Deciphering the Dynamic Coordination of Chromatin Accessibility and Gene Expression to Uncover the Mechanisms of Cellular Differentiation

The study of differentiation and development is in many ways the study of how cells change over time. As cells differentiate and mature, they must change their state by changing their gene expression, chromatin accessibility, morphology, and other characteristics. These changes both describe and define the steps a cell must pass through as it differentiates and matures. This proposal seeks to develop computational models that can identify this sequence of changes that a cell must undergo to mature and transform from one type to another and compare changes in a cell’s gene expression and chromatin accessibility state in order to see how these modalities coordinate to drive the maturation and differentiation of a cell. This will be done by modeling the sequence of cell state changes over time as movement, inferring both the rate of change within each modality as well as the direction of change, i.e., peaks and genes that define these changes. Together the rate and direction of change describe a velocity for each cell, showing how the cell changes as it matures. Recent advances in single cell assay development now allow for the measurement of both the gene expression and chromatin accessibility modalities from single cells, giving a higher resolution view into the maturation of a cell. However, current approaches for inferring the dynamics of cell state change either reveal only the direction of change or cannot be applied equally to each modality. The methods proposed in this application seek to use the distribution of cells in the cell state space to infer both the rate and direction of cell state change, allowing these methods to be equally applicable to the gene expression and chromatin accessibility modalities. This velocity will be inferred with two approaches: (1) For single timepoint datasets, psuedotime will be used to define the direction of cell state change while the density of cells in each state will be related to their rate of change; (2) For time series data the change in density over time will be used to describe the flow of cells down developmental trajectories over time and will be related to a velocity using the drift diffusion equation. The algorithms proposed in this application aim to deepen our understanding of development by detailing how cells change their gene expression and chromatin accessibility as they mature down differentiation trajectories, by modeling the dynamics of these changes over time in single cell multimodal data. Further, these algorithms will provide a toolkit to explore the coordination between the epigenome and transcriptome, offering insights into cell fate priming and plasticity. This will lead to a better understanding of cell fate specification and will enhance our ability to design therapies for developmental diseases and uncover new targets for human reprogramming and regeneration. Project Number: 1F31HD118770-01 | Fiscal Year: 2025 | NIH Institute/Center: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | Principal Investigator: Connor Finkbeiner | Institution: FRED HUTCHINSON CANCER CENTER, SEATTLE, WA | Award Amount: $44,843 | Activity Code: F31 | Study Section: Special Emphasis Panel[ZRG1 F08-L (20)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1F31HD11877001