ArticleNature communications2025
Tetrahedral DNA dendritic nanostructure-enhanced FISH for high-speed, sensitive spatial transcriptomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Engineered CRISPR/Cas12a2 Nanoprobe Imaging in Living Cells for Precise Tumor Diagnosis.Small methods · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Understanding where genes are active within tissues is essential to explain how cells build and maintain organs, yet many spatial RNA assays are slow and weak, limiting short-transcript detection and masking cellular diversity. Here we show that TDDN-FISH (Tetrahedral DNA Dendritic Nanostructure-Enhanced Fluorescence In Situ Hybridization), a rapid, enzyme-free method using self-assembling DNA nanostructures, accelerates and amplifies RNA detection. Per round, TDDN-FISH is ~eightfold faster than HCR-FISH and generates stronger signals than smFISH, enabling short-RNA detection and low-magnification tissue imaging with single-cell and subcellular resolution. Iterative, multiplexed hybridization produces color-coded readouts for many targets in the same specimen, supporting high-throughput spatial transcriptomics. We apply TDDN-FISH to cultured cells and tissue sections to map RNA distributions with high specificity and reveal complex expression patterns. This platform streamlines workflows and broadens access to spatial RNA profiling for studies of cellular heterogeneity, tissue organization, and disease mechanisms.
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Registered trials
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