In this study, Adell et al. investigated the bases of the selection of certain aneuploidy patterns in tumors, and by establishing time-resolved long-term adaptation protocols, they found that human cells adapt to persistent spindle assembly checkpoint (SAC) inhibition by acquiring specific chromosome arm gains and losses. They conclude that aneuploidy-induced gene dosage imbalances of individual mitotic regulators are sufficient for altering mitotic timing to reduce CIN. Both the presence of an abnormal complement of chromosomes (aneuploidy) and an increased frequency of chromosome missegregation  are hallmarks of cancer. Analyses of cancer genome data have identified certain aneuploidy patterns in tumors; however, the bases behind their selection are largely unexplored. By establishing time-resolved long-term adaptation protocols, we found that human cells adapt to persistent spindle assembly checkpoint (SAC) inhibition by acquiring specific chromosome arm gains and losses. Independently adapted populations converge on complex karyotypes, which over time are refined to contain ever smaller chromosomal changes. Of note, the frequencies of chromosome arm gains in adapted cells correlate with those detected in cancers, suggesting that our cellular adaptation approach recapitulates selective traits that dictate the selection of aneuploidies frequently observed across many cancer types. We further engineered specific aneuploidies to determine the genetic basis behind the observed karyotype patterns. These experiments demonstrated that the adapted and engineered aneuploid cell lines limit CIN by extending mitotic duration. Heterozygous deletions of key SAC and APC/C genes recapitulated the rescue phenotypes of the monosomic chromosomes. We conclude that aneuploidy-induced gene dosage imbalances of individual mitotic regulators are sufficient for altering mitotic timing to reduce CIN. The faithful segregation of replicated genetic material to daughter cells is a fundamental requirement of all living beings. Erroneous chromosome segregation during mitosis results in gains and losses of chromosomes and chromosome arms, resulting in aneuploidy. An increased rate of such chromosome missegregation events is termed chromosomal instability (CIN). Aneuploidy is associated with growth abnormalities and inviability in many organisms . Intriguingly, the frequencies of chromosome gains in the adapted cells correlate with those seen in cancer, suggesting general advantages that are largely independent of the cell type and cellular environment. We then used CRISPR/Cas9-based engineering of monosomic chromosomes to determine the genetic bases behind frequently acquired aneuploidies. We identified specific monosomies that directly rescue SAC inhibition. We show that these monosomies increase the mitotic duration and that changing the dosage of single genes is sufficient to reduce CIN. Together, these results demonstrate that alterations in single genes on aneuploid chromosomes are sufficient to affect mitotic timing and suppress SAC inhibition.p53-deleted cell lines with the MPS1 inhibitor reversine over 30, 60, and 90 d . Before initiating the adaptation process, we first engineered human cell lines suitable for observing adaptation to high rates of CIN. We started with six near-diploid human cell lines that comprise three categories: myeloid leukemia (diploidized HAP1 -[referred to here as dipHAP1] and EEB), colorectal cancer (HCT116 and DLD1), and epithelial noncancer (hTERT-RPE1 and hTERT-HME1). Near-diploid cell lines were chosen to reduce the likelihood of pre-existing CIN-tolerating mutations and to simplify the karyotype analyses. In cell lines that did not already have a p53 deletion, we homozygously deleted p53 so that they could continue to propagate despite ongoing chromosome missegregation . Most cell lines showed a cell type-dependent decrease in their capacity to arrest at the G1/S transition following p53 deletion, as previously described . On average, the dipHAP1 and RPE1 populations exhibited the highest degree of aneuploidy with 18% and 14% of the genome being aneuploid at 90 d, respectively. In contrast, the HCT116 populations showed the least amount of aneuploidy at ∼1% of the genome on average. We conclude that aneuploidy formation in response to long-term MPS1 inhibition is highly cell line-dependent.Most of the adapted cell populations showed a gradual increase in reversine resistance over time as measured by colony formation assays. Proliferation of cells in reversine increased from 0.4%–7.2% relative to untreated cells before the adaptation (time point 0) to ∼13%–15% at 90 d B. ProlifSupplemental Fig. S3A,B). The complete lack of whole-chromosome and arm-level losses in DLD1 and HCT116, respectively, indicated fundamental differences in how the diverse cell lines adapted to the drug . We conclude that early on in adaption to CIN, whole-chromosome changes are primarily acquired. At later stages, adapted populations develop more refined karyotypes by gaining and losing smaller chromosomal areas.We next measured which types of copy number changes  were most prevalent in the adapted populations and how this changed over time. For all cell lines combined, whole-chromosome aneuploidies made up >60% of all CNAs at the 30-d time point E. Over tSupplemental Fig. S2). We hypothesized that these decreases could be due to incompatibilities with other, more beneficial chromosomes. For each of the examples mentioned above, we identified strong negative correlations with other aneuploidies that show corresponding increases over time. Aneuploidy of chromosome 7q, 1q, and 10p was each mutually exclusive with chromosome 14q monosomy, chromosome 19q partial loss, and chromosome 12q gain, respectively . The anticorrelation with chromosome 1q gain was compared with both forms of 19q loss combined. These patterns indicate that distinct aneuploidies are lost when more beneficial aneuploidies take over the population. Indeed, for chromosome 7q trisomy versus chromosome 14q monosomy and for chromosome 10p monosomy versus chromosome 12q trisomy, the aneuploidy that emerged later in adaptation correlated better with growth in reversine . We conclude that optimized aneuploid karyotypes develop over time through both the gradual selection of highly beneficial aneuploidies and the loss of less beneficial aneuploidies acquired early on in the adaptation process.Although many aneuploidies increased in frequency over the course of the experiment, we were surprised to see that some aneuploidies that were frequently acquired early on in the adaptation process decreased in prevalence at later time points. These include the gain of chromosome 7q and chromosome 1q in dipHAP1 cells and the loss of chromosome 10p in RPE1 cells . By the 90-d time point, the vast majority of reversine-adapted populations had acquired recurring aneuploidies in all cell lines except for HCT116. Importantly, none of the cell populations underwent whole-genome duplication (WGD) during the 90 d of adaptation, demonstrating that WGD did not drive reversine resistance in these experiments . There were some aneuploidies that were acquired in adapted populations across multiple cell lines. These include the gain of chromosomes 5, 8, and 20 in three or four out of the six adapted cell lines . These include the loss of focal chromosome 3p trisomy in HME1 and chromosome X disomy in dipHAP1. The dipHAP1 cell line was generated through diploidization of male haploid chronic myeloid leukemia cells that lost chromosome Y. Therefore, dipHAP1 cells are aneuploid for chromosome X because they have two active copies. We conclude that both cell line-dependent and -independent aneuploidies are selected for during the adaptation to CIN.We next determined which specific aneuploidies were most enriched at the end of the adaptation. For these analyses, we focused on whole-chromosome and arm-level CNAs, as these were most frequently observed. Cultivation of cell populations in the absence of reversine did not change karyotypes during the 90 d, with the exception of the loss of chromosome Xp in dipHAP1 . In addition, we combined the data of all of the reversine-adapted populations from the six cell lines and compared them with the combined data of the different cancer types in the three cancer databases. At all three time points, chromosome gain frequencies between cancers and the adapted cell lines were highly correlated . This pattern was largely driven by the high frequency of chromosome 8, 20, 5p, and 1q gains in both data sets. The only chromosome that is frequently gained across many different cancers but rarely in the adapted cell lines is chromosome 7. This difference may reflect the negative correlation between chromosome 7q trisomy and other, even more beneficial aneuploidies under reversine selection as described above , MSK-IMPACT, and the Cancer Cell Line Encyclopedia (CCLE) . Since wed above G. These Supplemental Fig. S7A,B).We next wanted to identify those aneuploidies that lead to reversine resistance. We therefore determined which of the recurrent arm aneuploidies in the adapted cell lines best correlated with improved growth in reversine. In addition to measuring growth in reversine, we also calculated growth in reversine relative to growth without the drug to account for potential negative effects on general fitness due to the aneuploidy burden. However, the complexity of the karyotypes at 90 d made the detection of significant correlations challenging. In addition, it was impossible to draw correlations in cases where aneuploidy of a particular chromosome was no longer observed. We therefore focused on looking for correlations with growth under reversine for aneuploidies that were found in 25%–80% of adapted populations at 90 d. For those with a frequency >80%, we looked at the 60-d time point when there were enough disomic populations for meaningful comparisons . In addition, we observed positive correlations with chromosome 6p loss in RPE1 cells and chromosome 14q loss in dipHAP1 cells after 90 d of adaptation. However, the growth improvement of chromosome 14q loss was just as strong in the absence of reversine, suggesting that the selection of this chromosome may not be reversine-specific. Positive correlations were also