Body Busters and Stop the Spread are 2 new e-Bug educational games.This study aimed to determine students’ baseline knowledge, views on the games, and knowledge improvement.Students in 5 UK educational provisions were observed playing 2 e-Bug games. Before and after knowledge and evaluation questionnaires were completed, and student focus groups were conducted.engaging and enjoyable, whereas Stop the Spread was fast-paced and challenging but increased vaccination and health behavior intentions.A total of 123 junior and 350 senior students completed the questionnaires. Vaccination baseline knowledge was high. Knowledge increased significantly about antibiotic use, appropriate sneezing behaviors, and vaccinations. In total, 26 student focus groups were conducted. Body Busters was e-Bug games are an effective learning tool for students to enhance knowledge about microbes, infection prevention, and antibiotics. Game-suggested improvements should help increase enjoyment. Educating children and young people is important in the fight against antibiotic resistance. Through education, we can raise awareness, enhance knowledge, and modify behavioral intentions about hygiene and antibiotic use in our future generation of antibiotic users. E-Bug, led by Public Health England (PHE), is an international health education resource that teaches children and young people about hygiene, the spread of infections, antibiotic use, and resistance. e-Bug includes Web-based lesson plans and activities for educators and educational games for students hosted on the e-Bug website. Evaluation of e-Bug activities to be undertaken in schools and science shows has been well-documented -6, and tGoogle Analytics has been used to monitor Web traffic to the e-Bug website since 2010 [Gamification, where features of gaming are used in other disciplines, has become increasingly popular in recent years, aiming to make science and health education more available and exciting to the general public. Serious games are those games where the primary focus is not entertainment but education and learning [The internet is a suitable tool for health promotion, and internet-based health interventions have been shown to change health behaviors. learning . A meta-learning .Evidence suggests that gamification and serious games for health and well-being are most effective when targeting health-related behaviors . For insThe e-Bug Web-based educational games  have beeThe aim of this study was to evaluate the 2 e-Bug educational games: Body Busters, previously evaluated by Hale et al and then modified with new content , and StoWhat is students’ baseline knowledge about the game-learning outcomes?What is students’ change in knowledge following the games?What are students’ views on the games to suggest improvements?The study was a mixed-method evaluation using quantitative and qualitative methods. Quantitative methods included before and after students’ knowledge questionnaires; qualitative methods included students’ focus groups and open-ended questions and responses from the students’ postgaming evaluation questionnaire.Educational providers, including schools and summer schools, were invited to take part in the study through convenience recruitment of educators at educational and scientific conferences and then through snowball sampling. Sampling aimed to ensure a representation of school-aged children across 3 local authorities in the United Kingdom, including rural and urban schools, different socioeconomics, and selective grammar and nonselective state schools see . Local aAll researchers who observed the sessions had a Disclosure Barring Check, through PHE, to work with children. This study did not require National Research Ethics Service approval as it was outside the National Health Service and was classed as a service evaluation. PHE provided written confirmation approving the service evaluation in July 2016. Educational providers gave informed written consent before the study took place; students were involved and their parents were given the option for students to opt out at any point during data collection. Teachers reported that no students opted out of the research. Consent was deemed accepted if the participants completed the before and after knowledge questionnaires. Questionnaires were collected in line with the Data Protection Act 1998 and Caldicott 1999 regulations on handling and distributing sensitive participant information. Focus group participants provided verbal informed consent for participation in the study, audio recording, and the publishing of anonymized quotes.Data collection took place between August 2016 and July 2017.general questions  students completed pregame-play questionnaire 1 alone without consultation, (2) students played on Body Busters for 5 min, (3) students played on Stop the Spread for 5 min, and (4) students completed postgame-play questionnaire 2 alone without consultation, which also included some additional open-ended evaluation questions. The game play was for 5 min to follow the methodology of previous evaluations , and as A researcher was present in each session to monitor and observe game play and hand out and collect questionnaires from the students. Data collection occurred in a convenient room where students had their own computer. The rationale for that was to model how the games might be played in a real-life teaching situation. McNemar test was used for each response from the multiple-choice question to determine the significance of the difference in the proportion of correct answers before and after game playing. Moreover, 95% CIs of the odds ratio were estimated to