Article

Quantitative and qualitative mutational impact ofionizing radiation on normal cells
Graphical abstract

Authors
Jeonghwan Youk, Hyun Woo Kwon,
Joonoh Lim, ..., Kyung Su Kim,
Tae Gen Son, Young Seok Ju

Correspondence
jh.chang@snu.ac.kr (J.H.C.),
kskim.cirt@snu.ac.kr (K.S.K.),
tgson@dirams.re.kr (T.G.S.),
ysju@kaist.ac.kr (Y.S.J.)

In brief

HighlightsdSingle-cell-resolution DNA sequencing reveals IR-associatedmutational signaturesdIR-associated mutations include short deletions and simple
and complex rearrangementsdSimultaneous multiple DSBs cause chromothripsis,
chromoplexy, and SVs by BFB cyclesdYouk, Kwon, Lim, Kim, Kim et al. usedsingle-cell-resolution DNA sequencing toinvestigate the quantitative andqualitative impact of ionizing radiation(IR) on the mammalian genome. They
found characteristic mutationalsignatures, comprised of specific shortdeletions and simple and complex
structural variations. Complex genomic
rearrangements, prevalent in cancer,
were induced in post-irradiated cells,suggesting their role in tumor
development. The study reveals
mutational rates proportional to IR doseand consistent across cell types,
underscoring IR’s universal mutagenic
impact on mammalian cells.

1 Gy IR exposure generates 2.33 mutations per Gb in
mammalian cells

Youk et al., 2024, Cell Genomics 4, 100499
February 14, 2024 ª 2024 The Author(s).
https://doi.org/10.1016/j.xgen.2024.100499

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ArticleQuantitative and qualitative mutational impactof ionizing radiation on normal cells
Jeonghwan Youk,1,21 Hyun Woo Kwon,2,3,21 Joonoh Lim,1,4,21 Eunji Kim,5,6,21 Taewoo Kim,1,21 Ryul Kim,1,4
Seongyeol Park,1,4 Kijong Yi,1,4 Chang Hyun Nam,1 Sara Jeon,7 Yohan An,1 Jinwook Choi,8,9 Hyelin Na,10,20
Eon-Seok Lee,11 Youngwon Cho,2,12 Dong-Wook Min,2,12 HyoJin Kim,11 Yeong-Rok Kang,11 Si Ho Choi,11 Min Ji Bae,11Chang Geun Lee,11 Joon-Goon Kim,1,13 Young Seo Kim,1,13 Tosol Yu,6,14 Won-Chul Lee,4 Jong-Yeon Shin,4Dong Soo Lee,2,15 Tae-You Kim,2,12,16 Taeyun Ku,1,13 Su Yeon Kim,1 Joo-Hyeon Lee,8,9 Bon-Kyoung Koo,10
Hyunsook Lee,7 On Vox Yi,17 Eon Chul Han,18 Ji Hyun Chang,6,19,* Kyung Su Kim,6,19,* Tae Gen Son,11,*and Young Seok Ju1,4,22,*
1Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141,
Republic of Korea
2Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul
National University, Seoul 08826, Republic of Korea
3Department of Nuclear Medicine, Korea University College of Medicine, Seoul, Republic of Korea
4Genome Insight, Inc., San Diego, CA 92121, USA
5Department of Radiation Oncology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 20, Boramae-ro 5
Gil, Dongjak-gu, Seoul 07061, Republic of Korea
6Department of Radiation Oncology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
7Department of Biological Sciences & IMBG, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
8Wellcome - MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, CB2 0AW Cambridge, UK
9Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3EL Cambridge, UK
10Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), Dr. Bohr-Gasse 3, 1030 Vienna,
Austria
11Research Center, Dongnam Institute of Radiological and Medical Science, Busan, Republic of Korea
12Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
13KI for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
14Department of Radiation Oncology, Dongnam Institute of Radiological and Medical Science, Busan 46033, Republic of Korea
15Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
16Department of Internal Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
17Department of Breast Surgery, Dongnam Institute of Radiological and Medical Science, Busan, Republic of Korea
18Department of Surgery, Dongnam Institute of Radiological and Medical Science, Busan, Republic of Korea
19Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea
20Present address: TONY Investment Co., Ltd., Seoul 06180, Republic of Korea
21These authors contributed equally
22Lead contact*Correspondence: jh.chang@snu.ac.kr (J.H.C.), kskim.cirt@snu.ac.kr (K.S.K.), tgson@dirams.re.kr (T.G.S.), ysju@kaist.ac.kr (Y.S.J.)
https://doi.org/10.1016/j.xgen.2024.100499

SUMMARY

The comprehensive genomic impact of ionizing radiation (IR), a carcinogen, on healthy somatic cells remains
unclear. Using large-scale whole-genome sequencing (WGS) of clones expanded from irradiated murine
and human single cells, we revealed that IR induces a characteristic spectrum of short insertions or deletions(indels) and structural variations (SVs), including balanced inversions, translocations, composite SVs (deletion-insertion, deletion-inversion, and deletion-translocation composites), and complex genomic rearrangements (CGRs), including chromoplexy, chromothripsis, and SV by breakage-fusion-bridge cycles. Our findings suggest that 1 Gy IR exposure causes an average of 2.33 mutational events per Gb genome, comprising
2.15 indels, 0.17 SVs, and 0.01 CGRs, despite a high level of inter-cellular stochasticity. The mutationalburden was dependent on total irradiation dose, regardless of dose rate or cell type. The findings were further
validated in IR-induced secondary cancers and single cells without clonalization. Overall, our study highlights
a comprehensive and clear picture of IR effects on normal mammalian genomes.

Cell Genomics 4, 100499, February 14, 2024 ª 2024 The Author(s). 1
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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ArticleINTRODUCTIONRESULTS

Somatic mutations accumulate in cells throughout an individual’s lifetime, often leading to diseases, such as cancer.1 As
these mutations are a joint result of specific DNA damage andrepair processes in cells,1 each mutational process leaves a
distinct pattern of mutations known as the ‘‘mutational signature.’’2 Recently, a sophisticated statistical deconvolution of
population-scale cancer genome data defined approximately
100 mutational signatures (available at the COSMIC database:https://cancer.sanger.ac.uk/signatures/).2,3 Some of the known
mutational signatures are associated with well-known carcinogens. For example, lung, skin, and liver cancer genomes
frequently exhibit single-base substitution (SBS) signaturesassociated with exposure to tobacco smoking,4 ultraviolet light,5and aristolochic acids,6 respectively. Additional mutationalsignatures have been identified under experimental conditions
using genome sequencing of cells exposed to various
physical, chemical, and biological genotoxins.7–9 These approaches have provided insight into DNA damage mechanismsof numerous carcinogens and downstream cellular repair
processes.
Ionizing radiation (IR) is a well-known potent carcinogenthat causes direct and indirect DNA damage10 and iscommonly encountered in the environment, for example asradon gas, and in medical practice. Due to its genotoxicity,
numerous studies have investigated its mutational impact,
particularly in the germline. Genome sequencing in model organisms has revealed a positive correlation between acquired
genomic mutations and irradiation dose in budding yeasts,
worms, and mice.8,11–14 In humans, survivors from atomic
bombings and nuclear power plant accidents have been
analyzed.15–17 To explore the mutational impact of IR on somatic cells, IR-irradiated induced pluripotent stem cells andradiation-induced secondary tumors have been investigated.7,18,19 However, the precise mutational signatures ofIR, particularly in normal human cells, remain incomplete, asprevious studies were limited by insufficient sample size, inattention to structural variations (SVs), or other confounding genetic and environmental factors impacting cancer cells.7,18–20
Due to similar reasons and the limited contribution of IR-associated secondary cancers in the discovery dataset, largescale statistical methods have not delineated IR-associatedmutational signatures.2
In this study, we conducted an in-depth genomic investigationof post-irradiated somatic cells employing whole-genome
sequencing (WGS) of clonally expanded post-irradiated cells(hereafter referred to as colonies)21,22 to elucidate the comprehensive IR-induced mutational spectrum and burden. Murine
colonies were subjected to 0–20 Gy gamma radiation undercontrolled experimental conditions, and human colonies were
established from adjacent normal tissues after radiation therapy
involving exposure to approximately 50 Gy radiation. We
also investigated the genomes of 22 radiation-induced secondary tumors.18,23 To explore potential selection bias from relying
on viable clones in our clonal-expansion approach, we additionally examined post-irradiated cells using various single-cellsequencing technologies.

Clonalization for accurate single-genome sequencing
WGS of clonally expanded cells allows comprehensive and accurate detection of all types of genomic variants accumulated
within a single cell22,24 but cannot be used to investigate the genomes of dead or non-proliferating cells. To efficiently expand
single cells, we used organoid culture techniques (Figures S1A
and S1B), which allow culturing of non-neoplastic normal single
cells.25 We used three different but complementary experimental
settings for IR exposure (Figure 1A), including (1) IR exposure on
cultured cells (IRvitro) using a 137Cs source; (2) IR exposure on
living tissues (IRvivo) using mostly a 60Co source (Figure S1C),
combined with a 137Cs source for long-term exposure experiments; and (3) post-radiotherapy (IRpost-RT) with gamma raysfrom the accelerator as the main IR source. Further, we analyzed
22 genomes of IR-associated secondary tumors to evaluate IRassociated mutations in human cancers (Figure 1A).
We investigated the genome sequences of 135 colonies,
including 88 colonies derived from post-irradiated normal cells
and 47 from non-irradiated normal cells (controls) (Tables S1and S2). The variant allele fraction (VAF) of acquired mutations(Figures 1B and S2–S4) confirmed that all colonies were monoclonal (n = 80; 59.3%) or dominant clonal (n = 55; 40.7%), thus
allowing detection of IR-induced mutations acquired in the predominant founder cell.

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Impact of IR on cell viability
We found that the organoid-forming efficiency of single cells was
negatively correlated with IR exposure level. Approximately 2 Gy
irradiation reduced the proliferation potential of mouse pancreatic organoids by 50% (Figure 1C). RNA sequencing of post-irradiated organoids revealed upregulation of DNA damage
response genes, such as Brca1, Brca2 (homologous recombination DNA repair pathway), Mdm2 (p53 pathway), and Cdkn1a(cell-cycle pathway), 2 h post-irradiation (Figure 1D). However,
the transcriptional activity of these genes returned to approximately baseline within 24 h, suggesting a short-term earlycellular response to IR exposure (Figure 1D).
Short insertion or deletion (indel) mutations induced byirradiation
Analysis of the genomes of the 135 colonies revealed an averageof 1,020 SBSs and 282 short indels per colony (Figure 2A). The
acquired alterations included IR-induced mutations, IR-unrelated mutations, and technical artifacts from cell culture andsequencing. To sort out IR-induced acquired mutations, we deconvoluted mutational signatures using these alterations.
Although IR exposure induces intracellular reactive oxygen species,10,26 which cause DNA damages and C:G>A:T base substitutions,27 we did not identify any SBS signatures exclusive to orenriched in the IR exposure group (Figures 2A and S5A;
Tables S3, S4, S5, and S6), indicating that IR exposure does
not lead to a substantial number of SBSs fixed in the genomeof proliferative cells.
In contrast, a specific signature of indel mutations (referred toas ID-A), which was newly delineated from the indel mutations
detected in this study, was almost exclusively observed in thell
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Figure 1. Overview of the experimental design(A) Experimental design for detecting mutations induced by ionizing radiation (IR) in single cells using organoid culture technique. We collected cells irradiated infour different but complementary experimental settings, including (1) IR exposure on cultured cells (IRvitro; top left), (2) IR exposure on living tissues (IRvivo; bottom
left), (3) post-radiotherapy (IRpost-RT; top right), and (4) IR-induced secondary cancers. The collected cells from the first three settings were clonally expanded intocolonies using organoid techniques followed by WGS. IR-induced secondary malignancies were whole-genome sequenced without the clonalization step. For
each experimental setting, we produced baseline (germline) genome sequences to sort out IR-associated somatic mutations from the colonies and cancer
tissues.
(B) Variant allele fraction (VAF) distribution of acquired single-base substitutions (SBSs) in non-irradiated (top) and irradiated (bottom) mouse colonies (pancreas).
The black solid lines are Gaussian curves fitted to the distribution, and the gray solid line is a density curve.
(C) Organoid-forming efficiencies of irradiated cells (bottom). Scale bar, 2.5 mm. Data are presented as mean ± SEM (the standard error of the mean) (top). n = 3.
(D) Gene expression changes in cells exposed to 2 Gy irradiation for 24 h. Genes in the p53, cell-cycle, DNA replication, and double-strand breakage pairpathways are colored red, green, purple, and blue, respectively.
See also Figures S1–S4 and S8.

irradiated group (shown in purple in Figures 2A and S5B;
Tables S7, S8, S9, and S10). Intriguingly, most indels observedin IR-associated secondary cancers were attributed to the ID-A
signature (Figure 2A). Two concordant versions of the ID-A
signature were independently extracted from murine and human(hID-A) colonies (cosine similarity = 0.924; Figures 2B and 2C).
The ID-A signature was characterized by (1) predominantly
base deletions (number of deletions:number of insertions =4.87:1), (2) a broad spectrum with no particular enrichment in a
specific sequence context, and (3) a marginally higher level ofsequence microhomology than randomly expected (1–4 bp; Figure 2D). Assuming that IR-induced DNA double-strand breaks(DSBs) are randomly distributed genome-wide, the ID-A signature suggests that DNA ligation through end resection is more
efficient when two breaks share sequence microhomology(Figures 2D and 2E). Interestingly, a component of the ID-A

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Figure 2. Landscape of SBSs and indels(A) Summary of the acquired base substitution and indel mutations in the 135 colonies and 22 IR-induced secondary cancers. Stacked bar plots showing absolute
number of SBS and indel mutations and the relative proportion of each SBS and indel signature. Full signatures delineated are shown in Figure S5. Annotation
tracks include relevant information for each sample. The color codes in the figure legend are used for all subsequent figures.
(B) Mutational spectrum of IR-associated indels delineated in mice (mID-A) (top). Additional mutational signatures (IR unrelated) are shown in Figure S5B. Expected mutational spectrum assuming random mutations is shown with y axis flipped (bottom).
(C) Mutational spectrum of IR-associated indels delineated in humans (hID-A). Additional mutational signatures (IR unrelated) are shown in Figure S5B. Expected
mutational spectrum assuming random mutations is shown with y axis flipped (bottom).
(D) Pie chart showing differences between observed and expected indel spectra in mice (inner circles) and humans (outer circles). The expected indel spectrum(lighter color in each circle) was calculated based on the reference genome sequence.
(E) Schematic representation of the formation of IR-induced DNA double-strand breaks (DSBs) and DNA repair processes for IRi-IDs.
See also Figure S5.

