Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis. They are characterized by early-onset persistent impairments in social communication and interaction as well as the presence of restricted, repetitive behaviors or interests3. Diagnosing ASD is a complicated, lengthy and time-consuming process, which requires outstanding and specific clinical expertise5. Although research makes constant progress in understanding the underlying genetic and neurobiological factors associated with ASD, there are currently no reliable biological markers for ASD and the diagnosis remains based on behavioral symptoms7. The current so-called “gold standard” of ASD diagnosis comprises the use of various standardized diagnostic instruments that assist clinicians in reaching a best-estimate clinical diagnosis9. Two of the most widely used diagnostic instruments are the Autism Diagnostic Observation Schedule (ADOS respectively ADOS-2 for the revised second edition)11 and the Autism Diagnostic Interview – Revised (ADI-R)12. The ADI-R is a semi-structured interview administered to parents or caregivers that focuses on developmental history and current symptom presentation. The ADOS is a standardized semi-structured diagnostic observation scale designed to assess important social-communicative behaviors as well as stereotypic and repetitive behavioral features. The ADOS includes four different modules for different age and language levels, with Module 4 intended for verbally fluent adolescents and adults. For each module, there is a diagnostic algorithm that allows for the classification of ASD or non-ASD14. The ADOS is one of the psychometrically best-evaluated diagnostic tools in ASD, especially in children (Modules 1 and 2)17, with fewer studies investigating the ADOS in adolescents and adults (Modules 3 and 4)21. Although a good psychometric quality has been widely demonstrated, existing studies suggest a lower diagnostic utility in naturalistic clinical settings as well as with older individuals24. While most cases with ASD are diagnosed in childhood, the diagnosis oftentimes remains unnoticed until adolescence and adulthood, particularly in those individuals with at least average cognitive and language abilities, better adaptive functions and more subtle symptom presentations26. Diagnosing ASD in high-functioning adolescents and adults  can be even more challenging compared to childhood due to various factors: a care-giver based developmental history as acquired with the ADI-R is oftentimes unavailable in older individuals25 and the individual’s self-report of symptoms may be diminished due to impaired self-referential cognition27. Additionally, learned compensatory skills might conceal impairments, thereby reducing diagnostic accuracy of observational tools such as the ADOS25. Furthermore, most adults with ASD have at least one comorbid psychiatric disorder28 and ASD symptoms frequently overlap with those of other psychiatric conditions23 thereby further complicating differential diagnosis. Nonetheless, establishing an accurate and timely diagnosis is of great importance for those affected and the planning of suitable psychosocial interventions in order to promote positive outcome31.Autism Spectrum Disorders (ASD) comprise a range of pervasive neurodevelopmental disorders with a population prevalence of approximately 1%32). A few authors have shown that efficiency and accessibility of existing pre-diagnostic screening questionnaires such as the Autism-Spectrum Quotient (AQ)35 or the Social Responsiveness Scale (SRS)37 can be improved using machine learning. Similar machine learning experiments have been run to identify subsets of behavioral features from clinician-administered diagnostic tools, namely ADOS (Module 1 to 3)42 and ADI-R43. Findings of these studies suggest that ASD detection in children can be achieved with substantially fewer items compared to the original ADOS and ADI-R algorithms while retaining high diagnostic accuracy, sensitivity and specificity. To our knowledge, this has not yet been examined in a sample of adolescents and adults as assessed with the ADOS Module 4. The purpose of the present study therefore was to expand on the existing literature with a specific focus on high-functioning adolescents and adults. Advances of the present study are a large and balanced routine clinical sample of adolescents and adults with best-estimate clinical diagnoses of ASD and relevant psychiatric differential diagnoses, thereby being a good representation of the actual population presenting to ASD assessment settings. We aim to identify the diagnostically most informative features from the ADOS Module 4 that accurately differentiate between individuals with ASD and individuals with other clinically complex presentations using a data-driven machine learning approach. Although all items of the ADOS focus on relevant behavioral concepts, some items may be more discriminative and have higher classification ability particularly in a challenging clinical sample of older individuals that were all initially suspected of ASD. Identifying essential subsets of behavioral features that distinguish ASD from non-ASD cases could contribute to an enhancement of the complex diagnostic process in multiple ways: to improve existing diagnostic tools , to shorten existing diagnostic tools such as the ADOS  and/or to inform the development of novel diagnostic tools and methods for initial screening based on these essential constructs.Previous studies have applied machine learning techniques to examine whether the process of diagnosing ASD can be improved by statistically identifying reduced subsets of features from existing diagnostic instruments reaching from self-administered screening questionnaires to clinician-administered diagnostic tools  and combined into one dataset for analysis. This procedure was approved by the Charité – Universitätsmedizin Berlin ethics committee (EA4/129/19) and due to the retrospective nature of data collection and analysis based on routinely obtained clinical data, the need for informed consent was waived by the local ethics committee. All methods were performed in accordance with the relevant institutional and international research guidelines and regulations.The study was conducted as a part of the ASD-Net, a research network with focus on ASD that is funded by the German federal ministry of education and research10), a standardized interview if parental informants were available ) and a differential diagnostic examination , which aided trained and experienced clinicians in reaching a best-estimate clinical diagnosis. Multiple assessments were available for some of the cases, however, only the most recent assessment was considered for each case.The diagnostic procedure involved a standardized behavior observation in all cases  and 43% did not receive a diagnosis of ASD but relevant differential diagnoses such as affective disorders, anxiety disorders, ADHD and/or personality disorders or no current psychiatric diagnosis  to 2  and sometimes 3 (profound severity), with additional codes of 7 and 8 for abnormal behavior or behavior not exhibited during the observation, and a code of 9 for missing values (i.e. answers omitted or left blank).The ADOS is a standardized observation scale designed to capture important social-communicative behaviors and stereotypic and repetitive behavioral featuresThe ADOS Module 4 provides a scoring algorithm consisting of a subset of the diagnostically most informative 11 items (see Table knnImpute) using the build-in preprocess function form the caret R package45. In our dataset, six items were missing in 4–10% of all cases , with all other items missing answers in less than 2.5% of the cases  were imputed using k nearest neighbor imputation with k = 5  were among the models that performed best42. Furthermore, SVM is one of the most frequently used algorithms that has been utilized for ASD classification due to its high predictive power32. Therefore, we decided to use SVM classification with radial kernel using svmRadial of the caret R package45 as our machine learning classifier. We performed an additional analysis using random forest, which showed slightly lower predictive performance. Due to readability and space constraints, we only present results for SVM. Results from our random forest analysis can be found in the supplement . All steps of data inspection and preprocessing, including imputation and analysis, were performed using R version 3.5.1 in Rstudio 1.1.456.knn-imputation) was performed separately and anew for the resampled training set folds during the model building process and once for the test set before application of the final models for prediction. The dependent variable was removed from the dataset prior to imputation to prevent information leakage. Thus, it was made sure that for imputation at each fold during cross-validation, the information about the distribution of any predictor variable in the training set was unaffected by the distribution of the same variable in the test set and of the outcome variable.To maximize the likelihood of unbiased results, rigorous guards against overfitting were implemented. The total sample was randomly split into a 75% partition as training set for building the models and hyperparameter testing, and a 25% partition as test set for evaluation of the predictive power of the final models in completely unseen cases not used to build the model Table . The ranrfFuncs) via the caret R package45 applying random forests46 to identify the most important features. The metric for hyperparameter tuning was Cohen’s kappa coefficient , and 5-times repeated 10-fold cross-validation was applied. The second step consisted of training our reduced feature model on the training set. During model training, 5-times repeated 10-fold cross-validation was applied to mitigate the risk of overfitting and to achieve stable prediction results. The metric for hyperparameter tuning was the area under the ROC curve (AUC) used to select the model with the largest AUC on the training set. The set of tuned hyperparameters was sigma and cost with a random search of 500 different combinations. All other tunable parameters were set to default values47. The third step was testing our reduced feature model on the dedicated test set to see how well the reduced model can separate ASD from non-ASD in completely unseen data of a separate hold-out set, i.e. data that was never used to build the model.Our machine learning approach consisted of multiple steps: First, we conducted a feature selection on the training set to identify a reduced feature