determine the odds of students answering correctly. Analysis was performed separately for junior and senior school-aged pupils, as knowledge change could differ between age groups. All statistical analysis was completed in STATA, version 14.2.The postgame-play questionnaire 2 included an additional 7 questions on game enjoyment, including 2 Likert scale questions  and 5 open-ended questions. Likert scale responses were inputted into MS Excel, and mean enjoyment scores for each game were calculated for junior, senior, and all students.In total, 5 open-ended questions on enjoyment were included in the postgame-play questionnaire 2 to provide deeper qualitative data from all students.Semistructured focus groups were facilitated immediately after the game intervention by VLY, CVH, and CVE who are all trained qualitative researchers for the e-Bug project, PHE. All 26 focus groups took place in person in a convenient room at the educational establishment. Focus groups of size 4 to 6 students, chosen by the class teacher and represented a mix of student abilities, lasted for 6 to 20 min depending on participant age. Focus groups were audio recorded, transcribed verbatim, and checked for accuracy by CVH or CVE. Furthermore, 26 focus groups were planned with all 5 schools participating; no new themes emerged from the later focus groups, and researchers agreed that data saturation had been reached.The topic guide for the focus groups was based on previous e-Bug evaluation topic guides for e-Bug and included additional questions on Stop the Spread learning outcomes. The schedule was piloted during the e-Bug game development in 3 testing sessions in schools in March 2016.All focus group data and open-ended responses on enjoyment were inputted into NVivo 10  qualitative analysis software. NVivo 10 was used to organize, code, and analyze the focus group transcripts by CVH and open-ended evaluation responses by CVE. A subset of focus group data (2 junior and 2 senior transcripts) was analyzed by a second researcher (CVE) to ensure reliability. Both researchers discussed the data and coding to agree on the emerging themes before developing a thematic framework. Any discrepancies between researchers were resolved through discussion until an agreement was reached. The thematic framework was discussed by the research team.The study recruited 473 students (123 junior and 350 senior students) aged 7 to 16 years from 5 educational providers across 3 local authorities in the United Kingdom (illustrated in Before and after knowledge questionnaires were completed by 473 students. Baseline knowledge about vaccinations was high in junior students (>60% correct responses) and was higher in senior students (>80% correct responses), except for 1 question for which the baseline knowledge 55%. Baseline knowledge about antibiotics was low in junior and senior students . Senior students had greater pregaming knowledge than junior students in 11 out of the 12 questions and had greater postgaming knowledge in 10 out of the 12 questions. However, knowledge change was greater in 9 out of the 12 questions for junior students.P<.05) about antibiotic use, appropriate sneezing behaviors, and vaccinations for both age groups: junior (7-11 years) and senior (11-16 years).Quantitative results showed significant improvements in knowledge  and 16 senior (11-16 years) with approximately 4 to 6 students per focus group. Researchers observed that students enjoyed playing Body Busters more, and they were keen to answer questions to gain more lives, whereas Stop the Spread was more difficult and on occasion required some researcher explanation. The mean enjoyment score for Body Busters was 8.4/10 for juniors and 7.2/10 for seniors; the mean enjoyment score for Stop the Spread was 6.2/10 for juniors and 5.1/10 for seniors  and senior (11-15 years) school-aged students, respectively. Body BustersWhen the user loses a life, do not slow the avatar down but either:increase the speed of harmful bacteria and viruses orincrease the size of harmful bacteria and virusesMake instructions clearerkeep the pictorial instructionsadd written instructionsHave a visible key for the different microbes in the game playAdd more levels in different areas of the bodyStop the SpreadSlow the pace of the game down to make it easierhave fewer children sneezing at the same timeslow the time down between students sneezingInclude more levels of different difficultyeasy levelhave an option for an have a tutorial or practice levelMake the instructions simpler and clearerOver 70% of junior students had high baseline knowledge of learning outcomes covered in questions 6, 7, and 11, and there was only a small nonsignificant increase in correct answers:P=.36)(6) All microbes are bad or harmful (7) You cannot infect other people around you through coughs and sneezes. True or false .(11) Vaccinations protect us from catching and spreading diseases  was seen in questions 1, 3, 4, and 10:P=.29)(1) Which of these microbes causes coughs and colds? Bacteria or fungus or virus (3) Antibiotics help cure colds? True or false (4) Which of these infections could antibiotics be used to treat? Bacterial or viral or fungal .(10) Antibiotics: Knowledge of the 7- to 11-year-old students significantly improved for 4 of the questions :P=.01)(4) Which of these infections could antibiotics be used to treat? Bacterial or viral or fungal (8) The more people are vaccinated; the more people are protected from that infection. True or false (10) Antibiotics: P=.01).(12) The best way to stop microbes in coughs and sneezes spreading is to: “catch coughs and sneezes in a tissue and throw the tissue away” :Knowledge improvement for 2 other questions (5 and 9) was approaching significance (P=.06)(5) Most coughs and colds get better without antibiotics. True or false .(9) By getting vaccinated, you can also protect others around you from infection. True or false , 8 , and 10 . Questions that saw the greatest improvement in knowledge for junior students were question 4 , which covered knowledge that could be indirectly gained from playing the 2 games.Kill good and bad bacteria).Senior school students had greater baseline knowledge than junior students in 10 out of the 12 questions. Senior students had high baseline knowledge (>92% correct scores) to the same 3 questions as junior students . In addition, the 2 questions (8 and 12) on vaccinations and sneezing behaviors had scores less than 70%. Senior students had significantly higher baseline knowledge scores for all bar 2 questions, so it was difficult to improve as much as the junior students. Low baseline knowledge (<40% correct scores) was seen in question 10 , which were the same 6 questions that showed significant improvements among junior students. Questions that saw the greatest improvement in knowledge for senior students were questions 9  and 10 . Question 6 , evaluating a learning outcome of Body Busters, and a general question 3  showed a significant decrease in knowledge .There was no evidence of a significant knowledge change for questions 7  and 11 (Vaccinations protect us from catching and spreading diseases), for which over 90% answered correctly before playing the games. Two other general questions, 1 (Which of these microbes causes coughs and colds?) and 2 (What is the best way to treat an infection with a virus?), did not see a significant change in knowledge.P<.05) positive effect on students’ knowledge on 6 out of the 12 questions:This study indicates that playing the 2 e-Bug games had a significant Antibiotics kill good and bad bacteria (question 10)P=.06; question 5)Most coughs and colds get better without antibiotics (junior The best way to stop microbes in coughs and sneezes spreading is to catch coughs and sneezes in a tissue and throw the tissue away (question 12)The more people are vaccinated, the more people are protected from that infection (question 8)P=.06; question 9).By getting vaccinated, you can also protect others around you from infection  Antibiotics help cure colds (6) All microbes are bad or harmful .Suggestions for this detrimental effect include the following: question 3 was perhaps not obvious that viruses cause colds in either of the games, and question 6 had a very high baseline knowledge of 95%; therefore, it would have been difficult to see an improvement in knowledge. Modifications to the games will be required to address this detrimental effect, and developers should consider the age group of their target audience.Another main finding of the study was the comparison between age groups of baseline knowledge and postintervention knowledge: junior (7-11 years) and senior (12-15 years). Baseline knowledge for senior students was higher than junior students on 11 out of the 12 questions; juniors scored 4.3% higher in question 9. Postintervention knowledge for senior students was higher than that for junior students in 10 out of the 12 questions; the 2 questions that were lower than juniors (2 and 9) were only lower by 0.3% and 4.3%, respectively. Positive knowledge change for juniors was greater in 9 out of the 12 questions compared with senior students, suggesting that the e-Bug games had a greater impact on junior student knowledge; the researchers therefore recommend that the e-Bug games should be targeted at junior school–aged children and should be further promoted to this age group.The high baseline knowledge for senior students could be a reflection that the questions were too easy for older students and perhaps senior students obtained other learning from the games, which researchers could have picked up with different or more difficult questions.Overall, both junior and senior students reported Body Busters to be more enjoyable than Stop the Spread on the Likert scale responses and thematic analysis of the focus group transcripts; this is supported by Google Analytics. During the academic year, September 1, 2016, to August 31, 2017, junior and senior students viewed Body Busters  more than Stop the Spread . During the same academic year, on average, junior and senior students played Body Busters  for longer than Stop the Spread . This may be because Body Busters was easier and more enjoyable; however, Stop the Spread lead to a greater improvement in knowledge, particularly about appropriate sneezing behaviors and vaccinations in this study. Suggestions for improvements on both games were provided by students, and the e-Bug team will consider the suggestions when making modifications for improvement.A mixed-method approach is a strength of the study; using both quantitative and qualitative methods of enquiry enables students’ knowledge to be measured and students’ views and intentions to be explored in some depth. The study is cross-sectional and representative of schools and students across the United Kingdom; a large number of students from a range of schools in different areas of the United Kingdom with different levels of deprivation were involved. This allows us to evaluate baseline knowledge about vaccinations and antibiotics in young people, the largest sample of this type. Baseline knowledge can be used to inform educational needs in different age groups within National Institute of Health and Care (NICE) recommendations. Qualitative focus groups enabled the exploration of a range of students’ views; it also brought synergism, snowballing of ideas, and stimulation of participants, which will assist in making improvements to the games.The study evaluation allowed students to play each game in a classroom setting for only 5 min. This mimicked a real-life class setting where they would usually play Web-based games and discuss the games together, and 5 min is the usual length of time it takes to play 1 game. However, the 5 min of game play might not replicate the normal duration for game play; 5 min per game might not have been long enough for some students to gain the desired knowledge. For example, in Body Busters, viruses only appeared in level 3, and some students might have struggled to reach this level, which could partly explain any variation in percentage correct answers. The intervention in this study was used in isolation and perhaps the learning outcomes can be achieved better when the games are used as a tool to reinforce teaching about each topic in the classroom or in the home environment.The questionnaires used in this study were based on questionnaires that have been used in previous e-Bug evaluations ,3,16. HoBaseline knowledge was very high in senior students, especially about vaccinations and sneezing behaviors, so there is little need for improvements; however, modifications, including adding more levels to the games or adding extra learning outcomes, are required.In the focus groups and evaluation questionnaires, junior students found it difficult to vocalize their thoughts beyond close-ended questions. Furthermore, many junior students found writing answers to the open responses on the evaluation form difficult to express their views, which was observed by researchers during data collection; however, data saturation was reached during the focus groups, suggesting no new themes would emerge.Improvements in students’ knowledge after the delivery of an e-Bug lesson ,3,4,6 anA previous evaluation  of an eaSystematic reviews and meta-analyses of gamification, serious games, and apps about health topics found that they can have a positive impact on health and well-being in the general population -20. OtheSuperbugs, showing this is a rapidly growing area of serious games. An infection prevention gamification tool called Germ Defence, encouraging individuals of any age to pledge to wash their hands more often, has been evaluated as part of a large RCT [Other antimicrobial games have been developed but very few have been evaluated and on such a large scale. Recently, there has been an increase in serious games about the topic of microbiology and antimicrobial resistance, such as the Longitude prize’s arge RCT . The RCTarge RCT , providikeep antibiotics working. Students had significant increase in knowledge and behavioral intentions about appropriate sneezing behaviors, which directly support the 2013 national campaign catch it, bin it, kill it. Future national infection-related public health campaigns could link to the e-Bug games to encourage schools to use the games in their teaching and reinforce the campaigns.Students had significant increases in knowledge about antibiotics, showing that e-Bug helps to reinforce the 2017 national antibiotics campaign Further research is required to investigate whether the knowledge gained from the e-Bug games is maintained or has changed future behavior. Additional qualitative research with teachers is needed to explore and understand how the e-Bug educational games can be used in a lesson to support learning.e-Bug will continue to follow NICE guidance and work with educators and students to develop and promote resources for teaching children and young people about microbes, infection, and antibiotics in a fun and interactive way.Science pedagogy Web-based games, including the e-Bug games, have the potential to engage and excite children and young people about important public health topics and aid in the learning of knowledge. To increase gaming, the e-Bug games should be both fun and challenging.This study shows that 2 e-Bug educational games, Body Busters and Stop the Spread, covering learning topics about microbes, infection prevention, and antibiotics, are valuable to school-aged children’s knowledge. Body Busters is greatly enjoyed by and engaged school-aged children; a few modifications about antibiotics that can kill good and bad bacteria are required to reinforce learning outcomes. Stop the Spread is enjoyed by school-aged children to a lesser degree and more modifications, including slowing the game down, are required to retain user engagement. However, learning outcomes are very well covered in Stop the Spread. Health commissioning schools should target and promote the Body Busters and Stop the Spread e-Bug games, especially toward junior students (aged 7-11 years), as they showed the greatest improvement in knowledge. Further levels with more learning outcomes will facilitate increased learning in older students (aged 12-15 years).