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Articlespectrum, particularly >1 bp deletions with microhomology(shown in purple in Figures 2B and 2C), appears highly similar
to the COSMIC reference signature ID8, the proposed mechanism of which involves non-homologous end joining (NHEJ), a
major repair mechanism for DNA DSBs.2,28 Our spectrum further
suggests that DSB repair pathways involving longer sequencehomology, such as alternative end joining (2–20 bp microhomology), single-strand annealing (>20 bp homology), and homologous recombination (>100 bp homology), do not dominantly
generate IR-induced indels.
SVs induced by irradiation
Analysis of copy-number changes and SVs revealed that chromosome and arm-level copy-number changes were 3.7 times
more frequent in colonies from irradiated cells, although the difference was not statistically significant (17.8% vs. 4.9% in irradiated and non-irradiated groups, respectively; p = 0.14, Fisher’s
exact test; Figure 3A; Table S11). The 135 colonies collectively
exhibited 2,596 SV breakpoints (Tables S12 and S13). SomeSV classes, including medium-sized deletions (100 bp–1 Mb),
tandem duplications, and templated insertions, were not considered IR associated, as they were also frequently present in the
non-irradiated colonies (Figure 3A). Except for complex genomic
rearrangements (CGRs; described in the following section),seven SV classes, whose number of breakpoints total 966,
were substantially enriched in the irradiated colonies and were
therefore defined as IR-inducible SVs (IRi-SVs) (Figure 3A). The
majority of the IRi-SV breakpoints exhibited 0–4 bp microhomology (94.5%), implying that NHEJ is the primary underlying DNA
repair mechanism leading to these rearrangements (Figure 3B).
Of the IRi-SV classes, balanced inversion was the most dominant type, present in approximately 60% of the colonies of theirradiated group. In contrast, colonies of the non-irradiatedgroup did not harbor any inversion events (58.0% vs. 0% for
the irradiated and non-irradiated groups, respectively; p =2.0e13, Fisher’s exact test; Figure 3A). These results corroborate a previous study that reported frequent inversions in IRmediated secondary malignancies.18 Similarly, balanced
translocation and long-deletion (R1 Mb) classes were almost
exclusive to the irradiated group (balanced translocation,
42.0% vs. 0% for the irradiated and non-irradiated groups,
respectively; p = 6.3e9; long-deletion, 30.7% vs. 2.1%, p =3.3e5, Fisher’s exact test; Figure 3A). Despite low frequency,
extrachromosomal DNA (ecDNA)30,31 was also exclusively foundin the irradiated group (2.3% vs. 0% for the irradiated and nonirradiated groups, respectively; p = 0.54, Fisher’s exact test; Figure 3A). Topologically, the formation of these four SV classes involves at least two simultaneous DSBs (Figure 3C).
Colonies derived from irradiated cells exhibited three additional SV classes with more interlaced breakpoints involving atleast three simultaneous DSBs (31% vs. 0% for the irradiatedand non-irradiated groups, respectively; p < 2.2e6, Fisher’s
exact test; Figure 3A). These included deletion-insertion (DelIns), deletion-inversion (Del-Inv), and deletion-translocation(Del-Tra) composites, which, to the best of our knowledge,
have not previously been systematically defined (Figures 3Dand S6A). We identified 17 such variants that were approximately 26-fold more frequent in the 22 IR-induced secondary

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malignancies than in IR-naive primary lung adenocarcinomas(Figure 3E; p < 2.2e16, independent two-population proportions test), confirming that these SV classes are likely IR induced.
We speculate that SVs involving multiple DSBs are prevalent incells exposed to IR, as IR can induce multiple DSBs simultaneously in a cell.
CGRs induced by irradiation
In the 135 colonies, we identified 14 CGRs, comprising
numerous breakpoints preferentially formed by a single catastrophic event.32 These CGR events were exclusive to the irradiated group (14.8% vs. 0% for the irradiated and non-irradiatedgroups, respectively; p = 4.2e3, Fisher’s exact test), implyingthat these CGRs were induced by IR exposure (Figure 3A). We
observed three typical CGR classes, including chromoplexy(n = 4), chromothripsis (n = 2), and SVs by breakage-fusionbridge (BFB) cycles (BFB-SVs; n = 1).
Chromoplexy, characterized by ‘‘closed-chain’’ multi-chromosomal balanced translocations, is a major mechanism forthe formation of oncogenic rearrangements in prostate, lung,bone, and soft-tissue cancers (Figure 4A).29,33,34 Of note, DSB
bursting in transcriptional hubs has been speculated to be an underlying mechanism of chromoplexy.34 Our findings indicate thatIR exposure may serve as a physical trigger for the induction ofchromoplexy via the generation of simultaneous multi-chromosomal broken ends and subsequent erroneous rejoining of theseends (Figure 4B).
Two colonies in the irradiated group exhibited chromothripsis,characterized by extensive DNA fragmentation and reassembly
localized on one or two chromosomes.35 One chromothripsis
event involved a 28-Mb-long region of chromosome 15, encompassing 47 breakpoints combined with copy-number oscillations between one and three copies in a colony derived from
cells exposed to 4 Gy (Figure 4C). We speculate that IR exposure
induced two DSBs in chromosome 15, which resulted in excisionof the 28-Mb-long segment and the formation of a ring structure(Figure 4D). Due to the absence of a centromere and telomeres,the ring structure is unstable and therefore likely to be massively
shattered in subsequent cell divisions.36 The other chromothripsis event involved joint fragmentation and reassembly of chromosomes 12 and X (Figure 4E). We speculate that IR exposure
contributed to disjoining the telomeres from the ends of thetwo chromosomes (Figure 4F), leading to the formation of adicentric chromosome. Dicentric chromosomes are also structurally unstable, forming anaphase chromatin bridges during
subsequent cell division, which can result in massive fragmentation and thus chromothripsis.37 Consistent with the mechanism,
we observed a localized hypermutation (kataegis) near the chromothripsis breakpoints (Figure S6B) that was acquired when theanaplastic DNA bridge was exposed to cytoplasmic TREX1.37
Finally, we identified a BFB-SV event exhibiting typical patterns,
including a stair-like increase in DNA copy number and enrichment of fold-back inversions (Figure 4G).38 BFB-SVs can arise
when two sister chromatids join end to end (Figure 4H). The genomes of IR-associated secondary cancers exhibited frequent
CGRs (Figure S7). However, as CGRs also frequently occur in
IR-naive primary cancer genomes,39 it is unclear whether all
the CGRs found in the secondary cancers were IR induced.

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Figure 3. Landscape of structural variations (SVs)(A) Summary of the acquired SVs in the 135 colonies. Each row indicates SV type, and each column represents the number of SVs observed in each sample.
Colonies are classified into two groups: non-irradiated (n = 47) and irradiated (n = 88). SV types considered non-specific to the irradiated group are presented on
the top. SV types specific to the irradiated organoids are presented on the bottom. Del-Inv, deletion-inversion composite; Del-Tra, deletion-translocation
composite; Del-Ins, deletion-insertion composite.
(B) Size distribution of microhomology in IR-induced SVs (IRi-SVs; purple). Compared to non-IRi-SVs (gray), breakpoints of IRi-SVs harbor shorter microhomology (p < 0.001, Kolmogorov-Smirnov test), which supports that non-homologous end joining is a predominant repair process for IR-induced DSBs.
(C) Schematic representation of the consequences of IR-induced double DSBs in one chromosome (top) and in two chromosomes (bottom).
(D) Schematic representation of the consequences of IR-induced triple DSBs in one chromosome (top) and in two chromosomes (bottom).
(E) Frequency of Del-Ins and Del-Inv-and-Del-Tra events in non-irradiated organoids (n = 47), irradiated organoids (n = 88), primary lung cancer (n = 138; data fromLee et al.29), and IR-induced secondary cancer (n = 22). ***p < 0.001 for Del-Ins and *p < 0.05 for Del-Inv-and-Del-Tra, independent two-population proportions
test. Data are presented as mean ± SEM.
See also Figure S6.

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Figure 4. Complex genomic rearrangements(A) Circos plots of four post-irradiated colonies exhibiting chromoplexy.
(B) Schematic representation of the mechanism of chromoplexy observed in a human breast colony (hBR_50Gy_3).
(C) Circos plot showing chromothripsis (red lines) on chromosome 15 in a 4 Gy irradiated organoid (PA_4Gy_7; top). The copy-number state in the catastrophic
segment oscillates between three and one (bottom).
(D) Schematic representation of a possible mechanism of chromothripsis observed in the 4 Gy irradiated organoid (PA_4Gy_7).
(E) Circos plot showing chromothripsis (red lines) localized in chromosomes 12 and X found in a 2 Gy irradiated organoid (PA_2Gy_12; top) and patterns ofrearrangements and copy-number states (bottom).

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Quantitative impact of IR exposure in colonies
The number of IRi indels (IRi-IDs), attributable to the ID-A signature, and IRi-SVs was positively correlated with IR dose
(Figures 5A and 5B). Although we observed a huge level of intercellular variation, colonies derived from IRvitro experiments exhibited 2.15 IRi-IDs per Gy per Gb of the genome (Figure 5A).
In comparison, colonies derived from tissues exposed in vivo
(IRvivo and IRpost-RT) and secondary tumor samples exhibitedslightly lower IRi-ID mutation rates (1.44 and 1.23 IRi-IDs per
Gy per Gb, respectively), which may be attributed to stronger
negative selection in these experiments. We speculate that cellswith an excessive number of DSBs were less viable, making
them not sequenceable due to decreased cell viability after IR
exposure (Figure 1C). The negative selection of hypermutatorcells in tissues was observed in a previous study, which reported
the removal of hypermutated cells from the bronchial epitheliumin ex-smokers.40
DNA DSB repair may result in either a seamless repair leaving
no mutations or a mutation-forming repair. To estimate the rateof seamless repair, we investigated breakpoints in IRi-SVs andfound predominantly end resections with gaps (approximately
87%; 1–20 bp) but some ligations with base insertions (approximately 7%; 1–9 bp) or without gaps (repair without end resection; approximately 6%) (Figure 5C). If the seamless repair rateof approximately 6% applies to indel formation, the rate of seamless DSB repair would be 0.15 per Gy per Gb for colonies derived
from IRvitro experiments.
The IRi-SV rate in colonies derived from IRvitro experiments
was 0.177 per Gy per Gb (Figure 5B), approximately 8% of therate of IRi-IDs (Figure 5D). This lower rate of SVs may be attributed to the more complex mechanism involved in SV formation
than indel mutations, requiring two or more simultaneous
DSBs and their end joining in an erroneous configuration. Interestingly, the relative frequency of IRi-SVs and IRi-IDs wasconsistent within colonies derived from the IRvitro experiments
within the IR dose range of 1–4 Gy (Figure 5D). However, the ratio
was reduced in colonies derived from IRvivo and IRpost-RT, whichwere exposed to a higher IR dose (8–50 Gy) (Figure 5D). As speculated in the IR-induced indel rate, it may be attributed to negative selection that weeds out cells harboring a large number of
DSBs. Cells that acquired many simultaneous DSBs, which are
more likely to generate SVs, will be less viable, as SVs are generally more deleterious than indels.
There were no significant differences in IR-associated mutational burden of the different cell types, although colon-derived
colonies exhibited slightly lower mutational burdens for both
IRi-IDs and IRi-SVs (Figure 5E). There results imply that mostcell types have similar DNA repair capacities in response to IR
exposure, despite different background transcriptional andepigenetic profiles.
To investigate whether the irradiation dose rate can change
the mutational impact, we established four colonies from miceusing 8 Gy gamma irradiation at a very low IR rate (approximately

Article10e4-fold; with approximately 5.5e5 Gy/min continuously for
100 days). We found that the number of IRi-IDs and IRi-SVs inthese colonies was not significantly different from thoseobserved in the IRvivo experiments using 0.714 Gy/min (Figure 5F), suggesting that the total dose of IR exposure, rather
than the dose rate, is the determining factor for the mutationalburden, at least within the given range of dose rates.
Finally, we compared the number of fixed IR-associated mutations with the number of acquired DSBs in the post-irradiatedcells. To accurately measure the number of DSBs induced by
IR irradiation, we counted gamma-H2AX foci in 50 cells exposed
to 2 Gy in vitro using three-dimensional super-resolution imaging
after physical expansion of cells.42 After adjusting for background levels, we found that 1 Gy IR generated 2.77 DSBs per
Gb in mouse cells (Figures 5G and 5H). The DSB burden wascomparable to the rate of observed IRi mutations in this study,
including 2.15 indels, 0.15 double-DSB-induced SVs (inversion,translocation, long deletion, and ecDNA), 0.02 triple-DSBinduced SVs (Del-Ins, Del-Inv, and Del-Tra), 0.01 CGR events,
and 0.15 seamless repairs (Figure 5H), indicating that our clonalization-based approaches allowed sensitive detection of IRassociated mutations and accurate estimation of the mutation rate.
Genomic distribution and selection of IR-induced
mutations in colonies
The IRi-IDs and IRi-SVs detected in the colonies were widelydistributed throughout the genome and were not strongly
associated with open/closed chromatin, specific histonemarkers, replication timing, or local GC content (Figure 5I).
The functional impact of IR-associated mutations was close
to random expectation. For example, approximately 1.8%
and 0.9% of the indels involved protein-coding sequences inmice and humans, respectively, which was not significantly
different from the background distribution (Figure 5J). Similarly, the proportions of gene-truncating balanced inversion
and translocation were also close to random expectation (Figure 5J). These results indicate that negative selection of postirradiated cells is not operative on pathogenic mutations after
DNA repair but on the number of DSBs prior to the completionof DNA repair.
Finally, we examined whether any IR-associated mutations
might have conferred selective advantage during clonal expansions. The 88 colonies from post-irradiated cells exhibitedfew gain-of-function mutations in known oncogenes and lossof-function mutations in tumor-suppressor genes. There wasonly one event in which the Myc oncogene was amplified via
an IR-induced BFB-SV event (Figure 4G), which resulted intranscriptional upregulation from 56 to 159 transcripts
per million (p < 0.001, two-sample t test; Figure 5K). Thesefindings suggest that IR-induced mutations in the colonies
are not particularly biased during our technical clonalization
steps.