seen in both DLD1 and HME1 cells with 20q. However, in dipHAP1 cells, the gain of chromosome 20 was negatively correlated with growth despite being present in ∼50% of the adapted populations, emphasizing the difficulties of identifying the contributions of individual chromosomes in complex karyotypes. The two strongest candidates for aneuploidies that lead to a strong reversine resistance are therefore chromosome 6p loss in RPE1 cells and chromosome 13 loss in multiple cell lines.We found positive correlations between reversine growth and 13q monosomy in both RPE1 and dipHAP1 cells at the 60-d time point and in HME1 cells at the 90-d time point . The analysis comprised 15 adapted cell populations (nine from RPE1 time point 60 d and three from dipHAP1 and three from HME1 time point 90 d) that showed extensive karyotype diversity in the most commonly acquired aneuploidies: 6p loss, 13q loss, and 20q gain.Next, we investigated whether increased reversine resistance was associated with decreased mitotic errors in the adapted cell lines. For this, we monitored mitotic timing and chromosome segregation fidelity by live-cell imaging of unsynchronized populations stained with SiR-DNA .Elongation of mitotic duration has been previously proposed as a resistance mechanism to MPS1 inhibition . AlthougSupplemental Fig. S8D,F). In addition to misaligned chromosomes, reversine addition also substantially increases the formation of lagging chromosomes and chromosome bridges in RPE1 and dipHAP1 cells. The adapted cell lines display a decrease in the rates of anaphase errors in the presence of reversine . These results indicate that the adapted cells limit reversine-induced mitotic error rates by extending mitotic duration.The shortened mitotic timing after reversine treatment greatly increases the number of misaligned chromosomes. To determine whether the adapted cells had improved chromosome alignment, we measured the percentage of cells with one or more chromosomes that where not properly aligned at the metaphase plate immediately before anaphase onset. Adapted cells exhibited very low chromosome misalignment rates in the presence or absence of reversine C. For RPSupplemental Fig. S7B), is also associated with increased mitotic length and decreased chromosome misalignment rates . Similar to reversine treatment, MAD2 depletion in unadapted cells led to a decrease in mitotic length and increased alignment errors . MAD2 depletion eliminated the mitotic delay phenotype exhibited by adapted RPE1 cells in the absence of reversine. Upon SAC inactivation, both unadapted and adapted RPE1 cells became extremely similar in mitotic duration and chromosome misalignment rates. These results indicate that in adapted RPE1 cells, the activity of the SAC is modulated to counteract the effects of partial MPS1 inhibition. In contrast to RPE1 cells, mitotic length in dipHAP1 cells was still significantly higher than in unadapted cells after MAD2 depletion. In addition, MAD2 depletion in the adapted dipHAP1 cells did not increase chromosome misalignment rates. These partial phenotypes could be due to either dipHAP1 cells adapting through mechanisms downstream from the SAC or only partial inhibition of SAC activity by the MAD2 depletion. However, nocodazole treatment led to a fully penetrant mitotic arrest in control cells and caused mitotic slippage in 100% of MAD2-depleted RPE1 and dipHAP1 cells, indicating robust SAC inactivation . We conclude that RPE1 cells adapt to reversine primarily by promoting SAC activity, while dipHAP1 cells adapt through a combination of increased SAC activity and additional effects downstream from the SAC such as the APC/C.Since MPS1 activates the SAC and we used reversine concentrations that do not completely inhibit its activity, cells could adapt either by increasing the residual SAC activity or by inhibiting activities downstream from the SAC such as the APC/C. We hypothesized that in reversine-adapted cells, SAC and/or APC/C activity could be altered to partially restore mitotic timing in the presence of the drug. To test this, we used small interfering RNA (siRNA) to deplete the essential SAC component MAD2 in RPE1 and dipHAP1 parental and adapted cell lines . To ascertain the specific roles of these monosomies in reversine resistance, we engineered the losses of chromosome arms 6p and 13q individually in the dipHAP1 cell line with CRISPR .Next, we sought to identify the basis behind the frequent selection of specific aneuploidies in the adapted cell lines. We focused on the most common chromosome losses, as we were able to efficiently engineer specific chromosome and gene losses as opposed to gains. We chose chromosome arm 13q loss, since it is one of the most frequently selected aneuploidies across all reversine-adapted cell lines A. In addSupplemental Fig. S10B; Supplemental Table S3; Supplemental Fig. S10C). Attempts to engineer chromosome losses in RPE1 cells were unsuccessful, potentially due to differences in DNA repair mechanisms.To generate cell lines with one specific chromosome arm loss, we selected sgRNAs that target the chromosome arm either at one site close to the centromere or at a repetitive region in the chromosome arm . Both monosomies showed a better growth in reversine relative to its absence when compared with wild-type dipHAP1 cells as measured by colony formation assays. However, for the 6p deletion, the added reversine resistance could not overcome the negative effect of the aneuploidy on proliferation, which may explain why this CNA was not observed in the adapted dipHAP1 cells.In the absence of reversine, engineered dipHAP1 cells monosomic for chromosome arm 13q showed a mild growth reduction, whereas deletion of one copy of 6p resulted in very poor growth . As mentioned earlier, the loss of chromosome X results in a relief from aneuploidy in dipHAP1 cells and was observed in all of the reversine-adapted populations . Similar to 6p and 13q monosomy, the loss of the whole X chromosome showed an increased reversine resistance compared with euploid wild-type cells . Loss of only the p-arm of X did not show this phenotype, suggesting that the genes affecting reversine resistance are on the q-arm. Loss of 19p was not observed in any of the adapted cell lines. We therefore used this aneuploidy as a control for the general effects of chromosome loss. Chromosome 19p loss made the cells more sensitive to reversine, demonstrating that reversine resistance is not a general property of monosomies . We conclude that the loss of one copy of chromosomes 6p, 13q, or X is sufficient to confer reversine resistance.Occasionally, other aneuploidies were observed during the screening process for 6p and 13q loss. These include cell lines whose only CNA is the loss of chromosomes X, Xp, and 19p . In the absence of reversine, the cell line with only a single aneuploidy grew better, but with the addition of reversine, the double-monosomic cell line quickly and reproducibly took over the population . These results demonstrate that multiple monosomic chromosomes can create stronger phenotypes and support the idea that the reversine resistance in the adapted cell lines results from the additive or synergistic contributions of multiple aneuploid chromosomes.Notably, the rescue phenotypes for the engineered monosomies were much weaker than what was observed in the adapted cell lines, suggesting that multiple aneuploidies act cooperatively during the adaptation process. To test whether multiple monosomies can act together to create stronger drug resistance phenotypes, we used a chromosome 13q deletion cell line that also acquired chromosome X loss during the generation of the cell line C. The coSupplemental Fig. S12A). Both of these deletions exhibited robust reversine resistance that was similar to the full arm deletion . This indicates that either one or more of the 86 genes closest to the telomere contribute to reversine resistance when heterozygously deleted or another genetic element in this region is responsible. In addition to full arm loss, we also detected focal losses on chromosome arm 13q in the adapted dipHAP1 and HCT116 cell lines . Focal losses of chromosome arm 13q in the adapted cell lines always contained our identified chromosomal region, further supporting the results from the engineered partial deletions.The losses of chromosome arms 6p and 13q decrease the copy number of hundreds of genes on the aneuploid chromosome. To narrow down the chromosomal regions that confer drug resistance, we engineered partial deletions of the chromosome arms. We engineered two partial deletions that eliminate either 165 or 86 of the 312 genes on chromosome 13q . Interestingly, although both of the smaller deletions grew better in the absence of reversine than the full arm deletion, only the larger 502 gene deletion still showed better growth in reversine than without reversine relative to the parental cell line . This narrows down the region of interest to the one in between the partial deletion sites containing 362 genes. Importantly, these results further demonstrate that the reversine resistance is due to the loss of specific regions and not a general effect of aneuploidy or the technique used for aneuploidy generation.The full deletion of chromosome arm 6p decreases the copy number of 585 genes. We again made two partial deletions—one that eliminated 502 genes and another that only removed 140 genes . Whole-genome sequencing of the identified cell line revealed that it also contained a deletion of chromosome X . We therefore used the chromosome X deletion cell line described above as the control for experiments with this cell line. CDC16+/− cells showed a strong resistance to reversine, substantially greater than the resistance observed for X loss alone . Additionally, cocultivating the CDC16 knockout cell line with a GFP-labeled X loss control cell line showed that the knockout cell line quickly overtook the population in medium containing reversine in two independent experiments . This level of resistance is consistent with the CDC16 gene being primarily responsible for the reversine resistance in the chromosome 13q deletion cell lines, indicating that the loss of CDC16 is likely the driving force behind the selection of chromosome 13q monosomy in the adapted cell