PSMB2 gene, and enrichment of proteasome genes among the hits. This selective dependency was replicated with a proteasome inhibitor, indicating a targetable drug-gene interaction. Many more plausible targets in challenging cell models can be efficiently identified with our approach.Perturbing expression is a powerful way to understand the role of individual genes, but can be challenging in important models. CRISPR-Cas screens in human induced pluripotent stem cells (iPSCs) are of limited efficiency due to DNA break-induced stress, while the less stressful silencing with an inactive Cas9 has been considered less effective so far. Here, we developed the dCas9-KRAB-MeCP2 fusion protein for screening in iPSCs from multiple donors. We found silencing in a 200 bp window around the transcription start site in polyclonal pools to be as effective as using wild-type Cas9 for identifying essential genes, but with much reduced cell numbers. Whole-genome screens to identify ARID1A-dependent dosage sensitivity revealed the  •CRISPRi is as powerful as Cas9 for CRISPR screening in iPSCs•The silencing window of dCas9-KRAB-MeCP2 is can extend up to 1.4 kb away•Monoclones performed better, but polyclonal screens were sensitive enough to estimate big effects•Whole-genome CRISPRi screens identified silencing ARID1A makes iPSCs more prone to proteotoxic stress The DNA break-induced stress upon Cas9 action limits the efficacy of whole-genome CRISPR screens in hiPSCs. Here, the authors established requirements for genome-wide CRISPR inhibition screens in hiPSC lines as a cost effective and efficient alternative to Cas9-based screens. By this method, they identified ARID1A gene interactions for stem cell growth including a targetable synthetic lethal interaction between the proteasome and ARID1A. Cell growth has to strike a balance between supporting development and survival across the lifespan against avoiding dysregulated proliferation. The genes required for this control have been mapped using genome-scale perturbation screens in different conditions and genetic backgrounds , and undThe CRISPR-Cas9 system has rapidly become the gold standard for pooled survival screens that collect this important information . In brieAn important context to study disease mechanisms, drug targets, and development is stem cells, and in particular induced pluripotent stem cells (iPSCs) . The firARID1A cancer and developmental disorder gene.Here, we develop an effective CRISPRi screening approach in iPSCs, and use it to map mutation-specific and drug-sensitive genetic dependencies. First, we compare multiple fusion proteins, and identify dCas9-KRAB-MeCP2 as the most potent one. We then establish the characteristics of successful gRNAs that effectively repress their target using this fusion, identifying both the activity window as well as highlighting the importance of correct transcript selection. Next, we conduct whole-genome screens to determine the coverage requirements for an effective dropout screen in iPSCs, and compare these to standard CRISPR screens. Finally, we demonstrate the efficiency of CRISPRi screens to identify nocodazole-dependent gene essentiality, as well as genes that impact growth differently as a result of a mutation to the Effective gene silencing with CRISPRi depends on the fusion protein used and the choice of gRNAs. Therefore, we first explored the potency of dCas9 alternatives in hiPSCs, and identified the effect of gRNA features, such as proximity to different TSSs.To identify the most potent dCas9 fusion construct, we compared three previously reported versions: dCas9-KRAB with piggyBAC delivery, KRAB-dCas9 with lentiviral delivery, and dCas9-KRAB-MeCP2 with piggyBAC delivery . We prodAfter picking the fusion protein to work with, we characterized the determinants of successful gene repression with dCas9-KRAB-MeCP2 in iPSCs, starting with TSS distance. To determine the optimal target window, we designed a diverse gRNA library against various non-coding regions  and coding sequences A.2 fold change = −2.5), with a small but significant decrease of efficacy elsewhere  in 100 base pairs (bp) downstream of the TSS for 252 essential genes. On average, downregulation was most potent in the 20–40 bp downstream window , with more than 21 kb distance between transcripts, showed a differential repression effect, with the remaining 12 alternative TSSs efficiently targeted , Bayesian analysis approaches can overcome some of the reduction in the signal , as well as worse resolution at 50× coverage to separate essential and non-essential genes (median AUC = 0.97 vs. 0.98 in monoclones). However, this performance is still above the range reported in large-scale resources such as the Cancer Dependency Map , with ale signal F and 3G,e signal H. Theref2 fold changes were moderately correlated between CRISPR and CRISPRi screens in the same cell line (Pearson’s R = 0.42), which is in line with concordances of both knockout and repression screens in different types of stem cells  but not CRISPR screening (LFC = 0.16). Thus, the nocodazole-dependent signal from BRD9 is a likely false positive because of its TSS proximity to the known TRIP13 causal gene. Therefore, CRISPRi screening can identify gene-drug interactions, but the outputs need to be carefully analyzed to avoid modes of false positives not observed in CRISPR screens.An important screening paradigm is identification of context-specific vulnerabilities for therapeutics and diagnostics. We therefore explored the utility of CRISPRi screens for mapping genome-scale drug-gene and allele-gene interactions. We first conducted pooled CRISPRi screens to identify genes that lead to nocodazole-dependent cell depletion . Nocodazand BRD9 A. TRIP13e arrest . BRD9, hARID1A-dependent vulnerabilities in iPSCs. The ARID1A gene produces a subunit of the ATP-dependent histone deacetylase SWI/SNF complex that has been implicated in developmental disorders, as well as 20% of all cancers , and one control line with wild-type sequence. The heterozygous mutation decreased ARID1A expression by 60% and resulted in slower growth compared with the wild-type line  and protein trafficking (AP1G1) had the most decrease. SEL1L encodes the adaptor subunit of ERAD ubiquitin ligase, which extracts misfolded proteins from the endoplasmic reticulum into the cytosol to be degraded by the proteasome (PSMB2) gene, a potential drug target, resulted in more cell death in the ARID1A+/− line  for 1 day and compared growth with non-treated controls, and the ARID1A+/− line. The vulnerability of hiPSCs to oprozomib treatment was dependent on ARID1A targeting, with gRNAs leading to stronger growth effect in hiPSCs also giving rise to stronger phenotypes upon proteasome inhibition, resembling the growth of the mutant line  as well as cancer  and Leopold Parts (leopold.parts@sanger.ac.uk).Further inquiries and requests for data and resources should be directed to corresponding authors, Sunay Usluer . Cells were passaged 1:20 every 4 days. Wild-type hiPSC (fiaj-1 and kolf2) lines were obtained from the HipSci platform, where its karyotype and phenotype were evaluated to be normal. hiPSC lines were cultured on Vitronectin-XF -coated plates and in mTeSR-Plus Media . For maintenance, they were passaged every 4 days as clumps using ReLeSR  according to the producer’s protocol. All cell lines were cultured at 37°C, 5% COThree different dCas9 fusion proteins were tested for dCas9 activity in both K562 and hiPSC lines: pLX_311-KRAB-dCas9 (Addgene 96918), pB-CAGGS-dCas9-KRAB-MeCP2 , and pPB-dCas9-KRAB (kindly provided by Dr. Qianxin Wu). The first is a lentiviral delivery vector, whereas the latter two rely on the piggyBac transposase for genome integration. We produced hiPSC and K562 lines stably expressing all three fusion proteins by transducing/transfecting them with the above vectors .2 in mTeSR-E8 medium supplemented with 10× CloneR . The medium was changed every day with blasticidin-supplemented mTeSR+Plus medium (10 μg/m). After 10 days, visible colonies were manually transferred into 12-well plates precoated with Vitronectin XF. Surviving monoclonal cell lines were expanded and used in later analyses.After 10 days of blasticidin selection, polyclonal cell populations were collected with accutase and 1,000 cells seeded into 6-cm dishes precoated with Synthemax II-SC Substrate  at a concentration of 5 μg/cmAfter 10 days of blasticidin selection, monoclonal lines were produced by serial dilution as follows: polyclonal cells were counted and serial diluted to an estimated 50 cells in 20 mL K562 medium and 200 μL of suspension was distributed into each well of a 96-well plate. In the first iteration, there was cell growth in 6 wells. Cells from these 6 wells were separately collected, diluted, and redistributed into 6 different 96-well plates, and one well from each plate was picked as monoclonal lines.Monoclonal and polyclonal hiPSC-dCas9 lines were transfected with pCS2-iREP-GFP-PGK-BFP-U6-gRNA-irep or pCS2-iREP-GFP-PGK-BFP-U6-gRNA-mock vectors  and cloned into lentiviral gRNA expression vector pKLV2-U6gRNA5(BbsI)-ccdb-PGKpuroBFP-W (AddGene: 67974)  supplem.gRNAs for iPSC-specific alternative TSSs for 10 genes were designed using the CRISPick tool of the Broad Institute .The Dolcetto library  was acqu7) were mixed with the lentiviral Dolcetto library to achieve an estimated 0.3 MOI and seeded on 2× vitronectin-coated 5-layer flasks at 36K cells/cm2 density in mTeSR-Plus medium with Rock inhibitor (Ri) . The next day, the medium was changed to remove Ri. On day 3 post infection, puromycin selection was started at 0.5 μg/mL, and continued through the screen duration (17–22 days). Cells were passaged at 90% confluency and seeded back at 16K cells/cm2 density for the rest of the screen. The remaining cell pellets were aliquoted and frozen for genomic DNA extraction.Cells (6 × 106) were resuspended in supplemented RPMI+ polybrene (8 μg/mL) medium and mixed with the Dolcetto library virus aiming for a MOI of 0.3. The cell + virus suspension was centrifuged at 1,000 rcf for 30 min. The cells were then resuspended in the same medium and plated into two T150cm2 plates for a final concentration of 0.45 × 106 cells/mL. The infected cells were selected with puromycin (2 μg/mL) starting from day 1 after infection until day 8. After complete selection, the cells in culture were passaged every 3 or 4 days, and plated back at 0.1 × 106 cells/mL density. Aliquoted pellets of at least 40 × 106 cells were frozen for genomic DNA extraction.Cells  × 106) + 1). Log2 fold change per guide was calculated by subtracting log2 transformed and normalized read counts of final time points from plasmid library sequences. To determine essential and non-essential genes, Bagel v.2 (ARID1A+/+ and ARID1A+/− lines, we calculated the mean and standard deviation of the difference across all genes, and assigned the genes with values of at least two standard deviations away from the mean to be differentially essential.gRNA sequences were counted from fasta files and aligned to corresponding library maps without allowing any mismatches. Uniformity of plasmid libraries was determined using the Gini index as implemented by agel v.2  was usedS.U. and L.P. conceptualized and initiated the study with the help of L.C. S.U. performed experiments with the help of Y.Z., K.U., and C.D. S.U. and P.H. analyzed the data with the help of J.S., G.N., and O.E.G. K.A. designed the TSS tiling library. B.N. created isogenic hiPSC lines with the supervision of S.S.G. and O.M.D. L.P. supervised the project. S.U. and L.P. wrote the manuscript with input from all authors.