(F) Schematic representation of the mechanism of chromothripsis observed in the 2 Gy irradiated organoid (PA_2Gy_12).
(G) Circos plot showing a BFB-SV identified in an 8 Gy irradiated human organoid (FT_8Gy_2; left) and patterns of rearrangements and copy-number states (right).
(H) Schematic representation of the mechanism of the BFB cycle observed in the 8 Gy irradiated human organoid (FT_8Gy_2).
See also Figure S7.

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Direct sequencing to reduce bias in clonalization
To further evaluate potential selection bias during clonalization,
we used various direct single-cell genome sequencing strategies, although such sequencing strategies generally exhibit alow signal-to-noise ratio and therefore limited sensitivity andspecificity for detecting de novo somatic mutations.43 First, 53
single cells were analyzed using whole-genome amplification(WGA) with multiple displacement amplification44 (MDA; 6 single
cells), WGA using primary-template-directed amplification45(PTA; 12 single cells), and Strand-seq46 (35 single cells) (Figure 6A). The WGA approaches focused on indels and SVs,
whereas Strand-seq targeted large-scale SVs. Second, we implemented two duplex sequencing strategies, including bottleneck sequencing47 (10 bulk organoids) and Concatenating
Original Duplex for Error Correction48 (CODEC; 6 bulk organoids), to accurately detect IR-induced indels in post-irradiatedbulk tissues without clonalization (Figure 6A). For IR exposurefor the experiments, we used the 137Cs source.
These techniques have variable capabilities for detection of
IR-induced mutations. WGA using MDA of six single cells (three
non-irradiated and three irradiated with 2 Gy) led to numerous
amplification artifacts (11,603 SBS calls per single cell, 95%
confidence interval [CI] [9,150–14,056], which is approximately
11.4-fold higher than SBSs detected from a colony, 95% CI[8.93–14.4]), unstable DNA copy numbers, and noisy distributionof the VAF of mutation calls (Figures 1B and S8A). Although stringent filter conditions to reduce erroneous calls in MDA provided
an overall higher burden of breakpoint calls in post-irradiatedcells (Figures 6B, S8B, and S8C), precise evaluation of the mutation rate and characterization of the mutational signature were
not feasible due to the high background noise in non-irradiatedcells.

ArticleCompared to MDA, WGA using PTA of 12 single cells (five nonirradiated and seven irradiated with 4 Gy) achieved more stable
mutation calls (630 SBS calls per single cell, 95% CI [334–974],
which was comparable to the number observed in the colonies;
Figures S9A and S9B). The indel calls allowed deconvolutionof an indel signature highly enriched in irradiated cells (PTAhID-A), which was similar to the ID-A signatures extracted fromthe colonies (Figures 6C and S9C). Considering the average indel
dropout rate, 4 Gy irradiated single cells acquired a median of 44
more hID-A indel calls (95% CI [6–130]) (Figures 6D and S9D),
with the estimated rate (1.81 per Gy per Gb, 95% CI [0.15–
5.23]) not significantly different from the rate observed in the colonies. The seven post-irradiated single cells analyzed using PTA
exhibited a median of 7.1 more SVs per genome (approximately
0.4 SVs per Gy per Gb, 95% CI [0–1.25]; Figure 6E), also notmeaningfully different from the colonies. Of note, one single cell
had approximately 80 adjusted SV breakpoints genome-wide,
implying that this was a hypermutator cell with numerous IRinduced DSBs.
Single-genome data from Strand-seq of 35 single cells(14 non-irradiated and 21 irradiated with 4 Gy) provided morerobust insight into SV formation in non-clonalized single cells(Figure 6F). We found that post-irradiated cells had a higher SV
burden than controls, including 2.47 long deletions (>8 Mb;
0.1/Gy/Gb), 1.38 inversions (>10 Mb; 0.05/Gy/Gb), and 2.26 sister-chromatid-exchange-like patterns (SCE-like patterns; 0.1/
Gy/Gb) (Figure 6G). Except for SCE-like patterns, which can bechallenging to interpret, as they may not represent real SVs (Figure S10), Strand-seq detected approximately 0.16 SVs (95% CI[0.11–0.20]), which is closely aligned with the rate observed in
the colonies. In contrast to PTA, no cell with an unusually highnumber of SVs was observed in the Strand-seq data.

Figure 5. Quantitative analysis of radiation-associated indels and SVs(A) Number of IR-induced indels per Gb with respect to radiation dose (Gy). Linear regression was used to estimate dose-response relationships. The data weredivided into three subsets (experiments from IRvitro, IRvivo, and IRpost-RT). Boxplot shows median (midline; red), interquartile range (IQR) (box), and whiskers
indicating minimum or maximum value within 1.53IQR from the quartiles.
(B) Number of IRi-SVs per Gb with respect to radiation dose (Gy). Linear regression was used to estimate dose-response relationships. The data were divided into
three subsets as shown above. Boxplot whiskers extend up to 1.53IQR from the quartiles.
(C) Size distribution of breakpoint gap in IRi-SVs. IR-induced DSBs are usually accompanied by some nucleotide deletions.
(D) Ratio of two means (mIRi-SV/mIRi-ID) with respect to irradiation type (IRvitro, IRvivo, and IRpost-RT). Marginal sums were compared between (1) IRvitro vs. IRvivo
groups and (2) IRvitro vs. IRvivo and IRpost-RT groups using Fisher’s exact test. The data are shown as the ratio of means with an 83% confidence interval (CI)
calculated by Fieller’s method41; *p < 0.05, not significant (ns) > 0.1.
(E) IRi-ID (middle), IRi-SV (bottom), and the ratio of the means (mIRi-SV/mIRi-ID; top) of in vivo, 8 Gy irradiated organoids with respect to tissue types. Only tissues withmore than two data points were included. Fisher’s exact test on the sum of IRi-IDs and IRi-SVs shows a significant difference between the colon and liver;*p < 0.05. Error bars indicate 83% CI.
(F) IRi-ID, IRi-SV, and the ratio (IRi-SV/IRi-ID) between high dose rate (one or four short exposures) and low dose rate (continuous exposure for 100 days) with a
total irradiation of 8 Gy. The high rate involved irradiating a single 8 Gy or four 2 Gy doses, and the low rate involved approximately 0.08 Gy per day for 100 days;
ns > 0.1. Boxplot whiskers extend up to 1.53IQR from the quartiles.
(G) Representative image of gamma-H2AX immunofluorescence staining (green) in 2 Gy irradiated mouse pancreas organoids following 3.23 tissue expansion
using magnified analysis of proteome method (left). Number of gamma-H2AX foci per cell in control and 2 Gy irradiated samples was counted; ***p < 0.001, twosample t test. Boxplot whiskers extend up to 1.53IQR from the quartiles.
(H) Pie chart showing direct DSB count from the gamma-H2AX experiment (inner circle) and estimated DSB count from the sequencing data (outer circle). We
estimated DSB counts from the sequencing data using the number of variants and their minimum number of DSBs necessary (for example, 1 DSB for an indel, 2
DSBs for an inversion, or 3 DSBs for composite SV). The number of variants was based on the coefficient of the linear model for the IRvitro experiment (Figures 5A
and 5B). The number of seamless indels was estimated from the proportion of the SV without a gap and the number of indels (Figure 5C). The counts were
adjusted to account for inaccessible and repetitive genomic regions.
(I) Enrichments of IRi-IDs and IRi-SVs in relation to genomic contexts. For an enrichment in the ATAC-seq (assay for transposase-accessible chromatin withsequencing) from the pancreas, SVs acquired from the pancreas colonies were used. Error bars indicate 95% CI.
(J) Functional consequences of IRi-IDs (left) and IRi-SVs (right) detected from colonies. Error bars indicate 95% CI.
(K) The difference of Myc expression in control and irradiated organoids (FT_8Gy_2). Data are presented as mean ± SEM. n = 3. TPM, transcripts per million.

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ArticleSV in MDA

B

A
IR exposure & single-cell genome sequencing (MDA and PTA)
Whole-genome
amplification (WGA)

Post-irradiatedsingle-cell

WGS(pre-IR)

gDNA fromsingle-cell

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(post-IR)

C17 C15 C14

WGS(pre-IR)

gDNA fromsingle-cell

Post-irradiatedsingle-cell
BrdU
1 cell-cycle

Irradiation(0 & 4Gy)

Single-celldissociation

T*
N

WGS
(post-IR)

Proportionof indels (%)

Organoid

R17 R18 R16>1bp deletionsat repeats

1bp deletion 1bp insertionC
IR exposure & Strand-seq

SVTYPE
INV
DUP
DEL
TRA

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10

15.1
11.3

C

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C

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WGS
(pre-IR)bulk-tissue
Duplex DNA
WGS(post-IR)

Irradiation(0 & 4 Gy)

IRi-ID in PTA

30

20
1.81
Gy-1Gb-1

10by themedians0
4Gy

in vitro

2

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10 11 12 13 14 15 16 17 18 19 20 21 22 X Y

ECount per cell

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4Gy

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y_ y_ y_ y_ y_y y _ _ _ y _0G 0G 0G 0G 0G 4G 4G4Gy 4Gy 4Gy 4G4Gy

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Sister chromatid exchange

250

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0Gy_120Gy_13

Genomic coordinate (Mb)

Watson strand

2 3(Insertion length)

SVTYPESV type
Others
INV
DEL
SCE

15
50

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0Gy
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0Gy

0

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Deletions withmicrohomology
5+IR-exposure in strand-seq.

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Adjusted count per cell

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DNA-seq

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4(Deletion length)

PTA-hID-A* T: template; N: nascent

IR exposure & Duplex DNA sequencing (Bot-seq & CODEC-seq)3

4Gy_11
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4Gy_2
4Gy_14
4Gy_18

Irradiation(0 & 2 Gy)

Single-celldissociation

2Gy

15

Count per cell

Organoid

0Gy

Figure 6. Direct single-cell genome sequencing(A) Experimental design for detecting IR-induced mutations in single cells without clonal expansions, using WGS of the whole-genome-amplified single cells(MDA and PTA), Strand-seq, and duplex DNA sequencing (bottleneck sequencing and Concatenating Original Duplex for Error Correction) techniques.
(B) SVs detected in the MDA experiments.
(C) Mutational signature of IR-associated indels delineated from the PTA experiments. Full signatures are shown in Figure S9.
(D) Number of indels observed in the PTA experiments, adjusted for allelic dropout rates. Boxplot whiskers extend up to 1.53IQR from the quartiles.
(E) Number of SVs observed in the PTA experiments, adjusted for allelic dropout rates.
(F) Example of Strand-seq result of a 4 Gy irradiated single cell.
(G) Number of SVs and sister-chromatid-exchange-like patterns observed in the Strand-seq experiments.
See also Figures S9–S11.

Finally, we applied the bottleneck sequencing and CODEC(both are duplex DNA WGS methods) to detect IR-induced point
mutations in post-irradiated bulk organoids. Despite much lower
sequencing error rates reported47,48 (10e6 to 10e7 errors perbp), these methods still generated a significant amount of back-ground noise, masking IR-induced point mutations (Figure S11).
Of note, an IR dose of 4 Gy generated 50 indels in the diploid
genome, which is equivalent to 8 3 10e9 indels per bp. Suchan amount of indels may be too low to be precisely detected
through the duplex DNA sequencing methods.

