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Updated: May 14, 2026
09:31
Visualization of Gut Microbiota-host Interactions via Fluorescence In Situ Hybridization, Lectin Staining, and Imaging
Published on: July 9, 2021
Cross hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH enables microbiome imaging with strain level
Emmanuel E Adade1,2, Ruogu Wang1,2, Colin M Henneberry1
1Department of Biology, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.
Biorxiv : the Preprint Server for Biology
|May 13, 2026
Summary
We developed Cross-hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH, a new method using machine learning to map microbial communities in hosts. This technique accurately identifies bacteria, revealing complex spatial structures previously hidden by traditional sequencing.
Area of Science:
- Microbiology
- Bioinformatics
- Spatial Biology
Background:
- Microbial community spatial organization is vital for host interactions.
- Species-level mapping is difficult due to high sequence homology and spectral overlap in FISH.
Purpose of the Study:
- To develop a novel pipeline, CIPHR-FISH, for high-resolution spatial mapping of microbial communities.
- To leverage probe cross-hybridization and spectral overlap as informative signals for taxonomic classification.
Main Methods:
- Integrated strategic probe design with supervised machine learning (SVM).
- Trained a support vector machine on empirical hybridization patterns from pure bacterial cultures in a gnotobiotic zebrafish model.
- Validated CIPHR-FISH on gnotobiotic zebrafish larvae.
Main Results:
- Achieved 99.2% macro-averaged accuracy, significantly outperforming linear unmixing (62.5%).
- Successfully discriminated bacterial strains with 99.7% sequence homology.
- Revealed distinct intestinal and skin biogeographies, with significant inter-individual variation in spatial structure.
Conclusions:
- CIPHR-FISH offers a robust, scalable framework for high-resolution spatial biology.
- Converts limitations of molecular labeling into a rich data source for taxonomic classification.
- Enables quantification of micro-scale ecological forces shaping host microbiomes.
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