# Input encoding in T-cell receptor signaling

> **NIH NIH R21** · UNIVERSITY OF WASHINGTON · 2021 · $194,375

## Abstract

ABSTRACT
Signal sensing circuits in cells often perform at a level that rivals or even exceeds that of man-made detection
devices. Understanding how these circuits perform optimally and robustly in a noisy cellular environment will
yield fundamental insights into their underlying design principles, and will enable us to re-purpose these circuits
for cell engineering. The signaling circuit that mediates antigen detection by T-cells exhibits particularly striking
sensing capabilities – it can selectively respond to even a few copies of antigenic ligand, while retaining an
ability to distinguish ligand levels over a five order of magnitude range. How T-cell receptor signaling circuits
can achieve such remarkable selectivity, sensitivity and dynamic range in their operation is not understood. To
address this question, we have developed a novel multi-pathway fluorescent reporter system that enables, for
the first time, simultaneous live tracking of the activity of the three primary signaling pathways downstream of
the T-cell receptor at the single-cell level. Here, we combine this multi-pathway reporter with quantitative live-
cell imaging, mathematical modeling and perturbation analysis to elucidate the control strategies underlying T-
cell ligand sensing, and determine how they are disrupted in T-cell dysfunction. Firstly, we will (I) perform a
systematic, quantitative characterization of input/output responses of individual pathways and T-cell receptor
engagement. This characterization will be performed at the single-cell level, using a combination of high
throughput live imaging and computational image analysis. Next, we will (II) elucidate regulatory feedback
loops underlying these responses. To do so, we will perform mathematical modeling of candidate feedback
architectures, followed by iterative experimental testing. Finally, we will (III) determine how these input/output
states are perturbed upon T-cell dysfunction, using live-cell imaging techniques in conjunction with mouse
models. These studies will generate fundamental insights into antigen sensing mechanisms by T-cells,
yielding guiding principles for engineering T-cells to treat cancer and other diseases. More broadly, this work
will also elucidate principles underlying architecture and design of mammalian signaling circuits, impacting
systems and signaling biology studies across diverse fields.

## Key facts

- **NIH application ID:** 10098320
- **Project number:** 5R21EB027327-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Hao Yuan Kueh
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $194,375
- **Award type:** 5
- **Project period:** 2019-05-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10098320

## Citation

> US National Institutes of Health, RePORTER application 10098320, Input encoding in T-cell receptor signaling (5R21EB027327-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10098320. Licensed CC0.

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