# Decoding mechanisms of phenotypic memory in single cells

> **NIH NIH DP5** · UNIVERSITY OF PENNSYLVANIA · 2021 · $406,250

## Abstract

PROJECT SUMMARY
 Drug resistance and metastasis are both deadly processes in cancer that remain poorly understood. In
many instances, resistance or metastasis arise from a small subset of the cells within an individual's tumor that
behave differently from the rest. Because these cells are only a small fraction of the cells in a patient's tumor,
they cannot be sequenced or profiled using traditional methods. Thus, single-cell analysis provides a window
into the variability between cells that underlie these harmful processes; however, methods such as single-cell
sequencing are performed after fixing or lysing of the cells, which prevents researchers from being able to
perform further downstream analysis such as testing the cells for resistant or invasive phenotypes. These
efforts catalogue the molecular variability at the single-cell level, but fail to determine how these variable
features relate to the phenotypes present in single cells.
 My research addresses this hurdle via development of a unbiased, high-throughput sequencing method
for identifying variability that is sufficiently ingrained in single cells to generate phenotypes. This approach is
based upon Luria and Delbrück's 1943 fluctuation analysis. It combines their clever experimental design with
the modern twist of high-throughput sequencing assays. When combined with RNA sequencing, our method
(MemorySeq) allows us to quantify gene expression dynamics in order to find single-cell gene expression
states that are slowly fluctuating and heritable through multiple cell divisions. We hypothesize that these slowly
fluctuating gene expression states allow for significant and ingrained changes in single-cells, which are
necessary to generate the detrimental phenotypes of resistance and metastasis in cancer. We aim to use our
new MemorySeq method to 1) test the hypothesis that long-lived fluctuations in gene expression underly
important phenotypes in cancer, specifically drug resistance and invasion, and to 2) identify transcription
factors, kinases, and epigenetic regulator proteins responsible for generating and maintaining these long-lived
fluctuations in gene expression. These aims will be accomplished using a highly innovative and
complementary approach that combines high-throughput sequencing, CRISPR/Cas9 genetic screening, and
single-cell imaging. This line of research will determine the single-cell gene expression signatures of rare
resistant and invasive populations in multiple cancer types, and will enumerate the transcription factors,
kinases, and epigenetic regulator proteins that govern these expression states.
 The results of this work will be significant to the cancer research community as they will yield new
therapeutic targets to specifically inhibit or destroy these undesirable rare cell populations. Furthermore, this
conceptual framework is generalizable and broadly accessible to the scientific research community. In the
future, these fluctuation analysis methods can be appli...

## Key facts

- **NIH application ID:** 10238987
- **Project number:** 5DP5OD028144-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Sydney Shaffer
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $406,250
- **Award type:** 5
- **Project period:** 2019-09-16 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10238987

## Citation

> US National Institutes of Health, RePORTER application 10238987, Decoding mechanisms of phenotypic memory in single cells (5DP5OD028144-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10238987. Licensed CC0.

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