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Collectively, our observations from the direct single-cellsequencing data suggested that (1) these techniques were notfully competent to comprehensively and precisely detect IRinduced mutations and (2) the best estimates of IR-induced mutation rates from these techniques were not substantiallydifferent from the rate observed in the colonies.
DISCUSSION
In this study, we investigated the mutational impact of IR on
normal cells at nucleotide-level resolution. Despite well-established mutagenicity, comprehensive mutational signatures of
IR in normal somatic cells have not been elucidated due to technical challenges posed by single-cell genomic analysis. Although
WGA techniques present a technically simple, direct approach7,9
and duplex DNA sequencing can be applied to bulk cells,47,49
these techniques are insufficient for accurately capturing small
numbers (10–100) of IR-induced somatic mutations acquired in
post-irradiated cells, as evidenced by our single-cell and duplex
genome sequencing results. Particularly, SVs, which are themain mutations induced by IR, are challenging to detect using
these techniques. Strand-seq is also insufficient, as it only detects large-scale SVs without breakpoint sequences. Although
our clonalization-based approach was labor intensive and only
applicable to cells with sustained proliferation potential, it allowed accurate and precise detection of mutations in single
cells, which is essential for obtaining accurate, comprehensive
mutational signatures.
The acquisition of indels and inversions after IR exposure waspreviously observed in a genome-sequence study of IR-associated secondary cancers.18 However, a clear picture of mutational signatures and rare classes of SVs were not elucidated
due to challenges in identifying direct mutations induced by IR
exposure amid coinciding, unrelated mutations in cancer genomes. Furthermore, although cytogenetic investigation of
post-irradiated cells may provide evidence of some rare SVs, it
inevitably overlooks information regarding SV breakpoints andlacks the resolution required for identifying indel mutations.50
This study is therefore the first to report a comprehensive profile
of IR mutational signatures in normal mammalian somatic cells.
The quantitative mutational impact of IR observed in the colonies requires cautious interpretation, as cells that have lost proliferation potential, and are more likely to harbor a higher mutational burden, could not be investigated. The mutation rate
observed in this study may therefore underestimate the absolute
mutational burden in post-irradiated cells or tissues. Indeed, results from the single-genome techniques indicated a slightly
higher mutational burden, although the observed rate may not
be precise due to high background noise. As severely damaged
cells are negatively selected in our tissues after real-world IR
exposure, our results from the clones likely reflect the biologicaleffects of IR more accurately.
Compared to IRi-IDs, the IRi-SV burden in post-irradiated cells
is more variable across cells, as CGRs are stochastically formed
via unstable intermediate chromosomal structures during therepair of multiple DSBs. Surprisingly, approximately 15% of cells
irradiated with R1 Gy acquired catastrophic CGRs, including
chromoplexy (approximately 5%), chromothripsis (approxi-

12 Cell Genomics 4, 100499, February 14, 2024

Articlemately 2%), and BFB-SV (approximately 1%). These CGRs
have a greater functional impact than indels and simple SVs,
contributing to the development of IR-associated secondary malignancy to a greater degree. Although it is quite challenging toaccurately estimate the functional consequences of CGRs, if asubstantial fraction of CGRs detected here activate cellular proliferation, then the frequency of CGRs in this study could beoverestimated.
During typical RT, millions of normal cells in adjacent target tissues are exposed to at least 50 Gy IR. Our results from colonies
established from post-RT cells imply that approximately 300 mutations are acquired in every cell under such circumstances.
Some of these post-RT cells will acquire pathogenic variants,
such as gain-of-function mutations in oncogenes or loss-offunction mutations in tumor-suppressor genes. Mutant cells
harboring these pathogenic alterations may facilitate diseasedevelopment, as well as silently persist until sufficient acquisitionof additional mutations allow cellular transformation. Futurestudies tracing phenotypic changes in mutant cells carrying
functionally important mutations in vivo will provide further insights into the tumorigenesis of IR-associated secondary
malignancies.
Limitations of the study
This study had some limitations. Most IR-induced mutations
observed in this study were identified in single-cell expandedcolonies (organoids), therefore representing genomic alterationsacquired in adult stem cells with proliferative potential. As
mentioned in the results, post-irradiated cells that acquired an
excessive number of DSBs may have been underrepresented
in the study. In addition, mutations that activate or inactivate
cell proliferations could be over- or under-detected, respectively. Furthermore, mutations acquired in terminally differentiated cells, such as neurons, could not be measured. A few direct
genome sequencing techniques applied in the study exhibited
insufficient competence to accurately detect IR-induced mutations. Exploring a larger number of genomes collected fromdiverse tissues through more advanced single-genome techniques will be necessary to achieve an unbiased assessmentof the qualitative and quantitative mutagenic impact of IR exposure across various cell types.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:d KEY RESOURCES TABLE
d RESOURCE AVAILABILITY

B Lead contact
B Materials availability
B Data and code availabilityd EXPERIMENTAL MODEL AND STUDY PARTICIPANT DE-

TAILSB Experimental mice
B Human normal samples
B Human cancer samples
B Pathology review

llOPEN ACCESS

Articled METHOD DETAILSAUTHOR CONTRIBUTIONS

B Irradiation experiments
B Organoid culture
B Single-cell-derived clonal organoid acquisition
B DNA extraction
B Whole-genome sequencing and read alignment
B Whole transcriptome sequencing of organoids
B Detection of somatic variants
B Publicly available datasets
B Monoclonal and polyclonal organoids
B Determination of VAF cutoffs for dominant clonal muta-tions
B Mutational signature analysis of single base substitu-

tions and indels
B Simulation of IR-inducible indel mutations
B Reconstruction of genomic rearrangements
B Adjusted count per Gb
B Analysis of double-strand breaks (DSBs) induced byirradiation
B Analysis of IR-associated copy number variants

Y.S.J., T.G.S., K.S.K., J.Y., and H.W.K. designed the experiments; H.W.K.,
T.G.S., H.K., Y.-R.K., C.G.L., and M.J.B. conducted the in vitro and in vivo
mouse irradiation experiments; K.S.K., O.V.Y., and E.C.H. provided adjacentnormal human tissue samples; E.K. and J.H.C. provided secondary malignancy samples; K.S.K., E.K., and J.H.C. oversaw all clinical data collection
and curation; J.Y., H.W.K., S.J., E.-S.L., T.Y., and T.G.S. performed organoidculture, clonal expansion, and DNA/RNA extraction, with S.H.C. and D.S.L.
providing advice; S.K. (pancreas), J.C. (lung), H.N. (Wnt), J.-H.L. (lung),
B.-K.K. (Wnt, colon), and H.L. (pancreas) provided training in organoid culture
technologies; H.W.K. and Y.C. performed cell viability analysis and organoid
staining; D.-W.M., T.-Y.K., H.W.K., J.-G.K., Y.S.K., and T. Ku performed organoid expansion and obtained high-resolution confocal images; T. Kim and
C.H.N. conducted single-cell WGS analysis; Y.A., W.-C.L., and J.-Y.S. conducted duplex sequencing analysis; J.Y. performed most bioinformatic analyses, including alignment, mutation calling, and data curation, with S.P.,
K.Y., R.K., and Y.S.J. providing advice; J.L. and J.Y. performed downstream
data analysis, refinement, and interpretation, with Y.S.J. providing advice;
J.L. conducted most of the statistical and statistical learning analyses,
including mutational signature analysis; S.Y.K. oversaw statistical analysis;and J.Y., H.W.K., J.L., K.S.K., T.G.S., and Y.S.J. wrote the manuscript. All authors finalized the manuscript. Y.S.J. supervised the project.

B Assay

for transposase-Accessible chromatin using
sequencing (ATAC-seq) analysis
B Association of IRi-SV incidences with genomic features
B Sanger validation of deletion-insertion composite (DelIns)
B Viability assay
B g-H2AX immunostaining and 3D visualization
B Physical expansion of organoids for super-resolution
imaging
B g-H2AX counting
B Selection pressure analysis
B Single-cell sequencing
B BotSeq and analysis
B CODEC and analysis
B Confidence intervals of statistics
d QUANTIFICATION AND STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
xgen.2024.100499.

ACKNOWLEDGMENTS
The authors thank Jinwoo Seong and Young-Yun Kong (Seoul National University) for mouse mammary gland acquisition and Myungsuk Choi (KAIST) for
technical help. This work was supported by the Suh Kyungbae Foundation(SUHF-18010082 to Y.S.J.); Young Investigator Grants from the Human Frontier Science Program (RGY0071/2018; to Y.S.J. and B.-K.K.); the National
Research Foundation of Korea (Leading Researcher Program NRF2020R1A3B2078973 and NRF-2020M3A9E4039670 to Y.S.J.; for the Brain
Pool Program NRF-2019H1D3A2A02061168 to Y.S.J. and S.Y.K.; Basic Science Research Program NRF-2020R1I1A1A01072873 to K.S.K.; and NRF2017R1A2B4003535 to J.H.C. and NRF-2021R1G1A1009606 to H.W.K.); theNational R&D Program funded by the Ministry of Science, ICT, and Future
Planning through the Dongnam Institute of Radiological & Medical Sciences(DIRAMS) under grants 50491-2018 and 50491-2019 (to T.G.S.); and research
grants from Korea University Anam Hospital, Seoul, Republic of Korea (grantnos. O1800741 and O2000681 to H.W.K.).

DECLARATION OF INTERESTS
Y.S.J. is a genomic co-founder and chairman of Genome Insight.
Received: December 6, 2022
Revised: October 23, 2023
Accepted: January 9, 2024
Published: February 14, 2024
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Cat#20015963

TruSeq Nano DNA Kit

Illumina

Cat#20015965

Accel-NGS 2S Plus DNA Library Kit

Swift

Cat#21024

RNeasy Mini Kit

QiagenCat#74104

TruSeq Stranded Total RNA Gold Kit

Illumina

Cat#20020599

TruSeq RNA Library Prep Kit

Illumina

Cat#RS-122-2001

Illumina Tagment DNA Enzyme and Buffer Kit

Illumina

Cat#20034197

Nextera DNA Library Kit

Illumina

Cat#FC-121-1030

NEBNext High-Fidelity 2X PCR Master Mix

New England Labs

Cat#M0541L

Power SYBR Green PCR Master Mix

Thermo Fisher Scientific

Cat#4367659

PrimeSTAR HS Taq polymerase

TakaraCat#R010A

Pfu DNA polymerase

Thermo Fisher Scientific

Cat#EP0501

Dual Luciferase reporter assay

Promega

Cat#E1910

CellTiter-Glo 3D Cell Viability Assay

Promega

Cat#G9681

Deposited data
Mouse organoid whole-genome sequence data

This study

KoNA:KAP230688

Mouse organoid transcriptome data

This study

KoNA:KAP230688

Human organoid whole-genome sequence data

This study

KoNA:KAP230688

Human organoid transcriptome data

This study

KoNA:KAP230688

Afamin-Wnt3a-producing HEK293

Mihara et al.51

N/A

Cultrex HA-R-Spondin 1-Fc 293T

TrevigenCat#3710-001-01

Mouse: BALB/c (male, 6–7 weeks old)Central Laboratory Animal Incorporated

N/A

Mouse: C57BL/6 (female, 8–9 weeks old)Central Laboratory Animal Incorporated

N/A

BIONICS

N/A

Experimental models: Cell lines

Experimental models: Organisms/strains

Oligonucleotides
PA_2Gy_11 deletionF, 50 -GCCTGTGTTCAAATTGGGGG-30(Continued on next page)e2 Cell Genomics 4, 100499, February 14, 2024

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REAGENT or RESOURCESOURCEIDENTIFIER

0

PA_2Gy_11 deletionR, 5 -AAAACCCATG
CCCTCTGCTT-30

BIONICS

N/A

PA_2Gy_11 insertionF, 50 -CCACATGGAACCTTATGCTGC-30

BIONICS

N/A

PA_2Gy_11 insertionR, 50 -TTCTGACTG
CCTTGGCACAG-30

BIONICS

N/A

Recombinant DNA
TOPcloner Blunt core Kit

Enzynomics

Cat#EZ012S

TOPcloner TA core Kit

Enzynomics

Cat#EZ011S

M50 Super 8x TOPFlashde Lau et al.52

Addgene plasmid #12456

M51 Super 8x FOPFlashde Lau et al.52

Addgene plasmid #12457

ResolveDNA Whole Genome Amplification Kit

BioSkryb

Cat#PN100068

R 3.6.0

The R Foundation

http://www.r-project.org/

Burrow-Wheeler Aligner

Li et al.53

https://github.com/lh3/bwa

Samtools

Li et al.54

http://htslib.org

PicardMcKenna et al.55

http://broadinstitute.github.io/picard

Genome Analysis Toolkit

McKenna et al.55

https://gatk.broadinstitute.org/hc/en-us

Cutadapt

Martin et al.56

https://github.com/marcelm/cutadapt

Software and algorithms

57

https://github.com/deweylab/RSEM

RSEM v1.3.1

Li et al.

ComplexHeatmap R package

Gu et al.58

https://github.com/jokergoo/ComplexHeatmap

Enrichr

Kuleshov et al.59

https://maayanlab.cloud/Enrichr

Strelka2

Kim et al.60

https://github.com/Illumina/strelka

Varscan2

Koboldt et al.61

https://varscan.sourceforge.net

Python 2.7.5

Python Software Foundation

https://www.python.org

Pysam Python package

Li et al.54

https://github.com/pysam-developers/pysam

Delly 0.7.6

Rausch et al.62

https://github.com/dellytools/delly

Integrative Genomics Viewer

Robinson et al.63

https://software.broadinstitute.org/software/igv

TraFiC-mem

Rodriguez-Martin et al.64

https://github.com/cancerit/TraFiC

Sequenza R package

Favero et al.65

https://cran.r-project.org/package=sequenza

CircosKrzywinski et al.66

http://circos.ca

67

https://cran.r-project.org/package=flexmix

flexmix R package

Grun et al.

mixtools R package

Benaglia et al.68

https://cran.r-project.org/package=mixtools

rootSolve R package

Soetaert et al.69

https://cran.r-project.org/package=rootSolve

numDeriv R package

Satopaa et al.70

https://cran.r-project.org/package=numDeriv

Julia 1.2.0

The Julia Project

https://julialang.org/

tolerance R package

Young71https://cran.r-project.org/package=tolerance

phangorn R package

Schliep et al.72

https://cran.r-project.org/package=phangorn

ape R package

Paradis et al.73

https://cran.r-project.org/package=ape

Bowtie2

Langmead et al.74

https://bowtie-bio.sourceforge.net/bowtie2

liftOverKent et al.75

https://genome.ucsc.edu/cgi-bin/hgLiftOver

ImageJ

ImageJ software

https://ImageJ.nih.gov/ij/Lmratios R package

Fieller et al.41

https://cran.r-project.org/package=mratios

Mutational signature analysis

This study

https://doi.org/10.5281/zenodo.7402806

Confocal microscope

Carl Zeiss

LSM 780

Cs-137 gamma irradiator

J.L. Shepherd and Associates

MK1-68

Cs-137 source

Chiyoda Technol Corp.

