However, there is a large group of compounds for which a standard four-parametric sigmoid curve fitting may be inappropriate for estimating AC50. In the present study, 22 polyphenol-rich compounds were prioritized from the least to the most toxic based on the total area under and over the dose-response curves (AUOC) in relation to baselines. The studied compounds were ranked across three key cell indicators  in a panel of five cell lines , using a high-content screening (HCS) assay. Regarding AUOC score values, naringin (negative control) was the least toxic phenolic compound. Aronox, spent hop extract and kale leaf extract had very low cytotoxicity with regard to mitochondrial membrane potential and cell membrane integrity, as well as nuclear morphology (nuclear area). Kaempferol (positive control) exerted strong cytotoxic effects on the mitochondrial and nuclear compartments. Extracts from buckthorn bark, walnut husk and hollyhock flower were highly cytotoxic with regard to the mitochondrion and cell membrane, but not the nucleus. We propose an alternative algorithm for the screening of a large number of agents and for identifying those with adverse cellular effects at an early stage of drug discovery, using high content screening analysis. This approach should be recommended for series of compounds producing a non-sigmoidal cell response, and for agents with unknown toxicity or mechanisms of action.The toxicity of  Plant polyphenols constitute a highly heterogeneous group of compounds which play a plethora of physiological and ecological roles in plants. Some phenolic compounds produced by plant tissues, like flavonoids, are widely distributed in the plant kingdom, but others are often restricted to specific genera or even families, making them convenient biomarkers for taxonomic studies . Flavonoin vitro effects of new drug candidates to be examined on living cells. The basic cytotoxic tests include those that measure metabolic activity of the cells, plasma membrane integrity, changes in cell number and morphology, cell growth/proliferation or the mechanisms of cell death [Toxicity testing of new compounds is essential for the drug development process. There are numerous conventional cytotoxicity methods which allow the ll death . Howeverll death .An important breakthrough in this field was achieved in 1997, when high content screening (HCS) technology, also known as high content analysis (HCA), was introduced into the market as a new, effective tool for the assessment of toxicity . High coin vitro models to human liver and hence they are the most appropriate candidates for use in cytotoxicity assays. Studies using human hepatocytes have shown that HCS is more effective than past methods for identifying cytotoxic substances in humans [As metabolically competent cells, human primary hepatocytes and the hepatoma HepG2 cell line provide the closest n humans . The abin humans ,10, human humans , intestin humans , neuronan humans , lymphocn humans , human on humans , and bovn humans . As a ren humans , recent n humans , fungal n humans , nanoparn humans  and plann humans ,18.50 value and the maximum effect. Hence, the present study presents an alternative algorithm to estimate the toxicity of polyphenol-rich compounds and prioritize them. This new approach allows the assessment of the degree of cytotoxicity of an unlimited number of compounds with potential health impacts. High content screening provides a powerful and reliable tool for the fast analysis of plant compound toxicity against different target cell lines.In the present study, HCS was used to prioritize nineteen plant extracts together with three well-known and widely-described phytochemicals, based on their toxicity towards five cell lines. As natural compounds can trigger various responses, it is not always possible to determine toxicity by estimating the ACNunc .The HepG2, Caco-2, A549, and 3T3 cell lines  were obtained from Sigma Chemical Co. , while the HMEC-1 cell line was obtained from ATCC . L-glutamine was obtained from PAA . Cell culture media, such as DMEM and MCDB 131 with supplements, were obtained respectively from GIBCO  and Cytogen . MitoTracker and YO-PRO 1 were from Invitrogen . Collagen (Cultrex Rat Collagen I) was obtained from R&D Systems . All solvents of reagent or analytical grade were purchased from POCH . All the remaining reagents were purchased from Sigma Chemical Co. . Plastic disposable flasks, 96-well tissue culture plates, pipettes, and tubes were purchased from The study used seventeen freshly prepared plant extracts, two commercial plant extracts (Aronox and Omnivir R), and three natural phenolic compounds of plant origin which are regularly consumed by humans: naringin, kaempferol and resveratrol.Salix L.) bark, oak (Quercus L.) bark, buckthorn  bark, hollyhock (Alcea rosea L. var. nigra) flowers, mallow  flowers and silverweed (Potentilla anserina L.) herb were supplied by the Kawon-Hurt Nowak Sp. J. . Spent hop (Humulus lupulus L.) extract, after the hop extraction with supercritical CO2, was supplied by the Fertilizer Research Institute Puławy (Poland). Black currant (Ribes nigrum L.) pomace was supplied by Agros Nova Sp. z o. o. . Raspberry (Rubus idaeus L.) seeds were separated from the fruits of the Polka variety. Red kale (Brassica oleracea L. var. sabellica) was purchased in a retail trade in Lodz. Other raw plant materials, such as walnut (Juglans regia L.) husks, rowan (Sorbus aucuparia L.) fruits, birch (Betula verrucosa Ehrh.) inflorescences and the leaves of black currant (Ribes nigrum L.), blackberry (Rubus fructicosus L.), oak (Quercus robur L.), and birch (Betula verrucosa Ehrh.) were collected from the natural environment near Lodz.Willow (Aronia melanocarpa (Michx.) Elliott), while Omnivir R  is produced from grape seeds (Vitis vinifera L.). Naringin, kaempferol and resveratrol have been experimentally documented to possess numerous biological properties and potential therapeutic applications [https://www.sigmaaldrich.com). Resveratrol was used along with the plant extracts, whereas naringin and kaempferol served as negative and positive controls, respectively. Naringin, which exerts a lower cytotoxic effect than kaempferol against various cells [see below).Aronox  is a supplement produced from chokeberry fruits  never exceeded 0.1% and had no influence on cell growth.2 at 37°C in 75 cm2 flasks. When the cells reached at least 70% confluence, they were rinsed with phosphate-buffered saline (PBS), detached from the flask by brief exposure to trypsin-EDTA solution, and counted. The wells of 96-well microplates were inoculated with 3,500 HepG2 cells, 10,000 Caco-2 cells, 3,500 A549 cells, 5,000 HMEC-1 cells or 5,000 3T3 cells suspended in 100 μl of suitable culture medium. In the case of HepG2 and Caco-2 cells, collagen-coated plates were used. After 24 hours, the cells were incubated for another 24 hours with polyphenol-rich compounds at final concentrations of 5, 20, 30, 40, and 50 μg/ml. Accordingly, the negative control wells contained 9–86 μM naringin, while the positive control wells included 18–175 μM kaempferol. The control wells contained medium alone.The following cell lines: HepG2 (human liver carcinoma cells), Caco-2 , A549 , and 3T3 (embryonic mouse fibroblasts) were cultured in DMEM with GlutaMAX supplemented with 10% FBS, 100 U/ml penicillin and 100 μg/ml streptomycin. The HMEC-1 cell line  was cultured in MCDB 131 supplemented with 10% FBS, 100 U/ml penicillin, 100 μg/ml streptomycin, 2 mM L-glutamine, 10 ng/ml epidermal growth factor, and 1 μg/ml hydrocortisone. All cell lines were cultured in a humidified atmosphere with 5% COThe effects of polyphenol compounds on selected cellular functions, such as mitochondrial membrane potential, cell membrane integrity and nuclear area, were measured by image cytometry, in which data was analyzed simultaneously at single-cell resolution. Fluorescent dyes were combined according to their optical compatibility in a flow cytometer. Mitochondrial membrane potential and cell permeability were measured respectively with MitoTracker and YO-PRO 1. The nuclei of treated cells were stained with Hoechst 33342 dye. Where applicable, media, cells and reagents were added by a JANUS Automated Workstation ; washing steps and the addition of fluorescent dyes were performed by an ELx405 Microplate Washer .Briefly, half an hour before the end of the 24-hour incubation with the polyphenol-rich compounds, MitoTracker and YO-PRO 1 were added to the culture medium at final concentrations of 0.1 and 1 μM respectively. Then, the cells were washed twice with PBS and fixed with 2% formaldehyde for 20 minutes at RT. Next, the fixed cells were washed three times with PBS and labeled with Hoechst 33342  for 30 minutes in the dark. After washing, the 96-well microplates were measured using Cellomics Array Scan HCS Reader . Measurements were performed using three channels with the following filters: XF93-Hoechst (Channel 1) for Hoechst 33342, XF93-FITC (Channel 2) for YO-PRO 1 and XF93-TRITC (Channel 3) for MitoTracker. Nuclear fluorescence was used for automatic focusing. To measure changes in mitochondrial membrane potential, cell membrane integrity and nuclear area evoked by polyphenolic compounds, the assay protocol was configured to analyze 5000 cells per well. The nuclear area (channel 1) and fluorescence intensities (channel 2 and 3) of single cells were averaged from duplicate wells and the mean nuclear area and fluorescence intensity collected from five independent experiments were quantified. Images were acquired at 10x magnification and analyzed using Multiparameter Cytotoxicity BioApplication V3 software . Data was collected in duplicate from three cytotoxicity assays: mitochondrial membrane potential, membrane integrity and nuclear area. These tests were performed in five cell lines: HepG2, Caco-2, A549, HMEC-1, and 3T3. All three assays were performed in five independent experiments after the incubation of cells with 5–50 μg/ml plant extracts. In parallel, the cells were incubated with medium alone (baseline), 5–50 μg/ml naringin  or 5–50 μg/ml kaempferol . To evaluate the toxicity of each polyphenol-rich compound, the areas under and over the curves (AUOC) were calculated in relation to relevant baselines. Such an approach was based on the observation that some concentration-response curves were not sigmoidal and hence, it was impossible to estimate the parameters of the four-parameter non-linear logistic equation. The individual AUOC values obtained for the examined extracts and control phytochemicals were normalized using the van der Waerden score method and averaged for each assay within each cell line. The mean AUOC scores were then placed in ascending order based on the method of measurement (assay) or the cell line, thus producing a ranking of nineteen plant extracts and three phytochemicals  in ascending order of toxicity.The sensitivity of cell lines to polyphenol-rich compounds for each assay was estimated by counting the number of mean AUOC score values above 0, which was directly proportional to cell sensitivity. The total sensitivity of the cell line was calculated by summing the counts for all assays. In the same way, the sensitivity of the assays was determined for the given polyphenol-rich compounds within each of five tested cell lines.To determine the least and the most toxic polyphenol-rich compound in the assay, regardless of cell line, the cumulative AUOC scores were estimated for each polyphenol-rich compound by summing the mean AUOC scores determined for each polyphenol-rich compound within each of five tested cell lines. In addition, regardless of the assay, the cumulative AUOC scores were estimated for each polyphenol-rich compound with regard to each cell line  by summing up the mean AUOC scores for each of the three assays. The least and most toxic polyphenol-rich compounds in the study were identified based on the global AUOC score. The global AUOC score for a tested compound was estimated by summing up of the cumulative AUOC scores for each of five