1
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Capponi S, Wang S. AI in cellular engineering and reprogramming. Biophys J 2024; 123:2658-2670. [PMID: 38576162 PMCID: PMC11393708 DOI: 10.1016/j.bpj.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/19/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024] Open
Abstract
During the last decade, artificial intelligence (AI) has increasingly been applied in biophysics and related fields, including cellular engineering and reprogramming, offering novel approaches to understand, manipulate, and control cellular function. The potential of AI lies in its ability to analyze complex datasets and generate predictive models. AI algorithms can process large amounts of data from single-cell genomics and multiomic technologies, allowing researchers to gain mechanistic insights into the control of cell identity and function. By integrating and interpreting these complex datasets, AI can help identify key molecular events and regulatory pathways involved in cellular reprogramming. This knowledge can inform the design of precision engineering strategies, such as the development of new transcription factor and signaling molecule cocktails, to manipulate cell identity and drive authentic cell fate across lineage boundaries. Furthermore, when used in combination with computational methods, AI can accelerate and improve the analysis and understanding of the intricate relationships between genes, proteins, and cellular processes. In this review article, we explore the current state of AI applications in biophysics with a specific focus on cellular engineering and reprogramming. Then, we showcase a couple of recent applications where we combined machine learning with experimental and computational techniques. Finally, we briefly discuss the challenges and prospects of AI in cellular engineering and reprogramming, emphasizing the potential of these technologies to revolutionize our ability to engineer cells for a variety of applications, from disease modeling and drug discovery to regenerative medicine and biomanufacturing.
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Affiliation(s)
- Sara Capponi
- IBM Almaden Research Center, San Jose, California; Center for Cellular Construction, San Francisco, California.
| | - Shangying Wang
- Bay Area Institute of Science, Altos Labs, Redwood City, California.
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2
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Reno A, Tang J, Sudbeck M, Custodio PF, Baldus B, McLaughlin E, Peng F, Xiao H. Evaluation of a Deep Learning Based Approach to Computational Label Free Cell Viability Quantification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.29.610252. [PMID: 39257757 PMCID: PMC11383692 DOI: 10.1101/2024.08.29.610252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
One of the most common techniques found in a cell biology or tissue engineering lab is the cytotoxicity assay. This can be performed using a variety of different dyes and stains and various protocols to result in a clear indication of dead and live cells within a culture to quantify the viability of a culture and monitor for sudden drops or increases in viability by a drug, material, viral vector, etc introduced into the culture. This assay helps cell biologists determine the health of their culture and what toxicity added substances may add to the culture and whether they are appropriate and safe to use with human cells. However, many of the dyes and stains used for this process are eventually toxic to cells, rendering the cells useless after testing and preventing real time monitoring of the same culture over a period of hours or days. Computation biology is moving cell biology towards novel and innovative techniques such as in silico labeling and dye free labeling using deep learning algorithms. In this work, we investigate whether it is feasible to train a Resnet CNN model to detect morphological changes in human cells that indicate cell death in order to classify cells as live or dead without utilizing a stain or dye. This work also aims to train one CNN model to count all cells regardless of viability status to get a total cell count, and then one CNN model that specifically identifies and counts all of the dead cells for an accurate dead and live cell total by utilizing both pieces of data to determine a general viability percentage for the culture. Additionally, this work explores the use of various image enhancements to understand if this process helps or impedes the deep learning models in their detection of total cells and dead cells.
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Affiliation(s)
- Allison Reno
- Department of Bioengineering, Clemson University, Clemson, SC, USA
| | - Jianan Tang
- Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA
| | - Madeline Sudbeck
- Department of Biological Sciences, Clemson University, Clemson, SC, USA
| | | | - Brandi Baldus
- Department of Materials Science and Engineering, Clemson University, Clemson, SC, USA
| | | | - Fei Peng
- Department of Materials Science and Engineering, Clemson University, Clemson, SC, USA
| | - Hai Xiao
- Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA
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3
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Chiang CC, Anne R, Chawla P, Shaw RM, He S, Rock EC, Zhou M, Cheng J, Gong YN, Chen YC. Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics. LAB ON A CHIP 2024; 24:3169-3182. [PMID: 38804084 PMCID: PMC11165951 DOI: 10.1039/d4lc00197d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024]
Abstract
Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic platforms have enabled high-throughput drug screening with 3D models, but current viability assays for 3D cancer spheroids have limitations in reliability and cytotoxicity. This study introduces a deep learning model for non-destructive, label-free viability estimation based on phase-contrast images, providing a cost-effective, high-throughput solution for continuous spheroid monitoring in microfluidics. Microfluidic technology facilitated the creation of a high-throughput cancer spheroid platform with approximately 12 000 spheroids per chip for drug screening. Validation involved tests with eight conventional chemotherapeutic drugs, revealing a strong correlation between viability assessed via LIVE/DEAD staining and phase-contrast morphology. Extending the model's application to novel compounds and cell lines not in the training dataset yielded promising results, implying the potential for a universal viability estimation model. Experiments with an alternative microscopy setup supported the model's transferability across different laboratories. Using this method, we also tracked the dynamic changes in spheroid viability during the course of drug administration. In summary, this research integrates a robust platform with high-throughput microfluidic cancer spheroid assays and deep learning-based viability estimation, with broad applicability to various cell lines, compounds, and research settings.
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Affiliation(s)
- Chun-Cheng Chiang
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
| | - Rajiv Anne
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Pooja Chawla
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Rachel M Shaw
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Sarah He
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Carnegie Mellon University, Department of Biological Sciences, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Edwin C Rock
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Mengli Zhou
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
- Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Jinxiong Cheng
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Yi-Nan Gong
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Immunology, University of Pittsburgh School of Medicine, 3420 Forbes Avenue, Pittsburgh, PA, 15260, USA
| | - Yu-Chih Chen
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
- CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
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4
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Tjeerdema E, Lee Y, Metry R, Hamdoun A. Semi-automated, high-content imaging of drug transporter knockout sea urchin (Lytechinus pictus) embryos. JOURNAL OF EXPERIMENTAL ZOOLOGY. PART B, MOLECULAR AND DEVELOPMENTAL EVOLUTION 2024; 342:313-329. [PMID: 38087422 DOI: 10.1002/jez.b.23231] [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: 05/16/2023] [Revised: 11/08/2023] [Accepted: 11/19/2023] [Indexed: 05/01/2024]
Abstract
A defining feature of sea urchins is their extreme fecundity. Urchins produce millions of transparent, synchronously developing embryos, ideal for spatial and temporal analysis of development. This biological feature has been effectively utilized for ensemble measurement of biochemical changes. However, it has been underutilized in imaging studies, where single embryo measurements are used. Here we present an example of how stable genetics and high content imaging, along with machine learning-based image analysis, can be used to exploit the fecundity and synchrony of sea urchins in imaging-based drug screens. Building upon our recently created sea urchin ABCB1 knockout line, we developed a high-throughput assay to probe the role of this drug transporter in embryos. We used high content imaging to compare accumulation and toxicity of canonical substrates and inhibitors of the transporter, including fluorescent molecules and antimitotic cancer drugs, in homozygous knockout and wildtype embryos. To measure responses from the resulting image data, we used a nested convolutional neural network, which rapidly classified embryos according to fluorescence or cell division. This approach identified sea urchin embryos with 99.8% accuracy and determined two-cell and aberrant embryos with 96.3% and 89.1% accuracy, respectively. The results revealed that ABCB1 knockout embryos accumulated the transporter substrate calcein 3.09 times faster than wildtypes. Similarly, knockouts were 4.71 and 3.07 times more sensitive to the mitotic poisons vinblastine and taxol. This study paves the way for large scale pharmacological screens in the sea urchin embryo.
