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Sheth V, Chen X, Mettenbrink EM, Yang W, Jones MA, M’Saad O, Thomas AG, Newport RS, Francek E, Wang L, Frickenstein AN, Donahue ND, Holden A, Mjema NF, Green DE, DeAngelis PL, Bewersdorf J, Wilhelm S. Quantifying Intracellular Nanoparticle Distributions with Three-Dimensional Super-Resolution Microscopy. ACS NANO 2023; 17:8376-8392. [PMID: 37071747 PMCID: PMC10643044 DOI: 10.1021/acsnano.2c12808] [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] [Indexed: 05/10/2023]
Abstract
Super-resolution microscopy can transform our understanding of nanoparticle-cell interactions. Here, we established a super-resolution imaging technology to visualize nanoparticle distributions inside mammalian cells. The cells were exposed to metallic nanoparticles and then embedded within different swellable hydrogels to enable quantitative three-dimensional (3D) imaging approaching electron-microscopy-like resolution using a standard light microscope. By exploiting the nanoparticles' light scattering properties, we demonstrated quantitative label-free imaging of intracellular nanoparticles with ultrastructural context. We confirmed the compatibility of two expansion microscopy protocols, protein retention and pan-expansion microscopy, with nanoparticle uptake studies. We validated relative differences between nanoparticle cellular accumulation for various surface modifications using mass spectrometry and determined the intracellular nanoparticle spatial distribution in 3D for entire single cells. This super-resolution imaging platform technology may be broadly used to understand the nanoparticle intracellular fate in fundamental and applied studies to potentially inform the engineering of safer and more effective nanomedicines.
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Affiliation(s)
- Vinit Sheth
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Evan M. Mettenbrink
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Wen Yang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Meredith A. Jones
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Ons M’Saad
- Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, 06520, USA
- Panluminate, Inc. New Haven, Connecticut, 06516, USA
| | - Abigail G. Thomas
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Rylee S. Newport
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Emmy Francek
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Lin Wang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Alex N. Frickenstein
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Nathan D. Donahue
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Alyssa Holden
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Nathan F. Mjema
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Dixy E. Green
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, 73126, USA
| | - Paul L. DeAngelis
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, 73126, USA
| | - Joerg Bewersdorf
- Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, 06520, USA
- Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut, 06510 USA
- Department of Physics, Yale University, New Haven, Connecticut, 06511, USA
| | - Stefan Wilhelm
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
- Institute for Biomedical Engineering, Science, and Technology (IBEST), Norman, Oklahoma, 73019, USA
- Stephenson Cancer Center, Oklahoma City, Oklahoma, 73104, USA
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Okada T, Iwayama T, Ogura T, Murakami S, Ogura T. Structural analysis of melanosomes in living mammalian cells using scanning electron-assisted dielectric microscopy with deep neural network. Comput Struct Biotechnol J 2022; 21:506-518. [PMID: 36618988 PMCID: PMC9807747 DOI: 10.1016/j.csbj.2022.12.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/16/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Melanins are the main pigments found in mammals. Their synthesis and transfer to keratinocytes have been widely investigated for many years. However, analysis has been mainly carried out using fixed rather than live cells. In this study, we have analysed the melanosomes in living mammalian cells using newly developed scanning electron-assisted dielectric microscopy (SE-ADM). The melanosomes in human melanoma MNT-1 cells were observed as clear black particles in SE-ADM. The main structure of melanosomes was toroidal while that of normal melanocytes was ellipsoidal. In tyrosinase knockout MNT-1 cells, not only the black particles in the SE-ADM images but also the Raman shift of melanin peaks completely disappeared suggesting that the black particles were really melanosomes. We developed a deep neural network (DNN) system to automatically detect melanosomes in cells and analysed their diameter and roundness. In terms of melanosome morphology, the diameter of melanosomes in melanoma cells did not change while that in normal melanocytes increased during culture. The established DNN analysis system with SE-ADM can be used for other particles, e.g. exosomes, lysosomes, and other biological particles.
