# Predictive engineering of cellular transcriptional state

> **NIH NIH DP2** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $2,655,000

## Abstract

PROJECT SUMMARY/ABSTRACT
Specific combinations of transcription factors (TFs) exhibit emergent properties when functioning together,
enabling the generation of diverse cell types and behaviors. However, identifying which combinations regulate
a behavior of interest requires overcoming a combinatorial explosion, as among the ~1,600 TFs in the human
genome there are ~1.3 million possible pairs alone. This scaling challenge has forced past efforts at
systematically mapping such genetic interactions (GIs) to rely on simple, parallelizable measures of phenotype
such as growth rate. Each GI is then characterized only by a single number, obscuring the mechanistic or
molecular basis for any particular interaction: put simply, there are many ways for cells to appear equally
“unfit.” Finally, many human cell types are quiescent or post-mitotic, so that the growth-based measures of
interaction that have been highly successful in model organisms such as yeast do not apply.
Here we address these challenges by introducing a new, massively parallel method for studying GIs in human
cells that combines rich phenotyping of single cells with an analytical framework for predicting which
combinations are most informative to measure. We leverage the recently developed Perturb-seq screening
technology, which allows pooled profiling of CRISPR-mediated genetic perturbations with single-cell RNA
sequencing as the phenotypic readout. This approach allows us to overexpress many programmed
combinations of TFs using CRISPR activation (CRISPRa) and obtain a direct readout of their transcriptional
consequences. The resulting rich phenotypes yield insight into the biological origins of GIs, and can for
example identify combinations of TFs that promote differentiation to diverse cell states. They also provide a
critical “handle” to apply modern machine learning methods. Using techniques from the field of compressed
sensing, we propose a predictive approach for searching combinatorial spaces of GIs that would be too large
to profile exhaustively by any experimental technology. Since the transcriptome is a direct readout of TF
function and TFs interact via specific mechanisms such as cooperative binding at target promoters, these
large-scale experiments can also be used to study deeper questions on how GIs emerge mechanistically, and
how neomorphic (i.e. entirely new or unpredictable) phenotypes are generated. Our research provides the first
scalable method for simultaneously finding and characterizing GIs in any system, a technique for rapidly
mapping the “levers” controlling cell fate in diverse models of development and disease, and a model for how
machine learning can be used to design the large combinatorial genetics experiments made possible by Cas9.

## Key facts

- **NIH application ID:** 10001677
- **Project number:** 1DP2GM140925-01
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Thomas Maxwell Norman
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $2,655,000
- **Award type:** 1
- **Project period:** 2020-09-30 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10001677

## Citation

> US National Institutes of Health, RePORTER application 10001677, Predictive engineering of cellular transcriptional state (1DP2GM140925-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10001677. Licensed CC0.

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