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Rutowicz K, Lüthi J, de Groot R, Holtackers R, Yakimovich Y, Pazmiño DM, Gandrillon O, Pelkmans L, Baroux C. Multiscale chromatin dynamics and high entropy in plant iPSC ancestors. J Cell Sci 2024; 137:jcs261703. [PMID: 38738286 PMCID: PMC11234377 DOI: 10.1242/jcs.261703] [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: 10/10/2023] [Accepted: 04/29/2024] [Indexed: 05/14/2024] Open
Abstract
Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.
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Affiliation(s)
- Kinga Rutowicz
- Plant Developmental Genetics, Institute of Plant and Microbial Biology, University of Zurich, 8008 Zurich, Switzerland
| | - Joel Lüthi
- Department of Molecular Life Sciences, University of Zurich, 8050 Zurich, Switzerland
| | - Reinoud de Groot
- Department of Molecular Life Sciences, University of Zurich, 8050 Zurich, Switzerland
| | - René Holtackers
- Department of Molecular Life Sciences, University of Zurich, 8050 Zurich, Switzerland
| | - Yauhen Yakimovich
- Department of Molecular Life Sciences, University of Zurich, 8050 Zurich, Switzerland
| | - Diana M. Pazmiño
- Plant Developmental Genetics, Institute of Plant and Microbial Biology, University of Zurich, 8008 Zurich, Switzerland
| | - Olivier Gandrillon
- Laboratory of Biology and Modeling of the Cell, University of Lyon, ENS de Lyon,69342 Lyon, France
| | - Lucas Pelkmans
- Department of Molecular Life Sciences, University of Zurich, 8050 Zurich, Switzerland
| | - Célia Baroux
- Plant Developmental Genetics, Institute of Plant and Microbial Biology, University of Zurich, 8008 Zurich, Switzerland
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Paunovic Pantic J, Vucevic D, Radosavljevic T, Corridon PR, Valjarevic S, Cumic J, Bojic L, Pantic I. Machine learning approaches to detect hepatocyte chromatin alterations from iron oxide nanoparticle exposure. Sci Rep 2024; 14:19595. [PMID: 39179629 PMCID: PMC11344034 DOI: 10.1038/s41598-024-70559-4] [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: 01/30/2024] [Accepted: 08/19/2024] [Indexed: 08/26/2024] Open
Abstract
This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.
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Affiliation(s)
- Jovana Paunovic Pantic
- Department of Pathophysiology, Faculty of Medicine, University of Belgrade, Dr. Subotica 9, 11129, Belgrade, Serbia
| | - Danijela Vucevic
- Department of Pathophysiology, Faculty of Medicine, University of Belgrade, Dr. Subotica 9, 11129, Belgrade, Serbia
| | - Tatjana Radosavljevic
- Department of Pathophysiology, Faculty of Medicine, University of Belgrade, Dr. Subotica 9, 11129, Belgrade, Serbia
| | - Peter R Corridon
- Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE.
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE.
- Center for Biotechnology, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE.
- Department of Biomedical Engineering and Biotechnology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE.
| | - Svetlana Valjarevic
- Faculty of Medicine, Clinical Hospital Center Zemun, University of Belgrade, Vukova 9, 11000, Belgrade, Serbia
| | - Jelena Cumic
- Faculty of Medicine, University of Belgrade, University Clinical Centre of Serbia, Dr. Koste Todorovića 8, 11129, Belgrade, Serbia
| | - Ljubisa Bojic
- Institute for Artificial Intelligence Research and Development of Serbia, Fruškogorska 1, 21000, Novi Sad, Serbia
| | - Igor Pantic
- Department of Medical Physiology, Faculty of Medicine, University of Belgrade, Visegradska 26/II, 11129, Belgrade, Serbia.
- University of Haifa, 199 Abba Hushi Blvd, Mount Carmel, 3498838, Haifa, Israel.
- Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, 84105, Be'er Sheva, Israel.
- Department of Pharmacology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE.
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Dong W, Xiong S, Wang X, Hu S, Liu Y, Liu H, Wang X, Chen J, Qiu Y, Fan B. Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma. J Cancer Res Clin Oncol 2023; 149:14901-14910. [PMID: 37604939 DOI: 10.1007/s00432-023-05263-3] [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: 06/28/2023] [Accepted: 08/08/2023] [Indexed: 08/23/2023]
Abstract
PURPOSE To explore the efficiency of a contrast-enhanced CT-based radiomics nomogram integrated with radiomics signature and clinically independent predictors to distinguish mass-like thymic hyperplasia (ml-TH) from low-risk thymoma (LRT) preoperatively. METHODS 135 Patients with histopathology confirmed ml-TH (n = 65) and LRT (n = 70) were randomly divided into training set (n = 94) and validation set (n = 41) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to obtain the optimal features. Based on the selected features, four machine learning models, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGBOOST), and random forest (RF) were constructed. Multivariate logistic regression was used to establish a radiomics nomogram containing clinically independent predictors and radiomics signature. Receiver operating characteristic (ROC), DeLong test, and calibration curves were used to detect the performance of the radiomics nomogram in training set and validation set. RESULTS In the validation set, the area under the curve (AUC) value of LR (0.857; 95% CI: 0.741, 0.973) was the highest of the four machine learning models. Radiomics nomogram containing radiomics signature and clinically independent predictors (including age, shape, and net enhancement degree) had better calibration and identification in the training set (AUC: 0.959; 95% CI: 0.922, 0.996) and validation set (AUC: 0.895; 95% CI: 0.795, 0.996). CONCLUSION We constructed a contrast-enhanced CT-based radiomics nomogram containing clinically independent predictors and radiomics signature as a noninvasive preoperative prediction method to distinguish ml-TH from LRT. The radiomics nomogram we constructed has potential for preoperative clinical decision making.