The combination of daphnetin with either vemurafenib or epirubicin showed an antagonistic interaction. Moreover, additive interactions were observed for the combinations of daphnetin with cisplatin and docetaxel. The most desirable synergistic interactions for human melanoma metastatic cell lines were observed for the combination of daphnetin with mitoxantrone. (4) The obtained results suggest that daphnetin should not be combined with vemurafenib or epirubicin in the treatment of malignant melanoma due to the abolition of their anticancer effects. The combination of daphnetin with mitoxantrone is beneficial in the treatment of metastatic melanoma due to their synergistic interaction.(1) The treatment of metastatic or drug-resistant melanoma is still a significant therapeutic problem. The aim of this study was to evaluate the anticancer potential of daphnetin  and its combinations with five different cytostatic drugs . (2) The viability, proliferation and cytotoxicity of daphnetin against four human malignant melanoma cell lines were evaluated. The interactions were assessed using isobolographic analysis for the combinations of daphnetin with each of the five cytostatic drugs. (3) Daphnetin showed anticancer activity against malignant melanoma, with IC Melanoma can appear anywhere on the body where there are pigment cells—melanocytes–including in mucous membranes, eyeballs and hair follicles. However, it most often appears on the skin. Melanoma is a cancer that has a high survival rate but only when it is diagnosed early. Compared with other skin cancers, melanoma patients are more likely to have metastases. As the stage of the cancer increases, the survival rate of patients decreases ,2. For sEarly diagnosis of melanoma allows for effective treatment  in its early stages . When thNowadays, it has become popular to search for new therapeutic substances of natural origin. Active substances of plant origin may be the foundation of less invasive treatments used in the future, especially due to the lack of or mild side effects. Each of the natural compounds is characterized by a different chemical structure and various biological activities. An interesting group of compounds are coumarins; it is estimated that, so far, about 1300 substances of this group have been isolated from plants, fungi or bacteria ,7.Daphne, from which the name of a compound is derived. Daphne plants are flowering shrubs  found in Europe, Asia and North Africa [One of the simple coumarins is daphnetin , which was first isolated from plants of the genus h Africa .Daphnetin possesses a number of biological activities, including anti-inflammatory, antioxidant, antibacterial, antidiabetic, immunosuppressive and anticancer activities ,10. NeurHelicobacter pylori. The mechanism of action is based on the release of specific proteins, the binding of ions and the prevention of colonization by H. pylori. This is of great importance for preventing bacterial infection but also, due to the colonization of the stomach, preventing the development of peptic ulcers or cancer of this organ [Daphnetin has a strong antibacterial effect, which has already been used in traditional Chinese medicine. It is important that bacteria do not develop resistance to the compound after its application. Experiments have shown that daphnetin is effective in neutralizing the dangerous bacterium is organ . The useis organ .2. Culture media used: for A375—DMEM high glucose; for SK-MEL28—EMEM; and for FM55P and FM55M2—RPMI1640. Previous publications have described in more detail the growth conditions of these cell line cultures [In this experiment, we used two metastatic (FM55M2 and SK-MEL28) and two primary (FM55P and A375) melanoma cell lines. From the European Collection of Authenticated Cell Cultures , we purchased FM55P and FM55M2 cell lines, and from the American Type Culture Collection , we purchased A375 and SK-MEL28 cell lines. Of note, the A375 cell line is the original and not a CRISPR-modified version of the cell line, and all the tested cell lines in this study have the BRAF V600E mutation. Experimental cell culture conditions: 37 °C in a humidified atmosphere of 95% air and 5% COcultures .Daphnetin (DAP) and the chemotherapeutics—mitoxantrone (MTX), docetaxel (DOCX) and vemurafenib (VEM)—were dissolved in DMSO. Other test compounds such as epirubicin (EPR) were dissolved in sterile hot water, and cisplatin (CDDP) was dissolved in PBS buffer . Compounds were dissolved to the tested concentrations in growth medium before adding compounds to the microtiter (96-well) plate. DMSO was maintained at the safe concentration of 0.1% and had no effect on cell growth.4 cells/mL), HEMa-LP melanocytes (density: 5 × 103 cells/mL) and four human malignant melanoma lines . After a day (24 h) of incubation, the medium was replaced with fresh medium to which was added increasing concentrations of the tested substances . The experimental steps of the MTT assay have been previously described [The effect on cell viability of DAP and the five chemotherapeutics  was assessed using the MTT assay. All tested cell lines—the HaCaT cell line model of human keratinocyte cells   was used to assess cell proliferation. The test was carried out in accordance with the manufacturer’s instructions provided. Initially, appropriate cell densities (as above) of melanoma and normal cells were plated on microtiter plates (NEST). After 24 h, the cells were exposed to increasing concentrations of DAP 2–200 µM). The next steps of the BrdU assay have been previously described ,20.0 µM. TheThe cytotoxicity of DAP was assessed using the LDH assay  based on the measurement of lactate dehydrogenase activity released into the medium from damaged cells. Specified densities (as above) of all the studied melanoma and normal human cell lines were plated on microtiter plates (NEST). After 24 h, the medium was replaced with fresh medium supplemented with DAP (at concentrations of 2–200 µM). The LDH test was carried out after 72 h incubation with DAP according to the manufacturer’s instructions. A more detailed description of the steps of the LDH test has been previously described ,19.50 values for every tested drug. The collaborative effect of two concentration–response lines was tested for the DAP + MTX, DAP + DOCX, DAP + VEM, DAP + EPR, DAP + CDDP combinations, as previously described [50 values, SEM, N, test of parallelism between the studied drugs) were computed automatically in an MS Excel spreadsheet with formulas originally derived from the log-probit method [50 values for the tested compounds when used alone. However, if the concentration-response curves are not parallel, the additivity on the isobologram is illustrated as an area bounded by two (upper and lower) lines of additivity [50 values (based on MTT assay) for the drugs administered alone, median additive inhibitory concentrations for the mixtures (IC50 add) (at the fixed ratio of 1:1) of DAP + MTX, DAP + DOCX, DAP + VEM, DAP + EPR, DAP + CDDP were calculated, as described previously [To thoroughly characterize the pharmacodynamic interactions between DAP and the five cytostatics in the malignant melanoma cell lines , we performed an isobolographic analysis of the MTT assay. On the basis of the results of the MTT test, the percentage of inhibition of cell viability in relation to the specific concentrations of the tested substances  was determined. Based on a computer-assisted log-probit method , dose-reescribed ,22,23,24t method , but thet method ,22,23,24eviously ,26. Moreeviously ,26,27,28eviously .50 and the IC50 mix) values for DAP and the five tested cytostatics  were calculated automatically with a computer-assisted log-probit method using an MS Excel spreadsheet, as described earlier [t-test with Welch’s correction was used to statistically compare the experimentally derived IC50 mix values  with their respective, theoretically calculated and presumed to be additive IC50 add values, as recommended elsewhere [GraphPad Prism  was used for statistical analysis of the data . The analyses were performed with a one-way ANOVA test followed by Tukey’s post-hoc test. Data were presented as means ± standard errors (SEMs). The median inhibitory concentration , mitoxantrone (MTX), docetaxel (DOCX), vemurafenib (VEM), epirubicin (EPR) and cisplatin (CDDP)—inhibited melanoma cell viability in a concentration-dependent manner. The main compound tested was the simple coumarin daphnetin, and its effect on viability in four human melanoma cell lines as well as in keratinocytes and melanocytes was assessed after 72 h incubation with DAP at concentrations of 2–200 µM . The melAll the tested malignant melanoma cells and normal cells were treated with increasing concentrations of DAP, displaying a concentration-dependent decrease in DNA synthesis . MeasureThe cell cytotoxicity of DAP was assessed by the LDH assay, which measures lactate dehydrogenase release into the medium . The rel50 values ± SEM). In our previous experiments, we had determined the mean inhibitory concentration (IC50) values for MTX (0.04 to 1.7 µM) [50 values for EPR and VEM, respectively, were also determined for the following cell lines: FM55P (0.29 ± 0.07 µM and 0.76 ± 0.26 µM), A375 (0.26 ± 0.05 µM and 6.07 ± 2.06 µM), FM55M2 (0.16 ± 0.03 µM and 0.62 ± 0.27 µM) and SK-MEL28 (0.39 ± 0.08 µM and 0.25 ± 0.13 µM) [50 of DAP was determined at 40.48 ± 10.90 µM in the FM55M2 cell line, 64.41 ± 9.02 µM in the FM55P cell line, 116.59 ± 18.35 µM in the SK-MEL28 cell line and 183.97 ± 18.82 µM in the A375 cell line.The inhibitory effects of the tested cytostatics—MTX, DOCX, VEM, EPR and CDDP—in combination with DAP on the viability of malignant melanoma cells were analyzed in the MTT assay. The main purpose of this work was to determine whether DAP could enhance the anti-cancer effects of the tested chemotherapeutic drugs. Concentration-response curves (CRC) were determined according to the log-probit method . From th 1.7 µM) , CDDP (1 1.7 µM) ,19 and D 1.7 µM) . The IC50.13 µM) . The IC5Parallelism of the experimentally determined curves of the concentration-response effects for all the tested