Optimized methods for mapping DNA double-strand-break ends and resection tracts and application to meiotic recombination in mouse spermatocytes.
Soonjoung KimShintaro YamadaKaku MaekawaScott KeeneyPublished in: bioRxiv : the preprint server for biology (2024)
DNA double-strand breaks (DSBs) made by SPO11 protein initiate homologous recombination during meiosis. Subsequent to DNA strand breakage, endo- and exo-nucleases process the DNA ends to resect the strands whose 5' termini are at the DSB, generating long 3'-terminal single-stranded tails that serve as substrates for strand exchange proteins. DSB resection is essential for meiotic recombination, but a detailed understanding of its molecular mechanism is currently lacking. Genomic approaches to mapping DSBs and resection endpoints, e.g., S1-sequencing (S1-seq) and similar methods, play a critical role in studies of meiotic DSB processing. In these methods, nuclease S1 or other enzymes that specifically degrade ssDNA are used to trim resected DSBs, allowing capture and sequencing of the ends of resection tracts. Here, we present optimization of S1-seq that improves its signal:noise ratio and allows its application to analysis of spermatocyte meiosis in adult mice. Furthermore, quantitative features of meiotic resection are evaluated for reproducibility, and we suggest approaches for analysis and interpretation of S1-seq data. We also compare S1-seq to variants that use exonuclease T and/ or exonuclease VII from Escherichia coli instead of nuclease S1. Detailed step-by-step protocols and suggestions for troubleshooting are provided.
Keyphrases
- single cell
- circulating tumor
- rna seq
- genome wide
- cell free
- dna repair
- single molecule
- dna damage
- escherichia coli
- high resolution
- nucleic acid
- copy number
- high density
- type diabetes
- electronic health record
- small molecule
- dna methylation
- pseudomonas aeruginosa
- adipose tissue
- machine learning
- young adults
- transcription factor
- mass spectrometry
- crispr cas
- biofilm formation
- oxidative stress
- insulin resistance
- klebsiella pneumoniae
- artificial intelligence
- childhood cancer