1
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Bardini R, Di Carlo S. Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review. Comput Struct Biotechnol J 2024; 23:601-616. [PMID: 38283852 PMCID: PMC10818159 DOI: 10.1016/j.csbj.2023.12.035] [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: 08/31/2023] [Revised: 12/22/2023] [Accepted: 12/23/2023] [Indexed: 01/30/2024] Open
Abstract
This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field.
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Affiliation(s)
- Roberta Bardini
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca Degli Abruzzi, 24, Turin, 10129, Italy
| | - Stefano Di Carlo
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca Degli Abruzzi, 24, Turin, 10129, Italy
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2
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Alahmari S, Schultz A, Albrecht J, Tagal V, Siddiqui Z, Prabhakaran S, El Naqa I, Anderson A, Heiser L, Andor N. Cell identity revealed by precise cell cycle state mapping links data modalities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.04.610488. [PMID: 39282313 PMCID: PMC11398313 DOI: 10.1101/2024.09.04.610488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.4 and 2.4 cells for sequencing and imaging data respectively. Data integration revealed thousands of pathways and organelle features that are correlated with each other, including several previously known interactions and novel associations. The ability to assign the transcriptome state of a profiled cell to its closest living relative, which is still actively growing and expanding opens the door for genotype-phenotype mapping at single cell resolution forward in time.
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Affiliation(s)
- Saeed Alahmari
- Department of Computer Science, Najran University, Najran 66462, Saudi Arabia
| | - Andrew Schultz
- Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Jordan Albrecht
- Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Vural Tagal
- Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Zaid Siddiqui
- Department of Radiation Oncology, Baylor College of Medicine, Houston, TX, USA
| | - Sandhya Prabhakaran
- Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alexander Anderson
- Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Laura Heiser
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Noemi Andor
- Department of Integrated Mathematical Oncology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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3
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Nabi IR, Cardoen B, Khater IM, Gao G, Wong TH, Hamarneh G. AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth. J Cell Biol 2024; 223:e202311073. [PMID: 38865088 PMCID: PMC11169916 DOI: 10.1083/jcb.202311073] [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: 11/15/2023] [Revised: 04/02/2024] [Accepted: 05/21/2024] [Indexed: 06/13/2024] Open
Abstract
Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers tremendous potential for the discovery of new biology, that, by definition, is not known and lacks ground truth. Herein, we describe the application of weakly supervised paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.
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Affiliation(s)
- Ivan R. Nabi
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, Canada
| | - Ben Cardoen
- School of Computing Science, Simon Fraser University, Burnaby, Canada
| | - Ismail M. Khater
- School of Computing Science, Simon Fraser University, Burnaby, Canada
- Department of Electrical and Computer Engineering, Faculty of Engineering and Technology, Birzeit University, Birzeit, Palestine
| | - Guang Gao
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
| | - Timothy H. Wong
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
| | - Ghassan Hamarneh
- School of Computing Science, Simon Fraser University, Burnaby, Canada
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4
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Hoffman J, Zheng S, Zhang H, Murphy RF, Dahl KN. Image-based discrimination of the early stages of mesenchymal stem cell differentiation. Mol Biol Cell 2024; 35:ar103. [PMID: 38837346 PMCID: PMC11321037 DOI: 10.1091/mbc.e24-02-0095] [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: 02/29/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/07/2024] Open
Abstract
Mesenchymal stem cells (MSCs) are self-renewing, multipotent cells, which can be used in cellular and tissue therapeutics. MSCs cell number can be expanded in vitro, but premature differentiation results in reduced cell number and compromised therapeutic efficacies. Current techniques fail to discriminate the "stem-like" population from early stages (12 h) of differentiated MSC population. Here, we imaged nuclear structure and actin architecture using immunofluorescence and used deep learning-based computer vision technology to discriminate the early stages (6-12 h) of MSC differentiation. Convolutional neural network models trained by nucleus and actin images have high accuracy in reporting MSC differentiation; nuclear images alone can identify early stages of differentiation. Concurrently, we show that chromatin fluidity and heterochromatin levels or localization change during early MSC differentiation. This study quantifies changes in cell architecture during early MSC differentiation and describes a novel image-based diagnostic tool that could be widely used in MSC culture, expansion and utilization.
