# Decoding the logic of cellular signaling through the integration of dynamic, single-cell and multiplexed methods

> **NIH NIH R35** · UNIVERSITY OF VIRGINIA · 2024 · $390,973

## Abstract

PROJECT SUMMARY – Responses of cells to signaling perturbations exhibit complex dynamics that vary
across diverse cell types or even genetically identical cells exposed to uniform conditions. Heterogeneity arises
partly because of the cells’ ability to process the signaling input in the context of their states controlled by their
epigenome, a regulatory network involving transcription factors and chromatin architecture which, although
contains heritable information about gene expression programs, remains plastic. Such plasticity enables cells
to transition from one phenotype to another and exhibit fractional responses to perturbations. Despite this
knowledge, what remains largely unknown is: (i) the nature of these dynamically fluctuating phenotypically
consequential cell states, and how they determine the context for signal transduction, (ii) mechanisms that
control the diversity of these states, their multi-stability or plasticity, and variation across cell types, and (iii)
mechanisms by which information embedded in these states is integrated with that from signaling input to elicit
state-specific decisions. Our proposed research is significant because it addresses these key gaps in our
understanding through the study of AP-1 transcription factor network, which we recently showed to serve as a
key node in linking MAPK signal transduction to diverse patterns of cell state plasticity. Individual cells can
adopt a variety of AP-1 states regulated by a combination of competitive homo- and heterodimeric interactions
between individual AP-1 proteins, whose levels and stability are also subject to transcriptional (auto)regulation
and posttranslational modification by MAP kinases. We have shown that the diversity of recurrent AP-1 states
and their single-cell frequencies vary across differentiation states and predict their phenotypic plasticity prior to
and following MAPK perturbations. We propose to determine how combinatorial patterns of AP-1 network
shape cell state heterogeneity and plasticity via analyzing a panel of model-guided, AP-1 perturbed, isogenic
cells to decompose the impact of diverse AP-1 dimers on chromatin features, networks of transcriptional
coregulators, the resulting gene expression programs, and single-cell responses to MAPK perturbations. We
will build the first generation of experimentally validated models of AP-1 regulated expression system based on
their dimerization-controlled, co-regulated, competitive interactions, and use them to reveal network features
underlying the diversity of multi-stable AP-1 states in different cell populations. Side-by-side virtual and wet-lab
single-cell experiments will uncover pharmacologically tractable targets to modulate population heterogeneity
via stabilizing or destabilizing certain AP-1 states. We will use multiplexed single-cell imaging in combination
with multivariate modeling to determine how the trajectory of adaptive responses to MAPK perturbations and
their phenotypic outcomes ar...

## Key facts

- **NIH application ID:** 10765216
- **Project number:** 2R35GM133404-06
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Mohammad Fallahi-Sichani
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $390,973
- **Award type:** 2
- **Project period:** 2019-09-01 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10765216

## Citation

> US National Institutes of Health, RePORTER application 10765216, Decoding the logic of cellular signaling through the integration of dynamic, single-cell and multiplexed methods (2R35GM133404-06). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10765216. Licensed CC0.

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