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Affiliation(s)
- Evan Tjeerdema
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
| | - Yoon Lee
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
| | - Rachel Metry
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
| | - Amro Hamdoun
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
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Xu Z, Chen Z, Yang S, Chen S, Guo T, Chen H. Passive Focusing of Single Cells Using Microwell Arrays for High-Accuracy Image-Activated Sorting. Anal Chem 2024; 96:347-354. [PMID: 38153415 DOI: 10.1021/acs.analchem.3c04195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Sorting single cells from a population was of critical importance in areas such as cell line development and cell therapy. Image-based sorting is becoming a promising technique for the nonlabeling isolation of cells due to the capability of providing the details of cell morphology. This study reported the focusing of cells using microwell arrays and the following automatic size sorting based on the real-time recognition of cells. The simulation first demonstrated the converged streamlines to the symmetrical plane contributed to the focusing effect. Then, the influence of connecting microchannel, flowing length, particle size, and the sample flow rate on the focusing effect was experimentally analyzed. Both microspheres and cells could be aligned in a straight line at the Reynolds number (Re) of 0.027-0.187 and 0.027-0.08, respectively. The connecting channel was proved to drastically improve the focusing performance. Afterward, a tapered microwell array was utilized to focus sphere/cell spreading in a wide channel to a straight line. Finally, a custom algorithm was employed to identify and sort the size of microspheres/K562 cells with a throughput of 1 event/s and an accuracy of 97.8/97.1%. The proposed technique aligned cells to a straight line at low Reynolds numbers and greatly facilitated the image-activated sorting without the need for a high-speed camera or flow control components with high frequency. Therefore, it is of enormous application potential in the field of nonlabeled separation of single cells.
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Affiliation(s)
- Zheng Xu
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
| | - Zhenlin Chen
- Department of Biomedical Engineering, College of Engineering, Kowloon, City University of Hong Kong, Hong Kong SAR, China
| | - Shiming Yang
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
| | - Siyuan Chen
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
| | - Tianruo Guo
- Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Huaying Chen
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China
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Hayashi M, Ohnuki S, Tsai Y, Kondo N, Zhou Y, Zhang H, Ishii NT, Ding T, Herbig M, Isozaki A, Ohya Y, Goda K. Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae. LAB ON A CHIP 2023; 23:4232-4244. [PMID: 37650583 DOI: 10.1039/d3lc00556a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.
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Affiliation(s)
- Mika Hayashi
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Yating Tsai
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Naoko Kondo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Yuqi Zhou
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Hongqian Zhang
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Natsumi Tiffany Ishii
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Tianben Ding
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Maik Herbig
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Akihiro Isozaki
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Mechanical Engineering, College of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo 113-8654, Japan
| | - Keisuke Goda
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Bioengineering, University of California, Los Angeles, California 90095, USA
- Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
- CYBO, Tokyo 135-0064, Japan
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7
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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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8
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Harrison PJ, Gupta A, Rietdijk J, Wieslander H, Carreras-Puigvert J, Georgiev P, Wählby C, Spjuth O, Sintorn IM. Evaluating the utility of brightfield image data for mechanism of action prediction. PLoS Comput Biol 2023; 19:e1011323. [PMID: 37490493 PMCID: PMC10403126 DOI: 10.1371/journal.pcbi.1011323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 08/04/2023] [Accepted: 07/02/2023] [Indexed: 07/27/2023] Open
Abstract
Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.
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Affiliation(s)
- Philip John Harrison
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- Science for Life Laboratory, Uppsala, Sweden
| | - Ankit Gupta
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Jonne Rietdijk
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- Science for Life Laboratory, Uppsala, Sweden
| | - Håkan Wieslander
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- Science for Life Laboratory, Uppsala, Sweden
| | - Polina Georgiev
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- Science for Life Laboratory, Uppsala, Sweden
| | - Carolina Wählby
- Science for Life Laboratory, Uppsala, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- Science for Life Laboratory, Uppsala, Sweden
| | - Ida-Maria Sintorn
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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Sun H, Xie W, Mo J, Huang Y, Dong H. Deep learning with microfluidics for on-chip droplet generation, control, and analysis. Front Bioeng Biotechnol 2023; 11:1208648. [PMID: 37351472 PMCID: PMC10282949 DOI: 10.3389/fbioe.2023.1208648] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023] Open
Abstract
Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.
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Affiliation(s)
- Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Wantao Xie
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Jin Mo
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Yi Huang
- Centre for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, China
| | - Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
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10
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 PMCID: PMC10141994 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [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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11
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Wang Y, Zhang W, Yip H, Qu C, Hu H, Chen X, Lee T, Yang X, Yang B, Kumar P, Lee SY, Casimiro JJ, Zhang J, Wang A, Lam KS. SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator. PATTERNS (NEW YORK, N.Y.) 2023; 4:100686. [PMID: 36873901 PMCID: PMC9982297 DOI: 10.1016/j.patter.2023.100686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/28/2022] [Accepted: 01/10/2023] [Indexed: 02/05/2023]
Abstract
As a measure of cytotoxic potency, half-maximal inhibitory concentration (IC50) is the concentration at which a drug exerts half of its maximal inhibitory effect against target cells. It can be determined by various methods that require applying additional reagents or lysing the cells. Here, we describe a label-free Sobel-edge-based method, which we name SIC50, for the evaluation of IC50. SIC50 classifies preprocessed phase-contrast images with a state-of-the-art vision transformer and allows for the continuous assessment of IC50 in a faster and more cost-efficient manner. We have validated this method using four drugs and 1,536-well plates and also built a web application. We anticipate that this method will assist in the high-throughput screening of chemical libraries (e.g., small-molecule drugs, small interfering RNA [siRNA], and microRNA and drug discovery).
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Affiliation(s)
- Yongheng Wang
- Department of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA
| | - Weidi Zhang
- Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA
| | - Hoyin Yip
- Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA
| | | | - Hongru Hu
- Integrative Genetics and Genomics, University of California, Davis, Davis, CA 95616, USA
| | - Xiaotie Chen
- Department of Mathematics, University of California, Davis, Davis, CA 95616, USA
| | - Teresa Lee
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Xi Yang
- Intel, Santa Clara, CA 95054, USA
| | - Bingjun Yang
- Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA
| | - Priyadarsini Kumar
- Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA
- Institute for Pediatric Regenerative Medicine, Shriners Hospital for Children Northern California, UC Davis School of Medicine, Sacramento, CA 96817, USA
| | - Su Yeon Lee
- Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA
| | - Javier J. Casimiro
- Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA
| | - Jiawei Zhang
- Department of Computer Science, IFM Lab, University of California, Davis, Davis, CA 95616, USA
| | - Aijun Wang
- Department of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA
- Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA
- Institute for Pediatric Regenerative Medicine, Shriners Hospital for Children Northern California, UC Davis School of Medicine, Sacramento, CA 96817, USA
| | - Kit S. Lam
- Department of Biochemistry and Molecular Medicine, UC Davis NCI-designated Comprehensive Cancer Center, University of California, Davis, Sacramento, CA 95817, USA
- Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis, Sacramento, CA 95817, USA
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12
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Qi R, Zou Q. Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level. RESEARCH (WASHINGTON, D.C.) 2023; 6:0050. [PMID: 36930772 PMCID: PMC10013796 DOI: 10.34133/research.0050] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 01/12/2023]
Abstract
Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.
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Affiliation(s)
- Ren Qi
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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13
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Sinha Mahapatra P, Ganguly R, Ghosh A, Chatterjee S, Lowrey S, Sommers AD, Megaridis CM. Patterning Wettability for Open-Surface Fluidic Manipulation: Fundamentals and Applications. Chem Rev 2022; 122:16752-16801. [PMID: 36195098 DOI: 10.1021/acs.chemrev.2c00045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Effective manipulation of liquids on open surfaces without external energy input is indispensable for the advancement of point-of-care diagnostic devices. Open-surface microfluidics has the potential to benefit health care, especially in the developing world. This review highlights the prospects for harnessing capillary forces on surface-microfluidic platforms, chiefly by inducing smooth gradients or sharp steps of wettability on substrates, to elicit passive liquid transport and higher-order fluidic manipulations without off-the-chip energy sources. A broad spectrum of the recent progress in the emerging field of passive surface microfluidics is highlighted, and its promise for developing facile, low-cost, easy-to-operate microfluidic devices is discussed in light of recent applications, not only in the domain of biomedical microfluidics but also in the general areas of energy and water conservation.