combinations—DAP + MTX, DAP + DOCX, DAP + VEM, DAP + EPR and DAP+CDDP see —was asseThe isobolographic analysis of the combination of DAP with VEM (at the fixed ratio of 1:1) showed a statistically significant antagonistic interaction in the A375 and FM55M2 cell lines c,e and aIn the isobolographic analysis, the combination of DAP with CDDP a,c,e,g aSynergy was observed for the combinations of DAP with MTX in the FM55M2 and SK-MEL28 metastatic melanoma cell lines c,d. The A polygonogram  was draw50 doses of daphnetin against the tested hepatocellular carcinoma lines Huh7 and SK-HEP-1 were 69.41 and 81.96 μM, respectively [50 values of daphnetin in this study ranged from 40.48 ± 10.90 µM to 183.97 ± 18.82 µM and are comparable to those obtained against hepatocellular carcinoma. A summary of the mechanisms of action of daphnetin in the in vitro model is presented in The results of our study confirmed that daphnetin dose-dependently inhibits the proliferation and viability of all the tested human malignant melanoma cell lines . Researchers have confirmed the inhibitory effect of daphnetin on the viability of the murine metastatic osteosarcoma LM8 cell line. Daphnetin at doses of up to 30 µM had no effect on cell viability; however, the drug at a dose of 100 µM exerted an 80% inhibition of cell viability . In laboectively . The IC5In vivo experiments on rats confirmed the anticancer effect of daphnetin in inhibiting liver cancer through antioxidant and anti-inflammatory effects. More specifically, Swiss Wistar rats were given diethylnitrosamine and phenobarbital to induce and progress hepatocellular carcinoma; then, the animals were given various doses of daphnetin to determine the effect of the compound on neoplastic lesions. The assessment of biochemical parameters showed a decrease in the levels of, among others, alkaline phosphatase, aspartate aminotransferase and total bilirubin, and an increase in the level of glutathione, catalase and superoxide dismutase. This proves that daphnetin alleviated the liver damage caused by diethylnitrosamine. In addition, daphnetin showed an anti-inflammatory effect that manifested as a decrease in the level of interleukins (IL-1β and IL-6), tumor necrosis factor-α (TNF-α), cyclooxygenase-2 and nuclear factor kappa B (NF-κB) . The strIn the case of studies on the effectiveness of daphnetin against melanoma, the experiments conducted on murine B16F10 cells confirmed its low cytotoxicity at doses of 1–50 µM . Our exp50 value of daphnetin in B16 murine melanoma cells was 54 ± 2.8 µM [50 values obtained in our experiments. For the mouse breast adenocarcinoma cells, the IC50 value for daphnetin was 74 ± 6.4 µM and for the mouse colon cancer cells, the IC50 value amounted to 108 ± 7.3 µM [In another experiment, daphnetin at a concentration of 160 µM evoked cytotoxicity in the mouse melanoma (B16), mouse colon cancer (C26) and mouse breast adenocarcinoma (MXT) cell lines . The IC5± 2.8 µM , which i± 7.3 µM . The inh± 7.3 µM . The hig± 7.3 µM .Importantly, daphnetin is safe to normal cells, which was confirmed in studies on normal ovarian cells (IOSE80), where concentrations of daphnetin (5 to 40 µg/mL ≈ 28–224 µM) were tested . Our exp50 values we obtained are much lower than the Cmax. Only for mitoxantrone was the Cmax value exceeded for the SK-MEL28 cell line, for which the IC50 was 1.74 ± 0.51 µM. Effective cancer therapy involves the use of a number of therapeutic methods, such as surgical methods, radiotherapy, chemotherapy or immunotherapy, depending on the localization and stage of the cancer. For most of the drugs used, numerous side effects are observed, and drug resistance appears after some time of use. Multidrug resistance is also becoming more and more common, which further complicates chemotherapy [The maximum concentrations (Cmax) of the tested cytostatics in the patients’ serum are as follows: MTX (0.715 µM), DOCX 5.47 µM), VEM (127 µM), EPR (16.6 µM) and CDDP (14.4 µM) [ µM, VEM otherapy ,49,50. Totherapy . In expeotherapy . Co-admiotherapy . The conotherapy . Our expotherapy . An addiotherapy  and osthotherapy  against 50 value of 253 μM [Deng et al. tested a combination of daphnetin with doxorubicin in in vitro tests on human esophageal squamous cell carcinoma cells (YM1 line). Daphnetin arrested the cell cycle in S phase, with an ICf 253 μM . In a mof 253 μM . Our expFrom the pharmacological point of view, the most desirable interaction is synergy, which proves the mutual intensification of the therapeutic effect of drugs in combination. Daphnetin in combination with mitoxantrone showed synergy in both metastatic human melanoma cell lines (FM55M2 and SK-MEL28), while in primary melanoma cell lines, the combination of daphnetin with mitoxantrone showed an additive interaction (A375) or additivity with a tendency toward synergy (FM55P).50 values of daphnetin range from 40.48 ± 10.90 µM to 183.97 ± 18.82 µM, depending on the cell line. Isobolographic analysis showed that the interaction for the combination of daphnetin with vemurafenib or epirubicin was antagonistic. An additive interaction was observed for the combinations of daphnetin with cisplatin and docetaxel. The most desirable synergistic interaction was observed for the combination of daphnetin with mitoxantrone in metastatic human melanoma lines (FM55M2 and SK-MEL28), while in primary melanoma lines, this combination showed an additivity with a tendency toward synergy. The interactions found in this study may have a clinical significance, but further research is needed to understand the mechanisms of action of the combinations, and additional pre-clinical studies on animals should shed more light on malignant melanoma treatment with daphnetin.Daphnetin, at lower concentrations of up to 100–150 µM, is safe for keratinocytes and melanocytes and does not limit their proliferation or viability. Above these concentrations, it causes only slight inhibition of the growth of normal cell lines. However, daphnetin exhibits anticancer activity by inhibiting viability and proliferation of the tested human malignant melanoma cell lines. The IC


A pipeline for metabolomics, based on UPLC-ESI-MS, was tested on two malignant breast cancer cell lines of the sub-types ER(+), PR(+), and HER2(3+) (MCF-7 and BCC), and one non-malignant epithelial cancer cell line (MCF-10A). This allowed us to quantify 33 internal metabolites, 10 of which showed a concentration profile associated with malignancy. Whole-transcriptome RNA-seq was also carried out for the three mentioned cell lines. An integrated analysis of metabolomics and transcriptomics was carried out using a genome-scale metabolic model. Metabolomics revealed the depletion of several metabolites that have homocysteine as a precursor, which was consistent with the lower activity of the methionine cycle caused by lower expression of the AHCY gene in cancer cell lines. Increased intracellular serine pools in cancer cell lines appeared to result from the over-expression of PHGDH and PSPH, which are involved in intracellular serine biosynthesis. An increased concentration of pyroglutamic acid in malignant cells was linked to the overexpression of the gene CHAC1. The deregulation of metabolism has been known to be a characteristic of cancer cells since the work of Warburg, almost a century ago . In thei13C-labeled substrates) and metabolomics enables measurement of the concentrations of intracellular metabolites. The relationships between these three types of variables  can be represented using so called genome-scale metabolic models (GSMMs) [Metabolism is a complex system that emerges from the interplay of metabolic enzymes and the small molecules (metabolites) transformed by these enzymes. Metabolism can be studied from different angles, depending on which experimental techniques are used. Proteomics and transcriptomics enable the measurement of changes in enzyme concentrations , fluxomics enables the measurement of reaction rates  . These m (GSMMs) ,10, and, (GSMMs) .13C-labeled substrates and mass spectroscopy. These studies revealed a key role of lipid metabolism in fighting oxidative stress [However, to date, it is not possible to establish a direct relationship between enzyme levels, metabolic fluxes and metabolite concentrations. It has been shown that most of the changes in the expression levels of metabolic genes (those coding metabolic enzymes) do not result in concomitant changes in metabolic fluxes , and thee stress , and thee stress .13C labeling pattern). The same metabolic flux distribution  could be achieved at different steady state concentrations of internal metabolites.There is also no direct relationship between metabolic fluxes and internal metabolite concentrations. Indeed, the methods used to infer metabolic flux distributions in steady state  do not rInternal concentrations of metabolites could have a key role in cancer progression by interacting with signaling pathways, and could also be used as informative biomarkers to assess malignancy . The firThe objective of this work is to identify perturbed metabolic processes characteristic of breast cancer cell lines of the subtypes ER(+), PR(+), and HER2(3+), which could form the basis of the identification of new markers for diagnosis and drug targets.First of all, we assessed whether the panel of 33 internal metabolites, quantified using the described analytical pipeline, can be used to cluster different cell types. The metabolite concentration data, in relative area units, are available in Principal Component Analysis (PCA) was carried out  and showThe results of PCA indicate that 36% of the variation in metabolite concentrations observed between the three cell lines is associated with malignancy. In order to identify the metabolites that contribute the most to separating the non-malignant MCF-10A cell line from the two others, the data are represented in the form of a heat-map . The sixAs discussed later, six perturbed metabolites are related to the methionine cycle or have homocysteine (an intermediate of the methionine cycle) as a metabolic precursor. These metabolites are: methionine, taurine, hypotaurine, choline, betaine and pyroglutamic acid.Box plots showing the concentration distribution of some of the discussed metabolites are shown in Differentially expressed genes were identified using a false discovery rate of 0.01 and a fold change of 2 as cut-off values. A total of 1495 genes showed significant up-regulation in MCF-7 compared MCF-10A and 1335, in which they were significantly down-regulated. For BCC, 1926 genes were up-regulated and 1669 down-regulated. Among the up-regulated genes, 274 were common to both cancer cell lines, and 563 of the down-regulated genes were observed in both cancer cell lines. The complete list of differentially expressed genes can be found in Among the 274 genes up-regulated in both cancer cell lines, 44 were metabolic genes, involved in 131 metabolic reactions. Among the 663 consensus down-regulated genes, 130 were metabolic genes, which are associated with 832 metabolic reactions. The large number of reactions affected by the down-regulated genes comes from the fact that many of these down-regulated genes are involved in cross-membrane transport. For example, the solute carrier family 36 member 4 gene, SLC36A4 (ENSG00000180773), is involved in four transport reactions . The transporter SLC6A15, also down-regulated in both cancer cell lines, is involved in the transport of 11 amino acids. A full list of the metabolic reactions linked to up- and down-regulated genes can be found in Among the perturbed metabolites that were identified in the previous analysis, three of them  can be traced back to a common metabolic precursor (homocysteine), which is an intermediate in the methionine cycle. Pyroglutamic acid is formed from the break-down of glutathione, which also can be traced back to homocysteine; however, its carbon atoms come from glutamine, which was incorporated during the synthesis of glutathione.l-homocysteine, showed significantly lower expression in MCF-7 and BCC cell lines compared to MCF-10A. AHCY has been previously identified as a tumor suppressor gene involved in p53-induced cell cycle arrest [The gene AHCY, coding the enzyme adenosylhomocysteinase, which breaks down S-adenosylhomocysteine into adenosine and e arrest . TogetheTwo more of the perturbed metabolites are linked to the methionine cycle (choline and betaine). As shown in The RNA-seq data revealed that two genes involved in serine biosynthesis from the glycolytic intermediate 3-phosphoglycerate (PHGDH and PSPH) are more expressed in the MCF-7 and BCC cell lines compared to MCF-10A. The gene PHGDH codes phosphoglycerate dehydrogenase and has been shown to be overexpressed in several cancers . It has Pyroglutamic acid is a metabolic dead end that results from the degradation of glutathione. Our transcriptomic analysis revealed the overexpression of the gene CHAC1 in both cancer cell lines, which show increased intracellular pools of pyroglutamic acid. The function of this gene product in breaking down glutathione into cysteinyl-glycine and pyroglutamic acid was characterized in 2012 . The knoOther perturbed metabolites are metabolically unrelated to the previously discussed metabolic processes. For example, increased acetyl-carnitine points to higher activity of the carnitine shuttle, which is responsible for the transfer of fatty acids from the cytosol to the mitochondria. The relationship between the carnitine shuttle and cancer is well established . FinallyThe expression levels of genes that could be directly linked to perturbations in intracellular metabolic pools are shown in 2 into bicarbonate are also predicted in malignant cells, as a result of the down-regulation of two carbonic anhydrases (CA6 and CA2). A list of the perturbed metabolic reactions and their associated genes can be found in The obtained gene expression data were used to constrain a genome-scale metabolic model using the python library pyTARG . FBA wasThere is high metabolic heterogeneity between different breast cancer cell lines, even between those belonging to the same molecular sub-types, (ER(+), PR(+), and HER2(3+)). However, some internal metabolites can be linked to malignancy. Transcriptomic data allowed us to establish relationships between the observed perturbations in internal metabolites and changes in the expression levels of key enzymes. Two main conclusions could be obtained from the integrated analysis of transcriptomic and metabolomic data. First of all, the MCF-7 and BCC cell lines showed lower internal methionine, taurine and hypotaurine levels, which, together with the down-regulation of AHCY (adenosylhomocysteinase), reveals decreased activity of the methionine cycle in cancer cells. On the other hand, a higher concentration of serine and higher expression of the genes PSPH and PHGDH are consistent with higher serine intracellular biosynthesis (which compensates for the lower extracellular serine uptake caused by the down-regulation of SLC36A4). Both methionine and serine are closely related to one-carbon metabolism. The methionine cycle is involved in the regeneration of the methyl donor SAM, while serine is involved in the regeneration of 5-methyl-tetrahydrofolate. SAM acts within the cell as a donor of methyl groups for the methylation of histones and DNA. High SAM concentrations have been shown to have anti-cancer effects (promoting apoptosis or down-regulating oncogenes through methylation); in particular, this effect has been shown on the MCF-7 cell line . 5-methyBesides its role as methyl donor, serine is also a precursor to the synthesis of glycine and other compounds, such as glutathione, involved in cellular resistance to oxidative stress. Serine can be synthesized de novo or uptaken from the exterior, and both processes have been shown to have an impact on cancer proliferation ,31. Seri2. Cells were incubated for 24 h. Metabolite extraction was performed as described earlier by Sellick and co-workers [MCF-7  and BCC cells  were cul-workers . m/z to 250 m/z range with a Xevo TQD triple quadrupole tandem mass spectrometer  coupled with an electrospray ionization (ESI) ion source. The column was set at 40 °C. Elution was applied with a mobile phase of 0.1% formic acid water solution (solvent A) and acetonitrile (solvent B). Flow rate was set to 0.4 mL/min. The starting conditions were set to 95% of solvent A. Gradient elution was applied with the following proportions of solvent A: 0 to 0.2 min was set to 95%, 0.2 to 1.5 min was set to 10%, 1.5 min to 1.8 min was maintained at 90%, and 1.8 to 2 min was set back to the starting conditions. The settings for the mass spectrometer were as follows: capillary voltage was set to negative 2 kV, source temperature to 150 °C, desolvation gas (nitrogen) temperature to 400 °C, gas flow to 700 L/h and cone gas flow to −20 L/h. Cone voltage was set to 25 V.The samples were analyzed following the protocol published by Virgiliou and co-workers , which aBased on the retention times reported by Virgiliou and co-workers ,38 diffeTM  by mixing 1 million cells with 500 µL of Shield stabilization solution. The company constructed RNA-Seq libraries from 500 ng of total RNA using the Zymo-Seq RiboFree Total RNA Library Prep Kit  according to the manufacturer’s instructions, before sequencing on an Illumina NovaSeq  to a sequencing depth of at least 30 million read pairs (150 bp paired-end sequencing) per sample.Three biological replicates from each of the three cell lines  were sent to Zymo Research for next-gen RNA sequencing. The samples were prepared for shipment using DNA/RNA Shieldt-test was used to identify up-regulated and down-regulated genes comparing malignant and non-malignant cell lines. The obtained values were corrected for multiple testing as described in previous work [The obtained pair-ended reads were aligned on a reference sequence (the complete list of human transcripts was obtained from Ensembl BioMart) using Bowtie2 . The resous work  . It is known that broad changes in metabolic flux distributions are typically driven by changes in the expression levels of a relatively small number of metabolic genes. In order to identify these flux-controlling genes, reactions showing a significant change in both the estimated reaction rate and the expression level of at least one of their associated genes were identified.In order to obtain meaningful relationships between metabolomics and transcriptomics, we used a GSMM  in orderThe described analytical pipeline allowed us to quantify enough metabolites to obtain a clear metabolic footprint of each of the tested cell lines. Among the identified metabolites, 10 of them appeared to be associated with malignancy. The intracellular concentration of methionine was significantly lower in both malignant cell lines compared to the non-malignant MCF-10A. Together with the down-regulation of the metabolic gene AHCY and the depletion of taurine and hypotaurine (which originate from homocysteine), this observation suggests lower activity of the methionine cycle in cancer cell lines, which would cause a lower re-methylation rate of SAM. The pro-apoptotic effects of SAM supplementation in breast cancer cells have already been shown. Our data suggest that the supplementation of methionine and betaine, leading to higher flux in the methionine cycle and the higher production of SAM, could also have the same effects. This is an interesting hypothesis that should be tested in the future. An increased intracellular pool of serine was observed, which is consistent with the increased intracellular synthesis rate caused by the over-expression of the genes PHGDH and PSPH. The targeting of serine biosynthesis could be a potential alternative for the treatment of breast cancer. Finally, the recently discovered relationship between the gene CHAC1 and the production of pyroglutamic acid was confirmed. The results of this work point to a possible role of pyroglutamic acid as a biomarker, and an oncogenic role of the gene CHAC1, which remains to be explored further. All the presented conclusions are based on two breast cancer cell lines, and whether they apply to other cell lines and cancer types remains to be tested.


