It is unknown how reliable these preprints are, and which ones will eventually be published in scientific journals. In this study, we use crowdsourced human forecasts to predict publication outcomes and future citation counts for a sample of 400 preprints with high Altmetric score. Most of these preprints were published within 1 year of upload on a preprint server (70%), with a considerable fraction (45%) appearing in a high-impact journal with a journal impact factor of at least 10. On average, the preprints received 162 citations within the first year. We found that forecasters can predict if preprints will be published after 1 year and if the publishing journal has high impact. Forecasts are also informative with respect to Google Scholar citations within 1 year of upload on a preprint server. For both types of assessment, we found statistically significant positive correlations between forecasts and observed outcomes. While the forecasts can help to provide a preliminary assessment of preprints at a faster pace than traditional peer-review, it remains to be investigated if such an assessment is suited to identify methodological problems in preprints.Many publications on COVID-19 were released on preprint servers such as  The dynamics of the pandemic necessitated research findings to be disseminated quickly to other researchers as well as policy and decision makers. Preprint servers helped to accelerate this process by making data and research findings accessible without delays associated with traditional publications including peer-review [medRxiv being COVID-19-related in every month between March 2020 and August 2021 [The quick rise of the COVID-19 pandemic was followed by an unprecedented explosion in COVID-19-related research . The larr-review . It has r-review ,3. COVIDust 2021 . PreprinTwitter, the difference between peer-reviewed and non-peer-reviewed research may not be understood and flawed studies may be disseminated through media [The benefit of a faster dissemination of results comes potentially at the cost of a lower reliability of the released findings ,7,11–13.gh media ,20–22. Tgh media .It has been estimated that only 5–14% of COVID-19-related preprints will be published ,23,24 anIn the COVID-19 preprint forecasting study, we investigate if forecasting can help to fill the gap from preprints lacking peer-review. It is currently unknown if crowd-based forecasting allows us to identify which preprints are useful and robust and which preprints have flaws which will prevent publication in an academic journal. We asked forecasters to predict publication outcomes and future citations of 400 preprints uploaded on preprint servers between 1 January 2020 and 31 August 2020.Altmetric scores. Altmetric scores are an alternative to traditional impact measures such as citations and are calculated based on metrics such as (but not limited to) mentions on social media, mainstream media coverage, discussion on research blogs and citations on Wikipedia [Altmetric. This sampling method was designed so we could test the most widely disseminated preprints across an extended period, which can be expected to include the most relevant findings. We conducted incentivized surveys in November 2020, asking forecasters to predict the probabilities of three possible futures of each preprint: (i) remaining unpublished within 1 year of dissemination, (ii) being published in a medium- or low-impact journal or (iii) being published in a high-impact journal. We defined medium or low impact as a journal impact factor (JIF) below 10 and high impact as a JIF of at least 10. In addition, we asked forecasters to predict how a preprint will rank in terms of citations received after 1 year, relative to the other preprints, with a rank of 0 assigned to the least cited preprint and a rank of 100 to the most cited preprint. We also elicited more subjective assessments such as ‘usefulness’ and replicability. Previous research has shown that forecasters can predict characteristics of papers including their replicability [The selection of preprints was based on ikipedia . We splicability –32. WhilThe nature of preprints provides an opportunity for a crowd to provide informal assessments prior or in parallel to more formal assessment through a few peer-reviewers and an editor . Our stu. 2medRxiv and bioRxiv COVID-19 SARS-CoV-2 collection (connect.biorxiv.org/relate/content/181) uploaded before the end of August 2020. Of these 400 preprints, 92 were found on bioRxiv and 308 were found on medRxiv. To focus on preprints that have been recognized by media and social media to be of high relevance, we divided the roughly 8000 preprints in the entire collection at the time of sampling into ten bins by time and selected the top 40 within each bin as ranked by the Altmetric score. This stratified sampling strategy results in an even distribution of preprints across time and avoids the sample being dominated by earlier preprints which had more time to gain a higher Altmetric score. Any preprints that had been indicated by medRxiv or