Molecular methods of diagnosing diseases promise fast and accurate approaches for an array of conditions. While many molecular diagnostics look for mutations in DNA, an increasing number analyze a sample’s RNA, and those are explored here. Manufacturers offer a variety of RNA-based diagnostics, and academic scientists continue to explore new approaches to using these molecules for medical analyses.
“One key benefit to RNA-based diagnostics is that there is an abundance of RNA in a cell as compared to DNA, which gives these tests a high level of sensitivity,” says Damon Getman, director, scientific affairs at diagnostic and medical imaging company Hologic. That sensitivity played a key role in developing a diagnostic for the sexually transmitted bacterium Mycoplasma genitalium. This bacterium “is the smallest characterized free-living organism capable of self-replication, and it is extremely difficult to grow in culture,” Getman explains. “In the early 2000’s, Hologic scientists collaborated closely with academic clinical investigators, providing research-use-only reagents for molecular detection of M. genitalium.”
On the U.S. Food and Drug Administration’s list of approved nucleic acid–based tests, many address cancer, which will not be considered here, and DNA still dominates RNA. Nonetheless, some RNA-based diagnostics exist for uses outside oncology. For heart-transplant patients, for instance, CareDx’s AlloMap Heart analyzes the RNA expressed by 11 genes related to organ rejection. This diagnostic provides what the company calls an “actionable score,” which can be used by a physician as part of a plan to determine the next steps for treating a patient.
RNA-based diagnostics also exist for various infectious diseases. For instance, Siemens Healthcare’s VERSANT HCV RNA 1.0 Assay is FDA-approved for detecting RNA from the hepatitis C virus in patients.
Companies continue to explore new ways to develop diagnostics from RNA. As an example, DxTerity designed its ongoing EMPOWER study to use RNA in an at-home test to monitor multiple sclerosis (MS). This diagnostic can monitor the disease as well as the impact of a treatment.
As the following examples will show, even more RNA-based tests lie ahead. Some of the tests even aim at healthcare disasters, including the ongoing pandemic. Tomorrow’s most effective diagnostics, though, might combine the information from nucleic acids and other molecules, which could lead to diagnostics that succeed where other methods failed (See “Scanning a Spectrum.”).
Around the world today, many healthcare providers and researchers concentrate on COVID-19. Much of this work involves RNA. On August 16, 2020, a search on ClinicalTrials.gov for the terms RNA and diagnostic related to COVID turned up 185 studies. With only 18 of those completed at the time of the search, much more data and many potential diagnostics surely lie ahead.
Miami-based infectious disease startup MAVIDx hopes to develop such a diagnostic. “MAVIDx is developing an orthogonal front-end implementation for the nCounter system, where RNA detection is multiplexed for patient samples instead of gene targets,” explains Krassen Dimitrov, CEO of MAVIDx. “The technology attaches proprietary nano-sized fluorescent barcodes to individual RNA molecules, allowing direct detection and counting of viral genome copies in a sample.”
This method provides a massive ability to multiplex. “More than 40,000 samples can be processed in a business day per instrument,” Dimitrov explains. “The simplicity of the approach, where RNA molecules are directly tagged with molecular barcodes and counted, eliminates the steps of RNA extraction, reverse transcription, and amplification, resulting in simple workflow, reduced reagent needs, and correspondingly much lower costs.”
MAVIDx is working to “develop, validate, and secure regulatory approvals for and commercialize its SARS-CoV-2 and other infectious disease tests on the nCounter Analysis System,” Dimitrov says. “Veracyte will supply MAVIDx’s infectious disease test kits and nCounter instruments to support laboratories and other entities in the United States and in global markets.” Veracyte recently acquired the global diagnostic rights to the nCounter system and plans to make its own RNA sequencing-based tests for various diseases available on the platform internationally. The company’s tests for improved diagnosis of several diseases, including idiopathic pulmonary fibrosis, are already offered through its CLIA-certified laboratory in the United States.
Measuring for Mendelian disorders
Mendelian disorders, such as sickle-cell anemia and Huntington’s disease, arise from mutations in a single gene. According to Fowzan Alkuraya, head of the developmental genetics unit at the King Faisal Specialist Hospital and Research Center in Riyadh, Saudi Arabia, several factors make RNA-based diagnostics good choices for Mendelian disorders. “There are many variants that completely evade detection, even when picked up at the DNA level, because their effect at the transcriptional level is not readily obvious using current in-silico tools, and only RNA-based diagnostics can confirm their pathogenicity,” he says. “Another advantage is that in some cases RNA sequencing, RNA-seq, can at once serve as first- and second-tier analysis—that is, it identifies the causal variant and confirms its pathogenesis without the need for DNA sequencing.”
Despite these benefits, some shortcomings must also be kept in mind. “There are no standards like we have for exome and genome,” says Alkuraya. “There is also the concern that some variants will only exert their RNA-deleterious effect in tissues other than the ones sampled.” In addition, he points out that “the degree of noise is also far greater than in DNA-based diagnostics.” On top of that, sample quality can create a challenge in RNA-based tests. As Alkuraya notes, RNA-seq “is very sensitive to RNA degradation, which is far more difficult to prevent than DNA degradation.”
However, the advantages of using RNA-based methods in developing diagnostics for Mendelian disorders make it worthwhile. “We certainly need more research to know that which we don’t know. That is, identify gaps in our knowledge about RNA as a diagnostic tool,” Alkuraya says.