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Affiliation(s)
- Pallab Sinha Mahapatra
- Micro Nano Bio-Fluidics group, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai600036, India
| | - Ranjan Ganguly
- Department of Power Engineering, Jadavpur University, Kolkata700098, India
| | - Aritra Ghosh
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois60607, United States
| | - Souvick Chatterjee
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois60607, United States
| | - Sam Lowrey
- Department of Physics, University of Otago, Dunedin9016, New Zealand
| | - Andrew D Sommers
- Department of Mechanical and Manufacturing Engineering, Miami University, Oxford, Ohio45056, United States
| | - Constantine M Megaridis
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois60607, United States
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14
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Koch M, Nickel S, Lieshout R, Lissek SM, Leskova M, van der Laan LJW, Verstegen MMA, Christ B, Pampaloni F. Label-Free Imaging Analysis of Patient-Derived Cholangiocarcinoma Organoids after Sorafenib Treatment. Cells 2022; 11:3613. [PMID: 36429040 PMCID: PMC9688926 DOI: 10.3390/cells11223613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/01/2022] [Accepted: 11/11/2022] [Indexed: 11/18/2022] Open
Abstract
Monitoring tumor growth dynamics is crucial for understanding cancer. To establish an in vitro method for the continuous assessment of patient-specific tumor growth, tumor organoids were generated from patients with intrahepatic CCA (iCCA). Organoid growth was monitored for 48 h by label-free live brightfield imaging. Growth kinetics were calculated and validated by MTS assay as well as immunohistochemistry of Ki67 to determine proliferation rates. We exposed iCCA organoids (iCCAOs) and non-tumor intrahepatic cholangiocyte organoids (ICOs) to sub-therapeutic concentrations of sorafenib. Monitoring the expansion rate of iCCAOs and ICOs revealed that iCCAO growth was inhibited by sorafenib in a time- and dose-dependent fashion, while ICOs were unaffected. Quantification of the proliferation marker Ki67 confirmed inhibition of iCCAO growth by roughly 50% after 48 h of treatment with 4 µM sorafenib. We established a robust analysis pipeline combining brightfield microscopy and a straightforward image processing approach for the label-free growth monitoring of patient-derived iCCAOs. Combined with bioanalytical validation, this approach is suitable for a fast and efficient high-throughput drug screening in tumor organoids to develop patient-specific systemic treatment options.
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Affiliation(s)
- Michael Koch
- Physical Biology, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Sandra Nickel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University of Leipzig Medical Center, 04103 Leipzig, Germany
- Division of General, Visceral and Vascular Surgery, University Hospital Jena, 07740 Jena, Germany
| | - Ruby Lieshout
- Department of Surgery, Erasmus MC Transplant Institute, University Medical Center Rotterdam, 3015 CN Rotterdam, The Netherlands
| | - Susanna M. Lissek
- Experimental Medicine and Therapy Research, University of Regensburg, 93053 Regensburg, Germany
| | - Martina Leskova
- Physical Biology, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Luc J. W. van der Laan
- Department of Surgery, Erasmus MC Transplant Institute, University Medical Center Rotterdam, 3015 CN Rotterdam, The Netherlands
| | - Monique M. A. Verstegen
- Department of Surgery, Erasmus MC Transplant Institute, University Medical Center Rotterdam, 3015 CN Rotterdam, The Netherlands
| | - Bruno Christ
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Francesco Pampaloni
- Physical Biology, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
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15
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Wei Z, Liu X, Yan R, Sun G, Yu W, Liu Q, Guo Q. Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging. Front Genet 2022; 13:1002327. [PMID: 36386823 PMCID: PMC9644055 DOI: 10.3389/fgene.2022.1002327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/26/2022] [Indexed: 01/25/2023] Open
Abstract
Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.
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Affiliation(s)
- Zhihao Wei
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Ruiqing Yan
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Weiyong Yu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qiang Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China,*Correspondence: Qianjin Guo,
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16
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Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells. Int J Mol Sci 2022; 23:ijms231810827. [PMID: 36142736 PMCID: PMC9504098 DOI: 10.3390/ijms231810827] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/10/2022] [Accepted: 09/13/2022] [Indexed: 11/23/2022] Open
Abstract
Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.
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17
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Rickert CA, Lieleg O. Machine learning approaches for biomolecular, biophysical, and biomaterials research. BIOPHYSICS REVIEWS 2022; 3:021306. [PMID: 38505413 PMCID: PMC10914139 DOI: 10.1063/5.0082179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/12/2022] [Indexed: 03/21/2024]
Abstract
A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.
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18
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Melanthota SK, Gopal D, Chakrabarti S, Kashyap AA, Radhakrishnan R, Mazumder N. Deep learning-based image processing in optical microscopy. Biophys Rev 2022; 14:463-481. [PMID: 35528030 PMCID: PMC9043085 DOI: 10.1007/s12551-022-00949-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/14/2022] [Indexed: 12/19/2022] Open
Abstract
Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places. Graphical abstract
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Affiliation(s)
- Sindhoora Kaniyala Melanthota
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Dharshini Gopal
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Shweta Chakrabarti
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Anirudh Ameya Kashyap
- Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
| | - Raghu Radhakrishnan
- Department of Oral Pathology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, 576104 India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India
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19
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Buchanan BC, Yoon JY. Microscopic Imaging Methods for Organ-on-a-Chip Platforms. MICROMACHINES 2022; 13:328. [PMID: 35208453 PMCID: PMC8879989 DOI: 10.3390/mi13020328] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/15/2022] [Accepted: 02/15/2022] [Indexed: 02/06/2023]
Abstract
Microscopic imaging is essential and the most popular method for in situ monitoring and evaluating the outcome of various organ-on-a-chip (OOC) platforms, including the number and morphology of mammalian cells, gene expression, protein secretions, etc. This review presents an overview of how various imaging methods can be used to image organ-on-a-chip platforms, including transillumination imaging (including brightfield, phase-contrast, and holographic optofluidic imaging), fluorescence imaging (including confocal fluorescence and light-sheet fluorescence imaging), and smartphone-based imaging (including microscope attachment-based, quantitative phase, and lens-free imaging). While various microscopic imaging methods have been demonstrated for conventional microfluidic devices, a relatively small number of microscopic imaging methods have been demonstrated for OOC platforms. Some methods have rarely been used to image OOCs. Specific requirements for imaging OOCs will be discussed in comparison to the conventional microfluidic devices and future directions will be introduced in this review.
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Affiliation(s)
| | - Jeong-Yeol Yoon
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, USA;
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20
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Light sheet based volume flow cytometry (VFC) for rapid volume reconstruction and parameter estimation on the go. Sci Rep 2022; 12:78. [PMID: 34997009 PMCID: PMC8741756 DOI: 10.1038/s41598-021-03902-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/06/2021] [Indexed: 11/08/2022] Open
Abstract
Optical imaging is paramount for disease diagnosis and to access its progression over time. The proposed optical flow imaging (VFC/iLIFE) is a powerful technique that adds new capabilities (3D volume visualization, organelle-level resolution, and multi-organelle screening) to the existing system. Unlike state-of-the-art point-illumination-based biomedical imaging techniques, the sheet-based VFC technique is capable of single-shot sectional visualization, high throughput interrogation, real-time parameter estimation, and instant volume reconstruction with organelle-level resolution of live specimens. The specimen flow system was realized on a multichannel (Y-type) microfluidic chip that enables visualization of organelle distribution in several cells in-parallel at a relatively high flow-rate (2000 nl/min). The calibration of VFC system requires the study of point emitters (fluorescent beads) at physiologically relevant flow-rates (500-2000 nl/min) for determining flow-induced optical aberration in the system point spread function (PSF). Subsequently, the recorded raw images and volumes were computationally deconvolved with flow-variant PSF to reconstruct the cell volume. High throughput investigation of the mitochondrial network in HeLa cancer cell was carried out at sub-cellular resolution in real-time and critical parameters (mitochondria count and size distribution, morphology, entropy, and cell strain statistics) were determined on-the-go. These parameters determine the physiological state of cells, and the changes over-time, revealing the metastatic progression of diseases. Overall, the developed VFC system enables real-time monitoring of sub-cellular organelle organization at a high-throughput with high-content capacity.