N/A

Other

(Continued on next page)

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ArticleContinued
REAGENT or RESOURCESOURCEIDENTIFIER

Co-60 irradiator

Best Theratronics

GBX200Radiochromic film

ISP Corporation

Gafchromic EBT3

Nanodrop 2000 spectrophotometer

Thermo Fisher Scientific

ND-2000

PCR Thermal Cycler

Applied Biosystems

Veriti Thermal Cycler

Luminescence Counter

Perkin Elmer

1420 Victor LightRESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Young
Seok Ju (ysju@kaist.ac.kr).
Materials availability
Organoids established in this study are available under a material transfer agreement. To do so, please contact the lead author (ysju@
kaist.ac.kr).
Data and code availability
Mouse and human whole-genome and transcriptome sequencing raw data produced in this study were deposited on the Korean
Nucleotide Archive (KoNA; https://www.kobic.re.kr/kona/). The project accession ID is KAP230688. All data were provided for review
purposes upon reviewers’ request. Tumor genome sequences analyzed in this study were obtained from previous studies(EGAS00001000138, EGAS00001000147, EGAS00001000195; EGAD00001004162).18,23 Essential in-house scripts used in thisstudy are available on Zenodo (DOI: https://doi.org/10.5281/zenodo.7402806).
EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS
Experimental mice
Male BALC/c (6–7 weeks old) and female C57BL/6 (8–9 weeks old) mice were purchased from Central Laboratory Animal Incorporated (Seoul, Korea). All experiment protocols were approved by Dongnam Institute of Radiological and Medical Sciences (DIRAMS
AEC-2018-007 and DIRAMS AEC-2019-007) and Seoul National University (SNU-180101-1-3). Animal studies were conducted inaccordance with guidelines established by the committee on Use and Care of Animals of Dongnam Institute of Radiological and Medical Sciences and Seoul National University Animal Care and Use Committee. Animals were treated humanely in accordance with the
Ministry of Food and Drug Safety on the ethical use of animals. Mice were sacrificed with carbon dioxide inhalation euthanasia to gettissues for organoid cultures and spleens for matched germline controls.
Human normal samples
For all patients, tumor-adjacent normal tissues or matched blood were collected from irradiated and non-irradiated individuals(controls). The protocol of this study was approved by the institutional review board of Dongnam Institute of Radiological andMedical Sciences (D-1804-023-002, D-1810-007-002, and D-2206-021-002). Radiation-free human normal colon tissues wereacquired from surgical specimens of two colorectal cancer patients (Table S1). The enrolled patients had no experience of previous chemotherapy or radiotherapy. Tumor adjacent normal colon tissues were cut out from a region >5 cm away from primarytumor mass. Irradiated normal colon samples were taken from a patient diagnosed with rectal cancer located 7cm away fromthe anal verge. The patient received preoperative radiotherapy (45 Gy/25 fx + 5.4 Gy/3 fx) with 5-fluorouracil and leucovorin topreserve the anus. After ultralow anterior resection surgery, a tumor-adjacent normal colon tissue in the radiation field was acquired from a region >5 cm away from primary tumor mass. Normal human breast samples were collected from adjacent normal
tissues from a total mastectomy of recurred breast cancer at Dongnam Institute of Radiological and Medical Sciences(Table S1). Patients had breast-conserving surgery 1 (HBIR-1) and 17 (HBIR-2) years ago, followed by prophylactic adjuvant
radiotherapy (50 Gy/25 fx + 10 Gy/5 fx as a boost for HBIR-1 and 50.4 Gy/28 fx + 9 Gy/5 fx as a boost for HBIR-2). The
HBIR-1 patient received neoadjuvant chemotherapy with Adriamycin, cyclophosphamide and docetaxel at initial diagnosis.
The HBIR-2 patient received adjuvant cyclophosphamide, methotrexate and 5-fluorouracil at initial diagnosis. The patient
took tamoxifen for several years. After recurrence, the HBIR-2 patient had neoadjuvant chemotherapy with doxorubicin, cyclophosphamide, docetaxel and trastuzumab before total mastectomy.

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Human cancer samples
After receiving an approval by the institutional review board of Seoul National University Hospital (H-1506-026-678), wecollected patients who met the following modified Cahan’s criteria for radiation-induced sarcoma between 2015 and 2019;(A) pathologically confirmed sarcoma which was histologically different from the primary cancer, (B) occurred in the field of radiation,and (3) developed at least 6 months after radiation therapy.76 Sixteen patients were identified and surgically resected in our institution, and we finally obtained whole-genome sequences of ten matched tumor and normal samples. We also collected clinical information including initial tumor diagnosis, radiation dose, latency period, and pathologic diagnosis of radiation-induced sarcoma(Table S1). The median prescribed radiation dose was 50.4 Gy. Radiation-induced sarcomas were located in the extremities(n = 5), trunk (n = 4), and head & neck (n = 1). Radiation-induced sarcomas included 4 undifferentiated pleomorphic sarcomas, 3 undifferentiated spindle cell sarcomas, 2 osteosarcomas, and 1 undifferentiated epithelioid sarcoma.
Pathology review
Tumors had been fresh frozen (n = 8) or formalin-fixed/paraffin-embedded (FFPE, n = 2). Normal samples were obtained from blood(n = 7) or adjacent normal tissues of FFPE (n = 1) and fresh frozen specimens (n = 2). Two experienced pathologists reviewed specimens and performed microdissection to isolate the tumor and normal tissues.
METHOD DETAILS
Irradiation experiments
For in vitro organoids irradiation, 137Cs gamma irradiator (MK1-68; J.L. Shepherd and Associates) was employed. Dose distribution in
the floor of the irradiation cavity was mapped by a 24-well plate containing PBS-submerged radiochromic film (RCF, GAFCHROMIC
EBT2, ISP Corporation). Film samples were digitized using a flatbed scanner (Expression 10000XL, Epson) with accompanying software (Silverfast Epson IT8). The optical density from RGB (red, green, and blue) uncompressed tagged film images was analyzed forradiation exposure. After the harvest and dissociation of organoids, the cell suspension was transferred to 1.5 mL Eppendorf tube
with culture media. Single cells were irradiated at a dose rate of 31 mGy/s. After the irradiation, the cells were sorted and inoculated
in Matrigel (Corning). For in vivo mice irradiation, a cobalt irradiator (GBX200, Best Theratronics) with 60Co source (5,000 Ci) wasused. An ionization chamber was used to ensure accurate dosimetry. Ionization chambers for whole-mouse irradiation measured air
kerma and an accurate dose was irradiated at an experimental radiation field. The field size of 60Co was 20 cm 3 15 cm, and the air
kerma was measured by inserting the ionization chamber into a 50 mL conical tube with a holed cap, which was placed at a sourceto-chamber distance (SCD) of 60 cm (Figure S1C). Whole-body g-ray irradiations of 2 ± 0.098 Gy (0.4763 Gy/min) were carried out inan irradiation room equipped with a 60Co source. The standard uncertainty of mouse irradiation using a cobalt irradiator is shown in
Table S15. A total 8 and 20 Gy were irradiated cumulatively by irradiating 2 Gy per day with 4 times and 10 times, respectively. The
animals were sacrificed 24 h and 2 weeks after irradiation for primary bulk organoid culture and primary single-cell/crypt culture,
respectively. For low-rate long-term irradiation, 8 Gy radiation was irradiated to whole-body with a rate of 3.33 mGy/h using 137Cs
source (185 GBq) (Chiyoda Technol Corp.)77 for 100 days, followed by sacrifice after three months from irradiation. Sham mice
were also placed in the same tube without irradiation and then sacrificed on the same day as the other irradiated mice.
Organoid culture
All organoid establishment procedures and media compositions were adopted from previous literature with slight modifications(Table S14). For crypt isolation of the mouse stomach (antrum),78 small intestine79 and colon,80 each tissue was dissected fromeuthanized mice. Each tissue was opened up longitudinally and washed in cold PBS 3 times. In the small intestine, villi were scrapped
off using coverslips. Tissues were cut into small 2–4 mm tissues and transferred to 10 mM EDTA (Invitrogen) in 50 mL conical tubes,
followed by incubation on an automatic rolling machine for 20–30 min at room temperature. After incubation, the tubes were shaken
to extract crypts from the tissues. The supernatant was transferred to new tubes and centrifuged at 300 g for 5 min. The pellet waswashed once with PBS. Isolated crypts were embedded in Matrigel and plated in a 12- or 24-well plate (TPP). The plates were transferred into an incubator at 37 C for 5-10 min to solidify Matrigel. Each well was overlaid with 0.5–1 mL of organoid culture media for a
24- or 12-well plate, respectively. For the mouse pancreas,81 liver,81 breast82 and fallopian tubes,83 each tissue was collected fromeuthanized mice and washed in cold HBSS (Gibco). Each tissue was placed in a 100-mm Petri dish and minced into small pieces
using scalpel blades. For the pancreas, liver and breast, 10 mg/mL collagenase P (Roche) and 0.1 mg/mL DNAse (Sigma) in
HBSS were prepared as digestion solutions. For fallopian tubes, 0.5 mg/mL collagenase I (Sigma) was prepared for digestion.
The minced tissue was transferred to a 50 mL conical tube, and 10 mL of prewarmed digestion buffer was added, followed by incubation at 37 C with shaking at 230 rpm for 20–45 min. The digested tissues were washed with PBS two times and shaken in PBS.
Because insufficiently digested tissues were frequently observed in the pancreas and liver, the tubes with digested tissues were allowed to stand for 20-30 s to remove the insufficiently digested tissues. Then, the supernatant was collected and centrifuged at 300
rcf for 3 min. For breast and fallopian tubes, the tubes with digested tissues were centrifuged at 300 rcf for 3 min. After centrifugation,pellets were embedded in Matrigel. In the case of the liver, isolation media was used for primary culture cases during the initial 3–
4 days. Then, expansion media was added to culture the liver organoids. For mouse lung organoids,84 a rib cage was opened up
in euthanized mice. Lung perfusion was carried out through the right ventricle using 10 mL DPBS in a 10 mL syringe with a 26 G

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Articleneedle. After removing the heart, the trachea was dissected and cut off at the most proximal portion. A 2 mL dispase (Corning) was
infused through the proximal opening of the trachea until the expansion of all lung lobes. The whole lung was separated from thethoracic cage by lifting the trachea. Five lobes were dissected from the whole lung using scissors, followed by digestion in 3 mL
PBS with 65 mL of collagenase/dispase (Sigma) in a 37 C shaking incubator with 230 rpm. After 35 min of digestion, 7.5 mL of DNase
I (Sigma) was added to the digestion solution. With 10–20 min of additional incubation, the digested solution was filtrated using a
40 mm strainer (Falcon) to remove insufficiently digested materials. Then, filtrates were centrifuged at 400 rcf for 5 min. Because
several RBCs were frequently observed in the pellet, the pellet was incubated in RBC lysis buffer (Sigma) for 1–1.5 min, followedby adding 10 mL of advanced DMEM/F12 and 500 mL of FBS (Gibco) at the bottom. After centrifuging at 400 rcf for 5 min, we discarded the supernatant. The pellet was resuspended in 200 mL of PF10 (10% FBS in DPBS). The resuspended solution was incubatedwith Anti-EpCAM PE antibody (BD Biosciences, 563477), anti-CD31 APC antibody (BD Biosciences, 550274), anti-CD45 APC antibody (BD Biosciences, 561018) and anti-Ly-6A/E APC-Cy7 antibody (BD Biosciences, 560654). After 30–60 min of incubation, 800 mL
PF10 was added and centrifuged at 13,000 rpm for spin-down. The pellet was washed two times with PF10. Then, type 2 alveolar
epithelial cells (EpCAM-positive, CD31-negative, CD45-negative and Ly-6A/E-negative) were isolated using a fluorescence-activated cell sorter (FACSAria II, BD Biosciences) (Figure S1B). The type 2 alveolar epithelial cells were embedded in a 1:1 mixtureof complete lung organoid media85 and Growth Factor Reduced Matrigel (Corning), and the droplets of organoids and Matrigel
mixture were seeded in 6.5 mm transwells (Corning) in a 24-well plate. After 20 min of incubation, 500 ml complete lung media
with 10 mM Y-27632 (Sigma) were added into each well. Plates were transferred to a humidified 37 C/5% CO2 incubator, and the
medium was changed every 2–3 days. Organoids were passaged by removing the medium and dissolving Matrigel by adding Cultrex
organoid harvesting solution (Trevigen) or cell recovery solution (Corning) for 40–50 min on ice. After removing the dissolving solution,
0.5–1 mL prewarmed TrypLE Express (Gibco) or prewarmed Accutase (Stemcell Technology) was added to disrupt organoids, followed by centrifuging at 300 rcf for 3 min. In the case of mouse breast organoids, a 1cc syringe with a 26 G needle was used to dissociate before centrifugation. Pellet was resuspended with Matrigel and seeded in a new 12- or 24-well plate at ratio of 1:4 to 1:6. Afterpolymerization of Matrigel in a 37 C incubator, 500–1000 mL complete media were added to each well. In the several complete organoid media, Wnt3a or R-Spondin-1 conditioned media was included. We prepared the conditioned media using Afamin-Wnt3a
producing HEK293 cell line51 and Cultrex HA-R-Spondin 1-Fc 293T cell line (Trevigen), respectively. The activity of the harvesting
conditioned media was tested using TOP/FOP assay.52,86
Single-cell-derived clonal organoid acquisition
For the clonalization of single cells, we used two alternative strategies. First, bulk organoids were harvested and single-cell dissociated using prewarmed TrypLE Express or Accutase. After centrifuging, supernatants were removed, and organoids were resuspended using AdDF+++ (Advanced DMEM/F12 with 10mM HEPES, 1X Glutamax and 1% penicillin/streptomycin). Organoid suspensions were filtered through a 40 mm strainer (Falcon). Using a cell sorter (FACSAria II, BD Biosciences), single cells were sorted into a
FACS tube with 2 mL of AdDF+++. Single cells were selected in the FACSDiva software based on forward- and side-scatter characteristics.87 Sorted cells were seeded with Matrigel (500–1500/well). Alternatively, single whole-crypts were collected from thestomach, small intestine, and colon after 14 days from whole-body irradiation. Of note, a crypt is physiologically clonalized in vivo
due to the competitive proliferation of single cells.88 When mono-to oligo-clonal organoids were acquired right after primary culture,digested materials were sparsely seeded with Matrigel at P0. After growing organoids, a single organoid distant from other organoids
was picked up using a 200 mL pipette under a microscope. Clonal organoid generation was evaluated by variant allele frequency ofsomatic point mutations.
DNA extraction
Genomic DNA was extracted using DNeasy Blood & Tissue Kit (Qiagen) or TaKaRa MiniBEST Universal Genomic DNA Extraction Kit
Ver. 5.0 (TaKaRa Bio Inc.). At the last step, the DNA sample was eluted twice with 40 mL of elution buffer. The concentration of DNA
was measured using a Nanodrop 2000 spectrophotometer (Thermo Scientific, ND-2000) and stored at 20 C or 80 C before use.
Whole-genome sequencing and read alignment
DNA libraries for whole-genome sequencing (WGS) were generated using Truseq DNA PCR-Free Library Prep Kits (Illumina) from>500 ng of genomic DNA or TruSeq Nano DNA Kit (Illumina) from >100 ng of genomic DNA, according to the manufacturer’s protocols. When genomic DNA was insufficient to use the PCR-Free Library Kits, Accel-NGS 2S Plus DNA Library Kit for Illumina (Swift)
was alternatively used to construct DNA libraries (n = 10; Table S1). The libraries were sequenced with paired-end (2x151bp) using
HiSeq 2500, HiSeq X10, or Novaseq 6000 platforms (Illumina) to generate a minimum of 30X depth for most samples except B3S100(100X), radiation-induced sarcomas (90X), and tumor-matched normal controls (60X). The raw FASTQ files were aligned to the humanreference genome, GRCh37/hg19, and the mouse reference genome, GRCm38/mm10, using Burrows-Wheeler Aligner mapping
tools.53 Samtools was used to convert sam to bam and to sort.54 Duplicated reads were marked and removed using the Picard package (https://broadinstitute.github.io/picard). Then, local realignments and base recalibration were done using the Genome Analysis
Toolkit.55

