Citation: Hang Xu, Shijie Lu, Yike Song, Jiale Zhou, Bin Shen, Yejun Zou, Zhuo Zhang, Yuzheng Zhao, Huifeng Wang. Artificial intelligence-driven multimodal image analyzer for genetically encoded fluorescence sensors on cell division metabolism dynamics[J]. Chinese Chemical Letters, ;2026, 37(8): 111757. doi: 10.1016/j.cclet.2025.111757 shu

Artificial intelligence-driven multimodal image analyzer for genetically encoded fluorescence sensors on cell division metabolism dynamics

Hang Xu ^{a, 1} ,
Shijie Lu ^{b, 1} ,
Yike Song ^{b, 1} ,
Jiale Zhou ^a ,
Bin Shen ^a ,
Yejun Zou ^c ,
Zhuo Zhang ^b ,
Yuzheng Zhao ^{b, *,} ,
Huifeng Wang ^{a, b, *,}

a.
Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
b.
Optogenetics & Synthetic Biology Interdisciplinary Research Center, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
c.
Tianfu Jincheng Laboratory, Chengdu 610212, China

yuzhengzhao@ecust.edu.cn

whuifeng@ecust.edu.cn

Received Date: 22 April 2025
Revised Date: 22 August 2025
Accepted Date: 25 August 2025
Available Online: 26 August 2025

Figures(5)

Genetically encoded fluorescence (FL) sensors play vital roles in monitoring cell metabolism dynamics, gene transcription, DNA repair, apoptosis, and nutrient sensing. In genetically encoded FL sensors study, microscope acquires multimodal images that contain bright-field (BF) and FL images. BF and FL images can provide different information on cell phenotypes and gray level. Here, we developed an artificial intelligence (AI)-driven system “AI-cell metabolism dynamic analyzer (AI-CMDA)” for high-throughput automatically cell division metabolism dynamics processing by fusing multimodal information. The system consists of a deep learning model based on multimodal images dedicated to division nodes extraction and an adaptive correlation filter tracker for associating cell sequences. The division extraction method enables fast filtering of dividing cells without manual selection, and the adaptive correlation filter can achieve robust, accurate tracking during cell transforms with time. We apply the system with 3 genetically encoded FL sensors for nicotinamide adenine dinucleotides (NAD⁺/NADH) ratio, reduced nicotinamide adenine dinucleotide phosphate (NADPH) and H₂O₂, respectively, to monitor the redox metabolism within cell division sequences. The results show that this system can reduce the processing time to seconds compared with several hours’ manual labeling and can achieve accuracy and fastness.
- Genetically encoded fluorescence sensor,
- Multimodal images,
- Artificial intelligence,
- Single-cell metabolism dynamics,
- Cell division metabolism,
- Cell tracking
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