cell lines, calculated across all three assays. Thus, evaluated measures of cytotoxicity were referred to as cumulative or global averaged normalized AUOC values. The example of the calculation of cumulative and global AUOC scores is given in post hoc multiple comparison Fisher’s least significant difference test were used to evaluate the statistical differences between the examined extracts and the control reference compounds (naringin and kaempferol). Block ANOVA was employed to demonstrate the impact of two grouping co-variates, an assay and a cell line, on the discrimination between the tested polyphenol-rich compounds, and to compare the extents of such discrimination without and upon the adjustment. The values of Fisher-Snedecore’s statistics (FANOVA) were used for these comparisons: a higher FANOVA value indicated better discrimination power, with the polyphenol-rich compound as a grouping variable.In addition, the individual van der Waerden normal scores were also calculated based on individual AUOC values for each set of data, which were further used for inference testing. Furthermore, for each cell line, the cumulative scores  were calculated based on five individual score values. The results were tested for normality and variance homogeneity. As the particular data did not meet the assumptions of normality and/or variance homogeneity, the Box-Cox transformation was used. To discriminate between the effects of various examined polyphenol-rich compounds, one-way ANOVA and block ANOVA, followed by the n, the total number of tested polyphenol-rich compounds, ri,ref, the rank of the ith polyphenol-rich compound in the reference approach, ri,examined, the rank of the ith polyphenol-rich compound in the examined approach, ri,ref-reversed, the rank of the ith polyphenol-rich compound in the rank sequence reversed with regard to that of the reference approach, divcumulative, cumulative divergence and AUC, the area under the curve describing the function of As these approaches for estimating of cytotoxicity indices produced slightly different rankings of toxicity, a few methods were used to compare them: the AC1 Gwet’s test for two raters, the one-tailed Cuzick’s test (which provides a Wilcoxon-type test for trend across a group of two or more variables) and the method of cumulative divergence, which describes the extent to which each rank departs from the rank of the reference method; it takes into account both the fraction of divergent data and the extent of divergence for each analysed rank:In addition, two graphical methods were used: a Bland-Altman plot  and a mountain plot, both of which are considered much better than a simple correlation analysis, as even a high correlation does not necessarily mean that the two methods have to be exchangeable .Statistica v.12.5 and 13.1, StatsDirect v.3.0.182, Resampling Stats Add-in for Excel v.4, GraphPad Prism v.5.To confirm that the evaluated associations were not observed by pure chance, the bootstrap-boosted Spearman’s rho rank correlation coefficient was used to assess associations among the measured variables (1000 iterations). Fisher’s exact test was used to compare the sensitivity of the cells through the assays or the assays through the cell lines. The statistical analyses were performed using the following software packages: 50, are shown in The representative dose-response curves obtained from the measurements of mitochondrial membrane potential, with the estimated total area and ICThe modulators of mitochondrial membrane potential which had the lowest AUOC scores (the lowest cytotoxicity), were naringin, Aronox, resveratrol and the extracts from spent hop and currant leaf . NaringiKeampferol and four extracts obtained from buckthorn bark, walnut husk, hollyhock flower and silverweed herb were among the three most cytotoxic compounds with regard to mitochondrial activity. The buckthorn bark extract had the highest AUOC scores in all the examined cell lines. Kaempferol and the extracts from walnut husk and hollyhock flower demonstrated the three highest AUOC scores in three of five cell lines. The silverweed herb extract was among the three most toxic to mitochondria in Caco-2 cells .Regardless of cell line, the overall (cumulative) AUOC scores, calculated for all tested polyphenol-rich compounds in the mitochondrial membrane potential assay, are given in Regarding membrane integrity, the lowest AUOC scores against the analyzed cell lines were demonstrated by the polyphenols naringin, resveratrol, Aronox and Omnivir R, and the extracts of spent hop, currant leaf, currant pomace, raspberry seed and willow bark. No matter which cell line was used, naringin always demonstrated one of the lowest AUOC scores. Interestingly, resveratrol, which had the strongest protective effect against membrane disruption (the lowest AUOC scores) in Caco-2, A549 and HMEC-1 cells and exerted relatively low toxicity to HepG2 cell membranes, strongly enhanced membrane permeability in 3T3 fibroblasts, which was reflected by the highest AUOC score observed in 3T3 cells. Similarly, depending on cell line, Omnivir R exhibited either extremely protective (very low AUOC score in 3T3 cells) or extremely destructive effects (the highest AUOC score in HepG2 cells) on membrane permeability. The remaining six compounds were the least cytotoxic (with the lowest AUOC scores), but only with regard to single selected cell lines .The extracts from buckthorn bark, walnut husk, hollyhock flower and silverweed herb were distinguished by their high toxicity regarding membrane integrity: they each demonstrated one of the three highest AUOC scores for cell permeability assay in two to three cell lines. The extracts from oak leaf (in HepG2 cells), birch inflorescences (in Caco-2 cells) and blackberry leaf (in HMEC-1 cells) were also among the agents exerting the most adverse effects on membrane integrity: each had one of the three highest AUOC values observed in single cell lines . The oakIrrespective of the cell line used, the overall (cumulative) AUOC score values for all plant extracts calculated in the assay of membrane integrity are listed in Naringin, Omnivir R, Aronox and the extracts from hollyhock flower, spent hop, oak bark, raspberry seed, mallow flower, birch inflorescence and walnut husk were some of the least toxic compounds in nuclear area measurements. Naringin gave one of the three lowest AUOC scores in all tested cell lines apart from A549 epithelial cells. Hollyhock flower extract demonstrated low AUOC scores against Caco-2 and HMEC-1 cells. Omnivir R had one of the three lowest AUOC scores in Caco-2 and A549 cells, but at the same time, it had one of the three highest AUOC scores in HepG2 and HMEC-1 cells. Likewise, the extracts from raspberry seed and mallow flower, which had one of the three lowest AUOC scores, respectively in 3T3 and HepG2 cells, were within the three most cytotoxic agents in A549 and Caco-2 cells, respectively. Each of the remaining five polyphenols  was ranked within the three least cytotoxic polyphenols, with the lowest AUOC scores found only in one of the five cell lines examined .Resveratrol and kaempferol had one of the highest AUOC scores concerning the nuclear area: they remained among the three most toxic polyphenols in this regard . ResveraThe toxicity of all polyphenol-rich compounds expressed by the values of overall (cumulative) AUOC scores obtained in the measurements of nuclear area are presented in Naringin, Aronox, resveratrol, and the extracts from birch inflorescence, mallow flower, raspberry seed, and willow bark were the least toxic agents in HepG2 cells Figs  and 4A. The values of overall (cumulative) AUOC scores calculated for all plant polyphenol-rich compounds used in HepG2 cells, across all three assays are given in The three least toxic agents, characterized by the lowest AUOC scores in Caco-2 cells, included naringin, resveratrol, Omnivir R and the extracts from currant leaf and hollyhock flower, irrespective of assay Figs  and 4B. In the study of mitochondrial membrane potential and membrane permeability, the extracts from buckthorn bark and silverweed herb were distinguished by the highest toxicity, showing the highest AUOC scores in these methods. High AUOC scores were also obtained for kaempferol in the measurements of mitochondrial membrane potential and nuclear area. Resveratrol, as well as the extracts from walnut husk, mallow flower, and birch inflorescence were in the group of the three highest AUOC scores found in only one used assay Figs  and 4B.The values of overall (cumulative) AUOC scores calculated for all plant extracts in the experiments with Caco-2 intestinal cells, across all the applied assays, are presented in Generally, naringin, Aronox, Omnivir R, resveratrol, and the extracts from spent hop and walnut husk were among the three least toxic polyphenol-rich compounds in the A549 cell line Figs  and 4C. The overall (cumulative) AUOC score values obtained in the analysis of toxicological effects of the examined polyphenol-rich compounds using A549 epithelial cells are listed in In all assays performed in HMEC-1 cells, naringin had one of the three lowest AUOC scores. Aronox, resveratrol, as well as the extracts from spent hop, currant pomace and rowan fruit had relatively low AUOC scores in the experiments with mitochondrial potential and cell membrane permeability, but, at the same time, they exhibited high scores when nuclear area was measured. In turn, the extracts from hollyhock flower and oak bark, which were the two least toxic extracts in the measurements of nuclear area, had relatively high AUOC scores in the remaining assays Figs  and 4D. All overall (cumulative) AUOC scores calculated for the examined plant extracts against human microvascular endothelial cells, irrespective of the method used, are shown in When the 3T3 cell line was used, some of the lowest AUOC scores were achieved by naringin  and Aronox (in two out of three methods) Figs  and 4E, The overall (cumulative) AUOC score values, obtained for the plant extracts in the toxicity analysis with 3T3 mouse fibroblasts, are given in P = 0.007), and A549 and HMEC-1 (P = 0.031). When nuclear area was measured, significant correlation coefficients were also observed in two out of ten possible pair combinations, namely between 3T3 and Caco-2 (P = 0.003) and HepG2 and HMEC-1 (P = 0.003)  .In all cell lines significant correlations were found between assays: one out of three associations was found in Caco-2, A549 cells, 3T3 and HepG2 cells and three associations were seen in HMEC-1 cells .Susceptibility of cell lines to the examined polyphenol-rich compounds, estimated by the number of mean AUOC scores above 0, varied depending on the method . In the The sensitivity of methods was assessed according to the number of mean AUOC scores exceeding 0 in relation to the cell line used . The celBased on the cumulative AUOC scores calculated within each line across the three assays  the toxicity of the examined polyphenol-rich compounds were compared with that of the control phytochemicals, naringin and kaempferol.P<0.05 or lower). Spent hop extract , kale leaf extract , willow bark extract (in HepG2 and Caco-2), currant pomace extract (Caco-2 and 3T3), blackberry leaf extract (in Caco-2 and A549), birch inflorescence extract (in A549 and 3T3), rowan fruit extract (in Caco-2 and HMEC-1), Aronox (in 3T3), currant leaf extract (in Caco-2) and oak leaf extract (in Caco-2) demonstrated insignificantly lower toxicity than that of naringin.In 80% of cases , the toxicity of naringin remained significantly lower than that observed in other polyphenol-rich compounds (for various comparisons: P<0.035), in A549 cells , and in Caco-2 cells .Considering all the tested cell lines, the toxicity of kaempferol was not different from that of the extracts from walnut husk, mallow flower and silverweed herb. In four out of five cell lines, the AUOC scores of kaempferol and the extracts from oak bark, blackberry leaf, oak leaf, birch leaf, and hollyhock flower were not significantly different. The