Cancer cells within tumors often exist in highly heterogeneous phenotypic states, which is a major challenge in cancer therapeutics. Although there have been great advances in studying cellular heterogeneity and discovering hidden phenotypes, little effort has been made in achieving mechanistic understandings of each phenotype in heterogeneous cell populations in living cells. Most of our understandings of cell biological processes are based on the ensemble average of experimental measurements drawn from cell population. However, biological systems are inherently heterogeneous even in isogenic cell cultures. One of the prominent examples of cellular/subcellular heterogeneity is cell protrusion, driven by actin polymerization pushing the leading edge plasma membrane. Actin cytoskeleton is a highly dynamic complex system which integrates numerous biochemical and mechanical signals. Hundreds of actin regulators collectively organize actin structures, which are spatiotemporally heterogeneous in micron length and minute time scales. Due to this heterogeneous issues, experimental outcomes are often context-dependent and difficult to interpret. Thus, it is necessary to develop a new quantitative live cell imaging method which allows us to achieve distinct molecular understandings of each cellular phenotype from heterogeneous cell population. Therefore, the goal of this project is to deconvolve the heterogeneity of cell protrusion to characterize each cellular/subcellular protrusive phenotype by combining quantitative live cell imaging and machine learning analysis, and we will determine the coordination of distinct sets of actin regulators with respect to each protrusive phenotype. This will lead to mechanistic understanding specific to each protrusive phenotype. The central hypothesis in this project is that heterogeneous cell protrusions are driven by the distinct sets of spatiotemporal coordinated actin regulators and traction forces at the leading edge. We will Evaluate how the coordination of actin regulators at the leading edge generates heterogeneous multiple protrusions (Aim 1). We will Evaluate how actin organization at the leading edge during the retraction generates reinforced multiple cell protrusion (Aim 2). Finally, we will determine how the traction forces at the leading edge play roles in heterogeneous cell protrusions (Aim 3). It is expected that the experimental and computational imaging tools developed in this project can be applied to many complex cytoskeleton related processes including cellular morphogenesis by revealing a wide range of molecular and cellular coordination at various length and time scales. We also anticipate the new insight on cellular/subcellular heterogeneity in cell protrusion will allow us to better understand the heterogeneity of cancer metastasis, where only small number of tumor cells metastasize.
This proposed project will be able to contribute to public health because new insight on the heterogeneity of cell protrusion will shed light on heterogeneity of cancer metastasis. This will allow us to find better therapeutic targets for more aggressive metastatic cells. The quantitative live cell imaging method developed in this project will enable the biomedical research community to tackle cellular heterogeneity to find more specific mechanism of specific phenotype.
| Wang, Chuangqi; Choi, Hee June; Kim, Sung-Jin et al. (2018) Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging. Nat Commun 9:1688 |
