Pub Date : 2024-01-25eCollection Date: 2024-01-01DOI: 10.1063/5.0172550
Meenakshi Pinnenti, Muhammad Ahsan Sami, Umer Hassan
Chronic myelogenous/myeloid leukemia (CML) is a type of cancer of bone marrow that arises from hematopoietic stem cells and affects millions of people worldwide. Eighty-five percent of the CML cases are diagnosed during chronic phase, most of which are detected through routine tests. Leukocytes, micro-Ribonucleic Acids, and myeloid markers are the primary biomarkers for CML diagnosis and are mainly detected using real-time reverse transcription polymerase chain reaction, flow cytometry, and genetic testing. Though multiple therapies have been developed to treat CML, early detection still plays a pivotal role in the overall patient survival rate. The current technologies used for CML diagnosis are costly and are confined to laboratory settings which impede their application in the point-of-care settings for early-stage detection of CML. This study provides detailed analysis and insights into the significance of CML, patient symptoms, biomarkers used for testing, and best possible detection techniques responsible for the enhancement in survival rates. A critical and detailed review is provided around potential microfluidic devices that can be adapted to detect the biomarkers associated with CML while enabling point-of-care testing for early diagnosis of CML to improve patient survival rates.
{"title":"Enabling biomedical technologies for chronic myelogenous leukemia (CML) biomarkers detection.","authors":"Meenakshi Pinnenti, Muhammad Ahsan Sami, Umer Hassan","doi":"10.1063/5.0172550","DOIUrl":"10.1063/5.0172550","url":null,"abstract":"<p><p>Chronic myelogenous/myeloid leukemia (CML) is a type of cancer of bone marrow that arises from hematopoietic stem cells and affects millions of people worldwide. Eighty-five percent of the CML cases are diagnosed during chronic phase, most of which are detected through routine tests. Leukocytes, micro-Ribonucleic Acids, and myeloid markers are the primary biomarkers for CML diagnosis and are mainly detected using real-time reverse transcription polymerase chain reaction, flow cytometry, and genetic testing. Though multiple therapies have been developed to treat CML, early detection still plays a pivotal role in the overall patient survival rate. The current technologies used for CML diagnosis are costly and are confined to laboratory settings which impede their application in the point-of-care settings for early-stage detection of CML. This study provides detailed analysis and insights into the significance of CML, patient symptoms, biomarkers used for testing, and best possible detection techniques responsible for the enhancement in survival rates. A critical and detailed review is provided around potential microfluidic devices that can be adapted to detect the biomarkers associated with CML while enabling point-of-care testing for early diagnosis of CML to improve patient survival rates.</p>","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"18 1","pages":"011501"},"PeriodicalIF":2.6,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10817778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139569875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-23eCollection Date: 2024-01-01DOI: 10.1063/5.0175178
Gianna A Slusher, Peter A Kottke, Austin L Culberson, Mason A Chilmonczyk, Andrei G Fedorov
In recent years, cell-based therapies have transformed medical treatment. These therapies present a multitude of challenges associated with identifying the mechanism of action, developing accurate safety and potency assays, and achieving low-cost product manufacturing at scale. The complexity of the problem can be attributed to the intricate composition of the therapeutic products: living cells with complex biochemical compositions. Identifying and measuring critical quality attributes (CQAs) that impact therapy success is crucial for both the therapy development and its manufacturing. Unfortunately, current analytical methods and tools for identifying and measuring CQAs are limited in both scope and speed. This Perspective explores the potential for microfluidic-enabled mass spectrometry (MS) systems to comprehensively characterize CQAs for cell-based therapies, focusing on secretome, intracellular metabolome, and surfaceome biomarkers. Powerful microfluidic sampling and processing platforms have been recently presented for the secretome and intracellular metabolome, which could be implemented with MS for fast, locally sampled screening of the cell culture. However, surfaceome analysis remains limited by the lack of rapid isolation and enrichment methods. Developing innovative microfluidic approaches for surface marker analysis and integrating them with secretome and metabolome measurements using a common analytical platform hold the promise of enhancing our understanding of CQAs across all "omes," potentially revolutionizing cell-based therapy development and manufacturing for improved efficacy and patient accessibility.