subset with similar predictive performance profiles. We used recursive feature selection .To compare model performances, we evaluated the AUCs of the predictions attained from our newly identified reduced feature subset vs. all 31 items of the ADOS vs. the subset of 11 items proposed by the ADOS algorithm. Additionally, we compared model performance to the 12-item classifier previously identified by Kosmicki and colleaguesBecause of an overall large age distribution in our sample , all of the above-mentioned steps were performed in the whole sample  as well as in age subgroups of adolescents aged ≤21 years ) and adults aged>21 years ). For further information on the age distribution of our sample and the age subgroups, see Supplementary Table 41 , B1 , B2 , B10  and B11 . All of the selected five features correspond to the domains of Social Interaction and Communication of the ADOS and are comprised in the original 11-feature ADOS algorithm as well as in the 12-item classifier proposed by Kosmicki and colleagues41  in the trainings set, which is comparable to the performance of the 11-feature model of the ADOS algorithm, the 31-feature model using all items of the ADOS and the 12-item subset identified by Kosmicki and colleagueset al.41  and the 31-feature model  and the 31-feature model .In a final step, we compared the models’ performances. No significant differences were found when comparing the AUCs of the reduced 5-feature model with the 11-feature model suggested by the ADOS algorithm , the 12-feature model proposed by Kosmicki Results separately for age subgroups (“adolescents” and “adults”) can be found in Table et al.41  in the test sets in the respective subgroups. For a detailed depiction of the comparison tests´ results as well as ROC curves for the age subgroups see Supplementary Figures 4 and 5.By splitting our “all ages” sample into age subgroups, we were able to increase the overall prediction accuracy of our models . In the present study, we sought to expand on the existing literature with a specific focus on the differentiation of adolescents and adults with ASD from individuals with other clinically complex presentations (ADOS Module 4).Although the search for objective biological markers associated with ASD is currently under way, the current standard of ASD diagnosis remains based on behavioral symptoms alone. One of the most widely used behavioral examination tools which aids clinicians in reaching a best-estimate clinical diagnosis is the ADOS. Although effective, administration and evaluation of the ADOS is time-consuming and requires outstanding specific clinical expertise. However, accurate and timely identification of ASD in adulthood has become an important clinical issueet al.41 on our dataset and compared performance with our models. Results showed that our reduced 5-feature model and the proposed 12-feature model (AUC of 84%) achieved similar predictive performance, with no significant differences when comparing the AUCs. In their original publication, Kosmicki et al.41 applied SVM with a radial basis function and reported model performance of 97.7% sensitivity and 97.2% specificity on their whole sample . For individuals for whom a best-estimate clinical diagnosis was available , their 12-feature classifier displayed 99.1% sensitivity and 70.9% specificity. Independently testing this proposed 12-item-subset on our dataset, we found an overall performance of 77% sensitivity and 82% specificity, which is lower compared to their original findings. However, their proposed 12-feature subset still achieved an overall good prediction performance that is in line with performances of our reduced subset as well as the ADOS algorithm and the full ADOS. A possible explanation for the observed lower prediction performance might lie in the composition of the investigated samples. First of all, their classifier was built using ADOS Module 3 data, with Module 3 being intended for children and adolescents. Consequently, their sample was considerably younger compared to ours and displayed an overall lower developmental level. Second, there was a large imbalance in their data between groups (ASD vs. non-ASD), with our dataset being more balanced. Lastly, while our sample was a complex clinical and naturalistic sample of individuals seen for comprehensive diagnostic evaluation, data of their investigated sample came from archival repositories and diagnosis was partly only based on ADOS results . Nonetheless, our independent evaluation of their proposed classifier shows encouraging results and lends additional support to the hypothesis that models using minimal feature subsets from the ADOS are accurate for ASD classification.Using an SVM-based approach, we identified a reduced subset of 5 behavioral features from the ADOS Module 4 that showed good specificity (83%) and sensitivity (71%) on our whole sample. Furthermore, with an AUC of 82%, our reduced classifier reached performance close to that of the 11-feature algorithm proposed by the ADOS manual and the full ADOS consisting of 31 features (both AUCs of 84%) with no significant differences in overall performance. Additionally, we evaluated model performance of the previously proposed 12-item-subset by Kosmicki et al.41); “adults”: AUCs of 84% (5-feature) vs. 87% (11-feature) vs. 87% (31-feature) vs. 85% ) in the respective subgroups.By splitting up our sample further into age subgroups of adolescents (≤21 years) and adults (>21 years), we were able to even further increase prediction accuracy of our abbreviated 5-feature subsets . Similar to the whole sample , we found no significant differences when comparing the overall prediction performance of the different models (“adolescents”: AUCs of 90% (5-feature) vs. 88% (11-feature) vs. 87% (31-feature) vs. 84%  were part of the original 11-feature algorithm proposed by the ADOS manual, thereby supporting the relative diagnostic importance of these items and possibly preserving some of its diagnostic validity. Looking more closely at the age-subgroups, results show that the majority of items overlaps between the groups  and that all selected items stem from the ADOS’ domains of Social Interaction and Communication. Interestingly, there is a substantial overlap of 4 items between the two age subgroups, possibly reflecting their relative importance independent of developmental level. The outstanding difference between the two age subgroups were the inclusion of the more “basic” social-communicative item B1  in the adolescent group and the more “complex” qualitative and summative item B10  in the adult group. These findings suggest that adolescents with ASD show more saliently unusual eye contact compared to adults with ASD whereas adults show more “complex” social interaction impairments. This finding is in line with previous studies suggesting that there may be differences in ASD symptom presentation over the lifespan due to learnt compensatory behaviors and general developmental gains41 in their experiments on Module 3, we find a correspondence of all of the items identified in our classifiers apart from item B3. Regarding their experiments looking at ADOS Module 241, there was an overlap of 4 items between our reduced classifiers and their identified 9-feature classifier (Gestures (A9), Unusual Eye Contact (B1), Quality of Social Overtures (B9) and Amount of Reciprocal Social Communication (B11)). Similar overlaps can be found looking at the experiments conducted by Levy and colleagues42, who identified reduced 5-feature classifiers for Module 2 and Module 3 with an overlap of 3 items for Module 2 , Facial Expressions Directed to Others (B2) and Quality of Social Response (B10)) as well as an overlap of 3 items for Module 3 , Quality of Social Overtures (B9) and Quality of Social Response (B10)). Not yet published results of our own group also identified reduced 5-feature classifiers for Module 1, 2 and 3 using Support Vector Machine. Here, we find overlaps of feature B1 for Module 1 and features B2, B9 and B11 for Module 2 and 3 with our reduced classifier. Taken together, there are marked similarities in the features selected across different studies and age groups, particularly for items B1, B2, B9, B10 and B11, which all stem from the Social Interaction domain of the ADOS.Comparing our findings with those of previously published minimal behavior subsets from ADOS Module 1 to 3, we find parallels in the features selected across different studies and age groups. Looking at the reduced 12-feature classifier proposed by Kosmicki and colleagues52, for example, recently showed that the items stemming from the social interaction and communication domains of the ADOS Module 3 algorithm could be further separated into two subdomains: impairments in “Basic Social Communication”  and impairments in “Interaction Quality” including items that measure more complex aspects of social interaction , with the former being impaired in children with ASD and mostly intact in children without ASD but other clinical diagnoses, and the latter being impaired in both groups. In line with this finding, Drimalla and colleagues53 for example recently developed an alternative screening method for ASD detection and showed that it was possible to accurately detect ASD compared to healthy controls by automatically analyzing the subject´s facial expression, vocal features and gaze patterns during a video-based simulated social interaction. Although all items of the ADOS algorithm focus on relevant behavioral concepts of social-communication impairments, our abbreviated classifiers might contain relevant “core” features from the ADOS that are more specific to adolescents and adults with ASD. Thus, focusing on a reduced subset of items that are most specific to ASD and less influenced by other clinical presentations could assist clinicians in the difficult process of differential diagnosis.Impairments in social interaction in general are very common in many different psychiatric disorders. However, Bishop and colleagues39, however, that administration time for the ADOS cannot simply be reduced by reducing the number of items coded, as ADOS items are not directly tied to any subtask. In order to code the items, it is always necessary to administer the whole-length ADOS exam  and consequently the time saved by coding only a smaller number of behavioral features is marginal. In contrast to this view, however, recent findings have shown that ASD classification of children can be achieved by applying the ADOS items to shorter, unstructured social interactions56 and even by relying solely on written extracts of