Morphological characterization of the human corneal epithelium by in vivo confocal laser scanning microscopy

Background

Regarding the growing interest and importance of understanding the cellular changes of the cornea in diseases, a quantitative cellular characterization of the epithelium is becoming increasingly important. Towards this, the latest research offers considerable improvements in imaging of the cornea by confocal laser scanning microscopy (CLSM). This study presents a pipeline to generate normative morphological data of epithelial cell layers of healthy human corneas.

Methods

3D in vivo CLSM was performed on the eyes of volunteers (n=25) with a Heidelberg Retina Tomograph II equipped with an in-house modified version of the Rostock Cornea Module implementing two dedicated piezo actuators and a concave contact cap. Image data were acquired with nearly isotropic voxel resolution. After image registration, stacks of en-face sections were used to generate full-thickness volume data sets of the epithelium. Beyond that, an image analysis algorithm quantified en-face sections of epithelial cells regarding the depth-dependent mean of cell density, area, diameter, aggregation (Clark and Evans index of aggregation), neighbor count and polygonality.

Results

Imaging and cell segmentation were successfully performed in all subjects. Thereby intermediated cells were efficiently recognized by the segmentation algorithm while efficiency for superficial and basal cells was reduced. Morphological parameters showed an increased mean cell density, decreased mean cell area and mean diameter from anterior to posterior (5,197.02 to 8,190.39 cells/mm²; 160.51 to 90.29 µm²; 15.9 to 12.3 µm respectively). Aggregation gradually increased from anterior to posterior ranging from 1.45 to 1.53. Average neighbor count increased from 5.50 to a maximum of 5.66 followed by a gradual decrease to 5.45 within the normalized depth from anterior to posterior. Polygonality gradually decreased ranging from 4.93 to 4.64 sides of cells. The neighbor count and polygonality parameters exhibited profound depth-dependent changes.

Conclusions

This in vivo study demonstrates the successful implementation of a CLSM-based imaging pipeline for cellular characterization of the human corneal epithelium. The dedicated hardware in combination with an adapted image registration method to correct the remaining motion-induced image distortions followed by a dedicated algorithm to calculate characteristic quantities of different epithelial cell layers enabled the generation of normative data. Further significant effort is necessary to improve the algorithm for superficial and basal cell segmentation.

Automatic three-dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images

Time-lapse confocal fluorescence microscopy images from mouse embryonic stem cells (ESCs) carrying reporter genes, histone H2B-mCherry and Mvh-Venus, have been used to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. Accurate cell nucleus segmentation is required to analyse the ESC dynamics and differentiation at a single cell resolution. Several methods used concavities on nucleus contours to segment overlapping cell nuclei. Our proposed method evaluates not only the concavities but also the size and shape of every 2D nucleus region to determine if any of the strait, extrusion, convexity and large diameter criteria is satisfied to segment overlapping nuclei inside the region. We then use a 3D segmentation method to reconstruct simple, convex, and reasonably sized 3D nuclei along the image stacking direction using the radius and centre of every segmented region in respective microscopy images. To avoid false concavities on nucleus boundaries, fluorescence images of the H2B-mCherry reporter are used for localisation of cell nuclei and Venus fluorescence images are used for determining the cell colony ranges. We use a series of image preprocessing procedures to remove noise outside and inside cell colonies, and in respective nuclei, and to smooth nucleus boundaries based on the colony ranges.  We propose dynamic data structures to record every segmented nucleus region and solid in sets (volumes) of 3D confocal images. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei on microscopy images and that the segmentation method effectively segment out every nucleus with a reasonable size and shape. All 3D nuclei in a set (volume) of confocal microscopy images can be accessed by the dynamic data structures for 3D reconstruction. The 3D nuclei in time-lapse confocal microscopy images can be tracked to calculate cell movement and proliferation in consecutive volumes for understanding the dynamics of the differentiation characteristics about ESCs. LAY DESCRIPTION: Embryonic stem cells (ESCs) are considered as an ideal source for basic cell biology study and producing medically useful cells in vitro. This study uses time-lapse confocal fluorescence microscopy images from mouse ESCs carrying reporter gene to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. To automate analyses of ESC differentiation behaviours, accurate cell nucleus segmentation to distinguish respective cells are required. A series of image preprocessing procedures are implemented to remove noise in live-cell fluorescence images but yield overlapping cell nuclei. A segmentation method that evaluates boundary concavities and the size and shape of every nucleus is then used to determine if any of the strait, extrusion, convexity, large and local minimum diameter criteria satisfied to segment overlapping nuclei. We propose a dynamic data structure to record every newly segmented nucleus. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei and that the segmentation method effectively detects overlapping nuclei. All segmented nuclei in confocal images can be accessed using the dynamic data structures to be visualised and manipulated for quantitative analyses of the ESC differentiation behaviours. The manipulation can be tracking of segmented 3D cell nuclei in time-lapse images to calculate their dynamics of differentiation characteristics.

A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models

This paper presents a novel computer vision algorithm to analyze 3D stacks of confocal images of fluorescently stained single cells. The goal of the algorithm is to create representative in silico model structures that can be imported into finite element analysis software for mechanical characterization. Segmentation of cell and nucleus boundaries is accomplished via standard thresholding methods. Using novel linear programming methods, a representative actin stress fiber network is generated by computing a linear superposition of fibers having minimum discrepancy compared with an experimental 3D confocal image. Qualitative validation is performed through analysis of seven 3D confocal image stacks of adherent vascular smooth muscle cells (VSMCs) grown in 2D culture. The presented method is able to automatically generate 3D geometries of the cell's boundary, nucleus, and representative F-actin network based on standard cell microscopy data. These geometries can be used for direct importation and implementation in structural finite element models for analysis of the mechanics of a single cell to potentially speed discoveries in the fields of regenerative medicine, mechanobiology, and drug discovery.