近年来，细胞疗法改变了医疗方式。这些疗法在确定作用机理、开发精确的安全性和有效性检测方法以及实现低成本规模化产品生产方面面临着诸多挑战。问题的复杂性可归因于治疗产品错综复杂的成分：具有复杂生化成分的活细胞。识别和测量影响疗法成功与否的关键质量属性 (CQAs) 对于疗法的开发和生产都至关重要。遗憾的是，目前用于识别和测量 CQA 的分析方法和工具在范围和速度上都很有限。本视角探讨了微流控质谱（MS）系统全面表征细胞疗法CQAs的潜力，重点关注分泌组、细胞内代谢组和表面组生物标记物。最近针对分泌物组和细胞内代谢组推出了功能强大的微流控采样和处理平台，该平台可与 MS 结合使用，对细胞培养物进行快速、局部采样筛选。然而，表面组分析仍然受到缺乏快速分离和富集方法的限制。开发用于表面标记分析的创新微流控方法，并使用通用分析平台将其与分泌组和代谢组测量相结合，有望增强我们对所有 "omes "的CQAs的了解，从而有可能彻底改变细胞疗法的开发和生产，提高疗效和患者的可及性。
{"title":"Microfluidics enabled multi-omics triple-shot mass spectrometry for cell-based therapies.","authors":"Gianna A Slusher, Peter A Kottke, Austin L Culberson, Mason A Chilmonczyk, Andrei G Fedorov","doi":"10.1063/5.0175178","DOIUrl":"10.1063/5.0175178","url":null,"abstract":"<p><p>In recent years, cell-based therapies have transformed medical treatment. These therapies present a multitude of challenges associated with identifying the mechanism of action, developing accurate safety and potency assays, and achieving low-cost product manufacturing at scale. The complexity of the problem can be attributed to the intricate composition of the therapeutic products: living cells with complex biochemical compositions. Identifying and measuring critical quality attributes (CQAs) that impact therapy success is crucial for both the therapy development and its manufacturing. Unfortunately, current analytical methods and tools for identifying and measuring CQAs are limited in both scope and speed. This Perspective explores the potential for microfluidic-enabled mass spectrometry (MS) systems to comprehensively characterize CQAs for cell-based therapies, focusing on secretome, intracellular metabolome, and surfaceome biomarkers. Powerful microfluidic sampling and processing platforms have been recently presented for the secretome and intracellular metabolome, which could be implemented with MS for fast, locally sampled screening of the cell culture. However, surfaceome analysis remains limited by the lack of rapid isolation and enrichment methods. Developing innovative microfluidic approaches for surface marker analysis and integrating them with secretome and metabolome measurements using a common analytical platform hold the promise of enhancing our understanding of CQAs across all \"omes,\" potentially revolutionizing cell-based therapy development and manufacturing for improved efficacy and patient accessibility.</p>","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"18 1","pages":"011302"},"PeriodicalIF":2.6,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10807926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139544975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.
{"title":"Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry","authors":"Jian Wei, Wenbing Gao, Xinlong Yang, Zhuotong Yu, Fei Su, Chengwu Han, Xiaoxing Xing","doi":"10.1063/5.0181287","DOIUrl":"https://doi.org/10.1063/5.0181287","url":null,"abstract":"Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"116 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139556603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
96-well microtiter plates, widely used in immunoassays, face challenges such as prolonged assay time and limited sensitivity due to the lack of analyte transport control. Orbital shakers, commonly employed to facilitate mass transport, offer limited improvements and can introduce assay inconsistencies. While microfluidic devices offer performance enhancements, their complexity and incompatibility with existing platforms limit their wide adoption. This study introduces a novel microfluidic 96-well cover designed to convert a standard 96-well plate to a mass-transport-controlled surface bioreactor. The cover employs microfluidic methods to enhance the diffusion flux of analytes toward the receptors immobilized on the well bottom. Both simulation and experimental results demonstrated that the cover significantly enhances the capture rate of analyte molecules, resulting in increased signal strength for various detection methods and a lower detection limit. The cover serves as an effective add-on to standard 96-well plates, offering enhanced assay performance without requiring modifications to existing infrastructure or reagents. This innovation holds promise for improving the efficiency and reliability of microtiter plate based immunoassays.
