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. 2026 Mar 10;19:173. doi: 10.1186/s13104-026-07761-2

Targeted native long-read sequencing of DNA methylation alterations following CRISPR-Cas9-induced double-strand breaks in human cells

Yingzi Zhang 1,2,#, Mengge Wang 1,2,#, Chongwei Bi 1, Mo Li 1,2,✉
PMCID: PMC13088674  PMID: 41808208

Abstract

Objectives

CRISPR-Cas9 nucleases are widely used to introduce targeted DNA double-strand breaks (DSBs) for genome engineering, but the long-term impact of these lesions on local epigenetic information remains poorly characterized. In a companion research article, we used Cas9-assisted targeted nanopore sequencing (CTS) to reveal that CRISPR–Cas9–induced DSBs can disrupt local epigenetic maintenance across multiple genomic contexts and cell systems. Here, we present a structured description of the raw and minimally processed datasets underlying the study. These datasets provide base-resolution measurements of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) at the differentially methylated regions (DMRs) of several imprinted loci, two heterochromatic regions, a cancer-associated promoter epimutation region, and the SNRPN DMR at early/late passages of a clonal line. They enable re-analysis and methodological benchmarking of DSB-associated epigenetic instability.

Data description

We provide aligned BAM files and per-CpG methylation calls for multiple genomic contexts under both CRISPR-targeted and non-targeting control conditions. Specifically, the collection includes: (i) imprinted loci in human embryonic stem cells (hESCs), including small nuclear ribonucleoprotein polypeptide N (SNRPN), paternally expressed 10 (PEG10), and KCNQ1 opposite strand/antisense transcript 1 (KCNQ1OT1), (ii) heterochromatic regions in hESCs, including urothelial cancer associated 1 (UCA1), and cysteine rich C-terminal 1 (CRCT1)), (iii) the epimutation locus of MutL homolog 1 (MLH1) in RKO cells, and (iv) the DMR of SNRPN locus in early- and late-passage derivatives of a single hESC clone. For each collection, there is a dataset that includes both the raw aligned Nanopore sequencing reads (BAM) deposited in the NCBI Sequence Read Archive (SRA) and the corresponding processed per-CpG 5mC/5hmC matrices deposited in Zenodo. All higher-level analyses in the research article–such as DMR calling, haplotype-resolved analyses, and structural variant (SV) characterization–are fully reproducible using these deposited data. Additional processed analyses are comprehensively documented in the companion article and are therefore not duplicated here. Together, these datasets offer a rich resource for benchmarking long-read methylation analysis workflows and further investigation of DSB-associated epigenetic instability across diverse genomic contexts.

Keywords: CRISPR-Cas9, Epigenetic instability, DNA methylation, Double-strand break, Nanopore sequencing, Long-read sequencing, Genomic imprinting, Human embryonic stem cell, Colorectal cancer cell

Objective

CRISPR-Cas9 has transformed genome engineering and functional genomics, yet accumulating evidence suggests that its activity can have consequences beyond intended sequence changes [1–3], potentially altering epigenetic marks near induced lesions [4]. In particular, how DNA methylation is maintained following CRISPR-Cas9 DSB induction remains incompletely understood.

Long-read sequencing on the Oxford Nanopore Technologies (ONT) platform offers key advantages for resolving complex genetic and epigenetic alterations after CRISPR-Cas9 editing. By generating reads tens to hundreds of kilobases in length, ONT enables haplotype resolution, accurate reconstruction of SVs, and simultaneous detection of native DNA methylations—capabilities not achievable with short-read technologies. Multiple studies have demonstrated the utility of ONT long-read technologies for precisely characterizing genetic instability [5] and CRISPR outcomes [1–3]. Long-read nanopore sequencing has been instrumental in uncovering pervasive unintended SVs at Cas9-induced breaks, highlighting previously unrecognized genotoxic risks of CRISPR editing [1, 3], and has been used as a quantitative, unbiased tool to evaluate genomic repair outcomes [2].

