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Picazo-Bueno JÁ, Sanz M, Granero L, García J, Micó V. Multi-Illumination Single-Holographic-Exposure Lensless Fresnel (MISHELF) Microscopy: Principles and Biomedical Applications. Sensors (Basel) 2023; 23:1472. [PMID: 36772511 PMCID: PMC9918952 DOI: 10.3390/s23031472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.
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Affiliation(s)
- José Ángel Picazo-Bueno
- Department of Optics, Optometry and Vision Science, University of Valencia, 46100 Burjassot, Spain
- Biomedical Technology Center of the Medical Faculty, University of Muenster, Mendelstr. 17, D-48149 Muenster, Germany
| | - Martín Sanz
- Department of Optics, Optometry and Vision Science, University of Valencia, 46100 Burjassot, Spain
| | - Luis Granero
- Department of Optics, Optometry and Vision Science, University of Valencia, 46100 Burjassot, Spain
| | - Javier García
- Department of Optics, Optometry and Vision Science, University of Valencia, 46100 Burjassot, Spain
| | - Vicente Micó
- Department of Optics, Optometry and Vision Science, University of Valencia, 46100 Burjassot, Spain
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Jiang S, Guo C, Wang T, Liu J, Song P, Zhang T, Wang R, Feng B, Zheng G. Blood-Coated Sensor for High-Throughput Ptychographic Cytometry on a Blu-ray Disc. ACS Sens 2022; 7:1058-1067. [PMID: 35393855 DOI: 10.1021/acssensors.1c02704] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The Blu-ray drive is an engineering masterpiece that integrates disc rotation, pickup head translation, and three lasers in a compact and portable format. Here, we integrate a blood-coated image sensor with a modified Blu-ray drive for high-throughput cytometric analysis of various biospecimens. In this device, samples are mounted on the rotating Blu-ray disc and illuminated by the built-in lasers from the pickup head. The resulting coherent diffraction patterns are then recorded by the blood-coated image sensor. The rich spatial features of the blood-cell monolayer help down-modulate the object information for sensor detection, thus forming a high-resolution computational biolens with a theoretically unlimited field of view. With the acquired data, we develop a lensless coherent diffraction imaging modality termed rotational ptychography for image reconstruction. We show that our device can resolve the 435 nm line width on the resolution target and has a field of view only limited by the size of the Blu-ray disc. To demonstrate its applications, we perform high-throughput urinalysis by locating disease-related calcium oxalate crystals over the entire microscope slide. We also quantify different types of cells on a blood smear with an acquisition speed of ∼10,000 cells per second. For in vitro experiments, we monitor live bacterial cultures over the entire Petri dish with single-cell resolution. Using biological cells as a computational lens could enable new intriguing imaging devices for point-of-care diagnostics. Modifying a Blu-ray drive with the blood-coated sensor further allows the spread of high-throughput optical microscopy from well-equipped laboratories to citizen scientists worldwide.
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Affiliation(s)
- Shaowei Jiang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Chengfei Guo
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Tianbo Wang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Jia Liu
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Pengming Song
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Terrance Zhang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Ruihai Wang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Bin Feng
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Guoan Zheng
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
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Kabir MA, Kharel A, Malla S, Kreis ZJ, Nath P, Wolfe JN, Hassan M, Kaur D, Sari-Sarraf H, Tiwari AK, Ray A. Automated detection of apoptotic versus nonapoptotic cell death using label-free computational microscopy. J Biophotonics 2022; 15:e202100310. [PMID: 34936215 DOI: 10.1002/jbio.202100310] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/09/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
Identification of cell death mechanisms, particularly distinguishing between apoptotic versus nonapoptotic pathways, is of paramount importance for a wide range of applications related to cell signaling, interaction with pathogens, therapeutic processes, drug discovery, drug resistance, and even pathogenesis of diseases like cancers and neurogenerative disease among others. Here, we present a novel high-throughput method of identifying apoptotic versus necrotic versus other nonapoptotic cell death processes, based on lensless digital holography. This method relies on identification of the temporal changes in the morphological features of mammalian cells, which are unique to each cell death processes. Different cell death processes were induced by known cytotoxic agents. A deep learning-based approach was used to automatically classify the cell death mechanism (apoptotic vs necrotic vs nonapoptotic) with more than 93% accuracy. This label free approach can provide a low cost (<$250) alternative to some of the currently available high content imaging-based screening tools.
