# Computational Modeling of Lineage Decisions using Single-cell Data

> **NIH NIH R35** · FRED HUTCHINSON CANCER CENTER · 2024 · $425,737

## Abstract

Project Summary
Cellular differentiation is a fundamental biological process through which complex multi-cellular organisms
develop from single-cell embryos and maintain tissue homeostasis throughout life. Cells integrate signals from
the microenvironment and transmit them to downstream transcriptional regulators, which execute the expression
and chromatin changes to define phenotypic state transitions in differentiation trajectories. Elucidating the
principles of how cells choose their fate, and the path they take to get there, is a major challenge in the field.
Single-cell (sc) RNA sequencing technologies are revolutionizing our understanding of the cellular spatio-
temporal trajectories that shape differentiation. The emergence of additional high throughput, multimodal
technologies such as paired RNA&ATAC-seq, scCUT&Tag and spatial technologies provide unprecedented
opportunities to extract mechanistic insights into the lineage decisions that underly differentiation trajectories.
This proposal aims to exploit this enormous potential by developing sophisticated new algorithms that integrate
single-cell measurements to model and interpret complex biology. Through analysis of multiple single-cell RNA-
seq datasets, we demonstrate that phenotypic asymmetries are a pervasive feature of lineage decisions. We will
develop algorithms to unravel the mechanisms that drive lineage decisions and the underlying asymmetries in
three broad research directions. We will investigate the role of: (i) enhancer priming and transcriptional
regulation, (ii) open and heterochromatin dynamics, and (iii) cell communication in shaping differentiation
trajectories. Our studies will lead to novel insights surrounding cell-autonomous and non-autonomous
mechanisms engaged by cells as they navigate the phenotypic landscape. Successful completion of this
research will provide a robust mechanistic basis to delineate normal differentiation events, decipher
dysregulation of these mechanisms in disease, understand repurposing of differentiation mechanisms in wound
healing and regeneration, and reconstruct differentiation processes in vivo and ex vivo to unlock the therapeutic
potential of cell engineering.

## Key facts

- **NIH application ID:** 10889170
- **Project number:** 5R35GM147125-03
- **Recipient organization:** FRED HUTCHINSON CANCER CENTER
- **Principal Investigator:** Manu N Setty
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $425,737
- **Award type:** 5
- **Project period:** 2022-09-17 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10889170

## Citation

> US National Institutes of Health, RePORTER application 10889170, Computational Modeling of Lineage Decisions using Single-cell Data (5R35GM147125-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10889170. Licensed CC0.

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