children´s medical and educational records58, thereby suggesting that a reduction in time associated with ASD detection might be feasible. For example, Fusaro and colleagues54 have assessed the feasibility of applying all of the ADOS Module 1 codes, but not the behavior observation exam, to short (<10 minutes) and unstructured home-videos collected from YouTube. They collected and rated 100 videos of children (age 1–15) with self-reported ASD and non-ASD diagnoses and achieved classification accuracy of about 97% (with 94% sensitivity and 100% specificity). While certainly not all ADOS items were relevant to the YouTube videos, the authors did find, however, that a majority of the items could be applied. In particular, the items from Module 1 that overlap with the Module 4 items included in our identified 5-feature subsets , facial expressions directed to others (B2), gestures (A9) and quality of social overtures (B9)) were readily detectable (frequency of “not applicable” ratings was at <0.044% per video). These findings suggest that short, unstructured interactions can provide sufficient information to rate ADOS codes and to detect ASD. Building upon this work, Tariq and colleagues55 recently investigated how the reduced feature subsets from the ADOS modules 1 to 3 that were identified in previous machine learning experiments42 could be translated into clinical practice. For this purpose, they created a mobile web portal and asked video raters to assess the previously identified minimal feature subsets in short home videos (<5 minutes) of children with and without ASD. Results showed that all video raters took a median rating time of 4 minutes to detect ASD with high accuracy (AUC at 90%). Although constrained by an unbalanced sample size, a comparison group of typically-developing children as well as relying on self-report diagnosis of ASD, these results are nonetheless promising and an important first step in translating machine-learning based behavioral models into clinical practice. Furthermore, these results support the possibility of achieving ASD detection in briefer, unstructured social interactions such as home videos of under 5- to 10-minute length, which is considerably shorter than the full ADOS exam assessment time (which is on average 30–60 minutes).Our findings suggest that reduced subsets of only 5 items are equally accurate in predicting ASD as the 11-item algorithm proposed by the ADOS manual or even the whole ADOS exam consisting of 31 items. Compared to the full ADOS, however, our abbreviated 5-feature classifiers present a substantial reduction of 84% of the number of behavioral features that have to be coded in order to assess ASD. It has been argued beforeConsidering these findings, the minimal 5-feature subsets identified in our study might have the potential for utility in shorter format approaches such as assessment in unstructured home videos or brief interactions with clinicians, thereby possibly reducing redundancy in the existing diagnostic algorithm and accompanying assessment time. Furthermore, the features identified could inform the development of novel diagnostic screening tools that specifically build upon these core features. Further examination is certainly needed to evaluate whether the behaviors identified with our classifiers prove essential and stable in their ability to discriminate between adolescents and adults with ASD and other complex clinical presentations and whether they may also be adequately assessed in shorter social interactions with adolescents and adults. Nonetheless, these findings support the claim that accurate ASD detection in adolescents and adults can be performed using smaller sets of behavioral features, thereby potentially allowing for a reduction in the complexity of the diagnostic procedure.60. Future studies should investigate differences in more specific clinical comparison groups  as well as specific gender groups.The main strength of this study is the large and balanced routine clinical sample of individuals all initially suspected of having ASD. The non-ASD group was a diagnostically very diverse and heterogeneous group, with ASD-like behavioral characteristics that originate, however, from different underlying psychiatric conditions such as affective disorders, anxiety disorders, ADHD and/or personality disorders or no current psychiatric diagnosis. Furthermore, there was a large age range in our sample and our sample consisted of male as well as female participants, thereby being a good representation of the routine clinical population presenting for ASD diagnostics in adolescence and adulthood. Evaluating age-restricted subgroups, we found slight differences in the items selected, hence the predictive value of our reduced algorithms might also differ in clinical as well as gender subgroupsAdditionally, our sample consisted of high-functioning individuals of whom most presented late in life for an ASD diagnosis, thereby probably belonging to the mild end of the spectrum. Therefore, results cannot be generalized to the entire ASD spectrum, especially to those individuals with lower intellectual functioning.41). However, even though the ADOS was usually factored into the clinical decision making, it did not solely determine the diagnosis.Lastly, our outcome criterion  was not independent of the features utilized for building the prediction algorithm, therefore possibly confounding our results. This circularity problem has been previously discussed, however, there are currently no ways of satisfyingly addressing this issue as there is no independent external criterion available (for a more detailed discussion seeTaken together, our results are an important step forward towards improving ASD detection in older individuals and shed some light particularly in the difficult issue of differential diagnosis among clinically complex cases. We identified reduced subsets of behavioral features from the ADOS Module 4 for the whole sample as well as adolescents and adults separately that showed comparable classification performance to that of the full ADOS and the existing ADOS algorithm. While all items of the ADOS focus on relevant behavioral concepts, the items identified may have a higher ability to differentiate individuals with ASD from individuals with other clinically complex presentations in adolescence and adulthood. Although further studies are needed to evaluate these reduced classifiers’ ability to generalize to completely new and unseen data and to determine its clinical value, these results may help to improve the complicated ASD diagnostic process in adolescents and adults by encouraging future efforts to improve existing diagnostic instruments such as the ADOS, thereby aiding clinicians particularly in the difficult question of differential diagnosis, as well as to develop novel diagnostic instruments for ASD detection.Supplementary Material.


This ecological study evaluates the public mobility and social media attention associated with COVID-19 spread and societal interventions from February 15 to June 14, 2020, in Denmark and Sweden. We assessed public mobility and social media attention associated with COVID-19 spread and societal interventions from February 15 to June 14, 2020, in Denmark and Sweden.To reduce the spread of coronavirus disease 2019 (COVID-19), countries have implemented different societal interventions, and evaluation of the effect on public response is needed.2 We focused on mobility across retail and recreational spaces based on previous findings.3 Daily volume of tweets including COVID-19–related hashtags and keywords (eTable in the STROBE) reporting guideline that are relevant to ecological studies.For this ecological study, public mobility was measured by Google mobility reports, providing daily percentage change in the number of visitors to public spaces compared with baseline .4 and key societal interventions and announcements  targeting public mobility. Interrupted time series analyses were conducted to evaluate the association between societal interventions and mobility, including both a level (β1) and a slope change (β2) that can be interpreted as the modeled percentage change in mobility from before to after the intervention.5 Statistical analyses were performed using R, version 3.6.3 . Details on measures and interrupted time series analyses are provided in the eMethods in the The exposures that we focused on were disease spread The percentage of mobility change, the number of COVID-19–related tweets, and the number of confirmed cases of COVID-19 are plotted with key societal interventions in the 1, –20.19; 95% CI, –27.79 to –12.59) . Phase 1 and 2 of the Danish reopening were associated with immediate reductions in mobility but increased mobility in the subsequent days. In Sweden, only the ban on public gatherings of more than 500 people was associated with a significant decrease in mobility . Neither the prime minister’s nor the King’s address to the nation showed a statistically significant association with mobility. In both countries, higher numbers of daily confirmed COVID-19 cases were associated with reduced mobility .The interrupted time series analyses showed that the Danish lockdown was associated with a 20% immediate decrease in mobility (β –12.59) . The ban6 Social media attention to COVID-19 on Twitter was greater for early interventions and disease spread than for later interventions and disease spread, with nearly identical patterns of decreasing COVID-19–related Twitter volumes across Denmark and Sweden despite marked difference in number of cases and the type of societal interventions. Better understanding of how interventions are associated with public mobility and attention may guide policy in relation to the resurgence of COVID-19 currently observed in many countries. Our findings are limited to short-term effects of specific interventions and do not capture interactions between these nor other important factors likely to be associated with public mobility and attention . Any conclusions are limited by the representativeness of Twitter and Goggle mobility data.In this cross-country comparison, stricter interventions were associated with larger reductions in mobility. Bans on public gatherings showed stronger associations with mobility in both countries compared with national announcements by country leaders and public recommendations, which were the primary intervention types in Sweden. We also found a delay in reaching maximum reduction in mobility after restrictions and for normalization of mobility after relaxation of restrictions, in line with prior research.