Beyond genetic characterization, ONT provides direct, base-resolution detection of DNA modifications, allowing simultaneous mapping of epigenetic and genetic outcomes on the same reads. Insights from methylation biology further motivate this approach. Yuta et al. [6] showed that targeted perturbations—such as insertion of CpG-free DNA—can induce stable de novo CpG island methylation, generate pathogenic epimutations, or correct imprinting defects in human pluripotent stem cells. Additional studies have demonstrated how methylation states contribute to disease traits and may influence epigenetic inheritance [7]. Meanwhile, recent benchmarking efforts have enabled highly accurate ONT-based base-resolution DNA methylation detection [8–12]. Enrichment workflows such as CTS support high-depth profiling of selected genomic regions of interest (ROIs) without PCR amplification [13]. Together, these developments position ONT long-read sequencing as a powerful platform for interrogating how CRISPR-Cas9-induced DSBs influence the maintenance and stability of DNA methylation.

In the companion research article, we used CTS to examine how Cas9-induced DSBs affect DNA methylation at several genomic contexts in human cells, focusing on (i) canonical imprinted loci with well-defined parental methylation asymmetry, (ii) heterochromatic regions, and (iii) a cancer-relevant epimutant locus in the MLH1 promoter (Fig. 1). We obtained deep coverage of predefined ROIs in H1 hESCs and RKO colorectal cancer cells. CTS datasets were also generated from early- and late-passage derivatives of an SNRPN-DMRMe/Me clonal line to assess locus-specific methylation stability during prolonged culture. With read lengths typically spanning tens of kilobases and enabling haplotype resolution, methylation calling, and SV detection, these data allow characterization of the interplay between DSB repair and epigenetic maintenance.

Fig. 1.

Fig. 1

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Schematic overview of the experimental design and data-processing pipeline. H1 hESCs or RKO cells were transfected with either locus-specific or non-targeting control sgRNAs and cultured for 72 h prior to genomic DNA isolation. Target regions—including imprinted loci (SNRPN, PEG10, KCNQ1OT1), heterochromatic loci (UCA1, CRCT1), and the MLH1 epimutation locus—were enriched using CTS. Sequenced reads were processed through a unified workflow comprising basecalling, methylation calling, phasing, structural variant detection, and differential methylation analysis

The purpose of this Data Note is to describe the raw and minimally processed datasets needed for future research applications. By depositing raw reads, processed methylation calls, and sample metadata, we aim to support reproducibility of DSB-associated epigenetic analyses and facilitate downstream applications such as benchmarking long-read methylation callers, evaluating DMR detection frameworks, assessing relationships between SVs and local epigenetic perturbations, and generating hypotheses on mechanisms underlying DSB-induced epigenetic instability.

Data description

Experimental design and sample groups

CTS was performed in two human cell lines to investigate how CRISPR–Cas9-induced DSBs affect the maintenance of CpG methylation across diverse genomic contexts (Fig. 1; Table 1). In H1 hESCs, CTS was applied to interrogate three classical imprinted loci (SNRPN, PEG10, and KCNQ1OT1) with strong allelic methylation asymmetry and two densely methylated heterochromatic regions (UCA1 and CRCT1). For each locus, cells were transfected with either a single-guide RNA (sgRNA) targeting an internal site to induce a DSB or with a non-targeting sgRNA serving as a control. This design enabled high-depth profiling of parental-allele–specific methylation states at loci with well-defined imprinting asymmetry, as well as the examination of methylation stability within heterochromatin.

Table 1.

Experimental metadata annotation

Dataset Experimental group Sample Cell line Targeted sequencing region DSB induction site
1 CTS experiments targeting H1 imprinted loci (SNRPN, PEG10, KCNQ1OT1) and the NonTarget control Target-SNRPN H1 SNRPN, PEG10, and KCNQ1OT1 SNRPN
Target-PEG10 H1 SNRPN, PEG10, and KCNQ1OT1 PEG10
Target-KCNQ1OT1 H1 SNRPN, PEG10, and KCNQ1OT1 KCNQ1OT1
Non-Target H1 SNRPN, PEG10, and KCNQ1OT1 None
2 CTS experiments targeting H1 heterochromatin loci (UCA1, CRCT1) and the NonTarget control Target-UCA1 H1 UCA1, and CRCT1 UCA1
Target-CRCT1 H1 UCA1, and CRCT1 CRCT1
Non-Target H1 UCA1, and CRCT1 None
3 CTS experiments targeting RKO epimutation locus (MLH1) and the NonTarget control Target-MLH1 RKO MLH1 MLH1
Non-Target RKO MLH1 None
4 CTS experiments targeting the SNRPN locus in early- and late-passage SNRPN-DMRMe/Me cells SNRPN-DMRMe/Me clone, early-passage H1 clone SNRPN SNRPN
SNRPN-DMRMe/Me clone, early-passage H1 clone SNRPN SNRPN
non-target control clone, early passage H1 clone SNRPN SNRPN
non-target control clone, late passage H1 clone SNRPN SNRPN
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CTS was also performed in the RKO colorectal cancer cell line to interrogate an epimutation in the MLH1 promoter. RKO cells were subjected to CRISPR-Cas9 editing at the MLH1 locus or treated with a non-targeting sgRNA, enabling direct comparison of methylation and genetic outcomes in a cancer-relevant context.