{"title":"A microfluidic cover converts a standard 96-well plate into a mass-transport-controlled immunoassay system","authors":"Sheng Wang, You Zhou, Zhenyu Li","doi":"10.1063/5.0183651","DOIUrl":"https://doi.org/10.1063/5.0183651","url":null,"abstract":"96-well microtiter plates, widely used in immunoassays, face challenges such as prolonged assay time and limited sensitivity due to the lack of analyte transport control. Orbital shakers, commonly employed to facilitate mass transport, offer limited improvements and can introduce assay inconsistencies. While microfluidic devices offer performance enhancements, their complexity and incompatibility with existing platforms limit their wide adoption. This study introduces a novel microfluidic 96-well cover designed to convert a standard 96-well plate to a mass-transport-controlled surface bioreactor. The cover employs microfluidic methods to enhance the diffusion flux of analytes toward the receptors immobilized on the well bottom. Both simulation and experimental results demonstrated that the cover significantly enhances the capture rate of analyte molecules, resulting in increased signal strength for various detection methods and a lower detection limit. The cover serves as an effective add-on to standard 96-well plates, offering enhanced assay performance without requiring modifications to existing infrastructure or reagents. This innovation holds promise for improving the efficiency and reliability of microtiter plate based immunoassays.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"30 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computer vision has become a powerful tool in the field of biosensing, aiding in the development of innovative and precise systems for the analysis and interpretation of biological data. This interdisciplinary approach harnesses the capabilities of computer vision algorithms and techniques to extract valuable information from various biosensing applications, including medical diagnostics, environmental monitoring, and food health. Despite years of development, there is still significant room for improvement in this area. In this perspective, we outline how computer vision is applied to raw sensor data in biosensors and its advantages to biosensing applications. We then discuss ongoing research and developments in the field and subsequently explore the challenges and opportunities that computer vision faces in biosensor applications. We also suggest directions for future work, ultimately underscoring the significant impact of computer vision on advancing biosensing technologies and their applications.
{"title":"A perspective on computer vision in biosensing","authors":"Li Liu, Ke Du","doi":"10.1063/5.0185732","DOIUrl":"https://doi.org/10.1063/5.0185732","url":null,"abstract":"Computer vision has become a powerful tool in the field of biosensing, aiding in the development of innovative and precise systems for the analysis and interpretation of biological data. This interdisciplinary approach harnesses the capabilities of computer vision algorithms and techniques to extract valuable information from various biosensing applications, including medical diagnostics, environmental monitoring, and food health. Despite years of development, there is still significant room for improvement in this area. In this perspective, we outline how computer vision is applied to raw sensor data in biosensors and its advantages to biosensing applications. We then discuss ongoing research and developments in the field and subsequently explore the challenges and opportunities that computer vision faces in biosensor applications. We also suggest directions for future work, ultimately underscoring the significant impact of computer vision on advancing biosensing technologies and their applications.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"1 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cancer spatial and temporal heterogeneity fuels resistance to therapies. To realize the routine assessment of cancer prognosis and treatment, we demonstrate the development of an Intelligent Disease Detection Tool (IDDT), a microfluidic-based tumor model integrated with deep learning-assisted algorithmic analysis. IDDT was clinically validated with liquid blood biopsy samples (n = 71) from patients with various types of cancers (e.g., breast, gastric, and lung cancer) and healthy donors, requiring low sample volume (∼200 μl) and a high-throughput 3D tumor culturing system (∼300 tumor clusters). To support automated algorithmic analysis, intelligent decision-making, and precise segmentation, we designed and developed an integrative deep neural network, which includes Mask Region-Based Convolutional Neural Network (Mask R-CNN), vision transformer, and Segment Anything Model (SAM). Our approach significantly reduces the manual labeling time by up to 90% with a high mean Intersection Over Union (mIoU) of 0.902 and immediate results (<2 s per image) for clinical cohort classification. The IDDT can accurately stratify healthy donors (n = 12) and cancer patients (n = 55) within their respective treatment cycle and cancer stage, resulting in high precision (∼99.3%) and high sensitivity (∼98%). We envision that our patient-centric IDDT provides an intelligent, label-free, and cost-effective approach to help clinicians make precise medical decisions and tailor treatment strategies for each patient.
