read RNA directly. Reverse transcriptase converts it into complementary DNA (cDNA), which is what methods like RT-qPCR actually amplify and measure. Reverse transcriptase options differ in performance (Zucha et al., 2020), which adds one more variable to control. Gene fusions are a good example: rearrangement breakpoints usually fall in introns that are hard to cover on DNA, while the transcript presents the spliced junction directly (Heyer et al., 2019). However, reverse transcription and amplification also add steps that can introduce variability of their own.
Choose your RNA collection and extraction workflow based on the species you want to measure
With the unique post-collection challenges for different types of RNA molecules, it’s clear that there is no one-size-fits-all approach for collecting, stabilizing, and analyzing RNA. A collection approach built to hold the cellular profile has different concerns than one built for cfRNA, for example. If a lab needs both plasma cfRNA and the cellular transcriptome, those are two different collection and extraction designs from the same patient.
A lab that collects into a tube that lyses cells and later needs cell-free RNA has no route back to the plasma fraction, and no downstream step recovers it. A workflow like that can still pass validation, because validation confirms the workflow is internally consistent, not that the right material was collected. The correction is new samples and a new validation.
The RNA you are measuring is what sets the collection requirement. Understand the analyte and what can change it after the draw, then choose the collection approach and the extraction workflow to match.
Reading list
- Larson MH, Pan W, Kim HJ, et al. A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection. Nat Commun. 2021;12:2357. DOI 10.1038/s41467-021-22444-1. PMID 33883548.
- Tsui NBY, Ng EKO, Lo YMD. Stability of endogenous and added RNA in blood specimens, serum, and plasma. Clin Chem. 2002;48(10):1647–1653. PMID 12324479.
- Kim HJ, Rames MJ, Goncalves F, et al. Selective enrichment of plasma cell-free messenger RNA in cancer-associated extracellular vesicles. Commun Biol. 2023;6:885. DOI 10.1038/s42003-023-05232-z. PMID 37644220.
- Tao S-C, Guo S-C, Zhang C-Q. Platelet-derived extracellular vesicles: an emerging therapeutic approach. Int J Biol Sci. 2017;13(7):828–834. DOI 10.7150/ijbs.19776. PMID 28808416.
- Kim HJ, Rames MJ, Tassi Yunga S, et al. Irreversible alteration of extracellular vesicle and cell-free messenger RNA profiles in human plasma associated with blood processing and storage. Sci Rep. 2022;12:2099. DOI 10.1038/s41598-022-06088-9.
- Myklebust MP, Rosenlund B, Gjengstø P, et al. Quantitative PCR measurement of miR-371a-3p and miR-372-3p is influenced by hemolysis. Front Genet. 2019;10:463. DOI 10.3389/fgene.2019.00463.
- Hu W-P, Chen Y-C, Chen W-Y. Improve sample preparation process for miRNA isolation from the culture cells by using silica fiber membrane. Sci Rep. 2020;10:21132. DOI 10.1038/s41598-020-78202-8. PMID 33273557.
- Zucha D, Androvic P, Kubista M, Valihrach L. Performance comparison of reverse transcriptases for single-cell studies. Clin Chem. 2020;66(1):217–228. DOI 10.1373/clinchem.2019.307835. PMID 31699702.
- Heyer EE, Deveson IW, Wooi D, et al. Diagnosis of fusion genes using targeted RNA sequencing. Nat Commun. 2019;10:1388. DOI 10.1038/s41467-019-09374-9. PMID 30918253.