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Affiliation(s)
- Justin Hoffman
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Shiyuan Zheng
- Department of Biomedical Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Huaiying Zhang
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Robert F. Murphy
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Kris Noel Dahl
- Department of Biomedical Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
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5
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Chen V, Yang M, Cui W, Kim JS, Talwalkar A, Ma J. Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments. Nat Methods 2024; 21:1454-1461. [PMID: 39122941 PMCID: PMC11348280 DOI: 10.1038/s41592-024-02359-7] [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: 10/27/2022] [Accepted: 06/24/2024] [Indexed: 08/12/2024]
Abstract
Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers.
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Affiliation(s)
- Valerie Chen
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Muyu Yang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Wenbo Cui
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Joon Sik Kim
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ameet Talwalkar
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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6
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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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7
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Yoshikawa C, Nguyen DA, Nakaji-Hirabayashi T, Takigawa I, Mamitsuka H. Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers. ACS Biomater Sci Eng 2024; 10:2165-2176. [PMID: 38546298 DOI: 10.1021/acsbiomaterials.3c01888] [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: 04/09/2024]
Abstract
Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been developed to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however this is practically intractable. To address this issue, we present a graph-neural network-based simulator (GNS) that can be trained by using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of six different conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e., changing a pair of the training and testing data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario, in which the experimental conditions differed between the training and testing data. Practically, this means that the GNS trained by the first 24 h images could predict the cell types obtained 3 weeks later. This result could reduce the number of experiments required to find the optimal conditions for generating cells with desired 3D structures. Ultimately, our approach could accelerate progress in regenerative medicine.
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Affiliation(s)
- Chiaki Yoshikawa
- Research Center for Functional Materials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0047, Japan
| | - Duc Anh Nguyen
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
| | - Tadashi Nakaji-Hirabayashi
- Graduate School of Science and Engineering, University of Toyama, Toyama, Toyama 930-8555, Japan
- Graduate School of Innovative Life Science, University of Toyama, Toyama, Toyama 930-0194, Japan
| | - Ichigaku Takigawa
- Center for Innovative Research and Education in Data Science (CIREDS), Institute for Liberal Arts and Sciences, Kyoto University, Kyoto, Kyoto 606-8315, Japan
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
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8
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Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M, Reyes M, Riegler MA, Wiesenfarth M, Kavur AE, Sudre CH, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Rädsch T, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko MB, Cardoso MJ, Cheplygina V, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kofler F, Kopp-Schneider A, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rajpoot N, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Jäger PF. Metrics reloaded: recommendations for image analysis validation. Nat Methods 2024; 21:195-212. [PMID: 38347141 PMCID: PMC11182665 DOI: 10.1038/s41592-023-02151-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
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Affiliation(s)
- Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Department of Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Informatics, Goethe University Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine, University Medicine Essen, Essen, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Manuel Wiesenfarth
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Québec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, IU Health Information and Translational Sciences Building, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Matthew B Blaschko
- Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Nuffield Orthopaedic Centre, Oxford, UK
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
- Australian Institute for Machine Learning AIML, University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Robert Haase
- Technische Universität (TU) Dresden, DFG Cluster of Excellence 'Physics of Life', Dresden, Germany
- Center for Systems Biology, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University, Leipzig, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Peter Mattson
- Google, 1600 Amphitheatre Pkwy, Mountain View, CA, USA
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Québec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
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9
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Minamino N, Fujii H, Murata H, Hachinoda S, Kondo Y, Hotta K, Ueda T. Analysis of Plant-Specific ANTH Domain-Containing Protein in Marchantia polymorpha. PLANT & CELL PHYSIOLOGY 2023; 64:1331-1342. [PMID: 37804254 DOI: 10.1093/pcp/pcad118] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/06/2023] [Accepted: 09/28/2023] [Indexed: 10/09/2023]
Abstract
Membrane trafficking is a fundamental mechanism for protein and lipid transport in eukaryotic cells and exhibits marked diversity among eukaryotic lineages with distinctive body plans and lifestyles. Diversification of the membrane trafficking system is associated with the expansion and secondary loss of key machinery components, including RAB GTPases, soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) and adaptor proteins, during plant evolution. The number of AP180 N-terminal homology (ANTH) proteins, an adaptor family that regulates vesicle formation and cargo sorting during clathrin-mediated endocytosis, increases during plant evolution. In the genome of Arabidopsis thaliana, 18 genes for ANTH proteins have been identified, a higher number than that in yeast and animals, suggesting a distinctive diversification of ANTH proteins. Conversely, the liverwort Marchantia polymorpha possesses a simpler repertoire; only two genes encoding canonical ANTH proteins have been identified in its genome. Intriguingly, a non-canonical ANTH protein is encoded in the genome of M. polymorpha, which also harbors a putative kinase domain. Similar proteins have been detected in sporadic lineages of plants, suggesting their ancient origin and multiple secondary losses during evolution. We named this unique ANTH group phosphatidylinositol-binding clathrin assembly protein-K (PICALM-K) and characterized it in M. polymorpha using genetic, cell biology-based and artificial intelligence (AI)-based approaches. Our results indicate a flagella-related function of MpPICALM-K in spermatozoids, which is distinct from that of canonical ANTH proteins. Therefore, ANTH proteins have undergone significant functional diversification during evolution, and PICALM-K represents a plant-unique ANTH protein that is delivered by neofunctionalization through exon shuffling.