GSTM1 and PALD1, genes linked to oxidative stress, inflammation and endothelial junction stability, as vascular aging markers. A functional assay performed on PALD1 KD VECs demonstrated a recovery in vascular permeability. We found that iSMCs from HGPS donors overexpressed bone morphogenetic protein (BMP)−4, which plays a key role in both vascular calcification and endothelial barrier damage observed in HGPS. Strikingly, BMP4 concentrations are higher in serum from HGPS vs. age-matched mice. Furthermore, targeting BMP4 with blocking antibody recovered the functionality of the vascular barrier in vitro, hence representing a potential future therapeutic strategy to limit cardiovascular dysfunction in HGPS. These results show that iVECs and iSMCs retain disease-related signatures, allowing modeling of vascular aging and HGPS in vitro.Vascular dysfunctions are a common feature of multiple age-related diseases. However, modeling healthy and pathological aging of the human vasculature represents an unresolved experimental challenge. Here, we generated induced vascular endothelial cells (iVECs) and smooth muscle cells (iSMCs) by direct reprogramming of healthy human fibroblasts from donors of different ages and Hutchinson-Gilford Progeria Syndrome (HGPS) patients. iVECs induced from old donors revealed upregulation of  Physiological and pathological aging represent a major risk factor for the onset of cardiovascular diseases, the leading cause of death worldwide . The in ETV2 alone, which is a master regulator of VEC development and early vasculogenesis (MYOCD) was able to strongly induce both smooth and cardiac muscle genes in human foreskin fibroblasts and mesenchymal stem cells  is a relatively new research field which can potentially affect both tissue engineering applications and in vitro disease modeling. Recently, several attempts have been made to generate reprogrammed vascular cells , but a cMYOCD or ETV2, respectively. iSMCs expressed cell-identity markers including alpha smooth muscle actin (αSMA)  . Importan (αSMA) , thus min (αSMA)  and othen (αSMA) . In partn (αSMA) . In paran (αSMA)  were abln (αSMA)  or iSMCsn (αSMA)  in 3D fin (αSMA) .ACTA2   was 6.62 log2FoldChange (FC) higher in primary VECs vs. fibroblasts and 6.38 log2FC higher in iVECs vs. fibroblasts. At the same time, another endothelial-specific gene (i.e. CDH5) was upregulated in both primary cells (log2FC = 6.7) and iVECs (log2FC = 4) compared to fibroblasts, although at different levels   vs. primary SMCs  or skin fibroblasts (N = 6 donors) vs. primary VECs  . This ant levels . Similarroblasts . Notably TWIST2) .Finally, TWIST2) , while i TWIST2) .Taken together, the majority of the markers expressed by reprogrammed cells remained fibroblast-like. After carefully considering this critical point, we would like to highlight that reprogrammed SMCs and VECs expressed typical cell identity genes, self-organized into 3D vascular structures mimicking simple vascular networks and showed a global gene expression signature representing a gradual transition towards fully mature cells.MYOCD and ETV2, respectively. It is important to highlight that the expression of MYOCD and ETV2 was comparable in reprogrammed cells derived from young and old donors . DE analysis highlighted a set of genes  that changed according to the age of the donor for either iSMCs , which is involved in the vascular calcification of blood vessels during aging and disease  vs. young (3 months) animals , we performed knockdown (KD) experiments on VECs and SMCs (PALD1 KD in VECs decreased vascular permeability compared to luciferase control (51.5 ± 3.4% vs. 100.0 ± 4.3%)   in co-culture with old VECs. Strikingly, we found that the presence of HGPS iSMCs determined a significant increase in the vascular leakiness compared to young donors . Although we cannot separate the contribution of these two aspects, it is likely that the combination of physical support and secreted factors contributes to the observed differences in the barrier functionality in presence of young vs. old iSMCs.Strikingly, we found that reprogrammed cells from young vs. old donors were also characterized by functional differences, as demonstrated by the compromised barrier function in presence of iSMCs or iVECs from old individuals. We noticed that the levels of permeability were about 50% higher in presence of reprogrammed cells from old vs. young donors. The observed difference in permeability seems to reflect intrinsic differences between young and old cells, considering that the expression level of a small set of cell identity genes was not affected by the age of the donor. It is important to keep in mind that our microscale models do not fully recapitulate the vascular niche, including tissue-specific cells and immune cells, which might contribute to changes in vascular functionality during aging and disease. The absence of these components might partially explain why we observed limited (~50%) differences in vascular functionality. Further, it is important to mention that the contribution of SMCs to the vascular barrier could be in terms of both paracrine secretion and direct support to the endothelial monolayer. While confocal images showed that iSMCs from old donors did not physically interact with iVECs, differential expression analysis highlighted that iSMCs from old donors upregulated genes which might interfere with the integrity of the barrier  in R (R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/) on the row-wise z-scaled log-transformed count per million values (CPM). A pseudo-count of 5 was added to each CPM value before log-transformation to reduce noise. Dendrograms on the heatmap were calculated using Ward. D2 clustering algorithm and 1-Pearson’s correlation as distance measurement.The quality of cDNA libraries was checked with Tape Station (Agilent) and the amount of cDNA quantified through Qubit (ThermoFisher). HiSeq2500 was used to sequence the samples (single-end reading). Reads were mapped by STAR  to the hg19 reference genome with default parameters. Homer eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Jessica Tyler as the Senior Editor.Thank you for submitting your article "Direct reprogramming human vascular cells reveals defects associated with aging and Hutchinson-Gilford Progeria Syndrome" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:Direct reprogramming of skin fibroblasts is a valuable strategy to obtain large amounts of specific cell types that are centrally involved in human disease processes. This manuscript reports direct reprogramming of skin fibroblasts to address age-specific and disease-specific changes in vascular cells during normative aging and in accelerated aging in Hutchinson-Gilford Progeria Syndrome. The authors report the transcriptional and functional characterization of vascular cell types generated by direct reprogramming. This strategy may have substantial impact by providing a tool to study changes in vascular function and potentially that of other tissues during physiological and pathological aging.Essential revision requirements:The authors addressed most comments appropriately with new experimental results or new statistical analyses. However, the question of similar reprogramming efficiency between young and old samples , was still not fully answered. In the revised manuscript, the authors compared the expression of ETV2 and MYOCD and found no statistical difference. However, these are comparisons of the expression level of reprogramming factors, not the reflection of similar reprogramming efficiency between young and old samples – that is, whether the reprogrammed state into iVECs or iSMC as an output, is similar between young and old samples. This is a central point that needs to be addressed. [Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]Bersini et al. provide a conceptual advance in the use of direct reprogramming to model age-associated dysfunction of vascular cells. This is a novel and exciting proposed approach to study mechanisms of age-related vascular dysfunction and may provide actionable in vitro models of age-related vascular disease. The data presented, however, does not support efficacy of the reprogramming approach at the transcriptional or functional level. Critical validation of the approach is lacking. In the functional comparisons between reprogrammed cells from young and old donors, it is unclear that reprogramming efficiencies are comparable. Substantial weaknesses in the statistical analyses of the data were identified. Some of the supplementary materials provided may be superfluous and could be replaced by informative panels included in the main figures. BMP2 is identified in one set of experiments, but subsequent experiments focus on a different gene, BMP4. From an RNA-seq experiment that was performed data from only 2 genes are shown. Overall, while the focus of the studies reported is of high interest, these and other weaknesses decrease the priority for publication of these studies in their current form. A revised manuscript adequately addressing the main concerns raised in the peer-review process, that will require additional experimental work, might be suitable for publication. A revised Tools and Resources article also needs to provide additional insights into how the authors' reprogramming techniques and biological resources will advance the field.GSTM1 in skin biopsies, expression of Calponin in reprogrammed SMCs and analysis of endothelial cell-cell junction quality.We have extensively revised the previous version of the manuscript by performing additional analyses and experiments. Specifically, we have addressed the concerns regarding the