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Ku J, Asuri P. Stem cell-based approaches for developmental neurotoxicity testing. FRONTIERS IN TOXICOLOGY 2024; 6:1402630. [PMID: 39238878 PMCID: PMC11374538 DOI: 10.3389/ftox.2024.1402630] [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: 03/18/2024] [Accepted: 08/05/2024] [Indexed: 09/07/2024] Open
Abstract
Neurotoxicants are substances that can lead to adverse structural or functional effects on the nervous system. These can be chemical, biological, or physical agents that can cross the blood brain barrier to damage neurons or interfere with complex interactions between the nervous system and other organs. With concerns regarding social policy, public health, and medicine, there is a need to ensure rigorous testing for neurotoxicity. While the most common neurotoxicity tests involve using animal models, a shift towards stem cell-based platforms can potentially provide a more biologically accurate alternative in both clinical and pharmaceutical research. With this in mind, the objective of this article is to review both current technologies and recent advancements in evaluating neurotoxicants using stem cell-based approaches, with an emphasis on developmental neurotoxicants (DNTs) as these have the most potential to lead to irreversible critical damage on brain function. In the next section, attempts to develop novel predictive model approaches for the study of both neural cell fate and developmental neurotoxicity are discussed. Finally, this article concludes with a discussion of the future use of in silico methods within developmental neurotoxicity testing, and the role of regulatory bodies in promoting advancements within the space.
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Affiliation(s)
- Joy Ku
- Department of Bioengineering, Santa Clara University, Santa Clara, CA, United States
| | - Prashanth Asuri
- Department of Bioengineering, Santa Clara University, Santa Clara, CA, United States
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2
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Thite NG, Tuberty-Vaughan E, Wilcox P, Wallace N, Calderon CP, Randolph TW. Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning. J Pharm Sci 2024; 113:2114-2127. [PMID: 38710387 DOI: 10.1016/j.xphs.2024.05.001] [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] [Received: 03/07/2024] [Revised: 05/01/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.
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Affiliation(s)
- Nidhi G Thite
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Emma Tuberty-Vaughan
- Dosage Form Design & Development (DFDD), BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Paige Wilcox
- Dosage Form Design & Development (DFDD), BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Nicole Wallace
- Dosage Form Design & Development (DFDD), BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Christopher P Calderon
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, USA; Ursa Analytics, Denver, CO 80212, USA
| | - Theodore W Randolph
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
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3
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Chen K, Swanson S, Bizheva K. Line-field dynamic optical coherence tomography platform for volumetric assessment of biological tissues. BIOMEDICAL OPTICS EXPRESS 2024; 15:4162-4175. [PMID: 39022542 PMCID: PMC11249681 DOI: 10.1364/boe.527797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 07/20/2024]
Abstract
Dynamic optical coherence tomography (dOCT) utilizes time-dependent signal intensity fluctuations to enhance contrast in OCT images and indirectly probe physiological processes in cells. Majority of the dOCT studies published so far are based on acquisition of 2D images (B-scans or C-scans) by utilizing point-scanning Fourier domain (spectral or swept-source) OCT or full-field OCT respectively, primarily due to limitations in the image acquisition rate. Here we introduce a novel, high-speed spectral domain line-field dOCT (SD-LF-dOCT) system and image acquisition protocols designed for fast, volumetric dOCT imaging of biological tissues. The imaging probe is based on an exchangeable afocal lens pair that enables selection of combinations of transverse resolution (from 1.1 µm to 6.4 µm) and FOV (from 250 × 250 µm2 to 1.4 × 1.4 mm2), suitable for different biomedical applications. The system offers axial resolution of ∼ 1.9 µm in biological tissue, assuming an average refractive index of 1.38. Maximum sensitivity of 90.5 dB is achieved for 3.5 mW optical imaging power at the tissue surface and maximum camera acquisition rate of 2,000 fps. Volumetric dOCT images acquired with the SD-LF-dOCT system from plant tissue (cucumber), animal tissue (mouse liver) and human prostate carcinoma spheroids allow for volumetric visualization of the tissues' cellular and sub-cellular structures and assessment of cellular motility.