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21
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From imaging a single cell to implementing precision medicine: an exciting new era. Emerg Top Life Sci 2021; 5:837-847. [PMID: 34889448 PMCID: PMC8786301 DOI: 10.1042/etls20210219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 11/17/2022]
Abstract
In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.
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22
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Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
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Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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23
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Shigene K, Hiasa Y, Otake Y, Soufi M, Janewanthanakul S, Nishimura T, Sato Y, Suetsugu S. Translation of Cellular Protein Localization Using Convolutional Networks. Front Cell Dev Biol 2021; 9:635231. [PMID: 34422790 PMCID: PMC8375474 DOI: 10.3389/fcell.2021.635231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 07/15/2021] [Indexed: 12/15/2022] Open
Abstract
Protein localization in cells has been analyzed by fluorescent labeling using indirect immunofluorescence and fluorescent protein tagging. However, the relationships between the localization of different proteins had not been analyzed using artificial intelligence. Here, we applied convolutional networks for the prediction of localization of the cytoskeletal proteins from the localization of the other proteins. Lamellipodia are one of the actin-dependent subcellular structures involved in cell migration and are mainly generated by the Wiskott-Aldrich syndrome protein (WASP)-family verprolin homologous protein 2 (WAVE2) and the membrane remodeling I-BAR domain protein IRSp53. Focal adhesion is another actin-based structure that contains vinculin protein and promotes lamellipodia formation and cell migration. In contrast, microtubules are not directly related to actin filaments. The convolutional network was trained using images of actin filaments paired with WAVE2, IRSp53, vinculin, and microtubules. The generated images of WAVE2, IRSp53, and vinculin were highly similar to their real images. In contrast, the microtubule images generated from actin filament images were inferior without the generation of filamentous structures, suggesting that microscopic images of actin filaments provide more information about actin-related protein localization. Collectively, this study suggests that image translation by the convolutional network can predict the localization of functionally related proteins, and the convolutional network might be used to describe the relationships between the proteins by their localization.
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Affiliation(s)
- Kei Shigene
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Yuta Hiasa
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Yoshito Otake
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Mazen Soufi
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Suphamon Janewanthanakul
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Tamako Nishimura
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Yoshinobu Sato
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.,Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan
| | - Shiro Suetsugu
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.,Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan.,Center for Digital Green-Innovation, Nara Institute of Science and Technology, Ikoma, Japan
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24
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Thakur S, Dasmahapatra AK, Bandyopadhyay D. Functional liquid droplets for analyte sensing and energy harvesting. Adv Colloid Interface Sci 2021; 294:102453. [PMID: 34120038 DOI: 10.1016/j.cis.2021.102453] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 05/25/2021] [Accepted: 05/25/2021] [Indexed: 02/06/2023]
Abstract
Over the past century, rapid miniaturization of technologies has helped in the development of efficient, flexible, portable, robust, and compact applications with minimal wastage of materials. In this direction, of late, the usage of mesoscale liquid droplets has emerged as an alternative platform because of the following advantages: (i) a droplet is incompressible and at the same time deformable, (ii) interfacial area of a spherical droplet is minimum for a given amount of mass; and (iii) a droplet interface allows facile mass, momentum, and energy transfer. Subsequently, such attributes have aided towards the design of diverse droplet-based microfluidic technologies. For example, the microdroplets have been utilized as micro-reactors, colorimetric or electrochemical (EC) sensors, drug-delivery vehicles, and energy harvesters. Further, a number of recently reported lab-on-a-chip technologies exploit the motility, storage, and mixing capacities of the microdroplets. In view of this background, the review initiates discussion by highlighting the different attributes of the microdroplets such as size, shape, surface to volume ratio, wettability, and contact line. Thereafter, the effects of the surface or body forces on the properties of the droplets have been elaborated. Finally, the different aspects of such liquid droplet systems towards technological adaptations in health care, sensing, and energy harvesting have been presented. The review concludes with a tight summary on the potential avenues for further developments.
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Affiliation(s)
- Siddharth Thakur
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam 781039, India
| | - Ashok Kumar Dasmahapatra
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam 781039, India; Centre for Nanotechnology, Indian Institute of Technology Guwahati, Assam 781039, India
| | - Dipankar Bandyopadhyay
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam 781039, India; Centre for Nanotechnology, Indian Institute of Technology Guwahati, Assam 781039, India.
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25
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Accorsi A, Box AC, Peuß R, Wood C, Sánchez Alvarado A, Rohner N. Image3C, a multimodal image-based and label-independent integrative method for single-cell analysis. eLife 2021; 10:65372. [PMID: 34286692 PMCID: PMC8370771 DOI: 10.7554/elife.65372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well-characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering, and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell clustering pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and detect changes between different conditions. Therefore, Image3C expands the use of image-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.
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Affiliation(s)
- Alice Accorsi
- Stowers Institute for Medical Research, Kansas City, United States.,Howard Hughes Medical Institute, Stowers Institute for Medical Research, Kansas City, United States
| | - Andrew C Box
- Stowers Institute for Medical Research, Kansas City, United States
| | - Robert Peuß
- Stowers Institute for Medical Research, Kansas City, United States.,Institute for Evolution and Biodiversity, University of Münster, Münster, Germany
| | - Christopher Wood
- Stowers Institute for Medical Research, Kansas City, United States
| | - Alejandro Sánchez Alvarado
- Stowers Institute for Medical Research, Kansas City, United States.,Howard Hughes Medical Institute, Stowers Institute for Medical Research, Kansas City, United States
| | - Nicolas Rohner
- Stowers Institute for Medical Research, Kansas City, United States.,Department of Molecular and Integrative Physiology, KU Medical Center, Kansas City, United States
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26
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Lee KCM, Guck J, Goda K, Tsia KK. Toward deep biophysical cytometry: prospects and challenges. Trends Biotechnol 2021; 39:1249-1262. [PMID: 33895013 DOI: 10.1016/j.tibtech.2021.03.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/15/2021] [Accepted: 03/15/2021] [Indexed: 12/13/2022]
Abstract
The biophysical properties of cells reflect their identities, underpin their homeostatic state in health, and define the pathogenesis of disease. Recent leapfrogging advances in biophysical cytometry now give access to this information, which is obscured in molecular assays, with a discriminative power that was once inconceivable. However, biophysical cytometry should go 'deeper' in terms of exploiting the information-rich cellular biophysical content, generating a molecular knowledge base of cellular biophysical properties, and standardizing the protocols for wider dissemination. Overcoming these barriers, which requires concurrent innovations in microfluidics, optical imaging, and computer vision, could unleash the enormous potential of biophysical cytometry not only for gaining a new mechanistic understanding of biological systems but also for identifying new cost-effective biomarkers of disease.
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Affiliation(s)
- Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - Jochen Guck
- Max Planck Institute for the Science of Light, and Max-Planck-Zentrum für Physik und Medizin, 91058 Erlangen, Germany; Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan; Institute of Technological Sciences, Wuhan University, Hubei 430072, China; Department of Bioengineering, University of California, Los Angeles, California 90095, USA
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong; Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong.
