Quantifying time-varying cellular secretions with local linear models.

Area of Science:

• Biochemistry
• Cell Biology
• Biophysics

Background:

• Extracellular protein concentrations and gradients drive crucial cellular responses like motility, growth, proliferation, and death.
• Understanding inter-cellular communication necessitates spatio-temporal data on secreted factors and their link to cell phenotypes.
• Real-time cellular secretion detection techniques are advancing, but robust, generalizable data analysis for quantifying concentrations remains a challenge.

Purpose of the Study:

• To develop a generalizable data analysis methodology for quantifying time-varying secreted protein concentrations from affinity-based biosensor measurements.
• To apply a probabilistic approach combining local-linear models and the law of mass action for accurate concentration determination.
• To validate the method using simulated data and real-world measurements of antibody secretion from hybridoma cells.

Main Methods:

• A probabilistic approach was developed, integrating local-linear models with the law of mass action.
• The methodology was tested using simulated datasets featuring static and dynamic concentration profiles.
• The technique was applied to quantify antibody concentrations secreted by 9E10 hybridoma cells using nanoplasmonic biosensors.

Main Results:

• The probabilistic method successfully quantified time-varying secreted protein concentrations.
• Simulated data analysis demonstrated the approach's capability with both steady-state and dynamic profiles.
• Real-time measurements revealed antibody concentrations ranging from 230 pM steady-state near the cell surface to transient peaks of 56 nM.

Conclusions:

• The developed probabilistic method provides a robust tool for quantifying dynamic protein concentrations from biosensor data.
• This approach enhances the understanding of inter-cellular communication by enabling accurate analysis of secreted factors.
• The findings demonstrate significant dynamic range in antibody secretion from hybridoma cells, crucial for cell signaling studies.