# Lineage Analysis of Cellular States Predicting Reprogramming into iPSCs

> **NIH NIH F30** · UNIVERSITY OF PENNSYLVANIA · 2022 · $34,096

## Abstract

Project Summary
Induced pluripotent stem cells (iPSCs) derived from differentiated somatic cells via ectopic expression of a
cocktail of reprogramming factors are a promising, patient-specific resource for disease modeling and
regenerative medicine. However, only a rare subset of cells (<1%) exposed to the reprogramming factors actually
become iPSCs. Furthermore, we do not know what, if anything, is different about these rare cells capable of
reprogramming. While variability in reprogramming outcomes is often ascribed to technical issues, low
reprogramming efficiency remains even when the reprogramming factors are integrated clonally and stably into
the genome. This suggests that this variability is instead due to single-cell differences in chromatin state, gene
expression, and protein signaling (i.e. cell states). Here, we demonstrate evidence of distinct and stable cell
states in the rare subset of cells “primed” to reprogram. We hypothesize that cells can fluctuate in and out of
these primed states whose acquisition enables successful reprogramming into iPSCs. The underlying goal of
our proposal is to identify, characterize, and eventually manipulate these primed states to increase iPSC
reprogramming efficiency. Yet, identifying post-facto relevant factors marking this rare subset of primed cells
represents a major conceptual and technical challenge. Therefore, we propose to use a cellular “Time Machine”
to rewind back time from the ultimate phenotype to identify cells primed to become iPSCs in the original
population via a novel combination of barcoding, RNA FISH, imaging, and flow sorting. Our preliminary data
demonstrate that this method can label, isolate, and profile specific cells based on their future propensity to
reprogram into iPSCs when exposed to the reprogramming factors. In Aim 1, we will use this method to isolate
cells that would later give rise to iPSCs from several different starting cell types. By performing RNA-seq and
ATAC-seq on the isolated cells, we will identify markers and epigenetic regulators of these primed cells and
validate them functionally using chemical and CRISPR-based perturbations. In addition to baseline
reprogramming, we want to understand how perturbations that increase iPSC reprogramming efficiency (i.e.
boosters) specifically increase the fraction of cells becoming iPSCs. In Aim 2, we will use Time Machine to
isolate and profile the extra cells that give rise to iPSCs only when reprogrammed with booster. We will determine
how they are different from the initial subset of reprogrammable cells without booster by comparing molecular
signatures. Then, we will identify and validate factors mediating reprogramming in these extra cells with a specific
booster or across boosters to molecularly understand how boosters recruit additional subsets of cells to become
iPSCs. This work is poised to answer longstanding questions about the existence and nature of rare cells primed
for reprogramming. More broadly, it wi...

## Key facts

- **NIH application ID:** 10470914
- **Project number:** 5F30HD103378-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Naveen Jain
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $34,096
- **Award type:** 5
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470914

## Citation

> US National Institutes of Health, RePORTER application 10470914, Lineage Analysis of Cellular States Predicting Reprogramming into iPSCs (5F30HD103378-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10470914. Licensed CC0.

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