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.
Understand and control microenvironment impact on chronic liver disease progression. We TRANSLATE technologies and knowledges into solutions for drug development, diagnostics and therapeutics.
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.
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).
Sheetz, M., and Yu, H. (2018) The Cell as a Machine, Cambridge University Press, ISBN: 9781107052734.
This unique introductory text has been written by a Lasker Award winning cell biologist; and his PhD student who turned into a biomedical engineer who innovates multiple technologies into award winning companies. It is based on the content and to serve as the text for a course, "Cell as a Machine”, offered by leading universities in the USA and Asia. The course started in 2003 as W3150/4150 in Columbia University, and then taught as course 2.799 in Massachusetts Institute of Technology, and MB5101 in National University of Singapore since 2009, which was extended in different years to Penn, Georgia Tech, UIUC, UC Berkeley/San Diego/Irvine/Davis/Merced, City College of New York, Minnesota, Wash U St Louis, Wisconsin, Boston U and Texas A&M in the US, Hong Kong UST and Zhejiang University in Asia.
The text explains cell functions using the engineering principles of robust devices. Adopting a process-based approach to understanding cell and tissue biology, it describes the molecular and mechanical features that enable the cell to be robust in operating its various components, and explores the ways in which molecular modules respond to environmental signals to execute complex functions. The design and operation of a variety of complex functions are covered, including engineering lipid bilayers to provide fluid boundaries and mechanical controls, adjusting cell shape and forces with dynamic filament networks, and DNA packaging for information retrieval and propagation. Numerous problems, case studies and application examples help readers connect theory with practice, and solutions for instructors and videos of lectures accompany the book online. Assuming only basic mathematical knowledge, this is an invaluable resource for graduate and senior undergraduate students taking single-semester courses in cell mechanics, biophysics, mechanobiology, and cell/tissue biology from a fresh perspective.
Uniquely links together the biology, biophysics, and engineering principles underlying cell functions
Avoids complex mathematical treatments, making it accessible to students with only a basic mathematical background
Additional information about many of the functions described in the book can be found online at www.mechanobio.info
Liu, Y.P., Raymond, L.L., Wu, X.L., Chua, P.W.L., Ling, S.Y.H., CHan, C.C., Chan, C.R.Y., Loh, J.X.Y., Song, M.X.Y., Ong, M.Y.Y., Ho, P.Y., Mcbee, M.E., Springs, S.L., Yu, H., and Han, J.Y. (2024) Electrostatic microfiltration (EM) enriches and recovers viable microorganisms at low-abundance in large-volume samples and enhances downstream detection. Lab on a Chip, 10 September 2024; 24(18):4275-4287. doi: 10.1039/D4LC00419A.
Rapid and sensitive detection of pathogens in various samples is crucial for disease diagnosis, environmental surveillance, as well as food and water safety monitoring. However, the low abundance of pathogens (<10 CFU) in large volume (1 mL-1 L) samples containing vast backgrounds critically limits the sensitivity of even the most advanced techniques, such as digital PCR. Therefore, there is a critical need for sample preparation that can enrich low-abundance pathogens from complex and large-volume samples. This study develops an efficient electrostatic microfiltration (EM)-based sample preparation technique capable of processing ultra-large-volume (≥500 mL) samples at high throughput (≥10 mL min-1). This approach achieves a significant enrichment (>8000×) of extremely-low-abundance pathogens (down to level of 0.02 CFU mL-1, i.e., 10 CFU in 500 mL). Furthermore, EM-enabled sample preparation facilitates digital amplification techniques sensitively detecting broad pathogens, including bacteria, fungi, and viruses from various samples, in a rapid (≤3 h) sample-to-result workflow. Notably, the operational ease, portability, and compatibility/integrability with various downstream detection platforms highlight its great potential for widespread applications across diverse settings.
Jiang, X., Wu, Y., Cheng, J. Qi, J., Hang, C., Dong, R., Low, B.C., and Hanry Yu (2024) Three-dimensional liquid metal-based neuro-interfaces for human hippocampal organoids. Nature Communications, 15 May 2024; 15: 4047. doi: 10.1038/s41467-024-48452-5.
Human hippocampal organoids (hHOs) derived from human induced pluripotent stem cells (hiPSCs) have emerged as promising models for investigating neurodegenerative disorders, such as schizophrenia and Alzheimer’s disease. However, obtaining the electrical information of these free-floating organoids in a noninvasive manner remains a challenge using commercial multi-electrode arrays (MEAs). The three-dimensional (3D) MEAs developed recently acquired only a few neural signals due to limited channel numbers. Here, we report a hippocampal cyborg organoid (cyb-organoid) platform coupling a liquid metal-polymer conductor (MPC)-based mesh neuro-interface with hHOs. The mesh MPC (mMPC) integrates 128-channel multielectrode arrays distributed on a small surface area (~2*2 mm). Stretchability (up to 500%) and flexibility of the mMPC enable its attachment to hHOs. Furthermore, we show that under Wnt3a and SHH activator induction, hHOs produce HOPX+ and PAX6+ progenitors and ZBTB20+PROX1+ dentate gyrus (DG) granule neurons. The transcriptomic signatures of hHOs reveal high similarity to the developing human hippocampus. We successfully detect neural activities from hHOs via the mMPC from this cyb-organoid. Compared with traditional planar devices, our non-invasive coupling offers an adaptor for recording neural signals from 3D models.
Luo, X., Wang, J., Tan, C., Dou, Q., Han, Z., Wang, Z., Tasnim, F., Wang, X., Zhan, Q., Li, X., Zhou, Q., Cheng, J., Liao, F., Yip, H.C., Jiang, J., Tan, R.T., Liu, S., and Yu, H. (2024) Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases with an Artificial Intelligence Contextual Framework.Gastroenterology, 4 April 2024; S0016-5085(24)00365-2. doi: 10.1053/j.gastro.2024.03.039.
Background & Aims
Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis (UC), Crohn’s disease (CD), ischemic colitis (IC) and intestinal tuberculosis (ITB) share similar phenotypes with different etiologies and treatment strategies. To accurately diagnose closely-related diseases like UCDs, we hypothesize that contextual learning is critical in enhancing the ability of the artificial intelligence models to differentiate the subtle differences in lesions amidst the vastly divergent spatial contexts.
Methods
White light colonoscopy datasets of patients with confirmed UCDs and normal controls were retrospectively collected. We developed a Multi-class Contextual Classification (MCC) model that can differentiate among the mentioned UCDs and normal controls by incorporating the tissue object contexts surrounding the individual lesion region in a scene and spatial information from other endoscopic frames (video-level) into a unified framework. Internal and external datasets were used to validate the model’s performance.
Results
Training datasets included 762 patients, and the internal and external testing cohorts included 257 patients and 293 patients, respectively. Our MCC model provided a rapid reference diagnosis on internal test sets with a high averaged AUROC (image level: 0.950 and video-level: 0.973) and balanced accuracy (image-level: 76.1% and video-level: 80.8%), which was superior to junior endoscopists (accuracy: 71.8%, p<0.0001) and similar to experts (accuracy: 79.7%, p=0.732). The MCC model achieved a AUROC of 0.988 and balanced accuracy of 85.8% using external testing datasets.
Conclusions
These results enable this model to fit in the routine endoscopic workflow, and the contextual framework to be adopted for diagnosing other closely-related diseases.
