Arnav Moudgil

  • Grosse Pointe Woods, MI

  • Stanford U. (2010)

  • Computational and Systems Biology

  • Rob Mitra, Ph.D.

  • Deconvolving Genomic Regulatory Heterogeneity with Self-Reporting Transposons

  • amoudgil@wustl.edu

Research

A cell’s identity is a function of the genes expressed in that cell, which are in turn regulated by transcription factors. Over the last decade, single-cell RNA sequencing (RNA-seq) has emerged as a powerful class of techniques to characterize cellular diversity in heterogeneous tissues. These methods barcode transcripts by their cell-of-origin and assign them to specific genes. The resulting high-dimensional data are further processed to reveal clusters of cells sharing transcriptional states. Annotating these clusters, based on either known or discovered marker genes, offers a glimpse into the dynamic composition of an organ or biological process.


While single-cell RNA-seq excels at describing cell states, it alone does not inform us about the mechanisms maintaining a particular state. In recent years, multi-modal single cell technologies have flourished, combining single cell RNA-seq with at least one other genomic modality. As a result, joint assays now exist for assaying gene expression simultaneously with genotype, with methylation, with chromatin accessibility, or with lineage. Collectively, these methods aim to connect gene expression to regulatory processes in the genome, thereby gaining insight into the molecular foundations underpinning cellular identity.


Transcription factors are key protein regulators of gene expression. Master transcription factors organize gene regulatory networks to promote differentiation or homeostasis and are often used as markers of cell type. Unfortunately, no methods exist to measure single-cell RNA-seq and map transcription factor binding in those same cells. Such a technique would be uniquely poised to identify both the identity of a cell and candidate regulatory elements contributing to that identity. The Mitra Lab has developed transposon calling cards as an alternative assay to map transcription factor binding, using transcription factor-transposase fusions to mark binding sites with deposited transposon sequences. Here, I present a single cell extension of this technique using a novel construct, the self-reporting transposon, whose genomic location can be mapped from single-cell RNA-seq libraries. Thus, in one workflow, single cell calling cards identifies cell types in complex systems and deconvolves cell-type-specific regulatory elements bound by a transcription factor in those cell types.

Graduate Publications:

Kfoury N, Qi Z, Prager BC, Wilkinson MN, Broestl L, Berrett KC, Moudgil A, Sankararaman S, Chen X, Gertz J, Rich JN, Mitra RD, Rubin JB. 2021 Brd4-bound enhancers drive cell-intrinsic sex differences in glioblastoma. Proc Natl Acad Sci USA, 118(16):e2017148118.

Cammack AJ, Moudgil A, Chen J, Vasek MJ, Shabsovich M, McCullough K, Yen A, Lagunas T, Maloney SE, He J, Chen X, Hooda M, Wilkinson MN, Miller TM, Mitra RD, Dougherty JD. 2020 A viral toolkit for recording transcription factor-DNA interactions in live mouse tissues. Proc Natl Acad Sci USA, (Epub ahead of print): pii: 201918241.

Moudgil A, Wilkinson MN, Chen X, He J, Cammack AJ, Vasek MJ, Lagunas T Jr, Qi Z, Lalli MA, Guo C, Morris SA, Dougherty JD, Mitra RD. 2020 Self-Reporting Transposons Enable Simultaneous Readout of Gene Expression and Transcription Factor Binding in Single Cells. Cell, 182(4):992-1008.e21.

Moudgil A. (2017) Lineage Tracing With Single Cell Calling Cards. NHGRI Genome Analysis Training Program Conference, St. Louis, MO, Abstract.

Last Updated: 8/24/2017 3:29:04 PM

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