1
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Lo MCK, Siu DMD, Lee KCM, Wong JSJ, Yeung MCF, Hsin MKY, Ho JCM, Tsia KK. Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307591. [PMID: 38864546 PMCID: PMC11304271 DOI: 10.1002/advs.202307591] [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: 10/11/2023] [Revised: 05/17/2024] [Indexed: 06/13/2024]
Abstract
Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.
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Affiliation(s)
- Michelle C. K. Lo
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Dickson M. D. Siu
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Kelvin C. M. Lee
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Justin S. J. Wong
- Conzeb LimitedHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
| | - Maximus C. F. Yeung
- Department of Pathology, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Michael K. Y. Hsin
- Department of Surgery, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - James C. M. Ho
- Department of Medicine, Li Ka Shing Faculty of MedicineThe University of Hong KongPokfulam RoadHong Kong000000Hong Kong
| | - Kevin K. Tsia
- Department of Electrical and Electronic EngineeringThe University of Hong KongHong Kong000000Hong Kong
- Advanced Biomedical Instrumentation CentreHong Kong Science Park, New TerritoriesHong Kong000000Hong Kong
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2
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Hisada T, Imai Y, Takemoto Y, Kanie K, Kato R. Prediction of antibody production performance change in Chinese hamster ovary cells using morphological profiling. J Biosci Bioeng 2024; 137:453-462. [PMID: 38472072 DOI: 10.1016/j.jbiosc.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/13/2024] [Accepted: 01/15/2024] [Indexed: 03/14/2024]
Abstract
Monoclonal antibodies (mAbs) represent a significant segment of biopharmaceuticals, with the market for mAb therapeutics expected to reach $200 billion in 2021. Chinese Hamster Ovary (CHO) cells are the industry standard for large-scale mAb production owing to their adaptability and genetic engineering capabilities. However, maintaining consistent product quality is challenging, primarily because of the inherent genetic instability of CHO cells. In this study, we address the need for advanced technologies for quality monitoring of host cells in biopharmaceuticals. We highlight the limitations of traditional cell assessment techniques such as flow cytometry and propose a noninvasive, label-free image-based analysis method. By utilizing advanced image processing and machine learning, this technique aims to non-invasively and quantitatively evaluate subtle quality changes in suspension cells. The research aims to investigate the use of morphological analysis for identifying subtle alterations in mAb productivity of CHO cells, employing cells stimulated by compounds as a model for this study. Our results show that the mAb productivity of CHO cells (day 8) can be predicted only from their early morphological profile (day 3). Our study also discusses the importance of strategic methods for forecasting host cell mAb productivity using morphological profiles, as inferred from our machine learning models specialized in predictive score prediction and anomaly prediction.
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Affiliation(s)
- Takumi Hisada
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
| | - Yuta Imai
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
| | - Yuto Takemoto
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
| | - Kei Kanie
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan; Department of Biotechnology and Chemistry, Faculty of Engineering, Kindai University, 1 Umanobe, Takaya, Higashi-Hiroshima 739-2116, Japan
| | - Ryuji Kato
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan; Institute of Nano-Life-Systems, Institute for Innovation for Future Society, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya 464-8601, Japan.
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3
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Chang YT, Prompsy P, Kimeswenger S, Tsai YC, Ignatova D, Pavlova O, Iselin C, French LE, Levesque MP, Kuonen F, Bobrowicz M, Brunner PM, Pascolo S, Hoetzenecker W, Guenova E. MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides. Nat Commun 2024; 15:752. [PMID: 38272918 PMCID: PMC10810852 DOI: 10.1038/s41467-024-45083-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 01/15/2024] [Indexed: 01/27/2024] Open
Abstract
Cancer-associated immune dysfunction is a major challenge for effective therapies. The emergence of antibodies targeting tumor cell-surface antigens led to advancements in the treatment of hematopoietic malignancies, particularly blood cancers. Yet their impact is constrained against tumors of hematopoietic origin manifesting in the skin. In this study, we employ a clonality-supervised deep learning methodology to dissect key pathological features implicated in mycosis fungoides, the most common cutaneous T-cell lymphoma. Our investigations unveil the prominence of the IL-32β-major histocompatibility complex (MHC)-I axis as a critical determinant in tumor T-cell immune evasion within the skin microenvironment. In patients' skin, we find MHC-I to detrimentally impact the functionality of natural killer (NK) cells, diminishing antibody-dependent cellular cytotoxicity and promoting resistance of tumor skin T-cells to cell-surface targeting therapies. Through murine experiments in female mice, we demonstrate that disruption of the MHC-I interaction with NK cell inhibitory Ly49 receptors restores NK cell anti-tumor activity and targeted T-cell lymphoma elimination in vivo. These findings underscore the significance of attenuating the MHC-I-dependent immunosuppressive networks within skin tumors. Overall, our study introduces a strategy to reinvigorate NK cell-mediated anti-tumor responses to overcome treatment resistance to existing cell-surface targeted therapies for skin lymphoma.
