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Chromothripsis as an on-target consequence of CRISPR–Cas9 genome editing

Nature Genetics volume 53, pages 895–905 (2021)Cite this article

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Abstract

Genome editing has therapeutic potential for treating genetic diseases and cancer. However, the currently most practicable approaches rely on the generation of DNA double-strand breaks (DSBs), which can give rise to a poorly characterized spectrum of chromosome structural abnormalities. Here, using model cells and single-cell whole-genome sequencing, as well as by editing at a clinically relevant locus in clinically relevant cells, we show that CRISPR–Cas9 editing generates structural defects of the nucleus, micronuclei and chromosome bridges, which initiate a mutational process called chromothripsis. Chromothripsis is extensive chromosome rearrangement restricted to one or a few chromosomes that can cause human congenital disease and cancer. These results demonstrate that chromothripsis is a previously unappreciated on-target consequence of CRISPR–Cas9-generated DSBs. As genome editing is implemented in the clinic, the potential for extensive chromosomal rearrangements should be considered and monitored.

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Fig. 1: Micronucleation is an on-target consequence of CRISPR–Cas9 genome editing.
Fig. 2: Summary of genomic outcomes after the division of 18 micronucleated cells.
Fig. 3: CRISPR–Cas9 genome editing can cause chromothripsis.
Fig. 4: The impact of p53 status on the ability of micronucleated cells to undergo division.
Fig. 5: CRISPR–Cas9 genome editing induces chromosome bridge formation, adding to the genome complexity from micronuclei.
Fig. 6: Hallmark cytological features of chromothripsis after a genome-editing approach for the treatment of sickle cell disease.

Data availability

Whole images of cells presented in Figs. 1f and 6e,i and Extended Data Fig. 2e and filtered SV calls are available at https://doi.org/10.5281/zenodo.4533299. Original images and videos that contribute to analyses in Figs. 1e, 4 and 6c,h, Extended Data Figs. 1d, 2 and 5c and Look-Seq experiments (Figs. 2, 3 and 5 and Extended Data Fig. 3) were not published due to constraints of file size but are available upon reasonable request. CD34+ HSPC-derived FISH and SKY images and analyses (Fig. 6d–g) were generated by the St. Jude Cytogenetic Shared Resource Laboratory and derived data supporting the findings in Fig. 6d–g are available from the corresponding authors upon request. Sequence read data are available in the Sequencing Read Archive under BioProject PRJNA676146. Source data are provided with this paper.

Code availability

Scripts used for sequencing data analysis (allelic copy number calculation and rearrangement detection) and for image analyses performed in Extended Data Fig. 2 are available at https://github.com/chengzhongzhangDFCI/CN_and_SV.

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Acknowledgements

We are grateful to R. Jaenisch, S. Markoulaki, A. Spektor and members of the Pellman and Weiss laboratories for discussions, I. Cheeseman for the doxycycline-inducible Cas9 RPE-1 cell line, D. Cullins for assistance with single-cell sorting of HSPCs and N. Mynhier for help with data visualization. This work was supported by the National Science Foundation Graduate Research Fellowship under grant no. DGE1144152 (M.L.L.), the National Cancer Institute career transition award K22CA216319 (C.-Z.Z.), the Howard Hughes Medical Institute (D.P.), NIH grants R01 CA213404 (D.P.), F32 DK118822 (P.A.D.) and P01 HL053749 (M.J.W.), the Assisi Foundation (M.J.W.), the Doris Duke Charitable Foundation (M.J.W.) and St. Jude/ALSAC. The St. Jude Cytogenetic and Center for Advanced Genome Engineering Shared Resource Laboratories are supported by NIH grant P30 CA21765 and by St. Jude/ALSAC. We thank the members of the St. Jude Children’s Research Hospital Center for Advanced Genome Engineering and Cytogenetics core facilities.