differences were found in HMEC-1 cells , whereas in Caco-2 and A549 it was significantly higher than for willow bark (P<0.03 or less), currant pomace (P<0.04 or less), currant leaf (P<0.015 or less) and rowan fruit (P<0.005 or less). In contrast, kaempferol had significantly lower toxicity than the buckthorn bark extract in Caco-2 and A549 cells (P<0.006 or less). In three out of five cell lines, kaempferol was considerably more toxic than Omnivir R , spent hop extract , and raspberry seed extract . In all cell lines, Aronox had significantly lower scores than kaempferol, except for HepG2 (P<0.006 or less). In all cell lines, the differences between the toxicity of kaempferol and the examined polyphenols remained statistically significant for naringin (P<0.025 or less) and the extracts from kale leaf (P<0.015 or less) and birch inflorescence (P<0.04 or less).In Caco-2 and HMEC-1 cells the toxicity of kaempferol was significantly higher than that of resveratrol , the second resulted in over twofold of F statistics . When adjusted at once for two grouping co-variates, the assay and the cell line, the resultant Fblock ANOVA statistics did not improve considerably . Again, the toxicity of naringin remained significantly lower than other tested polyphenol-rich compounds (P<<0.0001). Aronox appeared significantly less toxic than nearly all other polyphenol-rich compounds but the extracts from birch inflorescence, spent hop and kale leaf (P<0.05 or less). Kaempferol and buckthorn bark were the most toxic compounds with regard to all the remaining polyphenol-rich compounds.When we applied block analysis to adjust for the effects of the cell line and the assay, we demonstrated that while the first showed merely a negligible impact , spent hop extract (P<0.0001), kale leaf extract (P<0.0001), birch inflorescence extract (P<0.0001), currant pomace extract (P<0.0001) and rowan fruit extract (P<0.0001). The differences between the most toxic compounds and the positive control (kaempferol) were as follows: buckthorn bark extract (NS), hollyhock flower extract (P<0.05), walnut husk extract (P<0.05), silverweed herb extract (P<0.02) and blackberry leaf extract (P<0.01).The least toxic compounds, based on a double adjustment for both the assay and the cell line, were , the extracts from spent hop, kale leaf, birch inflorescence and currant pomace. The most toxic were the extract from buckthorn bark, (kaempferol), the extracts from hollyhock flower, walnut husk, silverweed herb and blackberry leaf. Overall, the differences between the least toxic compounds and the negative control (naringin), evaluated by the 1) global normalized AUOC scores , (2) non-adjusted averaged individual normal scores of AUOC , and (3) averaged individual normal scores of AUOC adjusted for both the assay and the cell line . All employed statistical and graphical techniques clearly indicated considerable agreement between three approaches (P = 0.50 by Cuzick’s test). The agreement between the approaches, given by AC1 Gwet’s method, was 42.46% and 42.86% for comparing ‘global AUOC’ with ‘non-adjusted normalized AUOC’ and ‘global AUOC’ with ‘double-adjusted normalized AUOC’, respectively, and 71.43% for comparing ‘non-adjusted’ with ‘double-adjusted normalized AUOC’. Likewise, the cumulative divergence values for these comparisons were 9.95%, 9.95% and 6.33%, clearly pointing to better agreement between non-adjusted and double-adjusted normalized AUOC than between global AUOC and normalized averaged individual AUOC. Also, the graphical methods, i.e. the Bland-Altman plots , as well as three well-known phytochemicals regularly consumed by humans, like resveratrol, naringin (negative control), and kaempferol (positive control).The present study uses the HCS assay to prioritize twenty-two polyphenol-rich compounds through five established cell lines, according to their The majority of the examined compounds (the exception can be naringin) demonstrated different toxicity profiles between the methods. Some polyphenol-rich compounds that were assessed as very safe  or quite safe  with regard to their cytotoxic effects on mitochondrial membrane potential and plasma membrane integrity were found to be harmful in the analysis of nuclear area . On the other hand, another group of extracts, like those from buckthorn bark, walnut husk, hollyhock flower, oak bark, oak leaf, silverweed herb, and blackberry leaf, were highly cytotoxic with regard to mitochondrial membrane potential and cell membrane permeability, but quite inert in mediating nuclear morphology abnormalities. This observation was most pronounced when the cumulative effect of polyphenol-rich compound action was analyzed  . Furthervs. polyphenol concentration), as an approach useful to assess toxicity of plant compounds. This approach allows cell response to polyphenol-rich compounds tested to be studied at only a few concentrations (five doses within the range of 5–50 μg/ml). Under such conditions, the AUOC was always possible to be calculated . For the purposes of the present study, this resveratrol data was used to compare the calculated values of IC50 with those of the AUOC; a significant negative correlation was found between the numerical values of IC50 and total areas (AUOC)   , indicat50 value [50. Although the problems with interpreting data from toxicological studies are not so rare, the measures of toxicity other than AC50 are seldom reported. Recently, Martin et al. proposed the use of the so called Z-factor, the measure of a statistical effect size in a high-content screening procedure for the identification of cytotoxic compounds based on cell morphology and cell proliferation markers [50 value and lowest efficacious concentration (LEC) [50 values is inappropriate in a number of compounds, because they can exert markedly differentiated effects at higher concentrations, the initial cell response is small, or the response consists of multiple phases. In the majority of these cases, the authors attempted to establish a toxicity profile of a given compound in an arbitrary manner, either using other IC values or analyzing only a part of the curve [Taken together, our results may indicate that our approach for rank ordering compound toxicity has great potential, and suggest that AUOC score may be used as an alternative measure of toxicity to the standard AC50 value . The pre markers . Furtheron (LEC) . It shouhe curve . For comhe curve . Also, ahe curve –36. Beinhe curve ,37,38. Fin vivo primary mechanism of toxicity in humans that can account for liver, kidney and neuronal effects [in vivo mechanically-induced plasma membrane disruption are cardiac and skeletal muscle cells, epithelial cells, and endothelial cells [High content screening technology enabled the simultaneous measurement of a few cell health indicators associated with mitochondrial function, plasma membrane integrity and nuclear morphology. Mitochondrial dysfunction is a well-documented  effects . Changesal cells . By analal cells ,42.in vitro toxicity assays: it has been validated for multiple compounds and approved by ICCVAM for Neutral Red Uptake cytotoxicity assay (https://ntp.niehs.nih.gov/iccvam/methods/acutetox/invidocs/phiiiprot/3t3phiii.pdf).To investigate the adverse biological effects of polyphenol-rich compounds, cell lines were chosen which are well characterized, easy to culture and have been already preferably used in published cytotoxicity measurements. Such selection criteria have been already proposed in the literature . In addivia oral route. These cell lines were supplemented by two additional cell lines, A549 and HMEC-1, which are derived respectively from lung and skin, and represent alternative routes of exposure to xenobiotics. However, it has to be stressed that polyphenol-rich compounds need to be screened primarily through metabolically competent cells, as their chemically-reactive metabolites, produced in the course of metabolic processes, account for > 50% human liver injury [Cell lines representing different phenotypes were chosen: HepG2, representing a phenotype of hepatocytes, and Caco-2, representing the intestinal epithelium, which is frequently employed in ADME-Tox studies and is directly related to organs that are critically important for the response to xenobiotics entering a body r injury . Apart fr injury .in vivo drug toxicity by high-content analysis [In the study of mitochondrial membrane potential, the degree of toxicity of each tested polyphenol-rich compound was found not to differ between the examined cell lines: significant positive correlations were observed between the AUOC scores of various cell lines in ten out of ten possible cell pair combinations . In contanalysis .vice versa. Of note, the significant associations between assays or between cell lines were not compliant with the outcomes of tests comparing ‘sensitivities’ of assays or ‘sensitivities’ of cell lines, and such discrepancy most likely originates from the relatively high number of variables and small body of analyzed data. It is also worth emphasizing that both early and late cytotoxicity were evaluated in this study. Whereas the alterations of mitochondrial function occur early after cellular injury, changes in nuclear morphology and a loss of membrane integrity are recognized as biomarkers of acute or late-stage cell toxicity [The analysis of cumulative AUOC scores calculated for three assays together allowed an assessment of the toxicity of polyphenol-rich compounds with regard to cell type . Naringitoxicity . Therefotoxicity . Second,Overall, we propose an alternative approach for evaluating and hierarchically clustering various compounds according to their toxicity potential screened with the use of high content screening analysis. Such an algorithm should be recommended for a large number of compounds which induce a non-sigmoidal response, or agents with unknown toxicity/mechanism of action, particularly at an early stage of drug discovery. The suggested algorithm provides a rapid tool for identification of compounds with adverse effects, where the control agents are important factors in a successful and reliable analysis. Further testing of this approach with series of drugs with already known and differentiated toxicities is required to further strengthen the validation of the presented methodology.S1 Fig50.(A) untransformed data for total area calculation (B) data after semi-log transformation for calculation of IC(PDF)Click here for additional data file.S2 FigPlots representing the comparisons between: (A) global averaged normalized AOUC and individual normal scores averaged for extracts non-adjusted for assay and cell line, (B) global averaged normalized AOUC and averaged individual normal scores adjusted for both assay and cell line and (C) averaged individual normal scores non-adjusted for assay and cell line vs. averaged individual normal scores adjusted for both assay and cell line. For better legibility all variables were normalized to the same scale range of . The limits for agreement between the used algorithms, marked by dashed red lines, estimated as the averaged difference between the compared indices ± 1.96* standard deviation of differences.(PDF)Click here for additional data file.S3 Figvs. non-adjusted (blue line) or adjusted averaged individual normal scores (red line), (B) comparison of non-adjusted vs. adjusted averaged individual normal scores. The plot was created by evaluating the percentiles for ascending differences between the indices calculated for the compared algorithms. To generate the plot the numerical values of (100—percentile order) were assigned to the percentiles of the order above 50.Individual normal scores averaged for tested polyphenolic extracts either non-adjusted or adjusted for assay and cell line were compared with the reference algorithm: global averaged normalized AOUC. (A) comparison of global averaged normalized AOUC (PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table50 values for individual repeats are given as mean . The IC(PDF)Click here for additional data file.


