Mapping Larger Tissue Sections Without Specialized Equipment

Spatial transcriptomics technologies have opened the door to new kinds of biological analysis by creating detailed maps of gene expression within tissues. However, most current methods rely on complex, expensive imaging techniques that require specialized equipment and significant time investment.

Researchers at the Broad Institute have developed a new method that could make spatial transcriptomics more accessible. This technique eliminates the need for imaging altogether, instead using computational algorithms to reconstruct the spatial positions of gene expression.

By removing the imaging step, the researchers were able to map larger areas of tissue more quickly and at a lower cost than traditional methods. Just as importantly, the new approach doesn't require advanced lab infrastructure, making it accessible to a broader community of scientists. The study was published in Nature Biotechnology.

“Our work converts imaging into molecular biology—just a reaction in a test tube. That means anybody can use this approach if they have the algorithm and some common materials.”
— Fei Chen, senior author, Core Institute Member at the Broad Institute, and Assistant Professor at Harvard University

“When biologists think about spatial locations, they might think they need to look at samples with light or electron microscopy. But we’ve found that we can computationally infer physical locations instead.”
— Chenlei Hu, first author and Harvard graduate student

Mapping with Beads

This new development builds on Slide-seq, a technique created in 2019 by Chen, fellow Broad core institute member Evan Macosko, and collaborators. Slide-seq produces high-resolution maps of gene expression by imaging a slide covered with DNA-barcoded beads. After dissolving a tissue sample onto the slide, the messenger RNA binds to the beads, and researchers sequence the captured RNA to create a gene expression map.

Previously, Chen’s lab performed the imaging step themselves—often using their microscopes nearly full-time to support other researchers. But the idea of using sequencing alone to determine the spatial positions of beads had long been on their minds.

They hypothesized that if the relative distances between the beads were known, their positions could be reconstructed—much like triangulating a phone’s location using satellites.

When Chenlei Hu joined the lab, she proposed a new strategy: measure how DNA molecules diffuse between beads to infer distance. The team created a modified bead array containing “transmitter” and “receiver” beads, each tagged with unique DNA barcodes. When exposed to UV light, the barcodes detach from the transmitter beads, diffuse across the slide, and are captured by nearby receivers.

The closer a receiver bead is to a transmitter, the more barcodes it captures. By quantifying these barcodes during sequencing, the team could determine not just gene expression, but the spatial positioning of beads. Hu then used the Uniform Manifold Approximation and Projection (UMAP) algorithm—widely used in single-cell genomics—to computationally reconstruct the bead layout on the slide.

When tested against the original, image-based Slide-seq method, the new technique yielded nearly identical results.

Without the imaging bottleneck, the team could map gene expression across much larger tissue sections. In one experiment, they analyzed mouse embryo tissue spanning 1.2 centimeters—far beyond the 3-millimeter range of previous maps. Collaborating with the Macosko lab, they’re now working on mapping areas up to 7 centimeters, approaching the scale of whole human organs.

“We are no longer limited by how long it takes us to image something,” Chen said.
“Eventually, we’d like to analyze the whole human brain. That just wasn’t possible with earlier technologies.”