reprogramming efficiency of cells derived from young and old donors and the differentiation level of directly reprogrammed cells. We have performed new statistical analyses suggested by reviewers #2 and #3 and we have provided additional quantifications such as pathway analysis for the RNAseq data on organ-specific mouse VECs, expression of PALD1 and BMP2 knock-down on primary cells (both VECs and SMCs) to show the functional relevance of these genes.We have also addressed comments related to the validation of the approach: i) inclusion of negative (fibroblasts) and positive (primary VECs) controls for the vascular barrier assay; ii) permeability measurements with BMP2 to BMP4) and a discussion on the contribution of the reprogramming approach for the advancement of the field.We also provide a clear rationale for the transition from physiological aging to HGPS . Unfortunately, we only have access to the serum of this mouse model, not the mice themselves. We also feel that these experiments might go beyond the scope of this manuscript as a Reviewer #1:Bersini et al. report the generation of induced vascular endothelial cells and smooth muscle cells by direct reprogramming of skin fibroblasts through enforced expression of single master regulator genes for the endothelial and smooth muscle fates respectively. The stated goal of the study is to characterize and validate directly reprogrammed vascular cells, then use to ascertain age-specific differences in their biology. The goal is important since the authors state that transcriptomic studies on direct reprogrammed cells are lacking. The studies in directly reprogrammed skin fibroblasts hinge on the effectiveness of direct reprogramming. This is not supported by the transcriptomic data of Figure 1G and I. Even though (as the author state) induced cells do not express all cell identity markers of the primary cell types that they model, the paucity of cell type-specific genes that are expressed in the induced smooth muscle and vascular endothelial cells that are presented in Figure 1G strongly suggest that these cells may still be largely of fibroblast nature. It could be argued that a handful of vascular-specific genes are sufficient to direct the functionally relevant reprogramming of fibroblasts. However, the lack of direct comparisons in functional barrier assays (i.e. lack of data from “negative” and “positive” control samples using primary fibroblasts (negative control) and primary vascular endothelial or smooth muscle cells (positive controls)) in all subsequent studies that use cells directly reprogrammed from young and old donors make interpretation of those data difficult. Further, the differences in permeability between barriers established with induced cells from old and young donors are very small, and may be related to small differences in integrity or health of induced cells that retain (as suggested by the transcriptomic studies) most of their fibroblast-like features. These are important concerns that need to be addressed.We thank the reviewer for the detailed comments on our manuscript. We agree with the reviewer that an effective reprogramming protocol is fundamental to obtain meaningful data and we apologize for the absence of key data supporting this issue in the original manuscript. We have now included additional results showing that:2FC=0.19 for ETV2 and log2FC=0.03 for MYOCD comparing young and old cells; no statistical difference comparing young and old cells). This point demonstrates that the reprogramming efficiency is not affected by the age of the donor and that the observed differences between young and old cells are not due to different levels of reprogramming. Details have been included in the manuscript. We would like to highlight that permeability values were about 50% higher in presence of reprogrammed cells from old vs. young donors. This difference was consistent across multiple replicates and donors. However, we need to highlight that our microscale models do not include several components of the vascular niche, including tissue-specific cells and immune cells, which might contribute to changes in vascular functionality during aging and disease. The absence of these components might partially explain why we observed limited (~50%) differences in vascular functionality. We have included a paragraph in the manuscript to discuss this specific point.1) The expression of the master regulators ETV2 and MYOCD is comparable in reprogrammed cells derived from young vs. old donors (log2) The expression of most key cell identity genes for both iVECs and iSMCs is comparable in reprogrammed and primary cells. In addition, the expression of key fibroblast genes is significantly lower in both reprogrammed and primary cells compared to the fibroblast population. Details have been included in Supplementary Information (Supplementary file 2). Together, these data show that reprogrammed cells are not only expressing cell identity genes, but also downregulating well established fibroblast markers.As pointed out by the reviewer, a “handful of vascular-specific genes might be sufficient to direct the functionality of reprogrammed cells”. However, in the original manuscript there was no direct comparison of vascular permeability among reprogrammed cells, primary VECs (positive control) and fibroblasts (negative control). We have performed additional experiments showing that the permeability of reprogrammed cells is significantly lower compared to fibroblasts, reaching values close to the permeability of primary VECs. See Figure 2I.Although we cannot claim that reprogrammed cells are transcriptionally and functionally identical to primary VECs, these data clearly demonstrate that reprogrammed cells have acquired a “minimum set of transcriptional and functional features”, which allows to employ them to capture a fraction of the vascular changes occurring during aging and disease. We have included these new data and their discussion in the manuscript. Please note that we performed experiments to measure the barrier functionality of iVECs compared to primary VECs and fibroblasts. Indeed, fibroblasts represent an appropriate negative control for the study of vascular functionality, since they do not contribute to the formation of the inner wall of blood vessels (which is formed by VECs). However, regarding SMCs we have only performed experiments comparing iSMCs and primary SMCs. The main reason is that fibroblasts normally populate the extracellular matrix surrounding blood vessels and directly contribute to the homeostasis of VECs. Hence, a comparison including fibroblasts would have been confusing. Collected data show that the vascular permeability in presence of young iSMCs is even lower compared to primary SMCs, demonstrating that reprogramming generates functional cells directly contributing to the maintenance of the vascular barrier . See Figure 2J.Reviewer #2:[…] Based on the heatmap data shown in Figure 1, it is not clear whether the transcriptome of reprogrammed cells represent fully reprogrammed and functional iVECs/iSMCs. The expression data indicate that both iSMCs and iVECs express more genes in common with fibroblasts than with primary VECs or SMCs. The authors state "transition towards full mature cells." The readers will be better informed if the authors clarify what is the measurement for transcriptomics and function for partial versus fully reprogrammed cells, or present additional data that either reject or support fully mature versus partial reprogramming. It is understandable that the goal here is to induce SMCs or VECs to the point that can capture some of the age-associated changes, not necessarily generating cells that will be identical to primary cells. Still, this will be useful information to the readers.2FC higher in primary VECs vs. fibroblasts and 6.38 log2FC higher in iVECs vs. fibroblasts. At the same time, CDH5 was upregulated in both primary cells (log2FC=6.7) and iVECs (log2FC=4) compared to fibroblasts, although at different levels. Similarly, both primary and reprogrammed SMCs upregulated key identity genes including ACTA2 and MYH10 compared to fibroblasts. However, the expression of MYH10 was lower in reprogrammed vs. primary SMCs. Finally, we would like to highlight that reprogrammed cells downregulated the expression of fibroblast-specific genes , again indicating a transition towards fully mature vascular cells.We thank the reviewer for the useful comments on our manuscript. We have included additional data to better clarify the concept of “partial maturation” of the reprogrammed cells. First, we have analyzed the expression level of key cell identity genes for both iVECs and iSMCs and we have compared it with primary cells and fibroblasts. We have included in Supplementary Information a detailed table with these genes to provide a quantification of our concept of “partial maturation” (Supplementary file 2). For example, the expression level of KDR  was 6.62 logAs a second step, we have performed additional experiments to compare the functionality of iVECs with primary VECs (positive control) and original fibroblasts (negative control). Our data show that the permeability of iVECs is significantly lower compared to fibroblasts, reaching values close to the permeability of primary VECs. Regarding SMCs we have only performed experiments comparing iSMCs and primary SMCs. The main reason is that fibroblasts normally populate the extracellular matrix surrounding blood vessels and directly contribute to the homeostasis of VECs. Hence, a comparison including fibroblasts would have been confusing. Collected data show that the vascular permeability in presence of young iSMCs is even