# Combinatorial Cell State Engineering

> **NIH NIH DP1** · STANFORD UNIVERSITY · 2024 · $1,080,800

## Abstract

Abstract
Genome-wide screens in mammalian cells have emerged as a powerful tool for determining the relationship of
individual genes to a chosen biological phenotype. However, biological systems often rely on the concerted
action of multiple genes at once to elicit phenotypes. Nowhere is this more evident than in cellular differentiation,
where cell state transitions often involve the modulation of 5-7 master regulatory factors. Consistent with this
observation, successful efforts to reprogram cells, from Yamanaka on, have generally found that simultaneous
expression of 3-5 transcription factors are needed to elicit cell state or type changes (similar to an “AND-gate-
like” genetic circuit), and others have improved the efficiency or accuracy of these transitions by further perturbing
other factors such as epigenetic remodelers. Given these observations, we posit that the ability to carry out highly
combinatorial forward genetic screens for cell state phenotypes would produce a “sea change” in our ability to
engineer cells with highly specific properties, transforming the quality of cells available for research and cell
therapy applications. To this end, we propose an iterative platform that leverages a large multiplicity of
perturbation (MOP) per cell, intelligent structuring of engineered perturbation libraries, and machine learning
approaches to both identify combinations of perturbations most likely to elicit specific cellular phenotypes, and
to engineer maximally informative new perturbation libraries. We have piloted this platform on a simple “toy
model” wherein the simultaneous expression of 6 different proteins (across a total universe of 30 different
potential factors) are required to elicit a phenotype. By overloading cells with ~14 perturbations per cell,
structuring a library of ~80 perturbation combinations, then identifying further observations that would provide
maximal information about the causative perturbation combination, we were able to confidently uncover this six-
input “AND-gate” underlying state logic. While this initial ability to “solve” highly polygenic phenotypes is exciting,
challenges to extending our platform to primary human cells include identification and minimization of dominant
negative perturbations, identification of optimal MOP for each biological question, perfection of methods for high
MOP of primary cells, exploration and optimization of the direction and mechanism of gene expression
perturbation, and the engineering or selection of state changes sufficiently durable for therapeutic utility. We plan
to initially apply this platform to the trans-differentiation of naive T cells into regulatory T cells and the generation
of inexhaustible T-cells for cell therapies, with an eye toward establishing collaborations to deploy this platform
to develop diverse cell types with regenerative or therapeutic value. In short, we posit that complex,
therapeutically relevant phenotypes demand a polygenic design language ...

## Key facts

- **NIH application ID:** 10917076
- **Project number:** 5DP1HG013599-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** William James Greenleaf
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,080,800
- **Award type:** 5
- **Project period:** 2023-09-30 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917076

## Citation

> US National Institutes of Health, RePORTER application 10917076, Combinatorial Cell State Engineering (5DP1HG013599-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10917076. Licensed CC0.

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