Author information

Author notes

  1. These authors contributed equally: Mitchell L. Leibowitz, Stamatis Papathanasiou.

Authors and Affiliations

  1. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Mitchell L. Leibowitz & David Pellman

  2. Department of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Mitchell L. Leibowitz, Stamatis Papathanasiou, Logan J. Blaine & David Pellman

  3. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Mitchell L. Leibowitz, Stamatis Papathanasiou, Logan J. Blaine & David Pellman

  4. Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Phillip A. Doerfler, Yu Yao & Mitchell J. Weiss

  5. Single-Cell Sequencing Program, Dana-Farber Cancer Institute, Boston, MA, USA

    Lili Sun

  6. Department of Biomedical Informatics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Cheng-Zhong Zhang

  7. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA

    Cheng-Zhong Zhang

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Contributions

M.L.L., S.P. and D.P. conceived the project; M.L.L., S.P., D.P. and P.A.D. designed the experiments; M.L.L. and S.P. performed the experiments, except for the human CD34+ HSPC experiments that were carried out by P.A.D. and Y.Y.; L.S. performed library preparation and sequencing for RPE-1 cells. M.L.L., S.P., L.J.B. and C.-Z.Z. analyzed data; C.-Z.Z. and L.J.B. developed and performed the computational analysis; M.L.L., S.P. and D.P. wrote the manuscript; all authors discussed the results and commented on the manuscript; M.J.W. supervised the human blood cell experiments; P.A.D. and L.J.B. made equal contributions to this work. D.P. supervised the study.

Corresponding authors

Correspondence to Mitchell J. Weiss or David Pellman.

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Competing interests

M.J.W. is a consultant for Rubius Inc., Cellarity Inc., Beam Therapeutics and Esperion; none of the consulting work is relevant to the current project. C.-Z.Z. is a scientific adviser for Pillar BioSciences. D.P. is a member of the Volastra Therapeutics scientific advisory board. All other authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Fyodor Urnov and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Micronucleus formation after CRISPR-Cas9 genome editing in several cell lines.

a, Experimental schemes. Top, RNP transfection. Bottom, inducible Cas9 expression with constitutive expression of gRNAs (RPE-1 cells). G0 cell cycle block was by serum starvation. Dividing cell cartoon represents approximate time of cell division. b, Micronucleation frequency after CRISPR-Cas9 RNP transfection in asynchronous cells. Left, editing efficiency. Right, frequency of micronucleation for these RNP transfections. (n = 3 experiments with 1339, 1231, 1220, 1236, and 1237 cells scored, left to right). Error bars: mean +/- SEM, two-tailed Fisher’s exact test. c, Representative Western blot of Cas9 levels at the indicated times after induction with doxycycline. 1st division is 24 hours after serum starve release, and 2nd division is 48 hours after release. Dox is doxycycline. n = 3 experiments. d, Number of cleaved chromosome arms contained within micronuclei for the indicated gRNAs and Cas9 expression strategies (RPE-1 cells) determined by FISH to detect the centromere (RNP Cas9) and/or subtelomere of the targeted chromosome (RNP Cas9 and Dox-inducible Cas9). RNP Cas9: for 2p: n = 2 experiments with 64 micronuclei counted, 4q: n = 2 experiments with 58 micronuclei counted, 5q: n= 3 experiments with 116 micronuclei counted, Xq: n = 2 experiments with 96 micronuclei counted; (Dox) Doxycycline-inducible Cas9; n = 3 experiments; 168 micronuclei counted per condition. e, Frequency of micronucleation in synchronized BJ fibroblasts after RNP transfection; (n = 3 experiments with 2378, 2487, 2423, 2714 cells, left to right). Error bars: mean +/- SEM, two-tailed Fisher’s exact test. f, Left, percentage of MN containing the targeted chromosome arm for the chr5q-targeting gRNA in BJ cells, as counted using subtelomeric FISH probes. Right, the number of chr5q chromosome arms per micronucleus in BJ cells, determined from centromere-specific and subtelomere-specific FISH probes. (n = 2 experiments counting 109 micronuclei). g, Cut site and FISH probe locations for allele-specific gRNA experiments. PAM sequence is in bold, with the polymorphic site in red. Orange star is the centromere FISH probe and green circle the subtelomere FISH probe. gRNAs target the reference allele. h, Editing efficiency after Cas9/gRNA RNP transfection with allele-specific gRNAs. (n = 3 experiments). Error bars: mean +/- SEM. i, Micronucleation frequency from samples in (h). (n = 3 experiments with 7066, 7041, 7253, cells scored for micronucleation, left to right). Error bars: mean +/- SEM, two-tailed Fisher’s exact test. (j) Left, percentage of MN containing the targeted chromosome arm for the allele-specific gRNAs, as scored using subtelomeric FISH probes. Right, pie chart of the number of targeted arms per micronucleus in RPE-1 cells, as determined from subtelomere-specific FISH probes. (n = 3 experiments counting 123 and 184 micronuclei, left to right) Error bars: mean +/- SEM.

