Cross hybridization inference for phylogenetic resolution-fluorescence in situ hybridization enables microbiome imaging with strain level taxonomic resolution
Abstract
The spatial organization of microbial communities is a critical determinant of host–microbe interactions, yet species-level mapping remains challenging due to high 16S rRNA sequence homology and spectral crosstalk in multiplexed fluorescence in situ hybridization (FISH). To address this challenge, we developed Cross-hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH, a pipeline that integrates strategic probe design with supervised machine learning. CIPHR-FISH transforms probe cross-hybridization, traditionally viewed as experimental noise, into informative molecular signatures. Using a gnotobiotic zebrafish model colonized with a defined mix of 10 zebrafish bacterial strains, we trained a support vector machine on empirical hybridization patterns from pure bacterial cultures. CIPHR-FISH achieved 99.2% macro-averaged accuracy, significantly outperforming standard linear unmixing (62.5%), and successfully discriminated strains with 99.7% sequence homology. Applying this tool to gnotobiotic zebrafish larvae revealed distinct biogeographies: the intestinal bulb hosted highly structured, multi-layered polymicrobial aggregates, while the skin exhibited sparse, uniformly dispersed individual bacterial cells. Notably, we observed significant variation in spatial community structure that was obscured by traditional bulk 16S rRNA sequencing. CIPHR-FISH provides a robust framework for high-resolution spatial biology by converting the limitations of molecular labeling into a rich data source for taxonomic classification. This approach enables the quantification of micro-scale ecological and stochastic forces that shape the microbiome across hosts.
Related
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BibTeX
@article{adade2026cross,
title = {{Cross hybridization inference for phylogenetic resolution-fluorescence in situ hybridization enables microbiome imaging with strain level taxonomic resolution}},
author = {Emmanuel E Adade and Ruogu Wang and Colin M Henneberry and Alex A Lemus and Rebecca J Stevick and David Perez-Pascual and Bianca Audrain and Alexa J Orsino and Dylan R Farnsworth and Jean-Marc Ghigo and Alex M Valm},
journal = {ISME Communications},
year = {2026},
month = oct,
doi = {10.1093/ismeco/ycag296},
url = {https://doi.org/10.1093/ismeco/ycag296},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Cross hybridization inference for phylogenetic resolution-fluorescence in situ hybridization enables microbiome imaging with strain level taxonomic resolution
AU - Emmanuel E Adade
AU - Ruogu Wang
AU - Colin M Henneberry
AU - Alex A Lemus
AU - Rebecca J Stevick
AU - David Perez-Pascual
AU - Bianca Audrain
AU - Alexa J Orsino
AU - Dylan R Farnsworth
AU - Jean-Marc Ghigo
AU - Alex M Valm
PY - 2026
DA - 2026/10/13
JO - ISME Communications
DO - 10.1093/ismeco/ycag296
UR - https://doi.org/10.1093/ismeco/ycag296
AB - The spatial organization of microbial communities is a critical determinant of host–microbe interactions, yet species-level mapping remains challenging due to high 16S rRNA sequence homology and spectral crosstalk in multiplexed fluorescence in situ hybridization (FISH). To address this challenge, we developed Cross-hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH, a pipeline that integrates strategic probe design with supervised machine learning. CIPHR-FISH transforms probe cross-hybridization, traditionally viewed as experimental noise, into informative molecular signatures. Using a gnotobiotic zebrafish model colonized with a defined mix of 10 zebrafish bacterial strains, we trained a support vector machine on empirical hybridization patterns from pure bacterial cultures. CIPHR-FISH achieved 99.2% macro-averaged accuracy, significantly outperforming standard linear unmixing (62.5%), and successfully discriminated strains with 99.7% sequence homology. Applying this tool to gnotobiotic zebrafish larvae revealed distinct biogeographies: the intestinal bulb hosted highly structured, multi-layered polymicrobial aggregates, while the skin exhibited sparse, uniformly dispersed individual bacterial cells. Notably, we observed significant variation in spatial community structure that was obscured by traditional bulk 16S rRNA sequencing. CIPHR-FISH provides a robust framework for high-resolution spatial biology by converting the limitations of molecular labeling into a rich data source for taxonomic classification. This approach enables the quantification of micro-scale ecological and stochastic forces that shape the microbiome across hosts.
SN - 2730-6151
ER -