Drosophila melanogaster cell lines represent distinct tissue types and exhibit cell-type specific gene expression profiles. They thus could provide a useful tool for investigating cell-type specific nucleosome organization of an organism’s genome. To evaluate this possibility, we compared genome-wide nucleosome positioning and occupancy in five different Drosophila tissue-specific cell lines, and in reconstituted chromatin, and then tested for correlations between nucleosome positioning, transcription factor binding motifs, and gene expression. Nucleosomes in all cell lines were positioned in accordance with previously known DNA-nucleosome interactions, with helically repeating A/T di-nucleotide pairs arranged within nucleosomal DNAs and AT-rich pentamers generally excluded from nucleosomal DNA. Nucleosome organization in all cell lines differed markedly from in vitro reconstituted chromatin, with highly expressed genes showing strong nucleosome organization around transcriptional start sites. Importantly, comparative analysis identified genomic regions that exhibited cell line-specific nucleosome enrichment or depletion. Further analysis of these regions identified 91 out of 16,384 possible heptamer sequences that showed differential nucleosomal occupation between cell lines, and 49 of the heptamers matched one or more known transcription factor binding sites. These results demonstrate that there is differential nucleosome positioning between these Drosophila cell lines and therefore identify a system that could be used to investigate the functional significance of differential nucleosomal positioning in cell type specification.Changes in the distribution of nucleosomes along the genome influence chromatin structure and impact gene expression by modulating the accessibility of DNA to transcriptional machinery. However, the role of genome-wide nucleosome positioning in gene expression and in maintaining differentiated cell states remains poorly understood.  Over 75% of eukaryotic DNA within a nucleus is compacted into chromatin fibers that contain long repeating arrays of nucleosomes. In each nucleosome unit, a segment of DNA is wrapped around a histone protein core . An essePositioning of nucleosomes is directed by two major factors: intrinsic DNA-histone interactions, and positioning of nucleosomes by remodeling complexes –22. For Drosophila L3 imaginal disc cell lines: leg, eye, antennal and haltere [Previously, cell differentiation was considered to be driven solely by controlled expression of transcription factors (TFs) –38. Howe haltere .Drosophila melanogaster is an attractive model to use because the relatively small genome of the organism allows for reasonable coverage of mapped reads during parallel sequencing. The various cultured Drosophila cell lines are extensively characterized and therefore provide a powerful model for understanding cell-type specification. Several studies have characterized the unique differential expression profiles for many of the available Drosophila cell lines [ll lines , 45. Whill lines , 34.Drosophila cell lines. We report in vivo nucleosome positioning maps for the standard S2 cell line that is of embryonic hemocyte origin, and for the antennal, eye, haltere and leg cell lines that are derived from imaginal discs. We characterized patterns of differential coverage by examining nucleosome occupancy and positioning throughout the genome. By comparing nucleosome maps to each other and to the map from in vitro reconstituted Drosophila chromatin, we uncovered differences from intrinsic nucleosome organization that correlate with possible binding sites for in vivo factors that may direct cell type specification.In this work, we compare nucleosome positioning over key genomic regions and DNA sequences in distinct D. melanogaster cell lines were obtained from the Drosophila Genomics Resource Center—DGRC (https://dgrc.cgb.indiana.edu/). The five cell lines used in this study were: S2 (late embryonic cell line); Cme-L1 ; ML-DmD11 ; ML-DmD20 ; and the ML-DmD17 . Cells were cultured in the DGRC recommended culture media at 24°C. S2 cells were cultured in Schneider's Drosophila medium (Invitrogen) supplemented with 10% FCS (Hyclone); Cme-L1 cells were maintained in M3 (Sigma-Aldrich), supplemented with 2% FCS, 5 μg/ml insulin (Sigma), and 2.5% fly extract, while ML-DmD11, ML-DmD20 and ML-DM17 cells were maintained in M3+BPYE supplemented with 10% FCS and 10 μg/ml insulin. For the experiments, cells were replated on 60 mm plastic dishes at a density of 0.5–1×106 cells/ml, and allowed to proliferate for 3–4 days until they became they reached ~85% confluency. Cell harvest required only gentle agitation to dislodge the semi-adherent cells, which were pelleted by centrifugation at 1200 x g then washed three times with PBS.The following 2; 0.5% NP-40; 0.15 mM spermine; 0.5 mM spermidine). PMSF and BZA (Sigma) were added to final concentrations of 1 mM and 0.4 mM respectively. Cells were lysed by a 5-minute incubation on ice, the nuclei were pelleted and then washed once with PBS. After gentle resuspension in MNase digestion buffer , chromatin was digested with Micrococcal nuclease (Sigma N3755) for 10 minutes at room temperature. Digestion was stopped with MNase stop solution  and 5 M NaCl (added to a ratio of 1:5 buffer volume). MNase-digested DNA was isolated from histones and other DNA binding proteins by phenol/chloroform extraction and ethanol precipitation. 10 mg/mL RNAse was added and the purified DNA was incubated for 30 minutes at 37°C to remove any residual RNA.~100 million cells were collected from healthy cell cultures, pelleted and washed with ice-cold PBS. The cell lines were cultured in parallel in identical conditions and digested samples were combined after bar coding, but before sequencing. Cells were resuspended in NP-40 lysis buffer . Nucleosomal DNA bands were visualized by UV illumination and mononucleosomal DNA (mnDNA) corresponding in size to 150 bp was excised from the gel. mnDNA was recovered by a mild “crush and soak” protocol . Brieflyin vitro reconstitutions, chicken erythrocytes were prepared as described previously [To obtain histone octamers for eviously . BrieflyDrosophila dm3 reference genome using the ABI BioScope™ software (Applied Biosystems). SOLiD sequencing generated 4 million to 12 million uniquely mapped reads for each sample. From the aligned reads, only unique, paired DNA fragments sized between 101 and 191 bp were retained for use in the analysis dataset. Nucleosome fragment length was estimated as the distance between paired reads and the midpoint of each mapped fragment was considered the nucleosome midpoint. To generate AA/AT/TA/TT and CC/CG/GC/GG frequency plots, we extracted dinucleotide counts surrounding every nucleosome midpoint. We then computed the frequency of d:A/d:T and d:C/d:G dinucleotides at each distance from the nucleosome midpoint. One sample from each cell line or in vitro chromatin reconstitution was prepared and sequenced. The samples were processed in parallel, and a high degree of similarity in nucleosome occupancy was observed between cell lines  and observed in nucleosome profiles shown in For sequencing, nucleosomal DNA fragments were gel-extracted, end-repaired  and ligated to adaptors using the recommended ABI SOLiD Fragment Library reagents and protocol (Applied Biosystems PN 4464412). The DNA fragments were amplified by PCR for 10 cycles or less prior to ABI SOLiD sequencing. PCR fragments were purified and loaded on a SoLiD flow cell for cluster generation. Nucleosomal reads were separated into separate library files based on their barcodes, and mapped to the 10 FPKM value as the expression measurement.The gene sets and annotations used in these analyses were from FlyBase BDGP Release 5. RNA-seq reads from S2 cells were obtained from modENCODE . The numDrosophila cell line in our study . Genes with medium and high expression show a well-positioned nucleosome configuration around the TSS . In contin vitro using purified genomic DNA from Drosophila S2 cells and purified histone octamers from chicken erythrocytes [in vitro nucleosome maps as previously described, capturing over 25 million unique read pairs [in vitro reconstituted chromatin than in the in vivo chromatin  being defined as promoters. The number of nucleosome midpoints within each region were counted and normalized against the total number of sequenced aligned reads from each experiment to determine nucleosome enrichment in that region . NucleosGiven that the inherent nucleosome organization is broadly similar in each cell line, we hypothesized that changes in chromatin structure associated with cell-type specific expression occur locally, within smaller regulatory regions. To investigate this possibility, we divided the genome into non-overlapping 200 bp regions and compared the nucleosome coverage of each base pair in each cell line to the coverage in the S2 cells. S2 cells are derived from embryonic hemocyte (macrophage-like) cells, and thus provide a comparison for the four imaginal disc cell lines derived from later stage larval epithelial tissue. Although nucleosome occupancy within the different cell lines is generally similar to that of the S2 cell line, a subset of regions are markedly different , with maDrosophila genome [To further resolve small differences between the four tissue-specific cell lines, we examined the nucleosomal occupancy over short kmers for each cell type. We used 7 bp kmers (i.e. heptamers) for analysis, reasoning that some differences between cell lines are likely to be at cell-type specific TF binding sites (TFBSs). TFBSs are short degenerate sequences, generally 7–11 bp, that occur throughout the genome , 65. Thea genome , but we in vitro reconstituted chromatin. In total we performed 15 pairwise comparisons, and for each comparison, we considered the 20 kmers with the largest absolute residuals from the regression line to be “outliers”. In total, there were 91 unique outlier heptamers that had the greatest differences in at least one pairwise comparison , the mean of all 4 cell lines, and the mparison .in vitro reconstituted chromatin . Notably, in some cases, the region of differential nucleosome occupancy was tightly centered on the heptamer sequences but phasing of nucleosomes extended to broader genomic regions , we compared the differential heptamers to those in databases of known Drosophila transcription factor binding sites. Of the 91 differentially occupied heptamers identified, 49 matched one or more known Drosophila TFBS consensus sequences  is a TF that is needed in appendage formation [We next asked whether any of the heptamers with differential occupancy between the cell lines match known regulatory sequences. Using the Tomtom motif comparison tool of the MEME suite of tools , a transcription factor required for appendage development [The possibility that these 42 heptamers could be functional binding sites for transcription factors or chromatin remodeling factors is supported by our finding that 49 of the differentially occupied heptamers corresponded to binding sites for known transcription factors. A particularly notable example is Bab1 Click here for additional data file.S2 Figin vitro reconstituted chromatin are indicated with black boxes, while example peaks that are strong in all cell lines but reduced in in vitro chromatin, or vice versa, are shown in red boxes and marked with an asterisk.MNase midpoint density profiles, smoothed using a 30 bp sliding window, along randomly chosen genomic regions demonstrate that the nucleosome arrays from each cell line (top four tracks) correspond well with one another. The in vitro nucleosome arrays from this study  correspond least well with nucleosome arrays generated from cells but maintain similar spacing and many of the strong and intermediate peaks. Example peaks that are similar between the cell lines and (PDF)Click here for additional data file.


