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Dibus M, Joshi O, Ivaska J. Novel tools to study cell-ECM interactions, cell adhesion dynamics and migration. Curr Opin Cell Biol 2024; 88:102355. [PMID: 38631101 DOI: 10.1016/j.ceb.2024.102355] [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: 12/01/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 04/19/2024]
Abstract
Integrin-mediated cell adhesion is essential for cell migration, mechanotransduction and tissue integrity. In vivo, these processes are regulated by complex physicochemical signals from the extracellular matrix (ECM). These nuanced cues, including molecular composition, rigidity and topology, call for sophisticated systems to faithfully explore cell behaviour. Here, we discuss recent methodological advances in cell-ECM adhesion research and compile a toolbox of techniques that we expect to shape this field in future. We outline methodological breakthroughs facilitating the transition from rigid 2D substrates to more complex and dynamic 3D systems, as well as advances in super-resolution imaging for an in-depth understanding of adhesion nanostructure. Selected methods are exemplified with relevant biological findings to underscore their applicability in cell adhesion research. We expect this new "toolbox" of methods will allow for a closer approximation of in vitro experimental setups to in vivo conditions, providing deeper insights into physiological and pathophysiological processes associated with cell-ECM adhesion.
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Affiliation(s)
- Michal Dibus
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omkar Joshi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Johanna Ivaska
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Department of Life Technologies, University of Turku, FI-20520 Turku, Finland; Western Finnish Cancer Center (FICAN West), University of Turku, FI-20520 Turku, Finland; Foundation for the Finnish Cancer Institute, Tukholmankatu 8, FI-00014 Helsinki, Finland.
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2
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Lightley J, Kumar S, Lim MQ, Garcia E, Görlitz F, Alexandrov Y, Parrado T, Hollick C, Steele E, Roßmann K, Graham J, Broichhagen J, McNeish IA, Roufosse CA, Neil MAA, Dunsby C, French PMW. openFrame: A modular, sustainable, open microscopy platform with single-shot, dual-axis optical autofocus module providing high precision and long range of operation. J Microsc 2023; 292:64-77. [PMID: 37616077 PMCID: PMC10953376 DOI: 10.1111/jmi.13219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/04/2023] [Accepted: 08/21/2023] [Indexed: 08/25/2023]
Abstract
'openFrame' is a modular, low-cost, open-hardware microscopy platform that can be configured or adapted to most light microscopy techniques and is easily upgradeable or expandable to multiple modalities. The ability to freely mix and interchange both open-source and proprietary hardware components or software enables low-cost, yet research-grade instruments to be assembled and maintained. It also enables rapid prototyping of advanced or novel microscope systems. For long-term time-lapse image data acquisition, slide-scanning or high content analysis, we have developed a novel optical autofocus incorporating orthogonal cylindrical optics to provide robust single-shot closed-loop focus lock, which we have demonstrated to accommodate defocus up to ±37 μm with <200 nm accuracy, and a two-step autofocus mode which we have shown can operate with defocus up to ±68 μm. We have used this to implement automated single molecule localisation microscopy (SMLM) in a relatively low-cost openFrame-based instrument using multimode diode lasers for excitation and cooled CMOS cameras.
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Affiliation(s)
- J. Lightley
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Francis Crick InstituteLondonUK
| | - S. Kumar
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Francis Crick InstituteLondonUK
| | - M. Q. Lim
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Department of Surgery and CancerImperial College LondonLondonUK
| | - E. Garcia
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Department of Surgery and CancerImperial College LondonLondonUK
| | - F. Görlitz
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
| | - Y. Alexandrov
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Francis Crick InstituteLondonUK
| | | | | | - E. Steele
- Cairn Research LtdFavershamKentEngland
| | - K. Roßmann
- Leibniz‐Forschungsinstitut für Molekulare PharmakologieBerlinGermany
| | - J. Graham
- Cairn Research LtdFavershamKentEngland
| | - J. Broichhagen
- Leibniz‐Forschungsinstitut für Molekulare PharmakologieBerlinGermany
| | - I. A. McNeish
- Department of Surgery and CancerImperial College LondonLondonUK
| | - C. A. Roufosse
- Department of Inflammation and ImmunologyImperial College LondonLondonUK
| | - M. A. A. Neil
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Francis Crick InstituteLondonUK
| | - C. Dunsby
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Francis Crick InstituteLondonUK
| | - P. M. W. French
- Photonics Group, Physics DepartmentImperial College LondonLondonUK
- Francis Crick InstituteLondonUK
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3
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Basumatary J, Baro N, Zanacchi FC, Mondal PP. Temporally resolved SMLM (with large PAR shift) enabled visualization of dynamic HA cluster formation and migration in a live cell. Sci Rep 2023; 13:12561. [PMID: 37532749 PMCID: PMC10397235 DOI: 10.1038/s41598-023-39096-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/20/2023] [Indexed: 08/04/2023] Open
Abstract
