RESEARCH

Forces are ubiquitous in life, and they directly impact the physiological functions of the cells. In response to externally applied forces like tension, shear, or electric field, cells alter their phenotypes and subsequently pathophysiological states. Our goal is to unravel, through quantitative experiments and analyses under simplified conditions, the physical mechanisms underlying fascinating cellular dynamics. Our lab’s primary research interests center around the emerging interdisciplinary mechanobiology and cell mechanics. We aim to find links between the physical stresses (tension, shear, electric field, or topology) and the pathophysiological responses of the cells in light of diagnostic and therapeutic applications.

I. Engineering tools for cell regulation

I.A Surface wettability dependent sorting of brain cells

Glial cells, including microglia, oligodendrocyte, and astrocytes, play an essential role in the development and progression of neurological diseases. The generation of in vitro models to study these neurological diseases first requires accurate and efficient isolation of target cells of interest. Our lab has established a novel cell sorting strategy by modulating the physical properties (i.e., wettability) of the culture substrate that led to the segregation of mixed glial cells based on their intrinsic adhesion characteristics. This novel protocol enables mixed-cells to be isolated quickly at a low cost while warranting a good purity compared to pre-existing conventional methods.

Current Research Projects and Collaborations

Project – RENEW: Rehabilitation Engineering for Neurological disorders Worldwide

Collaborations – 박형순, 김정 (KAIST,  Graduate School of Mechanical Engineering), 정재승 (KAIST,  Graduate Bio and Brain Engineering)

I.B Electric field-guided steering of epithelial monolayer

II. Disease model

II.A Cancer disease model

Many solid cancer tissues feature unique mechanical signatures that differentiate themselves from their healthy counterpart. These physical traits include stresses, material properties, and microarchitectures. Extensive in vitro data have shown how these mechanical concepts are bounded up with cancer progression. Still, it is tricky to recapitulate these concepts with conventional biology setups because they heavily rely upon a 2-dimensional plastic dish-based culture method that is not very consistent with the conditions of the human body.

In TheSHINLab, we adopt and improve 3-dimensional culture methods, taking account of the mechanical aspects of cancer cells. We build mini-tumors mimicking in vivo cellular interactions, and the resultant cancer models display enhanced motility, which is highly correlated with the deadly cancer metastasis. In the meanwhile, we don’t need any special cocktails containing hormones or growth factors.

Further, we evaluate our own models in the aspect of cell mechanics. Based on the mechanical engineering knowledge, we quantitatively measure the motile phenotypes(e.g., speed, directedness, collectivity) and traction forces in real-time, keeping the cells alive. For example, we can embed two different types of tumor spheroids in the tunable hydrogel and observe which one digs into surroundings more vigorously. The results, obtained in physiologically relevant setups, provides us the spatiotemporal information on cellular behaviors. And this information is more intuitive than the typical gene/protein expression assays, which often lead to contradictory interpretations.

By establishing cancer disease models and the quantifiable indices for them, we are dedicated to unraveling the mechanisms of cancer cell propagation. Glioblastoma, notorious for its extremely low five-year survival rate (8%), has its unique dynamics that cannot be explained by conventional epithelial-mesenchymal transition (EMT) theory. However, not much has been understood due to the hurdles of getting fully functioning GBM cells. In collaboration with KAIST GSME (의과학대학원), we are provided with grade IV GBM mouse model and establishing 3D in vitro cancer model. We are expecting to find key regulators of GBM invasion with this model.

In recent years, we are introducing deep learning techniques to enhance our cell mechanics indices and combining our study with the latest bioinformatics analysis.

To sum up, we fabricate cancer models on the basis of mechanobiology and again inspect their functionalities from the perspective of cell mechanics. Our goal is (i) to contribute to translational research by improving our models to function similarly to the patient cancer, and (2) to find mechanisms that effectively regulate the cancer cell motility to suggest a new candidate for cancer treatment strategy.

Current Research Projects and Collaborations

Projects – 교모세포종 침습제어 연구실 (Glioblastoma Invasion Regulation Laboratory)

Collaborations – 김인준, 구태윤, 김진국 (KAIST,  Graduate School of Medical Science and Engineering)

II.B Brain disease model

Current Research Projects and Collaborations

Project – RENEW: Rehabilitation Engineering for Neurological disorders Worldwide

Collaborations – 박형순, 김정 (KAIST,  Graduate School of Mechanical Engineering), 정재승 (KAIST,  Graduate Bio and Brain Engineering)

III. AI-based cell characterization

III.A  Automatic cell phenotyping

The complexity of studying cells stems from the fact that the cells which share the same genome show distinct phenotypes, represented by various morphology, gene expression, etc. Many efforts have been made to unravel this elusive characteristic using biochemical markers. However, the static property of molecular assays hinders the comprehensive understanding of dynamic fluctuation inherent in most cells. Single-cell morphology, which can allow holistic definition for the biological state of a cell, is a convenient measure to track in a time-dependent manner. The Shin lab is developing deep learning-based models with live-cell imaging technique which provides temporal information of cells to extract dynamical information of cells. This novel strategy bestows better interpretation than conventional static approaches and lays the groundwork for simplifying heterogeneous behaviors of cells.

Current Research Projects and Collaborations

Project – AI-based classification of phenotype and function of Cancer-associated fibroblasts for novel therapeutic strategy in pancreatic cancer, AI-based morphological classification of heterogeneous populations in breast cancer cells

Collaborations – 양세정(Yonsei University)