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27
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Abstract
As a high-throughput data analysis technique, photon time stretching (PTS) is widely used in the monitoring of rare events such as cancer cells, rough waves, and the study of electronic and optical transient dynamics. The PTS technology relies on high-speed data collection, and the large amount of data generated poses a challenge to data storage and real-time processing. Therefore, how to use compatible optical methods to filter and process data in advance is particularly important. The time-lens proposed, based on the duality of time and space as an important data processing method derived from PTS, achieves imaging of time signals by controlling the phase information of the timing signals. In this paper, an optical neural network based on the time-lens (TL-ONN) is proposed, which applies the time-lens to the layer algorithm of the neural network to realize the forward transmission of one-dimensional data. The recognition function of this optical neural network for speech information is verified by simulation, and the test recognition accuracy reaches 95.35%. This architecture can be applied to feature extraction and classification, and is expected to be a breakthrough in detecting rare events such as cancer cell identification and screening.
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28
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Luo S, Nguyen KT, Nguyen BTT, Feng S, Shi Y, Elsayed A, Zhang Y, Zhou X, Wen B, Chierchia G, Talbot H, Bourouina T, Jiang X, Liu AQ. Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection. Cytometry A 2021; 99:1123-1133. [PMID: 33550703 DOI: 10.1002/cyto.a.24321] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/19/2022]
Abstract
Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy >99.6%. The system can detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. The high-speed analysis reaches 346 frames per second, outperforming the state-of-the-art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications.
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Affiliation(s)
- Shaobo Luo
- ESIEE, Universite Paris-Est, Noisy-le-Grand Cedex, France.,Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore
| | - Kim Truc Nguyen
- Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore.,School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Binh T T Nguyen
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Shilun Feng
- Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore.,School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yuzhi Shi
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Ahmed Elsayed
- ESIEE, Universite Paris-Est, Noisy-le-Grand Cedex, France
| | - Yi Zhang
- School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Xiaohong Zhou
- Research Centre of Environmental and Health Sensing Technology, School of Environment, Tsinghua University, Beijing, China
| | - Bihan Wen
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | | | - Hugues Talbot
- CentraleSupelec, Universite Paris-Saclay, Saint-Aubin, France
| | | | - Xudong Jiang
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Ai Qun Liu
- Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore.,School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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29
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Zhou Y, Isozaki A, Yasumoto A, Xiao TH, Yatomi Y, Lei C, Goda K. Intelligent Platelet Morphometry. Trends Biotechnol 2021; 39:978-989. [PMID: 33509656 DOI: 10.1016/j.tibtech.2020.12.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/30/2020] [Accepted: 12/31/2020] [Indexed: 12/16/2022]
Abstract
Technological advances in image-based platelet analysis or platelet morphometry are critical for a better understanding of the structure and function of platelets in biological research as well as for the development of better clinical strategies in medical practice. Recently, the advent of high-throughput optical imaging and deep learning has boosted platelet morphometry to the next level by providing a new set of capabilities beyond what is achievable with traditional platelet morphometry, shedding light on the unexplored domain of platelet analysis. This Opinion article introduces emerging opportunities in 'intelligent' platelet morphometry, which are expected to pave the way for a new class of diagnostics, pharmacometrics, and therapeutics.
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Affiliation(s)
- Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan
| | - Akihiro Isozaki
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan; Kanagawa Institute of Industrial Science and Technology, Kanagawa 213-0012, Japan
| | - Atsushi Yasumoto
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, University of Tokyo, Tokyo 113-0033, Japan; Division of Laboratory and Transfusion Medicine, Hokkaido University Hospital, Sapporo 060-8648, Japan
| | - Ting-Hui Xiao
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, University of Tokyo, Tokyo 113-0033, Japan
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan; Institute of Technological Sciences, Wuhan University, Hubei 430072, China
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan; Institute of Technological Sciences, Wuhan University, Hubei 430072, China; Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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30
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Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021; 26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/28/2020] [Accepted: 01/15/2021] [Indexed: 01/17/2023]
Abstract
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.
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Affiliation(s)
| | - Paul R Riley
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
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31
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Cheng S, Fu S, Kim YM, Song W, Li Y, Xue Y, Yi J, Tian L. Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy. SCIENCE ADVANCES 2021; 7:eabe0431. [PMID: 33523908 PMCID: PMC7810377 DOI: 10.1126/sciadv.abe0431] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/19/2020] [Indexed: 05/08/2023]
Abstract
Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell-level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.
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Affiliation(s)
- Shiyi Cheng
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Sipei Fu
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Yumi Mun Kim
- Department of Philosophy & Neuroscience, Boston University, Boston, MA 02215, USA
| | - Weiye Song
- Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, MA 02118, USA
| | - Yunzhe Li
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Yujia Xue
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Ji Yi
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA.
- Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, MA 02118, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Lei Tian
- Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA.
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32
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Isozaki A, Harmon J, Zhou Y, Li S, Nakagawa Y, Hayashi M, Mikami H, Lei C, Goda K. AI on a chip. LAB ON A CHIP 2020; 20:3074-3090. [PMID: 32644061 DOI: 10.1039/d0lc00521e] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.
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Affiliation(s)
- Akihiro Isozaki
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Kanagawa Institute of Industrial Science and Technology, Kanagawa 213-0012, Japan
| | - Jeffrey Harmon
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Shuai Li
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and The Cambridge Centre for Data-Driven Discovery, Cambridge University, Cambridge CB3 0WA, UK
| | - Yuta Nakagawa
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Mika Hayashi
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Hideharu Mikami
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China and Department of Bioengineering, University of California, Los Angeles, California 90095, USA
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33
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Li Y, Di J, Wang K, Wang S, Zhao J. Classification of cell morphology with quantitative phase microscopy and machine learning. OPTICS EXPRESS 2020; 28:23916-23927. [PMID: 32752380 DOI: 10.1364/oe.397029] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/21/2020] [Indexed: 06/11/2023]
Abstract
We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifier including various individual models such as artificial neural network, extreme learning machine and generalized logistic regression. In another approach, we apply a pretrained convolutional neural network using transfer learning for the classification. As a validation, we show the performances of both approaches on classification between macrophages cultured in normal gravity and microgravity with quantitative phase imaging. The multilevel integrated classifier achieves average accuracy 93.1%, which is comparable to the average accuracy 93.5% obtained by convolutional neural network. The presented quantitative phase imaging system with two classification approaches could be helpful to biomedical scientists for easy and accurate cell analysis.
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34
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Zhao W, Guo Y, Yang S, Chen M, Chen H. Fast intelligent cell phenotyping for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:1-12. [PMID: 32495539 PMCID: PMC7267411 DOI: 10.1117/1.jbo.25.6.066001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 05/15/2020] [Indexed: 06/11/2023]
Abstract
SIGNIFICANCE The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed. AIM We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification. APPROACH We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime. The tested cell image datasets were acquired from high-throughput imaging of over 20,000 drug-treated and untreated cells with an optofluidic time-stretch microscope. RESULTS The framework we built beats other methods with an accuracy of over 97% and a classification frequency of 3000 cells / s. In addition, we determined the optimal structure of training sets according to model performances under different training set components. CONCLUSIONS The proposed XGBoost-based framework acts as a promising solution to processing large flow image data. This work provides a foundation for future cell sorting and clinical practice of high-throughput imaging cytometers.