bioRxiv to already be published were excluded from the sampling process. Because of the delay between sampling preprints and conducting the surveys and because publication indicators on preprint servers are not comprehensive, 153 preprints had already been published by the end of the survey period (10 November 2020), leaving 247 preprints that were not published by the end of the forecasting period. For hypothesis tests that were not related to forecasting, we used the full set of 400 preprints. Our primary analysis of forecasting performance will focus on these 247 preprints because forecasters could have been aware of the publication outcome for the remaining 153 preprints. However, we will provide tests on the full dataset as well.Our sample contains 400 COVID-19 preprints from the Twitter and Reddit. We had 49 participants in total. The participants are typically but not necessarily from academia or have academic backgrounds, and since the SCORE project focused on claims from the social and behavioural sciences, most participants had experience in these fields rather than in biomedical research. Of the 49 participants, 31 stated in an entry questionnaire to have at least one scientific publication, and 13 stated to have at least one publication in the biomedical sciences. In terms of career stage, 47% were graduate students, 27% were early career researchers  and 17% were mid-career researchers (e.g. senior research fellow or associate professors). Senior researchers make up only 3% of the participants. The majority of participants (61%) worked or studied in academia when the survey was taken, with a further 24% working in industry.Participants were recruited predominantly from forecasters in the Systematizing Confidence in Open Research and Evidence (SCORE)  project,Participants were assigned an initial random ‘batch’ of 10 preprints; once the initial batch was completed, participants had the option to complete additional batches. New batches were assigned to the participants after each batch they completed. Participants only learned about the preprints in a batch after it had been assigned to them. Participants could not select into assessing specific preprints and could after each batch solely choose to continue or discontinue with forecasting.M = 93, s.d. = 133). Conversely each preprint has a median number of forecasts of 11 . The surveys were open from 28 October 2020, through 10 November 2020. Participants had no access to other participants' forecasts. The abstract of the preprints was provided within the survey along with links to the full version of the online preprint. The surveys included four forecasting questions (with answers or prompts): —(Q1) Will this preprint be published in a peer-reviewed scientific journal within a year of first preprint posting? Provide a % probability between 0 and 100 for each option. The values must sum to 100: (option 1) No, not published, (option 2) Yes, in a journal with impact factor below 10, (option 3) Yes, in a journal with impact factor of at least 10. —(Q2) Rank this preprint's 1-year Google citations count relative to other preprints in this study. Select (using slider) a relative rank between 0 for least cited and 100 for most cited. —(Q3) What is the % probability that the findings presented in the preprint agree with the majority of results from similar future studies? Select (using slider) a value between 0 (impossible) and 100 (certain). —(Q4) Are the results presented in the preprint helpful to mitigate the impact of the COVID pandemic? Select (using slider) a value between 0 (no) and 100 (yes).Because experiences from previous projects suggest that participants often provide ‘conflated’ responses when asked about different aspects of a publication, we asked Q3 and Q4 in random order. Incentives for the surveys were provided through surrogate scoring [The median number of preprints forecasted by participants is 30  being indexed by Google Scholar. Where preprints were not resolved after 1 year (which applies to 86 preprints), citation counts were backdated using Google Scholar's citations by date function. Q3 and Q4 are not resolved but serve as a gauge of the expectations of the forecasters of the robustness and helpfulness (regarding the mitigation of the pandemic) of the preprints. We also conducted a prediction market to forecast above outcomes but decided not to use the corresponding data because the starting prices were set incorrectly, and prices on the dashboard did not update correctly. The combination of these technical issues coupled with a low participation of only around 30 traders led us to not analyse the prediction markets, although the unused data will be made available. This decision was made before any data were analysed.The questions Q1 and Q2 have been resolved 1 year after the preprint was uploaded to a preprint server. Publication outcomes were resolved manually using Open Science Framework  after the surveys were completed, towards the end of the data collection for the resolutions. We did not use the surveys or incomplete outcome data for any preliminary analyses. Any deviations from the preregistered analyses or any added non-preregistered tests will be mentioned in the text below. For statistical tests, we interpret the threshold of p < 0.005 as identifying statistical significance, and the threshold of p < 0.05 as identifying suggestive evidence [R’, using the ‘miceadds’ package for applying clustered standard errors [The experimental design and statistical analyses were preregistered on the evidence . The stad errors ,36.. 