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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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Xiao Q, Wang Y, Fan J, Yi Z, Hong H, Xie X, Huang QA, Fu J, Ouyang J, Zhao X, Wang Z, Zhu Z. A computer vision and residual neural network (ResNet) combined method for automated and accurate yeast replicative aging analysis of high-throughput microfluidic single-cell images. Biosens Bioelectron 2024; 244:115807. [PMID: 37948914 DOI: 10.1016/j.bios.2023.115807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/17/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Abstract
With the rapid development of microfluidic platforms in high-throughput single-cell culturing, laborious operation to manipulate massive budding yeast cells (Saccharomyces cerevisiae) in replicative aging studies has been greatly simplified and automated. As a result, large datasets of microscopy images bring challenges to fast and accurately determine yeast replicative lifespan (RLS), which is the most important parameter to study cell aging. Based on our microfluidic diploid yeast long-term culturing (DYLC) chip that features 1100 traps to immobilize single cells and record their proliferation and aging via time-lapse imaging, herein, a dedicated algorithm combined with computer vision and residual neural network (ResNet) was presented to efficiently process tremendous micrographs in a high-throughput and automated manner. The image-processing algorithm includes following pivotal steps: (i) segmenting multi-trap micrographs into time-lapse single-trap sub-images, (ii) labeling 8 yeast budding features and training the 18-layer ResNet, (iii) converting the ResNet predictions in analog values into digital signals, (iv) recognizing cell dynamic events, and (v) determining yeast RLS and budding time interval (BTI) ultimately. The ResNet algorithm achieved high F1 scores (over 92%) demonstrating the effectiveness and accuracy in the recognition of yeast budding events, such as bud appearance, daughter dissection and cell death. Therefore, the results conduct that similar deep learning algorithms could be tailored to analyze high-throughput microscopy images and extract multiple cell behaviors in microfluidic single-cell analysis.
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Affiliation(s)
- Qin Xiao
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China
| | - Yingying Wang
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China
| | - Juncheng Fan
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China
| | - Zhenxiang Yi
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China
| | - Hua Hong
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China
| | - Xiao Xie
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China
| | - Qing-An Huang
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China
| | - Jiaming Fu
- Nanjing Forestry University, College of Chemical Engineering, Longpan Road 159, Nanjing, 210037, China
| | - Jia Ouyang
- Nanjing Forestry University, College of Chemical Engineering, Longpan Road 159, Nanjing, 210037, China
| | - Xiangwei Zhao
- Southeast University, School of Biological Science and Medical Engineering, State Key Laboratory of Digital Medical Engineering, Sipailou 2, Nanjing, 210096, China
| | - Zixin Wang
- Sun Yat-Sen University, School of Electronics and Information Technology, Waihuan Dong Road 132, Guangzhou, 510006, China.
| | - Zhen Zhu
- Southeast University, School of Integrated Circuits, School of Electronic Science and Engineering, Key Laboratory of MEMS of Ministry of Education, Sipailou 2, Nanjing, 210096, China.
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Mouton SN, Boersma AJ, Veenhoff LM. A physicochemical perspective on cellular ageing. Trends Biochem Sci 2023; 48:949-962. [PMID: 37716870 DOI: 10.1016/j.tibs.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/18/2023]
Abstract
Cellular ageing described at the molecular level is a multifactorial process that leads to a spectrum of ageing trajectories. There has been recent discussion about whether a decline in physicochemical homeostasis causes aberrant phase transitions, which are a driver of ageing. Indeed, the function of all biological macromolecules, regardless of their participation in biomolecular condensates, depends on parameters such as pH, crowding, and redox state. We expand on the physicochemical homeostasis hypothesis and summarise recent evidence that the intracellular milieu influences molecular processes involved in ageing.
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Affiliation(s)
- Sara N Mouton
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Arnold J Boersma
- Cellular Protein Chemistry, Bijvoet Centre for Biomolecular Research, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Liesbeth M Veenhoff
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
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Reich C, Prangemeier T, Francani AO, Koeppl H. An Instance Segmentation Dataset of Yeast Cells in Microstructures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083295 DOI: 10.1109/embc40787.2023.10340268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Extracting single-cell information from microscopy data requires accurate instance-wise segmentations. Obtaining pixel-wise segmentations from microscopy imagery remains a challenging task, especially with the added complexity of microstructured environments. This paper presents a novel dataset for segmenting yeast cells in microstructures. We offer pixel-wise instance segmentation labels for both cells and trap microstructures. In total, we release 493 densely annotated microscopy images. To facilitate a unified comparison between novel segmentation algorithms, we propose a standardized evaluation strategy for our dataset. The aim of the dataset and evaluation strategy is to facilitate the development of new cell segmentation approaches. The dataset is publicly available at https://christophreich1996.github.io/yeast_in_microstructures_dataset/.
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Malik H, Idris AS, Toha SF, Mohd Idris I, Daud MF, Azmi NL. A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations. PeerJ Comput Sci 2023; 9:e1364. [PMID: 37346656 PMCID: PMC10280419 DOI: 10.7717/peerj-cs.1364] [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: 11/11/2022] [Accepted: 04/04/2023] [Indexed: 06/23/2023]
Abstract
Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.
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Affiliation(s)
- Hafizi Malik
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
| | - Ahmad Syahrin Idris
- Department of Electrical and Electronic Engineering, University of Southampton Malaysia, Iskandar Puteri, Johor, Malaysia
| | - Siti Fauziah Toha
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
| | - Izyan Mohd Idris
- Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health Malaysia, Shah Alam, Selangor, Malaysia
| | - Muhammad Fauzi Daud
- Institute of Medical Science Technology, Universiti Kuala Lumpur, Kajang, Selangor, Malaysia
| | - Nur Liyana Azmi
- Healthcare Engineering and Rehabilitation Research, Department of Mechatronics Engineering, International Islamic University Malaysia, Gombak, Selangor, Malaysia
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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