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Affiliation(s)
- Wentao Dong
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Situ Xiong
- Medical College of Nanchang University, Nanchang University, Nanchang, China
| | - Xiaolian Wang
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Shaobo Hu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Yangchun Liu
- Department of Thoracic Surgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Hao Liu
- R&D, Yizhun Medical AI, Beijing, China
| | - Xin Wang
- R&D, Yizhun Medical AI, Beijing, China
| | | | - Yingying Qiu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
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Valjarevic S, Jovanovic MB, Miladinovic N, Cumic J, Dugalic S, Corridon PR, Pantic I. Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1220-1227. [PMID: 37749686 DOI: 10.1093/micmic/ozad042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/05/2023] [Accepted: 03/10/2023] [Indexed: 09/27/2023]
Abstract
Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.
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Affiliation(s)
- Svetlana Valjarevic
- University of Belgrade, Faculty of Medicine, Clinical Hospital Center "Zemun", Vukova 9, RS-11080 Belgrade, Serbia
| | - Milan B Jovanovic
- University of Belgrade, Faculty of Medicine, Clinical Hospital Center "Zemun", Vukova 9, RS-11080 Belgrade, Serbia
| | - Nenad Miladinovic
- University of Belgrade, Faculty of Medicine, Clinical Hospital Center "Zemun", Vukova 9, RS-11080 Belgrade, Serbia
| | - Jelena Cumic
- University of Belgrade, Faculty of Medicine, University Clinical Centre of Serbia, Dr. Koste Todorovića 8, RS-11129, Belgrade, Serbia
| | - Stefan Dugalic
- University of Belgrade, Faculty of Medicine, University Clinical Centre of Serbia, Dr. Koste Todorovića 8, RS-11129, Belgrade, Serbia
| | - Peter R Corridon
- Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE
- Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE
- Center for Biotechnology, Khalifa University of Science and Technology, Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE
| | - Igor Pantic
- University of Belgrade, Faculty of Medicine, Department of Medical Physiology, Višegradska 26/2, RS-11129 Belgrade, Serbia
- Department of Pharmacology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Shakhbout Bin Sultan St - Hadbat Al Za'faranah - Zone 1 - Abu Dhabi, UAE
- University of Haifa, 199 Abba Hushi Blvd, Mount Carmel, Haifa IL-3498838, Israel
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Pantic IV, Cumic J, Valjarevic S, Shakeel A, Wang X, Vurivi H, Daoud S, Chan V, Petroianu GA, Shibru MG, Ali ZM, Nesic D, Salih AE, Butt H, Corridon PR. Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization. Front Bioeng Biotechnol 2023; 11:1105377. [PMID: 37304146 PMCID: PMC10250676 DOI: 10.3389/fbioe.2023.1105377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/11/2023] [Indexed: 06/13/2023] Open
Abstract
Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix. Metrics used to evaluate extracellular matrix performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary machine learning (ML) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing machine learning biosensing systems for evaluating subtle morphological changes in decellularized scaffolds, which are crucial for assessing their functionality.
