University of Michigan researchers have developed a novel method and subsequent algorithm that uses a traditional sequencing machine to analyze gene expression at microscopic resolution.
First described in the journal Cell in 2021, Seq-Scope was a method developed to revolutionize how biologists study gene expression. Since the original publication, the team further improved Seq-Scope to enhance its versatility and developed an algorithm, called FICTURE, to allow for high-resolution spatial data analysis which was recently published in Nature Methods.
“Basically, we are hacking DNA sequencing machines and letting them do all of the hard work,” said Kang, a professor of biostatistics at the University of Michigan.
In their work, the team demonstrated that Seq-Scope could be repurposed to profile spatially resolved transcriptomes. With further improvements, the team reduced the cost of high-resolution spatial transcriptome profiling from over $10,000 to just $500.
Additionally, using the new FICTURE method developed by the team, investigators can analyze large amounts of data and reach accurate inferences at the micrometer level. By reaching this resolution, researchers can identify where cell transcripts are located without bias.
Using traditional analysis, “even if you have cell segmentation, if you don’t know exactly which cells are being transcribed and stained, the analysis can be misleading or unclear,” said Hyun Min Kang. “Using FICTURE, for example, you can see that skeletal muscle tissue from a developing mouse embryo is differentiating into long striated muscle cells from myoblasts.”
The next steps for the researchers include developing a way to make the method even more accessible. “I think it’s important for computational and experimental investigators to work together to generate new types of data and methods. This is a good example of that type of collaboration,” Kang concluded.