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Affiliation(s)
- Tomoko Okada
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Central 6, Higashi, Tsukuba, Ibaraki 305-8566, Japan
| | - Tomoaki Iwayama
- Department of Periodontology, Osaka University Graduate School of Dentistry, 1-8 Yamada-oka, Suita, Osaka 565-0871, Japan
| | - Taku Ogura
- Chemical Business Unit, Nikko Chemicals Co., Ltd., Itabashi-ku, Tokyo 174-0046, Japan
| | - Shinya Murakami
- Department of Periodontology, Osaka University Graduate School of Dentistry, 1-8 Yamada-oka, Suita, Osaka 565-0871, Japan
| | - Toshihiko Ogura
- Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Central 6, Higashi, Tsukuba, Ibaraki 305-8566, Japan,Correspondence to: Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Higashi 1-1-1, Tsukuba, Ibaraki 305-8566, Japan.
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Botifoll M, Pinto-Huguet I, Arbiol J. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook. NANOSCALE HORIZONS 2022; 7:1427-1477. [PMID: 36239693 DOI: 10.1039/d2nh00377e] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.
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Affiliation(s)
- Marc Botifoll
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Ivan Pinto-Huguet
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Jordi Arbiol
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Catalonia, Spain
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Checa M, Jin X, Millan-Solsona R, Neumayer SM, Susner MA, McGuire MA, O'Hara A, Gomila G, Maksymovych P, Pantelides ST, Collins L. Revealing Fast Cu-Ion Transport and Enhanced Conductivity at the CuInP 2S 6-In 4/3P 2S 6 Heterointerface. ACS NANO 2022; 16:15347-15357. [PMID: 35998341 DOI: 10.1021/acsnano.2c06992] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Van der Waals layered ferroelectrics, such as CuInP2S6 (CIPS), offer a versatile platform for miniaturization of ferroelectric device technologies. Control of the targeted composition and kinetics of CIPS synthesis enables the formation of stable self-assembled heterostructures of ferroelectric CIPS and nonferroelectric In4/3P2S6 (IPS). Here, we use quantitative scanning probe microscopy methods combined with density functional theory (DFT) to explore in detail the nanoscale variability in dynamic functional properties of the CIPS-IPS heterostructure. We report evidence of fast ionic transport which mediates an appreciable out-of-plane electromechanical response of the CIPS surface in the paraelectric phase. Further, we map the nanoscale dielectric and ionic conductivity properties as we thermally stimulate the ferroelectric-paraelectric phase transition, recovering the local dielectric behavior during this phase transition. Finally, aided by DFT, we reveal a substantial and tunable conductivity enhancement at the CIPS/IPS interface, indicating the possibility of engineering its interfacial properties for next generation device applications.
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Affiliation(s)
- Marti Checa
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Xin Jin
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37235, United States
- Institute of Physics and University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Ruben Millan-Solsona
- Institut de Bioenginyeria de Catalunya (IBEC), The Barcelona Institute of Science and Technology (BIST), c/Baldiri i Reixac 11-15, 08028 Barcelona, Spain
- Departament d'Enginyeria Electrònica i Biomèdica, Universitat de Barcelona, Martí i Franqués 1, 08028 Barcelona, Spain
| | - Sabine M Neumayer
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Michael A Susner
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433, United States
| | - Michael A McGuire
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Andrew O'Hara
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Gabriel Gomila
- Institut de Bioenginyeria de Catalunya (IBEC), The Barcelona Institute of Science and Technology (BIST), c/Baldiri i Reixac 11-15, 08028 Barcelona, Spain
- Departament d'Enginyeria Electrònica i Biomèdica, Universitat de Barcelona, Martí i Franqués 1, 08028 Barcelona, Spain
| | - Petro Maksymovych
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sokrates T Pantelides
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Liam Collins
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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5
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Sotres J, Boyd H, Gonzalez-Martinez JF. Locating critical events in AFM force measurements by means of one-dimensional convolutional neural networks. Sci Rep 2022; 12:12995. [PMID: 35906466 PMCID: PMC9338096 DOI: 10.1038/s41598-022-17124-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/20/2022] [Indexed: 11/09/2022] Open
Abstract
Atomic Force Microscopy (AFM) force measurements are a powerful tool for the nano-scale characterization of surface properties. However, the analysis of force measurements requires several processing steps. One is locating different type of events e.g., contact point, adhesions and indentations. At present, there is a lack of algorithms that can automate this process in a reliable way for different types of samples. Moreover, because of their stochastic nature, the acquisition and analysis of a high number of force measurements is typically required. This can result in these experiments becoming an overwhelming task if their analysis is not automated. Here, we propose a Machine Learning approach, the use of one-dimensional convolutional neural networks, to locate specific events within AFM force measurements. Specifically, we focus on locating the contact point, a critical step for the accurate quantification of mechanical properties as well as long-range interactions. We validate this approach on force measurements obtained both on hard and soft surfaces. This approach, which could be easily used to also locate other events e.g., indentations and adhesions, has the potential to significantly facilitate and automate the analysis of AFM force measurements and, therefore, the use of this technique by a wider community.
