Translational Mechanobiology Lab

Vision.

We share the VISION of EDUCATING ambitious young scientists and engineers to make impacts beyond individual efforts through team projects and collaborative learning in academia and industry.

Research Mission

Understand and control microenvironment impact on chronic liver disease progression. We TRANSLATE technologies and knowledges into solutions for drug development, diagnostics and therapeutics.

Research Goals

Quantitative analysis of the dynamic process of liver regeneration and chronic liver diseases.

Investigating the contraction and propagation mechanism of secretory lumen such as bile canaliculi.

Developing novel and useful biomaterials, cell sources, and analytics for long-term maintenance of highly functional epithelial cells in culture.

Developing robust, scalable, low cost and predictive in vitro drug and toxin testing platforms.

Shared Values

Respect : every TMBL member is important and yet consciously sensitive to other members and the collective impacts. Decisions incorporate inputs from all the stakeholders for fairness and transparency.

Professionalism: every TMBL member strives to attain ever higher quality and standard of her/his own work through mutual empowerment, critique and support to each other.

No-walls culture: solutions to real-life problems can never be confined within artificially-created boundaries (organizational, disciplinary, cultural, inter-personal, or mental inertia).

Featured Book

Featured Recent Publications

Tasnim, F., Xing, J., Huang, X., Mo, S., Wei, X., Tan, M.-H. and Yu, H. (2019) Generation of Mature Kupffer Cells from Human Induced Pluripotent Stem Cells. Biomaterials, February 2019; 192: 337-391. DOI: 10.1016/j.biomaterials.2018.11.016

Liver macrophages, Kupffer cells (KCs), play a critical role in drug-induced liver injury (DILI) and liver diseases including cholestasis, liver fibrosis and viral hepatitis. Application of KCs in in vitro models of DILI and liver diseases is hindered due to limited source of human KCs. In vivo, KCs originate from MYB-independent macrophage progenitors, which differentiate into liver-specific macrophages in response to hepatic cues in the liver. Here, we recapitulated KCs ontogeny by differentiation of MYB-independent iPSCs to macrophage-precursors and exposing them to hepatic cues to generate iPSC-derived KCs (iKCs). iKCs expressed macrophage markers (CD11/CD14/CD68/CD163/CD32) at 0.3-5 folds of primary adult human KCs (pKCs) and KC-specific CLEC-4F, ID1 and ID3. iKCs phagocytosed and secreted IL-6 and TNFα upon stimulation at levels similar to pKCs but different from non-liver macrophages. Hepatocyte-iKCs co-culture model was more sensitive in detecting hepatotoxicity induced by inflammation-associated drugs, Acetaminophen and Trovafloxacin, and Chlorpromazine-induced cholestasis when compared to hepatocytes alone. Overall, iKCs were mature, liver-specific and functional. Furthermore, donor-matched iKCs and iPSC-hepatocyte co-culture exhibited minimal non-specific background response compared to donor-mismatched counterpart. iKCs offer a mature renewable human cell source for liver-specific macrophages, useful in developing in vitro model to study DILI and liver diseases such as cholestasis.

Yu, Y., Wang, J., Ng, C.W., Ma, Y., Mo, S., Fong, L.S.E., Xing, J., Song, Z., Xie, Y., Si, K., Wee, A., Welsch, R.E., So, P.T.C., and Yu, H. (2018) Deep learning enhances automated scoring of liver fibrosis stages. Scientific Reports, 30 October 2018; 8: 16016. DOI: 10.1038/s41598-018-34300-2

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.

Caldez, M.J., Hul, N.V., Koh, H.W.L., Teo, X.Q., Fan, J.J., Tan, P.Y., Dewhurst, M.R., Too, P.G., Talib, S.Z.A., Chiang, B.E., Fuhrer, T., Sauer, U., Stunkel, W., Yu, H., Lee, P., Choi, H., Björklund, M., and Kaldis, P. (2018) Metabolic remodeling during liver regeneration. Developmental Cell, 19 November 2018; 47(4): 425-438. DOI: 10.1016/j.devcel.2018.09.020

Liver disease is linked to a decreased capacity of hepatocytes to divide. In addition, cellular metabolism is important for tissue homeostasis and regeneration. Since metabolic changes are a hallmark of liver disease, we investigated the connections between metabolism and cell division. We determined global metabolic changes at different stages of liver regeneration using a combination of integrated transcriptomic and metabolomic analyses with advanced functional redox in vivo imaging. Our data indicate that blocking hepatocyte division during regeneration leads to mitochondrial dysfunction and downregulation of oxidative pathways. This resulted in an increased redox ratio and hyperactivity of alanine transaminase allowing the production of alanine and α-ketoglutarate from pyruvate when mitochondrial functions are impaired. Our data suggests that during liver regeneration, cell division leads to hepatic metabolic remodeling. Moreover, we demonstrate that hepatocytes are equipped with a flexible metabolic machinery able to adapt dynamically to changes during tissue regeneration.