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Affiliation(s)
- Igor V. Pantic
- Department of Medical Physiology, Faculty of Medicine, Visegradska 26/II, University of Belgrade, Belgrade, Serbia
- University of Haifa, Haifa, Israel
- Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be’er Sheva, Israel
- Department of Pharmacology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Jelena Cumic
- Faculty of Medicine, University of Belgrade, University Clinical Center of Serbia, Belgrade, Serbia
| | - Svetlana Valjarevic
- Faculty of Medicine, Clinical Hospital Center Zemun, University of Belgrade, Belgrade, Serbia
| | - Adeeba Shakeel
- Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Xinyu Wang
- Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Hema Vurivi
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Sayel Daoud
- Anatomical Pathology Laboratory, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Vincent Chan
- Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Georg A. Petroianu
- Department of Pharmacology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Meklit G. Shibru
- Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Zehara M. Ali
- Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Dejan Nesic
- Department of Medical Physiology, Faculty of Medicine, Visegradska 26/II, University of Belgrade, Belgrade, Serbia
| | - Ahmed E. Salih
- Department of Mechanical Engineering, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Haider Butt
- Department of Mechanical Engineering, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Peter R. Corridon
- Department of Immunology and Physiology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Biomedical Engineering, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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Wang H, Xie M, Chen X, Zhu J, Ding H, Zhang L, Pan Z, He L. Development and validation of a CT-based radiomics signature for identifying high-risk neuroblastomas under the revised Children's Oncology Group classification system. Pediatr Blood Cancer 2023; 70:e30280. [PMID: 36881504 DOI: 10.1002/pbc.30280] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND To develop and validate a radiomics signature based on computed tomography (CT) for identifying high-risk neuroblastomas. PROCEDURE This retrospective study included 339 patients with neuroblastomas, who were classified into high-risk and non-high-risk groups according to the revised Children's Oncology Group classification system. These patients were then randomly divided into a training set (n = 237) and a testing set (n = 102). Pretherapy CT images of the arterial phase were segmented by two radiologists. Pyradiomics package and FeAture Explorer software were used to extract and process radiomics features. Radiomics models based on linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were constructed, and the area under the curve (AUC), 95% confidence interval (CI), and accuracy were calculated. RESULTS The optimal LDA, LR, and SVM models had 11, 12, and 14 radiomics features, respectively. The AUC of the LDA model in the training and testing sets were 0.877 (95% CI: 0.833-0.921) and 0.867 (95% CI: 0.797-0.937), with an accuracy of 0.823 and 0.804, respectively. The AUC of the LR model in the training and testing sets were 0.881 (95% CI: 0.839-0.924) and 0.855 (95% CI: 0.781-0.930), with an accuracy of 0.823 and 0.804, respectively. The AUC of the SVM model in the training and testing sets were 0.879 (95% CI: 0.836-0.923) and 0.862 (95% CI: 0.791-0.934), with an accuracy of 0.827 and 0.804, respectively. CONCLUSIONS CT-based radiomics is able to identify high-risk neuroblastomas and may provide additional image biomarkers for the identification of high-risk neuroblastomas.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Mingye Xie
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Jin Zhu
- Department of Pathology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Hao Ding
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Zhang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Zhengxia Pan
- Department of Cardiothoracic Surgery, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Vorobjev IA, Bekbayev S, Temirgaliyev A, Tlegenova M, Barteneva NS. Imaging Flow Cytometry of Multi-Nuclearity. Methods Mol Biol 2023; 2635:87-101. [PMID: 37074658 DOI: 10.1007/978-1-0716-3020-4_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Multi-nuclearity is a common feature for cells in different cancers. Also, analysis of multi-nuclearity in cultured cells is widely used for evaluating the toxicity of different drugs. Multi-nuclear cells in cancer and under drug treatments form from aberrations in cell division and/or cytokinesis. These cells are a hallmark of cancer progression, and the abundance of multi-nucleated cells often correlates with poor prognosis.The use of standard bright field or fluorescent microscopy to analyze multi-nuclearity at the quantitative level is laborious and can suffer from user bias. Automated slide-scanning microscopy can eliminate scorer bias and improve data collection. However, this method has limitations, such as insufficient visibility of multiple nuclei in the cells attached to the substrate at low magnification.Since quantification of multi-nuclear cells using microscopic methods might be difficult, imaging flow cytometry (IFC) is a method of choice for this. We describe the experimental protocol for the preparation of the samples of multi-nucleated cells from the attached cultures and the algorithm for the analysis of these cells by IFC. Images of multi-nucleated cells obtained after mitotic arrest induced by taxol, as well as cells obtained after cytokinesis blockade by cytochalasin D treatment, can be acquired at a maximal resolution of IFC. We suggest two algorithms for the discrimination of single-nucleus and multi-nucleated cells. The advantages and disadvantages of IFC analysis of multi-nuclear cells in comparison with microscopy are discussed.
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Affiliation(s)
- Ivan A Vorobjev
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan.
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan.
- A.N. Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russian Federation.
- Biological Faculty, Lomonosov Moscow State University, Moscow, Russian Federation.
| | - Sultan Bekbayev
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
| | - Adil Temirgaliyev
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
| | - Madina Tlegenova
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
| | - Natasha S Barteneva
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
- Brigham Women's Hospital, Harvard University, Boston, MA, USA
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Davidovic LM, Cumic J, Dugalic S, Vicentic S, Sevarac Z, Petroianu G, Corridon P, Pantic I. Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:265-271. [PMID: 34937605 DOI: 10.1017/s1431927621013878] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.
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Affiliation(s)
| | - Jelena Cumic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
| | - Stefan Dugalic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
| | - Sreten Vicentic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Clinic of Psychiatry, Pasterova 2, RS-11000 Belgrade, Serbia
| | - Zoran Sevarac
- University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, RS-11000 Belgrade, Serbia
| | - Georg Petroianu
- Department of Pharmacology & Therapeutics, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
| | - Peter Corridon
- Department of Immunology and Physiology, College of Medicine and Health Sciences; Biomedical Engineering, Healthcare Engineering Innovation Center; Center for Biotechnology; Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
| | - Igor Pantic
- University of Belgrade, Faculty of Medicine, Department of Medical Physiology, Laboratory for Cellular Physiology, Visegradska 26/II, RS-11129 Belgrade, Serbia
- University of Haifa, 199 Abba Hushi Blvd. Mount Carmel, HaifaIL-3498838, Israel
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