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Affiliation(s)
- Naoki Minamino
- Division of Cellular Dynamics, National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
| | - Haruki Fujii
- Department of Electrical and Electronic Engineering, Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya, Aichi, 468-8502 Japan
| | - Haruhiko Murata
- Department of Electrical and Electronic Engineering, Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya, Aichi, 468-8502 Japan
| | - Sho Hachinoda
- Division of Cellular Dynamics, National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
- Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
| | - Yohei Kondo
- Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
- Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), 5-1 Higashiyama, Myodaiji, Okazaki, Aichi, 444-8787 Japan
| | - Kazuhiro Hotta
- Department of Electrical and Electronic Engineering, Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya, Aichi, 468-8502 Japan
| | - Takashi Ueda
- Division of Cellular Dynamics, National Institute for Basic Biology, Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
- Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Nishigonaka 38, Myodaiji, Okazaki, Aichi, 444-8585 Japan
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10
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Furuya K, Ikura M, Ikura T. Machine learning extracts oncogenic-specific γ-H2AX foci formation pattern upon genotoxic stress. Genes Cells 2023; 28:237-243. [PMID: 36565298 DOI: 10.1111/gtc.13005] [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: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022]
Abstract
H2AX is a histone H2A variant that becomes phosphorylated upon genotoxic stress. The phosphorylated H2AX (γ-H2AX) plays an antioncogenic role in the DNA damage response and its foci patterns are highly variable, in terms of intensities and sizes. However, whether characteristic γ-H2AX foci patterns are associated with oncogenesis (oncogenic-specific γ-H2AX foci patterns) remains unknown. We previously reported that a defect in the acetyltransferase activity of TIP60 promotes cancer cell growth in human cell lines. In this study, we compared γ-H2AX foci patterns between TIP60 wild-type cells and TIP60 HAT mutant cells by using machine learning. When focused solely on the intensity and size of γ-H2AX foci, we extracted the TIP60 HAT mutant-like oncogenic-specific γ-H2AX foci pattern among all datasets of γ-H2AX foci patterns. Furthermore, by using the dimensionality reduction method UMAP, we also observed TIP60 HAT mutant-like oncogenic-specific γ-H2AX foci patterns in TIP60 wild-type cells. In summary, we propose the existence of an oncogenic-specific γ-H2AX foci pattern and the importance of a machine learning approach to extract oncogenic signaling among the γ-H2AX foci variations.