In addition, to evaluate the stability of methylation alterations during extended culture, for the SNRPN locus, CTS was performed on early- and late-passage derivatives of an SNRPN-DMRMe/Me clonal line.

For each sample, high-molecular-weight genomic DNA was prepared without amplification, enriched for the ROIs through CTS using Cas9 ribonucleoprotein complexes assembled with external sgRNAs, and sequenced using one MinION flow cell (R9.4.1 chemistry) per sample. Basecalling, alignment, and methylation calling were performed against the hg38 reference genome (Fig. 1). Across experiments, CTS consistently achieved high on-target enrichment, yielding approximately 200× to over 1,100× coverage per ROI, which facilitated robust quantification of per-CpG methylation levels, allelic asymmetry, and DSB-associated epigenetic variation.

Raw sequencing data

All raw aligned sequencing data (BAM format with methylation tags) are deposited in the NCBI Sequence Read Archive under accession PRJNA1193628 [14], which includes multiple runs corresponding to different combinations of: cell line (H1 hESC, RKO), target locus (imprinted loci: SNRPN, PEG10, KCNQ1OT1; heterochromatin loci: UCA1, CRCT1; epimutation locus: MLH1), experimental condition (CRISPR-targeted DSB vs. non-targeting control), and culture stage (early- and late-passage SNRPN-DMRMe/Me cells). Individual sample accessions corresponding to each locus and condition are listed in Table 2. Basecalling was done by Dorado (v0.9.6) (https://github.com/nanoporetech/dorado). Alignment was done by minimap2 (v2.17). These data support reanalysis of nucleotide variants, phasing, methylation, and SVs using alternative tools; performing DMR calling under different statistical frameworks; integrating with additional annotations; or visualizing SNVs, SVs, and methylation landscapes with independent visualization tools.

Table 2.

Overview of datasets

Dataset Experimental dataset Raw aligned sequencing file format
(file extension)		 Data repository and identifier (DOI or accession number) Methylation metrics file format
(file extension)		 Data repository and identifier (DOI or accession number)
1 CTS experiments targeting H1 imprinted loci (SNRPN, PEG10, KCNQ1OT1) and the NonTarget control BAM (.bam)

Target-SNRPN (SRR35906854): http://identifiers.org/insdc.sra: SRR35906854 [19];

Target-PEG10 (SRR35906853): http://identifiers.org/insdc.sra: SRR35906853 [20];

Target-KCNQ1OT1 (SRR35797983): http://identifiers.org/insdc.sra: SRR35797983 [21];

NonTarget control (SRR35906856): http://identifiers.org/insdc.sra: SRR35906856 [22]

 bedMethyl (.bed) Zenodo (DOI: 10.5281/zenodo.18759160) [15]
2 CTS experiments targeting H1 heterochromatin loci (UCA1, CRCT1) and the NonTarget control BAM (.bam)

Target-UCA1 (SRR35171064): http://identifiers.org/insdc.sra: SRR35171064 [23];

Target-CRCT1 (SRR35171063): http://identifiers.org/insdc.sra: SRR35171063 [24];

NonTarget control (SRR35171061): http://identifiers.org/insdc.sra: SRR35171061 [25]

 bedMethyl (.bed) Zenodo (DOI: 10.5281/zenodo.17762285) [16]
3 CTS experiments targeting RKO epimutation locus (MLH1) and the NonTarget control BAM (.bam)

Target-MLH1 (SRR35171065): http://identifiers.org/insdc.sra: SRR35171065 [26];