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Affiliation(s)
- Yun-Tsan Chang
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Pacôme Prompsy
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Susanne Kimeswenger
- Department of Dermatology and Venerology, Medical Faculty, Johannes Kepler University, Linz, Austria
| | - Yi-Chien Tsai
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Desislava Ignatova
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Olesya Pavlova
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Christoph Iselin
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Lars E French
- Department of Dermatology and Allergology, Ludwig-Maximilians-University of Munich, Munich, Germany
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mitchell P Levesque
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - François Kuonen
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | | | - Patrick M Brunner
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Steve Pascolo
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Wolfram Hoetzenecker
- Department of Dermatology and Venerology, Medical Faculty, Johannes Kepler University, Linz, Austria.
| | - Emmanuella Guenova
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland.
- Department of Dermatology, Hospital 12 de Octubre, Medical School, University Complutense, Madrid, Spain.
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4
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Gupta A, Shaik SK, Balasubramanian L, Chakraborty U. MSCProfiler: a single cell image processing workflow to investigate mesenchymal stem cell heterogeneity. Biotechniques 2023; 75:195-209. [PMID: 37916466 DOI: 10.2144/btn-2023-0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023] Open
Abstract
Single cell cytometry has demonstrated plausible immuno-heterogeneity of mesenchymal stem cells (MSCs) owing to their multivariate stromal origin. To contribute successfully to next-generation stem cell therapeutics, a deeper understanding of their cellular morphology and immunophenotype is important. In this study, the authors describe MSCProfiler, an image analysis pipeline developed using CellProfiler software. This workflow can extract geometrical and texture features such as shape, size, eccentricity and entropy, along with intensity values of the surface markers from multiple single cell images obtained using imaging flow cytometry. This screening pipeline can be used to analyze geometrical and texture features of all types of MSCs across different passages hallmarked by enhanced feature extraction potential from brightfield and fluorescent images of the cells.
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Affiliation(s)
- Ayona Gupta
- Manipal Institute of Regenerative Medicine, Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | | | | | - Uttara Chakraborty
- Manipal Institute of Regenerative Medicine, Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
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5
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Pozzi P, Candeo A, Paiè P, Bragheri F, Bassi A. Artificial intelligence in imaging flow cytometry. FRONTIERS IN BIOINFORMATICS 2023; 3:1229052. [PMID: 37877042 PMCID: PMC10593470 DOI: 10.3389/fbinf.2023.1229052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/11/2023] [Indexed: 10/26/2023] Open
Affiliation(s)
- Paolo Pozzi
- Department of Physics, Politecnico di Milano, Milano, Italy
| | - Alessia Candeo
- Department of Physics, Politecnico di Milano, Milano, Italy
| | - Petra Paiè
- Department of Physics, Politecnico di Milano, Milano, Italy
| | - Francesca Bragheri
- Institute for Photonics and Nanotechnologies, Consiglio Nazionale delle Ricerche, Milano, Italy
| | - Andrea Bassi
- Department of Physics, Politecnico di Milano, Milano, Italy
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6
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Aslan MK, Ding Y, Stavrakis S, deMello AJ. Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis. Anal Chem 2023; 95:14526-14532. [PMID: 37733469 DOI: 10.1021/acs.analchem.3c03213] [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: 09/23/2023]
Abstract
We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.
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Affiliation(s)
- Mahmut Kamil Aslan
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
| | - Yun Ding
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
| | - Andrew J deMello
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
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7
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Timonen VA, Kerkelä E, Impola U, Penna L, Partanen J, Kilpivaara O, Arvas M, Pitkänen E. DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning. Cytometry A 2023; 103:807-817. [PMID: 37276178 DOI: 10.1002/cyto.a.24770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 05/16/2023] [Accepted: 06/02/2023] [Indexed: 06/07/2023]
Abstract
Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.