Source data

Extended Data Fig. 2 DNA damage, nuclear envelope rupture and reduced DNA replication in CRISPR-MN.

a, Nuclear envelope rupture frequency for CRISPR-MN as compared to spindle checkpoint inhibitor-induced micronuclei. Rupture was defined as an MN:PN ratio of lamin B receptor (LBR)49 intensity > 3 (n = 3 experiments with 201 and 167 micronuclei analyzed for chr5q, p = 0.2216 and 165 and 152 micronuclei counted for chr6q, p = 0.2034). Error bars: mean +/- SEM, two-tailed Fisher’s exact test. b, DNA replication defect of CRISPR-MN. EdU fluorescence intensity was measured after a 5-hour pulse. Only cells that had entered S-phase were scored (>150 a.u. EdU signal in primary nucleus). Dotted red line is normal levels of DNA replication in the micronucleus relative to the primary nucleus (n = 3 experiments with 109 and 97 micronucleated cells analyzed for chr5q, p = 0.1698 and 65 and 73 micronucleated cells analyzed for chr6q, p = 0.6948). Error bars: mean +/- SEM; two-tailed Mann-Whitney U-test. c, CRISPR-MN acquire DNA damage. Shown is the frequency of γH2AX positive micronuclei (> 3 standard deviations above mean signal in primary nuclei) for the indicated gRNAs using the inducible Cas9 system (n = 3 experiments with 203 and 184 micronucleated cells analyzed for chr5q, p = 0.6870 and 175 and 169 cells analyzed for chr6q, p = 0.8053). Error bars: mean +/- SEM, two-tailed Fisher’s exact test. d, CRISPR-MN acquire DNA damage (RNP Cas9 system). Shown is the frequency of γH2AX positive micronuclei for the indicated gRNAs (n = 2 experiments with 56, 46, 82, and 50 micronucleated cells analyzed, left to right). e, Example images of data from panel d, showing γH2AX labeling. White arrows: micronuclei. Scale bars, 5 μm. The γH2AX focus in the primary nucleus likely decorates the centric portion of the broken chromosome. Alternatively, or additionally, it may label a DNA break on the homolog.

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Extended Data Fig. 3 Haplotype copy number and SVs for the targeted chromosome for each sample in the paper.

Haplotype-resolved copy number and structural variant analysis for the targeted chromosome for each granddaughter pair. Red and blue dots represent 1 Mb copy number bins for each homolog, and curved lines represent structural variants of ≥ 1 Mb that could be on either homolog. Top, ‘granddaughter a’; middle, ‘granddaughter b’; bottom, sum copy number for each homolog for the pair of cells. Note that in most cases there should be a total of two red and two blue copies per granddaughter pair, and deviation from this represents certain missegregation or events, such as first-generation bridge formation. Copy number alterations occurring only in one daughter without a corresponding or reciprocal change in the other daughter were attributed to random noise due to variability in genome amplification quality. Text: inferred most likely explanation for each copy number and rearrangement profile. Note that alternative explanations exist for many samples, such as a G1 cut followed by replication of the cut chromosome.

Extended Data Fig. 4 Clustering of DNA breakpoints, indicative of chromothripsis, on the telomeric side of the CRISPR-Cas9-targeted cut site.

Breakpoint density for each daughter pair telomeric of the cut-site (red), relative to the rest of the genome (black), normalized by read depth. Data include both inter- and intra-chromosomal rearrangements. Significance is derived from a one-sided Poisson test28. p – values are rounded to the nearest exponent, except for those <10−30. Bolded p - values denote significance after Bonferroni correction. Bonferroni-corrected a = 0.0028.

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Extended Data Fig. 5 Chromosome bridge formation after CRISPR-Cas9 genome editing.

