Simultaneous quantification of protein-DNA interactions and transcriptomes in single cells with scDam&T-seq.
Corina M MarkodimitrakiFranka J RangKoos RooijersSandra S de VriesAlex ChialastriKim L de LucaSilke J A LochsDylan MooijmanSiddharth S DeyJop KindPublished in: Nature protocols (2020)
Protein-DNA interactions are essential for establishing cell type-specific chromatin architecture and gene expression. We recently developed scDam&T-seq, a multi-omics method that can simultaneously quantify protein-DNA interactions and the transcriptome in single cells. The method effectively combines two existing methods: DNA adenine methyltransferase identification (DamID) and CEL-Seq2. DamID works through the tethering of a protein of interest (POI) to the Escherichia coli DNA adenine methyltransferase (Dam). Upon expression of this fusion protein, DNA in proximity to the POI is methylated by Dam and can be selectively digested and amplified. CEL-Seq2, in contrast, makes use of poly-dT primers to reverse transcribe mRNA, followed by linear amplification through in vitro transcription. scDam&T-seq is the first technique capable of providing a combined readout of protein-DNA contact and transcription from single-cell samples. Once suitable cell lines have been established, the protocol can be completed in 5 d, with a throughput of hundreds to thousands of cells. The processing of raw sequencing data takes an additional 1-2 d. Our method can be used to understand the transcriptional changes a cell undergoes upon the DNA binding of a POI. It can be performed in any laboratory with access to FACS, robotic and high-throughput-sequencing facilities.
Keyphrases
- single cell
- rna seq
- circulating tumor
- cell free
- gene expression
- single molecule
- genome wide
- induced apoptosis
- high throughput
- nucleic acid
- transcription factor
- escherichia coli
- binding protein
- cell cycle arrest
- dna binding
- protein protein
- small molecule
- randomized controlled trial
- cell death
- amino acid
- magnetic resonance
- circulating tumor cells
- stem cells
- magnetic resonance imaging
- dna damage
- cystic fibrosis
- machine learning
- biofilm formation
- artificial intelligence
- electronic health record