1
|
Liu Y, Duan Z, Yuan J, Xiao P. Imaging assessment of conjunctival goblet cells in dry eye disease. Clin Exp Ophthalmol 2024; 52:576-588. [PMID: 38553944 DOI: 10.1111/ceo.14379] [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: 11/27/2023] [Revised: 03/06/2024] [Accepted: 03/13/2024] [Indexed: 07/03/2024]
Abstract
Dry eye disease (DED) is a widespread, multifactorial, and chronic disorder of the ocular surface with disruption of tear film homeostasis as its core trait. Conjunctival goblet cells (CGCs) are specialised secretory cells found in the conjunctival epithelium that participate in tear film formation by secreting mucin. Changes in both the structure and function of CGCs are hallmarks of DED, and imaging assessment of CGCs is important for the diagnosis, classification, and severity evaluation of DED. Existing imaging methods include conjunctival biopsy, conjunctival impression cytology and in vivo confocal microscopy, which can be used to assess the morphology, distribution, and density of the CGCs. Recently, moxifloxacin-based fluorescence microscopy has emerged as a novel technique that enables efficient, non-invasive and in vivo imaging of CGCs. This article presents a comprehensive overview of both the structure and function of CGCs and their alterations in the context of DED, as well as current methods of CGCs imaging assessment. Additionally, potential directions for the visual evaluation of CGCs are discussed.
Collapse
Affiliation(s)
- Yushuang Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Centre for Ocular Diseases, Guangzhou, China
| | - Zhengyu Duan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Centre for Ocular Diseases, Guangzhou, China
| | - Jin Yuan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Centre for Ocular Diseases, Guangzhou, China
| | - Peng Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Centre for Ocular Diseases, Guangzhou, China
| |
Collapse
|
2
|
Jang S, Kim S, Lee J, Choi WJ, Yoon CH, Yang S, Kim KH. Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva. Sci Rep 2023; 13:22839. [PMID: 38129447 PMCID: PMC10739799 DOI: 10.1038/s41598-023-49275-y] [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: 07/04/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area imaging and a robust analysis method is needed to provide GC information. In this study, we developed a deep learning framework for GC image analysis, named dual-channel attention U-Net (DCAU-Net). Dual-channel convolution was used both to extract the overall image texture and to acquire the GC morphological characteristics. A global channel attention module was adopted by combining attention algorithms and channel-wise pooling. DCAU-Net showed 93.1% GC segmentation accuracy and 94.3% GC density estimation accuracy. Further application to both normal and ocular surface damage rabbit models revealed the spatial variations of both GC density and size in normal rabbits and the decreases of both GC density and size in damage rabbit models during recovery after acute damage. The GC analysis results were consistent with histology. Together with the non-invasive high-contrast imaging method, DCAU-Net would provide GC information for the diagnosis of ocular surface diseases.
Collapse
Affiliation(s)
- Seunghyun Jang
- Department of Biomedical Engineering, Yonsei University, 1 Yonseidae-gil, Wonju-si, Gangwon-do, 26493, Republic of Korea
| | - Seonghan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyeoungbuk, 37673, Republic of Korea
| | - Jungbin Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyeoungbuk, 37673, Republic of Korea
| | - Wan Jae Choi
- Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Chang Ho Yoon
- Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Sejung Yang
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju-si, Gangwon-do, 26426, Republic of Korea.
- Department of Medical Informatics and Biostatistics, Graduate School, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| | - Ki Hean Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyeoungbuk, 37673, Republic of Korea.
| |
Collapse
|
3
|
Kim J, Lee J, Kim S, Yoon SH, Jo YC, Kim KH, Kim HK. Noninvasive Imaging of Conjunctival Goblet Cells as a Method for Diagnosing Dry Eye Disease in an Experimental Mouse Model. Transl Vis Sci Technol 2023; 12:22. [PMID: 38149964 PMCID: PMC10755591 DOI: 10.1167/tvst.12.12.22] [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: 05/08/2023] [Accepted: 09/11/2023] [Indexed: 12/28/2023] Open
Abstract
Purpose The purpose of this study was to evaluate a noninvasive conjunctival goblet cell (GC) imaging method for assessing dry eye disease (DED) in an experimental mouse model. Methods Moxifloxacin-based fluorescence microscopy (MBFM) was used to examine GCs noninvasively in 56 mice. Forty-two (42) DED-induced mice were divided into 2 groups and treated topically for 14 days with cyclosporine (CsA) or normal saline (NS). In vivo MBFM imaging and clinical DED evaluations were performed and goblet cell density (GCD) and goblet cell area (GCA) were obtained and compared with histological GCD using periodic acid-Schiff (PAS) staining. Correlation and receiver operating characteristic (ROC) analyses showed MBFM's high diagnostic value. Results The GCD and GCA of the DED mice obtained from in vivo MBFM imaging were highly correlated with clinical DED parameters and GCD obtained from PAS histology. The therapeutic effect of CsA, as observed by in vivo MBFM, was significant with respect to that of NS treatment. The ROC curves derived from in vivo MBFM showed high diagnostic value in assessing DED. Conclusions The proposed noninvasive method has high diagnostic value in assessing the severity of DED and the effect of treatment for this disease. Translational Relevance A noninvasive imaging method using moxifloxacin-based fluorescence microscopy was evaluated for assessing DED in an experimental mouse model. The method showed high diagnostic value in assessing the severity of DED and the effect of treatment, bridging the gap between basic research and clinical treatment. The study provides a promising tool for diagnosing and monitoring DED.
Collapse
Affiliation(s)
- Jeongho Kim
- Bio-Medical Institute, Kyungpook National University Hospital, Jung-gu, Daegu, Republic of Korea
| | - Jungbin Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, Republic of Korea
| | - Seonghan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, Republic of Korea
| | - Sook Hyun Yoon
- Department of Ophthalmology, Daegu Catholic University School of Medicine, Nam-gu, Daegu, Republic of Korea
| | - Yeong Chae Jo
- Department of Ophthalmology, Maryknoll Medical Center, Jung-gu, Busan, Republic of Korea
| | - Ki Hean Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, Republic of Korea
| | - Hong Kyun Kim
- Bio-Medical Institute, Kyungpook National University Hospital, Jung-gu, Daegu, Republic of Korea
- Department of Ophthalmology, School of Medicine, Kyungpook National University, Jung-gu, Daegu, Republic of Korea
| |
Collapse
|