lower compared to primary SMCs, demonstrating that reprogramming generates functional cells directly contributing to the maintenance of the vascular barrier .We have included these data in the manuscript. See Figure 2I and J.Overall, we think that these additional quantifications provide a better picture of vascular reprogramming and highlight that iVECs and iSMCs show transcriptional and functional features of VECs and SMCs, respectively, although they do not fully replicate primary cells. A discussion paragraph has been included in the manuscript.The choice of some of the SMC markers was not clear. For example, Calponin  appears to be expressed in cells that do not express the reprogramming factor, MYOCD, suggesting that Calponin could be also expressed in human fibroblasts. As such, it will be informative to the readers if the authors show the same immunostaining data for Calponin in fibroblasts.The image reported in Figure 1B showed MYOCD positive cells expressing Calponin. We noticed that a few cells can express Calponin despite the weaker MYOCD signal. We apologize if this aspect was confusing. To further clarify the expression of smooth muscle cell markers we have quantified the percentage of MYOCD positive cells co-expressing αSMA and Calponin. We have found that 70% MYOCD positive cells expressed αSMA, while 80% expressed Calponin. We have introduced these data in the manuscript.The main focus in Figure 2 is to define age-associated changes modeled through direct reprogramming of fibroblasts of young and old age groups. It is then essential to provide experimental evidence that the comparative analyses were performed in the realm of similar reprogramming efficiencies between young and old groups. This point is particularly crucial as fibroblasts from different ages may not reprogram with the same efficiency.2FC=0.19 for ETV2 and log2FC=0.03 for MYOCD comparing young and old cells; no statistical difference comparing young and old cells).We agree with the reviewer. We have provided additional data (Supplementary Information) showing that the reprogramming efficiency is comparable in cells obtained from young and old donors  in the old sample and none at the interface with iVECs, in contrast to the young sample. To further dissect the difference in permeability due to aging, not the lack of reprogrammed cells in the old samples, it will be informative to provide clear pictures showing the reprogrammed cells at the interface of the permeability assay.We thank the reviewer for the possibility to clarify this aspect. As discussed in the previous comment, we have provided additional data showing that the reprogramming efficiency was similar in cells obtained from young and old donors. Hence, differences in permeability are not due to different reprogramming efficiency.Regarding the distribution of iSMCs in the microscale assay, we realized that our description in the manuscript was not sufficiently clear. Indeed, iVECs and iSMCs are seeded in two separate channels, with iVECs forming a monolayer at the interface with the channel containing iSMCs (embedded in a 3D matrix). iSMCs homogeneously distribute inside the matrix channel. iSMCs from young donors tend to migrate towards the endothelial monolayer. However, the same migratory behavior is not observed with cells from old donors. Here we report a new Figure 2—figure supplement 6 showing a detail of the endothelial monolayer  between the pillars of the microscale model; smooth muscle actin (red) positive cells are close to the monolayer only in young donors .It is important to highlight that the contribution of SMCs to the vascular barrier could be in terms of both paracrine secretion and direct support to the endothelial monolayer. Overall, IF images showed that iSMCs from old donors did not physically interact with iVECs; at the same time, differential expression analysis highlighted that iSMCs from old donors upregulated genes which might interfere with the integrity of the barrier . Although we cannot separate the contribution of these two aspects, it is likely that the combination of physical support and secreted factors contributes to the observed differences in the barrier functionality in young vs. old conditions. We have included a paragraph in the Discussion section to better explain this point.As the end of Figure 2 was currently left with the correlation studies, additional mechanistic insights into the identified DE genes will greatly signify the authors' findings, for example by either i) reducing the expression of BMP2 or PALD1 in old cells, or ii) overexpressing those aging-associated genes in young cells to pinpoint whether the identified DE genes are underlying the observed phenotypes.PALD1 and BMP2 in primary VECs and SMCs, respectively, would have been even more relevant to clarify the contribution of these genes in the vascular functionality. Collected data showed that KD of PALD1 in VECs decreased vascular permeability compared to luciferase control . On the other side, KD of BMP2 in SMCs induced a weak, but reproducible, decrease in permeability compared to luciferase control .We have performed additional experiments to demonstrate the functional significance of the identified genes. We thought that KD of PALD1 expression by VECs is a key determinant of vascular permeability. On the other side, the contribution of BMP2, which is secreted by SMCs, is more limited. Based on this finding, it is possible that the effect of SMCs on the regulation of vascular permeability is the result of the action of multiple genes, each giving an incremental or synergistic contribution . Overall, these experiments show that reprogrammed cells are a promising tool to identify potential contributors to vascular aging. Data have been included in the manuscript. See Figure 2K and L.Together, these results clearly show that BMP2 was discovered to be upregulated in aging in Figure 2, but all the HGPS experiments in Figure 3 were associated with a different gene, BMP4. Based on the existing datasets, is BMP4 also upregulated in aged induced cells? Also, JAG1 is introduced in Figure 3, while there is no mention of PALD1 from Figure 2. The text will benefit from a more in-depth discussion of the link between findings of Figure 2 to Figure 3.We thank the reviewer for the possibility to clarify this point. We did not find overlapping genes comparing young vs. old and healthy vs. HGPS cells. This is the reason why JAG1 was not mentioned in Figure 2. However, it is interesting to highlight that BMP2  and BMP4  share a 62% sequence similarity and are functionally equivalent. Hence, BMP2/4 dysregulation might underlie a common mechanism leading to vascular degeneration during physiological aging and disease. We have clarified these aspects in the Discussion section.Reviewer #3:Direct reprogramming of fibroblasts from young, aged, and HGPS donors into vascular endothelial cells (VECs) and smooth muscle cells (SMCs), combined with transcriptomics analyses and co-culture assays, is a novel and interesting approach to study age-related alterations in EC-SMC crosstalk and discover new potential therapeutic targets to fight vascular dysfunction in normal and premature aging. However, RNAseq results from the physiological aging-related part lacks functional validation, and RNAseq results from the HGPS-related part presents weak in vivo/ex vivo validation.1) Regarding cells derived from normally aged donors, the authors show validation of three hits obtained from the RNAseq assays by immunofluorescence analysis of skin biopsies. However, no studies were performed to analyze the functional significance of such expression changes on endothelial permeability, as done for HGPS-related studies .PALD1 and BMP2 in VECs and SMCs, respectively, is coupled with a decrease in vascular permeability. More in detail, KD of PALD1 in VECs decreased vascular permeability compared to luciferase control . On the other side, KD of BMP2 in SMCs induced a weak, but reproducible, decrease in permeability compared to luciferase control .We agree with the reviewer that RNAseq data from the physiological aging part were not coupled with a functional validation of the identified genes. Hence, we have provided additional data showing that KD of PALD1 expression by VECs is a key determinant of vascular permeability. On the other side, the contribution of BMP2, which is secreted by SMCs, is more limited. Based on this finding, it is possible that the effect of SMCs on the regulation of vascular permeability is the result of the action of multiple genes, each giving an incremental or synergistic contribution . Overall, these experiments show that reprogrammed cells are a promising tool to identify potential contributors to vascular aging. Data have been included in the manuscript. See Figure 2K and L.Together, these results clearly show that 2) Although the HGPS-related part of the paper includes in vitro functional studies, ex vivo and in vivo validation of the RNAseq results appears rather limited. BMP4 ELISA data in Figure 2B comparing age-matched controls and HGPS patients is very far from reaching statistical significance (p=0.4), thus making very difficult to draw conclusion. I concur with the authors that these experiments are very challenging considering the limited number of available HGPS samples, and mouse studies may help overcome this limitation. Regarding mouse studies in Figure 3C, the authors used the t-test to compare serum BMP4 levels in young, aged and HGPS groups. However, t-test is not appropriate for studies including more than 2 experimental groups. These results should be re-analyzed using ANOVA and post-hoc analysis. Given the limitation