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and a multi-institutional team of researchers have created a computational toolkit with the detection power and precision of a spy satellite. But instead of keeping tabs of human traffic on the ground, or infrastructure development in a city, they’re focusing on RNA with unprecedented clarity at the subcellular level. 

Their intracellular spatial transcriptomic analysis toolkit, or InSTAnT, can analyze cellular data and chart RNA interactions, providing new insights into the molecular processes of life and advancing an evolving field of research.

“Conventional spatial transcriptomics maps RNA at the tissue level,” said Sinha, professor in the Wallace H. Coulter Department of Biomedical Engineering at ɫ���� and Emory University. “But InSTAnT represents a step forward. It provides, for the first time, an analytic technique to fully exploit single-molecule resolution. This means we can explore the intricate architecture, machinery, and activity of cells in ways that were not possible before.”

In addition to ɫ���� and Emory, the team included researchers from from the . With Anurendra Kumar, a grad student in the , as lead author, they explained their innovative work recently in .

Subcellular GPS

Spatial transcriptomics has enhanced the study of gene expression (how genes regulate cellular functions and behaviors), revealing molecular activity in its natural environment. The aim is to gain a deeper understanding of biology, health, and disease, with the hope of developing targeted treatments.

“One of the biggest challenges in the field was the lack of systematic tools to analyze spatial relationships at the subcellular level,” Sinha said. “We saw this gap as an opportunity to innovate and solve a problem that was truly spatial in nature.”

InSTAnT was designed to work in tandem with imaging-based spatial transcriptomics technologies like MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization, developed by Harvard in 2015), which can observe thousands of RNA molecules inside single cells, gathering detailed information about gene activity. 

“It’s like a GPS for tissue, looking all the way down to city street level,” said Sinha. “The little dots on this GPS aren’t people. They’re RNA molecules called gene transcripts. But we didn’t really know how to make sense of this distribution of molecules in the cytoplasm or the nucleus, or generally within the cell.”

InSTAnT translates what MERFISH gathers, using advanced statistical tests and algorithms, analyzing the distribution of RNA molecules that carry genetic information needed for various cell functions.

The Cities in Our Cells

If a cell was a busy little city, think of the gene transcripts — RNA molecules, the dots in Sinha’s GPS scenario — as workers moving around town, performing their important tasks.

 InSTAnT keeps tabs on this activity, investigating where and how these workers interact, and what they might be up to. So, InSTAnT identifies RNA pairs in specific areas, observing molecular interactions that are critical for cellular functions like protein production.

“Our toolkit provides a level of detail crucial for understanding complex biological processes and how they contribute to diseases,” said Sinha, whose team tested the toolkit on a variety of datasets, including human and mouse cells, and across multiple cell types and brain regions. 

He expects InSTAnT to transform how researchers study RNA interactions and explore unknown aspects of cellular organization and function.

“I think we’ve opened new possibilities for studying how cells coordinate their activities and adapt to challenges,” said Sinha, adding, “and it was a true team effort, with two other PIs from another institution, and a talented Ph.D. student as the lead author. This is a great example of how collaboration and data-driven science can uncover new biological frontiers.”

CITATION: Aunrendra Kumar, Alex Schrader, Bhavay Aggarwal, Ali Ebrahimpour Boroojeny, Marisa Asadian, JuYeon Lee, You Jin Song, Sihai Dave Zhao, Hee-Sun Han, Saurabh Sinha. “Intracellular spatial transcriptomic analysis toolkit (InSTAnT),” Nature Communications.

FUNDING: This research was supported by the National Institutes of Health, grant Nos. R35GM131819, R35GM147420, R21HG013180, and T32- 842 GM136629; Johnson & Johnson (WiSTEM2D Award for Science). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agency.