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 will help us identify new pathways to manipulate iPSC reprogramming and reveal the molecular basis of plasticity in seemingly differentiated cells.
Induced pluripotent stem cells (iPSCs) derived from somatic cells provide a valuable, patient-specific source for regenerative medicine, drug discovery, and disease modeling, but these clinical applications are limited because reprogramming is slow and inefficient. Only rare subsets of cells go on to become iPSCs upon exposure to reprogramming factors, but if we knew what was different about them beforehand we could design strategies to isolate or generate more for downstream use in clinical applications. Here we propose using a novel strategy combining single-cell barcoding, RNA FISH, imaging, and flow sorting to directly and isolate and characterize rare cells that give rise to iPSCs, enabling us to identify regulators of selective plasticity in differentiated cells with implications in stem cell biology, tissue repair, and cancer pathogenesis.