lines.Monosomy of chromosomes can induce phenotypes either through loss of heterozygosity (LOH) or decreased expression of genes on the chromosome. Since the monosomies were engineered in the dipHAP1 cell line that is already homozygous for nearly the entire genome (with the exception of a portion of chromosome 15), we can eliminate LOH as a possible contributor to the aneuploidy phenotypes. After further refining the regions of interest on chromosomes 13q and 6p, we took a candidate approach to identify genes that may contribute to the phenotype. The 86-gene region on 13q contains the gene for he APC/C A. A muta deleted . We therss alone B. CDC16 p31comet and CDKN1A.p21 p31comet is a negative regulator of the mitotic checkpoint complex (MCC) that was previously identified in a CRISPR/Cas9 screen for MPS1 resistance . For p31comet, we obtained a homozygous deletion cell line in dipHAP1 cells and a heterozygous deletion cell line in RPE1 cells . Both cell lines showed strong resistance to reversine . We conclude that the heterozygous deletion of p31comet is sufficient for robust reversine resistance and that the decrease in p31comet expression is likely the principal basis behind the selection of 6p loss in RPE1 cells adapted to reversine.The 362-gene region on chromosome 6p contains two genes of primary interest for potentially contributing to reversine resistance: sistance . p21CDKNregation . Althougll lines . Howevereversine D,E. p31cThe adapted dipHAP1 and RPE1 cell lines showed an increased time in mitosis compared with the parental cell lines A,B. In aCDC16 heterozygous deletion all had nearly identical NEBD-to-anaphase delays of ∼8 min in the absence of reversine . Addition of the guide RNAs eliminated the mitotic delay for the CDC16 heterozygous deletion and chromosome 6p monosomic cell lines with and without reversine. For chromosome 13 monosomy, mitotic timing was substantially, but not completely, reduced to the levels of the empty vector, indicating that genes other than CDC16 on this chromosome also contribute slightly to the anaphase delay.To determine whether the heterozygous loss of a system  using siSupplemental Fig. S15F,G). We conclude that the mitotic delay is not caused by monosomy in general in dipHAP1 cells and that the observed phenotypes are chromosome-dependent. Overall, these results are consistent with the reversine growth data, demonstrating a clear link between mitotic timing, chromosome alignment, and drug resistance for the engineered aneuploid and gene knockout cell lines.A recent study suggests that the presence of monosomic chromosomes generally increases mitotic duration in RPE1 cells . To deteIn this study, we developed a time-resolved adaptation assay for human cells to study complex karyotype formation over time in response to long-term MPS1 inhibition in six different cell lines. Reversine-mediated inactivation of the SAC leads to high rates of CIN that also serve as a selective force to adapt via aneuploidy A. Thus, MPS1 gene specifically in a cell line with a karyotype that was highly divergent from the other adapted populations. Altered aneuploid karyotypes resulting from specific mutations have also recently been reported in yeast  acquire very few chromosome gains and no chromosome losses. This observation is in line with the mutual exclusivity between MSI and CIN in colorectal cancers . The comcomet or CDC16 through the monosomies of chromosome arms 6p or 13q should both lead to decreased APC/C activity, which would delay anaphase onset. It has previously been shown that the deletion of the APC/C subunits APC7 or APC16, which also should lead to a decreased APC/C activity, rescues the lethality of MAD2 deletion and therefore SAC inactivation in HCT116 cells  were kindly provided by I.M. Cheeseman . The parental HCT116 cell lines (wild type and p53−/−) were gifts from B. Vogelstein (The Johns Hopkins Oncology Center). DLD1 was provided by M. Baccarini . The parental hTERT-HME1 cell line was obtained from Evercyte (CHT-044-0236), and the parental EEB is a cellosaurus cell line (Riken RCB2345). The parental HAP1 cell line was a gift from Joanna Loizou .All cell lines used in this study tested negative for mycoplasma contamination. Their tissue types, sources, authentication, and culture conditions are summarized in TP53 knockout: To generate the RPE1 TP53 single knockouts, Cas9 was induced by adding 1 µg/mL doxycycline hyclate (Sigma) every 24 h for 3 d before single-cell sorting. To generate the TP53 single knockouts in HME1, EEB, and DLD1, a CRISPR/Cas9 was provided on a plasmid (TP53 gene was cloned into pSpCas9(BB)-2A-GFP . The sgRNA plasmid was transfected into cells using FuGENE HD (Promega). Two days after transfection, cells were sorted for the presence of Cas9 (GFP-positive), and another 3 d later, single-cell sorting into 96-well plates was performed. Clonal populations were expanded gradually over the course of 3 wk. TP53 mutations were identified by Sanger sequencing, and karyotypes of the cell lines were validated by whole-genome sequencing. p21, p31comet, and CDC16 knockouts: The knockouts in dipHAP1 and RPE1 cells were generated similar to the protocol above, except XtremeGene 9 (Roche) was used as the transfection reagent for dipHAP1 cells and electroporation for RPE1 cells. All sgRNAs used for the generation of knockout cell lines are listed in Supplemental Table S5. The respective genotyping primers are listed in Supplemental Table S6. plasmid . sgRNA ap53−/− cell lines were split into 12 independent populations at day 0, and reversine (Axon Medchem BV) was added at the indicated concentrations for the respective cell lines where ∼90% of cells died . Three days later, single-cell sorting into 96-well plates was performed, and individualized cells were allowed to grow into colonies in the presence of reversine for the next 10–14 d. Around 20 independent cell populations for each cell line were expanded and continuously cultivated in medium containing reversine for up to 90 d. During this time period, the drug was replenished every 3–4 d. Reversine-adapted populations were analyzed at three time points—after 30, 60, and 90 d. For the last 30 d, reversine concentrations were increased to levels that are lethal for unadapted cells to further increase the selection of optimized adapted populations at 90 d .To initiate the adaptation process, parental http://bowtie-bio.sourceforge.net/bowtie2; http://samtools.sourceforge.net; http://bedtools.readthedocs.io; Genomic DNA from subconfluent populations was isolated using the QIAamp UCP DNA micro kit (Quiagen). NGS sample preparation was then performed as described previously in In-depth characterization of chromosome copy number changes was carried out in three steps: (1) segmentation and change point detection, (2) assignment of segments to corresponding wild-type segments and determination of change state, and (3) correction of oversegmentation.First, segmentation and change point detection were performed on copy number data that had been GC-debiased before using the Gingko online tool. The array of binned copy number data points (each point representing 500 kb) was first split into segments of equal mean copy numbers that did not contain change points (locations where the mean copy number abruptly changed). By definition, this can be a whole chromosome (no change point), a chromosome arm (one change point at the centromere and none within an arm), or a focal segment (at least one change point within a chromosome arm). The algorithm used to detect change points was PELT (pruned exact linear time) . By convFor accurate determination of the change state of a segment, a one-to-one assignment between a sample and its corresponding wild-type cell line was implemented. For this, the difference between the mean copy number values of the sample segments and the wild-type segments was measured. Changes of the mean copy number value between sample and wild type >0.5 were marked as gain, and changes less than −0.5 were marked as loss. By convention, changed segments needed to be at least 15 data points (=7.5 Mb) long to get counted. If a change point was found in the sample but not the wild-type or vice versa, an additional change point to ensure the one-to-one correspondence was introduced. For analyses that ignored pre-existing CNAs, certain segments were marked in the wild-type cell lines and excluded from further analysis. If a segment was flagged, all corresponding segments in the sample cell lines were also automatically flagged. The output of this analysis step was a list of segments, labeled with the change state . Flagged segments were labeled with the state i (ignore). The alignment step described above had the disadvantage of oversegmentation of chromosome data, since it copied change points from wild-type data to the sample data (and vice versa) if the change point was present only in the wild type. A second source of oversegmentation was the heuristic to split automatically at the centromere if at least one change point was found. For correction of this oversegmentation, subsegments of the same change state were merged.Supplemental Fig. S1A). For the engineered dipHAP1 cell lines, 300 nM reversine was used, and for the engineered RPE1 cell lines, 125 nM reversine was used. Colonies were fixed with 4% (v/v) formaldehyde in PBS (Thermo Fisher Scienitific) for 20 min, washed with purified water or PBS, stained for 20–30 min with Crystal Violet, washed with purified water several times, and dried. The plates were imaged using a ChemiDocMP imaging system (Bio-Rad), and analysis was done in ImageJ. The images were thresholded, and the fraction of area above the threshold was measured.On day 0, 600 cells were seeded into six-well plates and incubated for 12 d in their respective culture media supplemented with reversine at the indicated concentrations (+Rev) or with the corresponding amount of DMSO (−Rev). The following reversine concentrations were used for the adapted cell lines: 400 nM for dipHAP1, 50 nM for DLD1, 100 nM for HCT116, 125 for nM RPE1, and 100 nM for HME1 . On the day of imaging, cells were stained with 125 nM SiR-DNA or Spy650-DNA (Spirochrome) in culture medium for 3 h and afterward maintained in Opti-MEM supplemented with 12.5 nM SiR-DNA or Spy650-DNA