High-throughput screen to identify and optimize NOT gate receptors for cell therapy.

Area of Science:

• Synthetic biology
• Cellular engineering
• Biotechnology

Background:

• Logic-gated engineered cells represent an emerging therapeutic approach for targeted medical interventions.
• The complexity of these engineered systems presents challenges in predicting and optimizing their behavior.

Purpose of the Study:

• To design and test a flow cytometry-based screening system for rapid selection of functional inhibitory receptors.
• To identify inhibitory receptors functioning as NOT gates when paired with activating receptors.
• To generate data for machine learning models to enhance prediction and optimization of logic-gated cell therapeutics.

Main Methods:

• Development of a flow cytometry-based screening system.
• Screening of a pooled library of candidate inhibitory receptor constructs.
• Proof-of-concept experiments to validate the selection of functional receptors.

Main Results:

• Successful identification of inhibitory receptors capable of functioning as NOT gates.
• Demonstration of a rapid selection method for functional components of logic-gated cells.
• Establishment of a system for generating large datasets for machine learning.

Conclusions:

• The developed screening system efficiently selects functional inhibitory receptors for logic-gated cell therapeutics.
• This method facilitates the optimization of complex engineered cell systems.
• The approach supports the advancement of cell therapeutics through data-driven predictive modeling.