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Affiliation(s)
- Wanyue Zhao
- Tsinghua University, Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Beijing, China
| | - Yingxue Guo
- Tsinghua University, Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Beijing, China
| | - Sigang Yang
- Tsinghua University, Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Beijing, China
| | - Minghua Chen
- Tsinghua University, Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Beijing, China
| | - Hongwei Chen
- Tsinghua University, Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Beijing, China
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35
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Doan M, Case M, Masic D, Hennig H, McQuin C, Caicedo J, Singh S, Goodman A, Wolkenhauer O, Summers HD, Jamieson D, Delft FV, Filby A, Carpenter AE, Rees P, Irving J. Label-Free Leukemia Monitoring by Computer Vision. Cytometry A 2020; 97:407-414. [PMID: 32091180 PMCID: PMC7213640 DOI: 10.1002/cyto.a.23987] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/22/2020] [Accepted: 02/10/2020] [Indexed: 12/13/2022]
Abstract
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well‐recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913–1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on‐treatment bone marrow samples were labeled with an ALL‐discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright‐field and dark‐field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody‐free, single cell method is cheap, quick, and could be adapted to a simple, laser‐free cytometer to allow automated, point‐of‐care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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Affiliation(s)
- Minh Doan
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Marian Case
- Northern Institute for Cancer Research, Newcastle University, UK
| | - Dino Masic
- Northern Institute for Cancer Research, Newcastle University, UK
| | - Holger Hennig
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
| | - Claire McQuin
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Juan Caicedo
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Allen Goodman
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
| | - Huw D Summers
- College of Engineering, Swansea University, Bay Campus, Swansea, SA1 8EN, UK
| | - David Jamieson
- Northern Institute for Cancer Research, Newcastle University, UK
| | - Frederik V Delft
- Northern Institute for Cancer Research, Newcastle University, UK
| | - Andrew Filby
- Flow Cytometry Core Facility. Innovation, Methodology and Application Research Theme, Biosciences Institute, Newcastle University, NE2 4HH, UK
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Paul Rees
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,College of Engineering, Swansea University, Bay Campus, Swansea, SA1 8EN, UK
| | - Julie Irving
- Northern Institute for Cancer Research, Newcastle University, UK
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36
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Sun J, Tárnok A, Su X. Deep Learning-Based Single-Cell Optical Image Studies. Cytometry A 2020; 97:226-240. [PMID: 31981309 DOI: 10.1002/cyto.a.23973] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 01/03/2020] [Accepted: 01/10/2020] [Indexed: 12/17/2022]
Abstract
Optical imaging technology that has the advantages of high sensitivity and cost-effectiveness greatly promotes the progress of nondestructive single-cell studies. Complex cellular image analysis tasks such as three-dimensional reconstruction call for machine-learning technology in cell optical image research. With the rapid developments of high-throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large-scale image processing and analysis, which brings a new dawn for single-cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single-cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single-cell optical image studies. We explore the feasibility of applying deep learning techniques to single-cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end-to-end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single-cell optical image studies are reviewed, which include image segmentation, super-resolution image reconstruction, cell tracking, cell counting, cross-modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single-cell optical imaging techniques such as label-free cell optical imaging, high-content screening, and high-throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single-cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry.
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Affiliation(s)
- Jing Sun
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Attila Tárnok
- Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany.,Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Xuantao Su
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China
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37
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Wu Y, Zhou Y, Huang CJ, Kobayashi H, Yan S, Ozeki Y, Wu Y, Sun CW, Yasumoto A, Yatomi Y, Lei C, Goda K. Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging. OPTICS EXPRESS 2020; 28:519-532. [PMID: 32118978 DOI: 10.1364/oe.380679] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 12/13/2019] [Indexed: 05/24/2023]
Abstract
Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,000 cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning.
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Yalikun Y, Ota N, Guo B, Tang T, Zhou Y, Lei C, Kobayashi H, Hosokawa Y, Li M, Enrique Muñoz H, Di Carlo D, Goda K, Tanaka Y. Effects of Flow‐Induced Microfluidic Chip Wall Deformation on Imaging Flow Cytometry. Cytometry A 2019; 97:909-920. [DOI: 10.1002/cyto.a.23944] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 11/04/2019] [Accepted: 11/20/2019] [Indexed: 12/19/2022]
Affiliation(s)
- Yaxiaer Yalikun
- Center for Biosystems Dynamics Research (BDR) RIKEN 1‐3 Yamadaoka, Suita Osaka 565‐0871 Japan
- Division of Materials Science Nara Institute of Science and Technology Takayama, Ikoma Nara 630‐0192 Japan
| | - Nobutoshi Ota
- Center for Biosystems Dynamics Research (BDR) RIKEN 1‐3 Yamadaoka, Suita Osaka 565‐0871 Japan
| | - Baoshan Guo
- Department of Chemistry School of Science, The University of Tokyo Tokyo 113‐0033 Japan
| | - Tao Tang
- Division of Materials Science Nara Institute of Science and Technology Takayama, Ikoma Nara 630‐0192 Japan
| | - Yuqi Zhou
- Department of Chemistry School of Science, The University of Tokyo Tokyo 113‐0033 Japan
| | - Cheng Lei
- Department of Chemistry School of Science, The University of Tokyo Tokyo 113‐0033 Japan
- Institute of Technological Sciences, Wuhan University Wuhan 430072 China
| | - Hirofumi Kobayashi
- Department of Chemistry School of Science, The University of Tokyo Tokyo 113‐0033 Japan
| | - Yoichiroh Hosokawa
- Division of Materials Science Nara Institute of Science and Technology Takayama, Ikoma Nara 630‐0192 Japan
| | - Ming Li
- School of Engineering, Macquarie University Sydney 2109 Australia
| | - Hector Enrique Muñoz
- Department of Bioengineering University of California Los Angeles California 90095
| | - Dino Di Carlo
- Department of Bioengineering University of California Los Angeles California 90095
| | - Keisuke Goda
- Department of Chemistry School of Science, The University of Tokyo Tokyo 113‐0033 Japan
- Institute of Technological Sciences, Wuhan University Wuhan 430072 China
- Department of Bioengineering University of California Los Angeles California 90095
| | - Yo Tanaka
- Center for Biosystems Dynamics Research (BDR) RIKEN 1‐3 Yamadaoka, Suita Osaka 565‐0871 Japan
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Kobayashi H, Lei C, Wu Y, Huang CJ, Yasumoto A, Jona M, Li W, Wu Y, Yalikun Y, Jiang Y, Guo B, Sun CW, Tanaka Y, Yamada M, Yatomi Y, Goda K. Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia. LAB ON A CHIP 2019; 19:2688-2698. [PMID: 31287108 DOI: 10.1039/c8lc01370e] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 μL into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine.
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40
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Isozaki A, Mikami H, Hiramatsu K, Sakuma S, Kasai Y, Iino T, Yamano T, Yasumoto A, Oguchi Y, Suzuki N, Shirasaki Y, Endo T, Ito T, Hiraki K, Yamada M, Matsusaka S, Hayakawa T, Fukuzawa H, Yatomi Y, Arai F, Di Carlo D, Nakagawa A, Hoshino Y, Hosokawa Y, Uemura S, Sugimura T, Ozeki Y, Nitta N, Goda K. A practical guide to intelligent image-activated cell sorting. Nat Protoc 2019; 14:2370-2415. [PMID: 31278398 DOI: 10.1038/s41596-019-0183-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 04/18/2019] [Indexed: 02/08/2023]
Abstract
Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.
