Development of Human Stem Cell-Based Model for Developmental Toxicity Testing

Ph.DPH.D. IN MECHANOBIOLOGY (FOS

A method for human teratogen detection by geometrically confined cell differentiation and migration

Unintended exposure to teratogenic compounds can lead to various birth defects; however current animal-based testing is limited by time, cost and high inter-species variability. Here, we developed a human-relevant in vitro model, which recapitulated two cellular events characteristic of embryogenesis, to identify potentially teratogenic compounds. We spatially directed mesoendoderm differentiation, epithelial-mesenchymal transition and the ensuing cell migration in micropatterned human pluripotent stem cell (hPSC) colonies to collectively form an annular mesoendoderm pattern. Teratogens could disrupt the two cellular processes to alter the morphology of the mesoendoderm pattern. Image processing and statistical algorithms were developed to quantify and classify the compounds’ teratogenic potential. We not only could measure dose-dependent effects but also correctly classify species-specific drug (Thalidomide) and false negative drug (D-penicillamine) in the conventional mouse embryonic stem cell test. This model offers a scalable screening platform to mitigate the risks of teratogen exposures in human.Singapore. Agency for Science, Technology and ResearchJanssen Pharmaceutical Ltd. (Grant R-185-000-182-592)Janssen Pharmaceutical Ltd. (Grant R-185-000-228-592)Singapore-MIT Alliance Computational and Systems Biology Flagship Project (C-382-641-001-091)Mechanobiology Institute, Singapore (R-714-001-003-271

Modulation of integrin and E-cadherin-mediated adhesions to spatially control heterogeneity in human pluripotent stem cell differentiation

Heterogeneity in human pluripotent stem cell (PSC) fates is partially caused by mechanical asymmetry arising from spatial polarization of cell-cell and cell-matrix adhesions. Independent studies have shown that integrin and E-cadherin adhesions promote opposing differentiation and pluripotent fates respectively although their crosstalk mechanism in modulating cell fate heterogeneity remains unknown. Here, we demonstrated that spatial polarization of integrin and E-cadherin adhesions in a human PSC colony compete to recruit Rho-ROCK activated myosin II to different localities to pattern pluripotent-differentiation decisions, resulting in spatially heterogeneous colonies. Cell micropatterning was used to modulate the spatial polarization of cell adhesions, which enabled us to prospectively determine localization patterns of activated myosin II and mesoendoderm differentiation. Direct inhibition of Rho-ROCK-myosin II activation phenocopied E-cadherin rather than integrin inhibition to form uniformly differentiated colonies. This indicated that E-cadherin was the primary gatekeeper to differentiation progression. This insight allows for biomaterials to be tailored for human PSC maintenance or differentiation with minimal heterogeneity

Robust 2D Otsu’s Algorithm for Uneven Illumination Image Segmentation

Otsu’s algorithm is one of the most well-known methods for automatic image thresholding. 2D Otsu’s method is more robust compared to 1D Otsu’s method. However, it still has limitations on salt-and-pepper noise corrupted images and uneven illumination images. To alleviate these limitations and improve the overall performance, here we propose an improved 2D Otsu’s algorithm to increase the robustness to salt-and-pepper noise together with an adaptive energy based image partition technology for uneven illumination image segmentation. Based on the partition method, two schemes for automatic thresholding are adopted to find the best segmentation result. Experiments are conducted on both synthetic and real world uneven illumination images as well as real world regular illumination cell images. Original 2D Otsu’s method, MAOTSU_2D, and two latest 1D Otsu’s methods (Cao’s method and DVE) are included for comparisons. Both qualitative and quantitative evaluations are introduced to verify the effectiveness of the proposed method. Results show that the proposed method is more robust to salt-and-pepper noise and acquires better segmentation results on uneven illumination images in general without compromising its performance on regular illumination images. For a test group of seven real world uneven illumination images, the proposed method could lower the ME value by 15% and increase the DSC value by 10%

Evaluating Nanoparticles in Preclinical Research Using Microfluidic Systems

Nanoparticles (NPs) have found a wide range of applications in clinical therapeutic and diagnostic fields. However, currently most NPs are still in the preclinical evaluation phase with few approved for clinical use. Microfluidic systems can simulate dynamic fluid flows, chemical gradients, partitioning of multi-organs as well as local microenvironment controls, offering an efficient and cost-effective opportunity to fast screen NPs in physiologically relevant conditions. Here, in this review, we are focusing on summarizing key microfluidic platforms promising to mimic in vivo situations and test the performance of fabricated nanoparticles. Firstly, we summarize the key evaluation parameters of NPs which can affect their delivery efficacy, followed by highlighting the importance of microfluidic-based NP evaluation. Next, we will summarize main microfluidic systems effective in evaluating NP haemocompatibility, transport, uptake and toxicity, targeted accumulation and general efficacy respectively, and discuss the future directions for NP evaluation in microfluidic systems. The combination of nanoparticles and microfluidic technologies could greatly facilitate the development of drug delivery strategies and provide novel treatments and diagnostic techniques for clinically challenging diseases

A micropatterned human embryonic stem cell model for in vitro human developmental toxicity testing

Human embryonic stem cell (hESC)-based developmental toxicity models can help to reduce animal testing and mitigate inter-species variability. However, current hESC models do not fully recapitulate embryonic developmental structural changes, which are underpinned by spatio-temporally controlled differentiation and migration events. We show that mechanical polarization imposed by cell micropatterning could spatially pattern hESC differentiation and migration, and in turn reconstituted a rudimentary structural motif. Features of this structural motif were disrupted by paradigm developmental toxins but not a negative control drug. This novel micropatterned hESC model can potentially be scaled- up into an array format for high throughput developmental toxicity screening

In Vitro Micropatterned Human Pluripotent Stem Cell Test (μP-hPST) for Morphometric-Based Teratogen Screening

10.1038/s41598-017-09178-1Scientific Reports71Article number 849

Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment

10.1016/j.crtox.2021.07.003Current Research in Toxicology2282-29

Deep learning enables automated scoring of liver fibrosis stages

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated