The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines. Cell lines are widely used in the biological sciences, partly because they are able to multiply indefinitely. This property means that scientists can, in theory, exactly replicate previous studies and then build on these results . Howeverthe Cellosaurus database, housed at the Swiss Institute of Bioinformatics, contains extensive information on more than 100,000 cell lines  to create a register of contaminated and misidentified cell lines, increase awareness of the problem, and propose approaches to decrease the use of such cell lines . ICLAC all lines , includiDespite the availability of the ICLAC register, the overall rate of misidentified cell line use has not fallen across the literature . It has Research Resource Identifiers (RRIDs) are unique identifiers that can be included in the methods section of a research paper to define the cell line, antibody, transgenic organism or software used . These iRRID portal in 2016, and journals often require researchers to look up each antibody, animal or cell line on this website . This requirement creates a natural experiment to address the question: if researchers are alerted about the misidentification of a cell line before they publish, will they still report data from such a cell line?RRIDs for cell lines were first incorporated into the In this study, we used text mining to identify papers that included RRIDs and papers that listed cell lines, and then compared the prevalence of misidentified cell lines in these two samples. It should be stressed that the use of cell lines on the problematic list does not automatically mean that a given cell line is being employed improperly. For example, the problematic list includes cases where a cell line is ‘partially contaminated’, which does not affect cells purchased from, say, a stock center. The list also includes cell lines that have been labeled with the wrong type of cancer, but these may still be safely used if the researchers know the true identity of the line. We must, therefore, exercise caution when interpreting these results. More information about the problematic list please is given in the discussion section and To derive a dataset of reported cell lines from the general literature, we used text mining of the PubMed Central open access subset. This task requires the use of natural language processing, as cell-line names are not unique strings. For example, looking for a set of characters, such as ‘H2’, may reveal papers where H2 denotes a cell line, gene, protein, antibody or a figure. We used the SciScore tool, a ‘Named Entity Recognition-based algorithm’ specialized for scientific resources, which can in principle recognize the H2 cell line and can ignore its mentions referring to something other than a cell line.The algorithm was taught to consider features around the recognized term that are associated with cell lines. SciScore was trained using 1,457 annotations made by a curator as well as a complete list of cell lines and synonyms from Cellosaurus as a set of seed data. We split the data into 90% training, and 10% testing of random sets ten times, and obtained an average precision of 87.3% (+/- sd 4.65%), recall 61.9% (+/-sd 7.01%) with an overall F1 mean of 72.2% (+/- sd 5.39%).This algorithm was then deployed on the ~2 million articles in the open access subset of PubMed Central. It recognized 305,161 unique cell-line names from a total of 150,459 unique articles , which wAs cell-line names are not standardized in most papers, we used three different approaches to estimate the percentage of problematic cell lines in the general literature. First, to obtain a lower boundary, we determined whether the cell line name, as stated by the researcher, matched exactly a name or synonym of a problematic cell line in Cellosaurus, as this is the most stringent criterion .To determine an upper boundary, we used the cell line name and a wild-card character, so HeLa became HeLa*. In this loose condition, if researchers were to report "CF-1 mouse embryonic fibroblasts", which was recognized as the entity by SciScore, then the loose criterion would match this to CF-1. However, under the strict criterion, those would be considered different.The edit distance was also employed to come up with another estimate intermediate between the two. In this condition, an edit distance of 0 was set when the letters and numbers of the cell line matched exactly, and the hyphens, spaces and other special characters were omitted. If a researcher uses CF1 or CF-1, for example, the edit distance metric would consider these the same cell line. However, the "CF-10", "CAF-1" or "CF-1 mouse embryonic fibroblasts" would be an edit distance of more than 0, and therefore, would not match.We first considered the reporting of problematic cell lines as a function of the publication year . We did 2 calculation was used to assess whether there is a trend in the percentage of problematic cell lines used by year. From 1997 to 2012, the percent of reported cell lines on the problematic list appears to be increasing, although the R2 shows a rather weak correlation (0.648 strict criterion or 0.404 loose criterion). In the period between 2013 and 2018, the slope is reversed, although still a rather weak correlation (0.479 strict and 0.498 loose), suggesting that there may indeed be a trend to decrease the use of problematic cell lines.Awareness of misidentified cell lines and authentication testing also received a boost with the establishment of the ICLAC in 2012. Ever since, the ICLAC has been providing a focus for change. Thus, although there is certainly a long history of reporting of cell line problems, 2012 can be viewed as the seminal breakpoint. Both the strict and loose criteria were considered as a function of year as the lower and upper bounds of contamination rates, and the RTo assess the number of studies that used problematic cell lines, we asked what percentage of papers contain one or more cell lines where the name detected by SciScore matches a cell line on the problematic list. For this and all subsequent analyses, we used the edit distance criterion, as it is the measure between the strict and loose criteria and therefore a reasonable compromise. The number of cell lines that could be found on the problematic list was 8.7%  of total cell lines, corresponding to 16.1% of papers .The smallest number of cell lines on the problematic list is found in virology and botany related journals, while the largest is in cancer journals. This general observation is consistent with the identity of the problematic list, which has a high proportion of human cancer cell lines. Because of this composition and the aggressive growth of cancer cell lines taking over other cell lines, we expect that cell lines used in cancer journals would be over-represented, while cell lines from model organism would be underrepresented.To determine the quality of SciScore annotations beyond the training set, we used a single journal, Scientific Reports, that is fully open access . Furthermore, it has a relatively large number of cell line papers as a percentage of the total papers published. To address the issue of false positives and false negatives, we created a manual set of 1,000 papers by first retrieving the subset of Scientific Reports papers via PubMed that span the time frame covered by SciScore, and then selecting every 65th paper, for a total of 1,004 papers. One paper was rejected from the analysis because it was an erratum, leaving 1,003 papers. This method, while not completely random, does sample papers without assuming that the paper will have cell lines. Every methods section was read, and the curator noted the presence or absence of cell lines.Of the 1,003 papers, both the curator and SciScore did not find any cell lines in 815 papers, but identified cell lines in 138. In total, there was agreement between the algorithm and curator in 953 out of 1003 papers, or 95.01% agreement. Therefore, we did not seek to tune parameters further for SciScore. We assumed that if both curator and SciScore agree about the presence of cell lines in the paper, then the answer is correct and did not look more deeply into these data. We examined the 50 papers with a disagreement to determine whether the algorithm or the curator were incorrect in each case. The curator found 33 papers, which were not found by SciScore, while SciScore found 17 papers that the curator did not.To look at the false positive rate for SciScore, we examined more closely the 17 papers identified by SciScore but not the curator . Of thesResearchers that publish using the RRID syntax must look up the RRID in a central database . This daWe assessed the number of cell lines that were identified by researchers using the RRID syntax, by examining all papers from 2016 to 2017 that contain the term "RRID". Each RRID containing paper was annotated by SciBot, as described in the methods . SciBot The number of cell lines identified by RRIDs that matched the list of problematic cell lines obtained from Cellosaurus was 50 out of the 1,502 total cell lines, or 3.3% . This 3.The edit distance, total strict, and the 2016-2018 strict numbers, all are greater than 3.3% at the p<0.01 significance level. This percentage is also lower than that calculated for any of the individual journals in the top 100 journals  except 4PubMed Central currently contains roughly four million papers, and about two million of those are available under a ‘text mining is allowed’ license. Papers that contain RRIDs are under many different licenses, and only some are available for text mining, so we need to be careful in our comparisons of the RRID literature and the text-minable literature, because these are partially overlapping, but different subsets of the total literature.We considered if there was a difference in the percentage of cell lines on the problematic list, as we were sampling two different populations of journals. Some journals have higher, and some journals have lower numbers of papers using cell lines on the problematic list . We testThe rate of contamination was gathered from the data underlying We used a weighted average to determine what the rate of possible cell line contamination for each journal and found that the average was 17.1%, a value slightly larger than the overall average of 16.1%. Therefore, we do not believe that the journals using RRIDs are simply better than others in terms of cell line authentication. In fact, inclusion of an RRID is a more likely due to an intervention than a journal policy that reduces the overall use of cell lines on the problematic list.However, the presence of a cell line on the misidentified list in a paper does not mean that the statements or conclusions of the paper are questionable. In fact, many of the cell lines that are on the problematic list are explained by a mix up between cells originating from patients suffering from the same disease as the patient who was supposed to be the donor of the contaminated cell line, which is not something that should impact the conclusions of a study. Indeed, researchers may knowingly use a hepatocarcinoma cell line even if it may have been mislabeled as a hepatoblastoma, because their experiment calls for a hepatocarcinoma cell.Moreover, sometimes, it may not be necessary to know the exact origin of the cell line as their experiment simply calls for mammalian cells, especially when a cell line is only used as a vector for (over)expressing a specific gene. While it is not practical to look at thousands of papers to determine if the researchers knew that they were working with a misidentified cell line, it was practical to look at the 50 cell lines used in the RRID literature because presumably, they should have been alerted of a potential problem with their cell line.CVCL_0019) was contaminated in a laboratory separate to the originating laboratory, and several other laboratories who obtained the cell line used the contaminated version.Here, we found that one out of the of 50 cell lines on the list where an RRID was included by the researchers was a typo – they clearly used an uncontaminated cell line, but transposed the Cellosaurus number incorrectly. But 13 carry the ‘partially contaminated’ label, because the SH-SY5Y cell line (RRID:CVCL_0022) originally published as a glioblastoma cell line established in 1968 at the University of Uppsala, but this cell line is most likely a glioblastoma cell line of unknown origin. Therefore, as far as we can tell there is no cause for concern in these studies.Of these 13 papers, 11 clearly state that the cell line was obtained directly from a stock center such as ATCC, which contains an authentic version of the cell line. Ten papers used the U-87MG ATCC cell line  are less likely to be misidentified compared to secondary sources . EstimatHowever, while search strategies are a great way to help identify papers that use misidentified cell lines, they have several limitations. Many names, especially those for the oldest and thus most established experimental cell lines, are short , use generic words , or are provided in abbreviated form. It can be difficult to know if the name is referring to a gene or a part of a figure as opposed to a cell line. Here, we describe our use of text mining to look for sentences in the methods sections of open access papers that describe cell lines, based on sentence patterns and text features around the cell line name.Our approach for finding cell lines using automated detection in the PubMed Central corpus has significant limitations. Importantly, an uneven number of papers per year, including a partial year (2018), is likely to affect the accuracy of counts, especially in the older literature, where there are simply fewer papers per year. We report data as a percentage of total, yet this decision in a sense, hides the fact that the numbers of papers per year is different. Indeed, we see a larger variability year to year in early years as opposed to later years .Older papers – especially those before the year 2000 – which were entered into PubMed Central in a more one-off or manual manner from years prior to the creation of this data base, may be subject to more errors