of the human studies, a more thorough validation using the mouse model would significantly contribute to strengthen the biological significance of the results. For example, it would be very informative to perform qPCR and/or immunofluorescence assays to assess BMP4 and Jagged 1 levels in mouse arteries. I also miss experiments to examine in vivo endothelial permeability and the effects of neutralizing anti-BMP4 and anti-Jagged 1 antibodies in the mouse models of normal and premature aging.We appreciate that the reviewer concurs with us that experiments on HGPS are challenging considering the limited availability of samples and models. Based on the reviewer comments and on comments by another reviewer, we have re-analyzed data on serum from human donors by considering the aging perspective. While healthy donors show a significant decrease in BMP4 over age , this trend is lost in HGPS samples. This finding offers a novel way to analyze the potential contribution of BMP4 in vascular degeneration and has been included in the manuscript.We also agree with the reviewer that a t-test was not ideal to analyze the level of BMP4 in mouse samples given the presence of 3 experimental groups. For this reason, we have performed an ANOVA test followed by Holm-Sidak test finding that HGPS samples were statistically different compared to old (p<0.001) and young (p=0.001) samples. However, there was no difference comparing old and young samples (p=0.79). We have included these data in the manuscript.We agree with the reviewer that in vivo experiments (e.g. measurements of vascular permeability in presence of anti-BMP4 antibody) would significantly strengthen the conclusions of the manuscript. BMP4 might represent a robust biomarker of vascular damage in HGPS, with potential consequences for the development of new therapies. However, we think that these experiments might go slightly beyond the scope of this manuscript as a Tool and Resource article, which is “assessed in terms of the potential to facilitate experiments that address problems that to date have been challenging or even intractable”.3) Progerin expression should confirmed in HGPS-derived cells.-1.pdf).We have provided additional information about the source of HGPS cells. All HGPS cell lines were obtained from the Progeria Research Foundation and screened for mutation in the LMNA gene, as certified by the Progeria Research Foundation (https://www.progeriaresearch.org/wp-content/uploads/2019/11/PRF-AVAILABLE-CELL-LINES4) Several references appear to be incorrect in the introductory paragraph in the section of Results focused on HGPS. The authors state: "In particular, HGPS is characterized by dysfunctional SMCs, which promote vascular calcification , hypertension  and loss of vascular barrier functionality ". The cited papers address the role of BMPs in vascular calcification, hypertension and loss of vascular barrier functionality, but I believe none of them include studies with either HGPS patients or HGPS experimental models. The same comment regarding the studies by Wei et al., 2018 and Miriyala et al., 2006 applies to the Discussion: "The main cardiovascular complications observed in HGPS patients include loss of SMC coverage of blood vessels , abnormal vascular calcification  and hypertension ." Also note that all results in Varga et al., 2006 were derived from a mouse model of HGPS, not from HGPS patients, therefore references for SMC loss in patients should be cited instead of Varga et al., 2006 (or 1-2 review articles).We apologize for the incorrect references. We have modified the text accordingly.5) Figure 1A, B: Quantitative data should be presented .We have quantified the percentage of MYOCD positive cells co-expressing αSMA and Calponin. We have found that 70% MYOCD positive cells expressed αSMA, while 80% expressed Calponin. We have introduced these data in the manuscript.6) Figure 1D: The authors state that iSMCs contribute to the formation of microvascular networks in 3D fibrin matrices , but no control without iSMCs is shown, which is needed to ascertain if iSMC promote this process.We apologize for the misunderstanding. We wanted to highlight that iSMCs physically interact with VECs and partially wrap segments of capillary structures. Based on the reviewer comment, we have re-analyzed data generated in our lab regarding the area fraction covered by VECs (vascular structures) in presence of fibroblasts . The same culture conditions  were applied in the co-culture model with iSMCs. We have then quantified the area fraction covered by VECs in presence of fibroblasts or iSMCs considering regions of interests of the same dimensions. Our data show that there is not a statistical difference (t-test with p=0.61) when VECs are co-cultured with fibroblasts or iSMCs, supporting the idea that iSMCs contribute to the formation of vascular networks in vitro .7) The images in Figure 2E and 2F do not seem to be very representative according to the quantitative data shown in Figure 2C and 2D .We apologize, the images were selected to be representative for the differential expression of selected proteins. Image quantification is based on thresholded images (binarized) where low intensity pixels (which can be barely detected by eyes in the images) might be amplified resulting in the lower difference reported in the graphs.8) Figure 2G-J: To confirm that age-related increased permeability is due to compromised VEC junctions, confocal microscopy experiments should be performed in young and old VECs to visualize junctions within neighbouring cells, ideally in monoculture and in co-culture with young and old SMCs.We have included additional data showing the expression of Y658 VE-Cadherin (marker of cell-cell junction de-stabilization) in iVECs derived from young and old donors. Quantification of Y658 area fraction showed a significant over-expression of this marker in iVECs from old donors, complementing data on vascular permeability reported in Figure 2—figure supplement 5.9) Figure 3D, F and G: The experiments include 3 groups, therefore t-test is not adequate. Results should be re-analyzed using ANOVA and post-hoc analysis.We have re-analyzed the data using ANOVA test. Details are reported below.Figure 3D: ANOVA with Holm-Sidak test gives p<0.001 for all comparisonsFigure 3F: ANOVA with Holm-Sidak test gives p<0.001 for all comparisonsFigure 3G: ANOVA with Holm-Sidak test gives p=0.01 for control vs. Jagged1; p=0.043 for control vs. BMP4; p=0.897 for BMP4 vs. Jagged1Results have been included in the manuscript.10) Figure 3H-J: Quantitative results should be shown.Quantification for Figure 3H-J was already present in Figure 3G.11) Figure 2—figure supplement 3: Quantitative results of GSTM1 expression in young versus old ECs should be shown , was still not fully answered. In the revised manuscript, the authors compared the expression of ETV2 and MYOCD and found no statistical difference. However, these are comparisons of the expression level of reprogramming factors, not the reflection of similar reprogramming efficiency between young and old samples – that is, whether the reprogrammed state into iVECs or iSMC as an output, is similar between young and old samples. This is a central point that needs to be addressed.2FoldChange (FC) and the False Discovery Rate (FDR) for these genes. As you can see, the expression of these genes is not statistically different comparing young and old induced cells.We realize that our answer regarding the question about similar reprogramming efficiency between young and old samples by showing expression of the reprogramming factors ETV2 and MYOCD did not fully satisfied the reviewers. We now provide evidence showing that the reprogramming efficiencies are similar. We have performed additional analyses to show that the expression of key cell identity genes did not change comparing cells derived from young and old donors, considering both induced endothelial cells (iVECs) and induced smooth muscle cells (iSMCs). We have included a Supplementary File 3 reporting the logIn addition, we have generated heat-maps to better visualize how the expression of all these genes is not affected by the age group. As a reference, we have also included fibroblasts derived from young and old donors. These heat-maps have been included as Supplementary Figure (Z-score: ±1) . If you think that these additional images might better fit in the main text, we are open to include them in Figure 1.The main text has now been modified considering these details. We think that these data together with the expression of ETV2 and MYOCD will clarify the concept that differences between young and old reprogrammed cells are not dependent on their identity.We have now changed the way supplementary figures, videos and tables are cited. Moreover, supplementary methods and legends are now included in the main text. The Key Resources Table has now been included at the beginning of the Materials and methods Section. Regarding cell lines, relevant information have been added to the Supplementary Materials and methods section. In addition, we have provided references in the Key Resources Table to clarify that all cell lines were characterized for cell identity and tested negative for mycoplasma. We have provided a more detailed ethics statement, which is reviewed and approved yearly by the IRB. Regarding plasma from mice, we would like to highlight that we did not perform any kind of animal experiment. Plasma was obtained from Prof. Belmonte at Salk Institute. Relevant sequencing data are uploaded and publicly available in GEO (accession number GSE140898) and analyzed data are also included in Source Data 1, Source Data 2 and Source Code 1.