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Whole transcriptome sequencing of organoids
Mouse pancreas organoids in Matrigel were irradiated (2 Gy). After irradiation, the irradiated organoids were retrieved from Matrigel atfive different time points (0, 0.5 h, 2 h, 6 h, and 24 h; three biological replicates at each time point). After centrifugation at 300g for
3 min, total RNA was extracted from the organoid pellets using RNeasy Mini Kit (Qiagen, #74104). Total RNA sequencing library
was constructed using Truseq Stranded Total RNA Gold kit (Illumina) according to the manufacturer’s protocol. For validation of
Myc amplification in mouse fallopian tube organoids, total RNA was extracted from FT_8Gy_1 (Myc non-amplified; three biologicalreplicates) and FT_8Gy_2 (Myc amplified; three biological replicates). TruSeq RNA Library Prep Kit v2 (Illumina) was used to generate
cDNA libraries. We performed high-throughput sequencing with 2 x 101 bp using Hiseq 2500. Adapter contamination was eliminatedfrom the FASTQ files using Cutadapt.56 Then, the trimmed FASTQ files were aligned to GRCh38/mm10 using STAR v2.6.1day,89 andnormalized counts of RNA expression were calculated using RSEM v1.3.1.57 The top 2500 genes with the highest variations (standard deviations/mean of transcripts per million (TPM) values of each gene) in irradiated organoids at five time points (0, 0.5, 2, 6, and24 h) were demonstrated by a heatmap using the R package ‘‘ComplexHeatmap’’.58 Enriched pathways were obtained from the Enrichr website with the input of the top 2500 variable genes.59
Detection of somatic variants
Strelka2 and Varscan2 were used to find single base substitutions (SBSs) and small insertions and deletions (indels).60,61 After taking
a union of SBS and indel call sets passed in each caller, an in-house python script using pysam module was utilized to annotate read
information and filter out false positive calls. To exclude sequencing artifacts, we used germline samples to calculate loci-specificbackground mutation rates. In the case of mouse samples, normal organoid samples from other mice of the same strains werealso applied because the number of germline samples at each strain was not sufficient. This information was further utilized torule out false positive mutations. Structural variations (SVs), or genomic rearrangements, were called using Delly 0.7.6.62 Loci-specific background rearrangement rates were also calculated as described above. Accurate breakpoint positions and microhomology
sequences were determined using the ‘‘SA tag’’ of clipped reads. Further, read information around breakpoints was annotated by inhouse script with pysam module, including the number of wild-type read pairs spanning breakpoints with appropriate orientation andnormal insert size (%1000 bp) and the number of variant read pairs spanning breakpoints with inappropriate orientation or large insert
size (>1000 bp). Then, breakpoints suggesting false positive variants were removed by an in-house script. Briefly, variant calls thatmeet any of the following criteria were regarded as false positives and excluded from the downstream analysis: (i) breakpoints having
high depth of read pairs in a matched germline sample (read pairs R200), (ii) R2 SA tag being the same with a somatic sample at a
breakpoint in a matched germline sample, (iii) sequencing artifacts (rearrangements detected in R2 unmatched normal samples), or(iv) low supporting variants in somatic samples (less than ten discordant read pairs without supporting SA tag, less than three discordant read pairs with 1 supporting SA tag). Then, all rearrangements were visually inspected using Integrative Genomics Viewer (IGV)63
to remove remaining false positive variants and to rescue false negative variants near breakpoints. Gains and losses of <100 bp sequences were classified as indels. Somatic LINE-1 retrotranspositions were called using TraFiC-mem64 and manually investigatedusing IGV to filter false positive calls. Several false negative calls were rescued during visual inspection of structural variations.
Segmented copy number alterations (CNAs) of clonal organoids were estimated by Sequenza, an R package, with modification
for GRCm38/mm10 assembly.65 Except for a few organoids with chromothripsis or breakage-fusion-bridge cycles, purity and ploidy
were assigned as 1 and 2.0, respectively. When abrupt CNAs were without breakpoints at callable regions, we manually investigated
the regions to find false negative breakpoints using IGV. To show SVs and CNAs in each sample, we used the Circos package.66
Publicly available datasets
In addition to the newly sequenced mouse and human organoids and radiation-induced sarcomas, we obtained additional WGS frompreviously published cancer datasets to validate mutational signatures of ionizing radiation (IR). For validation of IR-induced indelsignatures, 12 radiation-induced cancer,18 64 bone sarcoma, and 31 soft tissue sarcoma data90 were used. For comparison of deletion-insertion composite (Del-Ins) incidence, 138 lung cancer29 and 369 diverse cancer data (liver, esophageal, prostate, bladder,stomach, biliary tract, rectal, and oral cancer, skin cutaneous melanoma) from the PCAWG datasets90 were used. Most WGS datasets were re-processed from raw FASTQ files as described above. For 12 radiation-induced tumor samples, we used available
variant call sets of SBSs, indels, and SVs for downstream analyses.
Monoclonal and polyclonal organoids
To extract precise mutational burden and signatures harbored in the original single-cell, we excluded polyclonal organoids lacking
sufficient clonal mutations due to noisy subclonal mutations (i.e., culture-associated mutations). To filter out polyclonal organoids, weused the distribution of variant allele frequency (VAF) of somatic SBSs, following a similar procedure from a previous report.88 In
particular, mouse organoids with a VAF peak <0.4 or not FACS-sorted were deemed polyclonal (Table S1). Of note, all human colonorganoid samples (n = 11) were monoclonal, and human breast organoids harboring IRi-SVs with VAF around 0.5 (n = 4) were regarded as monoclonal organoids. The rest of the organoids were polyclonal, but most of them had a clear, albeit not the most prominent, VAF peak between VAF 0.25 and 0.5 (mostly near 0.5), suggesting the presence of a dominant clone that contribute more than
50% of the cell pool with some minor clones (referred to as ‘dominant-clonal’ organoids). Among polyclonal organoids, organoids
that lack dominant clones (n = 20) based on manual inspection of SBS VAF distribution were excluded from further analysis (not listed

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Articlein Table S1). Although 3 organoids (CR_0Gy_3, LI_8Gy_8, LI_8Gy_9) did not meet the criteria, they had dominant clonal indel VAF
distribution and thus were reincluded. These are referred to as ‘non-dominant-clonal’ to be consistent with the naming based on
SBS VAF (Table S1). The discrepancy between SBS and indel VAF distribution in these samples implies that a certain subclone
gained substantial amount of SBSs for some reason while there is still only a handful of clones. The complete list of the excluded
samples is: BR_0Gy_7, BR_0Gy_8, BR_0G_9, BR_1Gy_1, BR_8Gy_1, BR_8Gy_2, FT_0Gy_1, LI_0Gy_6, Li_8Gy_7, LU_0Gy_1,
LU_0Gy_2, LU_8Gy_1, PA_0Gy_17, PA_0Gy_20, PA_4Gy_1, PA_4Gy_8, PA_8Gy_12, PA_8Gy_13, SI_0Gy_2, SI_8Gy_2. In addition,
an extreme outlier, FT_0Gy_2, bearing 2400 indels, was also excluded from further analysis. For SV VAF, many organoids had VAF
between 0.15 and 0.5 (mostly the middle of the range), lower than expected. This is likely because many informative sequencing
reads are missed due to the noisy nature of SV breakpoints, making it difficult to evaluate SV VAF accurately.
Determination of VAF cutoffs for dominant clonal mutations
To separate clonal mutations from subclonal mutations in the monoclonal and dominant clonal organoids, we determined VAF cutoffs
for each organoid so that only the mutations having higher VAF than the cutoff were taken to be clonal mutations. Because of theaforementioned slight discrepancy between SBS and indel VAF distributions, VAF cutoffs for both variants were calculated separately. Briefly, each VAF distribution was modeled as a Gaussian mixture, and a VAF cutoff was equated to the intersection of therightmost fitted Gaussian curve and the second rightmost curve. If a single Gaussian curve best explained the VAF distribution,
the VAF cutoff was set to 0, such as in some monoclonal organoids. The detailed procedure is as follows. Supposing that k is thenumber of Gaussian components, we used an R package ’flexmix’67,91 for k = 1 and ’mixtools’68 for k > 1 to fit Gaussian curves
to the distribution using expectation maximization. To promote accurate and stable solutions, we initialized ms as modes of the density curve fitted to the histogram (with bandwidth 0.03), ss as 0.1, and ls as 1/k, and calculated for k from 1 to 5. To find the modes, weused an R package ’rootSolve’ and ’numDeriv’ to solve for the first and second derivatives and chose VAF values that meet df(VAF) =0, d2f(VAF) < 0, and VAF <0.6.69 We computed Akaike information criteria for each k and used the Kneedle algorithm70 to infer a valueof k that best explains the VAF distribution. For each result, we manually examined whether the result from the chosen best k agreed
well with the VAF distribution. Wherever an apparent discrepancy was seen, we refined the fit by fixing k to be an appropriate value,
manually setting initial values of ms and ss, and re-ran the fitting procedure. We iterated the above procedure until all the solutions
were stably reproducible. The final Gaussian curve fits on SBS VAF distributions, and VAF cutoffs are shown in Figures S2–S4. The
numerical values of SBS and indel VAF cutoffs are listed in Table S1.
Mutational signature analysis of single base substitutions and indels
The representation of mutational signature was borrowed from the COSMIC database,92 where SBS and indel are summarized by amutation profile of 96 and 83 mutation features, respectively. The features are mutation types and sequence contexts.2 Briefly,
learning new mutational signatures here amounts to encoding the mutation profiles of a group of samples to a matrix and carryingout a mathematical technique called non-negative matrix factorization (NMF). Given an input matrix V ˛ Rm3n (i.e., m features and n
m3n. We seek for an
samples), our objective is to solve V = WH + ε for W and H where W ˛ R+m3k , H ˛ Rk3n
+ , and an error term ε ˛ R+
approximate solution by formulating it as an optimization problem:!
X
Vijargmin DðVkWHÞ where DðVkWHÞ = Vij + ðWHÞij :Vij logðWHÞijW; H
ijDðVkWHÞ is a generalized Kullback-Leibler divergence between V and WH.93 To avoid overfitting and promote stability of a solution,a bootstrap aggregating is used, where K-means clustering is used to obtain an averaged solution.94 For implementation, we referredto the original MATLAB code,94 a predecessor of the SigProfiler, one of the widely used tools for signature extraction.2 We followed
mainly the same procedure, except for manually choosing some of the computation and model parameters. In particular, great care
was taken to determine k, the number of presumed mutational processes, mainly through examining whether a set of signatures obtained from a chosen k leads to a coherent story without under or over-explaining the mutation history. We used a measure of stability
and reconstruction error to get a reasonable initial guess for k.94 For input data, 15 normal organoids and 22 radiation-induced cancers were included for human SBS and indel signature learning, and 67 monoclonal organoids were included for mouse SBS andindel signature learning. There were some peculiar signatures attributed uniquely to one or two samples. These were not consideredsolutions because they only introduced noises to the estimates of signature exposure in other samples without adding any meaningful information. Also, in the human indel signature solution set, we observed that one signature resembled ID18 from the COSMIC
database, which has been linked to E. coli and colibactin exposure. Our solution contained more signals that should presumably
have been attributed to other mutational signatures, a known phenomenon of the method. Therefore, we replaced it with ID18
from the COSMIC, which is a supposedly pure signal of E. coli and colibactin exposure.9 Here, it is referred to as hID-D to be consistent with the naming of signatures (Figure S5). Of note, in the learning phase, instead of using the learned VAF cutoffs, we used a
constant VAF cutoff 0.3 for all samples to minimize non-clonal signals and obtain a quality mutational signature set regarding radiation exposure. It was based on our observation that, in almost all cases except some monoclonal cases, it is inevitable to include
non-clonal signals regardless of the VAF cutoff, for a VAF distribution is almost always composed of overlapping Gaussian curves(Figures S2–S4). Then, we refitted our signature solution sets using a non-negative least squares method to all 135 organoid ande8 Cell Genomics 4, 100499, February 14, 2024

