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© 2021 Elacqua et al. This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/. DNA single-strand breaks (SSBs), or “nicks,” are the most common form of DNA damage. Oxidative stress, endogenous enzyme activities, and other processes cause tens of thousands of nicks per cell per day. Accumulation of nicks, caused by high rates of occurrence or defects in repair enzymes, has been implicated in multiple diseases. However, improved methods for nick analysis are needed to characterize the mechanisms of these processes and learn how the location and number of nicks affect cells, disease progression, and health outcomes. In addition to natural processes, including DNA repair, leading genome editing technologies rely on nuclease activity, including nick generation, at specific target sites. There is currently a pressing need for methods to study off-target nicking activity genome-wide to evaluate the side effects of emerging genome editing tools on cells and organisms. Here, we developed a new method, DENT-seq, for efficient strand-specific profiling of nicks in complex DNA samples with single-nucleotide resolution and low false-positive rates. DENT-seq produces a single deep sequence data set enriched for reads near nick sites and establishes a readily detectable mutational signal that allows for determination of the nick site and strand with single-base resolution at penetrance as low as one strand per thousand. We apply DENT-seq to profile the off-target activity of the Nb.BsmI nicking endonuclease and an engineered spCas9 nickase. DENT-seq will be useful in exploring the activity of engineered nucleases in genome editing and other biotechnological applications as well as spontaneous and therapeutic-associated strand breaks.

Mutation data reveal the dynamic equilibrium between DNA damage and repair processes in cells and are indispensable to the understanding of age-related diseases, tumor evolution, and the acquisition of drug resistance. However, available genome-wide methods have a limited ability to resolve rare somatic variants and the relationships between these variants. Here, we present lineage sequencing, a new genome sequencing approach that enables somatic event reconstruction by providing quality somatic mutation call sets with resolution as high as the single-cell level in subject lineages. Lineage sequencing entails sampling single cells from a population and sequencing subclonal sample sets derived from these cells such that knowledge of relationships among the cells can be used to jointly call variants across the sample set. This approach integrates data from multiple sequence libraries to support each variant and precisely assigns mutations to lineage segments. We applied lineage sequencing to a human colon cancer cell line with a DNA polymerase epsilon (POLE) proofreading deficiency (HT115) and a human retinal epithelial cell line immortalized by constitutive telomerase expression (RPE1). Cells were cultured under continuous observation to link observed single-cell phenotypes with single-cell mutation data. The high sensitivity, specificity, and resolution of the data provide a unique opportunity for quantitative analysis of variation in mutation rate, spectrum, and correlations among variants. Our data show that mutations arrive with nonuniform probability across sublineages and that DNA lesion dynamics may cause strong correlations between certain mutations., National Institute of Environmental Health Sciences (Core Center Grant P30-ES002109), National Institute of Allergy and Infectious Diseases (U.S.) (Grant R21AI110787)