henhaobio
Sign in

CellGenomicsPeer-reviewed journal article

Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

Arnav MoudgiliD, Michael WilkinsoniD, Xuhua CheniD, June He, Alexander J. CammackiD, Michael J. VasekiD, Tomás LagunasiD, Zongtai QiiD, Matthew A. LalliiD, Chuner GuoiD, Samantha A. MorrisiD, Joseph D. DoughertyiD, Robi David MitraiD

PDFDOIPreprint
Citations
89
FWCI
4.22
Field percentile
top 4%

Abstract

In situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISLalli '22Cammack '19Fu '20Reyes-Gopar '25Mullins '25He '24
63% semantic matchGenomics
Matthew A. Lalli, Allen Yen, Urvashi Thopte+6 more·NAR Genomics and Bioinformatics·Jul 9, 2022· 6 citations