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Affiliation(s)
- Javier Sotres
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506, Malmö, Sweden. .,Biofilms-Research Center for Biointerfaces, Malmö University, 20506, Malmö, Sweden.
| | - Hannah Boyd
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506, Malmö, Sweden.,Biofilms-Research Center for Biointerfaces, Malmö University, 20506, Malmö, Sweden
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Checa M, Millan-Solsona R, Glinkowska Mares A, Pujals S, Gomila G. Dielectric Imaging of Fixed HeLa Cells by In-Liquid Scanning Dielectric Force Volume Microscopy. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 11:1402. [PMID: 34070690 PMCID: PMC8226567 DOI: 10.3390/nano11061402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 05/21/2021] [Accepted: 05/23/2021] [Indexed: 01/16/2023]
Abstract
Mapping the dielectric properties of cells with nanoscale spatial resolution can be an important tool in nanomedicine and nanotoxicity analysis, which can complement structural and mechanical nanoscale measurements. Recently we have shown that dielectric constant maps can be obtained on dried fixed cells in air environment by means of scanning dielectric force volume microscopy. Here, we demonstrate that such measurements can also be performed in the much more challenging case of fixed cells in liquid environment. Performing the measurements in liquid media contributes to preserve better the structure of the fixed cells, while also enabling accessing the local dielectric properties under fully hydrated conditions. The results shown in this work pave the way to address the nanoscale dielectric imaging of living cells, for which still further developments are required, as discussed here.
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Affiliation(s)
- Martí Checa
- Nanoscale Bioelectric Characterization, Institut de Bioenginyeria de Catalunya (IBEC), The Barcelona Institute of Science and Technology (BIST), c/Baldiri I Reixac 11-15, 08028 Barcelona, Spain;
| | - Ruben Millan-Solsona
- Nanoscale Bioelectric Characterization, Institut de Bioenginyeria de Catalunya (IBEC), The Barcelona Institute of Science and Technology (BIST), c/Baldiri I Reixac 11-15, 08028 Barcelona, Spain;
- Departament d’Enginyeria Electrònica i Biomèdica, Universitat de Barcelona, c/Martí i Franquès 1, 08028 Barcelona, Spain;
| | - Adrianna Glinkowska Mares
- Nanoscopy for Nanomedicine, Institut de Bioenginyeria de Catalunya (IBEC), The Barcelona Institute of Science and Technology (BIST), c/Baldiri I Reixac 11-15, 08028 Barcelona, Spain;
| | - Silvia Pujals
- Departament d’Enginyeria Electrònica i Biomèdica, Universitat de Barcelona, c/Martí i Franquès 1, 08028 Barcelona, Spain;
- Nanoscopy for Nanomedicine, Institut de Bioenginyeria de Catalunya (IBEC), The Barcelona Institute of Science and Technology (BIST), c/Baldiri I Reixac 11-15, 08028 Barcelona, Spain;
| | - Gabriel Gomila
- Nanoscale Bioelectric Characterization, Institut de Bioenginyeria de Catalunya (IBEC), The Barcelona Institute of Science and Technology (BIST), c/Baldiri I Reixac 11-15, 08028 Barcelona, Spain;
- Departament d’Enginyeria Electrònica i Biomèdica, Universitat de Barcelona, c/Martí i Franquès 1, 08028 Barcelona, Spain;
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