3M= 151, s.d. = 81, Min = 14, Max = 362). Note that more preprints in our sample may have been published or will be published outside our 1-year cut-off. One hundred and twenty-seven of the 281 (45%) published papers were published in a high-impact journal (JIF of at least 10). Papers were published in 135 different journals with the most common being Nature (22 papers), Science (16 papers) and Nature Communications (14 papers).Of the 400 preprints in our sample, 281 (70%) were published in an academic journal within 1 year. Of the 247 preprints that were not published until the end of the forecasting period, 128 (52%) were published within 1 year. One hundred and nineteen preprints (30% of the full sample) were not published within a year after the upload to the preprint server. The median time between the release date on a preprint server and becoming published is 143 days . The median number of citations after one year (from the release date on a preprint server) is 54 . Fifty-five papers were cited less than 10 times, 200 papers were cited between 10 and 100 times, 138 papers were cited between 100 and 1000 times, and 8 papers were cited more than 1000 times. We transformed citation counts into relative ranks between 0 and 100, where a paper with rank 0 has the lowest citation count and a paper with rank 100 has the highest citation count. These ranks were used in our statistical analyses  with the advantage of avoiding inferential problems that may arise due to the strongly right-skewed distribution of citation count data.We also collected citation counts for each of the 400 preprints, combining r = 0.41, t398 = 8.91, p < 0.0001), with non-published preprints having an average citation rank of 32 compared with published preprints' average rank of 58, a statistically significant difference . For the preprints which have been published, citation ranks are statistically significantly correlated with the impact factor category  where impact factor category refers to a binary variable indicating publication in a high-impact or medium- to low-impact journal. Preprints published in journals with impact factors of at least 10 have an average citation rank of 73 compared with an average citation rank of 45 for preprints published in journals with a JIF below 10 . Preprints published in low- to medium-impact journals (JIF < 10) were cited statistically significantly more often than non-published preprints .Publication status (i.e. whether or not the paper has been published) is statistically significantly correlated with the paper's citation rank , potentially indicating that the forecasters were aware of at least some of the known publication outcomes. We also regressed individual responses against publication outcomes in a linear probability model with s.e. clustered at the forecaster level. The clustering of s.e. at the forecaster level is to avoid biased s.e. as the set of predictions by each forecaster is unlikely to be independently and identically distributed (i.e. within cluster correlation). This model provides suggestive evidence for a positive association between individual forecasts for publication in any journal and the binary publication outcome . When assessing binary accuracy, where we interpret an average survey forecast of less than 0.5 as a prediction of a preprint not being published, and an average survey forecast of 0.5 or above as a prediction of a preprint being published, forecasters had an accuracy of 59% in the sample of preprints that were not published by the end of the forecasting period. There is suggestive evidence that this accuracy rate is better than random chance .Using the subset of preprints that were not published by the end of the forecasting period, the mean forecasts for publication status  are statistically significantly positively correlated with the binary publication outcome : forecasters expected 65% of preprints to be published compared to the reality of 52% . Considering the full sample of preprints, the average forecasts of 68% match the observed publication rate of 70% much better, though this comparison comes with the caveat that some participants might have already known the correct publication outcome.Forecasters statistically significantly overestimated the overall publication rate when comparing binary publication outcomes and average forecasts of probability of publication . There is suggestive evidence that forecasters tended to underestimate the share of papers which were published in high-impact journals .We also tested if