Laboratories and faculty

Molecular Medicine and Cell Biology

Prof. Suetsugu Assoc.Prof. Nishimura
SUETSUGU Shiro mailアイコン
Associate Professor
NISHIMURA Tamako mailアイコン
Assistant Professor
KAWANA Hiroki mailアイコン
Labs HP

Outline of Research and Education

The cell membrane separates the inside and outside of the cell and is an indispensable structure for the cell to become a life. The cell membrane appears to be important for receiving the various stimuli that the cell receives. However, much remains unclear as to what combination of cell membranes and their binding proteins can shape cells and enable them to respond to stimuli. Our laboratory studies membrane-binding proteins that connect cell membranes to intracellular signaling in proliferation and morphological changes. The properties of the lipid molecules that make up the cell membrane are also investigated using membrane-binding proteins. Furthermore, using images showing the morphology of cells and images showing the localization of the above proteins, analysis of cell behavior, which is a change in cell morphology, is performed by deep learning.

Major Research Topics

Intracellular signaling depending on the morphology of the cell membrane and the proteins that form the morphology of the cell membrane, especially the association with cancer of the cell

Cell morphological changes accompany important diseases such as cancer formation and various diseases. The same applies to cell differentiation and reprogramming. However, it is still unclear how the binding proteins of the lipid membrane are altered or regulated in the formation of organelles such as clathrin-coated pores, caveolae, filopodia, lamellipodia, and podosomes. We will clarify the role of the actin cytoskeleton and the membrane sculpting proteins, including the BAR domain-containing proteins, as well as lipid membranes in cell structure construction and intracellular signaling. In vitro reconstitution of membrane morphogenesis by membrane-binding proteins will be performed, and then the correlation between the reconstitution and the cell function will be investigated. Through such research, for example, we discovered the formation mechanism of extracellular vesicles that mediates intracellular communications.

Cell morphological analysis by deep learning (AI)

Advances in image analysis technology show new possibilities in the analysis of cell morphology. For example, there are possibilities such as cell selection by automatic recognition of cell structure and prediction of future behavior of cells by analysis of images. We will discover new knowledge by deep learning using cell images such as the above proteins that form the morphology of cells acquired in the laboratory.

Fig. 1  Examples of proteins (membrane-binding proteins such as actin, BAR domain, F-BAR domain, and I-BAR domain) and lipids that are responsible for cell morphogenesis targeted in the laboratory (from Suetsugu et al., Phys Rev 2014) ).
The BAR domain acts as a polymer of protrusions (including filamentous and lamellipodia) and submicron scale invagination (eg, clathrin-coated holes and caveolae) to form microstructures. Typical sizes for clathrin-coated pores and caveolae are 100-200 nm in diameter and 50-100 nm in diameter, respectively. The BAR domain can be approximated as a 20-25 nm arc with a diameter of 3-6 nm. The thickness of the membrane is approximately 5 nm.
Fig. 2 Formation of extracellular fine particles (extracellular vesicles) by cutting cell processes. We discovered that the cell processes formed by the I-BAR domain are cleaved into extracellular microparticles that change the behavior of receptor cells. (Nishimura, T. et al., Dev. Cell, 2021)
Fig. 3 AI-based prediction of localization of actin-linked proteins from that of the actin cytoskeleton. Models based on the relationships between images by deep learning have been found to be able to predict the localization of focal adhesion proteins from that of acin ctytoskeleton (Shigene et al., Front. Cell). Dev Biol., 2021) (Joint research with Professor Yoshinobu Sato in Division ofinformation science)


  1. Sim PF., et al., J Biochem, 175, 57-67, 2023
  2. Wan Mohamad Noor, WNI., et al., Sci Adv., 9, eadf5143, 2023
  3. Mukherjee, A. et al., Advanced Science (Weinh), 10, e2207368, 2023
  4. D'Angelo, G. et al., Nature Reviews Molecular Cell Biology, 24, 81-82, 2023
  5. Nishimura, T. el al., PLoS One., 17, e0271003, 2022
  6. Osuga, M. et al., Molecular Biology of the Cell, mbcE21010044, 2021
  7. Shigene, K., et al., Frontiers in Cell and Developmental Biology, 9, 635231, 2021
  8. Hu, HT., et al., STAR Protocols, 2, 100625, 2021
  9. Snider, CE. et al., Trends in Cell Biology, 31, 644-655, 2021
  10. Nishimura, T. et al., Developmental Cell, 56, 842-859, 2021
  11. Gusmira, A. et al, Journal of Cell Science, 133, jcs246785, 2020
  12. Kitamata, M. et al., Genes to Cells, 25, 187-196, 2020
  13. Hanawa-Suetsugu, K., et al., Nature Communications, 10, 4763, 2019
  14. Kitamata, M. et al., iScience, 17, 101-118, 2019
  15. Tachikawa, M. et al., Scientific Reports, 7, 7794, 2017
  16. Senju, Y., et al., Journal of Cell Science, 125, 2766-2780, 2015
  17. Takahashi, N. et al., Nature Communications, 5, 4994, 2014
  18. Suetsugu, S. et al., Physiological Reviews, 94, 1219-1248, 2014
  19. Suetsugu, S. and Gautreau, A., Trends in Cell Biology, 22, 141-150, 2012
  20. Senju, Y., et al., Journal of Cell Science, 124, 2032-2040, 2011
  21. Takano, K., et al., EMBO journal, 27, 2817-2828, 2008
  22. Shimada, A., et al., Cell, 129, 761-772, 2007