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Azuaje-Hualde E, Alonso-Cabrera JA, de Pancorbo MM, Benito-Lopez F, Basabe-Desmonts L. Integration of secreted signaling molecule sensing on cell monitoring platforms: a critical review. Anal Bioanal Chem 2024:10.1007/s00216-024-05435-1. [PMID: 39048740 DOI: 10.1007/s00216-024-05435-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/10/2024] [Accepted: 06/28/2024] [Indexed: 07/27/2024]
Abstract
Monitoring cell secretion in complex microenvironments is crucial for understanding cellular behavior and advancing physiological and pathological research. While traditional cell culture methods, including organoids and spheroids, provide valuable models, real-time monitoring of cell secretion of signaling molecules remains challenging. Integrating advanced monitoring technologies into these systems often disrupts the delicate balance of the microenvironment, making it difficult to achieve sensitivity and specificity. This review explored recent strategies for integrating the monitoring of cell secretion of signaling molecules, crucial for understanding and replicating cell microenvironments, within cell culture platforms, addressing challenges such as non-adherent cell models and the focus on single-cell methodologies. We highlight advancements in biosensors, microfluidics, and three-dimensional culture methods, and discuss their potential to enhance real-time, multiplexed cell monitoring. By examining the advantages, limitations, and future prospects of these technologies, we aim to contribute to the development of integrated systems that facilitate comprehensive cell monitoring, ultimately advancing biological research and pharmaceutical development.
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Affiliation(s)
- Enrique Azuaje-Hualde
- Microfluidics Cluster UPV/EHU, BIOMICs Microfluidics Group, Lascaray Research Center, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
| | - Juncal A Alonso-Cabrera
- Microfluidics Cluster UPV/EHU, BIOMICs Microfluidics Group, Lascaray Research Center, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
| | - Marian M de Pancorbo
- BIOMICs Research Group, Lascaray Research Center, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain
| | - Fernando Benito-Lopez
- Microfluidics Cluster UPV/EHU, Analytical Microsystems & Materials for Lab-on-a-Chip (AMMa-LOAC) Group, Analytical Chemistry Department, University of the Basque Country UPV/EHU, Leioa, Spain.
- Microfluidics Cluster UPV/EHU, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain.
- Basque Foundation of Science, IKERBASQUE, María Díaz Haroko Kalea, 3, 48013, Bilbao, Spain.
| | - Lourdes Basabe-Desmonts
- Microfluidics Cluster UPV/EHU, BIOMICs Microfluidics Group, Lascaray Research Center, University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain.
- Microfluidics Cluster UPV/EHU, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain.
- Basque Foundation of Science, IKERBASQUE, María Díaz Haroko Kalea, 3, 48013, Bilbao, Spain.
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2
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Shah PA, Shrivastav PS, Ghate M, Chavda V. The fusion of microfluidics and artificial intelligence: a novel alliance for medical advancements. Bioanalysis 2024; 16:927-930. [PMID: 38979574 DOI: 10.1080/17576180.2024.2365528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/05/2024] [Indexed: 07/10/2024] Open
Affiliation(s)
- Priyanka A Shah
- Department of Forensic Sciences, National Forensic Sciences University, Dharwad, Karnataka 580011, India
| | - Pranav S Shrivastav
- Department of Chemistry, School of Sciences, Gujarat University, Ahmedabad, Gujarat 380009, India
| | - Manjunath Ghate
- Department of Forensic Sciences, National Forensic Sciences University, Dharwad, Karnataka 580011, India
- School of Pharmacy, National Forensic Science University, Gandhinagar, Gujarat 382007, India
| | - Vishwajit Chavda
- Applied Chemistry Department, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara 390002, India
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3
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Wang C, Qiu J, Liu M, Wang Y, Yu Y, Liu H, Zhang Y, Han L. Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401263. [PMID: 38767182 PMCID: PMC11267386 DOI: 10.1002/advs.202401263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/26/2024] [Indexed: 05/22/2024]
Abstract
Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.