than current papers that are processed at the publishers and sent as structured data. We have no good estimate about the proportion of errors in newer or older literature. While issues with character recognition would presumably creep into the subset of manually handled papers more frequently, SciScore should still recognize the word as a cell line, regardless of whether the word matches a particular cell line name. However, if a character error creeps into the cell line name, it would be less likely to match a problematic cell line, given our fairly stringent criteria for problematic cell line identification.Hypothes.is to provide updated information on a published paper when new information becomes available.Our understanding of cell lines continues to change, and this must be taken into consideration alongside the RRID. Inclusion in the ICLAC register of misidentified cell lines is based on published data, available samples, and existing test methods. New data may lead to cell lines being added or removed from the register, based on a discovery of authentic material or a new finding of misidentification. Similarly, information on problematic cell lines in Cellosaurus is based on available evidence and may change over time. Because RRIDs make it possible to search easily for papers that mention a particular cell line, it may be possible to use literature annotation tools like It is important to review the evidence on which these findings are based and make a considered judgment regarding their impact on published work. Cell line resources, such as Cellosaurus and the ICLAC register of misidentified cell lines, have been developed to improve awareness of cell line information. The inclusion of RRIDs will result in improved use of these resources and better reporting of cell lines and other research materials in publications.SCR_016251). SciScore is a text analysis tool suite that uses Named Entity Recognition to extract words or named entities from text documents. Here, we focused on words indicating cell lines. A word like HeLa is relatively unambiguous, however this is not the case for many other cell-line names. Therefore, the SciScore algorithm works only partially by recognizing these names, and mainly identifies them based on the sentence context of the word. SciScore is thus capable of finding new cell-line names that it was never specifically trained to recognize. This approach has been described previously .Sentence 1 HeLa(correct annotation) or HCN-A94(correct annotation) cells were grown in 24 well plates and transfected with 0.1 μg phRL-TK-10BOXB plasmid, 0.1 μg of pGL3 promoter plasmid and with 0.7 μg of one of the six pCl- λN-HA-tagged UPF3B expression constructs.For luciferase activity assays, Sentence 2 HeLa or RAW264.7(correct annotation) cells were seeded into 96-well plates and allowed to attach for 24h.For cellular uptake kinetics study, SCR_004166), retrieved on [3/23/2018], which constituted 1,950,740 total papers published between January 1975 and March 2018. SciScore identified 673,272 cell-line names in the methods section of 150,459 unique papers. According to this analysis, 7.7% of papers in the open-access subset of PubMed Central use cell lines. In those, we found 305,161 unique cell-line names in total. Therefore, in our sample of about 150,000 papers, each methods section describes an average of two cell lines and each cell line is mentioned twice.SciScore ran on the open access subset of PubMed Central . To improve our chances of finding 100 or more exemplary papers, we selected Scientific Reports, a journal that publishes many cell line papers and also is fully open access. The curator searched PubMed and downloaded the basic metadata  were chosen. The curator was blinded to the output of the algorithm while curating papers.Briefly, after searching PubMed for all papers published in Scientific Reports, we downloaded a CSV file from PubMed containing the basic metadata for all these papers. Review and non-research papers lacking an abstract were removed, resulting in a total of 65,085 articles for analysis. Of these, every 65th paper, by date of publication, was selected for curatorial review, leaving 1,004 papers to be reviewed for cell line presence. Each of the these was then manually reviewed by a curator to determine if cell lines had been used and if so, which ones. One record was an erratum and was removed from this final list, leaving 1,003 papers to examine.Cellosaurus Frequently Asked Questions section and contains cell lines from the ICLAC Register of Misidentified Cell Lines (both approved cell lines and those submitted for ICLAC review) and additional problematic cell lines reported by other sources. For the purposes of our primary analysis, we did not distinguish between different categories of cell line problems. However, we considered the differences in a subsample of the RRID papers, where it was important to determine whether the researchers knowingly used a misidentified cell line and if so, if they knew the identity of it.We extracted a list of problematic cell lines from Cellosaurus database Version 25 (March 2018), and copied the 810 cell line identifiers, 1,811 names and synonyms into a document . The comTo determine if cell-line names were highlighted in Cellosaurus as misidentified or otherwise problematic, we matched the data we obtained from SciScore, with the extracted list using three methods: strict, loose, and edit distance. The strict method involves loading the data into excel and using the "vertical lookup" function in Excel (VLOOKUP) to match whether the exact name for a cell line found by SciScore was one of the names or synonyms. For the loose method, we added an asterisk * to the end of each cell line name to match additional possibilities. The strict and loose methods for matching names represent what we consider the outer boundaries as percentages for a cell-line presence on the problematic list. Excel treats matches in a case-insensitive manner, so "HeLa" and "HELA" would both be considered a match.The third method used was the edit distance method to solve the string-to-string correction problem . The minSciBot  for RRID mentions. SciBot is an annotation-based curation workflow that works with the Hypothesis annotation tool . The curation workflow comprises the following steps:We searched for all papers that contain RRIDs by monitoring Google Scholar, ScienceDirect (Elsevier), Wiley and PubMed Central every week for four years for mentions of the string RRID. Each paper that contained the term "RRID" was opened by a curator and listed in our database. For the past two years, each paper was examined by our semi-automated tool, Document identification: for each paper, SciBot first identifies the digital object identifier (DOI) and the PubMed identifier (PMID) by locating the DOI on the html webpage and then querying the PubMed application programming interface to respond with a PMID for the specified DOI. These identifiers are attached to the document URL through the Hypothesis annotation service. If the automated methods fail, the curator fills in the PMID using the Hypothesis annotation client.Scicrunch resolving service, the results of which are then provided to the curator as a linked annotation viewable in Hypothesis .RRID Dataset: The RRID dataset as of March 2018 included over 2,000 cell line annotations. RRIDs that were supplied by the curator ("Missing") were excluded from this data set, leaving 1,554 cell lines where the researcher had added the RRID. Of the 1,554 cell-line RRIDs, 1,502 cell lines were found in papers associated with a PMID. The data set as of 4/01/2018 contains 686 unique papers; 634 of these were associated with a PMID. Papers without a PMID were not considered in the paper-level analysis, as we could not guarantee that they were unique.Social Science Statistics online calculator, RRID:SCR_016762). We made the one-tailed assumption as we assumed that researchers would only decrease their use of problematic cell lines and no longer publish data generated using problematic cell lines.To determine if the RRID literature and the general literature represent distinct populations, we used the population proportion z-score statistic to deal with the difference between the total number of cell lines reported in papers and the cell lines reported via RRID  of correctly recognized labels refers to the number of words that are recognized as cell lines, which were also a cell line according to the curators. The number (#) of true good labels refers to the total number of cell lines according to the curators. P, R, and F1 are calculated on each 10% test set.P= (# of correctly recognized good labels) / (# of recognized good labels)R= (# of correctly recognized good labels) / (# of true good labels)F1=(2*P*R) / (P+R) In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife for consideration as a Feature Article. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by two staff editors. The following individuals involved in review of your submission have agreed to reveal their identity: Malcolm R MacLeod (Reviewer #1) and Christopher Korch (Reviewer #3). Reviewer #2 remains anonymous.Thank you for submitting your article "Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines" to We invite you to submit a revised version of your manuscript that addresses the points made by the reviewers (which are summarised below).Summary:Using misidentified cell lines for biomedical research is a problem that has persisted long after it was identified in the 1960s and highlighted by the work of Stanley Gartler, Walter Nelson-Rees, many others. The frequency of misidentified cell lines in various collections of cell lines ranges between 10% -100%, depending on the sample size and the origin of the tissue. This persisting problem probably causes many irreproducible results.This article describes a large-scale survey of the scientific literature and aims to see if using Resource Reference Identification tags (RRIDs) to describe cell lines in research articles has reduced the use of misidentified cell lines, for which no known authentic samples exist. The authors found that using RRIDs reduced the incidence of misidentified cell lines to about 40%, compared to articles that did not use RRIDs. This major decrease illustrates a hopeful approach to tackling the use of known misidentified cell lines.Essential revisions:a) Materials and Methods section: Was the study design articulated in a study protocol, and is a dated version of this available? That is, can the authors demonstrate that their primary measures of interest and statistical analysis plan were articulated prior to data collection? If not, can you give some indication of any other analyses that were performed but are not reported? It might be worth listing these (if there are any).b) Results section: Please report the results of all analyses in the Results section – it is not sufficient to state that "all are different".c) The ICLAC register of misidentified cell lines consists of two lists. Table 1 is a list of misidentified cell lines for which there does not exist any known authentic samples. Table 2 is a list of misidentified cell lines, but for which there exist authentic samples. Please clarify which subset of the ICLAC register was used in this study?d) The Discussion section does not cover some important aspects of the study, such as:- The inclusion of a partial calendar year (2018) in the analysis and how this may have affected the results.- The predictably low use of cell lines (in a searchable format) in older papers and how this may have affected the resultant correlation coefficient.- The 14% miss rate  – this seems a rather high number given the human assessment is presented as the standard against which the automated search is compared.- There is a rather large assumption that researchers check the cell database early in a project and would cease to use problematic cells if a warning is seen . Evidence to support this assumption is missing.- Was the potential for older scanned papers to not be of sufficient resolution/ quality for text mining considered? If so, how could this have affected the results?e) Discussion section. In this paragraph the authors mention the range of 15-30%. It seems that they do not understand that in the two references to which they refer for these values, these numbers are the range of frequencies of misidentified or cross-contaminated cell lines in collections, but are not frequencies of the number of publications using misidentified cell lines. Moreover, the Masters, 2012 reference is a letter to Nature that refers to an editorial in Nature from 2009, and not to a study that actually determined these values. This frequency information is used incorrectly in the context of this paragraph that discusses search strategies for identifying published articles that have used misidentified cell lines.Furthermore, the frequency of misidentified cell lines ranges more broadly, between 10% and 100%, depending on the sample size and type of cell lines. For the frequency of misidentified cell lines in collections of cell lines I would suggest referring to instead Drexler et al., 2003 (18-36%) and Zhao et al., 2011 (30%). Perhaps one could also include more recent studies that show higher frequencies, some close to 50% . At the lower end, there is Liang-Chu et al., 2015, who observed in a large collection a frequency of 9.6% in which they compared many different cell lines. Based on these ranges, a more meaningful range of the frequency of cross-contaminated or misidentified cell lines in collections would be 10% to 48%.This information could be used as the first sentence in the paragraph to say how frequently misidentified cell lines are found. Then followed by referring to the Neimark, 2015 reference, and followed by a reference to other recent studies. For references to published articles that have examined the frequency that misidentified cell lines have been used in published studies (which would be more appropriate for this paragraph), I suggest weaving in the story of KB cells by Vaughn et al., 2017 and the findings of Horbach and Halffman, 2017. These authors used different strategies for identifying publications that used misidentified cell lines, which is the topic of this paragraph.f) Supplementary data: It's not clear whether the Supplementary data are a summary (it appears to be) or the entire dataset. I know that the entire dataset is evolving and available, but if what is presented here is a summary then I'd prefer that you deposited a snapshot of the data at the time the analysis was done, to allow attempted replication using the exact same dataset. But I may have got this wrong.g) Although beyond the scope of this study, the use of RRIDs does not verify that a cell line used for a study is indeed the claimed cell line. That would require each lab to authenticate the samples of the cell lines they are using by STR or SNP genotyping. If journals and funding agencies would enforce this, it may help to prevent misidentified cell lines from being unknowingly used in biomedical research. Please consider adding a sentence or two on this topic. Essential revisions:a) Materials and Methods section: Was the study design articulated in a study protocol, and is a dated version of this available? That is, can the authors demonstrate that their primary measures of interest and statistical analysis plan were articulated prior to data collection? If not, can you give some indication of any other analyses that were performed but are not reported? It might be worth listing these (if there are any).eLife/ff683ecc/rrids-how-did-we-get-here-and-where-are-we-goingWe certainly should have registered this study and failed to do so. This was a mistake. However, the idea originated in this Blog, published on May 11, 2017: https://elifesciences.org/inside-At the time of the blog, there were only about 100 papers using RRIDs to tag the cell lines used in the paper and we had two examples of problematic lines in that set, a percentage that seemed quite small. We did not write a full paper because the data set was preliminary and no comprehensive set of data existed for use of misidentified or contaminated cell lines in the broader literature. We subsequently created a larger data set in the course of creating text mining algorithms to identify research resources in the literature . When the algorithm training was performing sufficiently for our needs, we ran it once over the total biomedical literature, which we routinely parse for other reasons, to gather the data for this paper. Within a day of the algorithm run, we took all RRID data and put it into a file to analyze and did not re-curate or fix any of it. The papers described in this manuscript as lacking a PubMedIDs are almost guaranteed to now have those in our systems, and the RRID data today (11/8/2018) includes 4,018 cell lines because this is a rapidly growing data set. The use of the population proportions z test statistic was done after consulting with a UCSD statistician, Dr. Anthony C. Gamst, to make sure that we were not making a mistake regarding the type of statistical test that we used. We did no analysis on these data other than what is reported in the paper. We did retest after review, using an online social statistics calculator (now cited) to verify the z scores in a place easily accessible to readers.b) Results section: Please report the results of all analyses in the Results section – it is not sufficient to state that "all are different".We have added the following language in subsection “Considerations in comparing RRID and open access subsets of literature”, which includes an online calculator where the numbers can be plugged in to verify our claims and the exact z score obtained:“This 3.3% is significantly lower than 8.7% obtained by the edit distance criterion for all papers . Furthermore, it is also significantly lower than the strict match criterion shown in Figure 1 . Limiting the range of dates for the strict criterion to 2016, 2017, and 2018 did not change the average percent of cell lines on the problematic list . Whether we consider the edit distance, total strict, or the 2016-2018 strict numbers, all are different from 3.3% at the p<0.01 significance level.”- We note that the statistical calculator actually states that “The Z-Score is 7.3353. The p-value is 0.” for the edit distance metric, however we did not think that it would be wise to say that our p is zero, we therefore used the p<0.00001 as an approximation.c) The ICLAC register of misidentified cell lines consists of two lists. Table 1 is a list of misidentified cell lines for which there does not exist any known authentic samples. Table 2 is a list of misidentified cell lines, but for which there exist authentic samples. Please clarify which subset of the ICLAC register was used in this study?We updated the Discussion section to address this question. In short, the list of cell lines which were flagged as problematic in this paper are the ones that have a "CC" comment line "Problematic cell line" in the Cellosaurus. The text now reflects the link to Cellosaurus database FAQ and the paragraph copied below:“We extracted a list of problematic cell lines from Cellosaurus version Version 25 (March 2018), and copied the 810 cell line identifiers, 1,811 names, and synonyms, into a document . The composition of this list is described in the Cellosaurus Frequently Asked Questions section (https://web.expasy.org/cgi-bin/cellosaurus/faq#Q17) and contains misidentified cell lines from the ICLAC Register of Misidentified Cell Lines, and cell lines as well as additional problematic cell lines reported by other sources. It must be noted that this list contains both ICLAC approved cell lines and those that have been submitted for ICLAC review.”d) The Discussion section does not cover some important aspects of the study, such as:We have added a paragraph in the Discussion section to address some of these points. We also address specific concerns here.- The inclusion of a partial calendar year (2018) in the analysis and how this may have affected the results.It is relatively unlikely that the inclusion of the partial calendar year has seriously affected the results. While the absolute number of papers for 2018 is indeed smaller than 2017, it is still larger than most of the early data prior to 2012. There is no valid reason that we can think of to suggest that papers published early in a year would use more or less problematic cell lines.- The predictably low use of cell lines (in a searchable format) in older papers and how this may have affected the resultant correlation coefficient.PubMed Central started around the year 2000 so there are few papers in the years prior to 2000 as shown in figure 1 and in the data file. We therefore can be less confident of the total number of cell lines used in earlier literature because our sample is much smaller. However, the open access subset of the literature, as it is added to PMC, is consistently coded in JATS XML format by the National Library of Medicine staff as the papers are processed, which is true for new and early papers. Papers, regardless of age, that are only available as PDFs are not visible to our robots therefore are excluded from our sample. Therefore, it is highly unlikely that recognition of cell lines is substantially different in the older vs more recent literature. Typographical errors can always creep in, but we do not use optical character recognition or any other mechanism to gather papers that are not formatted by professionals- The 14% miss rate  – this seems a rather high number given the human assessment is presented as the standard against which the automated search is compared.We believe that this point was unclear in our writing and we have added the percent error into the relevant text. The curator did make 14 errors out of the total 1003 papers or 1.4%, which is certainly far from perfect, but not 14%. Subsection “Cell lines identified by RRID” was updated to include the percentage.- There is a rather large assumption that researchers check the cell database early in a project and would cease to use problematic cells if a warning is seen . Evidence to support this assumption is missing.We thank the reviewer for pointing this out, we have removed some of the excessive “hand waving” language from the Discussion section. We have no evidence for why authors report fewer cell lines. We also changed the sentence structure in subsection “Considerations in comparing RRID and open access subsets of literature” to ensure that the only thing we know is that the author must find the number at some point during the study and the journals instructions direct them to the RRID portal.- Was the potential for older scanned papers to not be of sufficient resolution/ quality for text mining considered? If so, how could this have affected the results?The text mining algorithm uses the data in xml format directly from PubMed Central, and while it was certainly seeded with the names of all cell lines, it is not bound to only recognize those names. The algorithm recognizes the context of the cell line in the sentence, including the most significant sentence features such as other noun phrases surrounding the putative cell line and the structure of the sentence. This allowed the algorithm to find cell lines that are not in the Cellosaurus database, and the more commonly found cell lines detected by the algorithm were subsequently added to Cellosaurus. It is certainly possible that transcription errors from PDF files submitted to the National Library of Medicine are present and many more of these would be expected in the older literature, however the detection of cell lines should not be affected. If errors did creep into the core documents, we would expect that cell lines that were on the problematic list would likely be affected because while a single character change should still trigger the algorithm to detect the cell line, but our analysis of how close the cell lines are to the list of problematic cell lines would be potentially no longer a “zero” edit distance from a problematic cell line.e) Discussion section. In this paragraph the authors mention the range of 15-30%. It seems that they do not understand that in the two references to which they refer for these values, these numbers are the range of frequencies of misidentified or cross-contaminated cell lines in collections, but are not frequencies of the number of publications using misidentified cell lines. Moreover, the Masters, 2012 reference is a letter to Nature that refers to an editorial in Nature from 2009, and not to a study that actually determined these values. This frequency information is used incorrectly in the context of this paragraph that discusses search strategies for identifying published articles that have used misidentified cell lines.Furthermore, the frequency of misidentified cell lines ranges more broadly, between 10% and 100%, depending on the sample size and type of cell lines. For the frequency of misidentified cell lines in collections of cell lines I would suggest referring to instead Drexler et al., 2003 (18-36%) and Zhao et al., 2011 (30%). Perhaps one could also include more recent studies that show higher frequencies, some close to 50% . At the lower end, there is Liang-Chu et al., 2015, who observed in a large collection a frequency of 9.6% in which they compared many different cell lines. Based on these ranges, a more meaningful range of the frequency of cross-contaminated or misidentified cell lines in collections would be 10% to 48%.This information could be used as the first sentence in the paragraph to say how frequently misidentified cell lines are found. Then followed by referring to the Neimark, 2015 reference, and followed by a reference to other recent studies. For references to published articles that have examined the frequency that misidentified cell lines have been used in published studies (which would be more appropriate for this paragraph), I suggest weaving in the story of KB cells by Vaughn et al., 2017 and the findings of Horbach and Halffman, 2017. These authors used different strategies for identifying publications that used misidentified cell lines, which is the topic of this paragraph.We thank the reviewers for such a thorough evaluation of the background material and the many helpful suggestions about more appropriate references. We have made changes to the manuscript to reflect this more accurate language and references.f) Supplementary data: It's not clear whether the Supplementary data are a summary (it appears to be) or the entire dataset. I know that the entire dataset is evolving and available, but if what is presented here is a summary then I'd prefer that you deposited a snapshot of the data at the time the analysis was done, to allow attempted replication using the exact same dataset. But I may have got this wrong.We have added an additional set of data to the Figure.Data worksheet, which includes the snapshot of the raw RRID data. We have also added text to the figure legend for the Supplementary data stating that the included data snapshot is rather out of date and that far more data is available via SciCrunch and CrossRef, which are regularly updated.g) Although beyond the scope of this study, the use of RRIDs does not verify that a cell line used for a study is indeed the claimed cell line. That would require each lab to authenticate the samples of the cell lines they are using by STR or SNP genotyping. If journals and funding agencies would enforce this, it may help to prevent misidentified cell lines from being unknowingly used in biomedical research. Please consider adding a sentence or two on this topic.SNP profiling is critical for authenticating cell lines in the literature, we have added text  to strengthen our position on this point. However, as the reviewer rightly points out, the presence or absence of genetic profiling of cell lines is not currently in scope for RRIDs or this paper. One heartening point may be that we have begun to train the SciScore algorithm to detect STR profiling sentences. In the future, we would expect that the tool would be able to provide to reviewers a note about STR profiling presence in any paper where cell lines are used. If it works and is used, the tool would act as a check for not only cell line identity, but also authenticity.


Our objective was to improve Dox delivery for producing significant antitumor efficacy in Dox-resistant (R) breast cancer cell lines with minimum toxicity to healthy cells. The results of our experiments revealed that Dox was successfully loaded inside a transferrin (Tf)-conjugated polymeric nanoparticle composed of poloxamer 407 (F127) and 123 (P123) (Dox/F127&P123-Tf), which produced nanosized particles (~90 nm) with a low polydispersity index (~0.23). The accelerated and controlled release profiles of Dox from the nanoparticles were characterized in acidic and physiological pH and Dox/F127&P123-Tf enhanced Dox cytotoxicity in OVCAR-3, MDA-MB-231, and MDA-MB-231(R) cell lines through induction of cellular apoptosis. Moreover, Dox/F127&P123-Tf inhibited cell migration and altered the cell cycle patterns of different cancer cells. In vivo study in MDA-MB-231(R) tumor-bearing mice demonstrated enhanced delivery of nanoparticles to the tumor site when coated in a targeting moiety. Therefore, Dox/F127&P123-Tf has been tailored, using the principles of nanotherapeutics, to overcome drug-resistant chemotherapy.In this study, a transferrin (T Nanotechnology contributes greatly to the design and development of chemotherapeutic drug formulations to overcome various shortcomings of traditional chemotherapy by improving therapeutic efficacy . NowadayPolymeric nanomedicines are designed by using amphiphilic organic molecules such as polypeptides, lipids, and block copolymers to encapsulate chemotherapeutic agents, like paclitaxel, cisplatin, and doxorubicin . Polymerf) to optimize the active targeting delivery of the chemotherapeutics drugs [f is a blood plasma glycoprotein known to be an iron transporter and can be used as a targeting site for cancer-specific drug delivery to enhance therapeutic efficacy against various cancer cells that overexpress Tf receptors [f-conjugated PSNPs, creating an excellent tool for favoring the accumulation of non-selective chemotherapeutic drugs at targeted areas while reducing the exposure of normal healthy cells to these drugs. In this way, overall therapeutic efficacy has been improved by increasing intracellular concentration and, consequently, anticancer activity [The surface of polymeric nanoparticle can be modified by fixing specific homing ligands such as monoclonal antibodies, peptides, nucleic acids, aptamers, and small molecules including epidermal growth factor, folic acid, CD44 or CD22, and transferrin , an anthracycline, is considered one of the most powerful chemotherapeutic agents and is commonly used in multiple cancers, including ovarian and breast cancer. Dox can directly kill tumor cells via DNA damage  and induf-targeted PSNPs. To this end, as shown in f (Dox/F127&P123-Tf) were prepared and characterized in vitro. In addition, the in vivo biodistribution of Dox/F127&P123-Tf was determined in xenograft mouse models bearing the Dox-resistant breast cancer cell line, MDA-MB-231(R).The aim of this study was to overcome Dox resistance in chemotherapy by using T® F-127, F127), poloxamer P123 , holo-transferrin (Tf), 3--2,5-diphenyltetrazolium bromide (MTT), 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC), N-hydroxysuccinimide (NHS), and dimethylsulfoxide (DMSO) were obtained from Sigma-Aldrich Co. . Tetramethoxysilane (TMOS) and pyrene were purchased from Aladdin Industrial Corporation . The MDA-MB-231 cell line was obtained from the Korean Cell Line Bank . DSPE-PEG2000 was purchased from Avanti Polar Lipid . Coumarin-6 and LysoTracker Red were purchased from Thermo Fisher Scientific Inc. . Primary antibodies anti-BAX, anti-Bcl-2, anti-caspase-3 and -8, anti-p27, and anti-p53 were purchased from Cell Signaling Technology . All other chemicals were of reagent grade and used without further purification.Dox was obtained from Dong-A Pharmaceutical Company . Poloxamer 407  cells were analyzed to determine characteristics such as efficacy of Dox in this cell line, levels of ABCB1/p-glycoprotein (P-gp) and the apoptosis-stimulating protein p53, and cellular uptake of Dox, and these were compared with those of the original MDA-MB-231 cells  .Poloxamer 407 (F127) was carboxylated using a modified method [1H NMR)  to identf [f. The amine group of Tf was conjugated to the Dox/F127&P123 nanoparticle using an EDC/NHS-based carbodiimide method. Briefly, equivalent amounts of EDC and NHS (0.25 M) were added to Dox/F127&P123 nanoparticle and stirred for 15 min at room temperature. To this solution, 130 μg of Tf in PBS (pH 7.4) was added and the mixture was incubated overnight. The unbound protein was separated from the nanoparticle by ultracentrifugation at 93,000g (RCF) for 30 min. A BCA protein assay was used to estimate the coupling efficiency [A modified thin-film hydration method with TMOS was used to prepare Dox/F127&P123 and Dox/F127&P123-Tf ,29. BrieThe Z-average hydrodynamic particle size, polydispersity index (PDI), and zeta-potential of nanoparticles were determined using a dynamic light scattering (DLS) method  with Nano DTS software, version 6.34  . Mean vaThe morphology of formulated nanoparticles was characterized using a transmission electron microscope (TEM) . Briefly, the prepared samples were stained with phosphotungstic acid solution and loaded on carbon-coated copper grids and dried at room temperature. Images were obtained using transmission electron microscopy at 120 kV .f was compared with the spectra of Dox, F127&P123 nanoparticles and Dox/F127&P123-Tf using a ‘Thermo Scientific Nicolet Nexus 670 FTIR spectrophotometer [To confirm the exact morphology of the nanoparticles, atomic force microscopy (AFM) images were taken using a Nanoscope IIIa scanning probe microscope . The X-ray diffraction (XRD) patterns of freeze-dried nanocapsules were obtained to identify their crystalline properties using a vertical goniometer and X-ray diffractometer   at 25 °Cotometer .f, an Amicon centrifugal filter device  was used [v/v, pH 3.0), an Inertsil C18 column  was used at a flow rate of 1.0 mL/min and column temperature of 25 °C. A 20-μL sample was injected for each analysis and the absorbance was measured at a wavelength of 480 nm. The percentages of EE and LC were calculated according to the following equation:WD is the weight of Dox encapsulated in Dox/F127&P123-Tf and WT is the total weight of Dox in the formulation; andWTD, WUD, and WTN are the weights of total Dox, unbound Dox, and total F127&P123 nanoparticle, respectively.To calculate the entrapment efficiency (EE) and loading capacity (LC) of Dox in Dox/F127&P123-Twas used . The forwas used . For isof, a predetermined volume of formulation was added to a Spectra/Por 3500 Da-MWCO membrane tube and immersed in 30 mL of either acetate-buffered saline  or phosphate-buffered saline , according to the most versatile and popular method, the dialysis method. The experiment was performed at 100 rpm and 37 °C. Required samples were withdrawn at specified times, and buffer was replaced with fresh media. The extent of drug release was determined using HPLC as reported previously.To assess the in vitro release of Dox from Dox/F127&P123 and Dox/F127&P123-Tf in cancer cell lines, fluorescence-activated cell sorting (FACS) analysis was performed. OVCAR-3, MDA-MB-231, and MDA-MB-231(R) cells (2 × 105 cells per well) were seeded into 6-well plates and incubated for 24 h [f in Dox-resistant MDA-MB-231 cells, all cell lines were treated with free Dox (100 µg/mL) and Dox/F127&P123-Tf. After incubation period (1 h), the cells were washed with cold PBS and uptake of Dox and Dox/F127&P123-Tf in individual cells was determined using FACS Verse . The uptake of the nanoparticle in all cell lines was also determined using the same procedure. The role of Tf in uptake was determined by pretreating with Tf in one group of cells from each cell line before treatment with each test formulation [To determine quantitative cellular uptake of F127&P123 PSNPs and Dox/F127&P123-Tfor 24 h . Untreatmulation .5 /well) in a 12-well plate. Free Dox (100 µg/mL), Dox/F127&P123, and Dox/F127&P123-Tf were added to appropriate wells and incubated. After 24 h of treatment, the images of wound edges were recorded using a fluorescence microscope  and contrasted with conditions before treatment [To examine the anti-metastatic properties of each formulation, a scratch wound assay was performed in all three cell lines. The straight cell-free ‘scratch’ was created on monolayer of cells using a 20 µL pipette tip and washed twice with 1X PBS after all cells had been seeded  and red fluorescence (propidium iodide) and imaged using fluorescence microscopy (Nikon Eclipse Ti).The potential cytotoxic effects of Dox/F127&P123-Tad assay ,41. Indi1, S, G2, and M phases of the mammalian cell cycle in, cell cycle analysis was carried out using the Cell-ClockTM Assay kit . All seeded cells (1 × 105 cells per well) in a 12-well plate were treated with free Dox, Dox/F127&P123, or Dox/F127&P123-Tf and incubated. After 24 h, incubated cells were stained with redox dye (Cell-Clock Dye Reagent) for 1 h at 37 °C. Images were taken by fluorescence microscope (Nikon Eclipse Ti) and the percentage of cells arrested in each phase was estimated from digitized photomicrographs using ImageJ software 1.46jar [To estimate the relative percentages of treated cells in the G 1.46jar .f and protein concentrations were quantified using the BSA Protein Assay Kit . All proteins were separated on 10% Bis-Tris polyacrylamide gel (at 210 mA for 120 min) and transferred to polyvinyl fluoride membrane. The membranes were blocked for 1 h with 5% nonfat milk powder suspension in TBST and incubated overnight with purified primary antibodies: anti-Bax, anti-Bcl-2, anti-p53, anti-p27, anti-caspase-3, and anti-caspase-8. The samples were incubated with suitable secondary antibodies for 1 h, then protein bands were examined with a luminol solution (Thermo Fisher Scientific) and photographed using enhanced chemiluminescence.To detect specific apoptosis-related protein levels in all treated cell lines, Western blot analysis was carried out using previously described methods . BrieflyTo track nanoparticle distribution in the bodies of experimental animals, F127&P123 PSNPs with or without the targeting ligand were injected into the tail veins of BALB/c nude mice bearing MDA-MB-231(R) xenografts with cyanine 5.5 as the fluorescent dye. Fluorescence signals were observed at 6, 12, and 24 h using a FOBI fluorescence imaging system  . After 2t-test (for pairs of groups) were used. P < 0.05 was considered statistically significant. All the obtained experimental results are expressed as mean ± standard deviation (SD).To evaluate the statistical differences between the groups, one-way analysis of variance (ANOVA) and Student’s f were 72.5 ± 1.5 nm and 90.8 ± 2.1 nm, respectively with polydispersity indexes  suggesting uniform particle size distribution (f-conjugated nanoparticles (‒16.5 ± 0.9 mV) was slightly more negative than that of Dox/F127&P123 NPs (−9.8 ± 1.2 mV)  B. The neeric NPs . TEM imaM images C. MoreovM images D showed s  and (=C‒H) groups arising from Dox appeared at 1119 cm−1 and 2891 cm−1, respectively. Finally, an amide band was observed at 1665 cm−1, corresponding to the amide group of Tf, proving that the surface of Dox/F127&P123-Tf was successfully coated in Tf [Functional groups and covalent bonding in Dox/F127&P123-Ted in Tf .f was less crystalline than Dox/F127&P123 and other pure ingredients, as determined by the XRD pattern , and acidic media, acetate-buffered saline . As shown in f in acidic media was 89.5 ± 6.5% and 78.5 ± 7.5%, respectively, after 48 h. Both these values were higher than the rates of Dox release in PBS, which were 51.8 ± 2.8% (Dox/F127&P123) and 42.9 ± 3.5% (Dox/F127&P123-Tf) at pH 7.4 and 58.5 ± 3.4% (Dox/F127&P123) and 44.4 ± 2.5% (Dox/F127&P123-Tf) at pH 6.5. Modification of NPs with Tf may affect release rates to some degree, potentially due to the surface-shielding effects that allow for controlled release of drug and subsequent improvement of half-life [To estimate Dox release from Dox/F127&P123 and Dox/F127&P123-Tf and compare these with the uptake patterns of Dox/F127&P123-Tf with and without Tf pretreatment. Enhancement in the uptake of Dox/F127&P123-Tf was evident with higher time and concentration of treatment in all cell lines (f occurred even in the Dox-resistant cell line MDA-MB-231(R). In contrast, the uptake of Tf-conjugated nanoparticles was significantly prohibited following Tf pretreatment because Tf receptors on cells had been blocked by free Tf molecules (f was successfully achieved with the aid of targeted PSNPs and the role receptor-mediated cellular uptake plays in targeted drug delivery has been corroborated by these results [Fluorescence-activated cell sorting (FACS) analysis was performed on OVCAR-3, MDA-MB-231, and MDA-MB-231(R) cells to study the time- and concentration-dependent uptake patterns of Dox/F127&P123-Tll lines . In partolecules B. The up results .f-targeting, and the results were compared with control. As shown in f invaded and migrated, but the antimigratory effect of Dox/F127&P123-Tf in MDA-MB-231(R) cells was significantly higher than that of free drug treatment and negative control. These results proved that our targeted drug-loaded nanoparticle can be used effectively in Dox-resistant cell lines [Wound healing, scratch, and invasion assays were conducted to assess the progression of cancer cell development in both Dox-sensitive and -resistant cell lines, following treatment with free Dox and formulations with or without Tll lines .f of OVCAR-3, MDA-MB-231, and MDA-MB-231(R) cells was determined using the standard cell viability test, the MTT assay. The assay proved that the cytotoxic effect of Dox/F127&P123-Tf was higher than that of free Dox in not only the Dox-sensitive (OVCAR-3 and MDA-MB-231), but also the Dox-resistant MDA-MB-231(R) cell lines  had the lowest cell viability following treatment with Dox/F127&P123-Tf, suggesting that our Tf-targeted Dox-loaded NPs inhibit cell proliferation of Dox-resistant breast cancer. This information was further proved by the IC50 values of the treated groups, calculated using the standard curve of each cell line  cell line, the IC50 value of Dox/F127&P123-Tf was more significantly lower than other cell lines. The high IC50 value of free Dox in the MDA-MB-231(R) cell line confirmed that the cell line had successfully developed resistance. The mechanism underlying this weak potency is the removal of Dox from cells, following its uptake, via the ATP-dependent efflux transporters that are overexpressed in MDR cell lines [The susceptibility to Dox/F127&P123-Tll lines A. At theell line . The IC5ll lines .f-targeting. The intensity of red fluorescence for all three cell lines that treated with Dox/F127&P123-Tf was higher than that of non-targeted NPs and free drug, especially in the Dox-resistant MDA-MB-231(R) cell line. Therefore, the cytotoxic effect of targeted NPs must be more effective than non-targeted PSNPs not only in the Dox-sensitive cancer cell lines, but also in the resistant cell line, due to the specific receptor-mediated internalization of targeted NPs and increased cellular uptake of the drug [The ability of nanoparticles to induce cell death and apoptosis was evaluated using a live/dead assay in all three cancer cell lines. the drug .f showed lower populations in the G2/M phase, while the number of MDA-MB-231(R) cells treated with Dox in the G2/M phase was similar to that observed for the control group. Therefore, Dox/F127&P123-Tf inhibits mitosis (cell division) in Dox-sensitive and Dox-resistant cells by promoting the phase-specific mechanism of action of Dox, which affects cells in the S and G2 phases of the cell cycle due to higher cellular internalization in both the sensitive and resistant cancer cells [As it is one of the fundamental methods for studying cellular apoptosis, cell cycle analysis was carried out in OVCAR-3, MDA-MB-231, and MDA-MB-231(R) cells; the results are presented in . All threr cells .f were estimated via Western blot analysis. The comparative results of three cell lines are shown in  cells treated with Dox, Dox/F127&P123, and Dox/F127&P123-Tshown in . The levll lines .f in xenograft mouse models bearing the Tf-receptor expressing Dox-resistant cell line MDA-MB-231(R) was evaluated using an in vivo imaging apparatus and compared to that of cyanine 5.5 loaded non-targeted nanoparticles using an injection model of mice . In vitro data of Dox/F127&P123-Tf treatment in MDA-MB-231(R) suggest that drug resistance can be overcome by the accumulation of Dox in the nuclear region of the cancer cell via inhibition of P-gp mediated efflux. In addition, Dox/F127&P123-Tf successfully accumulated in xenograft mouse models bearing the Dox-resistant cell line MDA-MB-231(R), with minimum toxicity to healthy organs. Therefore, Tf-targeted NPs can be used as safe and effective drug carriers for the treatment of both Dox-sensitive and -resistant tumors.The present study successfully developed a T


Healthy non-tumorigenic, MCF-10A and breast cancer, MDA-MB-231, Hs578T, T47D and MCF-7 cell lines were electrically characterized using the open-ended coaxial probe technique from 200 MHz to 13.6 GHz. The dielectric constant, dielectric loss and conductivity between breast non-tumorigenic and breast cancer cells lines were analyzed and their differences determined. Our results showed that the four breast cancer cell lines analyzed exhibited higher dielectric properties when compared to healthy cells. Interestingly, we found that breast and colon cancer cells have different dielectric properties as well, thus suggesting that each type of cancer has a unique microwave signature. This study shows that microwave characterization of breast cancer cell lines is reliable with potential in biomedical applications such as designing electromagnetic models for detection of tumorous cells in healthy tissues.Here we investigated the feasibility of using microwave spectroscopy for characterization of normal and breast cancer cell lines cultured  Cancer originates when certain genetic abnormalities arise in a cell or group of cells. Such cells do not respond to normal signaling system of the body and hence divide and multiply uncontrollably forming bulk cancerous tissues. Cancer can be classified depending on the type of cell in which they originate. Breast, lung and colorectal cancers are the most common cancer types with highest incidence rates worldwide.2. The larger dielectric properties of cancerous tissues arise mainly from its increased water content and can contribute to the increased scattering of microwaves. An extensive amount of studies has been carried out to electrically characterize living tissues using microwave spectroscopy techniques17. Some of the earliest dielectric studies using open ended coaxial probe method were performed on animal tissues. Joines et al. measured electrical properties of rat tissues from 30 MHz to 2 GHz3, whereas Stuchly et al. characterized ex vivo cat tissue samples from 0.01 to 1 GHz4. The dielectric studies were later extended to human tissues. Joines et al. reported dielectric characterization of different types of human tissues ex vivo from 50 to 900 MHz5. It was estimated that the contrast in permittivity and conductivity between malignant tissues and normal tissues was 6.4:1 and 3.8:1, respectively. Lazebnik et al. performed the most extensive study to characterize breast tissues from 0.5 to 2 GHz12. His experiments unveiled the variation of breast tissue dielectric properties with respect to breast heterogeneity and adipose content. Martellosio