Automated Measurement of Cobblestone Morphology for Characterizing Stem Cell Derived Retinal Pigment Epithelial Cell Cultures

Abstract

Purpose: Assessing the morphologic properties of cells in microscopy images is an important task to evaluate cell health, identity, and purity. Typically, subjective visual assessments are accomplished by an experienced researcher. This subjective human step makes transfer of the evaluation process from the laboratory to the cell manufacturing facility difficult and time consuming.

Methods: Automated image analysis can provide rapid, objective measurements of cultured cells, greatly aiding manufacturing, regulatory, and research goals. Automated algorithms for classifying images based on appearance characteristics typically either extract features from the image and use those features for classification or use the images directly as input to the classification algorithm. In this study we have developed both feature and nonfeature extraction methods for automatically measuring "cobblestone" structure in human retinal pigment epithelial (RPE) cell cultures.

Results: A new approach using image compression combined with a Kolmogorov complexity-based distance metric enables robust classification of microscopy images of RPE cell cultures. The automated measurements corroborate determinations made by experienced cell biologists. We have also developed an approach for using steerable wavelet filters for extracting features to characterize the individual cellular junctions.

Conclusions: Two image analysis techniques enable robust and accurate characterization of the cobblestone morphology that is indicative of viable RPE cultures for therapeutic applications.

FIG. 1.

Illustrating the preprocessing and analysis steps. Phase and ZO-1 fluorescence microscopy images manually identified as category 5 (left). Analysis steps include SWFs and NCD. The SWF analysis first skeletonizes the image and finds the branch points (SWF panel, left). The filter (SWF panel, center) is evaluated at each branch point. The distribution of responses is shown as a box plot (SWF panel, right) with the red line indicating the median value, the blue box extending from the 25th through the 75th percentile, and data beyond the whiskers considered outliers. The NCD classifies each image against the training set. The NCD and SWF are complementary techniques that can be applied to either imaging modality. Scale bars indicate 50 μm. NCD, normalized compression distance; SWF, steerable wavelet filters.

FIG. 2.

Phase contrast RPE images. Each image is manually labeled into classes 1 (least cobblestone) through 5 (most cobblestone). Classification values detailed in Table 1. All 34 images were correctly classified using compression distance. Scale bar indicates 50 μm. RPE, retinal pigment epithelium.

FIG. 3.

SWFs detect cobblestone morphology. The SWF shape (number of arms), rotation, and scale are each controlled with a single parameter. A zoomed view of RPE cells with 3 SWFs overlaid. The relative response of each of the 3 filters encoded by color as they are rotated from 0° to 360°. This is shown for the maximum response of the red filter (left) and the minimum response for the red filter (right).

FIG. 4.

Cobblestone junction detection using SWFs. A preprocessed ZO-1 image (A) is skeletonized (B). Branch points on the skeleton are marked with yellow dots. The normalized SWF response at each branch point is indicated on the original image (C). Branch points whose response was below an empirically determined threshold are suppressed. Box plot showing distribution of the filter response for the fluorescence ZO-1 images pooled by manually labeled cobblestone class (D) shows SWF response has a statistically significant increase with each class.