Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics
Abstract Single-cell analysis is crucial for deciphering cellular heterogeneity and understanding complex biological systems. However, most existing single-cell sample manipulation (SCSM) systems suffer from various drawbacks such as high cost, low throughput, and heavy reliance on human interventio...
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| Format: | Article |
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Nature Publishing Group
2025-07-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-025-00962-y |
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| author | Zhiqiang Jia Chen Jiang Jiahao Li Yacine Belgaid Mingfeng Ge Li Li Siyi Hu Xing Huang Tsung-Yi Ho Wenfei Dong Zhiwen Yu Hanbin Ma |
| author_facet | Zhiqiang Jia Chen Jiang Jiahao Li Yacine Belgaid Mingfeng Ge Li Li Siyi Hu Xing Huang Tsung-Yi Ho Wenfei Dong Zhiwen Yu Hanbin Ma |
| author_sort | Zhiqiang Jia |
| collection | DOAJ |
| description | Abstract Single-cell analysis is crucial for deciphering cellular heterogeneity and understanding complex biological systems. However, most existing single-cell sample manipulation (SCSM) systems suffer from various drawbacks such as high cost, low throughput, and heavy reliance on human interventions. Currently, large language models (LLMs) have been used in robotic platforms, but a limited number of studies have reported the application of LLMs in the field of lab-on-a-chip automation. Consequently, we have developed an active-matrix digital microfluidic (AM-DMF) platform that realizes fully automated biological procedures for intelligent SCSM. By combining this with a fully programmable lab-on-a-chip system, we present a breakthrough for SCSM by combining LLMs and object detection technologies. With the proposed platform, the single-cell sample generation rate and identification precision reach up to 25% and 98%, respectively, which are much higher than the existing platforms in terms of SCSM efficiency and performance. Furthermore, a three-class detection method considering droplet edges is implemented to realize the automatic identification of cells and oil bubbles. This method achieves a 1.0% improvement in cell recognition accuracy according to the $${\rm{AP}}_{75}^{\rm{test}}$$ AP 75 test metric, while efficiently distinguishing obscured cells at droplet edges, where approximately 20% of all droplets contain cells at their edges. More importantly, as the first attempt, a ubiquitous tool for automatic SCSM workflow generation is developed based on the LLMs, thus advancing the development and progression of the field of single-cell analysis in the life sciences. |
| format | Article |
| id | doaj-art-e46071b8cbef43adb77434d389bb11cc |
| institution | Kabale University |
| issn | 2055-7434 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Microsystems & Nanoengineering |
| spelling | doaj-art-e46071b8cbef43adb77434d389bb11cc2025-08-20T03:45:49ZengNature Publishing GroupMicrosystems & Nanoengineering2055-74342025-07-0111111510.1038/s41378-025-00962-yIntelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidicsZhiqiang Jia0Chen Jiang1Jiahao Li2Yacine Belgaid3Mingfeng Ge4Li Li5Siyi Hu6Xing Huang7Tsung-Yi Ho8Wenfei Dong9Zhiwen Yu10Hanbin Ma11CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesDepartment of Computer Science and Engineer, The Chinese University of Hong KongACX Instruments Ltd, St John’s Innovation Centre, Cowley RoadACX Instruments Ltd, St John’s Innovation Centre, Cowley RoadCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSchool of Computer Science, Northwestern Polytechnical UniversityDepartment of Computer Science and Engineer, The Chinese University of Hong KongCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSchool of Computer Science, Northwestern Polytechnical UniversityCAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesAbstract Single-cell analysis is crucial for deciphering cellular heterogeneity and understanding complex biological systems. However, most existing single-cell sample manipulation (SCSM) systems suffer from various drawbacks such as high cost, low throughput, and heavy reliance on human interventions. Currently, large language models (LLMs) have been used in robotic platforms, but a limited number of studies have reported the application of LLMs in the field of lab-on-a-chip automation. Consequently, we have developed an active-matrix digital microfluidic (AM-DMF) platform that realizes fully automated biological procedures for intelligent SCSM. By combining this with a fully programmable lab-on-a-chip system, we present a breakthrough for SCSM by combining LLMs and object detection technologies. With the proposed platform, the single-cell sample generation rate and identification precision reach up to 25% and 98%, respectively, which are much higher than the existing platforms in terms of SCSM efficiency and performance. Furthermore, a three-class detection method considering droplet edges is implemented to realize the automatic identification of cells and oil bubbles. This method achieves a 1.0% improvement in cell recognition accuracy according to the $${\rm{AP}}_{75}^{\rm{test}}$$ AP 75 test metric, while efficiently distinguishing obscured cells at droplet edges, where approximately 20% of all droplets contain cells at their edges. More importantly, as the first attempt, a ubiquitous tool for automatic SCSM workflow generation is developed based on the LLMs, thus advancing the development and progression of the field of single-cell analysis in the life sciences.https://doi.org/10.1038/s41378-025-00962-y |
| spellingShingle | Zhiqiang Jia Chen Jiang Jiahao Li Yacine Belgaid Mingfeng Ge Li Li Siyi Hu Xing Huang Tsung-Yi Ho Wenfei Dong Zhiwen Yu Hanbin Ma Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics Microsystems & Nanoengineering |
| title | Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics |
| title_full | Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics |
| title_fullStr | Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics |
| title_full_unstemmed | Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics |
| title_short | Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics |
| title_sort | intelligent single cell manipulation llms and object detection enhanced active matrix digital microfluidics |
| url | https://doi.org/10.1038/s41378-025-00962-y |
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