NonTarget control (SRR35171062): http://identifiers.org/insdc.sra: SRR35171062 [27]

 bedMethyl (.bed) Zenodo (DOI: 10.5281/zenodo.17762504) [17]
4 CTS experiments targeting the SNRPN locus in early- and late-passage SNRPN-DMRMe/Me cells BAM (.bam)

SNRPN-DMRMe/Me clone, early passage (SRR35171060): http://identifiers.org/insdc.sra: SRR35171060 [28];

SNRPN-DMRMe/Me clone, late passage (SRR35171059): http://identifiers.org/insdc.sra: SRR35171059 [29];

non-target control clone, early passage (SRR35171058): http://identifiers.org/insdc.sra: SRR35171058 [30];

non-target control clone, late passage (SRR35171057): http://identifiers.org/insdc.sra: SRR35171057 [31]

 bedMethyl (.bed) Zenodo (DOI: 10.5281/zenodo.17762655) [18]
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Processed methylation data

To maximize reusability while avoiding duplication, we provide the essential processed datasets required for downstream analysis. The processed data include per-CpG 5mC and 5hmC methylation matrices for each sample, which were generated using Modkit (v0.5.1) on Dorado-basecalled, hg38-aligned BAM files [15–18]. Each resulting matrices file, provided in bedMethyl format, reports per-CpG genomic coordinates, modification type, coverage, raw read counts, and methylation proportions. The output contains 18 columns, defined as follows (see the Modkit documentation for further details: https://github.com/nanoporetech/modkit): Columns 1–3 denote genomic coordinates in BED format (chromosome name, 0-based start coordinate, and 0-based exclusive end position), Column 4 indicates the modified base code (m for 5mC or h for 5hmC), Column 5 reports the score, which is equal to the value in Column 10. Column 6 indicates the reference strand. Columns 7–9 correspond to the start position, end position, and RGB color code for visualization compatibility, respectively. Column 10 reports the valid coverage at the site. Column 11 provides the specified modification percentage. Columns 12–18 provide the raw count breakdown used to derive methylation proportions: Column 12 indicates the number of calls classified as a residue with a specified base modification; Column 13 indicates the number of calls classified as the canonical base rather than modified; Column 14 indicates the number of calls classified as other modification types; Column 15 indicates the number of reads with a deletion at this reference position; Column 16 the indicates number of calls where the probility of the call was below the threshold (by default); Column 17 indicates the number of reads with a base other than the canonical base for this modification; and Column 18 indicates the number of reads aligned to this reference position, with the correct canonical base, but lacking a modification call.

These primary processed data allow users to re-perform differential methylation analyses under alternative statistical frameworks, integrate with additional annotations, or visualize methylation landscapes using customized analytical pipelines.

The analytical outputs are not duplicated in this Data Note since they are already publicly available in the companion research article and can be reproduced from the raw data [4].

Limitations

The datasets include raw aligned Nanopore sequencing reads, while raw signal-level files (POD5) are available upon request. Because signal-level data are not contained within BAM files, benchmarking or re-analysis at the raw electrical signal level cannot be performed using the deposited BAM files alone.

The dataset presented here is subject to several limitations inherent to the experimental design. First, CTS provides targeted rather than genome-wide coverage; therefore, the data are restricted to selected imprinted loci, heterochromatic regions, and a cancer-associated promoter, and are not designed to present comprehensive methylome-wide patterns. Second, although we selected two distinct cell lines (H1 hESCs and RKO colorectal cancer cells) and consistently observed that DSBs perturb local methylation patterns, all experiments were nevertheless performed and validated exclusively in in vitro cell line systems. As no in vivo assays were conducted, the methylation dynamics and SV patterns reported here may not fully recapitulate the chromatin architecture, DNA repair processes, or epigenetic responses that occur under physiological conditions.

Additionally, this study specifically investigates DNA DSBs induced by CRISPR–Cas9. Other forms of chromosomal breakage—such as those induced by ionizing radiation, or chemical genotoxins—were not assessed. Consequently, the extent to which these findings generalize to other sources of DNA damage remains uncertain. Finally, methylation calling and downstream analyses focused specifically on 5mC and 5hmC in the CpG context using the models available at the time of the study. Other base modifications or non-CpG methylation states were not curated, although such signals may be present in the raw nanopore reads and could be explored with future analytical tools.

