Microproteomics, the profiling of protein expressions in small cell populations or individual cells, is essential for understanding complex biological systems. However, sample loss and insufficient sensitivity of anal...Microproteomics, the profiling of protein expressions in small cell populations or individual cells, is essential for understanding complex biological systems. However, sample loss and insufficient sensitivity of analytical techniques pose severe challenges to this field. Microfluidics, particularly droplet-based microfluidics, provides an ideal approach by enabling miniaturized and integrated workflows to process samples and offers several advantages, including reduced sample loss, low reagent consumption, faster reaction times, and improved throughput. Droplet-based microfluidics manipulates droplets of fluids to function as discrete reaction units, enabling complex chemical reactions and biological workflows in a miniaturized setting. This article discusses a variety of on-chip functions of droplet-based microfluidics,including cell sorting, cell culture, and sample processing. We then highlight recent advances in the mass spectrometry(MS)-based analysis of single cells using droplet-based microfluidic platforms, including digital microfluidics(DMF). Finally, we review the integrated DMF–MS systems that enable automated and parallel proteomic profiling of single cells with high sensitivity and discuss the applications of the technology and its future perspectives.展开更多
Background Chronic obstructive pulmonary disease(COPD)is a severe public health problem.Cigarette smoke(CS)is a risk factor for COPD and lung cancer.The underlying molecular mechanisms of CS-induced malignant transfor...Background Chronic obstructive pulmonary disease(COPD)is a severe public health problem.Cigarette smoke(CS)is a risk factor for COPD and lung cancer.The underlying molecular mechanisms of CS-induced malignant transformation of bronchial epithelial cells remain unclear.In this study,we describe a lung-on-a-chip to explore the possible mechanistic link between cigarette smoke extract(CSE)-associated COPD and lung cancer.Methods An in vitro lung-on-a-chip model was used to simulate pulmonary epithelial cells and vascular endothelial cells with CSE.The levels of IL-6 and TNF-αwere tested as indicators of inflammation using an enzyme-linked immune sorbent assay.Apical junction complex mRNA expression was detected with qRT-PCR as the index of epithelial-to-mesenchymal transition(EMT).The effects of CSE on the phosphorylation of signal transduction and transcriptional activator 3(STAT3)were detected by Western blotting.Flow cytometry was performed to investigate the effects of this proto-oncogene on cell cycle distribution.Results Inflammation caused by CSE was achieved in a lung-on-a-chip model with a mimetic movement.CSE exposure induced the degradation of intercellular connections and triggered the EMT process.CSE exposure also activated the phosphorylation of proto-oncogene STAT3,while these effects were inhibited with HJC0152.Conclusions CSE exposure in the lung-on-a-chip model caused activation of STAT3 in epithelial cells and endothelial cells.HJC0152,an inhibitor of activated STAT3,could be a potential treatment for CS-associated COPD and lung cancer.展开更多
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
An active-matrix electrowetting-on-dielectric(AM-EWOD)system integrates hundreds of thousands of active electrodes for sample droplet manipulation,which can enable simultaneous,automatic,and parallel on-chip biochemic...An active-matrix electrowetting-on-dielectric(AM-EWOD)system integrates hundreds of thousands of active electrodes for sample droplet manipulation,which can enable simultaneous,automatic,and parallel on-chip biochemical reactions.A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps.In this work,we demonstrated an artificial intelligence(Al)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks.We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods.We used the YOLOv8 model to monitor the droplet-splitting process online.A 97.76% splitting success rate was observed with 18 different AM-EWOD chips.A 99.982% model precision rate and a 99.980%model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets.Compared with manual verification,the model achieved 99.260%and 99.193%precision and recall rates,respectively.In addition,single-cell droplet sorting and routing experiments were demonstrated.With an Al-based smart detection system,AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.展开更多
基金supported by National Natural Science Foundation of China (62103050)National Key Research and Development Program of China (2022YFA1207100 and 2023YFE0112400)+3 种基金Beijing Natural Science Foundation (2242018)BIT Research and Innovation Promoting Project (2023CX01002)Open Research Fund of State Key Laboratory of Digital Medical Engineering (2023-K02)Open Research Fund of State Key Laboratory of Optoelectronic Materials and Technologies (Sun Yat-sen University) (OEMT-2022KF-09)。
文摘Microproteomics, the profiling of protein expressions in small cell populations or individual cells, is essential for understanding complex biological systems. However, sample loss and insufficient sensitivity of analytical techniques pose severe challenges to this field. Microfluidics, particularly droplet-based microfluidics, provides an ideal approach by enabling miniaturized and integrated workflows to process samples and offers several advantages, including reduced sample loss, low reagent consumption, faster reaction times, and improved throughput. Droplet-based microfluidics manipulates droplets of fluids to function as discrete reaction units, enabling complex chemical reactions and biological workflows in a miniaturized setting. This article discusses a variety of on-chip functions of droplet-based microfluidics,including cell sorting, cell culture, and sample processing. We then highlight recent advances in the mass spectrometry(MS)-based analysis of single cells using droplet-based microfluidic platforms, including digital microfluidics(DMF). Finally, we review the integrated DMF–MS systems that enable automated and parallel proteomic profiling of single cells with high sensitivity and discuss the applications of the technology and its future perspectives.
