A newly developed algorithm shows how a gene works

They say a picture is worth a thousand words.

A new method developed by University of Michigan researchers creates images worth gigabytes of data, and it could revolutionize the way biologists study gene expression. Developed by Seq-Scope Jun Hee Lee, Ph.D.Hyun Min Kang, Ph.D. and colleagues, first described in Cell in 2021 as: First method to analyze gene expression at submicrometer-scale spatial resolution.

For comparison, the width of a single human hair varies between 20 and 200 micrometers.

The team has since improved Seq-Scope, making it more versatile, scalable and accessible; this was recently published in Nature Protocols. Additionally, the same group developed an algorithm called FICTURE, described below, to analyze high-resolution spatial data from Seq-Scope and other technologies. Nature Methods.

“We basically hack the DNA sequencing machines and let them do all the hard work,” said Kang, a professor of biostatistics at the UM School of Public Health.

Researchers use these machines to produce outputs of the transcriptome, which is a collection of all RNAs transcribed from genes by a particular cell or tissue. Traditionally, biologists studying genes within a cell or tissue have to contend with the fact that a transcriptome has tens of thousands or more RNAs. While it involves millions of cells, too many genes are expressed to reach a conclusion without the help of a computer.

“The problem is that traditionally there have been no computational methods that allow us to understand this data set at microscopic resolution,” said Lee, professor of Molecular and Integrative Physiology at the UM School of Medicine.

Lee and Kang’s proof-of-concept method, Seq-Scope, demonstrated that a sequencing machine could be repurposed to profile spatially resolved transcriptomes, allowing scientists to see how and where a gene is expressed at microscopic resolution. The team then created Seq-Scope. is even more cost-effective, reducing the cost of high-resolution spatial transcriptome profiling from $10,000 to approximately $500.

Moreover, the new FICTURE method allows researchers to analyze huge amounts of data by aggregating surrounding data to make a more accurate inference at the micrometer level. By doing this, they showed that you can see where cell transcripts are located without any bias.

The method produces incredibly detailed images of tissues and cells from microscopic resolution analysis.

For example, in traditional analysis, Kang said, “Even if you have cell compartmentalization, if you don’t know exactly which cells are transcribed and stained, the analysis can be misleading or imprecise.”

“For example, using FICTURE you can see that skeletal muscle tissue from a developing mouse embryo differentiates from myoblasts into long-striated muscle cells.”

“We get a lot of emails from companies and other researchers who previously assumed they wouldn’t be able to do these types of experiments and analyses. Now they’re in the realm of possibility,” Lee said.

U-M’s Advanced Genomics Core co-authored the Seq-Scope protocol document and contributed by optimizing the use of DNA sequencers. The facility is currently working to make the Seq-Scope method even more accessible and aims to disseminate this technology to UM and the broader scientific community.

AGC Director Ph.D. “This is the type of technology we want to bring to as many labs as possible, both here at UM and beyond,” said Olivia Koues.

“Our goal is to empower more researchers with state-of-the-art spatial transcriptomics capabilities.”

Lee and Kang next hope to develop a way to make the method more accessible to researchers and allow them to examine genomic expression from start to finish.

“I think it’s important for computational and experimental researchers to work together to create new types of data and methods,” Kang said. “This is a good example of this type of collaboration.”

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