and the indicated drugs during microscopy. For a few cell lines (explicitly labeled in the figure legends), 10 µM verapamil  was added to the staining and imaging medium to increase SiR-DNA incorporation. Automated microscopy of dividing human cells was performed on a Celldiscoverer 7 screening microscope (Zeiss) using a 50× water immersion objective (Zeiss plan-apochromat 50×/1.2-W autocorr and autoimmersion objective) with a 0.5× zoom lens and recorded as a three-dimensional time series. Each time series was 2.5 h long, with images taken every 3 min. Imaging was controlled by Zen 2.5 (blue edition) software. Cells were maintained at 37°C in a humidified atmosphere of 5% COSupplemental Table S7. A nontargeting scrambled siRNA was used as a negative control. siRNA transfections were performed using Lipofectamine RNAiMAX (Thermo Fisher) according to the manufacturer's instructions and at room temperature. Diploid HAP1 and RPE1 cells were plated at 25%–30% confluence 12 h prior to siRNA transfection in six-well glass-bottom plates. Nine microliters of RNAiMAX was diluted in 150 µL of Opti-MEM (Gibco), and 3 µL of 10 µM siRNA (30 pmol) was dissolved in 150 µL of Opti-MEM. Both solutions were combined, mixed by pipetting, and incubated for 5 min. The siRNA–lipid complex was then added dropwise to six wells, and cells were incubated for 24 h in the presence of the siRNA. Cells were subsequently subjected to SiR-DNA staining and live-cell imaging as described above. Nocodazole was used at 250 ng/mL as a readout for siRNA efficacy.The siRNA (Dharmacon) used in this study is listed in −1 Hoechst 33342 (Thermo Fisher Scientific) for 30 min at 37°C. Analysis by flow cytometry immediately after incubation was done on a BD FACSAria IIu cell sorter (BD) using an argon laser tuned for UV (353–365 nm) and fluorescence detection at 480 nm.Human cells were trypsinized and stained with 0.2 µg mLFACS was performed on a BD FACSAria IIu cell sorter (BD) or a BD FACSMelody cell sorter (BD). For all cell lines and FACS experiments, medium containing PBS and 10% FBS was used. FACS was performed either in bulk or single-cell-sorted into 96-well plates depending on the experiment. Growth medium for FACS-sorted cells contained normocin (Fisher Scientific) following the first 2 d after sorting to suppress mycoplasma, bacterial, and fungal contamination.p53+/+ and p53−/− cell lines were treated with 6.22 Gy γ-irradiation (Co60-Dmax), collected, and fixed in ice-cold ethanol for at least 20 min. After ethanol removal, fixed cells were washed twice with PBS and then permeabilized with 0.25% Triton X-100 in PBS for 10 min at 4°C. Cells were blocked for 1 h with 1% BSA in PBS and stained for 2 h with a mouse monoclonal MPM-2 antibody . After several washes, cells were stained with 10 µg/mL Alexa 488-conjugated goat antimouse secondary antibody (Thermo Fisher Scientific). DNA was stained with propidium iodide-RNase solution  for another 20 min in the dark at room temperature and then subjected to FACS analysis.https://guidescan.com [http://crispor.tefor.net; http://www.rgenome.net/cas-offinder; Supplemental Table S3 and Supplemental Figure S10B. The number of protein-coding genes was calculated based on the MANE project -2A-GFP plasmid was transfected with XtremeGene9 (Roche). Forty-eight hours after transfection, cells were FACS-sorted for GFP expression either in bulk or as single cells in 96-well plates. Three days later, bulk-sorted cells were single-cell-sorted into 96-well plates. Cell clones were expanded to confluency in a 12-well plate. Genomic DNA was extracted with 0.5× lysis buffer . All qPCR reactions were performed on Mastercycler ep Realplex platforms (Eppendorf). Each calculated cycle threshold (Ct) value was normalized to the Ct value generated by a primer pair targeting the euploid chromosome 4 (ALB control primer). Samples and controls were measured in triplicates. In addition to qPCR analysis, the karyotypes of all engineered aneuploid cell lines were verified by whole-genome sequencing according to the above-described protocol. The aneuploid cell lines were frozen in multiple aliquots, and for each experiment, fresh aliquots were thawed and cultivated for a maximum of six passages to reduce the formation of secondary karyotypic changes.For full and partial chromosome arm deletions, dipHAP1 s buffer , and DNASupplemental Figs. S11B, S13F). Mixed cell lines were cultured in standard growth medium or growth medium supplemented with 300 nM reversine in three or six replicates for each growth condition, and 50,000 mixed cells were added into each well of a six-well plate. The remaining mixed cells were used to determine the exact distribution of GFP+ and dark cells (or mCherry+) at day 0 by flow cytometry. After 4 d, cell mixtures were passaged, and the relative abundance of GFP was measured by flow cytometry. All later measurements were done in intervals of 3–4 d for a total duration of 13–19 d.GFP-expressing and nonfluorescent (dark) or mCherry-expressing cell lines were seeded separately in 10-cm dishes prior to FACS sorting. The two cell lines were then mixed in a 50:50 ratio by bulk-sorting 400,000 cells each into 4 mL of growth media with 1:500 normocin. Setting up the gates was done using a nonfluorescent (dark) parental cell line and GFP- or mCherry-expressing cell lines as controls , and anti-α-Tubulin .For protein preparation, 2 million or 4 million cells were lysed in 75 µL of ice-cold NP40 buffer with cOmplete protease inhibitor cocktail (Roche) and incubated for 1 h on ice. Samples were then incubated with 0.1 µL of benzonase nuclease (Sigma) for 30 min at 37°C. Twenty-five microliters of 4× protein sample buffer was added and boiled for 5 min at 95°C. Protein size was estimated using the prestained protein Ladder midrange molecular weight (Abcam). Coomassie staining was used to equalize the amount of each sample for loading. SDS-PAGE was performed at 120 V for 90 min. Wet transfer onto a nitrocellulose membrane was performed at 250 mA for 150 min at 4°C. Membranes were blocked for 1 h in 5% BSA (Sigma) or 5% skim milk (Gerbu Biotechnik GmbH) in Tris-buffered saline with 0.01% Tween (TBST), depending on the primary antibody (see below). Primary antibodies diluted in the respective blocking buffers were incubated overnight at 4°C on an orbital shaker. The membranes were washed for 30 min in TBST with three buffer exchanges before and after secondary antibody incubation. Horseradish peroxidase (HRP)-conjugated antimouse IgG secondary antibody  diluted in the respective blocking buffers was incubated for 1 h at room temperature. Chemiluminescence was developed using the Amersham ECL Prime detection reagent (Cytiva) and detected with the ChemiDocMP imaging system (Bio-Rad). Quantification of protein bands was carried out using ImageJ. The following primary antibodies were used: anti-CDC16 , anti-p31To generate dCas9-VPR-mCherry-expressing dipHAP1 cell lines for CRISPRa, HEK293HiEx cells were transfected using XtremeGene9 (Roche) with an envelope plasmid, packaging plasmid, and dCas9-VPR-mCherry expression plasmid for lentivirus production . The expression plasmid had both dCas9-VPR and mCherry under the EF1α promoter separated by P2A. After 3 d, the supernatant-containing virus was filtered through a 0.45-µm filter, and the cell lines were transduced with a 1:1 dilution of the virus. After 3 d of incubation, the cells were FACS single-cell-sorted for mCherry fluorescence. Clonal colonies were expanded, and cell lines stably expressing a moderate to high level of mCherry were selected.Supplemental Table S8. For the overexpression of p31comet, three sgRNAs were used together. Plasmid-expressing cells were selected with 1.5–2 mg/mL G418. After at least 5 d of selection, mitotic timing was measured as described above, and cells were pelleted for RT-qPCR to measure the change in mRNA expression levels.For the induction of overexpression, the dCas9-VPR-mCherry cell lines were transfected with GFP-expressing sgRNA plasmids containing guides targeting the promoter of the gene of interest or the empty vector as a control using XtremeGene9 (Roche). The guide sequences are listed in Ct) values were normalized to the Ct value generated by a primer pair targeting GAPDH. Samples and controls were measured in triplicate. The RT-qPCR primers used are listed in Supplemental Table S9.For RT-qPCR, RNA was isolated using the RNeasy mini kit (Qiagen). Ten micrograms of RNA was treated with RNase-free DNase I (NEB). cDNA was generated following the ProtoScript II reverse transcriptase (NEB) protocol with oligo d(T)23VN (NEB) and RNasin ribonuclease inhibitor (Promega). cDNA concentration was measured with the Qubit dsDNA HS assay (Invitrogen), and samples were normalized to the same concentration for RT-qPCR (4–8 ng). Expression levels were measured with the same qPCR protocol as described above. All qPCR reactions were performed on Mastercycler ep Realplex platforms (Eppendorf). Cycle threshold  unless otherwise indicated.All experiments were repeated multiple times, and the indicated number of experiments always refers to biological replicates. Statistical analysis was performed using Python 3.4 and GraphPad Prism software. For statistical analysis of differences versus the wild-type cell lines, the unpaired Student's


