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...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-ona-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 labon-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 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.展开更多
基金the National Key R&D Program of China(2023YFF0721500)The National Natural Science Foundation of China(Nos.62374102,82172077,22201298,and 62027825)+3 种基金The Innovation and Entrepreneurship Team of Jiangsu Province(JSSCTD202145)The Science and Technology Innovation Project of Foshan,Guangdong Province,China(No.1920001000047)The Science and Technology Development Project of Jilin Province(No.20210204110YY and 20250204092YY)The Suzhou Basic Research Project(SSD2023013).
文摘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-ona-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 labon-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 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.
