Measuring transcription factor binding and gene expression using barcoded self-reporting transposon calling cards and transcriptomes. Issue 3 (31st August 2022)

• Record Type: Journal Article
• Authors: Lalli, Matthew; Yen, Allen; Thopte, Urvashi; Dong, Fengping; Moudgil, Arnav; Chen, Xuhua; Milbrandt, Jeffrey; Dougherty, Joseph D; Mitra, Robi D
• Is Part Of: NAR genomics and bioinformatics. Volume 4:Issue 3(2022)
• Publication Date: 2022-08-31
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqac061
• Languages: English
• ISSNs: 2631-9268

Abstract

Abstract: Calling cards technology using self-reporting transposons enables the identification of DNA–protein interactions through RNA sequencing. Although immensely powerful, current implementations of calling cards in bulk experiments on populations of cells are technically cumbersome and require many replicates to identify independent insertions into the same genomic locus. Here, we have drastically reduced the cost and labor requirements of calling card experiments in bulk populations of cells by introducing a DNA barcode into the calling card itself. An additional barcode incorporated during reverse transcription enables simultaneous transcriptome measurement in a facile and affordable protocol. We demonstrate that barcoded self-reporting transposons recover in vitro binding sites for four basic helix-loop-helix transcription factors with important roles in cell fate specification: ASCL1, MYOD1, NEUROD2 and NGN1. Further, simultaneous calling cards and transcriptional profiling during transcription factor overexpression identified both binding sites and gene expression changes for two of these factors. Lastly, we demonstrated barcoded calling cards can record binding in vivo in the mouse brain. In sum, RNA-based identification of transcription factor binding sites and gene expression through barcoded self-reporting transposon calling cards and transcriptomes is an efficient and powerful method to infer gene regulatory networks in a population of cells.