CIC) is a gene that is frequently mutated in several cancer types, including stomach cancers and certain subtypes of brain tumours and sarcomas. CIC, the protein encoded by the CIC gene, has been shown to play a multitude of roles in both normal and cancer cell functions; however, most studies exploring these roles focus on a single aspect of CIC function and may therefore overlook complex interconnected activities in which CIC is involved. In this study, we have used multiple complementary approaches to obtain a broader view of CIC’s complex functional networks. We observed novel interactions  between CIC and genes/proteins involved in various aspects of cellular function, including regulation of cell division and processing of RNA molecules. Altogether, our work characterises the complexity of CIC’s functional network and expands our understanding of its potential roles in cancer.Capicua (CIC encodes a transcriptional repressor and MAPK signalling effector that is inactivated by loss-of-function mutations in several cancer types, consistent with a role as a tumour suppressor. Here, we used bioinformatic, genomic, and proteomic approaches to investigate CIC’s interaction networks. We observed both previously identified and novel candidate interactions between CIC and SWI/SNF complex members, as well as novel interactions between CIC and cell cycle regulators and RNA processing factors. We found that CIC loss is associated with an increased frequency of mitotic defects in human cell lines and an in vivo mouse model and with dysregulated expression of mitotic regulators. We also observed aberrant splicing in CIC-deficient cell lines, predominantly at 3′ and 5′ untranslated regions of genes, including genes involved in MAPK signalling, DNA repair, and cell cycle regulation. Our study thus characterises the complexity of CIC’s functional network and describes the effect of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing, thereby expanding our understanding of CIC’s potential roles in cancer. In addition, our work exemplifies how multi-omic, network-based analyses can be used to uncover novel insights into the interconnected functions of pleiotropic genes/proteins across cellular contexts. CIC alterations and/or loss of CIC activity have been associated with inferior survival outcomes in several cancer types [Capicua (CIC) is a transcriptional repressor that is impacted by somatic mutations or rearrangements in several cancer types, including undifferentiated small round cell sarcomas (~60% of cases) , oligodeer types ,13,14,15er types  and resier types ,17,18. TPTPN9, SHC3/4) and members of regulatory feedback mechanisms  [ETV1, ETV4, and ETV5 [Drosophila, where Cic function was originally described [AURKA, CDK4, BUB1) across multiple cancer types [CIC is a transducer of receptor tyrosine kinase (RTK) signalling and functions through a default repression mechanism in which RTK activation leads to reduced CIC activity and subsequent de-repression of its target genes . TargetsED1/2/3) ,22,23,24and ETV5 ,9,10. Inescribed , Cic inhescribed ,27. Althescribed , loss ofescribed ,17,22,28er types . CIC haser types . It has er types , ATXN1L er types , and SINer types  to mediaer types  demonstrCic knockout (KO) mouse model, we demonstrate that loss of CIC is associated with an increased frequency of mitotic defects. Finally, motivated by the observation that CIC’s genetic and proteomic networks include several RNA processing and splicing factors, we perform a differential splicing analysis in which we compare CIC wild type (WT) and KO human cell lines and find differential exon usage patterns predominantly at 3′ untranslated regions (UTRs). Overall, our systematic characterization of CIC’s complex functional network reveals the effects of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing stability, thus providing new insights into CIC’s potential role(s) in different biological contexts, including cancer. We argue that, in addition to revealing novel CIC functions, our study provides a blueprint for multi-omic characterization of pleiotropic genes to uncover novel connections and functions at play across cellular and subcellular contexts.The increasing frequency of CIC-related publications in recent years appears to indicate a heightened level of interest in this apparently pleiotropic protein that, to date, is understood to be involved in transcriptional regulation and signal transduction to mediate phenotypes across a wide array of developmental and oncogenic contexts. Many of these CIC-related studies focus on a singular aspect of its function  in a unique context . The rapid development of genomic techniques and technologies in recent years , along with access to large public datasets generated by them, have enabled powerful investigations of complex biological relationships from different angles through layering of multiple -omic technologies  heat-inactivated (HI) FBS . Stable cell lines expressing F-CIC-S, F-CIC-L, or F-vec were cultured in DMEM + 10% HI FBS with Zeocin (Thermo Fisher Scientific) selection. Cell culture was performed in a humidified, 37 °C, 5% CO2 incubator.The IDH1-stable NHA cell line was obtained from Applied Biological Materials (ABM) Inc. . The HEK293A cell line was obtained from Dr. Gregg Morin  and authenticated by Genetica DNA Laboratories . The HOG cell line was obtained from Dr. G. Dawson . Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10%  and HEK293A (HEK-CICKO-A9 and HEK-CICKO-D10) cells as described previously [CIC knockout status was verified by Western blot and Sanger sequencing. N-terminal FLAG-CIC-short form (FLAG-CIC-S) and long-form (FLAG-CIC-L) plasmids were prepared as described previously [CRISPR-Cas9 sgRNA sequences were designed to target exon 2 of the eviously . CIC knoeviously .Single-cell 3′ RNA-seq libraries were generated using the Chromium Single Cell 3′ Library & Gel Bead Kit v1  following the manufacturer’s protocol. Full details can be found in the quickCluster function was used to cluster cells for normalisation (with min.mean = 0.1 as suggested for UMI data), and the resulting clusters were used as input to the computeSumFactors function (with min.mean = 0.1). These factors were then used in the normalise function of the scater R package (v1.10.0).The CellRanger software (v1.2.1 and 2.1.1) was used to create fastq files  and to perform alignment, filtering, barcode counting, and unique molecular identifier (UMI) counting . The CellRanger hg19-3.0.0 reference was used for alignment. Data preprocessing was performed in the R statistical software (v3.5.0) , based oestimate_Schwabe_stage function implemented in the tricycle R package [CICWT cells, 554 NHA-CICKO-A2 cells, and 532 NHA-CICKO-H9 cells; S, 478, 186, and 270; G2, 237, 66, and 94; G2/M, 200, 226, and 234; M/G1, 660, 111, and 155. For the differential expression analysis, differential expression was calculated using the DESeq2 R package [CICWT vs. NHA-CICKO samples. Genes with a Benjamini Hochberg (BH)-corrected p-value < 0.05 and an absolute fold-change > 1.5 were considered to be significantly differentially expressed. To visualise these expression differences, DESeq2-normalised expression values were obtained using the counts function with normalised = TRUE. By default, DESeq2 performs independent filtering based on mean expression across samples in order to select a set of genes for multiple test correction that maximises the number of significantly differentially expressed genes found at the specified false discovery rate [p-value of “NA”); these comparisons are indicated with “NC”  in the relevant figures.Cells were scored for cell cycle phases as described by Macosko et al.  and Schw package  (v1.2.1) package  with defery rate . TherefoThe IP-MS analyses of nuclear fractions from HEK293A cells using anti-endogenous CIC or anti-Myc (for N-Myc-CIC-S) antibodies  were perhttps://depmap.org/portal/download/; accessed on 10 February 2020). Briefly, co-essentiality mapping uses fitness effect scores that were derived from normalised and copy number-corrected whole-genome CRISPR-Cas9 knockout screens targeting 18,333 genes performed in 739 cancer cell lines [CIC and each screened gene, and a gene pair was considered co-essential if its BH-adjusted p-value was <0.05 and its coefficient was >0.178 (the inflection point). Given the BH adjustments, we expect a <5% false discovery rate in our analysis. For non-syntenic co-essential maps, genes on chromosome 19 were removed and the thresholds were recalculated (with an inflection point coefficient of 0.138).Co-essentiality mapping and in silico genetic interaction screening were performed using the R statistical software (v4.0.2) package GRETTA (v0.5.0) , with Dell lines ,47. GRETCIC-mutant lines were identified using genomic data from the 739 DepMap cancer cell lines. We identified two lines (HCC366 and JHUEM1) with homozygous deleterious CIC alterations (CICHomDel), five lines  with potentially trans-heterozygous CIC mutations , 53 lines with a single heterozygous deleterious CIC mutation or partial copy number loss (CICHetDel), and 557 lines with no CIC alterations  [CIC and 17,067 genes from over 10,485 human samples assayed using RNA-seq (“hsa-r.6”), where a z-score of zero is a random level and a score of three is a false positive rate of 0.1% for any species or version. In this study, we used the top 50 genes co-expressed with CIC.Genes co-expressed with b (v8.1) . COXPRESb (v8.1)  and Wainb (v8.1)  to benchCIC ChIP-seq peaks for NHA cells were obtained from Lee et al.  . Interestingly, many of these genes have roles that have previously been linked to CIC, as described in Given that iability —that weriability ,46,47. WCIC, we next identified genes that were co-expressed with CIC [ERF, BICRA, and PLEKHG2 . ERF is located on the same chromosome band as CIC (chromosome 19q13.2) and is positioned directly adjacent to and transcribed in the opposite direction of CIC. Similar to CIC, ERF is an ETS domain transcriptional repressor and an ERK-dependent kinase that functions as an effector of the RTK signalling pathway [ETV1 expression in prostate cancer cells [BICRA encodes the SWI/SNF complex member GLTSCR1 [To complement our efforts to identify genes with fitness effect scores similar to with CIC . To do twith CIC , as perfwith CIC . The topregation ,67,68. N pathway ,70. Simi pathway , develop pathway . ERF ander cells , furtherer cells , the co- GLTSCR1 , further GLTSCR1 , cell cy GLTSCR1 , and cel GLTSCR1 , these rCIC. Specifically, we screened for genetic perturbations that, in CIC mutant cells, resulted in decreases or increases in cell viability relative to CICWT control cell lines ; 53 lines with heterozygous deleterious CIC mutations ; and 557 CICWT lines  were defined as candidate genetic interactors of CIC. We identified 70 such genes, all of which were predicted to be SL interactors of CIC . We have previously shown that CIC exerts distinct functions depending on its subcellular location  [CICWT). The immunoprecipitates were then analysed using liquid chromatography followed by tandem mass spectrometry . Given CIC’s known function as a transcriptional repressor, we were particularly interested in investigating whether candidate interactions with proteins