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Affiliation(s)
- Chao Wang
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Jiaoyan Qiu
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Mengqi Liu
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Yihe Wang
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Yang Yu
- Department of PeriodontologySchool and Hospital of StomatologyCheeloo College of MedicineShandong UniversityJinan250100China
| | - Hong Liu
- State Key Laboratory of Crystal MaterialsShandong UniversityJinan250100China
| | - Yu Zhang
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
| | - Lin Han
- Institute of Marine Science and TechnologyShandong UniversityQingdao266237China
- Shandong Engineering Research Center of Biomarker and Artificial Intelligence ApplicationJinan250100China
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4
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Lu J, He R, Liu Y, Zhang J, Xu H, Zhang T, Chen L, Yang G, Zhang J, Liu J, Chi H. Exploiting cell death and tumor immunity in cancer therapy: challenges and future directions. Front Cell Dev Biol 2024; 12:1416115. [PMID: 38887519 PMCID: PMC11180757 DOI: 10.3389/fcell.2024.1416115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024] Open
Abstract
Cancer remains a significant global challenge, with escalating incidence rates and a substantial burden on healthcare systems worldwide. Herein, we present an in-depth exploration of the intricate interplay between cancer cell death pathways and tumor immunity within the tumor microenvironment (TME). We begin by elucidating the epidemiological landscape of cancer, highlighting its pervasive impact on premature mortality and the pronounced burden in regions such as Asia and Africa. Our analysis centers on the pivotal concept of immunogenic cell death (ICD), whereby cancer cells succumbing to specific stimuli undergo a transformation that elicits robust anti-tumor immune responses. We scrutinize the mechanisms underpinning ICD induction, emphasizing the release of damage-associated molecular patterns (DAMPs) and tumor-associated antigens (TAAs) as key triggers for dendritic cell (DC) activation and subsequent T cell priming. Moreover, we explore the contributions of non-apoptotic RCD pathways, including necroptosis, ferroptosis, and pyroptosis, to tumor immunity within the TME. Emerging evidence suggests that these alternative cell death modalities possess immunogenic properties and can synergize with conventional treatments to bolster anti-tumor immune responses. Furthermore, we discuss the therapeutic implications of targeting the TME for cancer treatment, highlighting strategies to harness immunogenic cell death and manipulate non-apoptotic cell death pathways for therapeutic benefit. By elucidating the intricate crosstalk between cancer cell death and immune modulation within the TME, this review aims to pave the way for the development of novel cancer therapies that exploit the interplay between cell death mechanisms and tumor immunity and overcome Challenges in the Development and implementation of Novel Therapies.
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Affiliation(s)
- Jiaan Lu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Ru He
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Yang Liu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jinghan Zhang
- Department of Anesthesiology, Southwest Medical University, Luzhou, China
| | - Heng Xu
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Tianchi Zhang
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of General Surgery, Dazhou Central Hospital, Dazhou, China
| | - Li Chen
- Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Department of General Surgery, Dazhou Central Hospital, Dazhou, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Jun Zhang
- Department of General Surgery, Dazhou Central Hospital, Dazhou, China
| | - Jie Liu
- Department of General Surgery, Dazhou Central Hospital, Dazhou, China
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, China
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5
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Liu Q, Zheng J, Xie A, Chen M, Gong RY, Sheng Y, Chen HL, Qi CB. Exosome, a Rising Biomarkers in Liquid Biopsy: Advances of Label-Free and Label Strategy for Diagnosis of Cancer. Crit Rev Anal Chem 2024:1-12. [PMID: 38669199 DOI: 10.1080/10408347.2024.2339961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
Cancer is commonly considered as one of the most severe diseases, posing a significant threat to human health and society due to various serious challenges. These challenges include difficulties in accurate diagnosis and a high propensity to form metastasis. Tissue biopsy remains the gold standard for diagnosing and subtyping cancer. However, concerns arise from its invasive nature and the potential risk of metastasis during these complex diagnostic procedures. Meanwhile, liquid biopsy has recently witnessed the rapid advancements with the emergence of three prominent detection biomarkers: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Whereas, the very low abundance of CTCs combined with the instability of ctDNA intensify the challenges and decrease the accuracy of these two biomarkers for cancer diagnosis. While exosomes have gained widespread recognition as a promising biomarker in liquid biopsy due to their relatively low-invasive detection method, excellent biostability, rich resources, high abundance, and ability to provide valuable information about cancer. Therefore, it is crucial to systematically summarize recent advancements mainly in exosome-based detection methods for early cancer diagnosis. Specifically, this review will primarily focus on label-based and label-free strategies for detecting cancer using exosomes. We anticipate that this comprehensive analysis will enhance readers' understanding of the significance and value of exosomes in the fields of cancer diagnosis and therapy.
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Affiliation(s)
- Qian Liu
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Jing Zheng
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - An Xie
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Min Chen
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Rui-Yue Gong
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Yuan Sheng
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Hong-Lei Chen
- Department of Pathology, School of Basic Medical Sciences, Wuhan University, Wuhan, China
| | - Chu-Bo Qi
- Department of Pathology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
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6
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Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. LAB ON A CHIP 2024; 24:1307-1326. [PMID: 38247405 DOI: 10.1039/d3lc01012k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.