Ascl1, a previously known oscillatory gene in NSCs. Pharmacological blockade of Ntsr2, which is transcriptionally co-regulated with Ascl1, showed that Ntsr2 may play an important role in the differentiation from NSCs to astrocytes. This study shows the importance of characterizing transcriptional heterogeneity and rearrangement of the co-regulation network between different cell states. It also highlights the potential for identifying novel regulators of cell differentiation that will further increase our understanding of the molecular mechanisms underlying the differentiation process.There are an increasing number of reports that characterize the temporal behavior of gene expression at the single-cell level during cell differentiation. Despite accumulation of data describing the heterogeneity of biological responses, the dynamics of gene expression heterogeneity and its regulation during the differentiation process have not been studied systematically. To understand transcriptional heterogeneity during astrocyte differentiation, we analyzed single-cell transcriptional data from cells representing the different stages of astrocyte differentiation. When we compared the transcriptional variability of co-expressed genes between the undifferentiated and differentiated states, we found that there was significant increase in transcriptional variability in the undifferentiated state. The genes showing large changes in both “variability” and “correlation” between neural stem cells (NSCs) and astrocytes were found to be functionally involved in astrocyte differentiation. We determined that these genes are potentially regulated by  In addition to average gene expression levels across stem cell populations, the variation in gene expression between single cells triggered by biological oscillations can provide new insights. A team led by Hiroyuki Honda at Nagoya University in Japan introduced the concept of analyzing gene expression heterogeneity within a cell population to elucidate the mechanism of astrocyte differentiation. By scoring the variability and correlation within single-cell gene expression data, the expression heterogeneity in a cell population was found to increase before differentiation. The analysis highlighted key differentiation regulators like Ascl1, Sox9, and Ntsr2. This work underscores the importance of understanding transcriptional heterogeneity to identify key genes in stem cell differentiation that could not be found through conventional “level-focused” expression analysis. To understand the complexity of biological processes, it is important to investigate the mechanism of variability in gene expression and the co-expression relationships.The variability in gene expression among individual cells is known to increase the complexity of cellular phenotypes due to the population effect.7 During neuronal and glial differentiation, genes in the notch-signaling cascade have been reported to oscillate, however, there is both spatio- and temporal heterogeneity between cells.8 In neuronal stem cells (NSCs), oscillations in gene expression are also less synchronized, therefore gene expression levels among individual cells show a large degree of diversity, resulting in a high degree of variability in gene expression in the cell population.Transcriptional variability is caused by factors that were greatly affected by the complexity of the cell population, such as individual differences in the cell state, the cell cycle, and in their biological profiles. One of the features which best describes such heterogenic states in a cell population is the mis-synchronization of oscillations in gene expression.Dll1 and Ngn2) that are co-expressed in order to synchronize their gene expression oscillations in a single cell.9 Such transcriptional co-regulation changes as the cell progresses from the progenitor cellular state toward the differentiation state is known to play an important role in the decision of cell fate.9With such oscillation in gene expression, synchronization among different genes, even within a single cell, is another important feature that must be considered. During neuronal differentiation, for example, it is known that there are pairs of genes  the variability of gene expression within the same state, which reflects the potential variation in the expression of a particular gene measured for a number of times using a single cell or bulk of cells, and (2) the correlation, which reflects a similar pattern of expression for multiple genes (co-expression) in the same state. The concept of DVC analysis is illustrated in Fig. 14 Changes in biological responses are typically expressed as the average of gene expression rates for specific genes. In these analyses, bulk group of cells are lysed and measured to obtain their average gene expression, and such expression data has been known to reflect their biological status. However, it is difficult to use this concept to explore genes expressed at low average levels but greatly impact the biological event as such as a trigger. When gene expression rates are averaged within a group of cells, minor genes expressed only in few sub-populations of cells or over short time in individual cells, such gene effect detection is difficult. Considering that such genes have either large variance among groups of cells or exhibit the sudden appearance/disappearance among time series, our DVC analysis is designed to provide new aspects for analyzing transcriptomic data. Our previous work indicated that even using the average expression data from bulk cells, the gene sets identified by DVC analysis (DVC genes) were found to play important roles in neuronal differentiation13 ”.19 However, the importance of variability in the expression developmental regulator genes remains unclear. In this study, in order to extend our previous work on finding key functional genes in the neuronal differentiation process through a DVC analysis , we evaluated alterations in the co-expressed gene sets in NSCs.First, to obtain the co-expressed genes in the NSC state, which we defined as the most undifferentiated state, a hierarchical clustering analysis was performed on their single-cell gene expression data. Using a correlation coefficient, which describes the similarity of gene