U-FISH: a fluorescent spot detector for imaging-based spatial-omics analysis and AI-assisted FISH diagnosis

Imaging-based spatial-omics advances biomedical discoveries with subcellular resolution and high sensitivity, but accurately identifying signal spots from diverse images remains challenging. We develop U-FISH, a deep learning method that enhances images for consistent spot detection across various spatial-omics data. We establish a comprehensive FISH image dataset from seven spatial-omics methods. Benchmark analysis shows U-FISH has superior accuracy and generalizability compared to existing methods and can effectively decode 3D FISH data. U-FISH is the first spot detection software integrated with large language models, as demonstrated in AI-assisted diagnostics. Our study provides a valuable tool for spatial-omics analysis and diagnostics.

Authors

Institutions

Publication Details

Journal
Genome biology
Published
2025-09-01
DOI
https://doi.org/10.1186/s13059-025-03736-x
Citations
4
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
3.22

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

U-FISH: a fluorescent spot detector for imaging-based spatial-omics analysis and AI-assisted FISH diagnosis

Chengwen Li, Changzhi Liu, Florian Mueller, He Jin et al.
4 citations
Genome biology
Single-cell and spatial transcriptomics
3.22
article

U-FISH: a fluorescent spot detector for imaging-based spatial-omics analysis and AI-assisted FISH diagnosis

Chengwen Li, Changzhi Liu, Florian Mueller, He Jin, Jinxia Dai, Huaiyuan Cai, Wei Ouyang, Hao Chen, Qian Zhang, Jiajun Yang, Xiaofeng Wu, Weize Xu, Zhengze Wang, Gang Cao, Chenghua Cui
article en
4 citations

Abstract

Imaging-based spatial-omics advances biomedical discoveries with subcellular resolution and high sensitivity, but accurately identifying signal spots from diverse images remains challenging. We develop U-FISH, a deep learning method that enhances images for consistent spot detection across various spatial-omics data. We establish a comprehensive FISH image dataset from seven spatial-omics methods. Benchmark analysis shows U-FISH has superior accuracy and generalizability compared to existing methods and can effectively decode 3D FISH data. U-FISH is the first spot detection software integrated with large language models, as demonstrated in AI-assisted diagnostics. Our study provides a valuable tool for spatial-omics analysis and diagnostics.

Genome biologyVol. 26(1)
Institut Pasteur (FR), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Hong Kong University of Science and Technology (HK), Université Paris Cité (FR), Huazhong Agricultural University (CN), Science for Life Laboratory (SE), Beike Biotechnology (China) (CN), Shenzhen University Health Science Center (CN), Shenzhen Institutes of Advanced Technology (CN), Institute of Hematology & Blood Diseases Hospital (CN), Shenzhen Technology University (CN), KTH Royal Institute of Technology (SE), Stanford University (US)
National Natural Science Foundation of China, Science, Technology and Innovation Commission of Shenzhen Municipality
Openalex Percentile: Top 8%
Single-cell and spatial transcriptomics
3.22
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.