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Affiliation(s)
- Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jianpei Dong
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Hongwei Hou
- Beijing Life Science Academy, Beijing 102209, China
| | - Lu Huang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jinghong Li
- Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
- New Cornerstone Science Laboratory, Shenzhen 518054, China
- Beijing Life Science Academy, Beijing 102209, China
- Center for BioAnalytical Chemistry, Hefei National Laboratory of Physical Science at Microscale, University of Science and Technology of China, Hefei 230026, China
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7
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Lou C, Yang H, Hou Y, Huang H, Qiu J, Wang C, Sang Y, Liu H, Han L. Microfluidic Platforms for Real-Time In Situ Monitoring of Biomarkers for Cellular Processes. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307051. [PMID: 37844125 DOI: 10.1002/adma.202307051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/05/2023] [Indexed: 10/18/2023]
Abstract
Cellular processes are mechanisms carried out at the cellular level that are aimed at guaranteeing the stability of the organism they comprise. The investigation of cellular processes is key to understanding cell fate, understanding pathogenic mechanisms, and developing new therapeutic technologies. Microfluidic platforms are thought to be the most powerful tools among all methodologies for investigating cellular processes because they can integrate almost all types of the existing intracellular and extracellular biomarker-sensing methods and observation approaches for cell behavior, combined with precisely controlled cell culture, manipulation, stimulation, and analysis. Most importantly, microfluidic platforms can realize real-time in situ detection of secreted proteins, exosomes, and other biomarkers produced during cell physiological processes, thereby providing the possibility to draw the whole picture for a cellular process. Owing to their advantages of high throughput, low sample consumption, and precise cell control, microfluidic platforms with real-time in situ monitoring characteristics are widely being used in cell analysis, disease diagnosis, pharmaceutical research, and biological production. This review focuses on the basic concepts, recent progress, and application prospects of microfluidic platforms for real-time in situ monitoring of biomarkers in cellular processes.
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Affiliation(s)
- Chengming Lou
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, P. R. China
| | - Hongru Yang
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, P. R. China
| | - Ying Hou
- Institute for Advanced Interdisciplinary Research (IAIR), University of Jinan, Jinan, 250022, P. R. China
| | - Haina Huang
- Institute for Advanced Interdisciplinary Research (IAIR), University of Jinan, Jinan, 250022, P. R. China
| | - Jichuan Qiu
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, P. R. China
| | - Chunhua Wang
- Institute for Advanced Interdisciplinary Research (IAIR), University of Jinan, Jinan, 250022, P. R. China
| | - Yuanhua Sang
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, P. R. China
| | - Hong Liu
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, P. R. China
- Institute for Advanced Interdisciplinary Research (IAIR), University of Jinan, Jinan, 250022, P. R. China
| | - Lin Han
- Institute of Marine Science and Technology, Shandong University, Qingdao, Shandong, 266000, P. R. China
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8
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Kokabi M, Tahir MN, Singh D, Javanmard M. Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis. BIOSENSORS 2023; 13:884. [PMID: 37754118 PMCID: PMC10526782 DOI: 10.3390/bios13090884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.
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Affiliation(s)
| | | | | | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA; (M.K.); (M.N.T.); (D.S.)
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9
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Gao Z, Li Y. Enhancing single-cell biology through advanced AI-powered microfluidics. BIOMICROFLUIDICS 2023; 17:051301. [PMID: 37799809 PMCID: PMC10550334 DOI: 10.1063/5.0170050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/23/2023] [Indexed: 10/07/2023]
Abstract
Microfluidic technology has largely benefited both fundamental biological research and translational clinical diagnosis with its advantages in high-throughput, single-cell resolution, high integrity, and wide-accessibility. Despite the merits we obtained from microfluidics in the last two decades, the current requirement of intelligence in biomedicine urges the microfluidic technology to process biological big data more efficiently and intelligently. Thus, the current readout technology based on the direct detection of the signals in either optics or electrics was not able to meet the requirement. The implementation of artificial intelligence (AI) in microfluidic technology matches up with the large-scale data usually obtained in the high-throughput assays of microfluidics. At the same time, AI is able to process the multimodal datasets obtained from versatile microfluidic devices, including images, videos, electric signals, and sequences. Moreover, AI provides the microfluidic technology with the capability to understand and decipher the obtained datasets rather than simply obtaining, which eventually facilitates fundamental and translational research in many areas, including cell type discovery, cell signaling, single-cell genetics, and diagnosis. In this Perspective, we will highlight the recent advances in employing AI for single-cell biology and present an outlook on the future direction with more advanced AI algorithms.
