The 20,000 genes in each human cell operate like intricate gears in a complex machine, each one turning precisely to drive specific biological processes. By tracking which gears turn and where they do, scientists can decipher how the human body’s cellular systems fail when disease occurs.
Today, a cutting-edge technique called spatial transcriptomics is transforming medical research and diagnostics by allowing scientists to analyse all those genes at once, creating visual landscapes of cell activity across tissues and organs. Past methods to pinpoint disease-related genes could only probe a few genes at a time, leaving researchers to rely heavily on educated guesses and painstaking tests.
“Spatial transcriptomics gives us both an unbiased overview of the biological pathways that underlie disease, and a detailed look at specific genes of interest,” said Principal Scientist Kok Hao Chen and Research Fellow Nigel Chou from A*STAR’s Genome Institute of Singapore (GIS).
However, the cost and time required for spatial transcriptomics currently limit its wider use in clinics and labs; imaging 20,000 genes with the latest multiplexed fluorescence in situ hybridisation (FISH) tool involves advanced equipment and weeks of data collection and analysis.
To address those barriers, Chen, Chou and colleagues at GIS developed a streamlined form of FISH by focusing on essential aspects of disease analyses—such as identifying cell types and tissue patterns—without needing the high-resolution view of single RNA molecules.
Working with GIS’s computational and clinical teams, they created a modified approach dubbed Fluorescence In Situ Hybridisation of Cellular Heterogeneity and gene expression Programs (FISHnCHIPs). The approach leverages a natural pattern seen in gene expression: co-expressed genes within a single cell tend to cluster together in tissues, which allows FISHnCHIPs to target groups of genes as a whole.
“FISHnCHIPs differs from traditional multiplexed FISH methods in that it doesn’t try to profile each gene,” said Chen and Chou. “Instead, it directly labels cell types or gene programmes—containing up to 40 genes—and creates far more intense fluorescence signals. These signals can then be harnessed to increase imaging throughput, capturing more cells per image.”
Testing the approach on mouse kidney and brain tissue samples, as well as a human colorectal cancer biopsy, the team found FISHnCHIPS not only enhanced the robustness of gene probing, but also boosted cell typing sensitivity 2- to 20-fold compared to conventional FISH. This enabled them to image up to 30 times more cells using a wider, less sensitive lens.
“FISHnCHIPs allows the assay to be performed on a standard microscope, with the combined signals from multiple genes boosting detection sensitivity,” Chen and Chou added.
While robust, FISHnCHIPs still depends on costly reference datasets which may hinder its wider adoption. However, the researchers noted that projects like the Human Cell Atlas, which aims to create a comprehensive database of healthy human tissues, can pave the way for artificial intelligence-driven universal panel tailored to multiple human tissues, making the technique a more accessible diagnostic tool.
The A*STAR-affiliated researchers contributing to this research are from the Genome Institute of Singapore (GIS).
