Understanding Cellular Information Processing Using Microfluidic Single Cell Analysis
Cells receive dynamic inputs from their environment, use gene networks to process these signals, and generate functional outputs. Characterizing this dynamic input-output relationship at the single cell level helps understanding the underlying regulatory mechanisms and allows building predictive models of complex biological systems that can guide building synthetic functions. To enable this, we developed automated, high-throughput microfluidic single-cell and single-molecule analysis systems with unprecedented capabilities and measurement accuracy, and used them to understand and model immune coordination during response to infection [1-4]. Our recent efforts have resulted in a set of powerful technologies, including microfluidic systems to measure single-cell cytokine secretion dynamics, ultra high-throughput cell culture systems that create thousands of programmable culture conditions in a single experiment, devices to simulate and measure cell-cell communication during immune response, and methods for digital quantification of proteins and mRNA in the same cell. I will then talk about new biological insight that emanated from our experimental and modeling efforts on how single-cells detect, encode and decode dynamic input signals using the immune pathway NF-κB, and how molecular noise improves dynamic signal processing [1, 2]. I will also talk about our recent findings on spatiotemporal information processing via NF-κB among signal sending and receiving immune cells. The main goal of our combined technology/experimental/computational effort is to ultimately develop in vitro and in silico models of tissue-level immune responses that will serve as rapid test-bed for drug or genetic perturbations of disease pathways.
1. Kellogg & Tay. Cell, 2015.
2. Kellogg, Tian, Lipniacki, Quake, Tay. eLife, 2015.
3. Junkin, Kaestli, Cheng, Jordi, Albayrak, Hoffmann, Tay. Cell Reports, 2016.
4. Albayrak, Zechner, Jordi, Bichsel, Khammash, Tay. Molecular Cell, 2016.