forecasters could predict the impact factor of the journal the preprint will be published in. Focusing on the subset of preprints not published before the end of the forecasting period, we found a statistically significant correlation between the mean survey predictions for being published in a high-impact journal (given that it is published) and the binary outcome . The strength of this relation is illustrated in r = 0.69, t245 = 14.803, p < 0.0001).Forecasters can predict which preprints will have few or many citation counts after 1 year. Using the full sample, citation ranks (from 0 to 100) are statistically significantly correlated with the mean forecasts for citation ranks . In addition, we also correlated days between upload to preprint server and the end of the survey period and absolute forecasting error for publication and JIF outcomes. Forecasts of publication outcomes or JIF are not correlated with how early the preprint was released .As the preprints in our sample were released at different times, we tested if papers which were released early, and therefore had more time to get indications of citations, had a systematically lower absolute error. We correlated days between upload to preprint server and the end of the survey period (10 of November 2020) and absolute citation rank error in an un-preregistered test. We found no evidence that being released earlier correlates with more accurate forecasts . We also correlated overall users' SSR estimated AUC and Brier Scores with actual AUC and Brier Scores. We found no statistically significant correlation between overall SSR estimates and actual AUC  or Brier Score . SSR can also be used to aggregate forecasts by taking an average of only the forecasters with highest estimated AUC, with the aim to remove uninformative forecasts. We found evidence that squared error of SSR aggregated citation rank is higher than the squared error of the mean aggregated citation rank .The survey responses were incentivized using SSR which provides estimates of a forecaster's AUC or Brier score. AUC (Area under the ROC Curve) and the Brier score are both measures of accuracy of a predictor of a binary outcome. The AUC of a predictor is equal to the probability that the predictor will rank a random positive outcome higher than a random negative outcome. The Brier score is equivalent to the mean squared error . We testn = 247). We found that average responses to Q3 (What is the % probability that the findings presented in the preprint agree with the majority of results from similar future studies?) correlate positively with forecasts for publication , forecasted citation rank  and forecasted usefulness . In terms of outcomes, forecasts for agreement (Q3) are also positively correlated with being published , being published in a high-impact journal , and the citation rank . The assessed usefulness of preprints (as measured by Q4) is also correlated with forecasts of being published  and forecasts of being published in a high-impact journal . Moreover, the forecasts for usefulness are positively correlated with observed publication in a high-impact journal  and with actual citation ranks .In addition to the resolvable questions of publication and citation outcomes, we also asked forecasters to provide subjective judgements on the replicability and usefulness (with respect to the mitigation of the pandemic) of the preprints. While not being able to determine the accuracy of these forecasts, they serve as a gauge of the expectations of how the findings in our sample sit in the wider body of COVID-19 literature. We used the full sample of 400 preprints for all tests here, except for tests that include forecasts of publication and journal outcomes for which we used the subset of preprints where publication outcome was unknown at time of the surveys . No statistically significant order effects were found for Q4 . All correlations including Q3 were split by whether it was asked first or second can be found in As participants may provide conflated answers for Q3 and Q4, we randomized the order of these questions with half of the participants being asked Q3 first; the other half Q4 first. To test for order effects, we compared responses to Q3 and Q4 between those who answered Q3 or Q4 first using unpaired . 4Research Excellence Framework [The forecasting of scientific outcomes can help to improve science ,31,38–41ramework , this isramework . The undAltmetric-based sampling we used in our study likely translates into a higher publication rate for our sample compared to more comprehensive COVID-19 preprint samples.Our results show that forecasters can predict if preprints will be published within 1 year, despite overestimating the share of preprints in our sample which will be published. In total, we found 70% of preprints were published within a year—a number that can be expected to increase as some preprints will take longer than a year to publish. This rate of publication is in line with other research which has shown that around two-thirds of preprints are eventually