involved in transcription regulation and chromatin organisation could be replicated. We therefore performed a variation of the IP-MS technique in which ‘trigger peptides’ [p-value < 0.05) that clustered in 26 groups of terms with similar enrichment memberships, including the groups summarised in CIC  ; howeveretry MS; . This yietry MS; , includieptides’  were used in CIC . These rCIC interacting with members of the SWI/SNF complex , several members of the SWI/SNF complex were identified among the high-confidence candidate interactors of CIC, including SMARCA2, SMARCC1, SMARCC2, and PBRM1 (CICWT line (CICKO HEK cells stably expressing a FLAG-tagged construct of the short CIC isoform (HEK-CICKO-D10 + F-CIC-S) and observed a striking apparent colocalisation between F-CIC-S and ARID1A, ARID2, SMARCA2, and SMARCC1  [CICKO2 + F-CIC-S cells also displayed similar localisation dynamics as endogenous CIC  , observinous CIC . To detenous CIC . CIC didCICKO cells appeared to display a higher frequency of defective mitosis  compared to CICWT cells. To investigate this further, additional IF assays in the NHA lines were used to confirm that NHA-CICKO lines had an increased frequency of mitotic defects compared to the parental NHA-CICWT cells (CICKO lines (NHA-CICKO-A2 and NHA-CICKO-H9) showed 2.2–2.5-fold increases in metaphase alignment defects  and 2–2.3-fold increases in chromosome segregation defects  at telophase/cytokinesis . We also observed a significant increase in metaphase defects in HEK-CICKO cell lines compared to HEK-CICWT cell lines  and from heterozygous control mice  [Cic-null cells in the mitotically active ventricular zone displayed an increased frequency of mitotic defects, including lagging chromosomes and micronuclei  . ConsistCICWT, NHA-CICKO-A2, and NHA-CICKO-H9 cells. To characterise the transcriptional profiles of CIC-proficient and CIC-deficient cells at different phases of the cell cycle, we first scored cells for defined cell cycle phases  [CICWT and CICKO cells assigned to that phase (ETV1/4/5 were among the genes found to be significantly more abundantly expressed in CICKO cells at each phase (ETV4) or some phases , supporting the validity of our approach. Genes overexpressed in CICKO cells were also consistently found to be enriched for targets of RAS/MAPK signalling , as has previously been shown using bulk transcriptome profiling of CIC-deficient cell lines and tumours [CICKO cells in at least one phase included HIPK2, which encodes a serine-threonine kinase that regulates the activity of a wide range of transcription factors and chromatin regulators [TNRC6B and DGCR8, both of which are involved in RNA processing [ATF4, CREB3L2, FOSL1, MLXIP, RXRA, SMAD3, SOX9/12, SREBF1/2, and STAT6, were also found to be more abundantly expressed in CICKO cells, especially at the G1/S and S phases. Conversely, genes encoding proteins involved in transcriptional repression were also identified among those overexpressed in CICKO cells, including, e.g., the polycomb repressive complex 1 (PRC1)-related genes CBX6 and PHC2, the nucleosome remodelling and deacetylase (NuRD) complex members CHD3, MBD3, and GATAD2A, and the histone deacetylase HDAC7.Given CIC’s dynamic pattern of nuclear localisation and the association between its loss and increased mitotic defects, we hypothesised that CIC’s effects on gene expression regulation may depend on the cell cycle. Since dysregulated expression of dynamically expressed genes may not be detectable in asynchronous bulk cell populations and synchronisation through either chemical or physical means can introduce confounding artefacts ,108, we  phases)  using ap phases) ,40  of all significantly differentially expressed genes displayed lower expression in CICKO cells (CCNB1 (significantly less abundant in S and G2/M CICKO cells), CDK1 (G1/S), MAD2L1 (S and G2/M), CENPK (M/G1), and CCNE2 . Echoing the results of our IP-MS analysis, we also observed an enrichment of genes involved in RNA processing and splicing among genes with lower expression in CICKO cells. These include the small nuclear ribonucleoprotein core protein family members SNRPB2 and SNRPD2, the heterogeneous nuclear ribonucleoproteins HNRNPH3 and HNRNPA2B1, the splicing factor 3B subunit SF3B4, and the spliceosome component PRPF31, all of which were significantly under-expressed in G1/S-phase CICKO cells. Interestingly, our results also indicated that NPM1 had significantly lower expression in G2/M-phase CICKO cells, although it showed a similar trend at all phases, especially in the CICKO-A2 cell line , ribosome biogenesis, the p53-mediated stress response, and inhibition of cell growth and proliferation [Although elevated expression of target genes in KO cells a. This iE2 G1/S; c. YWHAQ activity  and whicactivity , were alell line c. NPM1 iferation . NotablyCICKO cells might be directly regulated by CIC, we compared our gene list with a set of 150 high-confidence CIC binding sites that our group previously identified in the NHA-CICWT line using chromatin immunoprecipitation followed by sequencing (ChIP-seq) [DUSP4, DUSP5, ETV4, ETV5, FOSL1, HMGA1, MAFF, OSGIN1, TBC1D14, and TBC1D22A—were differentially abundant between NHA-CICWT and NHA-CICKO cells in at least one cell cycle phase in our scRNA-seq data  . Of the seq data . All of contexts . Severalcontexts , which iOverall, these results indicate that CIC appears to regulate the expression of genes involved in a wide range of functions, including MAPK signalling, transcriptional regulation, mitotic checkpoint activity, and RNA processing. Notably, our results also indicate that CIC regulates the expression of similar groups of genes across different phases of the cell cycle. Furthermore, candidate direct targets of CIC transcriptional regulation appear enriched for genes involved in functions that we have repeatedly identified in this study, including MAPK signalling, AP-1 activity, and apoptosis, further implicating CIC activity within these functional groups.CCND1/Ccnd1, DUSP4/Dusp4, ETV4/Etv4, ETV5/Etv5, GPR3/Gpr3, LRP8/Lrp8, PTPN9/Ptpn9, SPRED2/Spred2, and SPRY4/Spry4. Of the 108 peaks identified in the mESC CIC ChIP-seq dataset (see p = 1.38 × 10−12), 60 with BRD9 peaks (p = 1.71 × 10−40), 84 with BRD4 peaks (p = 2.26 × 10−38), and 102 with SMARCA4 peaks (p = 8.66 × 10−32). Notably, of the 100 genes associated with these 108 mESC CIC peaks, the human orthologues for 10 of them were found to be differentially expressed in at least one cell cycle phase in our scRNA-seq data . The 14 peaks associated with these 10 genes all overlapped with SWI/SNF peaks . We did not observe differences in the overall distribution of entropy scores between the NHA-CICWT line and either the NHA-CICKO-A2 or NHA-CICKO-H9 lines , TSSs , and retained introns  [CICKO cells affects NRG1 isoforms 3, 6, 7, and 8 , which share a common TSS and which belong to types II  and III  [Given that UTR length and intron retention events have both been shown to affect gene expression ,130,131,CWT line d, showedKO cells f, was siKO cells . We alsols  . The altneurons) . Since tneurons) ,137, dysIn this study, we used bioinformatics, mass spectrometry, genomics, single-cell RNA-seq, and microscopy techniques to expand our understanding of CIC function in human and mouse cells and tissues. Results from these orthogonal approaches converged to implicate CIC in cell cycle regulation and splicing in addition to its known function as an effector of MAPK signalling  with a wide range of proteins and genes involved in RNA processing, splicing, and mRNA stability. For example, the list of 156 proteins we identified as high-confidence candidate physical interactors of CIC included at least 38 proteins annotated as being involved in functions related to RNA splicing, rRNA/mRNA metabolism and processing, and ribosome biogenesis, which is consistent with the notion that CIC physically interacts with RNA processing complexes. Furthermore, genetic interactions were also observed between  3′ UTRs ,79,80 an 3′ UTRs  and whicteractor . ThroughCIC’s co-essential network, which is composed of genes with which CIC is likely to share functionality, included several known regulators of MAPK signalling, including DUSP6, RASA1, and ERF. Notably, CIC and ERF have been shown to co-regulate the expression of ETV1 in prostate cancer, where their combined loss and the subsequent increase in ETV1 expression contribute to disease progression [It is worth noting that, in addition to the putative novel roles discussed above, our study also reinforced CIC’s known role as an effector of MAPK signalling and provided clues as to interactions between this established function and CIC’s possible novel ones. For example, gression . As mentgression , suggestCIC mutant lines included a lung cancer and an endometrial carcinoma line; CIC wild-type lines included 557 lines from 29 cancer contexts) and protein interactors (NHA and HEK cell lines). We also highlighted not only interacting genes/proteins but also the broad functional pathways that were enriched in multiple assays , which are known to be more conserved [While our multi-omic analysis uncovered several putative CIC functions, we acknowledge that there are limitations to such analyses. For example, both genetic interaction networks  and protonserved . While tOverall, our study provides a rigorous exploration of CIC’s genetic and proteomic interaction networks and considerably expands our understanding of the diverse cellular processes in which CIC appears to play a role. Beyond the insights it provides into CIC function specifically, this work also presents a model for how multi-omic, network-based analyses that leverage both public and novel datasets can reveal novel and interconnected roles for pleiotropic genes/proteins across cellular and subcellular contexts. For instance, our results raise the intriguing possibility that CIC may be required to ensure faithful mitotic division and that this function may be related to its interactions with SWI/SNF complexes and/or with RNA processing and spliceosomal complexes. This work thus introduces novel avenues through which CIC can act as a pan-cancer tumour suppressor, and we anticipate that it will catalyse further innovative investigations into this intriguing protein’s varied roles.