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Article22 tumor samples on the variants filtered by learned VAF cutoffs accordingly with respective species and variant types. To accountfor inflated proportions of unexplained mutations from ignoring the peculiar signatures and refitting the learned signatures, wedefined ‘‘unexplained’’ counts to quantify to what extent our solution, W, cannot explain the observed mutations. For that, wecast each mutational signature spectrum to a multinomial distribution. Then, we regarded each mutation as a sample from probability
distribution constructed from a convex combination of these distributions. The residual was then considered a deviation from themean by random sampling. The rationale is that residuals that do not fall within a certain expected interval should be attributed to
unknown mutational processes. More precisely, given that fitting on a sample resulted in the residual r = v  vb where vb is our reconstructed mutational spectrum, we defined an unexplained count for the sample as
m
X








b  ;
max 0; 1ri R 0 jri j  CI+95% ð vbi Þ + 1ri < 0 jri j  CI
95% ð vi Þiwhere m is the number of features (i.e., 96 for SBS and 83 for indel) and 1C ðxÞ is an indicator function that becomes x only when thebbcondition C is met and 0 otherwise. CI+95% ð vbi Þ (or CI
95% ð vi Þ) is a value obtained by subtracting vi from the upper (or lower) bound of
95% confidence interval of vbi . We assumed that vbi is a reasonable mean estimate of the sum of binomial random variables and used
the Jeffreys method to compute the binomial confidence interval using an R package ‘tolerance’.95,71 A caveat is that our mean estimate for the mutation count of each feature can be an underestimate of the actual count by the binomial sum variance inequality.96
Also, ignoring counts that fall within the 95% CI of vbi acts against the unexplained count. Nevertheless, our definition is conservativein that the greater unexplained count strongly suggests the presence of potentially unknown processes.
Simulation of IR-inducible indel mutations
We examined the IR-associated indel signature (ID-A) to determine if there is any bias in the mutation types, hoping to understandpossible mechanistic underpinnings of the indel process of IR. We ran an indel simulation to infer the expected signature spectrum
when a random mutation is assumed for the IR-associated indel signature. First, the learned IR indel signatures were reduced to aprobability vector (p) of deletions and insertions of length from 1 to 5+ by summing up the probabilities of the respective features.
Then, we sampled random positions in the mouse or human genome without replacement. Subsequently, a deletion or insertion
was introduced for each position depending on a random sample from p. We sampled an arbitrarily large number (e.g., 10000) of
samples that is supposedly enough to accurately capture the true underlying distribution.
Reconstruction of genomic rearrangements
Given confident rearrangement call sets (n = 1,298) from 135 organoids, we reconstructed the rearrangement patterns and classified
them into 16 types of SVs (Tables S12 and S13), including (i) medium-sized simple-deletion (<1 Mb), (ii) tandem duplication, (iii) templated insertion, (iv) retrotransposition, (v) miscellaneous type, (vi) balanced inversion, (vii) balanced translocation, (viii) long simpledeletion (R1 Mb), (ix) ecDNA formation, (x) deletion-inversion composite (Del-Inv), (xi) deletion-translocation composite (Del-Tra), (xii)deletion-insertion composite (Del-Ins), (xiii) chromoplexy, (xiv) chromothripsis, (xv) SV by Breakage-fusion-bridge cycle (BFB-SV),and (xvi) other complex genomic rearrangements (CGRs). All the balanced inversions and balanced translocations showed no overlapping segments or overlapping less than 10 bp, except in one case (225 bp in one 20 Gy irradiated liver organoid). If a pattern of SV
suggests balanced inversion or balanced translocation combined with >500 bp copy number loss in one of the breakpoints, weconsidered those SVs as Del-Inv or Del-Tra. An SV was defined as ecDNA formation if one read of a deletion-type rearrangement
and one read of a duplication-type rearrangement are located at one breakpoint and the other reads of the two rearrangements
are located at another breakpoint together with copy number gain or loss. Del-Ins was an ectopic insertion of a short (typically
100–1000 bp) deleted segment like cut-and-paste type mobilization of DNA transposons. Chromoplexy was defined as a clusterof reciprocal rearrangements involving three or more chromosomes.34 Chromothripsis is a cluster of localized (usually involving 1
or 2 chromosomes) rearrangements with massive breakpoints exhibiting copy number oscillation between two or three copy number
states.35 BFB-SV was defined as a type of SV best explained by repetitive BFB cycles, characterized by a cluster of rearrangementswith multiple copy number amplifications and a foldback inversion pattern.97 Other CGRs were characterized by rearrangements ofthree or more segments by a cut-and-paste mechanism. Some breakpoints (n = 21) were defined as miscellaneous if the rearrangement is not involved in the other 15 types.
Adjusted count per Gb
To convert the unit of count into per Gb, we divided the count accordingly by either the mouse or human diploid genome size(of autosomal and X chromosomes), which is 5,267,553,344 bp or 6,072,607,692 bp, respectively, and then multiplied it by 109
and a correction factor. The correction factor was used to account for missed calls in (1) genomic regions of insufficient readcoverage and (2) repetitive regions where indel calls are more likely to be missed. The correction factor for indel counts was
1.122 for mice and 1.143 for humans, which take both (1) and (2) above into account. For SV counts, the correction factor was
1.097 for mice and 1.123 for humans. The correction factors were derived from read coverages of representative clones for miceand humans and the reference genome sequences.

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ArticleAnalysis of double-strand breaks (DSBs) induced by irradiation
DSBs must reflect the extent of initial damage induced by irradiation. Therefore, we used the number of DSBs to quantify the damage
and consequences of irradiation (Figure 5H). DSB is not directly measurable from the sequencing data but can be estimated from thenumber of breakpoints associated with indel and SVs. However, some breakpoints may not be observable if they have been repaired
seamlessly. To estimate the frequency of such seamless repairs, we looked into breakpoints in IR-induced SVs, except deletions,chromothripsis and BFB cycle (Figure 5C), examining traces of end-repairs occurred during junctional ligations, assuming that the
proportion of seamless repair for total DSBs would be similar across all SV types. Our estimate for the seamless repair is 6.7% of
all DSBs induced by irradiation. For observable breakpoints, each indel and SV was converted to DSB counts using the following
conversion rules: (1) an indel is comparable to 1 DSB, (2) balanced inversion, balanced translocation, long deletion, ecDNA, and chromothripsis result from 2 DSBs, (3) Del-Inv, Del-Tra, Del-Ins, chromoplexy, and CGRs generally result from 3 DSBs. A few exceptions
to this rule were three organoid samples: (1) Chromoplexy found in LI_20Gy_1 was accompanied by Del-Ins and can be explained by
4 initial DSB events (and so only 1 DSB was counted toward the Del-Ins), (2) CGRs found in both PA_2Gy_5 and LI_8Gy_1 can be
explained by 4 DSBs. To validate the conversion approach to quantify radiation damage, we compared the estimated DSBs fromthe sequencing data with direct DSB count from the g-H2AX immunostaining (the details of which will be followed; Figure 5G).
We used these DSB counts to estimate the number and types of mutations induced by 1 Gy of irradiation (Figure 5H). The expected
number of indel and SVs were taken from the coefficient of the regressors for in vitro samples, as shown in Figures 5A and 5B,
respectively.
Analysis of IR-associated copy number variants
Manual inspection was carried out on the genome view results of Sequenza.65 Primarily only arm-level copy number changes wereconsidered CNVs, yet a few >10 Mb-sized copy number changes missing from SV calls were counted toward CNV. We observed thatsome CNVs, mostly of whole chromosome level, appear to be of the same origin across different samples. These CNVs could beancestral variants, recurrent variants that occurred independently, or both. Another possibility being a germline variant was ruledout since not all the samples that are known to originate from the same mother cell shared the variant. To account for this potential
source of bias and count CNVs that are only relevant to the IR exposure, we assessed whether these ‘‘common’’ CNVs are ancestralby examining a phylogenetic relationship of the samples. We inferred three phylogenetic trees, each from mouse pancreas organoids, mouse breast organoids, and human tumors, groups where common CNVs were found. In the trees, we looked for mutations
attributed to the terminal branches, which correspond to the time points where the samples were irradiated. Accordingly, CNVs
satisfying any of the following criteria were removed from the final CNV count: 1) a CNV shared across samples more than once
in non-irradiated (0 Gy) samples, which is deemed culture-associated, 2) a CNV shared across samples that are tightly
grouped in the phylogenetic tree, deemed an ancestral variant, 3) deletion or duplication (of a large size) that have been alreadyincluded in structural variation calls, and 4) depth change at 0.5 unit, deemed 50:50 subclones (i.e., no dominant clones). To reconstruct the phylogenetic tree, we first merged somatic SBS variant-call-formatted files (VCFs) using samtools. We then calculated
pairwise p-distance using an R package ‘phangorn’72 and built a tree using the neighbor-joining algorithm using an R package‘ape’.73 The phylogeny was manually confirmed by examining the BAM files through the IGV at random positions among samples
in the same lineage, whether the same variants are shared only in the samples. In addition, we also checked whether all the samples
having the same common CNV had any shared mutation, which turned out there were no shared mutations.
Assay for transposase-Accessible chromatin using sequencing (ATAC-seq) analysis
To investigate whether chromatin status was associated with the formation of IRi-SVs, we performed ATAC-seq on control mousepancreas organoids (n = 4, biological replicates). According to the published protocol,98 nuclei isolation was initially performed asfollows. Organoids (approximately 10k-100k cells) were retrieved from Matrigel using Cell Recovery Solution (Corning). After centrifugation at 300g for 3 min, 10k-100k cells were prepared and resuspended in 100 mL cold lysis buffer (Tris$HCl pH7.4 10 mM, NaCl
10 mM, MgCl2 3 mM, BSA 1%, Tween 20 0.1%, NP40 0.1% (Sigma), Digitonin 0.01% in distilled water). After 3 min incubation, 1 mL
wash buffer (Tris$HCl pH7.4 10 mM, NaCl 10 mM, MgCl2 3 mM, BSA 1%, Tween 20 0.1% in distilled water) was added and centrifuged at 500 g for 5 min. The supernatant was discarded, and nuclei were resuspended in 95 mL DPBS. Approximately 10-20k nuclei
were prepared and centrifuged at 500 g for 5 min. The supernatants were discarded, and the remaining nuclei were gently resuspended in 16.5 mL of DPBS. After the nuclei isolation, a transposition reaction mixture (16.5 mL nuclei in DPBS, 12.5 mL 2x TD buffer(Illumina), 0.5 mL 1% digitonin, 0.5 mL 10% tween 20, and 2.5 mL transposase (Illumina) in 5 mL distilled water) was prepared and incubated at 37 C for 45 min. A post-clean-up process was performed using MinElute PCR Purification Kit (Qiagen), and the transposed
DNA was eluted in a 10 ml Elution Buffer. PCR amplification of the transposed DNA was performed with the following in PCR tubes(10 mL of transposed DNA, 2.5 mL Nextera index primer1 (Illumina), 2.5 mL Nextera index primer2, 25 mL NEBNext High-Fidelity 2x PCR
Master Mix (New England Labs) in 10 mL distilled water). The thermal cycle was as follows: 72 C 5 min, 98 C 30 s, 5 cycles of(98 C 10 s, 63 C 30 s, 72 C 60 s), then 4 C hold. To determine the appropriate number of qPCR cycles, 5 mL of PCR amplified
DNA was run with 5 mL NEBNext High-Fidelity 2x PCR Master Mix, 0.5 mL Nextera index primer1, 0.5 mL Nextera index primer2 in
3.85 mL distilled water +0.15 mL SYBR Green I (Invitrogen). A cycle was as follows: 98 C 30 s, 20 cycles of (98 C 10 s, 63 C 30 s,
72 C 1 min). The number of cycles corresponding to 1/3 of maximum fluorescent intensity was applied to qPCR for a 45 mL remnantof the PCR-amplified DNA. Lastly, we performed double library size selection using SPRIselect. Briefly, 22.5 mL (0.5x) of SPRIselecte10 Cell Genomics 4, 100499, February 14, 2024