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Affiliation(s)
- Zhaolong Gao
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics—Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Systems Biology Theme, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yiwei Li
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics—Hubei Bioinformatics and Molecular Imaging Key Laboratory, Department of Biomedical Engineering, Systems Biology Theme, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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10
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Pillai S, Kwan JC, Yaziji F, Yu H, Tran SD. Mapping the Potential of Microfluidics in Early Diagnosis and Personalized Treatment of Head and Neck Cancers. Cancers (Basel) 2023; 15:3894. [PMID: 37568710 PMCID: PMC10417175 DOI: 10.3390/cancers15153894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Head and neck cancers (HNCs) account for ~4% of all cancers in North America and encompass cancers affecting the oral cavity, pharynx, larynx, sinuses, nasal cavity, and salivary glands. The anatomical complexity of the head and neck region, characterized by highly perfused and innervated structures, presents challenges in the early diagnosis and treatment of these cancers. The utilization of sub-microliter volumes and the unique phenomenon associated with microscale fluid dynamics have facilitated the development of microfluidic platforms for studying complex biological systems. The advent of on-chip microfluidics has significantly impacted the diagnosis and treatment strategies of HNC. Sensor-based microfluidics and point-of-care devices have improved the detection and monitoring of cancer biomarkers using biological specimens like saliva, urine, blood, and serum. Additionally, tumor-on-a-chip platforms have allowed the creation of patient-specific cancer models on a chip, enabling the development of personalized treatments through high-throughput screening of drugs. In this review, we first focus on how microfluidics enable the development of an enhanced, functional drug screening process for targeted treatment in HNCs. We then discuss current advances in microfluidic platforms for biomarker sensing and early detection, followed by on-chip modeling of HNC to evaluate treatment response. Finally, we address the practical challenges that hinder the clinical translation of these microfluidic advances.
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Affiliation(s)
| | | | | | | | - Simon D. Tran
- McGill Craniofacial Tissue Engineering and Stem Cell Laboratory, Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC H3A 0C7, Canada; (S.P.); (J.C.K.); (F.Y.); (H.Y.)
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11
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Ma X, Guo G, Wu X, Wu Q, Liu F, Zhang H, Shi N, Guan Y. Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems. MICROMACHINES 2023; 14:mi14050972. [PMID: 37241596 DOI: 10.3390/mi14050972] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/20/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023]
Abstract
Microfluidics attracts much attention due to its multiple advantages such as high throughput, rapid analysis, low sample volume, and high sensitivity. Microfluidics has profoundly influenced many fields including chemistry, biology, medicine, information technology, and other disciplines. However, some stumbling stones (miniaturization, integration, and intelligence) strain the development of industrialization and commercialization of microchips. The miniaturization of microfluidics means fewer samples and reagents, shorter times to results, and less footprint space consumption, enabling a high throughput and parallelism of sample analysis. Additionally, micro-size channels tend to produce laminar flow, which probably permits some creative applications that are not accessible to traditional fluid-processing platforms. The reasonable integration of biomedical/physical biosensors, semiconductor microelectronics, communications, and other cutting-edge technologies should greatly expand the applications of current microfluidic devices and help develop the next generation of lab-on-a-chip (LOC). At the same time, the evolution of artificial intelligence also gives another strong impetus to the rapid development of microfluidics. Biomedical applications based on microfluidics normally bring a large amount of complex data, so it is a big challenge for researchers and technicians to analyze those huge and complicated data accurately and quickly. To address this problem, machine learning is viewed as an indispensable and powerful tool in processing the data collected from micro-devices. In this review, we mainly focus on discussing the integration, miniaturization, portability, and intelligence of microfluidics technology.
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Affiliation(s)
- Xingfeng Ma
- School of Communication and Information Engineering, Shanghai University, Shanghai 200000, China
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
| | - Gang Guo
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
| | - Xuanye Wu
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
- Shanghai Industrial μTechnology Research Institute, Shanghai 200000, China
| | - Qiang Wu
- Shanghai Aure Technology Limited Company, Shanghai 200000, China
| | - Fangfang Liu
- Shanghai Industrial μTechnology Research Institute, Shanghai 200000, China
| | - Hua Zhang
- Shanghai Aure Technology Limited Company, Shanghai 200000, China
| | - Nan Shi
- Shanghai Industrial μTechnology Research Institute, Shanghai 200000, China
- Institute of Translational Medicine, Shanghai University, Shanghai 200000, China
| | - Yimin Guan
- Department of Microelectronics, Shanghai University, Shanghai 200000, China
- Shanghai Aure Technology Limited Company, Shanghai 200000, China
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12
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Gebreyesus ST, Muneer G, Huang CC, Siyal AA, Anand M, Chen YJ, Tu HL. Recent advances in microfluidics for single-cell functional proteomics. LAB ON A CHIP 2023; 23:1726-1751. [PMID: 36811978 DOI: 10.1039/d2lc01096h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.