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Affiliation(s)
- Veera A Timonen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erja Kerkelä
- Advanced Cell Therapy Centre, Finnish Red Cross Blood Service, Vantaa, Finland
| | - Ulla Impola
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Leena Penna
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Outi Kilpivaara
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Medical and Clinical Genetics, Medicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HUSLAB Laboratory of Genetics, HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Mikko Arvas
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
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8
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Zhou S, Chen B, Fu ES, Yan H. Computer vision meets microfluidics: a label-free method for high-throughput cell analysis. MICROSYSTEMS & NANOENGINEERING 2023; 9:116. [PMID: 37744264 PMCID: PMC10511704 DOI: 10.1038/s41378-023-00562-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/21/2023] [Accepted: 04/10/2023] [Indexed: 09/26/2023]
Abstract
In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.
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Affiliation(s)
- Shizheng Zhou
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
| | - Bingbing Chen
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
| | - Edgar S. Fu
- Graduate School of Computing and Information Science, University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - Hong Yan
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
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9
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Wang Y, Huang Z, Wang X, Yang F, Yao X, Pan T, Li B, Chu J. Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms. LAB ON A CHIP 2023; 23:3615-3627. [PMID: 37458395 DOI: 10.1039/d3lc00194f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion blur in cell images, making it challenging to identify cell types from the raw images. In this study, we present a real-time single-cell imaging and classification system based on a fluorescence microscope and deep learning algorithm, which is able to directly identify cell types from motion-blur images. To obtain annotated datasets of blurred images for deep learning model training, we developed a motion deblurring algorithm for the reconstruction of blur-free images. To demonstrate the ability of this system, deblurred images of HeLa cells with various fluorescent labels and HeLa cells at different cell cycle stages were acquired. The trained ResNet achieved a high accuracy of 96.6% for single-cell classification of HeLa cells in three different mitotic stages, with a short processing time of only 2 ms. This technology provides a simple way to realize single-cell fluorescence IFC and real-time cell classification, offering significant potential in various biological and medical applications.
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Affiliation(s)
- Yiming Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Ziwei Huang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Xiaojie Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Fengrui Yang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Xuebiao Yao
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Tingrui Pan
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, China
| | - Baoqing Li
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Jiaru Chu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
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10
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Hourani T, Perez-Gonzalez A, Khoshmanesh K, Luwor R, Achuthan AA, Baratchi S, O'Brien-Simpson NM, Al-Hourani A. Label-free macrophage phenotype classification using machine learning methods. Sci Rep 2023; 13:5202. [PMID: 36997576 PMCID: PMC10061362 DOI: 10.1038/s41598-023-32158-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 03/23/2023] [Indexed: 04/01/2023] Open
Abstract
Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.
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Affiliation(s)
- Tetiana Hourani
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, 3050, Australia
| | - Alexis Perez-Gonzalez
- Melbourne Cytometry Platform, Department of Microbiology and Immunology, The University of Melbourne, at The Peter Doherty Institute of Infection and Immunity, Parkville, VIC, 3010, Australia
| | | | - Rodney Luwor
- Department of Surgery, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, 3050, Australia
- Fiona Elsey Cancer Research Institute, Ballarat, Victoria, 3350, Australia
- Federation University Australia, Ballarat, Victoria, 3350, Australia
| | - Adrian A Achuthan
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, 3050, Australia
| | - Sara Baratchi
- School of Health & Biomedical Sciences, RMIT University, Bundoora, Victoria, 3083, Australia
| | - Neil M O'Brien-Simpson
- ACTV Research Group, Division of Basic and Clinical Oral Sciences, Centre for Oral Health Research, Melbourne Dental School, Royal Dental Hospital, The University of Melbourne, 720 Swanston Street, Carlton, VIC, 3010, Australia
| | - Akram Al-Hourani
- School of Engineering, RMIT University, Melbourne, Victoria, 3000, Australia.
