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University of Michigan researchers have refined a unique approach for visualizing gene expression at unprecedented resolution, marking a major advance in spatial transcriptomics. First introduced in Cell in 2021, Seq-Scope offers biologists a powerful tool to map gene activity down to the sub-micrometer scale. The method, originally developed by Jun Hee Lee, Hyun Min Kang, and their team, has now been further enhanced, increasing its versatility, accessibility, and cost-effectiveness, as reported in Nature Protocols. Complementing Seq-Scope is a new computational tool, FICTURE, described in Nature Methods, which enables precise analysis of spatially resolved gene data.
Advancing Spatial Transcriptomics
Seq-Scope utilizes standard DNA sequencing technology to map gene expression across tissues with intricate detail. Conventional transcriptomic methods struggle to separate gene expression patterns due to the vast quantity of genes and cells involved. However, by reconfiguring DNA sequencers, Seq-Scope can capture spatially resolved transcriptomes—providing scientists with high-definition "maps" of gene activity that reveal where specific genes are expressed within tissues.
Hyun Min Kang, a biostatistics professor at U-M, explains, “Basically, we are hacking DNA sequencing machines and letting them do all of the hard work.” Seq-Scope converts sequencing output into images, offering spatial clarity on gene expression at microscopic levels. To highlight the technique's precision, the sub-micrometer scale of Seq-Scope’s analysis is narrower than a human hair—ranging from 20 to 200 micrometers.
Cost-Effective and High-Resolution Data Processing
Since its initial release, Seq-Scope has undergone substantial improvements, including a significant reduction in cost. Where high-resolution spatial transcriptomics might previously have cost up to $10,000 per sample, the enhanced Seq-Scope process now achieves this for around $500, expanding its feasibility for more researchers.
Lee, professor of molecular and integrative physiology at U-M, explains, “The problem is traditionally, there are no computational methods that allow us to understand this data set at microscopic resolution.” The team’s Seq-Scope approach resolves this limitation by transforming sequencing instruments into spatial transcriptomics tools, making high-resolution data acquisition far more accessible.
In addition, the team’s newly developed FICTURE algorithm enables researchers to analyze large datasets at the micrometer level. The tool uses surrounding data to enhance inference accuracy, allowing for unbiased detection of transcript locations within cells. “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,” says Kang, describing its application to mouse embryonic tissue.
Applications and Community Impact
The impact of Seq-Scope extends beyond basic research. Olivia Koues, Director of U-M’s Advanced Genomics Core (AGC), emphasized the technology’s potential to reach a wider scientific audience. AGC, a co-author on the Seq-Scope protocol paper, has been instrumental in refining the method for use on DNA sequencing machines and aims to extend Seq-Scope’s reach to additional laboratories. “This is exactly the kind of technology we want to bring to as many labs as possible, both here at U-M and beyond,” said Koues.
With Seq-Scope and FICTURE, investigators can obtain highly specific visualizations of gene expression, providing clarity in situations where conventional methods fall short. As Kang notes, traditional cell segmentation methods can yield misleading results if researchers cannot accurately identify which cells are actively transcribed and stained. Seq-Scope mitigates this issue by offering direct spatial profiling.
Interest in the technology has been high, with Lee noting, “We’re getting a lot of emails from companies and other investigators who previously assumed they wouldn’t be able to do such experiments and analyses. Now they are in the realm of possibility.”
Looking Ahead
While Seq-Scope and FICTURE mark considerable progress in spatial transcriptomics, Lee and Kang continue to work toward even greater accessibility. Their vision includes expanding this technology to allow broader, more comprehensive studies of genomic expression. Kang reflects on the importance of collaboration in achieving these advances, saying, “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.”
Advancing Spatial Transcriptomics
Seq-Scope utilizes standard DNA sequencing technology to map gene expression across tissues with intricate detail. Conventional transcriptomic methods struggle to separate gene expression patterns due to the vast quantity of genes and cells involved. However, by reconfiguring DNA sequencers, Seq-Scope can capture spatially resolved transcriptomes—providing scientists with high-definition "maps" of gene activity that reveal where specific genes are expressed within tissues.
Hyun Min Kang, a biostatistics professor at U-M, explains, “Basically, we are hacking DNA sequencing machines and letting them do all of the hard work.” Seq-Scope converts sequencing output into images, offering spatial clarity on gene expression at microscopic levels. To highlight the technique's precision, the sub-micrometer scale of Seq-Scope’s analysis is narrower than a human hair—ranging from 20 to 200 micrometers.
Cost-Effective and High-Resolution Data Processing
Since its initial release, Seq-Scope has undergone substantial improvements, including a significant reduction in cost. Where high-resolution spatial transcriptomics might previously have cost up to $10,000 per sample, the enhanced Seq-Scope process now achieves this for around $500, expanding its feasibility for more researchers.
Lee, professor of molecular and integrative physiology at U-M, explains, “The problem is traditionally, there are no computational methods that allow us to understand this data set at microscopic resolution.” The team’s Seq-Scope approach resolves this limitation by transforming sequencing instruments into spatial transcriptomics tools, making high-resolution data acquisition far more accessible.
In addition, the team’s newly developed FICTURE algorithm enables researchers to analyze large datasets at the micrometer level. The tool uses surrounding data to enhance inference accuracy, allowing for unbiased detection of transcript locations within cells. “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,” says Kang, describing its application to mouse embryonic tissue.
Applications and Community Impact
The impact of Seq-Scope extends beyond basic research. Olivia Koues, Director of U-M’s Advanced Genomics Core (AGC), emphasized the technology’s potential to reach a wider scientific audience. AGC, a co-author on the Seq-Scope protocol paper, has been instrumental in refining the method for use on DNA sequencing machines and aims to extend Seq-Scope’s reach to additional laboratories. “This is exactly the kind of technology we want to bring to as many labs as possible, both here at U-M and beyond,” said Koues.
With Seq-Scope and FICTURE, investigators can obtain highly specific visualizations of gene expression, providing clarity in situations where conventional methods fall short. As Kang notes, traditional cell segmentation methods can yield misleading results if researchers cannot accurately identify which cells are actively transcribed and stained. Seq-Scope mitigates this issue by offering direct spatial profiling.
Interest in the technology has been high, with Lee noting, “We’re getting a lot of emails from companies and other investigators who previously assumed they wouldn’t be able to do such experiments and analyses. Now they are in the realm of possibility.”
Looking Ahead
While Seq-Scope and FICTURE mark considerable progress in spatial transcriptomics, Lee and Kang continue to work toward even greater accessibility. Their vision includes expanding this technology to allow broader, more comprehensive studies of genomic expression. Kang reflects on the importance of collaboration in achieving these advances, saying, “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.”