Rickettsia raoultii using generally available laboratory equipment and Rhipicephalus microplus cells, further demonstrating the usefulness of continuous tick cell lines. R. raoultii is one of the causative agents of tick-borne lymphadenopathy (TIBOLA) and is, together with its vector Dermacentor reticulatus, emerging in novel regions of Europe, giving rise to an increased threat to general public health.Continuous culture of tick cell lines has proven a valuable asset in isolating and propagating several different vector-borne pathogens, making it possible to study these microorganisms under laboratory conditions and develop serological tests to benefit public health. We describe a method for effective, cost- and labor-efficient isolation and propagation of Dermacentor reticulatus ticks were collected in the Donau-Auen (Lobau) national park in Vienna, Austria. The hemolymph of ten collected ticks was screened by PCR-reverse line blot for the presence of rickettsial DNA. A single tick tested positive for R. raoultii DNA and was used to infect Rhipicephalus microplus BME/CTVM2 cells.R. raoultii-infected tick, we observed intracellular bacteria in the cultured cells. On the basis of microscopy we suspected that the intracellular bacteria were a species of Rickettsia; this was confirmed by several PCRs targeting different genes. Subsequent sequencing showed 99–100 % identity with R. raoultii. Cryopreservation and resuscitation of R. raoultii was successful. After 28 days identical intracellular bacteria were microscopically observed.Sixty-five days after infection of the tick-cell line with an extract from a R. raoultii was successfully isolated and propagated from D. reticulatus ticks using R. microplus BME/CTVM2 cells. The isolated strain shows significant molecular variation compared to currently known sequences. Furthermore we show for the first time the successful cryopreservation and resuscitation of R. raoultii. Dermacentor reticulatus, also known as the ornate dog tick, is a dark-colored tick with a light-colored pattern on its scutum , which is also known as Dermacentor-borne necrosis erythema and lymphadenopathy (DEBONEL) in humans , and PCR-grade water . A C1000 Touch Thermal Cycler  was used for the PCR reactions, starting with a touch-down protocol in which the annealing temperature was lowered by 1° per cycle for the initial 10 cycles  followed by 45 cycles with a fixed annealing temperature . The initial touch-down cycles were used to increase the specificity of the PCR reactions. All PCR products were used in the RLB assay as described previously [Rickettsial DNA was detected using PCR followed by reverse line blot (RLB) hybridization targeting the escribed , 23. Ricere used . Brieflyeviously  and wereR. raoultii DNA. The surface of the tick was disinfected using thiomersal, benzalkonium chloride and 70 % ethanol solutions followed by 2 rinses in sterile demineralized water, as described by Bell-Sakyi [A single tick from the ten that were tested gave a positive result for presence of ll-Sakyi . After dll-Sakyi  using a R. microplus [The tick cell line BME/CTVM2  originating from icroplus  was mainicroplus , at 28 °g for 5 min. The obtained smears were stained with Giménez stain [Rickettsia-like intracellular bacteria.After the weekly change of medium, tick cells were resuspended using a glass Pasteur pipette. From this suspension ≈ 100 μl was used to prepare cytocentrifuge smears using a Basic Cytology Rotor in a Rotofix 32A centrifuge  with a single cytology funnel . Centrifugation conditions were 700× ez stain . StainedRickettsia-like intracellular bacteria, DNA was extracted from 200 μl cell suspension using the NucleoSpin Tissue kit  according to the manufacturer’s guidelines. The presence and identity of suspected rickettsial bacteria was confirmed using several PCRs targeting different genes and regions: gltA, ompA, ompB, sca4, 16S ribosomal RNA, and the 23S-5S ribosomal RNA intergenic spacer (see Table http://www.ncbi.nlm.nih.gov/genbank/) under accession numbers KX500092–KX500097.After observing R. raoultii strain, a new strain-specific probe was designed (R. raoultii strain Jongejan probe: 5′-TCA ACT AAT AAA TTT GCT GAG TA-3′) to be used in a PCR-RLB assay targeting the 23S-5S ribosomal RNA intergenic spacer [To detect the isolated c spacer . The proc spacer , togetheCombining and modifying previously published methods , 31, cryD. reticulatus with the tick cell line R. microplus BME/CTVM2, and after 65 days we observed intracellular bacteria in the cultured cells  identity with R. raoultii sequences available in the NCBI GenBank database (Table R. raoultii strain Khabarovsk (CP010969.1)  Fig. . A strai.1) Fig. . FurtherR. raoultii strain Jongejan.Viability of the newly isolated strain was assessed by (sub)inoculating either 100 μl cell suspension or 100 μl medium (taken without disturbing the cell layer). Both methods successfully infected BME/CTVM2 cells: identical intracellular bacteria were present in cytocentrifuge smears of these newly infected cultures within 14 to 21 days. We also used a PCR-RLB assay targeting the 23S-5S ribosomal RNA intergenic spacer and the newly designed strain-specific probe to confirm the successful subculture of R. raoultii strain Jongejan proved successful as both, 80 °C and in liquid nitrogen stored cell suspensions successfully infected BME/CTVM2 cells; after 28 days identical intracellular bacteria were observed while analyzing the cytocentrifuge smears.Cryopreservation and resuscitation of R. raoultii in VERO and L929 mammalian cell line cultures [R. raoultii, e.g. embryo-derived culture cell lines originating from R. sanguineus (RLM-RSE) [D. reticulatus [D. reticulatus cell lines, propagation of R. raoultii was successful in a number of tick cell lines originating from D. albipictus, D. nitens, and R. microplus [R. microplus cell line as used in our study. It is possible that the initial cultivation period of 14–16 months aided the adjustment of R. raoultii towards the laboratory cultivation conditions, making subcultivations more likely to succeed. We show for the first time the capability of the R. microplus cell line BME/CTVM2 as initial isolation host for R. raoultii. Successful cryopreservation and resuscitation of R. raoultii has, to the best of our knowledge, not been documented before.Previously, the centrifugation-shell vial technique described by Marrero & Raoult  was usedcultures . HoweverRLM-RSE)  or embryiculatus . After aicroplus , using tR. raoultii strain Jongejan showed differences within the 23S-5S ribosomal RNA intergenic spacer and the sca4 gene when compared with the full genome sequence of strain Khabarovsk (CP010969.1). The 23S-5S spacer is non-coding, therefore selective pressure is low and mutations within the spacer can occur frequently. Due to the non-coding nature of the spacer, the deletion most likely does not influence the behavior or the pathogenicity of the bacteria. The sca4 gene, together with the ompA and ompB genes, are part of a group coding the cell surface antigen proteins (immunodominant surface protein antigens) forming the outer cell surface of Rickettsia bacteria [R. raoultii strain Marne (DQ365807.1); Strain Jongejan varies to strain Marne within the 16 s rRNA and ompB genes by 1 % (identity 99 %). The specific phenotypical change due to the sca4 mutation needs to be further studied in order to assess the bacterial behavior and possible change to the pathogenicity of this strain. Considering the distinct molecular characteristics of strain Jongejan, we designed a specific probe for its detection in field specimens, thus enabling screening on a geographical basis and determination of the distribution of this strain.The sequences obtained from the newly named bacteria . In patibacteria , and thebacteria . FurtherRickettsia species from clinical samples. Our results also demonstrate that tick cell lines might be useful in culturing tick-borne pathogens that are currently uncultivable, such as Candidatus Neoehrlichia mikurensis. Moreover, geographical screening of D. marginatus and D. reticulatus would be beneficial in investigation of the distribution of different R. raoultii strains, both locally in Austria and in larger areas within Eurasia.Together with previous work , 27, 34,R. raoultii has been successfully isolated and propagated. Genetic analysis of the isolated strain revealed significant differences compared to the currently available data in the NCBI GenBank database. These molecular variations might lead to differences in phenotype and the behavior of the microorganism, potentially increasing the risk for human health. Furthermore our study shows for the first time the successful cryopreservation and resuscitation of R. raoultii.During our study