The blinking properties of a single molecule are critical for single-molecule localization microscopy (SMLM). Typically, SMLM techniques involve recording several frames of diffraction-limited bright spots of single-molecules with a detector exposure time close to the blinking period. This sets a limit on the temporal resolution of SMLM to a few tens of milliseconds. Realizing that a substantial fraction of single molecules emit photons for time scales much shorter than the average blinking period, we propose accelerating data collection to capture these fast emitters. Here, we put forward a short exposure-based SMLM (shortSMLM) method powered by sCMOS detector for understanding dynamical events (both at single molecule and ensemble level). The technique is demonstrated on an Influenza-A disease model, where NIH3T3 cells (both fixed and live cells) were transfected by Dendra2-HA plasmid DNA. Analysis shows a 2.76-fold improvement in the temporal resolution that comes with a sacrifice in spatial resolution, and a particle resolution shift PAR-shift (in terms of localization precision) of [Formula: see text] 11.82 nm compared to standard SMLM. We visualized dynamic HA cluster formation in transfected cells post 24 h of DNA transfection. It is noted that a reduction in spatial resolution does not substantially alter cluster characteristics (cluster density, [Formula: see text] molecules/cluster, cluster spread, etc.) and, indeed, preserves critical features. Moreover, the time-lapse imaging reveals the dynamic formation and migration of Hemagglutinin (HA) clusters in a live cell. This suggests that [Formula: see text] using a synchronized high QE sCMOS detector (operated at short exposure times) is excellent for studying temporal dynamics in cellular system.
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Affiliation(s)
- Jigmi Basumatary
- Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| | - Neptune Baro
- Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| | | | - Partha Pratim Mondal
- Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India.
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Fan D, Bajgiran SR, Samghabadi FS, Dutta C, Gillett E, Rossky PJ, Conrad JC, Marciel AB, Landes CF. Imaging Heterogeneous 3D Dynamics of Individual Solutes in a Polyelectrolyte Brush. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023. [PMID: 37290000 DOI: 10.1021/acs.langmuir.3c00868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Understanding molecular transport in polyelectrolyte brushes (PEBs) is crucial for applications such as separations, drug delivery, anti-fouling, and biosensors, where structural features of the polymer control intermolecular interactions. The complex structure and local heterogeneity of PEBs, while theoretically predicted, are not easily accessed with conventional experimental methods. In this work, we use 3D single-molecule tracking to understand transport behavior within a cationic poly(2-(N,N-dimethylamino)ethyl acrylate) (PDMAEA) brush using an anionic dye, Alexa Fluor 546, as the probe. The analysis is done by a parallelized, unbiased 3D tracking algorithm. Our results explicitly demonstrate that spatial heterogeneity within the brush manifests as heterogeneity of single-molecule displacements. Two distinct populations of probe motion are identified, with anticorrelated axial and lateral transport confinement, which we believe to correspond to intra- vs inter-chain probe motion.
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Affiliation(s)
- Dongyu Fan
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
| | - Shahryar Ramezani Bajgiran
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
| | - Farshad Safi Samghabadi
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Chayan Dutta
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302, United States
| | - Emil Gillett
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Peter J Rossky
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Smalley Curl Institute, Rice University, Houston, Texas 77005, United States
| | - Jacinta C Conrad
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Amanda B Marciel
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
| | - Christy F Landes
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States
- Smalley Curl Institute, Rice University, Houston, Texas 77005, United States
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5
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Barrantes FJ. Fluorescence microscopy imaging of a neurotransmitter receptor and its cell membrane lipid milieu. Front Mol Biosci 2022; 9:1014659. [PMID: 36518846 PMCID: PMC9743973 DOI: 10.3389/fmolb.2022.1014659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 11/01/2022] [Indexed: 05/02/2024] Open
Abstract
Hampered by the diffraction phenomenon, as expressed in 1873 by Abbe, applications of optical microscopy to image biological structures were for a long time limited to resolutions above the ∼200 nm barrier and restricted to the observation of stained specimens. The introduction of fluorescence was a game changer, and since its inception it became the gold standard technique in biological microscopy. The plasma membrane is a tenuous envelope of 4 nm-10 nm in thickness surrounding the cell. Because of its highly versatile spectroscopic properties and availability of suitable instrumentation, fluorescence techniques epitomize the current approach to study this delicate structure and its molecular constituents. The wide spectral range covered by fluorescence, intimately linked to the availability of appropriate intrinsic and extrinsic probes, provides the ability to dissect membrane constituents at the molecular scale in the spatial domain. In addition, the time resolution capabilities of fluorescence methods provide complementary high precision for studying the behavior of membrane molecules in the time domain. This review illustrates the value of various fluorescence techniques to extract information on the topography and motion of plasma membrane receptors. To this end I resort to a paradigmatic membrane-bound neurotransmitter receptor, the nicotinic acetylcholine receptor (nAChR). The structural and dynamic picture emerging from studies of this prototypic pentameric ligand-gated ion channel can be extrapolated not only to other members of this superfamily of ion channels but to other membrane-bound proteins. I also briefly discuss the various emerging techniques in the field of biomembrane labeling with new organic chemistry strategies oriented to applications in fluorescence nanoscopy, the form of fluorescence microscopy that is expanding the depth and scope of interrogation of membrane-associated phenomena.