Autoinflammatory diseases are a disease entity caused by the dysregulation of innate immune cells. Typical autoinflammatory diseases are monogenic disorders and often very rare. As a result, there is a relative lack of understanding of the pathogenesis, poor diagnosis and little available treatment. Induced pluripotent stem (iPS) cells are a new technology being applied to in vitro disease modeling. These models are especially useful for the analysis of rare and intractable diseases including autoinflammatory diseases. In this review, I will provide a general overview of iPS cell models for autoinflammatory diseases and a brief description of the results obtained from individual reports. Autoinflammatory diseases are a disease entity first proposed in 1999 as a group of diseases in which inflammatory findings show no apparent triggers and no evidence of existing autoantibodies or autoreactive T cells ,3.Because autoinflammatory diseases are a relatively new disease concept and because they are mainly composed of rare genetic diseases, they are not widely recognized by physicians or scientists. Therefore, there remains little in terms of diagnostic and therapeutic techniques and understanding of the pathogenic mechanisms. In this regard, research using induced pluripotent stem (iPS) cells for the analysis of autoinflammatory diseases has grown. In this paper, I present examples of iPS cell applications for the pathological analysis of autoinflammatory diseases and discuss future perspectives.iPS cells are pluripotent stem cells established by Shinya Yamanaka and colleagues ,5. BecauFor these reasons, iPS cell models are expected to accelerate the understanding and treatment of autoinflammatory diseases, especially those caused by single gene mutations. From patient iPS cells, researchers can prepare the cell types, such as neutrophils, monocytes, and macrophages, that show the disease phenotype.There already exist many differentiation systems to obtain the above innate immune cells from iPS cells ,13,14,15NLRP3 gene and present systemic inflammation due to excessive IL-1β production caused by hyperactivation of the inflammasome [NLRP3 mutations as low frequency somatic mosaicisms rather than constitutive in about 30–40% of patients [NLRP3 mutant cells in somatic cells is about 10%, the phenotype is comparable to that of patients with mutations in all cells in the body, raising the question of whether only these low-frequency NLRP3 mutant-positive cells are responsible for the disease or whether cells other than those with NLRP3 mutations also contribute to the pathogenesis.Chronic infantile neurocutaneous arthropathy  syndrome, also known as neonatal onset multisystem inflammatory disease (NOMID), is an autoinflammatory syndrome with a triad of non-pruritic urticarial rash, meningitis, and joint lesions that develops in the neonatal period  and is tammasome ,32. The patients ,34,35. HNLRP3-mutant and wild-type iPS cells were established from CINCA syndrome patients with a somatic NLRP3 mutation [NLRP3-mutant macrophages produced a large amount of IL-1β. Interestingly, when mutant macrophages were mixed with wild-type macrophages to create a pseudo-mosaic state and stimulated, IL-1β production was enhanced compared to mutant macrophages alone. In other words, in the somatic cell mosaicism, mutant cells mainly produce IL-1β, but wild-type cells also contribute to inflammation in some way. Thus, iPS cells can be used for detailed analysis of the unique pathologies associated with somatic cell mosaicisms.One of the characteristics of iPS cells is that the source of each iPS cell clone is derived from a single somatic cell . Therefomutation . When thNLRP3 mutant clones derived from the same patient was larger than that from wild-type iPS cells, and calcification occurred in a disordered manner. A detailed examination of the mechanism of cartilage hypertrophy suggested that the mechanism lies behind the increased expression of the SOX9 gene due to enhancement of the cAMP-cAMP responsive element binding protein (CREB) pathway. These results are important for understanding cartilage lesions in CINCA syndrome and for developing novel therapies, and provide an example of how iPS cells can be used to analyze cartilage lesions, which was previously difficult to do.In CINCA syndrome, contractures with periarticular bone hypertrophy are often observed, and the pathophysiology has been thought to be the hyperplasia of growing cartilage . HoweverNLRP3. However, the remaining 10% of patients with clinical symptoms of CINCA syndrome do not have mutations in the NLRP3 gene [NLRP3 mutation, induced them to differentiate into macrophages, and confirmed the production of IL-1β [NLRC4 gene were commonly detected in the diseased clones. When we knocked out the NLRC4 gene in the iPS cell clones with the mutated NLRC4 gene, the production of IL-1β decreased and the clone recovered to the normal phenotype. These results proved that the somatic mosaicism of the NLRC4 gene mutation is the genetic background of this CINCA syndrome patient. To date, no other case of somatic mosaicism of NLRC4 has been reported, nor has NLRC4 been reported as the causative gene of CINCA syndrome. Thus, this study showed that iPS cell technology can play an essential role to accurately diagnose extremely rare cases and that dissection of iPS cell-derived phenotypes can be useful in diagnosing sporadic patients for whom the causative gene is difficult to identify.As mentioned above, in CINCA syndrome, about 90% of patients have constitutive or somatic mosaic mutations in RP3 gene . Since mof IL-1β . Each clNOD2 gene, which leads to granulomatous lesions in the skin, joints, and eyes in childhood and severe complications such as blindness and joint contractures [NOD2 mutation and isogenic iPS cells in which the mutation was repaired [NOD2 mutation knocked into control iPS cells to create two pairs of iPS cell clones that are genetically homogeneous except for the difference in the NOD2 genotype. When these iPS cells were induced to differentiate into macrophages and their cytokine production capacity was compared, it was found that IFN-γ-dependent activation of the NF-κB pathway and production of pro-inflammatory cytokines were induced in the NOD2 mutant clones. Considering that granulomas in Blau syndrome patients are IFN-γ positive [Blau syndrome  is a disease caused by a heterozygous gain-of-function mutation in the ractures ,39,40. Iractures . Thereforepaired . In addipositive  and thatpositive ,44,45, wPSMB8 gene encoding β5i, a component of the immunoproteasome [PSMB8 mutations [PSMB8 mutation in order to develop a pathological model of NNS [PSMB8 mutation gene and generated two pairs of pluripotent stem cells that are genetically uniform except for the difference in PSMB8 genotype, as in the case of Blau syndrome. After differentiation of these pluripotent stem cells into monocytic cells, they were stimulated with IFN-γ and TNF-α, both of which are immunoproteasome-inducing stimuli. The results showed that the function of the immunoproteasome was significantly reduced in PSMB8 mutant monocytes compared to normal monocytes, while the secretion of pro-inflammatory cytokines and chemokines was enhanced. Interestingly, mitochondria-derived reactive oxygen species production was enhanced in the mutant cells, and the addition of antioxidants suppressed the production of pro-inflammatory cytokines and chemokines. Thus, oxidative stress was shown to be involved in the enhancement of the inflammatory response by PSMB8 mutation. This study demonstrates the usefulness of patient iPS cells for modeling proteasome-associated autoinflammatory diseases.Nakajo–Nishimura syndrome (NNS), also known as proteasome-associated autoinflammatory syndrome 1  is a disease characterized by chronic inflammation and lipomuscular atrophy caused by homozygous loss-of-function mutations in the oteasome ,47. Receutations . Howeverl of NNS . In paraIL-10 is one of the most important cytokines for the maintenance of intestinal homeostasis and regulates inflammation by inhibiting macrophage activation ,50,51. INLRP7 mutations [NLRP7 is an inflammasome protein, but the mechanism by which NLRP7 dysfunctions lead to complete hydatidiform moles is unknown. The NLRP7 gene does not have a mouse orthologue, which makes it difficult to construct animal models. Since the origin of hydatidiform moles is trophoblasts, it is necessary to induce this cell type from iPS cells. In a report on the establishment of iPS cells from a case of HYDM1 with a compound heterozygous mutation, phenotypic analysis was attempted using a system to induce trophoblasts in vitro [NLRP7-deficient iPS cells and trophoblast-related markers were upregulated. Furthermore, these phenotypes were dependent on BMP4 signaling, and inhibition of the BMP pathway corrected the excessive trophoblast differentiation of the patient-derived iPS cells. Although functional analysis was not performed in detail in this study, gene expression profiling analysis led to interesting results and insights. For example, the model provides a new method for analyzing human placental diseases. In addition, NLRP7, an inflammasome-related molecule, was shown to be an important regulator of cell fate during human development. Autoinflammatory diseases show a wide variety of inflammatory lesions, but there are also lesions that do not have inflammatory cell infiltration. For example, the cartilage lesions of CINCA syndrome described above are also non-inflammatory lesions. Detailed analysis of lesion tissues using iPS cells may be useful for the analysis of such non-inflammatory lesions.Familial recurrent hydatidiform moles  is an inherited disorder that causes recurrent complete hydatidiform moles . It is cutations ,61,62. NAs illustrated in this review, iPS cell-based analysis is useful for the diagnostic and pathological analysis of autoinflammatory diseases. However, there may be cases and diseases where the correct cell to reproduce the phenotype in vitro using iPS cells has not been identified. In these cases, careful selection of the desired cell type and optimization the differentiation and analysis protocols are needed. As seen in the case of Blau syndrome, it is also important to select the appropriate stimulus to obtain the phenotype associated with autoinflammation. Another problem that is often faced in the study of autoinflammatory diseases is the small number of cases. When there are only one or two cases for which iPS cells can be established, it is often difficult to obtain statistically reliable quantitative phenotypes, especially if the control clones are chosen at random. In these cases, the generation of isogenic counterparts by genome editing technology allows us to more reliably evaluate the phenotypic impact of the mutation.As mentioned in the introduction, the concept of autoinflammation is continuously expanding. Indeed, it has recently been reported that Down syndrome also has a spectrum of autoinflammation, since multiple interferon-related molecules are encoded on chromosome 21 . There aIn autoinflammatory diseases, it is important to analyze the functions of not only blood cells but also cells in various inflammatory tissues, such as the musculoskeletal system and vascular endothelial cells. Technology for the in vitro differentiation of iPS cells into various cell types or tissues has progressed considerably. Therefore, it is possible to gain a comprehensive understanding of autoinflammatory diseases by differentiating iPS cells into these other cell types. In addition to understanding autoinflammatory diseases better, iPS cell models are expected to lead to the development of new therapeutic methods.