# Deciphering dynamic signals in control of cell fate decisions

> **NIH NIH R35** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $391,250

## Abstract

PROJECT SUMMARY
In the long-term, our goal is to understand how single cells integrate and process information to make irreversible
decisions such as whether to proliferate, differentiate or die. Inflammatory factors that participate in many normal
and diseased cell fate decisions initiate signals by dynamically re-organizing proteins within the cell. For
example, ligand-bound TNF receptors transiently organize large protein complexes near the plasma membrane,
and these are visible within the cell as discrete punctate structures, whereas other proteins translocate between
cellular compartments such as the cytoplasm and the nucleus. It is an emerging principle that dynamic properties
of molecules within signal transduction circuits provide temporal codes (including rate of change, amplitude,
duration or frequency among others) that are critical to each cell’s response to stimulus. Given that there is
substantial cell-to-cell heterogeneity, even in clonal cell lines, static measurements at fixed time points cannot
reveal the mechanisms of dynamic information processing. We hypothesize that components of the same
signaling pathway are deterministically linked to one another in a single cell, even though there is substantial
heterogeneity between cells. Here, we propose to multiplex expression of live-cell fluorescent reporters for up-
and down-stream components of the same signaling pathway in the same cell, and correlate time-varying signals
from live-cell microscopy data. Using a hybrid of quantitative imaging, microfluidics and computational
techniques we will extract time-varying data from 100s-1000s of single cells in each experimental condition, and
compare them across several different cell lines. We will also compare cellular responses across different
inflammatory factors that share signaling modules and converge on the NF-κB transcriptional system, such as
TNF, LPS or IL-1 among others. Using a rich single-cell dataset, we will use transfer entropy to measure mutual
information between features of time-varying signals in the same pathway, and infer mechanisms of signal
transduction in addition to correlations with cell fate. Data from live-cell experiments will be incorporated into
mechanistic models to formalize our understanding of how information is relayed through the signaling network
into transcription, and suggest perturbations to test predicted mechanisms. We anticipate that increasingly
accurate models may lead to non-intuitive strategies to manipulate decisions in single cells. Through a detailed
understanding of how dynamic molecular signals encode, process, and decode information, we have the
potential to understand biological problems that are deeply rooted in disease, and use this knowledge to rationally
design therapies that impact cell fate decisions.

## Key facts

- **NIH application ID:** 9950861
- **Project number:** 5R35GM119462-05
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Robin E. C. Lee
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $391,250
- **Award type:** 5
- **Project period:** 2016-09-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9950861

## Citation

> US National Institutes of Health, RePORTER application 9950861, Deciphering dynamic signals in control of cell fate decisions (5R35GM119462-05). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/9950861. Licensed CC0.

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