et al. successfully evaluated breast tissue dielectric properties over a broad frequency range up to 50 GHz17.The increased incidence of cancer is a subject of major concern worldwide. Timely diagnosis of cancer can help in more effective treatment and reduction in mortality rate. Microwave based cancer diagnosis and treatment is one of the most relevant areas of research nowadays. Such research stems from the basic knowledge about the considerable contrast in the dielectric parameters of normal and cancerous tissues18. Cancer cell lines are frequently used as in vitro models of cancer and serve as powerful tools in learning cancer biology and testing cancer treatments. The main advantage of using cell lines is that when grown in a standard medium, they provide an unlimited supply of self-replicating homogeneous cell population. To a good extent, cell lines preserve the genomic properties of the original tissues from which they have been derived21. The degrees to which the cell lines represent their parent tissues are still being investigated. In this paper, we have studied the present the electrical signatures of different cancer cell lines dielectric properties of breast cell lines and determined the difference in electrical properties of normal and cancerous breast cells.In our studies, we experimented a different approach in which dielectric measurements are performed on cultured cell lines as reported inHuman breast cancer cells MDA-MB-231(HTB-26), Hs578T (HTB126), normal mammary epithelial cell MCF-10A (CRL-10317) and colon cancer cell line HT-29 were obtained from ATCC. Human breast cancer cells MCF-7 (300273) and T47D (300353) were obtained from Cell line service (CLS)-GmbH. MDA-MB-231, MCF-7 and HT-29 cells were maintained in DMEM  complemented with 10% fetal bovine serum (FBS). T47D cells were maintained in RPMI  complemented with 10% fetal bovine serum (FBS). MCF-10A were maintained in DMEM-F12 (In vitrogen) complemented with 15% FBS and 20 ng/mL human EGF recombinant protein (Sigma-Aldrich). All media were supplemented , 100 U/ml penicillin/streptomycin .5 cells/well and placed in 37 °C incubator with 5% CO2 for 24 hours to allow cells to form homogeneous confluent monolayer. Prior to measurement, cells were washed three times in phosphate-buffered saline solution (PBS) at room temperature and then resuspended in 1 ml cell culture media.For sample preparation, cells were seeded in triplicate in 12-well plates at a density of 4 × 10The open-ended coaxial probe (DAK-3.5 Dielectric Probe) developed by Speag was employed to determine the dielectric properties of cell line samples. An R&S®ZVL vector network analyzer 9 KHz to 13.6 GHz) and a Dielectric Assessment Kit software (DAK-3.5) were the key components of the measuring set up  to be measured was kept under the probe in such a way that the probe tip was slightly immersed in it without any air bubbles on the probe surface, and the probe kept at very close proximity from the base of the cell line plate, at about 0.75 mm. The prepared sets of samples were analyzed with and without culture media. For analysis without media, the solution was carefully removed by means of a sterile pipette. To avoid any contamination, the probe was wiped with ethanol in between measurements. It was also ensured that the probe or the cable was not moved during the measurements so as to avoid any kind of distortions in the amplitude and phase of the reflected waves through the cable. All measurements were acquired at room temperature in the 200 MHz-13.6 GHz frequency band. VNA computes the reflection coefficients at the probe tip and the DAK software converts it into dielectric parameters.ε′ is its real part representing the energy stored and ε″ is its imaginary part representing losses.ω is the angular frequency and εo is the free space permittivity.Microwave dielectric characterization by open ended coaxial cable method serves as a low cost, easy and fast technique to identify the dielectric properties of biological samples. The way biological samples interact with microwave radiation is governed by its dielectric properties, which in turn are the result of cellular or molecular level chemical constituents of the samples. The interaction of microwaves with biological matter is attributed to two different processes: energy storage caused by dipole polarization in response to external applied field and energy dissipation when the dipoles do not align instantly with varying field. The dielectric properties are therefore represented in terms of complex relative permittivity as23:fr is the relaxation frequency at which the dipole molecules fail to align with the field resulting in larger variations in real and imaginary parts of permittivity. At this particular frequency, real part tends to decrease and imaginary part shows a maximum value.Water, a polar molecule being the major chemical constituent of biological tissues contributes mainly to the interaction with electromagnetic radiation. The dielectric behavior of water molecules can be reasonably approximated using the well-known Debye relationIn addition to water molecules, cells contain other species such as free ions, carbohydrates, proteins and other organic chemical compounds. Ionic species contribute to energy dissipation and hence their behavior can be explained by adding an additional term in Equation  as5\docuSimilarly, the other species are also polar in nature and behave differently in an electric field. The effect of these species can be modeled by adding to these equations, appropriate parameters specific to their behavior.Biological cell characterization using microwaves is indeed more reliable as the waves with these frequencies completely penetrate the cell membranes, leading to accurate identification of the dielectric nature of intra cellular contents.p < 0.05, **p < 0.005 and ***p < 0.001).Statistical analysis were done using SPSS version 21 software. All statistical analysis were performed using one-way ANOVA test followed by Tukey’s HSD test. Significance for all statistical comparisons was set at p < 0.05  and two less invasive and metastatic (T47D and MCF-7) breast cancer cell lines along with the non-tumorigenic breast cell line (MCF-10A) have been characterized 16. The α-dispersion falls in the lower frequency range (kHz range) and is related to ionic diffusions at cell membranes. The β-dispersion which arises from cell membrane polarization occurs at the medium frequency range (lower MHz range). Finally, the With three replicates per sample, a total of 39 readings per experiment were taken from the samples including the culture medium. Experiment was repeated three times at different days and with freshly thawed cells each time. The dielectric data corresponding to each sample were analyzed and averaged. The permittivity and conductivity values of the five cell lines evaluated with and without the culture media are shown in Figs In this study, β-dispersion and most of the ε′ = 50 at 13.6 GHz. This type of behavior clearly indicates the high cytoplasm conductivity of the cell upon wave penetration. Hence, each cancer cell line can be differentiated based on their respective inflection point and dielectric responses. The point of inflection representing the frequency at which change in the behavior of each cell line is noted was 6.0, 4.7, 4.7 and 4.3 GHz for T47D, MCF-7, MDA-MB-231 and Hs578t cells, respectively. Dielectric constant of the non-tumorigenic cell line MCF-10 A, on the other hand, had much lower value, did not show inflection point and varied over the measurement spectrum at a relatively lower rate than that of cancer cell lines. Also, dielectric loss as well as conductivity exhibited a similar trend with respect to cancer cell line type but showed larger values to increase in frequency except for the non-tumorigenic cell line the values of which decreased at higher frequencies. As it is shown in Table 25. The frequency variation of dielectric parameters follows the Cole–Cole pattern23. The interactions of biological cells with microwaves are translated to relaxations and resonances of dielectric parameters. Our results are consistent with other published results for bulk tissues13.Between 4–7 GHz, the dielectric response was dominated by broad asymmetric peaks (points of inflection) in the spectra of dielectric constants of cancer cell lines. These inflection points denoted critical transition points after which the dielectric response reversed its trend, with T47D showing the highest value followed by MCF-7, MDA-MB-231 and Hs578T. In the On the other hand, in the absence of culture medium, cancer cell lines displayed lower dielectric values varying at a slower rate over the measurement frequency spectrum. The non-tumorigenic cell line exhibited the least dielectric response with slight or no variation with respect to frequency. Based on the results obtained from cell lines without the media, the contrast in the dielectric constant and conductivity of cancerous and normal cell lines was determined. The dielectric and conductivity ratio, at three specific frequencies, between normal and cancerous cells are summarized in Table Next, we analyzed the dielectric properties of the human colon cancer cell line HT-29. Figure In this study, we determined the dielectric response of breast and colon cell lines. The comparison of dielectric properties of breast cell lines revealed a significant dielectric contrast between normal and cancer cells. The dielectric response exhibited relaxations and resonances, which can be attributed to the dielectric relaxations of cells. Our analysis shows that microwave characterization of cell lines is reliable and has a strong potential in biomedical applications like cancer detection and designing electromagnetic models of healthy and tumorous tissues.