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Affiliation(s)
- Francisco J. Barrantes
- Biomedical Research Institute (BIOMED), Catholic University of Argentina (UCA)–National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
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Lightley J, Görlitz F, Kumar S, Kalita R, Kolbeinsson A, Garcia E, Alexandrov Y, Bousgouni V, Wysoczanski R, Barnes P, Donnelly L, Bakal C, Dunsby C, Neil MAA, Flaxman S, French PMW. Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy. J Microsc 2022; 288:130-141. [PMID: 34089183 DOI: 10.1111/jmi.13020] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 04/17/2021] [Accepted: 05/05/2021] [Indexed: 11/27/2022]
Abstract
We presenta robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, for example due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilize a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/-100 μm. We characterize the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localization microscopy.
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Affiliation(s)
- Jonathan Lightley
- Photonics Group, Physics Department, Imperial College London, London, UK
| | - Frederik Görlitz
- Photonics Group, Physics Department, Imperial College London, London, UK
| | - Sunil Kumar
- Photonics Group, Physics Department, Imperial College London, London, UK
- Francis Crick Institute, London, UK
| | - Ranjan Kalita
- Photonics Group, Physics Department, Imperial College London, London, UK
| | | | - Edwin Garcia
- Photonics Group, Physics Department, Imperial College London, London, UK
| | - Yuriy Alexandrov
- Photonics Group, Physics Department, Imperial College London, London, UK
- Francis Crick Institute, London, UK
| | - Vicky Bousgouni
- Institute of Cancer Research, Chester Beatty Laboratories, London, UK
| | - Riccardo Wysoczanski
- Photonics Group, Physics Department, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Peter Barnes
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Louise Donnelly
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Chris Bakal
- Institute of Cancer Research, Chester Beatty Laboratories, London, UK
| | - Christopher Dunsby
- Photonics Group, Physics Department, Imperial College London, London, UK
- Francis Crick Institute, London, UK
| | - Mark A A Neil
- Photonics Group, Physics Department, Imperial College London, London, UK
- Francis Crick Institute, London, UK
| | - Seth Flaxman
- Department of Mathematics, Imperial College London, London, UK
| | - Paul M W French
- Photonics Group, Physics Department, Imperial College London, London, UK
- Francis Crick Institute, London, UK
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7
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Fast DNA-PAINT imaging using a deep neural network. Nat Commun 2022; 13:5047. [PMID: 36030338 PMCID: PMC9420107 DOI: 10.1038/s41467-022-32626-0] [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: 12/08/2021] [Accepted: 08/09/2022] [Indexed: 11/08/2022] Open
Abstract
DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution technique with relatively easy-to-implement multi-target imaging. However, image acquisition is slow as sufficient statistical data has to be generated from spatio-temporally isolated single emitters. Here, we train the neural network (NN) DeepSTORM to predict fluorophore positions from high emitter density DNA-PAINT data. This achieves image acquisition in one minute. We demonstrate multi-colour super-resolution imaging of structure-conserved semi-thin neuronal tissue and imaging of large samples. This improvement can be integrated into any single-molecule imaging modality to enable fast single-molecule super-resolution microscopy.