expression profiles in each individual cell, the co-expressed gene modules in the NSC state were identified. Within the 12,147 genes, 17 modules of co-expressed genes were identified Fig. .Fig. 2Idq < 0.001). Between NSCs and astrocytes, 13.5% of all genes also showed differential variability (Levene test q < 0.001). These data show that 1637 genes changed their expression profile, either in a harmonized or in a heterogeneous pattern, as the cell state changed from undifferentiated to differentiated. By examining the overlap between genes with differential variability in expression and each co-expressed gene set, some modules were found to be enriched in genes that changed their variability as a result of the cell state transition. When all the modules were examined for the direction of differential variability  and astrocytes (p = 3.69e−70), respectively. The red and purple modules showed that >1/3 of the module member genes decreased their expression variability in both TAPs  and astrocytes .By comparing TAPs/astrocytes vs. NSCs, we evaluated the differences in gene expression variability using the single-cell gene expression data Fig. . Betweenity Fig. , it was By comparing TAPs/astrocytes vs. NSCs, differential correlations were also evaluated Fig. . When weFor further analysis of differential gene expression profiles between cell states, we carried out our DVC analysis to identify candidate genes that could be predictive of an upcoming drastic cell state transition. We measured the change in two parameters with our measure score, the system transition score (STS), which combines both an evaluation of the differential variability and the differential correlation of gene expression. We defined genes with a high STS as “DVC genes,” and selected candidate genes from a comparison of pairs of transition states, TAPs vs. NSCs, and astrocytes vs. NSCs. From the comparison of TAPs vs. NSCs, 474 DVC genes were found, and from astrocytes vs. NSCs comparison, 504 DVC genes were found . For example, Cdk6, which contributes mainly in G1 phase and proliferation, was found as the top DVC gene. This result suggest that the DVC analysis reflects the commonly known functional genes that is predominant differentiation pathway from progenitor to committed cell.In the DVC genes, the cell cycle genes, which is important for early proliferative phase was overrepresented (enrichment p = 6.15e−6) compared with DVC genes identified between the TAP vs. NSC states  expression rates of genes among different cell states, to identify differentially expressed genes (DEGs) (definition described in the Methods section), we rarely found astrocyte differentiation-related genes, even from both state comparisons (TAPs vs. NSC or astrocyte vs. NSC). This result suggests that the genes, which play important roles in astrocyte differentiation, vary in expression variability and correlation and not in average expression level.22 Our DVC analysis identified 97 highly ranked DVC genes from the second dataset .To further investigate the robustness of our DVC analysis applied to cells, which changed their state from stem cells to astrocytes, we evaluated the second dataset (53 NSCs and 13 astrocytes) obtained from an independent study.To further understand the function of the DVC genes, we examined if known biomarkers and regulators involved in neuron and astrocyte differentiations were also DVC genes.Ascl1, which promotes neuronal fate determination, was identified as being a DVC gene between the astrocyte vs. NSC states.23 As previously reported,9Ascl1 expression oscillates in NSCs, although it becomes stably suppressed following astrocyte differentiation. Similar changes in Ascl1 gene expression were observed in this study. Ascl1 showed a large variability in expression among individual cells in the NSC state, although this variability decreased in the TAP state. In the astrocyte state, it was expressed at a low level . These data also suggest that a disturbance of DVC genes is effective when their variability is large and their gene network is tight.To validate the functional importance of our DVC gene candidates, we searched for DVC genes, which could potentially be involved in the cl1 Fig. . The co-ate Fig. . Ntsr2 aate Fig. . Howeverity Fig. . This reeak Fig. . This in11 The “fragility” of various biological profiles is a new concept adopted to help understand complex biological phenotypes that are found during such critical transitions. It is now thought that both “variability” in gene expression levels and “co-expression” among heterogeneous populations of cells are empirical indicators, which are involved in any upcoming biological transition. Based on this theory, our DVC analysis offers objective measurement criterion, which correlate with the critical cell state transition. In this study, in order to investigate the use of our DVC analysis in understanding astrocyte differentiation, we analyzed single-cell transcriptome data to measure both the variability and correlation between cell states more accurately than cell population transcriptional data. Moreover, the robustness of DVC analysis was further confirmed in the independent second dataset.Cell fate decision in the differentiation processes is proposed to be a system that transits abruptly from one state to another in response to external stimulation based on dynamic systems theory. Such cell states transitions are referred to as critical transitions.By focusing on the evaluation of STS, a score incorporating both variability and co-expression, our analysis identified several candidate DVC genes, as being central regulating genes important in the transition from the NSC state to the astrocyte state. The functional enrichment of DVC genes important in astrocyte differentiation was more significant than that for conventional DEGs, indicating the importance of evaluating heterogeneity of gene expression data. Moreover, using a gene network analysis followed by pharmacological inhibition of a single DVC gene we demonstrated that the DVC analysis could identify key players in the transition from the NSC state. Our data also suggest that signatures that are involved in state transition are not easily identified using the conventional comparison of “expression averages.” This study therefore has an impact by improving our understanding of transcriptional regulation in differentiation processes.27 we consider that TAPs do not lie on the direct line of lineage from NSCs to astrocytes. In such considerations of lineage type differences, our STS score in DVC analysis, which rank the genes and identify the high DVC between cellular states, can be informative.Through our DVC analysis, the importance of expression “fragility” was clarified especially in the most undifferentiated NSC state. When we compared three states, represented by NSCs, TAPs, and astrocytes, the differences between NSCs and astrocytes were clear. However, the differences between TAPs and NSCs provided very few informative genes. Therefore, as proposed by Molofsky et al.,Dll1 and Dll3, are known to be targets of Ascl1.28 Notch signaling also upregulates the expression of Sox9, and induces differentiation into astrocytes.29 Moreover, Sox9 is known to bind to the genomic regions close to Ascl1, Dll1, and Dll3 from a Chip-seq study.26 Taken together, these data suggest that Sox9 may be both upstream and downstream of genes involved in notch signaling suggesting that a transcriptional loop could be formed. The change in the co-expression network between cell states might imply that there is a change in the network regulatory loop during the cell state transition.Ascl1, the central gene identified here from the DVC analysis, is a well-defined transcription factor. For example, the notch-signaling genes, 30 and is expressed in NSCs and astrocytes.22 However, its functional role in the cell state transition has not been previously described. An Ntsr antagonist suppressed Gfap expression and in addition had a small effect on Sox2 expression in NSCs. Type 1 NSCs are characterized by presence of both glial fibrillary acidic protein (Gfap) and Sox2 expression in the undifferentiated state.31 This type 1 state triggers the cellular state transit to type 2 in NSCs that express Sox2, but not Gfap,31 and is thought to be the state of self-renewal. It has been found that NSCs have the potential to differentiate into both neurons and astrocytes in their type 2 state.32 Use of the Ntsr antagonist might guide type 1 NSCs to become type 2 NSCs, which have a high capacity to give rise to neural lineages. This study suggests that Ntsr2 could be involved in the cell state transition in the early cell fate decision-making process. In the second data analysis, Ntsr2 was ranked at 304th and was not lost in the independent data. The sequencing depth of the second dataset was lower than that of the first dataset. This may have resulted in the loss of co-expression structure. Therefore, considering the difference in sequencing depth between the first and second datasets, we consider that our method provided reproducible results. However, since the Ntsr2 inhibition potentially affects Stat3 signaling that may lead Gfap expression change, we should evaluate Stat3 signaling more in detail to gain insight on Ntsr2 biology during astrocyte differentiation. To further analyze the Ntsr2-related cascade, we believe further development of inhibitor libraries are required. First, we could not find an appropriate small molecule inhibitor for directly inhibiting Ascl1. Second, although JMB449 are known to inhibit both types of Ntsr receptors, Ntsr1 and Ntsr2, there were no molecule that inhibits only Ntsr2. However, the expression read counts were not detectable for Ntsr1 in the data of all cell types; therefore, our experimental design was appropriate for studying Ntsr2 using JMB449.Ntsr2 is a G-protein coupled receptor that binds neurotensin,16 This report also suggested that variability in gene expression could be caused by other mechanisms. In this study, we identified oscillatory genes as DVC genes in the process of astrocyte differentiation. Our previous study showed that Hes1 is one DVC gene that is important during neuronal differentiation. Hes1 was also found to have oscillation in its expression levels before neuronal differentiation. Combining the data from our neuronal and astrocyte differentiation studies enhances our hypothesis that oscillations in gene expression in the undifferentiated state causes a high transcriptional variability before differentiation occurs. These oscillations may be one reason why the transcriptional system becomes “fragile” before an upcoming cell state transition such as cell fate decision. In the future, we intend to add more time-course data representing other cellular states in the differentiation process in order to extend DVC analysis to investigate the cause of high transcriptional variability in the differentiation process.Although the DVC analysis provided several possible clues, this study could not definitively explain why the variability in DVC gene expression is high in the undifferentiated state. A study of variability in the hematopoietic differentiation system demonstrated that both cell cycle and variations in cell size could, to a small extent, explain this variability in gene expression.+Prom+ cells (designated as NSCs) and Glast-Prom-Egfr+ cells (designated as TAPs) were isolated by fluorescence activated cell sorter (FACS) from the su-ventricular zone (SVZ). Glasthi (designated as astrocytes) were isolated from the striatum and somatosensory cortex according to archived data. NSCs can produce neural progenitor cells (TAPs or type C cells), which are a proliferative cell population expressing markers of early neuronal differentiation. Some NSCs can generate both neurons and astrocytes. TAPs are known to give rise to neuroblasts (type A cells) that differentiate into primarily interneurons. Although the complete lineages of neuronal and glial cells in the mammalian brain remains unclear, according to such lineage information, we hypothetically ordered the three types of cells  from the early lineage to the differentiated state. Another dataset for validating our findings by DVC analysis included cell populations isolated by FACS from GFAP-GFP transgenic mice: GFAP-GFP+PROM1+EGFR+ (astrocytes), GFAP-GFP+PROM1+ (NSCs).22 To further confirm our DVC analysis concept, we evaluated recent data (defined as the second dataset) from Llorens-Bobadilla et al.,20 which showed similar cell population data when representative biomarkers (Glast (Slc1a3) and Cd9) were compared. This data included 53 NSCs and 13 astrocytes after data filtering. The second dataset was preprocessed and DVC analysis was carried out in the same manner as for the first dataset. The reads were mapped to the mouse genome (ENSEMBL Release 83) using STAR.33 FeatureCounts was used to count the mapped reads for genes.34 TMM  normalization and CPM (counts per million) transformations were performed using EdgeR to compare the expression levels across the samples.35 A principal component analysis was applied to the CPM data to remove oligodendrocyte-like cells from the NPCs. Filtering out the genes with low expression levels (a read count < 2) for each cell type resulted in the detection of 12,147 commonly analyzable genes within the three cell states, and was used for the following analyses. The CPM data were log2 transformed and standardized to the Z-score for each gene across individual cells for each cell type.The single-cell RNA-seq data from three types of cells namely 92 NSCs, 27 TAPs, and 22 astrocytes were obtained from the Sequence Read Archive (SRP057125). According to archived data, GlastHierarchical clustering was applied to identify co-expressed genes as cluster modules. The gene expression values were standardized within individual cells. For the clustering, only the single-cell data in the NSC state, the first state in our analysis, were used to assemble the clustering tree. For the clustering, both the Pearson correlation coefficient (PCC) and Wald linkage method were used. For module detection, a dynamic tree-cutting algorithm  was used. All calculations were coded by R.13 The analysis was conducted using the Bioconductor package in the R language. The STS was used to rank the genes and identify those with high DVC between two different cellular statuses . The detailed description of STS calculation is shown in the 36 PCCs above 0.7 were shown as the connection between genes in the network figure.The detailed procedure for the DVC analysis has been described in our previous study.38 The ChIP-seq data showing the transcription factor binding sites and DNase-seq data showing open chromatin sites in NSCs were obtained from a previously published study.26 The genomic regions overlapping between ChIP-seq and DNase-seq data were discovered using bedtools. The genes within 1M bases from the overlapping regions were used in the transcription factor enrichment analysis to incorporate potential enhancer regions into the analysis. The enrichment significance was assessed using the cumulative hypergeometric probability with the phyper function in R. The enrichment test is one sided. Ingenuity pathways analysis  was used to examine the upstream regulators of the DVC genes. The reference dataset was set as the 12,147 genes that represented all the genes used in the functional enrichment analyses.The astrocyte differentiation-related gene sets (GO:0048708) for the functional enrichment analysis were obtained from the Gene Ontology database.As a comparison, we applied a functional enrichment analysis on the conventional “differently expressed genes (DEGs).” The DEG definition is described in the 5 cells/cm2 in T25 flasks coated with poly-l-ornithine hydrobromide  and natural mouse laminin  for maintenance and differentiation. The maintenance culture and differentiation culture (astrocyte differentiation) was performed according to the manufacturer’s protocol, with some modifications as described in our previous work.39 mNSC’s were seeded in triplicate into a coated six-well plate for the real-time PCR experiment, and into a 12-well plate for immunohistochemical staining. The primers used in this study are shown in Supplementary Table 39 The Ntsr2 antagonist (JMV449), a pan neurotensin receptor antagonist, was purchased from TOCRIS , and added to the mNSCs at final concentrations of 0.15 pM, 1.5 pM, 15 pM, 0.15 nM, and 1.5 nM.Fetal-derived mouse NSCs  at passage two were seeded at a density of 1.0 × 1040 The p-values were adjusted using the Benjamini–Hochberg method. These procedures were conducted in R. The statistical tests are two sided.The Leven test was applied to the RNA-seq data to evaluate the variability among individual cells in each cell type. The Voom-limma method was used to identify the DEGs between cell types.Supplementary information