Mosaic mutations can be used to track cell ancestries and reconstruct high-resolution lineage trees during cancer progression and during development, starting from the first cell divisions of the zygote. However, this approach requires sampling and analyzing the genomes of multiple cells, which can be redundant in lineage representation, limiting the scalability of the approach. We describe a strategy for cost- and time-efficient lineage reconstruction using clonal induced pluripotent stem cell lines from human skin fibroblasts. The approach leverages shallow sequencing coverage to assess the clonality of the lines, clusters redundant lines and sums their coverage to accurately discover mutations in the corresponding lineages. Only a fraction of lines needs to be sequenced to high coverage. We demonstrate the effectiveness of this approach for reconstructing lineage trees during development and in hematologic malignancies. We discuss and propose an optimal experimental design for reconstructing lineage trees.  Understanding the structure and dynamics of the human cell lineage tree in development and aging is a central question in medicine and biology. Understanding cell lineages in cancer development is also important for studying dynamics of cancer progression and metastasis. To map cellular ancestry in humans, recent studies typically collected biopsies from living or post-mortem individuals, sampled single cells, cloned them via different approaches, sequenced each clone and cross-compared the sequence across clones to discover mosaic mutations in the founder cell of each clone . MutatioTo mitigate the cost, some studies used shallower 15×) instead of standard 30×) sequencing coverage of clones 0× sequen. In our 5× insteaFor downsampling BAM files of 25 iPSC lines with high coverage that were aligned to the human reference genome hg19, we used SAMtools  with thesamtools view -@ 8 -s $} END {avg = coverages/reads; print 42 + 5/avg}’) -b high_coverage_line.bam > down_sampled_line.bam && samtools index -@ 8 down_sampled_line.bamCoverages of high-coverage data varied from 30× to >60× . To get http://broadinstitute.github.io/picard), and indel realignment and base quality score recalibration were performed with GATK  were mapped to the human reference genome hg19. The duplicated reads were marked and removed by Picard  that results in the minimum increase in total within-cluster distance variance after merging. We visualized the resulting clusters in a hierarchical tree with mutation counts in each line  mutation calls from all shallow-coverage iPSC lines were combined into the union call set; (ii) out of the set of union calls, 50%, 20%, 10% and 5% sites were randomly sampled; and (iii) in each iPSC line, only calls overlapping sampled sites were retained for clustering. While randomly downsampling to 50%, 20% and 10% mutation sites reproduced the same clusters as when using all discovered mutations, random downsampling to 5% (roughly 20–50 discovered mutations) resulted in some discrepancies in clustering, particularly for the ‘RA2C1’ cluster where iPSC lines had an overall lower mutation burden . ConsequWe used two strategies to generate BAM files from each cluster, which were used for identifying mosaic mutations across clusters and high-coverage iPSC lines. The first strategy is ‘sum all’, where (i) we summed all shallow-coverage lines in each cluster except the ‘Unique’ and (ii) among 13 iPSC lines in the ‘Unique’ cluster, 3 with shallow coverage (5×) were excluded and for other 10 sequenced at high coverage we utilized their full coverage. The second and slightly enhanced strategy is ‘pick one’, where (i) in seven clusters with >3 lines and except for the ‘Unique’ we merged data to get a BAM file with a coverage high enough to accurately discover mutations; (ii) we randomly picked a high-coverage line from four other small clusters that had 2–3 lines; (iii) we skipped the remaining two small clusters because they do not have any high-coverage line; and (iv) for lines in the ‘Unique’ cluster, the same strategy was applied as in the ‘sum all’.2  filtering strategy resulted in filtering out false positives to arrive at an accurate mutation call set . In such cases, the computations can be reduced by restricting comparisons to the sites of called germline-like variant  heterogeneous across compared lines .AllTm calculator tool available on the Thermo Fisher website. Primer specificity was initially confirmed in silico with UCSC Genome Browser (http://genome.ucsc.edu/index.html), and then confirmed on gel by the presence of a unique PCR product of the expected size. Amplicon DNA was then purified using the QIAquick PCR Purification Kit (Qiagen) and submitted for Sanger sequencing, together with its forward primer. Forward primer: GAGATCCCACCATTGCACTC; reverse primer: TGCACTTTATCCTCCCACCT.Amplicon-seq was performed to validate the newly discovered indel. Forward and reverse primers were designed by selecting a DNA template of 800 nucleotides containing the candidate variant (variant ± 400 bp), with amplicon size of 226 bp. For PCR amplification, we used the Phusion High-Fidelity DNA Polymerase (Thermo Fisher) to minimize the polymerase error rate; optimal annealing temperature was defined using the 2 tool  statistical test to retain calls that had heterogeneous frequency across lines and thus are unlikely to be a systematic low-frequency artifact of sequencing or alignment. This resulted in a set of 82 high-confidence mitochondrial mutations  matrix, where ρ is Pearson’s correlation coefficient of VAFs between samples. Ward’s minimum variance method was used for agglomerating clusters in the same way as we did for the nuclear genome. Two representative hierarchical trees with clusters using two set of mutations, all mutations  with accession code EGAD00001007714 (WGS colonies). We analyzed data for two patients: PD6629 and PD5117. Since no other samples  were sequenced in that study, we constructed a pseudo-bulk sample simulating (to the extent possible) other normal samples from the same individual. The pseudo-bulk construction was accomplished by combining data for outstanding clones in the trees for selected patients. Specifically, in patient PD6629 we used clones PD6629an, PD6629db_lo0016 and PD6629db_lo0026 because they belong to the outlier clade not harboring DNMT3A:p.R882H mutation. In patient PD5117, we used clones PD5117am, PD5117ck, PD5117dc and PD5117s that were chosen randomly out of colonies not harboring the JAK2:p.V617F mutation. Data for other clones were downsampled as for iPSC lines (see above). Initial mutations were called relative to constructed pseudo-bulks. Subsequent analyses were conducted as for iPSC lines. Particularly, in patient PD5117 we found a ‘Unique’ cluster  and custom R scripts. Scripts used for analysis and processing results are provided in the Supplementary Data.The manuscript describes strategy/approach that is based on the application of other tools: SAMtools , Mutect22  , we previously derived 74 iPSC lines from six biopsies . Out of 2 approach , we identified a total of 27 891 mosaic mutations . To test the efficacy of this strategy while capitalizing on existing data, we randomly picked an iPSC line with high-coverage sequencing data for each small cluster . By systematically comparing 21 lines/clusters, we missed only one early mutation, w  but hundreds and thousands of copies of the mitochondrial genome. Accordingly, during sequencing, coverage of the mitochondrial genome is hundreds to thousands times higher than that of the nuclear genome, enabling comprehensive ascertainment of the mitochondrial genome even if coverage is shallow. We investigated whether mutations in the mitochondrial genome could be used for precise lineage reconstructions. In LB, we discovered a total of 126 mutations in the mitochondrial genome across all iPSC lines . Clustering of iPSC lines based on these mutations mostly reproduced clusters based on mutations from the nuclear genome; however, a dozen lines were clustered incorrectly . For exaWhile most of the iPSC lines are clonal, occasionally some are derived from two or more cells . From hiJAK2, DNMT3A and TET2 genes.To demonstrate the applicability of this approach to study the progression of cancer, we used publicly available data used to reconstruct lineages in myeloproliferative neoplasms . We analThe analysis presented demonstrates how shallow-coverage sequencing data can be efficiently used to reconstruct cell lineages from clonal cell lines. We propose the following seven-step strategy for lineage reconstruction: (i) derive multiple lines (or clones) from biopsies/regions from a biological system of interest (e.g. body or cancer); (ii) sequence all lines at shallow 5× coverage (this can be done as soon as lines become available); (iii) discover mutations in each line relative to some bulk normal tissue (e.g. blood) and eliminate nonclonal lines; (iv) cluster the lines based on the discovered mutations; (v) define clusters with redundant lines and combine data for lines in each redundant cluster ; (vi) sequence unique lines (i.e. lines that do not share mutations with other lines and therefore cannot be clustered) to a coverage of 15× to 30×; and (vii) discover mutations in the clusters and unique lines and reconstruct the lineage tree. The described strategy is cost-efficient if compared to sequencing all derived clones at high coverage. For the reconstruction of lineage tree in LB, using the proposed strategy as opposed to sequencing all iPSC lines would have saved ∼$26 000, assuming a cost of $600 for 30× WGS and a cost of $150 for 5× WGS. The strategy is on par in cost but time-efficient if compared to our original strategy when iPSC lines were prescreened for marker mutations of early lineages  is unlikely to affect clustering of redundant lines but may require additional sequencing of lines from clusters with just two or three lines. Sequencing at 1× or 2× coverage, even if resulting in correct clustering of redundant lines , would likely require sequencing to high coverage of some lines from each cluster. Since at shallow coverage most of the sequencing cost is taken by library preparation, sequencing at 1–2× would only be beneficial if the same sequencing library can be reused, which is challenging to achieve in practice. Thus, beyond the strategy outlined here, it seems likely that one of the major next advances leading to further improvement in cost efficiency in lineage tracing would come from reduction in the cost of library preparation for WGS.https://nda.nih.gov/study.html?id=1057) for qualified researchers.All data presented in our work are included in the text and supplementary material. The primary data are accessible at NDAR (study no. 1057; gkad254_Supplemental_FilesClick here for additional data file.