There were no significant associations between klotho and telomerase activity in either group, across cell types assessed here. Our results suggest that klotho levels and telomere length may be associated through a coordinated downregulation of longevity factors occurring under higher stress caregiving conditions.Aging biomarkers may be related to each other through direct co-regulation and/or through being regulated by common processes associated with chronological aging or stress. Klotho is an aging regulator that acts as a circulating hormone with critical involvement in regulating insulin signaling, phosphate homeostasis, oxidative stress, and age-related inflammatory functioning. Both klotho and telomere length are biomarkers of biological aging and decrease with age; however, the relationship between them is not well understood. Here we test the association between klotho levels and the telomere length of specific sorted immune cells among a healthy sample of mothers caregiving for a child with autism spectrum disorder  or a child without ASD, covarying age and body mass index, in order to understand if high stress associated with caregiving for a child with an ASD may be involved in any association between these aging biomarkers. In 178 caregiving women ( Aging biomarkers are often related to each other and this could occur through many different mechanisms. First, age influences aging related biomarkers, by definition, and any study of potential co-regulation must covary chronological age. Secondly, there may be direct co-regulation of each other. Thirdly, they may be regulated by common processes not associated with age, but associated with lifestyle factors such as chronic stress. Here we examine the relationship between two important biomarkers of aging, klotho and telomere length, in a healthy sample stratified into groups based on a combination of (a) stressor exposure and (b) level of perceived stress .Klotho is an aging regulator primarily expressed in the kidneys and choroid plexus of the brain that acts as a circulating hormone once cleaved from its transmembrane form –4. Once Like klotho, telomere length is increasingly recognized as a key biomarker of biological aging. Telomeres are protective DNA-protein complexes that cap the ends of chromosomes and provide genomic stability , 22. Comin vitro work using human cortical neurons derived from human pluripotent stem cells showed an age-related association between telomere attrition and klotho mRNA expression [in vitro cultured cells, a 50% inhibition of klotho gene expression led to a shortening of telomere length [Despite evidence supporting klotho and telomere length as aging biomarkers, the relationship between them has been examined in only a handful of basic studies and is not well understood. In this regard, pression . In anote length , and in e length . In mouse length . Among ie length . When ine length . To dateChronic stress, such as the stress of caregiving for a child with a neurodevelopmental disability ), is associated with accelerated biological aging and the more rapid development of age-related conditions, including cardiovascular disease –49 and pn = 90 high-stress mothers caring for a child with ASD, n = 88 low-stress mothers caring for a neurotypical child) are shown in M = 21.90, SD = 4.69) than mothers caring for a neurotypical child  = −9.03, p < .001). For brevity moving forward, we refer to the mothers caring for a child with ASD as the “high-stress” group and the mothers caring for a neurotypical child as the “low-stress” group.Full sociodemographic characteristics of the sample  and shorter telomere length across cell types . Older age was also associated with reduced CD8+CD28+ T cell telomerase activity , but there were no other significant correlations between age and telomerase activity in any other cell type (ps: .44–.97). Higher BMI was associated with lower klotho levels  and shorter telomeres in CD19+ B cells , but not shorter telomeres (ps: .19–.52) or telomerase activity (ps: .49–.95) in any other cell types. Education and race were not associated with levels of klotho, telomerase activity, or telomere length in any cell types (ps > .11).Collapsing across groups, older age was associated with lower klotho levels , p = .025) and whole blood TL , p = .013). When broken down into specific cell types, there were no statistically significant associations observed for CD4+ T cell TL , p = .07), CD8+CD28− T cell TL , p = .06), CD8+CD28+ T cell TL , p = .21), or CD19+ B cells , p = .23). Granulocyte TL was also not significantly related to klotho levels in unadjusted models , p = .15). After covarying for age and BMI, only klotho levels and whole blood TL had any signal of an association; however, this association was not statistically significant , p = .052). There were no other statistically significant associations between klotho and TL across cell types in adjusted models (ps: .12–.67). Regarding telomerase activity (TA), there were no reliable associations between klotho levels and TA across cell types in unadjusted (ps: .14–.57) or adjusted (ps: .14–.55) models.In models unadjusted for covariates, klotho levels were significantly associated with PBMC TL , p = .006; adjusted: b = 0.17, 95% CI , p = .019; see b = 0.18, 95% CI , p = .015, pB-H = .036; adjusted: b = 0.15, 95% CI , p = .039, pB-H = .12; see b = 0.28, 95% CI , p = .014, pB-H = .036; adjusted: b = 0.23, 95% CI , p = .039, pB-H = .12; see b = 0.20, 95% CI , p = .018, pB-H = .036; adjusted: b = 0.14, 95% CI , p = .084, pB-H = .17; see b = 0.08, 95% CI , p = .30, pB-H = .30; adjusted: b = 0.07, 95% CI , p = .35, pB-H = .35. There was no evidence of a significant klotho by group interaction in whole blood or for granulocyte TL (ps > .13).We found evidence of a significant klotho by group (high- vs. low-stress) interaction for PBMC TL .There was no evidence that associations between levels of klotho and cell-specific telomerase activity differed significantly between the high- and low-stress mothers. Across each cell type assessed for telomerase activity , there was no evidence of the klotho by group interaction associated with telomerase activity , p = .017; adjusted: b = 0.14, 95% CI , p = .081). There were no overly influential observations in this regression analysis based on the DFBETAS values. There were no other overly influential observations or outliers in these analyses.There was one significant outlier based on a Bonferroni outlier test of the studentized residuals in the analyses with CD8+CD28+ T cell TL. When this observation was removed the effect of the klotho by group interaction was similar  membership, and telomerase activity across cell types assessed here. This study is the first to explore associations between sorted immune cell telomere length, telomerase activity, and circulating klotho in humans; our results indicate that klotho and telomere length may be linked through a coordinated downregulation of certain longevity factors under higher stress caregiving conditions. Because both klotho and telomere length are associated with insulin signaling, oxidative stress, and age-related inflammatory functioning, the relationship between the klotho and telomere length may be mediated through these common stress- and longevity-related pathways.Building on past findings in this sample that mothers of a child with ASD had lower klotho levels than mothers of a neurotypical child , we idenFor high-stress mothers of a child with ASD, there was preliminary evidence that klotho may be associated with telomere length in specific sorted immune cells that are important for the aging immune system, namely T cells. As people age, telomere shortening happens primarily in CD8+ T cells , 60, espn = 50), this non-significant effect is worth examining in larger sample sizes.We found no evidence of reliable associations between klotho and telomerase activity in this sample in either group. The impact of chronic stress itself on telomerase activity is not clear as some studies identify that chronic stress may suppress telomerase activity , 54, 69 These findings should be interpreted while recognizing the limitations of the current study design, which also highlight opportunities for important future directions. Although we focus on the stress of caregiving for a child with an ASD in this study, the caregiving experience encompasses far more than just stress , 73. BecThese limitations are well-balanced by several key strengths of this study, including carefully matched groups of caregiving women and examining sorted immune cell telomere length. Many studies examining telomere length rely on heterogeneous populations of leukocytes without subdividing these cells; by sorting immune cells for this study we were able to identify where there were clear associations for the high-stress group between klotho and telomere length , where there was some weak evidence of an association between klotho and telomere length following the same pattern (CD8+CD28+ T cells), and where there was no evidence of an association between klotho and telomere length .There appears to be an exciting and fruitful area for future investigations to further understand the impact of caregiving stress on aging biology. There was a wide range of power to detect an effect for telomerase activity related to group membership and klotho levels; thus, for all analyses but especially those with smaller sample sizes  future work may replicate or extend these findings with larger sample sizes. Given the importance of both the chronicity and timing of stress for its impact on aging biology , future In sum, we identified that klotho levels and telomere length of certain immune cells are linked for those experiencing higher stress caregiving conditions but not linked in lower stress caregiving conditions. This may be due to the common and powerful effects of chronic stress on stress related aging pathways. We found no evidence of this pattern of associations for klotho and telomerase activity. Future research is needed to investigate the characteristics of stress exposures that link these two markers of aging and the underlying biological mechanisms driving this connection.≥13 on the Perceived Stress Scale (PSS) were categorized as a high-stress maternal caregiver. Participants who were caring for a neurotypical child  and reported a PSS score of ≤ 19 were categorized as a low-stress maternal control. This eligibility criteria were based on prior national norms [Participants included 183 mothers recruited via schools, mailings, social media, and ads circulated through child development centers in the San Francisco Bay Area as well as direct recruitment through the University of California, San Francisco Autism Clinic; 178 of the participants had serum samples available for quantifying klotho and of those, all had DNA available for quantifying telomere length. Participants were eligible if they were non-smokers between ages 20 and 50 years, with at least one child between ages of 2 and 16 years. Participants who were caring for a child diagnosed with an autism spectrum disorder (ASD) and reported a score of al norms , 77. ThiProcedures for assaying klotho have been described for this sample . BrieflyIdentical methods for determining telomere length in this sample were detailed in earlier papers , with thN = 6, assayed in triplicate) was 8% and the inter-assay variability of PBMC samples  was 6.7%.Telomerase activity was determined identically to how it was previously described . Brieflylm function from the stats package [influence.measures function from the stats package [outlierTest function from the car package, as well as through visual examination using the boxplot function [ggpredict function from the ggeffects package [ggplot2 capabilities [describe function from the psych package [cor.test function in the stats package [All statistical analyses were completed in the R statistical environment using R Studio (v2022.07.2). Linear regressions were performed using the  package . Leverag package . We examfunction , 83. Plo package , which bbilities . Descrip package . Correla package .pB-H). Lastly, sensitivity analyses tested the robustness of any significant effects by removing any potential outliers or overly influential observations. We used a Bonferonni outlier test to examine studentized residuals of each regression. Regarding leverage statistics, observations were removed if the DFBETAS value was greater than |1.0|, which indicates that a single observation may be influencing the regression parameter estimates’ values. Klotho levels underwent log-10 transformation to approximate a normal distribution. Telomere length was non-normally distributed across all cell types apart from whole blood cells; thus, we used a log-10 transformation to approximate a normal distribution for all measures of telomere length besides whole blood cells. Each measure of telomerase activity was also non-normally distributed and we applied a log-10 transformation to better approximate a normal distribution for analyses.Of the 183 participants in the parent study, 178 had serum available (missingness across relevant variables is described in Supplementary Tables


Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies. Intra-cell line heterogeneity remains to be characterized. Here, the use of single multi-omics on a large panel of human cell lines identifies copy number variation, epigenetic variation and extrachromosomal DNA distribution as the main contributors to intra-cell line heterogeneity. In recent years, single-cell genomics has made incredible progress toward characterizing specific tumor subtypes and disentangling the cellular complexity of a given tumor3. However, most of the effort has been so far focused on profiling the clinical samples. Yet, the molecular understanding of what drives such heterogeneity remains elusive, which necessitates extensive follow-up studies in different model systems. Human cancer cell lines have long served as tools for cancer research and drug discovery, as they represent key properties of their original tumors and provide context for studying cellular or molecular mechanisms and testing therapy responses. Given the establishment of cancer cell lines involves the selection of tumor cells that adapt to in vitro culture conditions, cancer cell lines were often thought to be homogenous and unable to maintain the heterogeneity of the original tumor. However, it has been shown that cell lines could evolve and develop into distinct cell line strains4. Many recent studies have demonstrated that cellular diversity could be detected in established cell lines by using techniques including single-cell RNA-sequencing (scRNA-seq) and fluorescence-activated cell sorting (FACS)7. Transcriptome heterogeneity within cell lines has facilitated the discovery of critical regulators that drive the epithelial-mesenchymal transition (EMT) program and drug resistance in subclones8. If certain intra-cell-line heterogeneity is associated with therapeutic response, these cell lines could potentially serve as suitable models to study the molecular mechanisms underlying drug sensitivity and to test different treatment schemes. Therefore, understanding the heterogeneity of commonly used human cancer cell lines will not only provide important information for cell line-based biomedical research but also may help to identify suitable models for investigating novel mechanisms underlying specific cancer phenotypes. More recently, Kinker et al. systematically profiled 198 cancer cell lines by scRNA-seq, which mainly consisted of those derived from lung cancer, and revealed the landscape and recurring patterns of intra-cell-line heterogeneity. However, the molecular mechanisms underlying the heterogeneity still await to be uncovered.The evolution of cancer starts with malignant transformation, followed by progression to more aggressive and resistant forms, toward poor clinical outcomes. The intratumoral heterogeneity in human tumors plays a critical role in carcinogenesis and tumor evolution11. Although effective at resolving the heterogeneity in transcriptional programs, scRNA-seq is unable to reveal the underlying driving force, such as epigenetic factors. For this purpose, it requires techniques that capture cell-to-cell variations in their epigenetic landscape. Since chromatin accessibility to a large extent could reflect transcription factor binding, histone modifications and DNA methylation, and offers greater insights into the gene regulatory mechanisms, single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) has recently become the most widely used assay for epigenomic profiling at single-cell resolution. A major advantage of scATAC-seq, compared to scRNA-seq, is that it provides mechanistic insights into gene regulation modulated by transcription factors12. The joined application of scRNA-seq and scATAC-seq to cancer specimens helps to discern precise cis-regulatory elements and target genes as well as identify the key regulatory networks that govern tumor development.The origin of intratumoral heterogeneity was traditionally considered in genetic terms but has been extended into more facets, including epigenetics and dramatic changes in the microenvironmentTo characterize both transcriptomic and epigenetic heterogeneity within different cell lines, we perform scRNA-seq and scATAC-seq on dozens of human cell lines, mainly consisting of breast and colorectal origins. By integrating the scRNA-seq and scATAC-seq data, we investigate the molecular mechanisms that drive heterogeneity and find that copy number variation (CNV) only contributes partially to the observed transcriptomic heterogeneity. Epigenetic diversity and extrachromosomal circular DNA (ecDNA) distribution contribute significantly to the intra-cell-line heterogeneity. Moreover, through lineage tracing and hypoxia treatment, we demonstrate that the transcriptomic heterogeneity is plastic and could be reshaped under environmental stress. Taken together, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell line heterogeneity.15  . The cluster distribution was not associated with read counts or sequencing batches , were in consistence with their reported status17. For instance, ESR1 was mainly expressed in LA and LB subtypes, while ERBB2 was upregulated in LB and H subtypes , but neither basal epithelial  nor stromal markers , whereas triple-negative cell lines expressed high levels of basal cell marker genes, and TNB cell lines further showed high expression levels of stromal and epithelial-mesenchymal transition (EMT)-related genes . In colorectal cancer, however, cells of the same subtype were not completely clustered together, in particular for those of CMS1 type , luminal B (LB), Her2+ (H), triple-negative A (TNA), and triple-negative B (TNB), were adjacent to each other Fig. . The exppes Fig. . Moreovepes Fig. 18,19. Lu22. Briefly, we employed PCA to project all cells captured in scRNA-seq to an eigenvector space and defined the centroids of the individual cell line, and then calculated the diversity score as the average distance within individual cell lines to their specific centroids (“Methods”). In general, the discrete/continuous classification correlated well with the diversity score, where the majority of the cell lines with the top 25% highest diversity score were those showing discrete pattern  was used to search heterogeneously activated expression programs (termed NMF programs) within each cell line (“Methods”)  includes SPARC, CTGF, FN1, VIM and other genes, which resemble the partial EMT and are related to cancer metastasis6, while Program cluster 5 (EMT I) includes VIM, S100A4, TIMP1, KRT19, etc., which prompt the progression of different cancer via EMT25. The identification of different EMT-associated program clusters suggested EMT as a common but context-specific driver of intra-cell-line heterogeneity. Program cluster 3 contains interferon response genes, such as IFIT1, IFIT2, and IFIT3, whose heterogeneity has previously been observed in ovarian cancer26. Program cluster 4 harbors stress response genes, including DNA damage-induced genes DDIT3 and DDIT4, which are reported heterogeneously activated in HNSCC6. Program cluster 6, which is termed as estrogen response, was significantly enriched in breast cancer cell lines (11/12)  per cell line and median fragments of 11,702  per nucleus , while distinct clusters were observed in the differential ones . As a result, 62% of cell lines showed an indiscriminate pattern, while 38% showed a differential pattern Fig. . Interes29, where we calculated the average expression levels in windows of 400 genes around each locus in comparison to a set of reference normal cells (RPE-1 and HK2) (“Methods”). The analysis identified CNV subclones within 25 of 40 (62.5%) cancer cell lines  were inferred based on scRNA-seq data using inferCNVnes Fig. , which snes Fig. . Among tells RPE- and HK2 nes Fig.  middle. nes Fig. , suggest28 and ChromVAR30, to seek to infer potential transcription factors (TFs) that drive the differential chromatin accessibility between cell subclusters and focused on common TFs that appeared in at least three cell lines  ecDNAs are highly amplified; (2) ecDNAs have much higher accessibility37. Based on the scATAC-seq data, the read coverage showed multiple peaks in every single cell, of which the first peak was considered as the read coverage for the genome regions of two copies and therefore defined here as reference coverage ABL1 and NUP214 are located11. Oncogenes were significantly enriched in potential ecDNA regions  in its 3’UTR , demonstrating a stable cellular composition along with the cell expansion  and E2F  families were activated in subcluster 0-1 cells, which were sensitive to hypoxia. This is in line with the previous studies showing the role of ETS transcription factors in hypoxia-induced gene expression  family members were the differential TFs in cluster 0-1  distribution measured by scATAC-seq were found to contribute to the observed intra-cell-line heterogeneity. Through lineage tracing and hypoxia treatment, we found that transcriptomic heterogeneity is dynamic and could be reshaped by environmental stress.5. Srivatsan et al. also found heterogeneity in the cellular response to HDAC inhibitors, which is caused by the variation in cells’ acetate reservoirs46. The transcriptional variation revealed by scRNA-seq could provide information on how heterogeneously cells may react to drug treatment and how mechanistically drug resistance may occur. Moreover, scRNA-seq data provide more precise information for molecular subtype classification of the cancer cell lines, which may help to identify cell lines resembling specific features of human tumors. Meanwhile, scATAC-seq data reveal cellular heterogeneity at a different regulatory level. As a resource, it offers a comprehensive dataset for the analysis of intra-cell-line heterogeneity at both the transcriptomic and epigenomic levels, which could be used as a reference to choose suitable cell lines for intended studies, such as mechanistic investigation, drug tests, and high-throughput screening.The intra-cell-line