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11
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Serhatlioglu M, Jensen EA, Niora M, Hansen AT, Nielsen CF, Jansman MMT, Hosta-Rigau L, Dziegiel MH, Berg-Sørensen K, Hickson ID, Kristensen A. Viscoelastic Capillary Flow Cytometry. ADVANCED NANOBIOMED RESEARCH 2022. [DOI: 10.1002/anbr.202200137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Murat Serhatlioglu
- Department of Health Technology Technical University of Denmark Ørsteds Plads Building 345C 2800 Kongens Lyngby Denmark
| | - Emil Alstrup Jensen
- Department of Health Technology Technical University of Denmark Ørsteds Plads Building 345C 2800 Kongens Lyngby Denmark
| | - Maria Niora
- Department of Health Technology Technical University of Denmark Ørsteds Plads Building 345C 2800 Kongens Lyngby Denmark
| | - Anne Todsen Hansen
- Department of Clinical Immunology University of Copenhagen Blegdamsvej 9 2100 København Ø Denmark
| | - Christian Friberg Nielsen
- Center for Chromosome Stability Department of Cellular and Molecular Medicine University of Copenhagen 2200 København N. Denmark
| | | | - Leticia Hosta-Rigau
- Department of Health Technology Technical University of Denmark Ørsteds Plads Building 345C 2800 Kongens Lyngby Denmark
| | - Morten Hanefeld Dziegiel
- Department of Clinical Immunology University of Copenhagen Blegdamsvej 9 2100 København Ø Denmark
- Department of Clinical Medicine University of Copenhagen Blegdamsvej 3B 2200 København N. Denmark
| | - Kirstine Berg-Sørensen
- Department of Health Technology Technical University of Denmark Ørsteds Plads Building 345C 2800 Kongens Lyngby Denmark
| | - Ian David Hickson
- Center for Chromosome Stability Department of Cellular and Molecular Medicine University of Copenhagen 2200 København N. Denmark
| | - Anders Kristensen
- Department of Health Technology Technical University of Denmark Ørsteds Plads Building 345C 2800 Kongens Lyngby Denmark
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Stolarek I, Samelak-Czajka A, Figlerowicz M, Jackowiak P. Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data. iScience 2022; 25:105142. [PMID: 36193047 PMCID: PMC9526149 DOI: 10.1016/j.isci.2022.105142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/29/2022] [Accepted: 09/09/2022] [Indexed: 11/27/2022] Open
Abstract
Recent advances in imaging flow cytometry (IFC) have revolutionized high-throughput multiparameter analyses at single-cell resolution. Although enabling the discovery of population heterogeneities and the detection of rare events, IFC generates hyperdimensional datasets that demand innovative analytical approaches. Current methods work in a supervised manner, utilize only limited information content, or require large annotated reference datasets. Dimensionality reduction algorithms, including uniform manifold approximation and projection (UMAP), have been successfully applied to analyze the large number of parameters generated in various high-throughput techniques. Here, we apply a workflow incorporating UMAP to analyze different IFC datasets. We demonstrate that it out-competes other popular dimensionality reduction methods in speed and accuracy. Moreover, it enables fast visualization, clustering, and tagging of unannotated objects in large-scale experiments. We anticipate that our workflow will be a robust method to address complex IFC datasets, either alone or as an upstream addition to the deep learning approaches. UMAP dimensionality reduction provides fast and accurate method of IFC data analysis UMAP yields improved object clustering and tagging of the multispectral IFC data PCA decomposition allows multispectral signals merging for direct image embedding
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Affiliation(s)
- Ireneusz Stolarek
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Anna Samelak-Czajka
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Marek Figlerowicz
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Paulina Jackowiak
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
- Corresponding author
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Hilzenrat G, Gill ET, McArthur SL. Imaging approaches for monitoring three-dimensional cell and tissue culture systems. JOURNAL OF BIOPHOTONICS 2022; 15:e202100380. [PMID: 35357086 DOI: 10.1002/jbio.202100380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
The past decade has seen an increasing demand for more complex, reproducible and physiologically relevant tissue cultures that can mimic the structural and biological features of living tissues. Monitoring the viability, development and responses of such tissues in real-time are challenging due to the complexities of cell culture physical characteristics and the environments in which these cultures need to be maintained in. Significant developments in optics, such as optical manipulation, improved detection and data analysis, have made optical imaging a preferred choice for many three-dimensional (3D) cell culture monitoring applications. The aim of this review is to discuss the challenges associated with imaging and monitoring 3D tissues and cell culture, and highlight topical label-free imaging tools that enable bioengineers and biophysicists to non-invasively characterise engineered living tissues.
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Affiliation(s)
- Geva Hilzenrat
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
| | - Emma T Gill
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
| | - Sally L McArthur
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
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Summers HD. Practical machine learning for disease diagnosis. CELL REPORTS METHODS 2021; 1:100103. [PMID: 35474900 PMCID: PMC9017117 DOI: 10.1016/j.crmeth.2021.100103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification.
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Affiliation(s)
- Huw D. Summers
- Department of Biomedical Engineering, Swansea University, Swansea, UK
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