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Affiliation(s)
- Kanji Furuya
- Laboratory of Genome Maintenance, Department of Genome Biology, Radiation Biology Center, Graduate School of Biostudies, Kyoto University, Kyoto, Japan
| | - Masae Ikura
- Laboratory of Chromatin Regulatory Network, Department of Genome Biology, Radiation Biology Center, Graduate School of Biostudies, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Ikura
- Laboratory of Chromatin Regulatory Network, Department of Genome Biology, Radiation Biology Center, Graduate School of Biostudies, Kyoto University, Kyoto, Japan
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11
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Cardoen B, Wong T, Alan P, Lee S, Matsubara JA, Nabi IR, Hamarneh G. SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy. PLoS One 2022; 17:e0276726. [PMID: 36580473 PMCID: PMC9799313 DOI: 10.1371/journal.pone.0276726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/12/2022] [Indexed: 12/30/2022] Open
Abstract
Identification of small objects in fluorescence microscopy is a non-trivial task burdened by parameter-sensitive algorithms, for which there is a clear need for an approach that adapts dynamically to changing imaging conditions. Here, we introduce an adaptive object detection method that, given a microscopy image and an image level label, uses kurtosis-based matching of the distribution of the image differential to express operator intent in terms of recall or precision. We show how a theoretical upper bound of the statistical distance in feature space enables application of belief theory to obtain statistical support for each detected object, capturing those aspects of the image that support the label, and to what extent. We validate our method on 2 datasets: distinguishing sub-diffraction limit caveolae and scaffold by stimulated emission depletion (STED) super-resolution microscopy; and detecting amyloid-β deposits in confocal microscopy retinal cross-sections of neuropathologically confirmed Alzheimer's disease donor tissue. Our results are consistent with biological ground truth and with previous subcellular object classification results, and add insight into more nuanced class transition dynamics. We illustrate the novel application of belief theory to object detection in heterogeneous microscopy datasets and the quantification of conflict of evidence in a joint belief function. By applying our method successfully to diffraction-limited confocal imaging of tissue sections and super-resolution microscopy of subcellular structures, we demonstrate multi-scale applicability.
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Affiliation(s)
- Ben Cardoen
- Medical Image Analysis Laboratory, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
- * E-mail: (BC); (IRN); (GH)
| | - Timothy Wong
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Parsa Alan
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sieun Lee
- Department of Ophthalmology and Visual Sciences, Eye Care Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Joanne Aiko Matsubara
- Department of Ophthalmology and Visual Sciences, Eye Care Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ivan Robert Nabi
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- * E-mail: (BC); (IRN); (GH)
| | - Ghassan Hamarneh
- Medical Image Analysis Laboratory, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
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12
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Takao D, Kyunai YM, Okada Y, Satoh A. A Primer on Deep Learning-Based Cellular Image Classification of Changes in the Spatial Distribution of the Golgi Apparatus After Experimental Manipulation. Methods Mol Biol 2022; 2557:275-285. [PMID: 36512222 DOI: 10.1007/978-1-0716-2639-9_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The visual classification of cell images according to differences in the spatial patterns of subcellular structure is an important methodology in cell and developmental biology. Experimental perturbation of cell function can induce changes in the spatial distribution of organelles and their associated markers or labels. Here, we demonstrate how to achieve accurate, unbiased, high-throughput image classification using an artificial intelligence (AI) algorithm. We show that a convolutional neural network (CNN) algorithm can classify distinct patterns of Golgi images after drug or siRNA treatments, and we review our methods from cell preparation to image acquisition and CNN analysis.
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Affiliation(s)
- Daisuke Takao
- Department of Cell Biology and Anatomy and International Research Center for Neurointelligence (WPI-IRCN), Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Yuki M Kyunai
- Faculty of Engineering, Department of Applied Chemistry and Biotechnology, Okayama University, Okayama, Japan
| | - Yasushi Okada
- Department of Cell Biology and Anatomy and International Research Center for Neurointelligence (WPI-IRCN), Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Physics and Universal Biology Institute (UBI), Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Laboratory for Cell Polarity Regulation, Center for Biosystems Dynamics Research (BDR), RIKEN, Osaka, Japan
| | - Ayano Satoh
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan.
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13
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Iwata H, Hayashi Y, Hasegawa A, Terayama K, Okuno Y. Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning. Int J Pharm X 2022; 4:100135. [PMID: 36325273 PMCID: PMC9619299 DOI: 10.1016/j.ijpx.2022.100135] [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: 08/25/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development.
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Affiliation(s)
- Hiroaki Iwata
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Yoshihiro Hayashi
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan,Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd., 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan,Correspondence to: Y. Hayashi, Pharmaceutical Technology Division, Nichi-Iko Pharmaceutical Co., Ltd.; 205-1, Shimoumezawa Namerikawa-shi, Toyama 936-0857, Japan.
| | - Aki Hasegawa
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan,RIKEN Center for Computational Science, Kobe 650-0047, Japan,Correspondence to: Y. Okuno, Graduate School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan.
