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Hörmann M, Camargo FVA, van Hulst NF, Cerullo G, Liebel M. Ultrabroadband Optical Diffraction Tomography. ACS PHOTONICS 2024; 11:3680-3687. [PMID: 39310293 PMCID: PMC11413850 DOI: 10.1021/acsphotonics.4c00797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 09/25/2024]
Abstract
Optical diffraction tomography (ODT) is a powerful noninvasive 3D imaging technique, but its combination with broadband light sources is difficult. In this study, we introduce ultrabroadband ODT, covering over 150 nm of visible spectral bandwidth with a lateral spatial resolution of 150 nm. Our work addresses a critical experimental gap by enabling the measurement of broadband refractive index changes in 3D samples, crucial information that is difficult to assess with existing methodologies. We present broadband, spectrally resolved ODT images of HeLa cells, obtained via pulse-shaping-based Fourier transform spectroscopy. The spectral observations enabled by ultrabroadband ODT, combined with material-dependent refractive index responses, allow for precise three-dimensional identification of nanoparticles within cellular structures. Our work represents a crucial step toward time and spectrally resolved tomography of complex 3D structures with implications for life and materials science applications.
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Affiliation(s)
- Martin Hörmann
- Dipartimento
di Fisica, Politecnico di Milano, Piazza L. da Vinci 32, Milano 20133, Italy
| | - Franco V. A. Camargo
- Istituto
di Fotonica e Nanotecnologie-CNR, Piazza L. da Vinci 32, Milano 20133, Italy
| | - Niek F. van Hulst
- ICFO
− Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels - Barcelona 08860, Spain
- ICREA
− Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Giulio Cerullo
- Dipartimento
di Fisica, Politecnico di Milano, Piazza L. da Vinci 32, Milano 20133, Italy
- Istituto
di Fotonica e Nanotecnologie-CNR, Piazza L. da Vinci 32, Milano 20133, Italy
| | - Matz Liebel
- ICFO
− Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss, 3, Castelldefels - Barcelona 08860, Spain
- Department
of Physics and Astronomy, Vrije Universiteit
Amsterdam, De Boelelaan
1081, Amsterdam, HV 1081, The Netherlands
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2
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Yang J, Xue Q, Li J, Han B, Wang Y, Bai H. Deep ultraviolet high-resolution microscopic hyperspectral imager and its biological tissue detection. APPLIED OPTICS 2023; 62:3310-3319. [PMID: 37132831 DOI: 10.1364/ao.485387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Ultraviolet (UV) hyperspectral imaging technology is commonly used in the field of atmospheric remote sensing. In recent years, some in-laboratory research has been carried out for substance detection and identification. In this paper, UV hyperspectral imaging technology is introduced into microscopy to better utilize the obvious absorption characteristics of components, such as proteins and nucleic acids in biological tissues in the ultraviolet band. A deep UV microscopic hyperspectral imager based on the Offner structure with F # 2.5, low spectral keystone and smile is designed and developed. A 0.68 numerical aperture microscope objective is designed. The spectral range of the system is from 200 nm to 430 nm; the spectral resolution is better than 0.5 nm; and the spatial resolution is better than 1.3 µm. The K562 cells can be distinguished by transmission spectrum of nucleus. The UV microscopic hyperspectral image of the unstained mouse liver slices showed similar results to the microscopic image after hematoxylin and eosin staining, which could help to simplify the pathological examination process. Both results show a great performance in spatial and spectral detecting capabilities of our instrument, which has the potential for biomedical research and diagnosis.
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Gorti V, Kaza N, Williams EK, Lam WA, Robles FE. Compact and low-cost deep-ultraviolet microscope system for label-free molecular imaging and point-of-care hematological analysis. BIOMEDICAL OPTICS EXPRESS 2023; 14:1245-1255. [PMID: 36950241 PMCID: PMC10026585 DOI: 10.1364/boe.482294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Deep-ultraviolet (UV) microscopy enables label-free, high-resolution, quantitative molecular imaging and enables unique applications in biomedicine, including the potential for fast hematological analysis at the point-of-care. UV microscopy has been shown to quantify hemoglobin content and white blood cells (five-part differential), providing a simple alternative to the current gold standard, the hematological analyzer. Previously, however, the UV system comprised a bulky broadband laser-driven plasma light source along with a large and expensive camera and 3D translation stage. Here, we present a modified deep-UV microscope system with a compact footprint and low-cost components. We detail the novel design with simple, inexpensive optics and hardware to enable fast and accurate automated imaging. We characterize the system, including a modified low-cost web-camera and custom automated 3D translation stage, and demonstrate its ability to scan and capture large area images. We further demonstrate the capability of the system by imaging and analyzing blood smears, using previously trained networks for automatic segmentation, classification (including 5-part white blood cell differential), and colorization. The developed system is approximately 10 times less expensive than previous configurations and can serve as a point-of-care hematology analyzer, as well as be applied broadly in biomedicine as a simple compact, low-cost, quantitative molecular imaging system.
