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PQG Working Group

December 13, 2022 @ 1:00 pm - 2:00 pm

PhD Candidate
Johns Hopkins University


Investigating dynamic genetic regulation in diverse cell types and differentiation trajectories

Large-scale efforts to identify genetic variants associated with nearby gene expression levels (cis expression quantitative trait loci, or cis-eQTLs) have focused primarily on healthy adult tissues, potentially overlooking effects specific to transient cell states such as those arising during development or environmental exposures. The current limitations in identifying target genes and mechanisms underlying disease-associated genetic loci may arise partially due to the scarcity of data from developmental and dynamic contexts in disease-relevant cell types. By integrating established stem cell model systems with recent advances in single-cell RNA-sequencing technologies, we are now able to study these transient contexts at high resolution, bringing a clearer picture of gene regulatory dynamics into view. We first studied directed differentiation of induced pluripotent stem cells (iPSCs) into cardiomyocytes, where single-cell analysis enabled us to resolve bifurcating trajectories displaying distinct regulatory dynamics. Now, we are branching out to a wider array of trajectories in an efficient, unified experimental framework based on embryoid bodies. Embryoid bodies are three-dimensional aggregates of iPSCs that spontaneously differentiate into a wide variety of cell types, including derivatives of all three germ layers. We generated embryoid bodies from iPSCs from 53 donors, and collected single cell RNA-sequencing data for over 900,000 cells in total. We applied trajectory inference to characterize the complex, multifurcating differentiation landscape and annotated a wide array of cell types, many of which resemble those arising during human embryonic development. We found that different cellular lineages display dynamic expression of numerous genes, including gene modules relevant to complex traits such as schizophrenia, hypertension, and BMI. We identified hundreds of genetic regulatory effects including cell-type specific and dynamic eQTLs in various trajectories. Next, we used unsupervised machine learning to infer patterns of shared regulatory activity within and between differentiation trajectories to characterize how regulation varies between cellular contexts. This work will expand identification of genetic effects to transient cell states arising during differentiation in diverse lineages and improve attribution of regulatory mechanisms to disease risk loci.


Date: December 13, 2022
Time: 1:00 pm - 2:00 pm
Calendars: Lecture / Seminar


In Person


Amanda King