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Affiliation(s)
- Keyu Chen
- Department of Physics and Astronomy, University of Waterloo, Waterloo N2 L 3G1, ON, Canada
| | - Stephanie Swanson
- Department of Physics and Astronomy, University of Waterloo, Waterloo N2 L 3G1, ON, Canada
| | - Kostadinka Bizheva
- Department of Physics and Astronomy, University of Waterloo, Waterloo N2 L 3G1, ON, Canada
- School of Optometry and Vision Sciences, University of Waterloo, Waterloo, ON, Canada
- System Design Engineering Department, University of Waterloo, Waterloo, ON, Canada
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4
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Ren C, Hao S, Wang F, Matt A, Amaral MM, Yang D, Wang L, Zhou C. Dynamic contrast optical coherence tomography (DyC-OCT) for label-free live cell imaging. Commun Biol 2024; 7:278. [PMID: 38448627 PMCID: PMC10918170 DOI: 10.1038/s42003-024-05973-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
Dynamic contrast optical coherence tomography (DyC-OCT), an emerging imaging method, utilizes fluctuation patterns in OCT signals to enhance contrast, thereby enabling non-invasive label-free volumetric live cell imaging. In this mini review, we explain the core concepts behind DyC-OCT image formation and its system configurations, serving as practical guidance for future DyC-OCT users. Subsequently, we explore its applications in delivering high-quality, contrast-enhanced images of cellular morphology, as well as in monitoring changes in cellular activity/viability assay experiments.
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Affiliation(s)
- Chao Ren
- Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO, USA
- Imaging Science Ph.D. Program, Washington University in St Louis, St. Louis, MO, USA
| | - Senyue Hao
- Department of Electrical & Systems Engineering, Washington University in St Louis, St. Louis, MO, USA
| | - Fei Wang
- Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO, USA
| | - Abigail Matt
- Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO, USA
| | - Marcello Magri Amaral
- Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO, USA
- Biomedical Engineering, Universidade Brasil, Sao Paulo, Brazil
| | - Daniel Yang
- Division of Allergy and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Leyao Wang
- Division of Allergy and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chao Zhou
- Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO, USA.
- Imaging Science Ph.D. Program, Washington University in St Louis, St. Louis, MO, USA.
- Department of Electrical & Systems Engineering, Washington University in St Louis, St. Louis, MO, USA.
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Kardorff M, Mahler HC, Huwyler J, Sorret L. Comparison of cell viability methods for human mesenchymal/stromal stem cells and human A549 lung carcinoma cells after freeze-thaw stress. J Pharmacol Toxicol Methods 2023; 124:107474. [PMID: 37866798 DOI: 10.1016/j.vascn.2023.107474] [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] [Received: 06/25/2023] [Revised: 09/27/2023] [Accepted: 10/19/2023] [Indexed: 10/24/2023]
Abstract
For the safety and efficacy of frozen cell therapy products, determination of cellular viability is key. However, results of cell viability measurements do not only depend on the cell line or on the inflicted stress, but also on the assay used, making inter-experimental comparisons difficult. The aim of this study was thus to assess commonly used viability assays in clinically relevant human mesenchymal/stromal stem cells and human A549 lung carcinoma cells. Post freeze-thaw stress viability and proliferation were evaluated under different conditions using trypan blue, acridine orange/DAPI stain, alamarBlue, ATP, and neutral red assays. Significant differences in cell viability between metabolic assays were observed, likely due to their distinct intrinsic detection mechanisms. Membrane-integrity based assays generally overestimated cell viabilities in this study. Furthermore, noticeable differences in inter-assay sensitivities were observed. These differences highlight that cell viability methods should be meticulously selected and their associated results carefully interpreted in a relevant context to ensure reliable conclusions. Indeed, although cell membrane integrity based assays are a popular choice to determine cellular quality attributes after freezing and thawing, we demonstrate that metabolic assays may be more suitable in this context.
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Affiliation(s)
- Markus Kardorff
- Drug Product Services, Lonza AG, Hochbergerstrasse 60G, 4057 Basel, Switzerland; Division of Pharmaceutical Technology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | | | - Jörg Huwyler
- Division of Pharmaceutical Technology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Léa Sorret
- Drug Product Services, Lonza AG, Hochbergerstrasse 60G, 4057 Basel, Switzerland.
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Monfort T, Azzollini S, Brogard J, Clémençon M, Slembrouck-Brec A, Forster V, Picaud S, Goureau O, Reichman S, Thouvenin O, Grieve K. Dynamic full-field optical coherence tomography module adapted to commercial microscopes allows longitudinal in vitro cell culture study. Commun Biol 2023; 6:992. [PMID: 37770552 PMCID: PMC10539404 DOI: 10.1038/s42003-023-05378-w] [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: 02/24/2023] [Accepted: 09/20/2023] [Indexed: 09/30/2023] Open
Abstract
Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organization processes such as rosette formation and mitosis as well as cell shape and motility. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening.
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Affiliation(s)
- Tual Monfort
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012, Paris, France
- Paris Eye Imaging Group, Quinze-Vingts National Eye Hospital, INSERM-DGOS, CIC 1423, 28 rue de Charenton, Paris, 75012, France
| | - Salvatore Azzollini
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Jérémy Brogard
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Marilou Clémençon
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Amélie Slembrouck-Brec
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Valerie Forster
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Serge Picaud
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Olivier Goureau
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Sacha Reichman
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Olivier Thouvenin
- Institut Langevin, ESPCI Paris, Université PSL, CNRS, 75005, Paris, France
| | - Kate Grieve
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France.