{"title":"Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine","authors":"Haojun Hua, Yunlan Zhou, Wei Li, Jing Zhang, Yanlin Deng, Bee Luan Khoo","doi":"10.1063/5.0172146","DOIUrl":"https://doi.org/10.1063/5.0172146","url":null,"abstract":"Cancer spatial and temporal heterogeneity fuels resistance to therapies. To realize the routine assessment of cancer prognosis and treatment, we demonstrate the development of an Intelligent Disease Detection Tool (IDDT), a microfluidic-based tumor model integrated with deep learning-assisted algorithmic analysis. IDDT was clinically validated with liquid blood biopsy samples (n = 71) from patients with various types of cancers (e.g., breast, gastric, and lung cancer) and healthy donors, requiring low sample volume (∼200 μl) and a high-throughput 3D tumor culturing system (∼300 tumor clusters). To support automated algorithmic analysis, intelligent decision-making, and precise segmentation, we designed and developed an integrative deep neural network, which includes Mask Region-Based Convolutional Neural Network (Mask R-CNN), vision transformer, and Segment Anything Model (SAM). Our approach significantly reduces the manual labeling time by up to 90% with a high mean Intersection Over Union (mIoU) of 0.902 and immediate results (&lt;2 s per image) for clinical cohort classification. The IDDT can accurately stratify healthy donors (n = 12) and cancer patients (n = 55) within their respective treatment cycle and cancer stage, resulting in high precision (∼99.3%) and high sensitivity (∼98%). We envision that our patient-centric IDDT provides an intelligent, label-free, and cost-effective approach to help clinicians make precise medical decisions and tailor treatment strategies for each patient.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"6 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hye-Ran Moon, Nishanth Surianarayanan, Tarun Singh, Bumsoo Han
Microphysiological systems (MPSs), also known as organ-on-chip or disease-on-chip, have recently emerged to reconstitute the in vivo cellular microenvironment of various organs and diseases on in vitro platforms. These microfluidics-based platforms are developed to provide reliable drug discovery and regulatory evaluation testbeds. Despite recent emergences and advances of various MPS platforms, their adoption of drug discovery and evaluation processes still lags. This delay is mainly due to a lack of rigorous standards with reproducibility and reliability, and practical difficulties to be adopted in pharmaceutical research and industry settings. This review discusses the current and potential use of MPS platforms in drug discovery processes while considering the context of several key steps during drug discovery processes, including target identification and validation, preclinical evaluation, and clinical trials. Opportunities and challenges are also discussed for the broader dissemination and adoption of MPSs in various drug discovery and regulatory evaluation steps. Addressing these challenges will transform long and expensive drug discovery and evaluation processes into more efficient discovery, screening, and approval of innovative drugs.
{"title":"Microphysiological systems as reliable drug discovery and evaluation tools: Evolution from innovation to maturity","authors":"Hye-Ran Moon, Nishanth Surianarayanan, Tarun Singh, Bumsoo Han","doi":"10.1063/5.0179444","DOIUrl":"https://doi.org/10.1063/5.0179444","url":null,"abstract":"Microphysiological systems (MPSs), also known as organ-on-chip or disease-on-chip, have recently emerged to reconstitute the in vivo cellular microenvironment of various organs and diseases on in vitro platforms. These microfluidics-based platforms are developed to provide reliable drug discovery and regulatory evaluation testbeds. Despite recent emergences and advances of various MPS platforms, their adoption of drug discovery and evaluation processes still lags. This delay is mainly due to a lack of rigorous standards with reproducibility and reliability, and practical difficulties to be adopted in pharmaceutical research and industry settings. This review discusses the current and potential use of MPS platforms in drug discovery processes while considering the context of several key steps during drug discovery processes, including target identification and validation, preclinical evaluation, and clinical trials. Opportunities and challenges are also discussed for the broader dissemination and adoption of MPSs in various drug discovery and regulatory evaluation steps. Addressing these challenges will transform long and expensive drug discovery and evaluation processes into more efficient discovery, screening, and approval of innovative drugs.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"21 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139063015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presented a platform of multiplex fluorescence detection of single-cell droplet microfluidics with demonstrative applications in quantifying protein expression levels. The platform of multiplex fluorescence detection mainly included optical paths adopted from conventional microscopy enabling the generation of three optical spots from three laser sources for multiple fluorescence excitation and capture of multiple fluorescence signals by four photomultiplier tubes. As to platform characterization, microscopic images of three optical spots were obtained where clear Gaussian distributions of intensities without skewness confirmed the functionality of the scanning lens, while the controllable distances among three optical spots validated the functionality of fiber collimators and the reflector lens. As to demonstration, this platform was used to quantify single-cell protein expression within droplets where four-type protein expression of α-tubulin, Ras, c-Myc, and β-tubulin of CAL 27 (Ncell = 1921) vs WSU-HN6 (Ncell = 1881) were quantitatively estimated, which were (2.85 ± 0.72) × 105 vs (4.83 ± 1.58) × 105, (3.69 ± 1.41) × 104 vs (5.07 ± 2.13) × 104, (5.90 ± 1.45) × 104 vs (9.57 ± 2.85) × 104, and (3.84 ± 1.28) × 105 vs (3.30 ± 1.10) × 105, respectively. Neural pattern recognition was utilized for the classification of cell types, achieving successful rates of 69.0% (α-tubulin), 75.4% (Ras), 89.1% (c-Myc), 65.8% (β-tubulin), and 99.1% in combination, validating the capability of this platform of multiplex fluorescence detection to quantify various types of single-cell proteins, which could provide comprehensive evaluations on cell status.
