MADISON, Wis. — Genetic cycles, from circadian rhythms to the cycles involved in reproduction, are everywhere. Now, a recent University of Wisconsin-Madison study is putting a new tool in the arsenal of researchers when it comes to the ebb and flow of gene expression. The new statistical approach, named Oscope, lets researchers identify and characterize the rhythm of genes across the entire genome using single-cell RNA sequencing.
Common genetic cycles include the circadian rhythm that regulates our behaviors over the 24-hour period and those that govern the division and multiplication of cells. Cycles are evident across almost all living systems, and errors in the process often lead to a variety of diseases. To study these cycles using traditional technologies, researchers must synchronize a whole population of cells so they are at the same state. Unfortunately, such synchronization isn’t possible for many cell types and conditions.
However, new RNA sequencing technology allows scientists to probe the genome wide expression of a single cell. When the cell is harvested for sequencing, it is destroyed in the process so it can’t be sequenced again to uncover an oscillating gene pattern. But with that single-cell information, researchers were able to ‘reorder’ unsynchronized cells and uncover a pattern of expression.
Oscillating gene expression can be thought of like a sine wave, moving up and down on a graph as the gene is expressed, repressed, and expressed again over time. Oscope uses a dataset from unsynchronized, single-cell RNA sequencing.
Christina Kendziorski, a professor of biostatistics and medical informatics at the UW School of Medicine and Public Health, led the team that developed Oscope to address the drawbacks of existing methods used to explore gene expression. The project melded the statistical strengths of the Kendziorski lab with the cell biology expertise from the lab of James Thomson, pioneer stem cell biologist at the UW and researcher at the Morgridge Institute for Research in Madison.
“This project is a great example of the collaborative efforts that are required to address the most challenging problems facing scientists today,” said Kendziorski.
Techniques to study oscillating gene expression in individual cells have existed for a long time. They include fluorescent tagging, where a certain gene is ‘tagged’ with a light-emitting molecule and gene expression is measured in proportion to the light emitted by the tag. If the tag is emitting light, the gene is active. However, this technique is labor-intensive and can only examine a handful of genes at a time.
When genetic rhythms are of interest across the entire genome, researchers often use traditional or bulk RNA sequencing, where a researcher examines the average gene expression over a large population of cells at various times. But to look at different times, the technique requires multiple populations of cells because the population is destroyed when expression is measured. The other issue here is the populations must be synchronized—starting and continuing at the same state of expression—which doesn’t always hold over time.
“After looking at average gene expression for over a decade, the ability to see genome-wide gene expression in individual cells is particularly exciting. Unfortunately, we only have a snapshot, and that’s the tricky part. We want to study oscillatory gene expression, but we don’t have time course data,” said Kendziorski, “so we developed a statistical method that would allow us to look at oscillatory genes and reconstruct one cycle of their oscillation that doesn’t involve time course experiments or synchronization.”
While the technique offers a new way to study gene oscillations, it does have limitations. The method can only reconstruct one cycle of the oscillation instead of its full oscillatory path. Another limitation is that with time course, one can see much more detailed dynamic for many genes.
Yet, for those working with oscillatory genes but uninterested in studying their rhythms, Oscope may be helpful in weeding out unnecessary data, letting a researcher adjust for the genetic variance that comes with oscillation, thereby improving the power to see the signal they’re most interested in.
This study was published in Nature Methods on August 24, 2015. The work was supported by US National Institutes of Health grants GM102756, 4UH3TR000506 and 5U01HL099773, the Charlotte Geyer Foundation and the Morgridge Institute for Research.