Cisplatin, as a classic anti-tumor drugs, has been tested and discovered as a photon radiosensitizer in several cell lines, including head and neck squamous cell carcinoma (HNSCC). Hence, the aim of our study is to evaluate whether cisplatin can sensitize CIRT towards HNSCC cell lines Human nasopharyngeal carcinoma cell line CNE-2, human tongue squamous carcinoma cell line TCA 8113 and human hypopharynx squamous carcinoma cell line FADU were all irradiated with photon beam of 2, 4, 6, 8 Gy  and carbon ion beam of 1, 2, 3, 4 Gy  and treated with cisplatin. Cell survival was assessed by clonogenic survival assay.In vitro sensitizer enhancement ratios (SERs) of cisplatin in CNE-2, TCA 8113 and FA DU cell lines after photon irradiation were 1.33, 1.14 and 1.21, while after carbon ion irradiation were 1.02, 1.00 and 0.96, showed that cisplatin sensitized photon irradiation but showed no sensitization effect in carbon ion irradiation in all tested cell lines.CIRT showed significantly stronger cytotoxic effect than standard photon radiotherapy. The relative biological effectiveness (RBE) of carbon ion beam at 10% survival  CIRT was more effective than photon irradiation to prevent the proliferation of HNSCC cell lines. Additional treatment with cisplatin could sensitize photon irradiation but showed no effect on carbon ion irradiation. Head and neck cancers are the seventh most common type of cancer in the world . It inclWith the continuously progress in HNSCC research, concurrent chemoradiotherapy has gradually shown advantages over radiotherapy alone by improving survival in patients with resectable and nonresectable, advanced disease , 5. AmonHeavy ion radiotherapy, such as through the use of carbon ion radiotherapy (CIRT), has multiple advantages over conventional photon therapy. CIRT can spare surrounding normal tissues while killing tumors due to its superior physical dose distribution (i.e. Bragg Peak). As a high-LET radiation, CIRT mainly breaks DNA double-strands and damages tumor cells, while it is not affected by cell cycle and oxygen concentration. Therefore, CIRT is effective against tumors that are resistant to hypoxia and photon radiation .The sensitization of chemotherapy to carbon ions in tumor cell lines has been investigated for multiple chemotherapeutic agents  and tumor cell lines . In clinical practice, results of clinical trials of the National Institute of Radiological Sciences (NIRS) showed that the combination of CIRT and gemcitabine treatment is not superior to CIRT alone in treating locally advanced pancreatic cancer .The use of concurrent chemotherapy plus high-LET irradiation, such as CIRT, for radiosensitization has never been addressed in HNSCC. Therefore, this study aims to evaluate the cytotoxic effect of photon and carbon ion radiotherapy in combination with cisplatin in HNSCC cell lines, aiming to improve current and future clinical practice.The human nasopharyngeal carcinoma cell line CNE-2 was obtained from the Xiangya Hospital, Central South University, Changsha, Hunan, China, and was cultured in RPMI 1640 supplemented with 10% heat-inactivated fetal calf serum , 1% penicillin/streptomycin , at 37 °C in a humidified atmosphere containing 5% CO2.The human tongue squamous carcinoma cell line TCA 8113 and the human hypopharynx squamous carcinoma cell line FADU were purchased from Shanghai Zhong Qiao Xin Zhou Biotechnology. TCA 8113 was cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated fetal calf serum , 1% penicillin/streptomycin, at 37 °C in a humidified atmosphere containing 5% CO2, while FADU was cultured in minimum Eagle’s medium (MEM) supplemented with 10% heat-inactivated fetal calf serum , 1% penicillin/streptomycin, at 37 °C in a humidified atmosphere containing 5% CO2.Cells in the log-growth phase were plated in T-25 flasks  and irradiated with a 225 kVp X-ray beam  at room temperature. Photon radiotherapy was performed as a single exposure to physical doses of 2 Gy, 4 Gy, 6 Gy or 8 Gy.For CIRT, cells in the log-growth phase were plated in T-25 flasks  and irradiated with the carbon ion beam at room temperature by an IONTRIS intensity-modulated raster scan system with an energy of 333.82 Mev/u at the Shanghai Proton and Heavy Ion Center . The LET was approximately 29.1351 keV/μm. The homogeneous spread-out Bragg peak (SOBP) of the carbon ion beam was adjusted to the surface where the adherent cells attached. CIRT was performed as a single exposure to physical doses of 1 Gy, 2 Gy, 3 Gy or 4 Gy.All cell lines were incubated with cisplatin  for 4 hours, followed by a medium change and exposure to radiation (photon irradiation or carbon ion irradiation). Pre-experiments were performed in 6-well cell culture plates to determine the experimental concentration of cisplatin. Cells were incubated with 1, 2, 3, 5, 10 or 20μM cisplatin for 4 hours and irradiated with 4 Gy or 6 Gy photon radiotherapy. Doses were selected to allow 50~60% cell survival after combination therapy confirmed by microscopic inspection, to keep enough cells alive when they were subjected to carbon ion radiotherapy combined with cisplatin chemotherapy. After pre-experiments the following drug concentrations were chosen: 3μM for CNE-2, 1μM for TCA 8113 and 2μM for FADU.3 to 104) were seeded in 25 cm² flasks . After cell attachment, which was confirmed by microscopic inspection, cells were exposed to a 4-hour chemotherapeutic incubation and were irradiated immediately after cisplatin was removed. After 7-14 days, single cells grew into colonies with over 50 cells, then they were gently washed with phosphate buffer saline (PBS)  twice, fixed with 4% paraformaldehyde for 30 minutes, gently washed with PBS twice again, and stained with crystal violet  for 30 minutes. Upon a final wash, colonies were counted by a colony counter  to detect colonies with over 50 cells. There were four groups: X-ray only group, X-ray+cisplatin group, Carbon ion only group, and Carbon ion+cisplatin group. For each cell line, data were obtained by three independent experiments and each experiment was run in triplicates.Clonogenic survival assay, as the radiobiological gold standard, was performed to assess the response of cell lines to radiation and chemotherapy. Cells  according to cell plating efficiency and clonogenic survival. Cell survival data were fitted by the linear quadratic (LQ) model: 0 is the mean lethal dose, which is calculated from the reciprocal of the slope of the survival curve, that is,where S is the survival fraction, D is the dose in Gray, D0 of cells treated with irradiation alone to the D0 of cells treated with irradiation plus another treatment, which was used to quantify the sensitization effect of radiotherapy. Significant differences among different groups were determined by unpaired Student’s t-test with a 2-tailed distribution. P value<0.05 was considered statistically significant.Sensitizer enhancement ratios (SERs) were calculated as the ratio of the DFrom linear quadratic fits (LQ-fits), several radiobiological parameters for photon and carbon ion radiotherapies were determined in three cell lines see Table 1.The concept of RBE has been used to describe the efficiency of different types of radiation to produce biological effects. It’s defined as the ratio of a dose from the reference radiation, photons, to a dose from any other radiation quality to produce the same biological effect. The RBE of carbon ion beams at 10% and 37 % survival for each cell line was calculated from the LQ-fits to evaluate the advantages of carbon ion radiotherapy over photon radiotherapy see Table 2.The cytotoxicity of cisplatin with radiation was assessed by the clonogenic survival assay. Survival data were fit to a multitarget-single hit model to construct survival curves see Figure 1Cisplatin plus radiotherapy was more effective than radiotherapy alone in every cell line. However, further calculations were necessary to accurately evaluate the impact of cisplatin on X-ray and carbon ion irradiation.0 of cells treated with irradiation alone to the D0 of cells treated with irradiation plus another treatment. In our case, SERs values ranged from 0.96-1.33 depending on the cell line and applied irradiation  . Moreove10 and RBE37 were respectively: 3.07 and 2.29 for CNE-2, 2.33 and 2.54 for TCA 8113, and 2.36 and 2.38 for FADU. Therefore, the RBE for CIRT was calculated to be between 2.33 and 3.07 for HNSCC cell lines. These results can assist subsequent research on CIRT.In order to quantify the advantages of CIRT over conventional photon radiotherapy, researchers have consistently used RBE values to evaluate effects. RBE is a crucial indicator to compare the biological effects of different types of radiation since it is closely related to the determination of radiation dose, while it is a complex parameter that depends on both physical  and biological  parameters. In our study, the RBE0 of cells treated with irradiation alone and D0 of cells treated with irradiation plus another treatment  can only reflect a certain part of the survival curve, therefore their reference value is lower than that of D0.We evaluated whether cisplatin could act as a radiosensitizer for radiotherapy in HNSCC cell lines. In several previous studies, and in this one, SER was calculated as the ratio between Dreatment –21. In cment  values  values . If thisus study . In 2015us study , 28. Theus study , apoptosus study .The sensitization of chemotherapy to carbon ions in tumor cell lines has been shown to be related to the chemotherapeutic agent and tumor type. Combs et al. and Harrabi et al. reported that temozolomide showed no radiosensitizing effect on CIRT for glioblastoma cells , 32, whiFocusing on cisplatin, Sai et al. reported that cisplatin sensitized CIRT in triple-negative breast cancer stem-like cells  and mali2/week). The 2-year LC of this study was 71%, and the 2-year OS was 88% (Our study was performed in preparation for further clinical trials to investigate the potential of CIRT in the treatment of HNSCC. Clinical evidence shows that the addition of cisplatin increases the efficacy of X-ray radiotherapy toward patients with cervical cancer  and non- was 88% . This gr was 88% . Unfortu was 88% . TherefoIn conclusion, high LET CIRT was more effective than photon irradiation to prevent the proliferation of HNSCC cell lines. Additional treatment with cisplatin could sensitize photon irradiation but showed no effect on carbon ion irradiation. This study can provide information for current and future clinical trials and clinical practice.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.XF designed experiments, performed experiments, arranged the data, did statistical analysis and wrote the manuscript. All authors contributed to the article and approved the submitted version.