{"title":"Multiplex fluorescence detection of single-cell droplet microfluidics and its application in quantifying protein expression levels","authors":"Guang Yang, Chiyuan Gao, Deyong Chen, Junbo Wang, Xiaoye Huo, Jian Chen","doi":"10.1063/5.0179121","DOIUrl":"https://doi.org/10.1063/5.0179121","url":null,"abstract":"This study presented a platform of multiplex fluorescence detection of single-cell droplet microfluidics with demonstrative applications in quantifying protein expression levels. The platform of multiplex fluorescence detection mainly included optical paths adopted from conventional microscopy enabling the generation of three optical spots from three laser sources for multiple fluorescence excitation and capture of multiple fluorescence signals by four photomultiplier tubes. As to platform characterization, microscopic images of three optical spots were obtained where clear Gaussian distributions of intensities without skewness confirmed the functionality of the scanning lens, while the controllable distances among three optical spots validated the functionality of fiber collimators and the reflector lens. As to demonstration, this platform was used to quantify single-cell protein expression within droplets where four-type protein expression of α-tubulin, Ras, c-Myc, and β-tubulin of CAL 27 (Ncell = 1921) vs WSU-HN6 (Ncell = 1881) were quantitatively estimated, which were (2.85 ± 0.72) × 105 vs (4.83 ± 1.58) × 105, (3.69 ± 1.41) × 104 vs (5.07 ± 2.13) × 104, (5.90 ± 1.45) × 104 vs (9.57 ± 2.85) × 104, and (3.84 ± 1.28) × 105 vs (3.30 ± 1.10) × 105, respectively. Neural pattern recognition was utilized for the classification of cell types, achieving successful rates of 69.0% (α-tubulin), 75.4% (Ras), 89.1% (c-Myc), 65.8% (β-tubulin), and 99.1% in combination, validating the capability of this platform of multiplex fluorescence detection to quantify various types of single-cell proteins, which could provide comprehensive evaluations on cell status.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"45 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The development of physiologically relevant in vitro systems for simulating disease onset and progression and predicting drug metabolism holds tremendous value in reducing drug discovery time and cost. However, many of these platforms lack accuracy in replicating the tissue architecture and multicellular interactions. By leveraging three-dimensional cell culture, biomimetic soft hydrogels, and engineered stimuli, in vitro models have continued to progress. Nonetheless, the incorporation of the microvasculature has been met with many challenges, specifically with the addition of parenchymal cell types. Here, a systematic approach to investigating the initial seeding density of endothelial cells and its effects on interconnected networks was taken and combined with hepatic spheroids to form a liver-on-a-chip model. Leveraging this system, nicotine's effects on microvasculature and hepatic function were investigated. The findings indicated that nicotine led to interrupted adherens junctions, decreased guanosine triphosphate cyclohydrolase 1 expression, impaired angiogenesis, and lowered barrier function, all key factors in endothelial dysfunction. With the combination of the optimized microvascular networks, a vascularized liver-on-a-chip was formed, providing functional xenobiotic metabolism and synthesis of both albumin and urea. This system provides insight into potential hepatotoxicity caused by various drugs and allows for assessing vascular dysfunction in a high throughput manner.