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Affiliation(s)
- Viswanath Gorti
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Nischita Kaza
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Evelyn Kendall Williams
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Wilbur A. Lam
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- Aflac Cancer and Blood Disorders Center of Children’s Healthcare of Atlanta and Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Francisco E. Robles
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
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Ojaghi A, Kendall Williams E, Kaza N, Gorti V, Choi H, Torey J, Wiley T, Turner B, Jackson S, Park S, Lam WA, Robles FE. Label-free deep-UV microscopy detection and grading of neutropenia using a passive microfluidic device. OPTICS LETTERS 2022; 47:6005-6008. [PMID: 37219158 DOI: 10.1364/ol.472691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/26/2022] [Indexed: 05/24/2023]
Abstract
Neutropenia is a condition comprising an abnormally low number of neutrophils, a type of white blood cell, which puts patients at an increased risk of severe infections. Neutropenia is especially common among cancer patients and can disrupt their treatment or even be life-threatening in severe cases. Therefore, routine monitoring of neutrophil counts is crucial. However, the current standard of care to assess neutropenia, the complete blood count (CBC), is resource-intensive, time-consuming, and expensive, thereby limiting easy or timely access to critical hematological information such as neutrophil counts. Here, we present a simple technique for fast, label-free neutropenia detection and grading via deep-ultraviolet (deep-UV) microscopy of blood cells in polydimethylsiloxane (PDMS)-based passive microfluidic devices. The devices can potentially be manufactured in large quantities at a low cost, requiring only 1 μL of whole blood for operation. We show that the absolute neutrophil counts (ANC) obtained from our proposed microfluidic device-enabled deep-UV microscopy system are highly correlated with those from CBCs using commercial hematology analyzers in patients with moderate and severe neutropenia, as well as healthy donors. This work lays the foundation for the development of a compact, easy-to-use UV microscope system to track neutrophil counts that is suitable for low-resource, at-home, or point-of-care settings.
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Chen D, Li N, Liu X, Zeng S, Lv X, Chen L, Xiao Y, Hu Q. Label-free hematology analysis method based on defocusing phase-contrast imaging under illumination of 415 nm light. BIOMEDICAL OPTICS EXPRESS 2022; 13:4752-4772. [PMID: 36187242 PMCID: PMC9484434 DOI: 10.1364/boe.466162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/16/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
Label-free imaging technology is a trending way to simplify and improve conventional hematology analysis by bypassing lengthy and laborious staining procedures. However, the existing methods do not well balance system complexity, data acquisition efficiency, and data analysis accuracy, which severely impedes their clinical translation. Here, we propose defocusing phase-contrast imaging under the illumination of 415 nm light to realize label-free hematology analysis. We have verified that the subcellular morphology of blood components can be visualized without complex staining due to the factor that defocusing can convert the second-order derivative distribution of samples' optical phase into intensity and the illumination of 415 nm light can significantly enhance the contrast. It is demonstrated that the defocusing phase-contrast images for the five leucocyte subtypes can be automatically discriminated by a trained deep-learning program with high accuracy (the mean F1 score: 0.986 and mean average precision: 0.980). Since this technique is based on a regular microscope, it simultaneously realizes low system complexity and high data acquisition efficiency with remarkable quantitative analysis ability. It supplies a label-free, reliable, easy-to-use, fast approach to simplifying and reforming the conventional way of hematology analysis.
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Affiliation(s)
- Duan Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Ning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Xiuli Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiaohua Lv
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Li Chen
- Department of Clinical Laboratory, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yuwei Xiao
- Wuhan Hannan People’s Hospital, Wuhan 430090, China
| | - Qinglei Hu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- Ministry of Education (MOE) Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
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Kaza N, Ojaghi A, Robles FE. Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis. BME FRONTIERS 2022; 2022:9853606. [PMID: 37850166 PMCID: PMC10521747 DOI: 10.34133/2022/9853606] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/05/2022] [Indexed: 10/19/2023] Open
Abstract
Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods. We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion. This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.
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Affiliation(s)
- Nischita Kaza
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Ashkan Ojaghi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Francisco E. Robles
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
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