New method shows role of elusive RNA in muscle regeneration

In situ polyadenylation allows spatial profiling of non-coding and non-host RNAs. aWorkflow for STRS. b, Comparison of selected RNA biotypes between Visium and STRS datasets. The therethe axis shows the percentage of UMI for each point. The box shows the median and quartile values, and the whiskers show 1.5 times the interquartile range. vs, Coding and non-coding RNA detection between Visium and STRS workflows. The color scale shows the average IMU number normalized per log. The dot size indicates the percentage of dots in which each RNA was detected. DTo register_ten-transformed coverage of deduplicated reads mapped to sense (light gray) and antisense (dark gray) strands at Vaultrc5, ENSMUSG00002075551, and Rps8 loci. The annotations shown are from GENCODE M28 and include one of the five Rps8 isoforms as well as the four intragenic features in the Rps8 introns. e, Spatial maps of coding and non-coding transcriptions for Visium and STRS workflows. The spots in which the transcript was not detected are represented in gray. The color scale indicates the log-normalized expression. F, Detection of REOV transcripts using standard workflow, STRS and STRS with targeted enrichment. The points in which the virus was not detected are displayed in gray. Credit: Natural biotechnology (2022). DOI: 10.1038/s41587-022-01517-6

In recent years, scientists studying gene expression in cells have used a method that basically involves pinning a tail on RNA and tracking where it is located. However, some types of RNA escape the process. Now, a Cornell team has developed a way to label these molecules, allowing researchers to spatially map the entire spectrum of RNA in a cell’s transcriptome.

The method, total spatial RNA sequencing (STRS), has revealed the previously elusive role of RNA in skeletal muscle regeneration and viral myocarditis in mice.

The team’s article is published in Natural biotechnology . The lead author is PhD student David McKellar, who works jointly in the labs of co-lead authors Iwijn De Vlaminck and Ben Cosgrove, both associate professors of biomedical engineering at the College of Engineering.

To better understand how gene expression drives different kinds of biological phenomena, researchers are using a technique called spatial transcriptomics, which measures RNA in a tissue sample and maps the location of its activity.

To do this, spatial transcriptomics cleverly exploits the organic process by which a cell adds a polyadenylated (poly-A) tail to an RNA when it is transcribed in the nucleus. RNAs are trapped by their poly-A tail, then they are sequenced and computationally mapped to their spatial position.

This usually happens for messenger RNAs, but there are other types, such as non-coding RNAs, that never get a poly-A tail. Because they are difficult to map, and therefore difficult to study, noncoding RNAs are a “sort of dark matter in the transcriptome,” according to McKellar.

McKellar was inspired to improve on this approach after observing the work of his co-author and doctoral student Madhav Mantri, who, in research recently published in Nature Cardiovascular research, used two forms of transcriptomics – single-cell and spatial – to create a high-resolution transcriptome map of reovirus-induced myocarditis, i.e. inflammation of the heart muscle, in mice. In doing so, Mantri was able to document the role of inflamed endothelial cells in the response to viral infections.

“I saw he was studying these viral RNAs, but he couldn’t see where the virus was,” McKellar said. “He must have inferred this based on the host gene expression response.”

McKellar and his collaborators discovered that by applying an enzyme, poly-A polymerase, they could add adenine bases to every RNA, even the most cunning ones.

“If RNA already had a poly-A tail, now the poly-A tail is just longer; if RNA didn’t have a poly-A tail, now it does,” McKellar said. “We can now use existing RNA sequencing technologies to capture all of these other types of RNA that were previously overlooked.”

In a series of experiments, the team used STRS to show how non-coding RNAs regulate skeletal muscle regeneration. They also demonstrated that STRS can spatially map non-host viral RNA infection, in myocarditis, as well as host tissue response, simultaneously, with a single measurement.

“David came up with a simple trick to solve a common problem in spatial transciptomics,” said De Vlaminck. “It’s great that his method adds just one inexpensive step to commercially available protocols. We hope other groups will learn from it and can quickly adopt David’s method.”

Now that they are able to spatially map any type of RNA, the team plans to use STRS to analyze other biological systems, such as microbiome bacteria, other viral diseases and possibly, by tweaking the technology, some forms of bacteria-associated cancer.

The researchers also plan to develop higher-resolution technologies to see which genes are expressed in individual cells and how gene expression varies spatially. Perhaps most importantly, because STRS relies on widely used spatial RNA sequencing, the new method can be widely and quickly adopted by other researchers.

“There are hundreds or thousands of genes that are simply not detected by existing technologies,” McKellar said. “We are now able to capture this whole other side of the transcriptome. But really, what’s exciting about STRS is the flexibility. Any type of tissue, any type of disease really, we can now map gene expression and study the underlying biology.”