Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes

Xinrui Zhou, Wan Yi Seow, Norbert Ha, Teh How Cheng, Lingfan Jiang, Jeeranan Boonruangkan, Jolene Jie Lin Goh, Shyam Prabhakar, Nigel Chou () and Kok Hao Chen ()
Additional contact information
Xinrui Zhou: Agency for Science, Technology and Research (A*STAR)
Wan Yi Seow: Agency for Science, Technology and Research (A*STAR)
Norbert Ha: Agency for Science, Technology and Research (A*STAR)
Teh How Cheng: Agency for Science, Technology and Research (A*STAR)
Lingfan Jiang: Agency for Science, Technology and Research (A*STAR)
Jeeranan Boonruangkan: Agency for Science, Technology and Research (A*STAR)
Jolene Jie Lin Goh: Agency for Science, Technology and Research (A*STAR)
Shyam Prabhakar: Agency for Science, Technology and Research (A*STAR)
Nigel Chou: Agency for Science, Technology and Research (A*STAR)
Kok Hao Chen: Agency for Science, Technology and Research (A*STAR)

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.

Date: 2024
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DOI: 10.1038/s41467-024-46669-y

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