基金This work was supported by National Natural Science Foundation of China(Grant Nos.81672297,Grant Nos.61701493)Policy Guidance project(International Science and Technology Cooperation)of Jiangsu Province of China(BZ2018040)+3 种基金the Natural Science Foundation of Tianjin,P.R.China(18JCYBJC42100)Hundred Talents Program of the Chinese Academy of SciencesProject Funded by China Postdoctoral Science Foundation(2019M651959)Postdoctoral Research Funding Program of Jiangsu Province(2018K004B).
文摘Background Chronic obstructive pulmonary disease(COPD)is a severe public health problem.Cigarette smoke(CS)is a risk factor for COPD and lung cancer.The underlying molecular mechanisms of CS-induced malignant transformation of bronchial epithelial cells remain unclear.In this study,we describe a lung-on-a-chip to explore the possible mechanistic link between cigarette smoke extract(CSE)-associated COPD and lung cancer.Methods An in vitro lung-on-a-chip model was used to simulate pulmonary epithelial cells and vascular endothelial cells with CSE.The levels of IL-6 and TNF-αwere tested as indicators of inflammation using an enzyme-linked immune sorbent assay.Apical junction complex mRNA expression was detected with qRT-PCR as the index of epithelial-to-mesenchymal transition(EMT).The effects of CSE on the phosphorylation of signal transduction and transcriptional activator 3(STAT3)were detected by Western blotting.Flow cytometry was performed to investigate the effects of this proto-oncogene on cell cycle distribution.Results Inflammation caused by CSE was achieved in a lung-on-a-chip model with a mimetic movement.CSE exposure induced the degradation of intercellular connections and triggered the EMT process.CSE exposure also activated the phosphorylation of proto-oncogene STAT3,while these effects were inhibited with HJC0152.Conclusions CSE exposure in the lung-on-a-chip model caused activation of STAT3 in epithelial cells and endothelial cells.HJC0152,an inhibitor of activated STAT3,could be a potential treatment for CS-associated COPD and lung cancer.
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.
基金This research was funded by:The National Key R&D Program of China(2023YFF0721500)The National Natural Science Foundation of China(No.62374102,82172077,22201298,and 62027825)+2 种基金The Science and Technology Innovation Project of Foshan,Guangdong Province,China(No.1920001000047)The Science and Technology Development Project of jilin Province(No.20210204110YY)The Suzhou Basic Research Project.(SSD2023013).
文摘An active-matrix electrowetting-on-dielectric(AM-EWOD)system integrates hundreds of thousands of active electrodes for sample droplet manipulation,which can enable simultaneous,automatic,and parallel on-chip biochemical reactions.A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps.In this work,we demonstrated an artificial intelligence(Al)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks.We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods.We used the YOLOv8 model to monitor the droplet-splitting process online.A 97.76% splitting success rate was observed with 18 different AM-EWOD chips.A 99.982% model precision rate and a 99.980%model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets.Compared with manual verification,the model achieved 99.260%and 99.193%precision and recall rates,respectively.In addition,single-cell droplet sorting and routing experiments were demonstrated.With an Al-based smart detection system,AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.