Genetically engineered pigs serve as excellent biomedical and agricultural models. To date, the most reliable way to generate genetically engineered pigs is via somatic cell nuclear transfer (SCNT), however, the efficiency of cloning in pigs is low (1–3%). Somatic cells such as fibroblasts frequently used in nuclear transfer utilize the tricarboxylic acid cycle and mitochondrial oxidative phosphorylation for efficient energy production. The metabolism of somatic cells contrasts with cells within the early embryo, which predominately use glycolysis. We hypothesized that fibroblast cells could become blastomere‐like if mitochondrial oxidative phosphorylation was inhibited by hypoxia and that this would result in improved in vitro embryonic development after SCNT. In a previous study, we demonstrated that fibroblasts cultured under hypoxic conditions had changes in gene expression consistent with increased glycolytic/gluconeogenic metabolism. The goal of this pilot study was to determine if subsequent in vitro embryo development is impacted by cloning porcine embryonic fibroblasts cultured in hypoxia. Here we demonstrate that in vitro measures such as early cleavage, blastocyst development, and blastocyst cell number are improved  when donor cells are cultured in hypoxia before nuclear transfer. Survival probability was increased in clones from hypoxic cultured donors compared to controls (8.5 vs. 4.0 ± 0.2). These results suggest that the clones from donor cells cultured in hypoxia are more developmentally competent and this may be due to improved nuclear reprogramming during somatic cell nuclear transfer. The pig, in particular, conveys great similarity to humans in their anatomy, physiology, and genomics, therefore, allowing them to exhibit symptoms of human pathologies more accurately and reliably than other animal models  between hypoxic (HYP) and control (CON) cultured fibroblast donors and was not different amongst cell lines (p = 0.44). While the total proportion of embryos cleaved was not different between donor fibroblast oxygen culture treatments (p = 0.75), HYP reconstructed clones had a higher (p = 0.03) proportion cleave earlier (within the first 24 hr) compared to control in both experiments  however some of these measures were lower in the cell line used for preliminary in vitro experiments (p < 0.01).Fusion of reconstructed embryos was not significantly different p = 0.0 betweenp < 0.01; Table p < 0.0001; See Table p ≤ 0.01; 24%) and similar amongst the other cell lines used . In utero, the homozygous KO line had lower survival than the heterozygous KO line  but not different from the Ossabaw line . Ossabaw and the heterozygous KO line did not have significantly different survival in utero (p = 0.05). From 446 embryos transferred into 12 surrogates, 6 surrogates became pregnant carrying 20 HYP and 9 CON fetuses  and cell lines . Survival probability was increased when clones were from HYP versus CON donors , epigenetic reprogramming, degree of pluripotency, and antioxidant treatment of cells  were cultured under hypoxic conditions the metabolism of the donor cells would become more blastomere‐like and thus it might improve reprogramming and subsequent cloning efficiency. Therefore we designed these experiments to test our hypothesis and see if this mechanism (hypoxic culture) would be effective and worth pursuing further. It was desirable that this ‘reprogramming’ induction be through a simple mechanism that could be applied to any cell line, such as altering the metabolism during culture via hypoxia. Previously our laboratory reported that oxygen restriction in porcine fetal fibroblasts (the same line used in in vitro experiments in the current study) induced expression of In this study, we demonstrate that measures of in vitro development were improved when fibroblasts cultured in hypoxia were used as donors for SCNT. Clones from HYP fibroblasts consistently cleaved earlier and produced more blastocysts with higher cell numbers Table , 2 regarThe improvement in blastocyst production from HYP donors is modest (5.5%) however this translated to a 1.5‐fold increase in cell number and 2.1‐fold increase in the probability of survival (fetuses attained). Overall these changes lead to a 2.5‐fold increase in system efficiency . In this study, our Day 35 pregnancy rate was 50% (6 of 12 surrogate gilts became pregnant) which is higher than previous experiments in our laboratory involving the same personnel . The homozygous SIGLEC1 KO line produced few Day 5 blastocysts, limiting the number of embryos we could equally transfer from each treatment. Of the two replicates, two of the four gilts became pregnant and whereas they were not transferred on the same day, between the two gilts all combinations of the homozygous and heterozygous KO lines and donor treatments were represented. Due to the decreased blastocyst production however we decided to use a different cell line (Ossabaw) in combination with the SIGLEC1 heterozygous KO line, a line proven to be ‘clonable’ and yield live animals which has been used for editing genes by the National Swine Research and Resource Center . According to another study from Kuhholzer, Hawley, Lai, Kolber‐Simonds, and Prather, . In this study, in similar terms, of the 223 embryos from HYP donors, there were 20 fetuses (~9%); the caveat being future studies must be conducted which carry HYP clones to term to ascertain this rate of efficiency holds true. However, at the present, we thought that these results were novel and worth reporting to the scientific community to open further exploration into mechanisms and effectiveness of hypoxic culture on donor cells. From these experiments, we propose that the clones from donor cells cultured in hypoxia may be developmentally competent and this may be due to improved nuclear reprogramming during somatic cell nuclear transfer. Implications of our results may be a method for improved efficiency for cloning of pigs.4Chemicals and materials were purchased from Sigma‐Aldrich, St. Louis, MO unless otherwise specified.4.1All procedures performed in studies involving animals were approved and conducted in accordance with the ethical standards of the University of Missouri Institutional Animal Care and Use Committee at the University of Missouri in Columbia, Missouri. This study does not contain any experiments with human participants.4.2Porcine fetal fibroblast cell lines collected from dorsal regions  used in the study were established from d 35 pregnancies and utilized in previous experiments in our laboratory , a system of four nitrogen tanks connected by one gas line was assembled to ensure adequate nitrogen gas was available to incubators. The regulators of nitrogen tanks were set to sequentially empty the tanks one by one as culture experiments required a large quantity of nitrogen gas, and at least one of the tanks had to be exchanged for a full tank daily. During this, oxygen concentration of incubators was regularly monitored by a handheld gas monitor  which was professionally tested by Utility Lab Services  and proved to have an error of ±0.05% oxygen. Therefore the low oxygen treatment may have been as high as 1.25% or as low as 1.15% oxygen. Incubators were maintained at 38.5°C with a humidified atmosphere of 5.5% carbon dioxide during experiments.In experiments where the modular incubator chamber  was used (four replicates of embryo transfer experiments), petri dishes of sterile water were placed in the bottom of the chamber below the grate to provide humidity. Vented cell culture flasks were placed inside chambers, which were then sealed and flooded with mixed gas for several minutes to displace gas within and achieve hypoxic conditions. Afterward, air ports were sealed and chambers were placed in the standard incubators with controls.g) and either diluted and replated (Day 5) or approximately 2,000 cells/treatment were collected and placed in droplets to be used for selection during SCNT (Day 7).Cells from all treatments were plated at the same initial densities between treatments and either passaged on Day 5 of growth and plated at the same density (if needed) or had gas‐equilibrated media changed and allowed to grow 7 days consecutively. Passaging was done quickly, and dissociation reagents were kept cold in an effort to decrease the metabolism of cells and prevent the metabolism of oxygen as much as possible. Media that cells were plated in had been equilibrated to desired oxygen concentrations by incubating media in the incubators in petri dishes or within modular incubator chambers for an hour or more. At passaging, flasks were briefly rinsed with phosphate buffered saline (PBS) + 0.01 M ethylenediaminetetraacetic acid, and fibroblast cells were dissociated from flasks by brief incubation (37°C) with 1× TrypLE Express . Fibroblasts were pelleted  by two DC pulses  at 1.2 kV/cm for 30 µsec using a BTX Electro Cell Manipulator . After electric pulse fusion, fused embryos were also chemically activated with 200 µM thimerosal (in 1× PVA‐TL Hepes) for 10 min in the dark and 8 mM dithiothreitol (in 1× PVA‐TL Hepes) for 30 min  were surgically transferred into the oviductal ampullary‐isthmic junction of surrogate gilt recipients (3–5 days postobserved estrus). Surgical embryo transfer technique was conducted as previously established within our laboratory  at 35 of embryonic development. The reproductive tract was excised from the cervix to ovaries and opened along the entire length of uterine horns for fetal collection. Tissue was excised from fetuses and washed in 1× PBS then frozen until DNA extraction and genotyping. To establish which donor cell treatment survived better in utero, fetuses were genotyped Figure  via poly et al., . When ba4.6p < 0.05.For in vitro developmental measures, nine replicates  were used in analyses. Three replicates of the same donor cell line were used to compare cell number within blastocysts for donor cell oxygen culture treatments . Embryo transfer data consisted of 446 total blastocysts transferred into 12 surrogates (6 replicates of surrogate pairs). Data distributions and Shapiro–Wilk test of normality values were generated using the Univariate procedure of SAS ; log or square root transformations were made before statistical analysis where necessary to meet assumptions for analyses. All data were analyzed using a MIXED procedure in SAS for main effects of donor cell treatment and cell line; interactions were assessed however this effect was removed from analyses when found to be insignificant. For embryo transfer data, blastocysts transferred were nested within surrogate gilt and a random effect of replicate was used (mainly because it corresponds to the time/season differences for this study duration). Survival probability was considered the number of favorable outcomes (viable fetuses) from the total (embryos transferred). Analyzed variables were considered statistically different if the The authors declare that there are no conflict of interests.


Cystoisospora was created in 1977  cells  cells . Developexamined . DevelopC. canis was examined in MDCK cells and primary cells from embryonic canine kidneys, and embryonic canine intestine as well as embryonic bovine trachea and kidney (C. canis sporozoites using bovine turbinate and African green monkey kidney (CV-1) cells provided remarkedly different findings of no asexual multiplication but rather MZTC formation in both host cell types (C. belli MZTC in the tissues of humans (C. canis in mice (C. ohioensis in mice (C. felis in mice (C. canis using two canine, four human, one monkey, and one bovine cell lines (C. canis isolates by different research groups. It suggests that two different genetic types of C. canis exist and that one divides by endodyogeny in culture and the other produces MZTC. How this would translate to in vivo development and pathogenicity in dogs is also an interesting area needing research.Development of sporozoites of d kidney . Developll types . Examinaf humans , 18, C.  in mice , C. ohio in mice . The orgll lines . No asexll lines  and Houkll lines  with drall lines . AdditioC. suis in five types of mammalian host cells in culture indicated that development was by endodyogeny (C. suis development of Type I meronts by endodyogeny in these cell cultures (in vitro (C. suis was observed in a swine testicular cell line (The initial studies on the development of odyogeny  and stagodyogeny . Similarodyogeny . Howeverodyogeny . The ultcultures  was examcultures . Notablyin vitro . Completell line . Developell line . Oocystsell line . The stuell line .C. suis conforms to all other reported features of the genus.No evidence of MZTC formation has been reported in cell cultures. Similarly, studies in pigs and potential paratenic hosts have been negative , 29. ThiC. belli in human and other mammalian cell cultures (C. belli as are present in C. suis (in vitro but otherwise the structural findings were similar to those reported for C. suis. Stages of C. belli obtained from macrophage cell cultures were infectious for RMK cells upon subinoculation and development by endodyogeny occurred (Four studies have examined development of sporozoites obtained from oocysts of cultures –33. The cultures . The aut C. suis  while unoccurred  but MZTCCystoisospora species from mammals and to provide insight into future research directions. Development will occur in many host cell types but studies with C. suis illustrate that selection of the proper host cell type can lead to complete development thus increasing the utility of an in vitro system. Studies with C. canis suggest that genetic differences in Cystoisospora species exist that are reflected by the phenotype of development expressed in vitro. I look forward to seeing what advances in our knowledge of this group are revealed in the future.I have attempted to point out the similarities and important differences in the development of The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


