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Objective automated quantification of fluorescence signal in histological sections of rat lens
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Ophthalmology.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Ophthalmology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Ophthalmology.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Purpose: To develop an automated method to delineate lens epithelial cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section.

Methods: An automated algorithm was developed in Matlab™ to localize and quantify fluorescence signal in lens epithelial cells in histological images. A region of interest representing the lens epithelium was manually demarcated in each input image. Individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding. Fluorescence signal was quantified within nuclei and cytoplasms. The classification of fluorescence signal was based on local background. Classification of cells as labelled or not labelled was thereafter optimized as compared to visual classification of a limited dataset.

The performance of the automated classification was evaluated by asking eleven independent blinded observers to classify all cells (n=395) in one lens image. Time consumed by the automatic algorithm and visual /manual classification of nuclei, was recorded.

Results: On an average, 77 % of the cells were correctly classified as compared to the majority vote of the visual observers. The average agreement among visual observers was 83 %. However, variation among visual observers was high, and agreement between two visual observers was as low as 71 % in the worst case. Automated classification was on average 10 times faster than manual scoring.

Conclusion: The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal in a histological section of rat lens, with accuracy comparable to the variability between different visual observers. Furthermore, automated scoring is unbiased and reproducible, and results in a 10-fold increase in throughput.

Keyword [en]
Automatic analysis, cell counting, image analysis, lens epithelium, fluorescence
National Category
Neurosciences
Research subject
Ophtalmology
Identifiers
URN: urn:nbn:se:uu:diva-268312OAI: oai:DiVA.org:uu-268312DiVA: diva2:876457
Available from: 2015-12-03 Created: 2015-12-03 Last updated: 2017-01-25Bibliographically approved
In thesis
1. Caspase-3 in lens epithelium
Open this publication in new window or tab >>Caspase-3 in lens epithelium
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Purpose: To model the time evolution of active caspase-3 protein expression in a healthy lens, and in a lens exposed to UVR-300 nm (UVR-B). To develop an automated method to classify the fluorescent signal of biomarkers in the lens epithelial cells.

Methods: Six-week old Sprague-Dawley rats were used. Firstly, expression of active caspase-3 was studied in the lens epithelium of healthy rats. Secondly, rats were unilaterally exposed in vivo to 1 kJ/m2 UVR-B for 15 minutes. At 0.5, 8, 16, and 24 hours after the UVR-B exposure, the exposed and the contralateral non-exposed lenses were removed. Immunohistochemistry was done on three mid-sagittal sections from each lens. The florescent labelling for active caspase-3 in each lens section was counted three times. The time evolution of active caspase-3 expression in response to UVR-B exposure was modelled as a function of cell position in the lens epithelium. An automated objective method was developed to quantify the lens epithelial cells and to classify the fluorescent signal of active caspase-3. Active caspase-3 was selected as a model signal.

Results: Active caspase-3 was abundant in the anterior pole of the normal lenses. Spatial distribution of active caspase-3 labelling in the lens epithelium was fitted to a logistic model. The probability of active caspase-3 expression was higher in the UVR-B exposed lenses (95% CI = 0.12 ± 0.01). There was no difference in the expression of active caspase-3 between the 0.5 and the 24 hours groups or between the 8 and the 16 hours groups. A difference was noted, when comparing the 0.5 and 24 hours groups with the 8 and 16 hours groups (Test statistic 7.01, F1;36;0.95= 4.11). Exposure to UVR-B has an impact on the average probability of labelling for active caspase-3 as a function of cell position. The probability of labelling as a function of cell number also varied as a function of time after UVR-B exposure. The automated method counted the lens epithelial cells and estimated the proportion of active caspase-3 labelling in the lens epithelium.

Conclusions: Active caspase-3 is present in the healthy lens epithelial cells. Active caspase-3 exhibits higher expression at the anterior pole of the lens and the expression decreases towards the periphery. After UVR-B exposure, the expression of active caspase-3 in the lens epithelium increases with a peak of expression occurring around 16 hours after exposure. The average probability of labelling in the lens epithelium is dependent on both the UVR-B exposure and the time period elapsed after the exposure. The automated method enables objective and fast quantification of lens epithelial cells and the expression of fluorescent signal in the lens cells.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 40 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1168
Keyword
ultraviolet radiation, caspase-3, lens, cataract, apoptosis, Immunohistochemistry, spatial distribution, time evolution, modelling, automatic analysis, cell counting, image analysis.
National Category
Neurosciences
Research subject
Ophtalmology
Identifiers
urn:nbn:se:uu:diva-267543 (URN)978-91-554-9436-0 (ISBN)
Public defence
2016-02-05, Enghoffsalen, entrance 50, 1st floor, Akademiska Sjukhuset, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2016-01-21 Created: 2015-11-24 Last updated: 2016-02-12Bibliographically approved

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