TET1, TET2, TET3, SMUG1, and TDG genes were also assayed by RT-qPCR. Our results show a global erasure of 5-hydroxymethyl-2′-deoxycytidine and 5-carboxy-2′-deoxycytidine in DNA of cultured cells compared with DNA from primary malignant tissue. Moreover, malignant cells in culture have a quite different DNA epigenetic profile than cultured normal cells, and different types of malignant cells display different and characteristic profiles of DNA epigenetic marks. Similar analyses of a broader spectrum of epigenetic modifications, not restricted to 5-methyl-2′-deoxycytidine, could lead to better understanding of the mechanism(s) responsible for emergence of different types of cancer cells.Active demethylation of 5-methylcytosine moiety in DNA occurs by its sequential oxidation to 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxycytosine, catalysed by enzymes of the Ten-Eleven Translocation family proteins . Here we analyzed for the first time all the intermediate products of DNA demethylation pathway in the form of deoxynucleosides -2′-deoxycytidine, 5-formyl-2′-deoxycytidine and 5-carboxy-2′-deoxycytidine as well as 5-(hydroxymethyl)-2′-deoxyuridine) using automated isotope-dilution online two-dimensional ultra-performance liquid chromatography with tandem mass spectrometry. DNA was isolated from human malignant cell lines of colon adenocarcinoma (HCT 116), melanoma (Me45), myelogenous leukemia bone marrow blasts (K562), EBV-positive Burkitt’s lymphoma lymphoblasts (Raji), EBV-negative Burkitt’s lymphoma lymphoblasts , as well as normal neonatal dermal fibroblasts (NHDF-Neo). The expression levels of  DNA methylation is involved in diverse biological processes including gene expression, which in turn has a profound impact on cellular identity and organismal fate . It was Tissue type is a major modifier of the 5-hmdC level and there is a substantial, about 10-fold, reduction of this level in cell lines when compared with their tissue of origin , 5. MoreSince downstream steps of active demethylation processes resulting in products of iterative oxidation of  may be at least partially responsible for the loss of 5-hmdC, in this study we investigated all described to date TETs products in the form of 2′-deoxynucleosides generated by enzymatic hydrolysis of DNA samples, i.e. 5-mdC, 5-hmdC, 5-fdC and 5-cadC, as well as 5-hmdU. We used several malignant human cell lines: colon adenocarcinoma (HCT 116), malignant melanoma (Me45), myelogenous leukemia bone marrow blasts (K562), Burkitt’s lymphoma EBV-positive lymphoblasts (Raji), and Burkitt’s lymphoma EBV-negative lymphoblasts , as well as normal human cells, namely neonatal dermal fibroblasts (NHDF-Neo). In addition, we compared the pattern of demethylation products in tissue samples of human colorectal cancer (CRC) and cultured HCT 116 cells, as well as in leukocytes from acute myelogenous leukemia (AML) patients and K562 cell line.TETs 1–3, SMUG1, TDG) was also determined.DNA modifications were assayed using a recently-developed, isotope-dilution online two-dimensional ultra-performance liquid chromatography with tandem mass spectrometry (2D-UPLC-MS/MS) . The lev2 using DMEM/F-12 medium  for HCT 116, Me45 and NHDF-Neo cells and RPMI (Sigma-Aldrich) for K562, Raji, Ca46 and ST486 cells, supplemented with 10% fetal bovine serum (Eurx) and penicillin-streptomycin (Sigma-Aldrich). Cell doubling times were determined by counting cells in a hemocytometer. Cells were harvested in the exponential phase of growth for DNA isolation.NHDF-Neo, HCT 116, K562, Raji, BL (Ca46 and ST486) cells were obtained from ATCC. The Me45 cell line was derived in 1997 from a lymph node metastasis of skin melanoma of a 35 year-old male at the Radiobiology Department of the Centre of Oncology in Gliwice . Solutions of RNase A (Sigma-Aldrich) and RNase T1 (Sigma-Aldrich) were added to the final concentrations of 0.5 mg/mL and 100 U/mL, respectively, and vortexed again. Sodium dodecyl sulfate (Sigma-Aldrich) solution was added to the final concentration of 0.5% m/v and the mixture was gently mixed using a polypropylene Pasteur pipette. After 30 min incubation at 37°C, proteinase K (Sigma-Aldrich) was added to the final concentration of 1 mg/mL. The mixture was incubated at 37°C for 1 h, cooled to 4°C, transferred to a centrifuge tube with 1 volume of phenol/chloroform/isoamyl alcohol mixture [25:24:1 (v/v/v)] (Sigma-Aldrich) and vortexed vigorously. The aqueous phase separated by centrifugation was vortexed with a 1 volume of chloroform/isoamyl alcohol mixture [24:1 (v/v)] (J.T.Baker) and centrifuged again. Supernatant containing DNA was treated with two volumes of cold absolute ethanol in order to precipitate high molecular weight DNA. The precipitate was removed with a plastic spatula and washed with 70% (v/v) ethanol. DNA  was dissolved in 100 μL of 100 mM ammonium acetate (Sigma-Aldrich) containing 0.2 mM ZnCl2 , pH 4.3. The dissolved DNA sample was mixed with the solutions containing 1 U of nuclease P1 (Sigma-Aldrich), 10 μg tetrahydrouridine  as cytidine deaminase inhibitor, and incubated at 37°C for 1 h. Subsequently, 12 μL of 10% (v/v) NH4OH (J.T.Baker) and 1.3 U of alkaline phosphatase (Sigma-Aldrich) were added to the mixture followed by 1 h incubation at 37°C. Finally, DNA hydrolysate was ultrafiltered using cut-off 10 kDa multi-well plate and concentrated by evaporation in SpeedVac down to 20 μL system for approximately 40 min.Preparation of cell pellet from of CRC tissues was described previously . The pel3]-5-hmdC, -5-fdC, -5-cadC, and -5-hmdU. Chromatographic separation was performed with a Waters Acquity 2D-UPLC system with photo-diode array detector for the first dimension chromatography (used for quantification of unmodified deoxynucleosides and 5-mdC) and Xevo TQ-S tandem quadrupole mass spectrometer for second dimension chromatography. At-column-dilution technique was used between first and second dimension for improving retention at trap/transfer column. The columns used were: a Phenomenex Kinetex C-18 column  at the first dimension, a Waters X-select C18 CSH  at the second dimension and Waters X-select C18 CSH  as trap/transfer column. Chromatographic system operated in heart-cutting mode, indicating that selected parts of effluent from the first dimension were directed to trap/transfer column via 6-port valve switching, which served as “injector” for the second dimension chromatography system. The flow rate at the first dimension was 0.25 mL/min and the injection volume was 0.5–2 μL. The separation was performed with a gradient elution for 10 min using a mobile phase 0.1% acetate (A) and acetonitrile (B) . Flow rate at the second dimension was 0.35 mL/min The separation was performed with a gradient elution for 10 min using a mobile phase 0.01% acetate (A) and methanol (B) . All samples were analyzed in three to five technical replicates of which technical mean was used for further calculation. Mass spectrometric detection was performed using the Waters Xevo TQ-S tandem quadrupole mass spectrometer, equipped with an electrospray ionization source. Collision-induced dissociation was obtained using argon 6.0 at 3 x 10−6 bar pressure as the collision gas. Transition patterns for all the analyzed compounds, as well as specific detector settings were determined using the MassLynx 4.1 Intelli-Start feature.The analyses were performed using a method described earlier . BrieflyRNA was extracted with Total RNA mini kits (A&A Biotechnology) and reverse transcribed using NG dART kits (Eurx) using oligo(dT). RT-qPCR was performed on a BioRad CFX 96 System using the Real-Time 2x PCR Master Mix SYBR A kit (A&A Biotechnology). Reaction mixtures were incubated for 2 min at 50°C, 4 min at 95°C, followed by 40 cycles of 45 sec at 95°C, and 30 sec at 55–60°C (depending on the primer set). Levels of expression were estimated by the ΔCT method , 9. Sequwww.statsoft.com). Results are expressed as means±SD for variables with normal distribution for all cell lines, and as median with interquartile range for variables with nonparametric distribution.Statistical analysis was performed using STATISTICA version 12.5 –NIR test was performed to assess the differences in the levels of genes expression. The levels of transcripts of the genes 3 deoxynucleosides (dN). A significantly lower levels of 5-mdC were observed in K562 and Me45 cells with the mean values of 2.83/103dN±0.2261 and 7.17/103dN±1.1659, respectively. Also the 5-mdC level showed statistically difference in DNA from HCT 116 cell line (8.59/103dN±0.9316) when compared to K562, Me45, Raji  cell lines, as well as in DNA from Me45 compared to Raji and BL (8.60/103dN±0.5138) cell lines. Interestingly, in Burkitt’s lymphoma EBV-positive Raji cell line we observed significantly lower level of 5-mdC compared to the mean level calculated for EBV-negative cell lines BL (p = 0.0308)  and malignant  cell lines . The mea 0.0308) .3dN±0.0043. We observed statistically significant lower level of this modification in DNA isolated from malignant cell lines with the following mean values: HCT 116 (0.0067/103dN±0.0015), K562 (0.0059/103dN±0.0012), Me45 (0.0023/103dN±0.0007), Raji (0.0013/103dN±0.0009), and BL (0.0040/103dN±0.0005). Moreover, the level of this modification was very low in DNA from Me45 and Raji cell lines. We also observed that determination of 5-hmdC in DNA of Burkitt’s lymphoma EBV-negative cell lines BL revealed 3-fold higher level of this modification in comparison with EBV-positive Raji cell line  than in malignant HCT 116 (0.082/106dN±0.0486), K562 (0.042/106dN±0.0288), Me45 (0.044/106dN±0.0389), Raji (0.043/106dN±0.0249), as well as BL cell lines that demonstrated exceptionally low 0.013/106dN±0.0016 5-fdC content in DNA . We also observed statistically lower level of this modification in HCT 116 and Me45 cell lines, with respective mean values 0.73/106dN±0.2657 and 1.35/106dN±0.6373, as compared to normal NHDF-Neo cells. Moreover, statistically significant differences in 5-hmdU level were observed between HCT 116 and other malignant cell lines, with following mean values: K562 (2.55/106dN±0.8643), Me45 (1.35/106dN±0.6373), Raji (2.75/106dN±0.7780), and BL (1.79/106dN±0.3135) (The highest level of 5-hmdU was observed in DNA from NHDF-Neo cells (2.86/10±0.3135) .3dN (0.0589–0.1645) and 26.40/109dN (7.6614–63.9474), respectively. Thus, determination of these modifications in DNA from HCT 116 revealed respectively 15- and 16-fold lower level compared to CRC tissues (6dN (0.5668–0.8154)) compared to CRC tissues (0.23/106dN (0.1350–0.3372)) ) .3dN (0.0177–0.0478)) compared to corresponding K562 cell line (0.01/103dN (0.0048–0.0070)) (9dN (17.4653–67.3516)) as compared to K562 cell line (0.87/109dN (0.7000–2.4621)) (6dN (1.9545–2.9840)) compared to primary tissue (0.28/106dN (0.1783–0.3586)) ) . We also2.4621)) . On cont0.3586)) .Interestingly, levels of 5-fdC were significantly higher (4.5-fold) in leukocytes of AML pastients compared to K562. No significant differences were observed between levels of this modification in DNA from HCT 116 cell line and DNA from CRC tissues .GAPDH as reference gene the levels of TET1, TET2, TET3, SMUG1 and TDG transcripts were compared in NHDF-Neo, HCT 116, K562, Me45 and Raji cells  and K562 (p = 0.0027) cell lines. Significantly lower amounts of TET1 transcript were also detected in other malignant cell lines: K562, Me45 and Raji (p<0.0001) compared to HCT 116  with the level of TET1 transcript in all malignant cell lines used in this study (We found a low amount of  HCT 116 . Both TEis study .TET2 mRNA was