Ying Ge, PhD
Position title: Professor, Cell and Regenerative Biology
1111 Highland Ave, Room 8551
Madison, WI, 53705
- Lab Website
- Ge Research Group
Our research aims to understand the molecular and cellular mechanisms underlying cardiovascular diseases via systems biology approaches featuring cutting-edge high-resolution mass spectrometry (MS)-based comparative proteomics and metabolomics in conjunction with biochemical/biological/physiological functional studies. Our research is highly interdisciplinary within the interface of chemistry, biology, and medicine. Success in my proposed research will advance our understanding of the molecular basis of diseases and foster the development of new strategies for early diagnosis, prevention and better treatment of cardiovascular diseases.
Cardiovascular disease is the leading cause of morbidity and mortality in developed countries and is reaching epidemic proportions. Transformative insights from a holistic approach at the systems level have great potential to elucidate disease mechanisms and to develop new therapeutic treatments. Proteins and metabolites are important molecular entities of the cell downstream of genes. Hence in the post genomic era, proteomics and metabolomics (the large-scale global analysis of proteins and metabolites in a cell, organism, tissue, and biofluid), are essential for deciphering how molecules interact as a system and for understanding the functions of cellular systems in health and disease. However, there are tremendous challenges in proteomics and metabolomics due to the extreme complexity and dynamic nature of the proteome and metabolome.
To address such challenges, we are developing novel ultra high-resolution MS-based top-down comparative proteomics and metabolomics platforms for systems biology studies with high efficiency, specificity, sensitivity, and reproducibility. We globally identify, characterize, and quantify intact proteins and metabolites extracted from tissues/cells/biofluids and reveal all changes in the proteome and metabolome in response to extrinsic and intrinsic stresses in a label-free, automated, and high-throughput fashion. (Technology Component)
We then employ these technology platforms to study cardiovascular diseases in conjunction with biochemical and pathophysiological functional assays. Currently we are focused on two major directions (Biology and Medicine Components):
- Cardiac myofilaments: establish a global map of myofilament protein modifications under normal and diseased conditions by top-down comparative proteomics and determine the functional consequence of novel modifications in regulating cardiac contractility
- Cardiac regenerative biology: evaluate the efficacy of stem cell therapies for treatment of heart failure using integrated proteomics and metabolomics (and interactomics) approaches. My research is highly interdisciplinary within the interface of chemistry, biology, and medicine. Success in my proposed research will advance our understanding of the molecular basis of diseases and foster the development of new strategies for early diagnosis, prevention and better treatment of cardiovascular diseases.
- Tiambeng, T. N.; Roberts, D. S.; Brown, K.A.; Zhu, Y.; Chen, B.; Wu, Z.; Mitchell, S. D.; Guardado-Alvarez, T.M.; Jin, Y.; Ge, Y. Nanoproteomics enables proteoform-resolved analysis of low-abundance proteins in human serum., Nature Commun., 2020, 11, 1-12.
- Cai, W.; Zhang, J.; De Lange, W. J.; Gregorich, Z. R.; Karp, H.; Ferrell, E. T.; Mitchell, S. T.; Tucholski, T.; Lin, Z.; Biermann, M.; McIlwain, S.; Ralphe, J. C.; Kamp, T. J.; Ge, Y. Unbiased Proteomics Method to Assess the Maturation of Human Pluripotent Stem Cell-Derived Cardiomyocytes., Circ Res., 2019, 125, 936–953
- Brown, K. A.; Chen, B.; Guardado-Alvarez, T.; Lin, Z.; Hwang, L.; Ayaz-Guner, S.; Jin, S.; Ge, Y. A cleavable surfactant for top-down proteomics. Nature Methods, 2019, 16, 417-420.
- Cai, W.; Tucholski, T. M.; Gregorich Z. R.; Ge, Y. Top-down proteomics: technology advancements and application to heart diseases, Expert Rev. Proteomics, 2016, 13, 717-730.