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Affiliation(s)
- Akihiro Isozaki
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Hideharu Mikami
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | | | - Shinya Sakuma
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan
| | - Yusuke Kasai
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan
| | - Takanori Iino
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan
| | - Takashi Yamano
- Laboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan
| | - Atsushi Yasumoto
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yusuke Oguchi
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
| | - Nobutake Suzuki
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
| | | | | | - Takuro Ito
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Japan Science and Technology Agency, Saitama, Japan
| | - Kei Hiraki
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
| | - Makoto Yamada
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Satoshi Matsusaka
- Clinical Research and Regional Innovation, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Takeshi Hayakawa
- Department of Precision Mechanics, Chuo University, Tokyo, Japan
| | - Hideya Fukuzawa
- Laboratory of Applied Molecular Microbiology, Kyoto University, Kyoto, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Fumihito Arai
- Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan
| | - Dino Di Carlo
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Mechanical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.,California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Atsuhiro Nakagawa
- Department of Neurosurgery, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yu Hoshino
- Department of Chemical Engineering, Kyushu University, Fukuoka, Japan
| | - Yoichiroh Hosokawa
- Division of Materials Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Sotaro Uemura
- Department of Biological Sciences, The University of Tokyo, Tokyo, Japan
| | - Takeaki Sugimura
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Japan Science and Technology Agency, Saitama, Japan
| | - Yasuyuki Ozeki
- Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, Japan
| | - Nao Nitta
- Department of Chemistry, The University of Tokyo, Tokyo, Japan.,Japan Science and Technology Agency, Saitama, Japan
| | - Keisuke Goda
- Department of Chemistry, The University of Tokyo, Tokyo, Japan. .,Japan Science and Technology Agency, Saitama, Japan. .,Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
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41
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Lee KCM, Wang M, Cheah KSE, Chan GCF, So HKH, Wong KKY, Tsia KK. Quantitative Phase Imaging Flow Cytometry for Ultra-Large-Scale Single-Cell Biophysical Phenotyping. Cytometry A 2019; 95:510-520. [PMID: 31012276 DOI: 10.1002/cyto.a.23765] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 03/19/2019] [Accepted: 04/01/2019] [Indexed: 12/21/2022]
Abstract
Cellular biophysical properties are the effective label-free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell-based assays that involve large-scale single-cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single-cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label-free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi-ATOM, that enables label-free single-cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s-a capability that is otherwise inaccessible in current QPI. With the aim to translate multi-ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high-dimensional single-cell biophysical phenotypic profiles at ultra-large-scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label-free statistical power to classify multiple human leukemic cell types at high accuracy (~92-97%). This system could substantiate the significance of high-throughput QPI flow cytometry in enabling next frontier in large-scale image-derived single-cell analysis applied in biological discovery and cost-effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Kelvin C M Lee
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Maolin Wang
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kathryn S E Cheah
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Godfrey C F Chan
- Department of Pediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Hayden K H So
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Pokfulam, Hong Kong
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42
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Gong M, Sakidja R, Goul R, Ewing D, Casper M, Stramel A, Elliot A, Wu JZ. High-Performance All-Inorganic CsPbCl 3 Perovskite Nanocrystal Photodetectors with Superior Stability. ACS NANO 2019; 13:3714-3722. [PMID: 30689349 DOI: 10.1021/acsnano.9b00911] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
All-inorganic perovskites nanostructures, such as CsPbCl3 nanocrystals (NCs), are promising in many applications including light-emitting diodes, photovoltaics, and photodetectors. Despite the impressive performance that was demonstrated, a critical issue remains due to the instability of the perovskites in ambient. Herein, we report a method of passivating crystalline CsPbCl3 NC surfaces with 3-mercaptopropionic acid (MPA), and superior ambient stability is achieved. The printing of these colloidal NCs on the channel of graphene field-effect transistors (GFETs) on solid Si/SiO2 and flexible polyethylene terephthalate substrates was carried out to obtain CsPbCl3 NCs/GFET heterojunction photodetectors for flexible and visible-blind ultraviolet detection at wavelength below 400 nm. Besides ambient stability, the additional benefits of passivating surface charge trapping by the defects on CsPbCl3 NCs and facilitating high-efficiency charge transfer between the CsPbCl3 NCs and graphene were provided by MPA. Extraordinary optoelectronic performance was obtained on the CsPbCl3 NCs/graphene devices including a high ultraviolet responsivity exceeding 106 A/W, a high detectivity of 2 × 1013 Jones, a fast photoresponse time of 0.3 s, and ambient stability with less than 10% degradation of photoresponse after 2400 h. This result demonstrates the crucial importance of the perovskite NC surface passivation not only to the performance but also to the stability of the perovskite optoelectronic devices.
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Affiliation(s)
- Maogang Gong
- Department of Physics and Astronomy , University of Kansas , Lawrence , Kansas 66045 , United States
| | - Ridwan Sakidja
- Department of Physics, Astronomy, and Materials Science , Missouri State University , Springfield , Missouri 65897 , United States
| | - Ryan Goul
- Department of Physics and Astronomy , University of Kansas , Lawrence , Kansas 66045 , United States
| | - Dan Ewing
- Department of Energy's National Security Campus , Kansas City , Missouri 64147 , United States
| | - Matthew Casper
- Department of Energy's National Security Campus , Kansas City , Missouri 64147 , United States
| | - Alex Stramel
- Department of Energy's National Security Campus , Kansas City , Missouri 64147 , United States
| | - Alan Elliot
- Department of Energy's National Security Campus , Kansas City , Missouri 64147 , United States
| | - Judy Z Wu
- Department of Physics and Astronomy , University of Kansas , Lawrence , Kansas 66045 , United States
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43
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Das Mahapatra A, Das A, Ghosh S, Basak D. Defect-Assisted Broad-Band Photosensitivity with High Responsivity in Au/Self-Seeded TiO 2 NR/Au-Based Back-to-Back Schottky Junctions. ACS OMEGA 2019; 4:1364-1374. [PMID: 31459404 PMCID: PMC6648538 DOI: 10.1021/acsomega.8b03084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 01/04/2019] [Indexed: 06/10/2023]
Abstract
TiO2 nanorods (NRs) have generated much interest for both fundamental understanding of defect formation and technological applications in energy harvesting, optoelectronics, and catalysis. Herein, we have grown TiO2 NR films on glass substrates using a self-seeded approach and annealed them in H2 ambient to modify their surface defects. It has been shown that broad-band photosensing properties of Au/self-seeded TiO2 NR/Au-based two back-to-back Schottky junctions (SJs) for a broad wavelength of light are much superior as compared to those of the pristine and the control samples. Photoresponsivity values for the H2-annealed sample are 0.42, 0.71, 0.07, and 0.08 A/W for detecting, respectively, 350, 400, 470, and 570 nm lights. Very low dark current and high photocurrent lead to a gain value as high as 1.85 × 104 for 400 nm light. Unprecedentedly modified NR-based SJs show excellent photoresponsivity for detecting as low as 25, 36, 48, and 28 μW/cm2 power densities of 350, 400, 470, and 570 nm lights, respectively. It is found that Ti3+ defects play a key role in an efficient photoelectron transfer from TiO2 to Au. Our work, for the first time, highlights the simplicity and reveals the rationale behind the excellent properties of Au/self-seeded TiO2 NR film/Au back-to-back SJs.
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44
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Ozaki Y, Yamada H, Kikuchi H, Hirotsu A, Murakami T, Matsumoto T, Kawabata T, Hiramatsu Y, Kamiya K, Yamauchi T, Goto K, Ueda Y, Okazaki S, Kitagawa M, Takeuchi H, Konno H. Label-free classification of cells based on supervised machine learning of subcellular structures. PLoS One 2019; 14:e0211347. [PMID: 30695059 PMCID: PMC6350988 DOI: 10.1371/journal.pone.0211347] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/12/2019] [Indexed: 01/26/2023] Open
Abstract
It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.