noticed in HCT 116 cell line which was prominently higher than in the other investigated cell lines (A highest expression of ll lines .TET3 and SMUG1 transcripts differed significantly between cell lines. HCT 116 cells were the most efficient in expressing TET3 (p<0.0001) and SMUG1 (p<0.0001) mRNA, whereas the other studied cell lines expressed both genes at very low levels  and levels in malignant cell lines which decreased in the order HCT 116 > K562 > Me45 > Raji. The level of 5-hmdC inversely reflected to the level of expression of the ll lines .TET1 is primary responsible for 5-hmCyt formation, while TET2 may be the main enzyme involved in 5-hmdC to 5-cadC conversion [TET2 was observed (9 deoxynucleosides) were on the border line of the detection limit. The levels of 5-fdC in different cell lines resembled those of 5-hmdC  level of 5-hmdC and 5-fdC was respectively 5- and 4.5-fold higher in the primary tissue.As mentioned above, 5-hmdC is a relatively stable modification, and substantial differences in its level were shown to depend mostly on cell proliferation rate, with a linear negative correlation between the proliferation rate and the global level of 5-hmdC in several murine tissues . Using sHere we found that in the cultured cell lines studied there were approximately two orders of magnitude differences between the 5-hmdC and 5-fdC levels, which was ten times less than in the mammalian tissues studied. Moreover, we did not observe any correlation between the proliferation rates of the malignant cells  which had the lowest proliferation rate (doubling time 25 h) had the highest level of 5-hmdC and 5-fdC, in agreement with Bachman et al . Interese lowest . TogetheThese findings raise an important question: what is the molecular background of these differences between normal tissues and cultured cells? While the involvement of TETs in all the modifications analyzed here raises no controversies, still little is known about the regulation of this process; specifically, it is unclear whether the level of the main substrate for TETs, namely 5-mdC, influences the formation of the products of iterative oxidation. It is also unclear why oxidation of 5-mdC either stops at 5-hmdC or proceeds to 5-fdC and 5-cadC. One potential explanation is the different affinity of TETs for 5-mdC, 5-hmdC and 5-fdC (for review see ). It is Our study demonstrates that the level of 5-mdC (TET’s substrate), in cell culture, does not influence the level of 5-hmdC, the product of TET’s reaction, since the 5-mdC level in K562 cells is about two-fold lower than that in Me45 cells, while the 5-hmdC level is significantly higher . We did SMUG1 and TDG genes have a substantially higher expression in HCT 116 cells than in the other cell types  may contribute to the level of this modified base in DNA. Since the ll types  this mayOf all the cell lines studied the lowest level of 5-hmdC was observed in Raji cells, which are human Burkitt lymphoma cells latently infected with Epstein-Barr virus (EBV). Raji cells have a statistically lower level of 5-mdC than their counterpart—EBV-negative cells (BL). A more prominent difference between these two cell types was observed for 5-hmdC, with a level about three-fold higher in EBV-negative in comparison with the EBV-positive cells . These dIn conclusion, this work shows a global erasure of 5-hmdC in DNA of cultured cells compared to DNA from different tissues, in agreement with Nestor et al. . This isOur study shows, for the first time, that different cell lines in culture have different epigenetic profiles. Moreover, different types of malignant cells display characteristic profiles of DNA epigenetic marks which differ significantly. Recent work , 20 quesS1 Table(DOCX)Click here for additional data file.


Proper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented.Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors.The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research.The online version of this article (doi:10.1186/s12864-016-2911-z) contains supplementary material, which is available to authorized users. According to a recent World Health Organization report, breast cancer is the second most common type of cancer. Each year there are about 2300 new cases of breast cancer in men and 230,000 new cases in women in the U.S. . While aCell lines, originated from human tumors, have historically acted as the primary experimental model to investigate the cancer biology and molecular pharmacology. Parallel massive drug screening on these cancer cells characterize the diverse cancer cell reactions to drugs by genomic features. As a salient example, the Cancer Cell Line Encyclopedia (CCLE) project conducts a detailed genetic characterization of a large panel of 997 human cancer cell lines in DNA copy number, mRNA expression and mutation [Breast cancer is heterogeneous in nature. Cell lines study is only an interpretation from a context of artifacts introduced by selection and establishment in vitro, and there exists large differences between cancer cell lines and tissue samples especially in its molecular genome , 9. SeleThe Cancer Genome Atlas (TCGA)  aims to Sixty-eight breast cancer cell lines were extracted from CCLE  and litehttp://www.nccn.org/), in year 2015. Due to this, many breast tumor and cell line samples lacked of ER, PR, and HER2 status for breast cancer treatment classifications. As for the missing information of HER2 status, it has 182 in 1096 TCGA tumors and 15 in 68 CCLE cell lines. These samples are classified as subtypes of Luminal A, Luminal B, HER2amp, and Basal-like using the PAM50 signature. On the other hand, the RT-qPCR and mRNA-based PAM50 ER/PR/HER2 classification results are compared. Figure PAM50   is one oIn order to compare the similarity of the whole genome expression profiles between primary breast cancer tumors and breast cancer cells (i.e. CCLE samples), the breast cancer tumors in Gene Expression Omnibus (GEO) GSE41998 (279 tumors) were selected because they shared the same Affymetrics gene expression platform . On the other hand, HMEL, Hs 578 T, Hs 274.T, Hs 606.T, Hs 281.T, Hs 739.T, CAL-51, Hs 343.T and Hs 742.T had negative correlation coefficients with tumors samples (p < 10−2).The CNVs between cell lines and tumor samples of breast cancer are compared in sample segmentation mean and density calculation of copy number Fraction Genome Altered (FGA). Its calculation is presented in the method section. Figure CCLE sequenced only 1347 cancer genes in breast cancer, while TCGA has whole exome sequencing. Our comparative analysis is only based on those 1347 overlapping genes and their somatic mutations. In CCLE, in order to remove background germline mutation, mutations reported in the 1000 Genome Project and dbSNP were filtered out using ANNOVAR tool, including the gene-based single nucleotide variants (SNVs) and insertions/deletions .y-axis is the mutation rate per million bases and x-axis is mutation gene numbers. The wider the line is, the more the gene mutation number of samples is. It suggests that the gene mutation number in Luminal B subtype from TCGA is the largest. At the same time, its mutation rate is also higher than the other subtypes. Tumor and cell lines with Luminal A subtype have the lowest mutation numbers and mutation rate. Her2 subtype group in cell lines has a larger mutation number than the other subtypes. Figure Figure Thirty-one genes, reported in recent TCGA nature and science papers , 22–28, A comparison of mutation spectra across four subtypes Fig.  reveals Quantitative expression of 50 cancer-related proteins, phosphorylated-proteins by RPPA, were measured on 197 breast tumors and 38 cell lines. Pearson Correlation analysis and unsupervised hierarchical clustering analyses were conducted between cell lines and tumors  between mRNA and protein both in TCGA tumors and cell lines. Two important oncogenes, GATA3 and RP56KB1, both have high mRNA- protein correlation. The correlation for GATA3 is 0.79 in cell lines and 0.81 in tumors, while the correlation for RP56KB1 is 0.92 in cell lines and 0.78 in tumors. The small figure in Fig. p < 0.01). This strong signal indicates the consistency of gene expression and protein expression in both cell line and tumor. The potential discrepancy could be due to the stability of mRNA, the degradation of protein, the time dependent and site dependent nature of protein phosphorylation, and etc. The interesting result in the Fig. The correlations of the gene mRNA versus its phosphorylated protein was calculated in cell lines and tumors irrespectively. The average correlation coefficient Fig.  of 38 geGene expression profiles and proteins phosphorylation expressions of tumors and cell lines were compared to further corroborate our observations made on the CNV and mutation data. The correlations of four different molecular profiles of all cell line and tumor pairs were calculated Fig. . These fIn gene expression-based clustering analysis, cell lines possess similar clustering as with tumors using PAM50. At the same time, cell lines show stable genomic and expression patterns, as well as high correlation, with tumors in whole gene expression profile.From the mutation comparison between cell lines and tumors, some common features were found: the chromosome 1 and 8 regions show high frequency copy number amplification, and chromosome 13 and 16 display high frequency deletions. Some significant cancer-related genomic alterations: MYC, PVT1, RAD21, TRPS1, CDH1, RB1, PIK3CA, MAP2K4, and ANKRD11, are identified in both breast cancer tumors and cell lines. The results were verified partially in reference .Breast cancer is a highly complex disease. The subsets of breast tumors show diverse patterns of gene expression, CNV, mutation, and protein expression. A considerable amount of knowledge on breast carcinomas have been derived from in vivo and in vitro studies performed on breast cancer cell lines. Whether breast cancer cells are representative of the tumors remains debatable. In this study, the comparisons between cell lines and primary tumors from molecular profiles: gene expression, CNV, mutation, and protein expression, show that