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Towards a Quantitative Understanding of Cell Identity.
1Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Trends in Cell Biology
|October 13, 2018
Summary
This review explores quantitative descriptors for cell identity, moving beyond traditional protein expression. New methods offer unbiased, information-dense characterization for better cell biology understanding and engineering.
Area of Science:
- Cell biology
- Biophysics
- Computational biology
Background:
- Traditional cell characterization relies on limited protein expression, introducing bias and qualitative data.
- This approach overlooks rich molecular information within cell populations.
Purpose of the Study:
- To review quantitative descriptors for cell identity.
- To discuss these descriptors in the context of cell definition, creation, and stability.
Main Methods:
- High-throughput, information-dense experimental approaches.
- Data-driven computational methods for cell characterization.
Main Results:
- Quantitative descriptors enable unbiased and information-rich cell characterization.
- These methods address limitations of traditional, sparse descriptors.
Conclusions:
- Quantitative descriptors are crucial for defining cell identity, creation, and stability.
- Development of quantitative phenotypic landscapes will advance cell biology and engineering.
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