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8
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Gui D, Chen Y, Kuang W, Shang M, Wang Z, Huang ZL. Accelerating multi-emitter localization in super-resolution localization microscopy with FPGA-GPU cooperative computation. OPTICS EXPRESS 2021; 29:35247-35260. [PMID: 34808963 DOI: 10.1364/oe.439976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 10/03/2021] [Indexed: 06/13/2023]
Abstract
The real-time multi-emitter localization method is essential for advancing high-throughput super-resolution localization microscopy (HT-SRLM). In the past decade, the graphics processing unit (GPU) computation has been dominantly used to accelerate the execution speed of the multi-emitter localization method. However, if HT-SRLM is combined with a scientific complementary metal-oxide-semiconductor (sCMOS) camera working at full frame rate, real-time image processing is still difficult to achieve using this acceleration approach, thus resulting in a massive data storage challenge and even system crash. Here we take advantage of the cooperative acceleration power of field programming gate array (FPGA) computation and GPU computation, and propose a method called HCP-STORM to enable real-time multi-emitter localization. Using simulated images, we verified that HCP-STORM is capable of providing real-time image processing for raw images from a representative Hamamatsu Flash 4 V3 sCMOS camera working at full frame rate (that is, 2048×2048 pixels @ 10 ms exposure time). Using experimental images, we prove that HCP-STORM is 25 times faster than QC-STORM and 295 times faster than ThunderSTORM, with a small but acceptable degradation in image quality. This study shows the potential of FPGA-GPU cooperative computation in accelerating multi-emitter localization, and pushes a significant step toward the maturity of HT-SRLM technology.
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Gaire SK, Wang Y, Zhang HF, Liang D, Ying L. Accelerating 3D single-molecule localization microscopy using blind sparse inpainting. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200388R. [PMID: 33641269 PMCID: PMC7910702 DOI: 10.1117/1.jbo.26.2.026501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/21/2021] [Indexed: 05/14/2023]
Abstract
SIGNIFICANCE Single-molecule localization-based super-resolution microscopy has enabled the imaging of microscopic objects beyond the diffraction limit. However, this technique is limited by the requirements of imaging an extremely large number of frames of biological samples to generate a super-resolution image, thus requiring a longer acquisition time. Additionally, the processing of such a large image sequence leads to longer data processing time. Therefore, accelerating image acquisition and processing in single-molecule localization microscopy (SMLM) has been of perennial interest. AIM To accelerate three-dimensional (3D) SMLM imaging by leveraging a computational approach without compromising the resolution. APPROACH We used blind sparse inpainting to reconstruct high-density 3D images from low-density ones. The low-density images are generated using much fewer frames than usually needed, thus requiring a shorter acquisition and processing time. Therefore, our technique will accelerate 3D SMLM without changing the existing standard SMLM hardware system and labeling protocol. RESULTS The performance of the blind sparse inpainting was evaluated on both simulation and experimental datasets. Superior reconstruction results of 3D SMLM images using up to 10-fold fewer frames in simulation and up to 50-fold fewer frames in experimental data were achieved. CONCLUSIONS We demonstrate the feasibility of fast 3D SMLM imaging leveraging a computational approach to reduce the number of acquired frames. We anticipate our technique will enable future real-time live-cell 3D imaging to investigate complex nanoscopic biological structures and their functions.
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Affiliation(s)
- Sunil Kumar Gaire
- The State University of New York at Buffalo, Department of Electrical Engineering, Buffalo, New York, United States
| | - Yanhua Wang
- Beijing Institute of Technology, School of Information and Electronics, Beijing, China
| | - Hao F. Zhang
- Northwestern University, Department of Biomedical Engineering, Evanston, Illinois, United States
| | - Dong Liang
- Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen, Guangdong, China
| | - Leslie Ying
- The State University of New York at Buffalo, Department of Electrical Engineering, Buffalo, New York, United States
- The State University of New York at Buffalo, Department of Biomedical Engineering, Buffalo, New York, United States
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Möckl L, Moerner WE. Super-resolution Microscopy with Single Molecules in Biology and Beyond-Essentials, Current Trends, and Future Challenges. J Am Chem Soc 2020; 142:17828-17844. [PMID: 33034452 PMCID: PMC7582613 DOI: 10.1021/jacs.0c08178] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Indexed: 12/31/2022]
Abstract
Single-molecule super-resolution microscopy has developed from a specialized technique into one of the most versatile and powerful imaging methods of the nanoscale over the past two decades. In this perspective, we provide a brief overview of the historical development of the field, the fundamental concepts, the methodology required to obtain maximum quantitative information, and the current state of the art. Then, we will discuss emerging perspectives and areas where innovation and further improvement are needed. Despite the tremendous progress, the full potential of single-molecule super-resolution microscopy is yet to be realized, which will be enabled by the research ahead of us.
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Affiliation(s)
- Leonhard Möckl
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - W. E. Moerner
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
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PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10103382] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.
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