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012, Paris, France.
- Paris Eye Imaging Group, Quinze-Vingts National Eye Hospital, INSERM-DGOS, CIC 1423, 28 rue de Charenton, Paris, 75012, France.
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Azzollini S, Monfort T, Thouvenin O, Grieve K. Dynamic optical coherence tomography for cell analysis [Invited]. BIOMEDICAL OPTICS EXPRESS 2023; 14:3362-3379. [PMID: 37497511 PMCID: PMC10368035 DOI: 10.1364/boe.488929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/29/2023] [Accepted: 06/06/2023] [Indexed: 07/28/2023]
Abstract
Label-free live optical imaging of dynamic cellular and subcellular features has been made possible in recent years thanks to the advances made in optical imaging techniques, including dynamic optical coherence tomography (D-OCT) methods. These techniques analyze the temporal fluctuations of an optical signal associated with the active movements of intracellular organelles to obtain an ensemble metric recapitulating the motility and metabolic state of cells. They hence enable visualization of cells within compact, static environments and evaluate their physiology. These emerging microscopies show promise, in particular for the three-dimensional evaluation of live tissue samples such as freshly excised biopsies and 3D cell cultures. In this review, we compare the various techniques used for dynamic OCT. We give an overview of the range of applications currently being explored and discuss the future outlook and opportunities for the field.
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Affiliation(s)
- Salvatore Azzollini
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
| | - Tual Monfort
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012 Paris, France
| | | | - Kate Grieve
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012 Paris, France
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Giczewska A, Pastuszak K, Houweling M, Abdul KU, Faaij N, Wedekind L, Noske D, Wurdinger T, Supernat A, Westerman BA. Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures. Neurooncol Adv 2023; 5:vdad134. [PMID: 38047207 PMCID: PMC10691443 DOI: 10.1093/noajnl/vdad134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023] Open
Abstract
Background In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays. Methods We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15). Results Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time. Conclusions Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.
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Affiliation(s)
- Anna Giczewska
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
| | - Krzysztof Pastuszak
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of Gdańsk, Gdańsk, Poland
- Department of Algorithms and System Modeling, Gdansk University of Technology, Gdańsk, Poland
| | - Megan Houweling
- Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- The WINDOW Consortium (www.window-consortium.org)
| | - Kulsoom U Abdul
- Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- The WINDOW Consortium (www.window-consortium.org)
| | - Noa Faaij
- Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Laurine Wedekind
- Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - David Noske
- Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Thomas Wurdinger
- Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- The WINDOW Consortium (www.window-consortium.org)
| | - Anna Supernat
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland
- Center of Biostatistics and Bioinformatics, Medical University of Gdańsk, Gdańsk, Poland
| | - Bart A Westerman
- Department of Neurosurgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- The WINDOW Consortium (www.window-consortium.org)
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Ferraro P, Li Y, Miccio L, Shui L, Zhang Y. Biological Cells as Natural Biophotonic Devices: Fundamental and Applications-introduction to the feature issue. BIOMEDICAL OPTICS EXPRESS 2022; 13:5571-5573. [PMID: 36425638 PMCID: PMC9664888 DOI: 10.1364/boe.475704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Indexed: 06/16/2023]
Abstract
This feature issue of Biomedical Optics Express presents a cross-section of interesting and emerging work of relevance to the use of biological cells or microorganisms in optics and photonics. The technologies demonstrated here aim to address challenges to meeting the optical imaging, sensing, manipulating and therapy needs in a natural or even endogenous manner. This collection of 15 papers includes the novel results on designs of optical systems or photonic devices, image-assisted diagnosis and treatment, and manipulation or sensing methods, with applications for both ex vivo and in vivo use. These works portray the opportunities for exploring the field crossing biology and photonics in which a natural element can be functionalized for biomedical applications.
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Affiliation(s)
- Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems «E. Caianiello», Via Campi Flegrei 34, 80078 Pozzuoli, Naples, Italy
| | - Yuchao Li
- Institute of Nanophotonics, Jinan University, 511443 Guangzhou, China
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems «E. Caianiello», Via Campi Flegrei 34, 80078 Pozzuoli, Naples, Italy
| | - Lingling Shui
- School of Information and Optoelectronic Science and Engineering, South China Normal University, 510006 Guangzhou, China
| | - Yao Zhang
- Institute of Nanophotonics, Jinan University, 511443 Guangzhou, China
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