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Khadem H, Mangini M, Farazpour S, De Luca AC. Correlative Raman Imaging: Development and Cancer Applications. BIOSENSORS 2024; 14:324. [PMID: 39056600 PMCID: PMC11274409 DOI: 10.3390/bios14070324] [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: 05/24/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024]
Abstract
Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.
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Affiliation(s)
- Hossein Khadem
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
| | - Maria Mangini
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
| | - Somayeh Farazpour
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
| | - Anna Chiara De Luca
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
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Tang J, Zhang T, Gong Z, Huang X. High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt. Bioengineering (Basel) 2023; 10:1424. [PMID: 38136015 PMCID: PMC10740838 DOI: 10.3390/bioengineering10121424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/30/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods.
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Affiliation(s)
- Jing Tang
- State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;
| | - Ting Zhang
- MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;
| | - Zeyu Gong
- State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;
| | - Xianjun Huang
- School of Computer Science and Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510006, China;
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Dwapanyin GO, Chow DJX, Tan TCY, Dubost NS, Morizet JM, Dunning KR, Dholakia K. Investigation of refractive index dynamics during in vitro embryo development using off-axis digital holographic microscopy. BIOMEDICAL OPTICS EXPRESS 2023; 14:3327-3342. [PMID: 37497510 PMCID: PMC10368053 DOI: 10.1364/boe.492292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 07/28/2023]
Abstract
Embryo quality is a crucial factor affecting live birth outcomes. However, an accurate diagnostic for embryo quality remains elusive in the in vitro fertilization clinic. Determining physical parameters of the embryo may offer key information for this purpose. Here, we demonstrate that digital holographic microscopy (DHM) can rapidly and non-invasively assess the refractive index of mouse embryos. Murine embryos were cultured in either low- or high-lipid containing media and digital holograms recorded at various stages of development. The phase of the recorded hologram was numerically retrieved, from which the refractive index of the embryo was calculated. We showed that DHM can detect spatio-temporal changes in refractive index during embryo development that are reflective of its lipid content. As accumulation of intracellular lipid is known to compromise embryo health, DHM may prove beneficial in developing an accurate, non-invasive, multimodal diagnostic.
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Affiliation(s)
- George O. Dwapanyin
- SUPA, School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews, Fife, United Kingdom
| | - Darren J. X. Chow
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Australian Research Council Centre of Excellence for Nanoscale Biophotonics, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Tiffany C. Y. Tan
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Australian Research Council Centre of Excellence for Nanoscale Biophotonics, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Nicolas S. Dubost
- SUPA, School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews, Fife, United Kingdom
| | - Josephine M. Morizet
- SUPA, School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews, Fife, United Kingdom
| | - Kylie R. Dunning
- Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, Australia
- Australian Research Council Centre of Excellence for Nanoscale Biophotonics, The University of Adelaide, Adelaide, Australia
- Institute for Photonics and Advanced Sensing, The University of Adelaide, Adelaide, Australia
| | - Kishan Dholakia
- SUPA, School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews, Fife, United Kingdom
- School of Biological Sciences, The University of Adelaide, Adelaide, Australia
- Centre of Light for Life, The University of Adelaide, Adelaide, Australia
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Lelliott PM, Hobro AJ, Pavillon N, Nishide M, Okita Y, Mizuno Y, Obata S, Nameki S, Yoshimura H, Kumanogoh A, Smith NI. Single-cell Raman microscopy with machine learning highlights distinct biochemical features of neutrophil extracellular traps and necrosis. Sci Rep 2023; 13:10093. [PMID: 37344494 DOI: 10.1038/s41598-023-36667-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/07/2023] [Indexed: 06/23/2023] Open
Abstract
The defining biology that distinguishes neutrophil extracellular traps (NETs) from other forms of cell death is unresolved, and techniques which unambiguously identify NETs remain elusive. Raman scattering measurement provides a holistic overview of cell molecular composition based on characteristic bond vibrations in components such as lipids and proteins. We collected Raman spectra from NETs and freeze/thaw necrotic cells using a custom built high-throughput platform which is able to rapidly measure spectra from single cells. Principal component analysis of Raman spectra from NETs clearly distinguished them from necrotic cells despite their similar morphology, demonstrating their fundamental molecular differences. In contrast, classical techniques used for NET analysis, immunofluorescence microscopy, extracellular DNA, and ELISA, could not differentiate these cells. Additionally, machine learning analysis of Raman spectra indicated subtle differences in lipopolysaccharide (LPS)-induced as opposed to phorbol myristate acetate (PMA)-induced NETs, demonstrating the molecular composition of NETs varies depending on the stimulant used. This study demonstrates the benefits of Raman microscopy in discriminating NETs from other types of cell death and by their pathway of induction.
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Affiliation(s)
- Patrick Michael Lelliott
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan.
| | - Alison Jane Hobro
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan
| | - Nicolas Pavillon
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan
| | - Masayuki Nishide
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yasutaka Okita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yumiko Mizuno
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Sho Obata
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shinichiro Nameki
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hanako Yoshimura
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Open and Transdisciplinary Research Institute (OTRI), Osaka University, Osaka, Japan
| | - Nicholas Isaac Smith
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan.
- Open and Transdisciplinary Research Institute (OTRI), Osaka University, Osaka, Japan.
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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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Liu S, Yuan Z, Qiao X, Liu Q, Song K, Kong B, Su X. Light scattering pattern specific convolutional network static cytometry for label-free classification of cervical cells. Cytometry A 2021; 99:610-621. [PMID: 33840152 DOI: 10.1002/cyto.a.24349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/11/2021] [Accepted: 04/01/2021] [Indexed: 12/12/2022]
Abstract
Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS-net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label-free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C-33A and CaSki cells) is obtained. When applied for the subtyping of label-free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS-net cytometric technique. Furthermore, the three-way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS-net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label-free.
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Affiliation(s)
- Shanshan Liu
- School of Microelectronics, Shandong University, Jinan, China.,Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zeng Yuan
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Xu Qiao
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Kun Song
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Beihua Kong
- Department of obstetrics and gynecology, Qilu Hospital, Shandong University, Jinan, China
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
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Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry. Sci Rep 2020; 10:20724. [PMID: 33244129 PMCID: PMC7691359 DOI: 10.1038/s41598-020-77765-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 11/12/2020] [Indexed: 11/11/2022] Open
Abstract
Machine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of \documentclass[12pt]{minimal}
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\begin{document}$${18.6}\,\upmu \text {m}$$\end{document}18.6μm. To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations.
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