Wang Lab
Tissue Regeneration and Bioinformatics, GIBH
Research Interests
Wang Lab is dedicated to uncovering the principles of tissue regeneration, advancing tissue regeneration, and leveraging regenerative abilities for disease intervention.
During evolution, different species and tissues exhibit diverse regenerative abilities. We focus on the study of cross-species regeneration in the retina and liver. Non-mamalian species, such as zebrafish, have remarkable regenerative potential in both the liver and retina. In mammals, regenerative ability is retained in the liver but not in the retina. However, liver fibrosis can develop after chronic injury, and macular degeneration frequently occurs in the retinas of older individuals. These diseases hold potential for treatment through regenerative approaches.
To investigate the cellular and molecular mechanisms driving tissue regeneration and relevant diseases, we utilize high-throughput sequencing and organoid technologies, develop computational and statistical approaches, and conduct cellular and animal experiments. Wang Lab is committed to integrating computational science and experimental biology to explore tissue regeneration, fibrosis, and degeneration.
Biological Interests
Tissue Regeneration
Tissue regeneration holds great potential for interfering with tissue fibrosis, degenerative diseases, etc. In zebrafish, Müller glial cells play essential roles in retinal regeneration. Although mammalian retinas contain Müller glia, mammals can not automatically regenerate retinas after injury. It is still unclear which regulatory mechanisms in Müller glia leads to cross-species differences on retinal regeneration.
Tissue Fibrosis
Tissue fibrosis is from an excessive accumulation of extracellular matrix components and can affect any organ, including the liver.
Tissue Degeneration
Tissue degeneration frequently occurs in nervous systems of older people and leads to degenerative diseases such as age-related macular degeneration.
Computational Interests
Regulatory Networks
Regulatory networks are used to uncover gene regulatory relationships in diverse biological processes, e.g. tissue regeneration.
Multiomics analysis
Multiomics analysis including single-cell multiomics is performed to reveal biological insights of tissue regeneration.
Artifitial intellegence
Artifitial intellegence especially deep learning is powerful to predict biomedical outcomes through learning high-throughput sequencing data and large-scale images.