heterogeneity observed in commonly used cancer cell lines may have a profound influence on cell line-based biomedical research. It may change the way researchers interpret their results and provide new explanations for unsolved questions. Our previous study has shown that cellular response to PARP inhibitors could be affected by the EMT program, which was heterogeneously activated within the cell lineBesides illustrating the intra-cell-line heterogeneity at a given time, our study also measured the dynamic nature of cellular states by combining the cellular genetic barcode tracing technique and demonstrated that, in some cell lines, the daughter cells from the same progenitor might enter different transcriptomic statuses, suggesting that even apparently homogeneous cells may have latent variability. Such latent variability was also observed in the hypoxia experiment, where hypoxia treatment drove the heterogeneous cellular response from the same cell line, even from the same subpopulation. the emergence of a new transcriptomic subcluster in DLD-1 and SW620 cell lines. This suggests that transcriptomic heterogeneity within cancer cell lines may be far more complex than we observed under normal culture conditions, or the current scRNA-seq technique is not sensitive enough to reveal the latent variability. Therefore, we further investigated if the latent transcriptomic variability could be observed at the epigenomic level by using scATAC-seq, given epigenomic heterogeneity may reflect not only the present but also the past and future cell status. Indeed, within one transcriptomic subcluster, differential primed chromatin states driven by specific TFs preceded hypoxia. This reminds us that when targeting observed heterogeneous transcriptional programs for cancer therapy, cells may evolve new mechanisms to overcome the treatment and the knowledge of epigenetic heterogeneity may help us to predict and prevent it from happening.47, Circle-seq48, and Circle-Finder49 that are based on bulk sequencing, our method developed for analyzing single-cell ATAC-seq data could identify highly potential ecDNA regions at single-cell resolution and therefore reveal specific events in rare cell populations, which could be neglected as noise in bulk sequencing data49. We observed on average hundreds of ecDNA events in individual cell lines, including ecDNAs that were validated by FISH and other sequencing-based techniques11. The ecDNAs identified by our method potentially contained a higher fraction of false positives than bulk sequencing-based approaches, owing to the fact that with the limited scATAC-seq read length, we could not use junction site reads for further filtering as in those methods. Nevertheless, the results derived from our single-cell data could provide important resources for further validation experiments using imaging-based tools.Furthermore, in this study, scATAC-seq was utilized for the first time to characterize the ecDNAs. Our data revealed the pervasive presence of ecDNA in cancer cell lines. The ecDNAs could drive high gene expression of the contained genes and their variable copy numbers in individual cells contribute to the observed transcriptional heterogeneity. Compared to previously published techniques, including AmpliconArchitectIn conclusion, this study advances the understanding of cellular heterogeneity within commonly used cancer cell lines and provides insights into the molecular mechanism driving it. We envision that the data and analysis provided in this study will facilitate all sorts of cancer cell line-based biomedical research, form a basis for investigating intratumor heterogeneity using available cancer cell lines, enable new biological insights into different research models, and eventually contribute valuable insights into tumor biology and have implication for the further development of precision therapies.2 at 37 °C. Cell line identity was confirmed by the Multiplex human cell line Authentication Test  or Cell Line Authentication . We regularly checked all cell lines for mycoplasma contamination.The 786-O (CRL-1932), A-253 (HTB-41), A549 (CCL-185), BT-474 (HTB-20), BT-549 (HTB-122), Caco-2 (HTB-37), COLO 205 (CCL-222), DLD-1 (CCL-221), FaDu (HTB-43), HCC1937 (CRL-2336), HCT 116 (CCL-247), HCT-15 (CCL-225), HCT-8 (CCL-244), HeLa (CCL-2), Hep G2 (HB-8065), HK-2 (CRL-2190), Hs 578T (HTB-126), HT-29 (HTB-29), K-562 (CCL-243), LoVo (CCL-229), LS 174T (CL-188), MCF7 (HTB-22), MDA-MB-231 (HTB-26), MDA-MB-361 (HTB-27), MDA-MB-453 (HTB-131), MDA-MB-468 (HTB-132), RKO (CRL-2577), RPE-1 (CRL-4000), RPMI 8226 (CCL-155), SCC4 (CRL-1624), SK-BR-3 (HTB-30), SW480 (CCL-228), SW620 (CCL-227), T-47D (HTB-133) and ZR-75-1 (CRL-1500) cells were obtained from the ATCC. The HAP1 (C631) cells are from Horizon Discovery and Huh7 (SCSP-526) cells are from The Cell Bank of the Chinese Academy of Sciences. The SF268 and SNB75 cells are from NCI, and the SF295 (C0005005) cells are from AddexBio. The HUNSCCUM-02T and HUNSCCUM-03T cells were kindly gifted by Walter Birchmeier’s lab at Max-Delbrueck-Center for Molecular Medicine, Berlin. All cell lines used in this study were maintained in DMEM  or RPMI1640  medium supplemented with 10% fetal bovine serum , 1% Penicillin/Streptomycin  in a humidified atmosphere of 5% CO2 and 1% O2. After 24 h incubation, the cells, which were 80–90% confluent, were washed once by PBS, and then harvested with 0.05% Trypsin-EDTA. After neutralization with complete medium, centrifugation, and resuspension in PBS with 0.04% BSA, the cells were measured.The normoxic set of plates was placed in an aerobic incubator. To create hypoxia, the hypoxic set was moved to a Hypoxia Incubator Chamber (StemCell) which was equilibrated at 37 °C in a humidified atmosphere of 5% COThe vector encoding PuroR-T2A-GFP protein followed by a 28-SW sequence in its 3’UTR was constructed based on CROPseq-Guide-Puro (Addgene#86708) by inserting a T2A-GFP coding sequence after PuroR and replacing the U6 promoter and filler sequences with a 28-SW sequence.HEK293T cells were seeded one day before and were transfected with lentiviral plasmid, psPAX2 (Addgene), and pMD2.G (Addgene) at ratio of 4:3:1 using PEI (Sigma). The medium was replaced 12 h after transfection. The supernatant was collected after 48 h with centrifugation and filtering through a 0.45 μm filter. The transduction was worked by incubating the viral particles containing supernatant with the targeting cells overnight in the presence of polybrene (Sigma). To ensure each cell was labeled with only one distinct barcode, we transfected 150,000 cells with a barcode library consisting of 230,000 distinct barcodes at a MOI of 0.1.51. Cells were harvested with 0.05% Trypsin-EDTA. After neutralization with complete medium, centrifugation, and resuspension in PBS with 0.04% BSA, the cells were filtered through a 35-µm Cell Strainer (Corning 352235) and a single-cell suspension was prepared. Cells and barcoded beads were diluted at the concentration of 1000 cells/μL in cell resuspension buffer and 1000 beads/μL in lysis buffer, respectively. For each pool containing two or three cell lines in equal quantity, 100,000 single cells and an equal number of beads were used for droplet generation. After incubation at room temperature, the droplets were broken, and the beads were recovered, reverse transcribed, and amplified to generate sufficient cDNA.Single-cell RNA-seq of all cell lines, with and without hypoxia treatment, was performed with the DNBelab C4 Series Single-Cell Library Prep Set  according to the corresponding protocols50. Successful preparation of intact, isolated nuclei was confirmed through visual inspection in phase-contrast microscopy, and nucleus concentration was assessed before proceeding immediately to transposing with transposase . For each pool, 10,000 single nuclei containing four to six cell lines in equal quantity and an equal number of beads were used for droplet encapsulation. Through further procedures including pre-amplification, emulsion breakage, capture beads collection, DNA amplification, and purification, dsDNA fragments were generated.For all cell lines, single-cell ATAC-seq libraries were generated by DNBelab C Series Single-Cell ATAC Library Prep Set . Single cells (80% viability) were lysed and transposed to generate single nucleus suspensions according to the corresponding protocolsIndexed sequencing libraries were prepared according to the operating standards for MGI library preparation. The quality control of ssDNA libraries was performed using QubitTM ssDNA Assay Kit and DNA nanoballs (DNBs) were sequenced on BGISEQ500 sequencer or DIPSEQ T1 sequencer.Raw sequencing reads were processed using the PISA (v0.4). The reads were aligned to the hg38 reference genome by STAR (v.2.7.9). Aligned reads were then used to parse out the cell barcodes and unique molecular identifier (UMI). Next, PISA corr was used to correct the UMI or barcode sequence based on Hamming distance. UMI counting defined by PISA was further used with default parameters to generate a cell versus gene UMI count matrix. Cells with more than 1000 UMI and 500 genes were retained for further analysis.P-value < 0.01 and log2FC > 0.5.The Seurat (v.4.1.0) was applied to the expression matrix to perform quality control, normalization and scaling, detection of highly variable features, dimensional reduction and clustering: (1) data were normalized by the LogNormalize function with default parameters; (2) the top 2000 most variable genes of each sample were detected by FindVariableFeatures with the vst method; (3) dimensionality reduction of PCA was performed using RunPCA; (4) to cluster the cells, a KNN graph was constructed based on the Euclidean distance and modularity optimization techniques were applied using FindNeighbors and FindClusters (resolution = 0.3/0.15); (5) the top 15 PCs were used to perform UMAP to visualize the cells; (6) the differentially expressed feature genes across different clusters were detected by the FindAllMarkers function. The cluster biomarkers were defined as genes with adjusted In the process of setting up a UMAP profiling for all cell lines, filtered data in each cell line were used and the procedures of merged data were similar to those for a single cell line, except that we performed FindClusters with resolution 0.5 and RunUMAP with top 30 PCs on merged data.P < 0.01 and log2FC > 0.5. We then compared these marker genes with the highly expressed genes in external bulk RNA-seq data of the corresponding cell line from Harmonizome52 to assign the cells to the most similar cell line. For CNV-based methods, InferCNV (v1.8.1) was used for constructing the CNV profile in each cell line. We then compared the CNVs with the reported main CNVs in each cell line from cBioPortal53. To further confirm cell line identification, we also compared the single-cell expression data to bulk-seq data from the CCLE database or GEO 13. Only cell lines whose assignments were consistent among different methods were retained for further analysis.We used both expression-based and CNA-based methods to assign cells to cell lines. For the expression-based method, marker genes of unidentified cell lines were detected by the FindAllMarkers function in Seurat with adjusted 13, including A-253, HCT 116 and MCF7, with our scRNA-seq data. The analysis showed that pseudo-single cells deriving from the bulk RNA-seq dataset were distributed within the corresponding cell populations on UMAP, and differentially expressed genes across the cell lines were also consistently expressed in both bulk and single-cell datasets  Data