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beads were added, and the supernatant was transferred. Then, 58.5 mL (1.8x) of SPRIselect beads were added, and libraries attachedto the beads were eluted in 40 mL of Elution Buffer. The final product (2x101 bp) was sequenced by Hiseq 4000 (Illumina). Using theraw fastq files of control organoids (n = 4), we performed primary data processing according to the ENCODE ATAC-seq Pipeline(https://github.com/ENCODE-DCC/atac-seq-pipeline), including alignment to GRCm38/mm10 by Bowtie2 aligner.74 Then, peakcalling was done by Model-based Analysis for CHIP-Seq (MACSv2, https://github.com/macs3-project/MACS). We used narrow
peak calls in the downstream analysis.
Association of IRi-SV incidences with genomic features
To investigate whether IR-induced DSBs occurred more frequently in a certain genomic context, we first collected several genomic
contexts information from our experiment (i.e., ATAC-seq in mouse pancreas organoids) as well as from Mouse ENCODE, including
DNase-seq of the liver, H3K9ac CHIP-seq of the liver, H3K27me3 CHIP-seq of the liver (8 weeks old male C57BL/6), and Repli-chip ofembryonic fibroblast (13.5 days male C57BL/6 embryo).99 GC ratio in 50 bp windows was calculated using the mouse reference
genome sequence. If genomic coordinates were from mm9, we converted them to mm10 using liftOver.75 Using SVs identified
from the irradiated mouse organoids (IRi-SVs) and SVs from controls (SVs from non-irradiated mouse organoids), we tested enrichments of IRi-SVs in euchromatin (open chromatin), early replicating regions, or genomic regions with high GC ratio, using the proportions of breakpoints around regions spanning peaked position ±20 bp. For ATAC-seq data, we additionally tested the enrichmentof IRi-SVs detected in mouse pancreas organoids.
Sanger validation of deletion-insertion composite (Del-Ins)
For validation of Del-Ins, primers were designed to cover the candidate deletion and insertion area of the Del-Ins:
PA_2Gy_11 deletionF, 50 -GCCTGTGTTCAAATTGGGGG-3’; PA_2Gy_11 deletionR, 50 -AAAACCCATGCCCTCTGCTT-3’;
PA_2Gy_11 insertionF, 50 -CCACATGGAACCTTATGCTGC-3’; PA_2Gy_11 insertionR, 50 -TTCTGACTGCCTTGGCACAG-3’; These
regions were PCR-amplified using 500 ng genomic DNA, 53 buffer 10 mL, 0.25 mM of each dNTP, 0.2 mM primers and 0.5 unit ofPrimeSTAR HS Taq polymerase (Takara) in a final volume of 50 mL or 500 ng genomic DNA, 103 buffer 5 mL, 0.2 mM of eachdNTP, 0.2 mM primers and 1.25 unit of Pfu DNA polymerase (ThermoFisher) in a final volume of 50 mL at 95 C for 10 min; followed
by 30 cycles of 98 C for 10 s, 60 C for 10 s, 72 C for 30 s; with a final extension at 72 C for 10 min (Taq polymerase) or 98 C for
5 min; followed by 98 C for 10 s, 60 C for 30 s, 72 C for 30 s; with a final extension at 72 C for 7 min (Pfu polymerase). The PCR
reaction was run in the Veriti Thermal Cycler (Applied Biosystems) according to the manufacturer’s protocol. The PCR products
were cloned into pTOP Blunt V2 vector system (Enzynomics), and Sanger sequencing was carried out using M13 universal primer.
Viability assay
Dissociated pancreas organoids were irradiated (0, 1, 2, 4, and 8 Gy; three biological replicates at each dose) and re-seeded in a
24-well plate (25,000/well). The growth of irradiated organoids was visually monitored for up to 6 days. On day 6 after plating, organoids were harvested for quantitative viability assay using CellTiter-Glo 3D kit (Promega). The total ATP amount in each well wasmeasured by a luminometer (1420 Victor Light).
g-H2AX immunostaining and 3D visualization
Mouse pancreas organoids on 12 well plates were irradiated (0 Gy and 2 Gy) and incubated for 1 h at 37 C. Then the organoids were
harvested into 15 mL tube without dissociation procedure and centrifuged at 500 g for 3 min. Collected organoids were fixed in 4%
formaldehyde for 20 min and permeabilized with 0.5% Triton X-100 in PBS for 20 min at room temperature. Blocking was performed
with 4% bovine-serum albumin (BSA) in buffer (0.2% Triton X-100, 0.05% Tween 20 in PBS) for 1 h. The blocked samples were incubated with a primary antibody (phospho-histone H2A.X (Ser139) antibody, Cell signaling, #2577) in PBST (PBS with 0.1% (w/v) Triton
X-100 and 0.02% (w/v) sodium azide) with a 1:300 dilution at 37 C for 4 h, followed by washing at 37 C for 2 h in PBST three times.
The samples were then incubated with a secondary antibody (donkey anti-rabbit IgG antibody-Alexa Fluor Plus 488, Invitrogen,#A32790) in PBST with a 1:300 dilution at 37 C for 2 h, followed by washing at 37 C for 1 h in PBST three times. For nuclear staining,
the samples were incubated in 1 mg/ml DAPI (Invitrogen) in PBST for 30 min followed by brief PBST washing three times. The samples
were mounted on a slide glass with a spacer (iSpacer 0.15 mm, SUNJin Lab) filled with PBS and then covered with a coverslip. The
mounted samples were imaged by confocal laser-scanning microscopy (Zeiss LSM 780) with a 633 objective (C-Apochromat 63x/1.20 W Korr M27).
Physical expansion of organoids for super-resolution imaging
For super-resolution microscopy of organoids, we used a modified magnified analysis of proteome (MAP) protocol.42 Organoids
were embedded in dense polyelectrolyte hydrogels via a physical tissue-gel hybridization approach,100 for which a modified MAP
solution with a decreased concentration of acrylamide was used (20% (w/v) acrylamide (Sigma), 10% (w/v) sodium acrylate (AK Scientific), 0.1% (w/v) bis-acrylamide (BIORAD), 0.03% (w/v) VA-044 (Wako Chemical) in PBS). Immunolabeled organoid samples were
post-fixed with 4% PFA in PBS for 10 min followed by brief PBST washing three times. Post-fixed organoid samples were incubatedin the modified MAP solution for 2 h. To locate organoids efficiently in 100-mm-thick gels in which organoids were embedded, weused a 3 mm punch to take out regions of interest containing organoids. After expanding the 3 mm punched gel with distilled water

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Articlethree times, about a 3.2x linear expansion ratio was observed. Samples were mounted on a slide glass with distilled water using the
Blu-Tack adhesive (Bostik) as a spacer, sealing with a coverslip on the top. The mounted samples were imaged using the confocal
laser scanning microscope with the 633 objective.
g-H2AX counting
ImageJ101 was used for image handling. We divided the microscopic fields of view into 3-by-3 to select cells in organoid images. Fiftycells, recognized through the nuclear staining using DAPI, were randomly selected to count the numbers of g-H2AX foci found in theAlexa Fluor Plus 488 channel. When selecting the cells, those with broad and high-intensity signals of g-H2AX were avoided to minimize error. The number of DSBs in each selected cell was manually counted.
Selection pressure analysis
We conducted analysis of selection pressure exerted by IRi-ID and IRi-SV. First, we annotated all indel calls and breakpoints of SV
calls in the 135 colonies, using the Ensembl Variant Effect Predictor (VEP).102 For indel, we estimated the background distribution byrandomly sampling 100,000 positions from the reference genome. The number of indels that fell into coding-sequence (CDS) regions
were then compared between the observed and simulated data. If the proportion of IRi-ID hits in CDS regions was smaller than that in
the simulated data, the observed data was considered to be under negative selective pressure. To isolate IRi-ID, or indels that werecaused by IR damage, we calculated the likelihood of each variant being attributed to IR-associated indel signatures, or mID-A andhID-A, based on the estimated amounts of indel signatures present in the colony and distributions of indel features, or 83 indel
sequence contexts, within these signatures. For SV, we considered only two SV classes, balanced inversions and balanced translocations, as these were the only classes that allowed for proper definitions of the background distribution. As in the indel case, we
randomly sampled 100,000 positions from the reference genome. We compared breakpoints of the SVs located in exon, intron, andUTR regions between the observed and simulated data, categorizing them as potentially damaging variants.
Single-cell sequencing
Irradiation experiments for single cell sequencing were performed using 12 well plates. Organoids were seeded in neighboring 2 wellsof 12 well plates. For irradiation, plates were placed with organoids centered in the irradiator room. After irradiation, organoids wereincubated during 24–36 h at 37 C. Genomic libraries of single cells were obtained by direct single cell manipulation (see ‘single-cellderived clonal organoid acquisition’) or respective library generation experiments.
For whole-genome amplification using MDA, 2Gy radiation was irradiated in mouse pancreas organoids. Single cells placed in 96
well plates were processed by REPLI-g Advanced DNA Single Cell Kit (Qiagen) using the manufacturer’s protocol. Amplified gDNA
were sequenced through Novaseq 151bp * 2 with 100 Gb. Sequenced fastq files were aligned on GRCm38/mm10 with BWA-MEM.53
Structural variations were called using Delly 0.7.662 and filtered using an in-house script (SVAIR, https://github.com/phansol/
gremlin). Then, all variants were visually inspected using IGV.63 To exclude false positive calls generated during whole-genome
amplification, only variants with (i) distances between breakpoints larger than 10kb for deletions and duplications and 100kb for inversions and (ii) breakpoints not overlapping with RepeatMasker103 are included.
For primary template-directed amplification (PTA), single cells dissociated, 4Gy-irradiated human colon organoids were preparedfollowing ResolveDNA Whole Genome Amplification Kit’s instructions (BioSkryb cat # PN 100068). Samples were sequenced throughNovaseq 151bp * 2 with 100 Gb. Sequenced fastq files were aligned on GRCh37 with BWA-MEM. SNV and indels were called by theunion of Varscan and GATK Mutect2, and further filtered out with two additional modifications on previous filter criteria.21 First, mutations where the number of 28 PTA samples with VAF R3% is larger than 2 were filtered out. Second, for indel, mutations, where thenumber of reads which harbor different indel mutations from called mutation is larger than 1, were filtered out. Among 28 PTA samples, 9 inefficiently amplified single cells and 7 multi-clonal cells were filtered out. To acquire single-cell mutations, mutations with
VAF larger than 25% were used. To calculate the indel mutation drop-out rate, we used germline heterozygous SNPs from thebulk organoid. Total 10,000 heterozygous indel mutations with 40% < VAF <60% and depth R30 were listed, and calculate the number of mutations that pass our filter criteria for 12 single-cell PTA samples. We adjusted the number of mutations in each PTA sample
using its own drop-out rate. For structural variations, we first called mutations from Delly and followed the in-house script (SVAIR,
https://github.com/phansol/gremlin). Because of depth fluctuation, we considered only obvious breakpoints where the fraction of
split alignment reads at both breakpoints was greater than 20% and both breakpoint depths were greater than 20. For short SV error,
only indels longer than 7000 bp and duplication and deletion longer than 2000 bp were used.
For Strand-seq, to incorporate BrdU in human colon organoids, we added BrdU after a 4Gy irradiation experiment. A single nuclei
isolation experiment was performed 36 h after irradiation using a standard nuclei isolation buffer.46 After sorting G1 phase BrdU incorporated cells, we followed the research paper’s instructions with three additional modifications. We used adapters and unique indexes for paired-end libraries,104 302nm UV light, and 2X beads–based short library removal. Pooled libraries were sequenced
through Novaseq 151bp * 2 with average 3 Gb per sample. Sequenced libraries were aligned on GRCh37 with BWA-MEM. The number of Watson and Crick strand reads were calculated from the BAIT R package.105 Samples with uneven coverage or coverage lessthan 1% were removed. Two researchers independently examined for structural variations and reached a consensus.

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BotSeq and analysis
Mouse pancreas organoids were used for the BotSeq experiment. Samples from each well of the plate were considered as separate
ones. Genomic DNA was extracted from 2Gy-irradiated sample using the DNeasy Blood & Tissue Kit (Qiagen).
DNA libraries were constructed with Truseq DNA PCR-Free Library Prep Kits (Illumina, San Diego, CA) following the manufacturer’s
protocol. Subsequent steps for BotSeq libraries were carried out as described in the previous literature.47 First, quantification of DNA
libraries was performed with the KAPA Library Quantification Kit ILLUMINA Platforms (Roche, KK4824) following the manufacturer’s
protocol. Next, each library was diluted to the target concentration using EB buffer (QIAGEN, 19086). The diluted libraries underwent
20 cycles of PCR amplification using KAPA HiFi HotStart ReadMix (Roche, KK2602) following the manufacturer’s protocol, withcustom primers from IDT (Coralville, IA) with a phosphorothioate bond(*) at the 30 end.
Forward: 50 -AATGATACGGCGACCACCGAG*A-3‘
Reverse: 50 -CAAGCAGAAGACGGCATACGA*G-30
The amplified libraries were cleaned-up with SPRIselect beads (BECKMAN COULTER life Sciences, B23317) following the manufacturer’s protocols. The completed libraries were sequenced on the Novaseq platform with paired-end 151-bp reads, generating a
total of 100Gb.
Sequenced fastq files were aligned on GRCm38/mm10 with BWA-MEM. Short indels were called by Varscan2, and we retained
only variants that met the following criteria. (1) the number of F1R2 and F2R1 reads exceeds 4 each, with a VAF greater than 0.9 forboth F1R2 and F2R1 reads. (2) no variant-supporting reads were detected in the mother organoids. (3) VAF in a panel of normals
composed of mouse organoids was less than 0.1. (4) No additional mismatches are identified except within 5 bp of the variant in orderto remove artifacts due to misalignment while retaining complex indels. (5) the distance from the read ends was greater than 5 bp. (6)the number of simple base repeats near the variant was fewer than 5. Additionally, all variants underwent visual inspection using Integrative Genomics Viewer (IGV)63 to remove remaining false positive variants generated by misalignment.
CODEC and analysis
Human colon organoids were used for the CODEC experiment. Samples from each well of the plate were considered as separate
ones. Genomic DNA was extracted from 4Gy-irradiated samples using Exgene Cell SV mini kit (GeneAll). All CODEC sequencing
and bioinformatics analytic procedures were adopted from the previous literature.48 Each library was prepared with 20 ng of input
gDNA extracted from organoids. We produced 150 Gb of genome sequences from each of the 12 libraries using 2 x 150 bp
with the S4 flow cells in NovaSeq 6000 sequencers (Illumina Inc). Subsequently, they were mapped to the reference genome andthe proportion of genomic regions covered by duplex DNA reads was measured for each sample using scripts in the previous literature (Figure S11D).48 This information was used to calculate the adjust number of indels per diploid genome.
Confidence intervals of statistics
For IRi-SV and IRi-ID, we assumed Poisson distribution and computed F  1 ðp; n = 2nmÞ = 2n to derive the confidence interval of themeans, where F  1 is the inverse cumulative distribution function of Chi-square distribution, n is the degree of freedom, p is 0.915 forthe upper bound and 0.085 for the lower bound, m is the mean estimate, and n is the number of samples included in the analysis (Figure 5E). To estimate the confidence interval of the ratio of the means (mIRi-SV/mIRi-ID) for each dose group across different irradiation
types (in vitro, in vivo, and post RT), we calculated Fieller’s interval41 using ‘mratios’ R package (Figure 5D). A caveat is that Fieller’s
method assumes normality for the underlying distributions. IRi-ID may well be approximated by normal distribution but IRi-SV is not
because IRi-SV count is very small. This is in line with an observation that a dispersion relative to the mean, or the coefficient of standard deviation, is greater in lower doses (e.g., 1.33 vs. 0.52 for 1Gy vs. 4Gy). Therefore, comparisons between low doses (1Gy or 2Gy)and higher doses cannot be drawn confidently. To circumvent this, we used marginal sums for irradiation types and compared (1)in vivo vs. post-RT and (2) in vitro vs. in vivo plus post-RT (Figure 5D).
QUANTIFICATION AND STATISTICAL ANALYSIS
All statistical calculations were performed using R version 3.6.0 (R Core Team, Vienna, Austria). A simple linear regression (using ‘lm’
function) was used for the relationship between two continuous variables, and one-sample t-test was used to evaluate p values forthe learned coefficients. We used Fisher’s exact test to assess a meaningful difference in the proportion of the number of events between two groups. To compare the means of a continuous variable between two groups, we used the Wilcoxon rank-sum test.
p value < 0.05 was considered statistically significant. For the visual aid of assessing the meaningful difference between themean estimates at a near 0.05 p value level, an 83% confidence interval was used where appropriate.106 Otherwise, a 95% confidence interval was used. Detailed information for the calculations can be found in the figure legends. The ‘‘n’’ values appearing in
the text and figure refer to the number of independent samples.

Cell Genomics 4, 100499, February 14, 2024 e13