Acknowledgements

We thank all individuals and facilities recognized in the companion Genome Biology research article [4], whose contributions supported the generation of the original experimental datasets described in this Data Note.

Abbreviations

5mC

5-methylcytosine

5hmC

5-hydroxymethylcytosine

CRCT1

Cysteine rich C-terminal 1

CTS

Cas9-assisted targeted nanopore sequencing

DSB

Double-strand break

DMR

Differentially methylated region

hESC

Human embryonic stem cell

KCNQ1OT1

KCNQ1 opposite strand/antisense transcript 1

MLH1

MutL homolog 1

ONT

Oxford Nanopore Technologies

PEG10

Paternally expressed 10

ROI

Region of interest

SNRPN

Small nuclear ribonucleoprotein polypeptide N

SV

Structural variant

sgRNA

Single-guide RNA

UCA1

Urothelial cancer associated 1

Author contributions

M.L., M.W., and Y.Z. jointly designed the experiments. M.W. carried out the CRISPR-Cas9 editing and nanopore sequencing experiments. Data processing and analysis were performed by M.L., M.W., Y.Z., and C.B. Y.Z. and M.W. led the organization and curation of the datasets and drafted this Data Note under the supervision of M.L. All authors reviewed and approved the final version of the manuscript.

Funding

The research of the Li laboratory was supported by the KAUST Office of Sponsored Research (OSR) under award number BAS/1/1080-01-01. This work was also financially supported in part by funding from King Abdullah University of Science and Technology (KAUST) – KAUST Center of Excellence for Smart Health (KCSH), under award number 5932 (M.L.).

Data availability

The data described in this Database article can be freely and openly accessed on NCBI Sequence Read Archive SRR35906854 [19], SRR35906853 [20], SRR35797983 [21], SRR35906856 [22], SRR35171064 [23], SRR35171063 [24], SRR35171061 [25], SRR35171065 [26], SRR35171062 [27], SRR35171060 [28], SRR35171059 [29], SRR35171058 [30], SRR35171057 [31], under accession PRJNA1193628 [14] and Zenodo under DOIs 10.5281/zenodo.18759160 [15], 10.5281/zenodo.17762285 [16], 10.5281/zenodo.17762504 [17], and 10.5281/zenodo.17762655 [18]. Please see Table 2 and references for details and links to the data. Raw signal-level files (POD5) are available upon request.

Declarations

Ethics approval and consent to participate

The work was performed using established human cell lines and does not involve human participants, animal experiments, or data collected from social media platforms. All research procedures involving human pluripotent stem cells (PSCs) were conducted at King Abdullah University of Science and Technology (KAUST), following review and approval by the KAUST Institutional Biosafety and Bioethics Committee (IBEC). KAUST IBEC, as the local ethics committee registered with the National Committee of Bioethics (NCBE) of Saudi Arabia, fulfills the role of an Embryonic Stem Cell Research Oversight (ESCRO) committee. The H1 human embryonic stem cell (hESC) line used in this study was obtained from the WiCell Research Institute under Material Transfer Agreement 19-W0398. Informed consent was obtained from all original donors by the provider institution at the time of derivation. No human embryos or gametes were collected for this study. The human colorectal cancer cell line RKO was purchased from ATCC and cultured according to the supplier’s instructions. As a commercially available and anonymized cell line, its use does not require additional ethical approval or informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yingzi Zhang and Mengge Wang have contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data described in this Database article can be freely and openly accessed on NCBI Sequence Read Archive SRR35906854 [19], SRR35906853 [20], SRR35797983 [21], SRR35906856 [22], SRR35171064 [23], SRR35171063 [24], SRR35171061 [25], SRR35171065 [26], SRR35171062 [27], SRR35171060 [28], SRR35171059 [29], SRR35171058 [30], SRR35171057 [31], under accession PRJNA1193628 [14] and Zenodo under DOIs 10.5281/zenodo.18759160 [15], 10.5281/zenodo.17762285 [16], 10.5281/zenodo.17762504 [17], and 10.5281/zenodo.17762655 [18]. Please see Table 2 and references for details and links to the data. Raw signal-level files (POD5) are available upon request.

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