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14
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He Y, He S, Kandel ME, Lee YJ, Hu C, Sobh N, Anastasio MA, Popescu G. Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity. ACS PHOTONICS 2022; 9:1264-1273. [PMID: 35480491 PMCID: PMC9026251 DOI: 10.1021/acsphotonics.1c01779] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Indexed: 06/01/2023]
Abstract
Traditional methods for cell cycle stage classification rely heavily on fluorescence microscopy to monitor nuclear dynamics. These methods inevitably face the typical phototoxicity and photobleaching limitations of fluorescence imaging. Here, we present a cell cycle detection workflow using the principle of phase imaging with computational specificity (PICS). The proposed method uses neural networks to extract cell cycle-dependent features from quantitative phase imaging (QPI) measurements directly. Our results indicate that this approach attains very good accuracy in classifying live cells into G1, S, and G2/M stages, respectively. We also demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as cell population distribution across different stages of the cell cycle. We envision that the proposed method can become a nondestructive tool to analyze cell cycle progression in fields ranging from cell biology to biopharma applications.
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Affiliation(s)
- Yuchen
R. He
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Shenghua He
- Department
of Computer Science & Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United States
| | - Mikhail E. Kandel
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Young Jae Lee
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Neuroscience
Program, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Chenfei Hu
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Nahil Sobh
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- NCSA
Center for Artificial Intelligence Innovation, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Mark A. Anastasio
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Department
of Bioengineering, University of Illinois
at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Gabriel Popescu
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Department
of Bioengineering, University of Illinois
at Urbana−Champaign, Urbana, Illinois 61801, United States
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15
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Balluet M, Sizaire F, El Habouz Y, Walter T, Pont J, Giroux B, Bouchareb O, Tramier M, Pecreaux J. Neural network fast-classifies biological images through features selecting to power automated microscopy. J Microsc 2021; 285:3-19. [PMID: 34623634 DOI: 10.1111/jmi.13062] [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: 03/15/2021] [Accepted: 09/28/2021] [Indexed: 11/26/2022]
Abstract
Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.
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Affiliation(s)
- Maël Balluet
- CNRS, Univ Rennes, IGDR - UMR 6290, Rennes, France.,Inscoper SAS, Cesson-Sévigné, France
| | - Florian Sizaire
- CNRS, Univ Rennes, IGDR - UMR 6290, Rennes, France.,Present address Biologics Research, Sanofi R&D, Vitry-sur-Seine, France
| | | | - Thomas Walter
- Centre for Computational Biology (CBIO), MINES ParisTech, PSL University, Paris, France.,Institut Curie, Paris, France.,INSERM, U900, Paris, France
| | | | | | | | - Marc Tramier
- CNRS, Univ Rennes, IGDR - UMR 6290, Rennes, France.,Univ Rennes, BIOSIT, UMS CNRS 3480, US INSERM 018, Rennes, France
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16
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Saraiva BM, Krippahl L, Filipe SR, Henriques R, Pinho MG. eHooke: A tool for automated image analysis of spherical bacteria based on cell cycle progression. BIOLOGICAL IMAGING 2021; 1:e3. [PMID: 35036921 PMCID: PMC8724265 DOI: 10.1017/s2633903x21000027] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/18/2021] [Accepted: 09/07/2021] [Indexed: 01/18/2023]
Abstract
Fluorescence microscopy is a critical tool for cell biology studies on bacterial cell division and morphogenesis. Because the analysis of fluorescence microscopy images evolved beyond initial qualitative studies, numerous images analysis tools were developed to extract quantitative parameters on cell morphology and organization. To understand cellular processes required for bacterial growth and division, it is particularly important to perform such analysis in the context of cell cycle progression. However, manual assignment of cell cycle stages is laborious and prone to user bias. Although cell elongation can be used as a proxy for cell cycle progression in rod-shaped or ovoid bacteria, that is not the case for cocci, such as Staphylococcus aureus. Here, we describe eHooke, an image analysis framework developed specifically for automated analysis of microscopy images of spherical bacterial cells. eHooke contains a trained artificial neural network to automatically classify the cell cycle phase of individual S. aureus cells. Users can then apply various functions to obtain biologically relevant information on morphological features of individual cells and cellular localization of proteins, in the context of the cell cycle.