{"title":"Vascularized liver-on-a-chip model to investigate nicotine-induced dysfunction","authors":"Eric Wang, Melisa J. Andrade, Quinton Smith","doi":"10.1063/5.0172677","DOIUrl":"https://doi.org/10.1063/5.0172677","url":null,"abstract":"The development of physiologically relevant in vitro systems for simulating disease onset and progression and predicting drug metabolism holds tremendous value in reducing drug discovery time and cost. However, many of these platforms lack accuracy in replicating the tissue architecture and multicellular interactions. By leveraging three-dimensional cell culture, biomimetic soft hydrogels, and engineered stimuli, in vitro models have continued to progress. Nonetheless, the incorporation of the microvasculature has been met with many challenges, specifically with the addition of parenchymal cell types. Here, a systematic approach to investigating the initial seeding density of endothelial cells and its effects on interconnected networks was taken and combined with hepatic spheroids to form a liver-on-a-chip model. Leveraging this system, nicotine's effects on microvasculature and hepatic function were investigated. The findings indicated that nicotine led to interrupted adherens junctions, decreased guanosine triphosphate cyclohydrolase 1 expression, impaired angiogenesis, and lowered barrier function, all key factors in endothelial dysfunction. With the combination of the optimized microvascular networks, a vascularized liver-on-a-chip was formed, providing functional xenobiotic metabolism and synthesis of both albumin and urea. This system provides insight into potential hepatotoxicity caused by various drugs and allows for assessing vascular dysfunction in a high throughput manner.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"206 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Bazyar, M. H. Kandemir, J. Peper, M. A. B. Andrade, A. L. Bernassau, K. Schroën, R. G. H. Lammertink
Acoustic manipulation of particles in microchannels has recently gained much attention. Ultrasonic standing wave (USW) separation of oil droplets or particles is an established technology for microscale applications. Acoustofluidic devices are normally operated at optimized conditions, namely, resonant frequency, to minimize power consumption. It has been recently shown that symmetry breaking is needed to obtain efficient conditions for acoustic particle trapping. In this work, we study the acoustophoretic behavior of monodisperse oil droplets (silicone oil and hexadecane) in water in the microfluidic chip operating at a non-resonant frequency and an off-center placement of the transducer. Finite element-based computer simulations are further performed to investigate the influence of these conditions on the acoustic pressure distribution and oil trapping behavior. Via investigating the Gor’kov potential, we obtained an overlap between the trapping patterns obtained in experiments and simulations. We demonstrate that an off-center placement of the transducer and driving the transducer at a non-resonant frequency can still lead to predictable behavior of particles in acoustofluidics. This is relevant to applications in which the theoretical resonant frequency cannot be achieved, e.g., manipulation of biological matter within living tissues.
{"title":"Acoustophoresis of monodisperse oil droplets in water: Effect of symmetry breaking and non-resonance operation on oil trapping behavior","authors":"H. Bazyar, M. H. Kandemir, J. Peper, M. A. B. Andrade, A. L. Bernassau, K. Schroën, R. G. H. Lammertink","doi":"10.1063/5.0175400","DOIUrl":"https://doi.org/10.1063/5.0175400","url":null,"abstract":"Acoustic manipulation of particles in microchannels has recently gained much attention. Ultrasonic standing wave (USW) separation of oil droplets or particles is an established technology for microscale applications. Acoustofluidic devices are normally operated at optimized conditions, namely, resonant frequency, to minimize power consumption. It has been recently shown that symmetry breaking is needed to obtain efficient conditions for acoustic particle trapping. In this work, we study the acoustophoretic behavior of monodisperse oil droplets (silicone oil and hexadecane) in water in the microfluidic chip operating at a non-resonant frequency and an off-center placement of the transducer. Finite element-based computer simulations are further performed to investigate the influence of these conditions on the acoustic pressure distribution and oil trapping behavior. Via investigating the Gor’kov potential, we obtained an overlap between the trapping patterns obtained in experiments and simulations. We demonstrate that an off-center placement of the transducer and driving the transducer at a non-resonant frequency can still lead to predictable behavior of particles in acoustofluidics. This is relevant to applications in which the theoretical resonant frequency cannot be achieved, e.g., manipulation of biological matter within living tissues.","PeriodicalId":8855,"journal":{"name":"Biomicrofluidics","volume":"48 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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