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Affiliation(s)
- Sofani Tafesse Gebreyesus
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Gul Muneer
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | | | - Asad Ali Siyal
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
| | - Mihir Anand
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
| | - Hsiung-Lin Tu
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
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13
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Zuo Y, Xia Y, Lu W, Li Y, Xiao Y, Gao S, Zhou Z, Xu H, Feng X, Li C, Yu Y. A multifunctional black phosphorus nanosheet-based immunomagnetic bio-interface for heterogeneous circulating tumor cell capture and simultaneous self-identification in gastric cancer patients. NANOSCALE 2023; 15:3872-3883. [PMID: 36722904 DOI: 10.1039/d2nr04277k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A single epithelial cell adhesion molecule (EpCAM) for circulating tumor cell (CTCs) isolation has been proved to be low in efficiency as it fails to recognize EpCAM-negative CTCs. Meanwhile, the current immunocytochemical (ICC) identification strategy for the captured cells is tedious and time-consuming. To address these issues, we designed a dual-labeled fluorescent immunomagnetic nanoprobe (BP-Fe3O4-AuNR/Apt), by loading magnetic Fe3O4 nanoparticles and gold nanorods (AuNRs) onto black phosphorus (BP) nanosheets and then linking them with Cy3-labeled EpCAM and Texas red-labeled tyrosine protein kinase 7 (PTK7) aptamers, which created a high-performance bio-interface for efficient, heterogeneous CTC capture and rapid self-identification with high accuracy. As few as 5 CTCs could be captured from 1.0 mL PBS, mixed cell solution and lysed blood. What's more, the presence of BP and AuNRs on this capturing interface also allowed us to preliminarily investigate the potential photothermal therapeutic effect of the probe toward CTC elimination. The applicability of the probe was further demonstrated in gastric cancer patients. By detecting the number of CTCs in the blood of gastric cancer patients, the correlations between the CTC number and the disease stage, as well as distant metastasis were systematically explored.
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Affiliation(s)
- Yifan Zuo
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Yi Xia
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Wenwen Lu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Yue Li
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Yang Xiao
- School of Anesthesiology, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Shuai Gao
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Zhiyi Zhou
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Hao Xu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Xingqing Feng
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Chenglin Li
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
| | - Yanyan Yu
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, Jiangsu, China.
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14
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Aubry G, Lee HJ, Lu H. Advances in Microfluidics: Technical Innovations and Applications in Diagnostics and Therapeutics. Anal Chem 2023; 95:444-467. [PMID: 36625114 DOI: 10.1021/acs.analchem.2c04562] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Guillaume Aubry
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hyun Jee Lee
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.,Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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15
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Ahmadi F, Simchi M, Perry JM, Frenette S, Benali H, Soucy JP, Massarweh G, Shih SCC. Integrating machine learning and digital microfluidics for screening experimental conditions. LAB ON A CHIP 2022; 23:81-91. [PMID: 36416045 DOI: 10.1039/d2lc00764a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Digital microfluidics (DMF) has the signatures of an ideal liquid handling platform - as shown through almost two decades of automated biological and chemical assays. However, in the current state of DMF, we are still limited by the number of parallel biological or chemical assays that can be performed on DMF. Here, we report a new approach that leverages design-of-experiment and numerical methodologies to accelerate experimental optimization on DMF. The integration of the one-factor-at-a-time (OFAT) experimental technique with machine learning algorithms provides a set of recommended optimal conditions without the need to perform a large set of experiments. We applied our approach towards optimizing the radiochemistry synthesis yield given the large number of variables that affect the yield. We believe that this work is the first to combine such techniques which can be readily applied to any other assays that contain many parameters and levels on DMF.
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Affiliation(s)
- Fatemeh Ahmadi
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada.
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
- Centre for Applied Synthetic Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Mohammad Simchi
- Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, Ontario, M5S 3G8, Canada
| | - James M Perry
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Stephane Frenette
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Habib Benali
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada.
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Jean-Paul Soucy
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec, H3A 2B4, Canada
| | - Gassan Massarweh
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec, H3A 2B4, Canada
| | - Steve C C Shih
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada.
- PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
- Centre for Applied Synthetic Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
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