Alkuraya and his colleagues are one group working to fill the knowledge gaps and have published an article to describe their “experience with a large cohort of highly diverse Mendelian disorders, so we can learn lessons that can inform the widest possible range of applications.” Part of the problem is that different labs analyze RNA-seq data in different ways. “This is not going to work if we are to adopt this diagnostically, since standardization is key to diagnostics,” Alkuraya explains. “We also know that RNA quality is an issue that needs to be addressed by innovative sample preservation methods as well as more robust bioinformatics that can tolerate a wider range of RNA quality.”
Scientists at Hologic used RNA detection to develop its Aptima diagnostics for a range of sexually transmitted infections (STIs) including chlamydia, gonorrhea, trichomonas, Mycoplasma genitalium, high-risk human papillomavirus (HPV), herpes simplex viruses 1 and 2, hepatitis C virus, and HIV. These “diagnostics utilize sequence-specific target capture and transcription-mediated amplification chemistry to purify and directly amplify RNA targets, and either chemiluminescent or fluorescent probes to detect the resulting RNA amplicons, all in a single-tube reaction,” says Getman. “This provides for highly efficient sample processing.”
When asked about the key benefits of these diagnostics, Getman says that Hologic’s real-time transcription mediated amplification (TMA) “is an extremely sensitive amplification system that can detect just a few molecules of RNA, and this leads to highly accurate and reliable results, with sensitivities and specificities close to 100%.”
As an example, consider the Aptima HPV assay. It amplifies and detects the messenger RNA “expressed from the E6 and E7 viral oncogenes of high-risk HPV types, resulting in high sensitivity and superior specificity for cervical pre-cancer and cancer detection compared to molecular tests that target viral DNA genomes,” Getman says.
Today’s RNA-based diagnostics are poised to get even better. “The key challenge in clinically validating highly sensitive RNA diagnostic devices is finding comparator test methods with sufficient analytical sensitivity to establish true-positive status for the clinical specimens collected in registration trials,” Getman explains. “DNA-based molecular methods are often not sensitive enough to detect low-titer organisms in clinical specimens, and when they are used as part of a composite comparator reference standard these methods cause apparent false-positive results and artificially lowered specificity estimates for the more sensitive investigational RNA diagnostic.”
This problem could be solved with “reverse-transcription/nested PCR Sanger sequencing assays that are validated to isolate and amplify RNA and produce high-quality sequence results from specimens with low titers of microorganisms,” Getman explains. “Such methods are available and have been used in clinical studies before, but have not been widely used in the diagnostics industry.”
Scientists always look for ways to get more from existing technology and to drive innovation. COVID-19 fuels the need for such advances as much as anything has in years. Here, combining RNA-based testing with other technology could spawn important improvements. For example, a team of scientists in Taiwan reported “A rapid, inexpensive, and easy-to-use POC [point-of-care] diagnostic device integrated with a smartphone could reduce transportation needs, lower the risk of spreading infection, alleviate the strain on the healthcare system, and mitigate the cost of care for both individuals and the government.”
Along those lines, these scientists suggested that “research into the development of a paper-based RNA assay for use in combination with a smartphone application can provide new insights into designing POC COVID-19 diagnostics and ultimately improve the health care system to combat this and similar diseases.”
Scanning a Spectrum
At the University of Albany in New York, Professor Igor Lednev, Ph.D., runs an analytical chemistry laboratory that specializes in applications of spectroscopy. One application involves the diagnosis of Duchenne muscular dystrophy (DMD). In 2020, Lednev, his students Nicole Ralbovsky and Andrew Galfano, and biologists Bijan and Paromita Dey wrote: “The earlier the disease is identified, the better opportunity the afflicted individual has for seeking treatment opportunities to slow the progression of the disease phenotype.” But there’s been no easy way to make that diagnosis.
Using Raman hyperspectroscopic analysis of blood serum, Lednev’s team accurately diagnosed DMD in mice, and the nucleic acids in the sample play a role in the mechanism behind the diagnostic. “We know that DNA and RNA are important,” Lednev says, but it’s not necessary to know exactly how to make use of this diagnostic.
In fact, that’s one strength of this approach, because scientists don’t need to start with a target. Plus, Lednev says: “The advantage of a spectroscopic marker is that it potentially has contributions from multiple biochemical markers, so the specificity and sensitivity is expected to be higher.”
So far, Lednev and his colleagues applied their DMD method to an animal model. Next, they will try the diagnostic with humans. If it appears to work there, too, the diagnostic will be further tested in a clinical trial.
Other scientists also apply Raman spectroscopy to diagnostics. For example, Amit Dutt of the Tata Memorial Centre in India and colleagues used Raman spectroscopy to detect RNA from viruses in saliva. This diagnostic uses 65 features of a Raman spectrum and software that the scientists call an RNA Virus Detector (RVD). The scientists concluded: “This provides the essential framework for field-application of Raman Spectroscopy‐based RVD in monitoring and responding to the COVID‐19 pandemic.”
Overall, diagnostic approaches like those of Lednev and Dutt allow for the quicker development of diagnostics for new diseases. Instead of creating a completely new technique, only the computation or software needs to be adjusted. Both of these diagnostics depend on sophisticated analysis, such as the machine learning used in Lednev’s diagnostic. This type of approach could also produce a diagnostic that can adjust to changes within a disease, such as mutations in SARS-CoV-2 or other infectious agents. That could make these some of the most useful diagnostics of all.