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Updated: Aug 29, 2025

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Measuring transcription factor binding and gene expression using barcoded self-reporting transposon calling cards and

Matthew Lalli1,2,3, Allen Yen1,4, Urvashi Thopte3

  • 1Department of Genetics, School of Medicine, Washington University in St. Louis School of Medicine, Saint Louis, MO 63110, USA.

NAR Genomics and Bioinformatics
|September 5, 2022
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Summary
This summary is machine-generated.

We developed barcoded calling cards, a cost-effective method to identify DNA-protein interactions and gene expression. This technique efficiently maps transcription factor binding sites and infers gene regulatory networks in cell populations.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Gene Regulation

Background:

  • Calling cards technology identifies DNA-protein interactions via RNA sequencing.
  • Current bulk calling card methods are labor-intensive and require numerous replicates.
  • There is a need for more efficient and affordable methods to study transcription factor binding and gene expression.

Purpose of the Study:

  • To reduce the cost and labor of calling card experiments in bulk cell populations.
  • To enable simultaneous transcriptome measurement alongside DNA-protein interaction analysis.
  • To develop an efficient method for inferring gene regulatory networks.

Main Methods:

  • Introduction of DNA barcodes into self-reporting transposons for calling cards.
  • Incorporation of an additional barcode during reverse transcription for transcriptome measurement.
  • Application of barcoded calling cards and transcriptomics in vitro and in vivo.

Main Results:

  • Barcoded calling cards successfully identified in vitro binding sites for four key transcription factors.
  • Simultaneous profiling revealed transcription factor binding sites and gene expression changes.
  • Demonstrated in vivo binding site identification in the mouse brain.

Conclusions:

  • Barcoded calling cards and transcriptomics offer an efficient and affordable RNA-based method.
  • This approach facilitates the inference of gene regulatory networks in cell populations.
  • The technology advances the study of DNA-protein interactions and their functional consequences.