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Affiliation(s)
- Bruno M. Saraiva
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Ludwig Krippahl
- NOVA LINCS, Departamento de Informática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Sérgio R. Filipe
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
- UCIBIO-REQUIMTE, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- MRC Laboratory for Molecular Cell Biology, University College London, London, United Kingdom
| | - Mariana G. Pinho
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
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17
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Abstract
The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.
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18
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Pattarone G, Acion L, Simian M, Mertelsmann R, Follo M, Iarussi E. Learning deep features for dead and living breast cancer cell classification without staining. Sci Rep 2021; 11:10304. [PMID: 33986434 PMCID: PMC8119670 DOI: 10.1038/s41598-021-89895-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 05/04/2021] [Indexed: 12/17/2022] Open
Abstract
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several classifiers were trained based on well-known convolutional neural networks (CNN) backbones to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.
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Affiliation(s)
- Gisela Pattarone
- Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Faculty of Medicine, Albert Ludwigs University of Freiburg, Freiburg, Germany
| | - Laura Acion
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
- Universidad Tecnológica Nacional, Buenos Aires, Argentina
| | - Marina Simian
- Instituto de Nanosistemas, Universidad Nacional de San Martín, San Martín, Argentina
- Universidad Tecnológica Nacional, Buenos Aires, Argentina
| | | | - Marie Follo
- Dept. Medicine 1, Freiburg University Medical Center, Freiburg, Germany
| | - Emmanuel Iarussi
- Universidad Tecnológica Nacional, Buenos Aires, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina.
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19
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Christopher JA, Stadler C, Martin CE, Morgenstern M, Pan Y, Betsinger CN, Rattray DG, Mahdessian D, Gingras AC, Warscheid B, Lehtiö J, Cristea IM, Foster LJ, Emili A, Lilley KS. Subcellular proteomics. NATURE REVIEWS. METHODS PRIMERS 2021; 1:32. [PMID: 34549195 PMCID: PMC8451152 DOI: 10.1038/s43586-021-00029-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/15/2021] [Indexed: 12/11/2022]
Abstract
The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.
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Affiliation(s)
- Josie A. Christopher
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
| | - Charlotte Stadler
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Claire E. Martin
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Marcel Morgenstern
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yanbo Pan
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Cora N. Betsinger
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - David G. Rattray
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Diana Mahdessian
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Bettina Warscheid
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
- BIOSS and CIBSS Signaling Research Centers, University of Freiburg, Freiburg, Germany
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Ileana M. Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew Emili
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Kathryn S. Lilley
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
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20
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Woloshuk A, Khochare S, Almulhim AF, McNutt AT, Dean D, Barwinska D, Ferkowicz MJ, Eadon MT, Kelly KJ, Dunn KW, Hasan MA, El-Achkar TM, Winfree S. In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining. Cytometry A 2020; 99:707-721. [PMID: 33252180 DOI: 10.1002/cyto.a.24274] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/29/2020] [Accepted: 11/26/2020] [Indexed: 12/30/2022]
Abstract
To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.
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Affiliation(s)
- Andre Woloshuk
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Suraj Khochare
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Aljohara F Almulhim
- Department of Computer Science, Indiana University Purdue University, Indianapolis, Indiana, USA
| | - Andrew T McNutt
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Dawson Dean
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Daria Barwinska
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Michael J Ferkowicz
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Michael T Eadon
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Katherine J Kelly
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA.,Department of Medicine, Indianapolis VA Medical Center, Indianapolis, Indiana, USA
| | - Kenneth W Dunn
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Mohammad A Hasan
- Department of Computer Science, Indiana University Purdue University, Indianapolis, Indiana, USA
| | - Tarek M El-Achkar
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA.,Department of Medicine, Indianapolis VA Medical Center, Indianapolis, Indiana, USA.,Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Seth Winfree
- Department of Medicine, Division of Nephrology and Hypertension, Indiana University School of Medicine, Indianapolis, Indiana, USA.,Department of Medicine, Indianapolis VA Medical Center, Indianapolis, Indiana, USA.,Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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