G12D protein, but wildtype Kras was expressed by all of the cells. Signal transduction analysis  revealed expression levels similar to the wildtype. The results described herein indicate that knocking-out the KrasG12D mutation by CRISPR/Cas9 is possible. Additionally, under regular growth conditions, the knock-out clones resembled wildtype cells.In 90% of pancreatic ductal adenocarcinoma cases, genetic alteration of the proto-oncogene Kras has occurred, leading to uncontrolled proliferation of cancerous cells. Targeting Kras has proven to be difficult and the battle against pancreatic cancer is ongoing. A promising approach to combat cancer was the discovery of the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas) system, which can be used to genetically modify cells. To assess the potential of a CRISPR/CRISPR-associated protein 9 (Cas9) method to eliminate Kras mutations in cells, we aimed to knock-out the c.35G>A (p.G12D) Kras mutation. Therefore, three cell lines with a heterozygous Kras mutation (the human cell lines SUIT-2 and Panc-1 and the cell line TB32047 from a KPC mouse model) were used. After transfection, puromycin selection and single-cell cloning, proteins from two negative controls and five to seven clones were isolated to verify the knock-out and to analyze changes in key signal transduction proteins. Western blots showed a specific knock-out in the Kras Pancreatic cancer (PaCa) is the fourth leading cause of all cancer death in the United States of America and Germany. Smoking, chronic pancreatitis, heavy alcohol consumption or genetic predispositions, such as Lynch syndrome, are only a small variety of risk factors. Common symptoms, including weight loss, abdominal or back pain, mean that identification during the early stages can be difficult. With its disastrous five-year survival rate of about 8%, the chances of survival after diagnosis are remote. Besides chemotherapy or radiation therapy as therapeutic options to alleviate pain or extend survival, the only potentially curative prospect remains resection ,2,3,4. TIn about 90% of PaCa cases, a Kras mutation is detectable . Kras isIn recent years, whole-genome sequencing led to the discovery of clustered regularly interspaced short palindromic repeat (CRISPR) sequences and CRISPR-associated (Cas) genes in various bacteria and archaea . The ideG12D (c.35G>A) mutation. After transfection, puromycin selection and single-cell cloning, we produced about five to seven clones (Kras clones). Western blotting was performed to detect the knock-out effects introduced with the CRISPR/Cas9 gene editing system. In all tested cell lines, the results showed a knock-out with no protein expression of mutant protein KrasG12D. However, a normal total Kras protein expression level was observed for Panc-1 wildtype (WT) cell lines, negative controls  and Kras clones. In the SUIT-2 cell line, WT and Kras clones resembled each other, whereas the protein expression in the negative controls was higher. Total Kras expression in the TB32047 clones was lower than in the WT and negative controls  and a murine cell line (TB32047) were used to perform a CRISPR/Cas9-based knock-out of the mutated allele. All cell lines contained a heterozygous KRAScontrols .G12D. All knock-out clones had indel mutations in the sequence of KrasG12D. Most clones  had small indels (< 20 bp), while Panc-1 clones 2.7 and 2.14 had single-nucleotide insertions. Only three clones, one from each cell line , had large indels (>20 bp)  see .G12D protein knock-out, the effect on key signal transduction pathways was analyzed. Specifically, the (non-)phosphorylated proteins Erk, Akt, Stat3, AMPKα and c-myc were evaluated . Approximately half of the DEGs (218) were up-regulated, while the other half (199) were down-regulated (see Lamb1 (log2 fold-change = −5) and Slc27a6  data from eight TB32047 samples  were analyzed to determine RNA expression changes in the Krasples see A. A totaples see . PrincipPC1, see B. In addated see C. Among As part of the G-protein family, Kras is involved in the regulation of cell proliferation and survival. Inactivation of its GTPase activity, as a result of mutation in the protein, leads to hyperactive effector signaling. Therefore, mutated Kras plays a major role in and is the driver of PaCa initiation. Mouse models have shown that an elevated frequency of activation in Kras leads to precursor lesions and the onset of PaCa ,18,19. HG12D using the CRISPR/Cas9 gene editing system in all of the three analyzed Kras heterozygous cell lines . Interestingly, the cell cultivation showed no apoptosis or growth arrest, only a decreased growth rate compared to the wildtype could be observed. This could be due to knocking-out mutated Kras, which can result in a slower growth ability.We were able to successfully knock-out KrasWe verified the knock-outs using DNA sequencing and western blotting (WB). However, we observed no changes in the expression level of total Kras. These results suggest that Kras is not important for maintaining PaCa cells in vitro .G12D does not affect one common pathway of signal transduction, but that each cell line displays different effects. Others state that PI3K/Akt hyperactivation is important in PaCa cells lacking Kras [Furthermore, we looked for changes in key signaling pathways, such as mitogen-activated protein kinase (MAPK) or PI3K/Akt, which are known to be the major effector pathways of Kras activation. Our work indicates that the knock-out of Krasing Kras . But PanIf the clones are not using MAPK or PI3K/Akt pathway to survive, how can they resist apoptosis? Besides Erk and Akt, we analyzed the signal transduction proteins Stat3, AMPKα and c-myc. Stat3 has an anti-apoptotic effect induced by TGF-beta and is engaged in cell survival, whereas c-myc regulates cell cycle progression, cell growth, and apoptosis . Our resLamb1) and solute carrier family 27, member 6 (Slc27a6). As part of a subunit which assembles to a heterotrimeric isoform, Lamb1 facilitates cell differentiation, motility, adhesion and—as a ligand of the laminin receptor—it enables the ability of tumor cells to invade and build vessels [G12D knock-out these two necessary genes are down-regulated, which could negatively affect intensive angiogenesis, invasion and energy supply and therefore provide a useful tool to combat PaCa.Through RNA sequencing, we gained insight into the changed gene expression of edited TB32047 clones. We identified 417 differentially expressed genes. Two heavily down-regulated genes were Laminin B1 . Panc-1 cells were cultured in Roswell Park Memorial Institute (RPMI) medium 1640 . The SUIT-2 cell line was purchased from Japanese Collection of Research Bioresources Cell Bank . SUIT-2 cells were cultured in minimum essential medium . The TB32047 cell line was obtained with courtesy from Prof. Dave Tuveson, Cold Spring Harbor Laboratory. TB32047 cells were cultured in Dulbecco’s modified Eagle’s medium . Every culture medium was enriched with a final concentration of 10% fetal bovine serum (FBS) . All cell lines were grown under sterile conditions to avoid contamination. For cultivation the cells were incubated in monolayer culture at 37 °C, 5% CO2 and 80% air humidity. All cells were harvested by treatment with 0.25% trypsin-ethylenediaminetetraacetic acid .The human cell lines Panc-1 and SUIT-2 and the murine cell line TB32047 were used in this study. The Panc-1 cell line was bought from ATCCG12D was knocked out by the CRISPR/Cas9 gene editing system. The following primers were synthesized by Eurofins Genomics: Hs_KRASm_sg1-forward (5′-CACCGTAGTTGGA GCTGATGGCGT-3′), Hs_KRASm_sg1-reverse (5′-AAACACGCCATCAGCTCCAATAC-3′), Hs_KRASm_sg2-forward (5′-CACCGCTTCTGGTAGTTGGAGCTGA-3′), Hs_KRASm_sg2-reverse (5′-AAACTCAGCTCCAACTACCACAAGC-3′). Cloning was performed using pSpCas9(BB)-2A- Puro (PX459) V2.0 vector, which was a gift from Feng Zhang  [® 10G electrocompetent cells . Plasmid construction was performed according to protocol and confirmed by sequencing. The above-mentioned cell lines were transfected with KrasG12D knock-out plasmid by Lipofectamine® 3000 Transfection reagent  for 24 h. After one day the transfected cells were selected with puromycin (concentration 5 ng/μL). Mouse epidermal growth factor (mEGF) was added to the clone culture medium. Thereafter, single-cell cloning was performed in a 96-well plate to grow single clones. After growth, western blot and sequencing were performed to detect the knock-out.In this study, Krase_62988) . The lig® BCA Protein Assay Kit. Gel electrophoresis was performed with Bolt™ 4–12% Bis-Tris Plus Gels  and proteins were transferred to a nitrocellulose membrane. After blocking with 5% milk, the following primary antibodies were used: Akt (pan) , phospho-Akt (Ser 473) , AMPKα , phospho- AMPKα (Thr172) , c-myc , p44/42 MAPK (Erk1/2) , phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) , Ras , Ras (G12D Mutant Specific) , Stat3 , phospho-Stat3 (Tyr705) . β-Actin , glyceraldehyde 3-phosphate dehydrogenase  and Na,K-adenosine triphosphate (ATP)ase  served as loading controls. As secondary antibodies, horseradish peroxidase (HRP)-linked anti-mouse immunoglobulin G (IgG)  and HRP-linked anti-rabbit IgG  were utilized. The signals were detected with an Amersham Imager 600 with SignalFire™ (Elite) ECL Reagent . All western blot assays were performed more than three times to validate the reliability of the results.To analyze the protein expression of the genetically modified cell clones, the cells were lysed using radioimmunoprecipitation assay (RIPA) buffer. The protein concentration was determined photometrically using Pierce® Tissue  was used according to the manufacturer’s protocol. The DNA concentration was determined using the NanoDrop 2000 . PCR products for sequencing were amplified using Platinum™ SuperFi™ PCR Master Mix . PCR fragments were displayed using gel electrophoresis. The following primers were used for the human cell lines: hKras_f1 (AAGCGTCGATGGAGGAGTTT) and hKras_r1 (ACCCTCTCAC GAAACTCTGA); and for the murine cell line Mm_Kras_f1 (ACTCTGTACATCTGTAGTCACTG) and Mm_Kras_r1 (GCACGCAGACTGTAGAGCA) were used. PCR fragments were cloned into pMiniT 2.0 with the NEB® PCR Cloning Kit . For each single cell clone 10 bacteria colonies were picked and plasmid DNA was isolated . Plasmid DNA was sequenced by Eurofins Genomics and the resulting sequences were aligned using Snapgene .To isolate DNA from the cell lines, NucleoSpin® RNA Plus kit  was used to isolate RNA from the cell lines according to the manufacturer’s protocol. The RNA concentration and pureness were determined using a 2100 Bioanalyzer using the Agilent RNA 6000 Nano Kit .The NucleoSpin2 fold-change ≥ 2 or ≤ −2 were considered differentially expressed. Normalized rlog-transformed gene counts were calculated using the DESeq2’s rlog function.Profiling of whole-genome transcriptomic patterns was performed with 500 ng of high-quality total RNA per sample by RNA sequencing at the next generation sequencing (NGS) core unit of the Medical Faculty of the FAU Erlangen. Libraries of the samples were generated using the Illumina stranded mRNA library kit. After sequencing on an Illumina HiSeq-2500 platform, the sequences were assigned to their sample of origin according to the used indices. The quality of reads was assessed using FastQC v0.11.4 . Adapterhttp://david.abcc.ncifcrf.gov/) [Functional and pathway analysis was performed with the Database for Annotation and Integrated Discovery  ,35. In pG12D in human and mouse cell lines. This leads to cell clones that exhibit different aspects of Kras signal transduction.In conclusion, the CRISPR/Cas gene editing system can be used to knock-out Kras