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.

Molecular profiling of stem cell-derived retinal pigment epithelial cell differentiation established for clinical translation

“…We profiled differentiation of one research and two clinical grade cell lines (HS980, KARO1, and E1C3, respectively) at six time points (day 7 [D7], D14, D30, D38, D45, and D60; Table S1 ). Morphological evaluation using cobblestone junction scores confirmed that changes in cell shape and size followed differentiation as cells progressively assumed a tighter cobblestone monolayer of pigmented cells ( Joshi et al., 2016 ) ( Figures 1 B, S1 A, and S1B).

Figure 1 Global scRNA-seq characterization of hESC-RPE differentiation trajectory (A) Schematic of the hESC-RPE differentiation experimental protocol where scRNA-seq was performed at the seven time points (bolded; D, day) in three cell lines: HS980, KARO1, and E1C3.

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Web Similarity in Sets of Search Terms Using Database Queries

“…Rather than using a combination of simple pairwise information distances (NGD's), the spectral approach [26] constructs a representation of the objects being clustered using an eigen decomposition. In previous work, we have found such spectral approaches to be most accurate when working with compression-based distance measures [7,8,12]. Mapping from clusters to classes for the pairwise analysis is done following the spectral clustering step by using a majority vote.…”

Machine learning applications in cell image analysis

“…Consider a different example, where ML is applied to whole images. Joshi and colleagues categorised images of cultured stem cells as they differentiate into retinal pigment epithelium 46 . Automated categorisation of cultures according to the maturation level has applications in stem cell replacement therapy for conditions associated with loss of retinal pigment epithelium.…”

“…Joshi and colleagues categorised images of cultured stem cells as they differentiate into retinal pigment epithelium. 46 Automated categorisation of cultures according to the maturation level has Normalised compression distance 46 Normalised compression distance; 47 support vector machines 37 Unsupervised learning Instances without labels Non-negative matrix factorisation 13 Otsu thresholding 36 viSNE 53 Probabilistic graphical models 55 AL Interactively request labels AL for improving cell tracking 49 AL strategies for high-content screening 51…”