preprocessing was applied by normalization and transforming all negative values to zero. (2) NMF was performed with the number of factor k ranging from 6 to 9, and the top 50 genes were defined as feature genes for the NMF programs. (3) Robust NMF programs that were identified with different factor k were retained. (4) Common NMF programs across cell lines (>3 cell lines) were the focus of further analysis. (5) ClusterProfiler (v.4.2.2) was applied to identify the functional characteristics for each retained common program with adjusted p-value (<0.01) and the maximum matching counts.We analyzed each cell line separately using NMF to identify programs of expression heterogeneity as described before:p-value < 0.05 were retained.Function enrichment analysis was performed using the enricher function in clusterProfiler (v4.2.2). Only functions with 22. First, we employed PCA to project the expression profiles of all cells to derive PCs, which could capture key features and reduce noise. The top 30 PCs were selected. We then define diversity as the average distance of all cells within the cell line to the centroid. To exclude the impact of outliers, we removed the extreme values that were beyond the range of 3 standard deviations from the mean.To quantify intratumoral heterogeneity, we calculate the diversity score of each cell line based on the gene expression profilesWe calculated the program cluster score of individual cells using the AddModuleScore function of the Seurat R package with the default parameter. The input data was normalized scRNA-seq data of all cell lines. Then cells with the top 5% program cluster score were selected as representative cells for each program cluster. We assessed the cell cycle phase of representative cells using the Seurat CellCycleScoring function of the Seurat R package with default parameter.54. Deconvolution was performed to remove doublets in scATAC sequencing data. MACS2 (v2.2.7.1) was applied to call peaks. The ArchR (v.1.0.2) was applied to perform quality control, dimensionality reduction, clustering, calculation of gene scores and motif enrichment: (1) those with a low number of TSS proportion (<4) and a low number of unique fragments (<1000) were filtered out; (2) the doublet score for each cell was calculated by the addDoubletScores function and the predicted doublet cells were filtered according to the filterDoublets function with the default parameters; (3) due to the sparsity of the data, Latent Semantic Indexing (LSI) was first used for dimensionality reduction, then singular value decomposition (SVD) was applied to identify the most valuable information across samples in a lower dimensional space; (4) the addClusters function was used for clustering (resolution = 0.3); (5) the addGeneScoreMat function was performed to add gene score for each gene based on the weighted distance from each peak (start or end) to the gene body. The getMarkerFeatures function was used to mark feature identification: each cell group was compared to its own background group to determine if the given cell group had significantly higher accessibility. The plotEmbedding function was applied for visualization.PISA was used to parse out the cell barcodes and UMIs based on the raw FASTQ files of scATAC-seq data. Clean reads were aligned to the hg38 reference genome by BWA (v.2.2)28 to project the chromatin accessibility profiles of all cells to derive LSIs, which could capture key features and reduce noise, for the sparsity of the data. The top 30 LSIs were selected. We then define diversity as the average distance of all cells within the cell line to the centroid. To exclude the impact of extreme values on diversity score calculation, we used mean ± 3*standard deviation to detect extreme values and removed the extreme value-related cells for diversity calculation.To quantify intratumoral heterogeneity in scATAC-seq, we calculate the diversity score of each cell line based on chromatin accessibility. First, we employed Iterative Latent Semantic Indexing (LSI)28 and ChromVAR (v.1.16.0)30, were used for identifying TFs with motif enriched in differential ATAC peaks from subgroups of a same cell line. ArchR was employed to identify potential TFs that drive differential chromatin accessibility across cell subclusters. More specifically, for ArchR, addMotifAnnotations was used to determine the motif presented in the peak set and peakAnnoEnrichment was used to identify the motifs enriched in differentially accessible peaks. Based on the motif determined by addMotifAnnotations, ChromVAR was applied to predict the enrichment of TF activity on a per-cell basis. Initially, the chromVAR::getBackgroundPeaks function was used to get background peaks based on similarity in GC-content and the number of fragments across all samples using the Mahalanobis distance, then the addDeviationsMatrix function was used to compute per-cell deviations across all of our annotated motifs.To identify key TFs responsible for subgroup-specific accessible chromatin regions, two approaches, including ArchR (v.1.0.2)We obtained cluster-specific TF of each cell line which was enriched from differential peak using the ArchR package. Then, we generated a cluster by TF matrix, which only consists of 0 and 1 according to whether the TF existed in the cluster. After hierarchical clustering, five modules were identified. For each module, we selected TFs that existed in over 50% of clusters as main TFs. Finally, these main TFs were used to perform functional enrichment analysis by clusterProfiler package using the Hallmark gene set.The FindTransferAnchors function implemented in ArchR was used to integrate scATAC-seq and scRNA-seq data by comparing the scATAC-seq gene score matrix with the scRNA-seq gene expression matrix. The anchors were used to transfer cluster-label identifiers between the two data types using the TransferData function. Each cell in the query was assigned the cluster label with the highest prediction score, and only query cells with prediction scores above 0.9 were considered to have been successfully label transferred. Each cell in the scATAC-seq data was aligned to the most similar cell from the scRNA-seq data.55 or pySCENIC (v.0.12.0)56 workflow was applied to scRNA-seq data of individual cell lines to infer TFs and their target genes. First, GENIE3 (GRNboost) was used to identify potential targets for each TF based on the co-expression regulatory network. Then, RcisTarget identified direct-binding targets (regulons) in gene networks based on motif enrichment analysis. Furthermore, AUCell was applied to determine the AUC score for these regulons in each cell by calculating the enrichment of the regulon as an area under the recovery curve (AUC) across the ranking of all genes in a particular cell, whereby genes are ranked by their expression value.The SCENICGene set variation analysis  was used to estimate the activity of a set of genes by transforming an input gene-by-sample expression data matrix into a corresponding gene-set-by-sample expression data matrix. The average normalized expression for cells was obtained. Then, GSVA scores of gene sets associated with ecDNA or hypoxia were calculated.The copy number variation of single cells was calculated with InferCNV, which analyzes gene expression intensity across the genome based on scRNA_seq data. RPE-1 and HK2 cells were selected as reference normal cells. The inferCNV analysis was performed with parameters including “denoise”, default hidden Markov model (HMM) settings, and a value of 0.1 for “cut0ff”. A heatmap was generated to illustrate the relative expression intensities across each chromosome.Hierarchical clustering was employed to detect CNV subclones within individual cell lines. We concentrated on chromosomal locations that differed considerably and reasoned that the presence of multiple CNV subclones in a single cell line would be reflected in a multimodal distribution of CNV signal for at least one chromosome arm across cells. We then classified cell lines into three types . Within the A-type cell line, cells in the same CNV subclone were only present in one transcriptomic subcluster. Within the B-type cell line, cells in the same CNV subclone were scattered across multiple transcriptomic subclusters. C-type cell lines have no CNV subclones.To further investigate the contribution of CNV to transcriptional variability, we focused on A-type cell lines and extracted genes located within CNV regions. Then the hypergeometric test was used to assess whether these CNV genes overlapped significantly with differentially expressed genes between subclusters.EcDNA amplicon in single cells was detected based on scATAC-seq data by the following steps: (1) The whole genome was divided into bins of length 100,000 bp. Then, we calculated the ‘normalized coverage’ of each bin in each cell as (read counts in each bin) / (fragment counts in the whole chromosome) × 10,000. Given that the coverage distribution shows multiple peaks in every single cell, we defined the first peak as ‘reference coverage’ for the regions of two genomic copies in the cell and calculated ‘relative coverage’ as  / (reference coverage). (2) To define the ecDNA based on the relative coverage, we investigated the relative coverage of known ecDNA, CNV and random regions in K-562 and MDA-MB-231 cell lines. We selected following ecDNA regions: chr9:130,731,514-131,280,213 and chr22: 23,280,553-23,290,953 in K-562, and chr6: 33,066,224-43,717,063 in MDA-MB-231. CNV regions were selected based on the CCLE dataset. For the ‘random region’, we randomly selected 500 regions, each of which contains one gene and ±50,000 bp region. The result showed that the relative coverage of ecDNA was apparently higher than CNV and random region  was used to analyze differential expression between normoxia and hypoxia treatmentTo match cell subclusters before and after hypoxia treatment within the individual cell line, first, Seurat’s FindIntegrationAnchors was performed to identify anchors between the two datasets. Then, IntegrateData was used to integrate cells from two conditions. Clustering was performed on all cells from the integrated data. Then we matched the cell subclusters in two conditions with cell subclusters in the integrated data. If more than 80% of cells in the subclusters in two conditions are clustered together in the integrated data, these two subclusters were matched in two conditions.58. To evaluate how different cell subclusters responded differently, differential gene expression analysis and gene set enrichment analysis were applied to the matched subclusters. To explain why different cell subclusters respond differently from the epigenetic perspective, we apply the FindTransferAnchors function in ArchR to integrate scATAC-seq data before hypoxia treatment with scRNA-seq data after hypoxia treatment and identified key TFs by enriching motifs from subgroup-specific accessible peaks.To evaluate the response of both the entire cell lines and their subpopulations to hypoxia, we employed the AddModuleScore function of Seurat to score their HALLMARK_HYPOXIA and publicly available HIF signatureP-values were false discovery rate-corrected for multiple hypotheses testing where indicated. P < 0.05 was considered statistically significant. All data are presented as mean ± SD unless otherwise indicated.All data analyses were conducted in R v.4.2.1 and Python 3.11.4. Statistical analyses are described in the respective “Methods” subsections and are briefly described in the figure legends. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting SummarySource Data