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Affiliation(s)
- Md Alamgir Kabir
- Department of Physics and Astronomy, University of Toledo, Toledo, OH, USA
| | - Ashish Kharel
- Department of Electrical and Computer Science, University of Toledo, Toledo, OH, USA
| | - Saloni Malla
- Department of Pharmacology and Experimental Therapeutics, College of Pharmacy and Pharmaceutical Sciences, University of Toledo, Toledo, OH, USA
| | | | - Peuli Nath
- Department of Physics and Astronomy, University of Toledo, Toledo, OH, USA
| | - Jared Neil Wolfe
- Department of Mechanical Engineering, University of Toledo, Toledo, OH, USA
| | - Marwa Hassan
- Department of Pharmacology and Experimental Therapeutics, College of Pharmacy and Pharmaceutical Sciences, University of Toledo, Toledo, OH, USA
| | - Devinder Kaur
- Department of Electrical and Computer Science, University of Toledo, Toledo, OH, USA
| | - Hamed Sari-Sarraf
- Department of Electrical & Computer Engineering, Texas Tech University, Lubbock, TX, USA
| | - Amit K Tiwari
- Department of Pharmacology and Experimental Therapeutics, College of Pharmacy and Pharmaceutical Sciences, University of Toledo, Toledo, OH, USA
- Department of Cancer Biology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Aniruddha Ray
- Department of Physics and Astronomy, University of Toledo, Toledo, OH, USA
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Liu T, de Haan K, Bai B, Rivenson Y, Luo Y, Wang H, Karalli D, Fu H, Zhang Y, FitzGerald J, Ozcan A. Deep Learning-Based Holographic Polarization Microscopy. ACS Photonics 2020; 7:3023-3034. [PMID: 34368395 PMCID: PMC8345334 DOI: 10.1021/acsphotonics.0c01051] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically operate by analyzing images collected from two or more light paths in different states of polarization, which lead to relatively complex optical designs, high system costs, or experienced technicians being required. Here, we present a deep learning-based holographic polarization microscope that is capable of obtaining quantitative birefringence retardance and orientation information of specimen from a phase-recovered hologram, while only requiring the addition of one polarizer/analyzer pair to an inline lensfree holographic imaging system. Using a deep neural network, the reconstructed holographic images from a single state of polarization can be transformed into images equivalent to those captured using a single-shot computational polarized light microscope (SCPLM). Our analysis shows that a trained deep neural network can extract the birefringence information using both the sample specific morphological features as well as the holographic amplitude and phase distribution. To demonstrate the efficacy of this method, we tested it by imaging various birefringent samples including, for example, monosodium urate and triamcinolone acetonide crystals. Our method achieves similar results to SCPLM both qualitatively and quantitatively, and due to its simpler optical design and significantly larger field-of-view this method has the potential to expand the access to polarization microscopy and its use for medical diagnosis in resource limited settings.
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Affiliation(s)
- Tairan Liu
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Kevin de Haan
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Bijie Bai
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Yair Rivenson
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Yi Luo
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Hongda Wang
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - David Karalli
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
| | - Hongxiang Fu
- Computational and Systems Biology Department, University of California, Los Angeles, California 90095, United States
| | - Yibo Zhang
- Electrical and Computer Engineering Department, Department of Bioengineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - John FitzGerald
- Division of Rheumatology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, Department of Bioengineering, California NanoSystems Institute, and Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States
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Abstract
Lab-on-a-chip systems have been rapidly emerging to pave the way toward ultra-compact, efficient, mass producible and cost-effective biomedical research and diagnostic tools. Although such microfluidic and microelectromechanical systems have achieved high levels of integration, and are capable of performing various important tasks on the same chip, such as cell culturing, sorting and staining, they still rely on conventional microscopes for their imaging needs. Recently, several alternative on-chip optical imaging techniques have been introduced, which have the potential to substitute conventional microscopes for various lab-on-a-chip applications. Here we present a critical review of these recently emerging on-chip biomedical imaging modalities, including contact shadow imaging, lens-free holographic microscopy, fluorescent on-chip microscopy and lens-free optical tomography.
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Affiliation(s)
- Zoltán Göröcs
- Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA
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