the cell lines are similar to some but not all of the primary tumors. Among them, gene expressions have the highest while the mutation-based correlation was the lowest.In the single point mutation comparison, the six trans-version distribution modes of mutation spectrum demonstrates the similarity between tumors and cell lines in four breast cancer subtypes. High frequent C > T and C > G transitions are observed in both tumors and cell lines, while few A > T happens; Basal-like tumors and cells show the high concordance. These results were confirmed by Philip J. et al. . They suIn analyzing the cancer landmark genes, gene PIK3CA and TP53 in cell lines are the top 2 mutated genes that tumors have . In addic)In protein expression-based comparison, breast cancer subtype proteins ER, PR and HER2 have a high consistence in cell lines and in tumors. RPPA can identify breast cancer subtypes clearly and accurately not only in cell lines but also in tumors according to these protein statuses. RPPA is a sensitive and accurate technology to evaluate protein expression and activities. It helps the target identification, validation, and drug discovery , 30. Somd)There are many complicated post-transcriptional mechanisms in turning mRNAs into proteins. According to correlation analyses between gene expression and phosphorylated protein expression in both cell lines and tumors, significant results are found that important drug targets in breast cancer, such as ESR1, PGR, HER2, EGFR and AR show high correlated mRNA and protein levels. High mRNA-protein correlation. Two oncogenes GATA3 and RP56KB1 with high consistency correlation between mRNA and protein expression become a promising potential drug targets. On the other hand, the gene expression variation at the mRNA level is not necessarily consistent with its protein level, such as genes TP53, KDR, DECAM1, which has been well documented in the literature , 33. Mose)In the whole score overall comparison, cell lines and tumors show high gene expression-based correlations, but the correlations in mutation and protein expression level are low. The possible reason is that mutation data is discrete, and mutation rate is low.On the other hand, cell lines have more genetic aberrations than primary tumors. Amplification, deletion and mutation are more frequent in the cell lines than in the tumors. This is consistent with a similar study in ovarian cancer . One pothttp://www.pubmed.org) in year 2015, the number of citations for all breast cancer cell lines at CCLE is sorted Breast cancer subtypes in tumors and cell lines. The breast cancer cell line classification provides a cell modeling system to primary tumors. Our study addresses the classification results for cell lines and tumors based on PAM50  PAM50 gene expression differentiated four major breast cancer subtypes, such as Luminal A and B, HER2amp, and Basal-like, in both cells and tumors. Using whole genome expression arrays, strong correlations are observed between cells and tumors. (2) Consistent CNV patterns are observed between tumors and cells across the chromosome. High C > T and C > G trans-version rates are observed in both cell lines and tumors, while cells have slightly higher somatic mutation rates than tumors. (3) Although the ER/PR/HER2 show the consistent patterns between cells and tumors, the other proteins in the RPPA platforms do not. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in both cells and tumors. However, low correlations were observed between cells and tumors in phosphorylated proteins. (4) Nearly 50 % gene expressions are not consistent with their protein levels both in tumors and cell lines. The high and low of gene expression is irrelevant to the translation processing from mRNA to protein directly. Nevertheless, important drug targets in breast cancer, such as ESR1, PGR, HER2, EGFR and AR possess highly correlated in mRNA-protein expression both in tumors and cell lines. (5) A total similarity score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors.https://gdc-portal.nci.nih.gov/) with tumor matched selections and level 3 data. DNA exome sequencing data was available from 967 tumors. mRNA expression by AgilentG4502A_07_3 platform test was collected for 530 samples, while copy number alteration was detected using Affymetrix 6.0 single nucleotide polymorphism array (SNP- array) in 1033 tumors, and protein expression by RPPA in 197 tumors was obtained. The total number of breast cancer cell lines in CCLE was 59 [http://www.broadinstitute.org/ccle). According to reference [Four levels of molecular profiles: mRNA gene expression, CNV, mutation, and protein expression, were retrieved from TCGA, CCLE and GEO Table . The stuE was 59 . DNA copeference , 38 celleference –36. To ceference  with theBreast cancer classification, in clinic, is measured according to these features: histological type, tumor grade, lymph node status and markers, such as oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) , 6. BreaPAM50  test is a risk model to identify the intrinsic subtypes in recent 5 years according to 50 gene expressions, including gene ESR1(ERα), PGR(PR) and ERBB2(HER2) . This teAll raw files of microarray mRNA expression, in the form of ‘CEL’ files, were downloaded from GEO GSE36133 and GSE41998. These raw data were normalized by the Affymetrix Microarray Suite 5.0 (MAS5.0) algorithm in accordance with background adjustments, scaling, and aggregation to remove non-biological elements of the signal. Common 22,267 probe sets, corresponding to 14,970 genes, are used comparison analysis for cell line and tumors. All samples in cell lines and tumors are divided into four subtypes group based on ER, PR, HER2 status: luminal A, luminal B, HER2-enrichment and Basal-like as the description before had shown in Additional file 2(CN/2)) for further analysis. These segmentations were used to identify focal amplification/deletions and arm-level gains.A total copy number of changes of TCGA 1033 tumors and CCLE 59 cell lines was detected using Affymetrix 6.0 single nucleotide polymorphism array (SNP 6.0 array) across 28,918 genes. Copy number was measured by a probe corresponding to a segment. They were then inferred and normalized based upon specific linear calibration curves. The circular binary segmentation (CBS) algorithm was used to normalize the segmentations ) whose copy number (CN) segment is above the set threshold (T) and divide by sum of lengths of all segments (L(i)) [CNVs correspond to relatively large regions of the genome that have been deleted and inserted. To quantitate the extent of the genomic instability in each sample, we calculated the Fraction of Genome Altered  as formula . The equs (L(i)) . Hence, T is set to 0.2 for tumor samples and 0.3 for CCLE cell line samples. The threshold values are based on the average distribution density after samples CNV analysis. Cell lines always keep a copy number hyper-mutation degree than tumors’.Here, the threshold With the help of Bioconductor package called ‘CNTools’ , these sThe mutation data was obtained directly from DNA sequence mutation annotation format (.maf) files where Illumina GA platform is used to test. In TCGA, 997 breast invasive cancer Level 2 somatic data is bulk downloaded and hybrid capture 1650 genes in CCLE 59 samples are obtained. According to software ANNOVAR gene-based annotation , gene muGene mutational frequency can be described as a ratio of total number of gene mutations in samples to total number of samples. Actually, it is the measure of gene mutations probability in the breast cancer population.https://www.synapse.org/#!Synapse:syn1695394). Hence samples or gene mutations rates can be calculated through summing up all bases where read covered as mutations per Mb.The mutation number of bases for TCGA are detected from the bed files. The bed file contains a number of bases covered for each chromosome, in form of start and end location. Subtracting end from start gives number of bases covered by the reads. All bases obtained for each sample are summed together to obtain a whole number of bases covered, it is the given sample mutations rate per million bases (Mb). Bed files derive from ‘Wig’ format file. ‘Wig’ provides the number of reads for each region. In case of CCLE, the file can be downloaded from CCLE data portal. To TCGA, it is available from Synapse websites, a research-sharing platform (The patterns of six trans-version distributions were searched in the sequence annotation files from CCLE and TCGA irrespectively by R programming. Then, the mutation allele mode was obtained in each of the subtypes of breast tumors and cell lines. The correlation was calculated as mutation allele spectrum in each subtype between cell lines and tumors by Pearson-correlation method.All basal phosphorylation and protein abundance data were obtained by RPPA technology from reference  and TCGAscore by formula (The extent to which the breast cancer cell lines match genetic characteristics shared by the TCGA tumors was assessed using a whole  formula . The scoWhere A is the gene expression similarity between cell lines and tumors by Pearson-correlation; B is the correlation with CNV segment mean of breast tumors; C is the correlation of genes mutation variation with breast tumors; D is the protein expression-based correlation with tumors in breast cancer. The score serves to identify a better or poorer cell lines model of breast cancer in entity molecular level and rank the graduate.www.broadinstitute.org/cancer/software/GENE-E/.All data arranging was operated on Ubuntu Linux operating system by shell scripting programming. R and MATLAB was used to perform statistical analysis and plotting graphs . IntegraCCLE, cancer cell line encyclopedia; CNV, copy number variation; ER, estrogen receptor; FGA, fraction genome altered; GEO, gene expression omnibus; HER2, human epidermal growth factor receptor 2; IGV, integrative genomics viewer; MAS5, affymetrix microarray suite 5.0; NCCN, National Comprehensive Cancer Network; PAM50, prediction analysis for microarrays 50; PR, progesterone receptor; RPPA, reverse phase protein array; SNP, single nucleotide polymorphism array