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Affiliation(s)
- Yusuke Ozaki
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Hidenao Yamada
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka, Japan
- * E-mail:
| | - Hirotoshi Kikuchi
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Amane Hirotsu
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Tomohiro Murakami
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Tomohiro Matsumoto
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Toshiki Kawabata
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Yoshihiro Hiramatsu
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Kinji Kamiya
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Toyohiko Yamauchi
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka, Japan
| | - Kentaro Goto
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka, Japan
| | - Yukio Ueda
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka, Japan
| | - Shigetoshi Okazaki
- Department of Medical Spectroscopy, Institute for Medical Photonics Research, Preeminent Medical Photonics Education and Research Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Masatoshi Kitagawa
- Department of Molecular Biology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
- Laboratory Animal Facilities and Services, Preeminent Medical Photonics Education and Research Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Hiroya Takeuchi
- Second Department of Surgery, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Hiroyuki Konno
- Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
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3D Engineering of Ocular Tissues for Disease Modeling and Drug Testing. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1186:171-193. [DOI: 10.1007/978-3-030-28471-8_7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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46
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Pomerantz AK, Sari-Sarraf F, Grove KJ, Pedro L, Rudewicz PJ, Fathman JW, Krucker T. Enabling drug discovery and development through single-cell imaging. Expert Opin Drug Discov 2018; 14:115-125. [DOI: 10.1080/17460441.2019.1559147] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Andrea K. Pomerantz
- Analytical Sciences & Imaging, Novartis Institutes for BioMedical Research Inc., Cambridge, MA, USA
| | - Farid Sari-Sarraf
- Analytical Sciences & Imaging, Novartis Institutes for BioMedical Research Inc., Cambridge, MA, USA
| | - Kerri J. Grove
- Global Discovery Chemistry, Novartis Institutes for BioMedical Research Inc., Emeryville, CA, USA
| | - Liliana Pedro
- Global Discovery Chemistry, Novartis Institutes for BioMedical Research Inc., Emeryville, CA, USA
| | - Patrick J. Rudewicz
- Global Discovery Chemistry, Novartis Institutes for BioMedical Research Inc., Emeryville, CA, USA
| | - John W. Fathman
- Cancer Therapeutics, Genomics Institute of the Novartis Research Foundation, La Jolla, CA, USA
| | - Thomas Krucker
- Alliance Management and Partnering, Novartis Institutes for BioMedical Research Inc., Emeryville, CA, USA
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47
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Soldati G, Del Ben F, Brisotto G, Biscontin E, Bulfoni M, Piruska A, Steffan A, Turetta M, Della Mea V. Microfluidic droplets content classification and analysis through convolutional neural networks in a liquid biopsy workflow. Am J Transl Res 2018; 10:4004-4016. [PMID: 30662646 PMCID: PMC6325488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 11/07/2018] [Indexed: 06/09/2023]
Abstract
In a recent paper we presented an innovative method of liquid biopsy, for the detection of circulating tumor cells (CTC) in the peripheral blood. Using microfluidics, CTC are individually encapsulated in water-in-oil droplets and selected by their increased rate of extracellular acidification (ECAR). During the analysis, empty or debris-containing droplets are discarded manually by screening images of positive droplets, increasing the operator-dependency and time-consumption of the assay. In this work, we addressed the limitations of the current method integrating computer vision techniques in the analysis. We implemented an automatic classification of droplets using convolutional neural networks, correctly classifying more than 96% of droplets. A second limitation of the technique is that ECAR is computed using an average droplet volume, without considering small variations in extracellular volume which can occur due to the normal variability in the size of the droplets or cells. Here, with the use of neural networks for object detection, we segmented the images of droplets and cells to measure their relative volumes, correcting over- or under-estimation of ECAR, which was present up to 20%. Finally, we evaluated whether droplet images contained additional information. We preliminarily gave a proof-of-concept demonstration showing that white blood cells expression of CD45 can be predicted with 82.9% accuracy, based on bright-field cell images alone. Then, we applied the method to classify acid droplets as coming from metastatic breast cancer patients or healthy donors, obtaining an accuracy of 90.2%.
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Affiliation(s)
- Gabriele Soldati
- Department of Mathematics, Computer Science and Physics, University of UdineItaly
| | - Fabio Del Ben
- Immunopathology and Cancer Biomarkers, CRO Aviano National Cancer InstituteAviano, Italy
| | - Giulia Brisotto
- Immunopathology and Cancer Biomarkers, CRO Aviano National Cancer InstituteAviano, Italy
| | - Eva Biscontin
- Immunopathology and Cancer Biomarkers, CRO Aviano National Cancer InstituteAviano, Italy
| | - Michela Bulfoni
- M.T., M.B., Department of Medicine, University of UdineItaly
| | - Aigars Piruska
- A.P. Institute for Molecules and Materials, Radboud UniversityNijmegen, The Netherlands
| | - Agostino Steffan
- Immunopathology and Cancer Biomarkers, CRO Aviano National Cancer InstituteAviano, Italy
| | - Matteo Turetta
- M.T., M.B., Department of Medicine, University of UdineItaly
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of UdineItaly
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48
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Song L, Feng Y, Guo X, Shen Y, Wu D, Wu Z, Zhou C, Zhu L, Gao S, Liu W, Zhang X, Li Z. Ultrafast polarization bio-imaging based on coherent detection and time-stretch techniques. BIOMEDICAL OPTICS EXPRESS 2018; 9:6556-6568. [PMID: 31065449 PMCID: PMC6490988 DOI: 10.1364/boe.9.006556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 11/22/2018] [Accepted: 11/23/2018] [Indexed: 06/09/2023]
Abstract
Optical polarization imaging has played an important role in many biological and biomedical applications, as it provides a label-free and non-invasive detection scheme to reveal the polarization information of optical rotation, birefringence, and photoelasticity distribution inherent in biological samples. However, the imaging speeds of the previously demonstrated polarization imaging techniques were often limited by the slow frame rates of the arrayed imaging detectors, which usually run at frame rates of several hundred hertz. By combining the optical coherent detection of orthogonal polarizations and the optical time-stretch imaging technique, we achieved ultrafast polarization bio-imaging at an extremely fast record line scanning rate up to 100 MHz without averaging. We experimentally demonstrated the superior performance of our method by imaging three slices of different kinds of biological samples with the retrieved Jones matrix and polarization-sensitive information including birefringence and diattenuation. The proposed system in this paper may find potential applications for ultrafast polarization dynamics in living samples or some other advanced biomedical research.
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Affiliation(s)
- Lu Song
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Yuanhua Feng
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China
| | - Xiaojie Guo
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Yuecheng Shen
- School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
| | - Daixuan Wu
- School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
| | - Zhenhua Wu
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Congran Zhou
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Linyan Zhu
- Department of Pharmacology, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Shecheng Gao
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China
| | - Weiping Liu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou 510632, China
| | - Xuming Zhang
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhaohui Li
- School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
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Chantzi E, Jarvius M, Niklasson M, Segerman A, Gustafsson MG. COMBImage: a modular parallel processing framework for pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. BMC Bioinformatics 2018; 19:453. [PMID: 30477419 PMCID: PMC6257977 DOI: 10.1186/s12859-018-2458-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 10/03/2018] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Large-scale pairwise drug combination analysis has lately gained momentum in drug discovery and development projects, mainly due to the employment of advanced experimental-computational pipelines. This is fortunate as drug combinations are often required for successful treatment of complex diseases. Furthermore, most new drugs cannot totally replace the current standard-of-care medication, but rather have to enter clinical use as add-on treatment. However, there is a clear deficiency of computational tools for label-free and temporal image-based drug combination analysis that go beyond the conventional but relatively uninformative end point measurements. RESULTS COMBImage is a fast, modular and instrument independent computational framework for in vitro pairwise drug combination analysis that quantifies temporal changes in label-free video microscopy movies. Jointly with automated analyses of temporal changes in cell morphology and confluence, it performs and displays conventional cell viability and synergy end point analyses. The image processing algorithms are parallelized using Google's MapReduce programming model and optimized with respect to method-specific tuning parameters. COMBImage is shown to process time-lapse microscopy movies from 384-well plates within minutes on a single quad core personal computer. This framework was employed in the context of an ongoing drug discovery and development project focused on glioblastoma multiforme; the most deadly form of brain cancer. Interesting add-on effects of two investigational cytotoxic compounds when combined with vorinostat were revealed on recently established clonal cultures of glioma-initiating cells from patient tumor samples. Therapeutic synergies, when normal astrocytes were used as a toxicity cell model, reinforced the pharmacological interest regarding their potential clinical use. CONCLUSIONS COMBImage enables, for the first time, fast and optimized pairwise drug combination analyses of temporal changes in label-free video microscopy movies. Providing this jointly with conventional cell viability based end point analyses, it could help accelerating and guiding any drug discovery and development project, without use of cell labeling and the need to employ a particular live cell imaging instrument.
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Affiliation(s)
- Efthymia Chantzi
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden
| | - Malin Jarvius
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden
- SciLifeLab Drug Discovery and Development, In Vitro Systems Pharmacology Facility, Uppsala University, Uppsala, Sweden
| | - Mia Niklasson
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Anna Segerman
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Mats G. Gustafsson
- Department of Medical Sciences, Cancer Pharmacology and Computational Medicine, Uppsala University, Uppsala, Sweden
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