Abstract
DNA double-strand breaks (DSBs) are among the most lethal types of DNA damage and frequently cause genome instability. Sequencing-based methods for mapping DSBs have been developed but they allow measurement only of relative frequencies of DSBs between loci, which limits our understanding of the physiological relevance of detected DSBs. Here we propose quantitative DSB sequencing (qDSB-Seq), a method providing both DSB frequencies per cell and their precise genomic coordinates. We induce spike-in DSBs by a site-specific endonuclease and use them to quantify detected DSBs (labeled, e.g., using i-BLESS). Utilizing qDSB-Seq, we determine numbers of DSBs induced by a radiomimetic drug and replication stress, and reveal two orders of magnitude differences in DSB frequencies. We also measure absolute frequencies of Top1-dependent DSBs at natural replication fork barriers. qDSB-Seq is compatible with various DSB labeling methods in different organisms and allows accurate comparisons of absolute DSB frequencies across samples.
Highlights
Background estimation and removalTo quantify double-strand breaks (DSBs) likely resulting from broken forks near origins, we first removed background not related to replication
We reveal two orders of magnitude differences in break frequencies between the conditions we study; we show that qDSB-Seq provides accurate comparison of absolute DSB frequencies across samples
Relying on knowledge of exact genomic locations of spike-ins, their frequency, Bcut, is calculated from enzyme cutting efficiency, fcut. fcut is calculated based on numbers of cut and uncut DNA fragments covering cutting sites in genomic DNA
Summary
To quantify DSBs likely resulting from broken forks near origins, we first removed background not related to replication To define such background, we calculated DSB density in a 500 bp sliding window with a 50 bp step, the peak of this distribution was assumed to be background DSB frequency. We calculated DSB density in a 500 bp sliding window with a 50 bp step, the peak of this distribution was assumed to be background DSB frequency This background was subtracted from the data at each position, resulting negative values were assigned to zero. The number of unique UMIs around AsiSI cutting sites was counted in a ±100 bp interval to calculate DSBs induced by AsiSI, as proposed by Iannelli et al.[13]. SD for results of BLISS quantification was estimated assuming the Poisson distribution of UMI and cell pffiffiffiffiffiffiffi pffiffiffiffiffiffiffi counts and using the formula
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