a, A bridge formed during the first cell division after Cas9 addition yields shared losses (left granddaughter pair) or gains (right granddaughter pair) depending upon how the bridge breaks. This copy number alteration will be on the centromeric side of the CRISPR-Cas9 break. Cells and chromosomes are depicted as in Fig. 3. The non-micronucleated daughter cell is faded and not followed. In this example, the micronuclear chromosome from the first division is not reincorporated and becomes a micronucleus in one granddaughter. b, A bridge formed in the second cell division yields reciprocal copy number gains and losses centromeric of the break (comparing the granddaughters). The non-micronucleated daughter cell is faded and not followed. c, The frequency of detectable chromosome bridges by live-cell imaging after CRISPR-Cas9 genome editing in RPE-1 cells expressing a fluorescence reporter that marks chromosome bridges efficiently (GFP-BAF). DNA breaks were induced with the Chr5q-targeting inducible Cas9 system after treatment with siRNA against TP53 or non-targeting siRNA. Chromosome bridges frequently arise when a micronucleus forms in at least one daughter cell in the first division (MN+), whereas when a micronucleus is not formed, bridge formation is uncommon (MN-). In the second division, micronucleated cells are more prone to bridge formation (MN+) as compared to non-micronucleated cells (MN-). Bridge formation is more frequent in the second division, which may be explained by isolation of the acentric arm from the centric fragment of the chromosome (p53 siRNA: n = 6 experiments with 175 and 172 cell divisions imaged [division 1] and 136 and 132 divisions imaged [division 2]; non-targeting siRNA: n = 3 experiments with 89 and 90 cell divisions imaged [division 1] and 43 and 58 divisions imaged [division 2]). Error bars: mean +/- SEM, two-tailed Fisher’s exact test.

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Extended Data Fig. 6 Allele ratios of heterozygous SNPs from CD34+ HSPC colonies after editing.

a, Map of SNP locations, cut site, and the centromere (CEN) on chromosome 2 (not to scale). b, The distribution of A-allele frequencies for samples where A-allele and B-allele frequencies comprise greater than 90 % of the sequence reads. The p-values for SNPs 1–8 are p = 0.1089, 0.3140, 0.9967, 0.7792. 0.2751, 0.4659, 0.3178, and 0.2239 respectively (two-tailed Mann-Whitney U test). SNP5 exhibited a strong deviation from a 50:50 allelic ratio even in unedited controls, which may reflect a PCR amplification artifact. Because of this, SNP5 was excluded from subsequent analysis. c, Heatmap of allele frequency data for all samples (Cas9, left; Cas9 + Chr2p gRNA, right). The heatmap is divided into sections based on the minimum sequencing read depth. Minimum sequencing read depth was defined by the SNP with the lowest number of reads in the sample. Samples with low read depth exhibited high variability in allelic ratios, likely reflecting low input DNA from small colonies. Because we lack phasing information, any deviation from a 50:50 allele ratio for multiple adjacent SNPs suggests segmental copy number alterations. See Supplementary Note for methods and additional discussion. For this experiment, only several hundred clones could feasibly be grown and analyzed, whereas patients will receive tens to hundreds of millions of edited cells. From the several hundred clones in our experiment, we only expect ~20 cells containing micronuclei based on micronucleation rates measured in Fig. 6. Extrapolating from these data, patients will receive millions of micronucleated cells, each one with the potential to undergo chromothripsis and grow into a clone. We note that this assay will not detect copy-number neutral chromothripsis nor chromothripsis that maintains copy number and heterozygosity at the assayed SNPs, with rearrangements located on other segments of the edited chromosome. Moreover, this approach has a limited ability to detect copy number gains or subclonal events that result from ongoing genomic instability triggered by micronucleation or bridging derived from the initial editing.

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Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Video 1

Representative Look-Seq video of a reincorporated micronucleus, as in Fig. 3a,b (GFP–H2B). Pair 5.6. Timestamp shows relative time in minutes. Widefield imaging under a ×20 objective.

Supplementary Video 2

Representative Look-Seq video of a persistent micronucleus, as in Fig. 3c,d (GFP–H2B). Pair 5.10. Timestamp shows relative time in minutes. Widefield imaging under a ×20 objective.

Supplementary Tables

Supplementary Table 1. Summary of gRNA species. Table of gRNA sequences and coordinates used in this study. Supplementary Table 2. One-sided Poisson test for enrichment of rearrangements across all chromosome arms. One-sided Poisson tests were performed to test for enrichment of rearrangement intrachromosomal breakpoints on each chromosome arm in all granddaughter pairs. The table includes breakpoints present in each genome and on the targeted arm. Also included is the fraction of total genomic reads aligning to each arm. Supplementary Table 3. Primer sequences for editing efficiency analysis. Table of forward and reverse primers used to amplify DNA for analyses of editing efficiency.

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Leibowitz, M.L., Papathanasiou, S., Doerfler, P.A. et al. Chromothripsis as an on-target consequence of CRISPR–Cas9 genome editing. Nat Genet 53, 895–905 (2021). https://doi.org/10.1038/s41588-021-00838-7

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