published –47 and iForecasters were also able to predict the impact of journals in which the preprints will be published, despite underestimating the number of the papers which are published in high-impact journals. The observed fraction of 45% of the published preprints appearing in high-impact journals is larger than our expectation and is likely substantially influenced by topic and sampling methods. Similarly, forecasts of relative citation ranks are highly correlated with actual relative citation ranks. We also found that subjective assessments of replicability and usefulness are correlated with publication outcomes and citation ranks. Our forecasters were more accurate at predicting citation rank than publication outcomes. We excluded a number of preprints from our accuracy analysis due to them being already published by the time of the forecasting survey. Preprints which are published quickly may be more obviously ‘publishable’ and therefore are easier to forecast. Preprints which are delayed in being published and therefore are in our sample may be more difficult to forecast. In addition, there is a delay between the prints in our sample being uploaded to a preprint server and the forecasting survey. This means that participants had some information on citations when forecasting. We found that having more information on citations (from preprints being uploaded earlier from the surveys) is not statistically significantly correlated with more accurate predictions of final citation ranks. Finally, it may be that citations are a more fine-grained and continuous outcome than publication and therefore easier to forecast. Further study with forecasting happening shortly after preprints are released may be used to test these assumptions.Our findings are promising in that they indicate that forecasting can be used to help fill the gap left by the lack of peer-review on preprints. While assessments such as the ones used in our study can be driven by factors that are unrelated to the quality of the methodology behind a preprint, it would be in line with previous findings from forecasting projects associated with large-scale replication studies ,40 that


Lung development is precisely controlled by underlying gene regulatory networks (GRN). Disruption of genes in the network can interrupt normal development and cause diseases such as bronchopulmonary dysplasia (BPD) – a chronic lung disease in preterm infants with morbid and sometimes lethal consequences characterized by lung immaturity and reduced alveolarization. Here, we generated a transgenic mouse exhibiting a moderate severity BPD phenotype by blocking IGF1 signaling in secondary crest myofibroblasts (SCMF) at the onset of alveologenesis. Using approaches mirroring the construction of the model GRN in sea urchin’s development, we constructed the IGF1 signaling network underlying alveologenesis using this mouse model that phenocopies BPD. The constructed GRN, consisting of 43 genes, provides a bird’s eye view of how the genes downstream of IGF1 are regulatorily connected. The GRN also reveals a mechanistic interpretation of how the effects of IGF1 signaling are transduced within SCMF from its specification genes to its effector genes and then from SCMF to its neighboring alveolar epithelial cells with WNT5A and FGF10 signaling as the bridge. Consistently, blocking WNT5A signaling in mice phenocopies BPD as inferred by the network. A comparative study on human samples suggests that a GRN of similar components and wiring underlies human BPD. Our network view of alveologenesis is transforming our perspective to understand and treat BPD. This new perspective calls for the construction of the full signaling GRN underlying alveologenesis, upon which targeted therapies for this neonatal chronic lung disease can be viably developed. Development is precisely controlled by the genetic program encoded in the genome and is governed by genetic interactions . ElucidaDisruption of any gene in the network could interrupt normal development and conceivably cause diseases. Some diseases are caused by single gene disorders while others are complex and multifactorial such as bronchopulmonary dysplasia (BPD), a common cause of morbidity and mortality in preterm infants characterized by arrested alveolar development with varied severities within the subjects’ lungs . Both prIn recent clinical trials, therapy with recombinant human IGF1 protein showed initial promise for BPD, but significant limitations did not allow further clinical trials . One impIGF1 and IGF1R expression are significantly reduced in BPD lungs , fibroblast growth factor (FGF), sonic hedgehog (SHH), wingless/integrated (WNT), platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), NOTCH, bone morphogenetic protein (BMP), and insulin like growth factor 1 (IGF1) . IGF1 anPD lungs , suggestPD lungs . A recenPD lungs .Igf1 and Igf1r are primarily expressed in secondary crest myofibroblasts (SCMF) in postnatal mice lungs. As a result, we interrupted IGF1 signaling in SCMF by the inactivation of Igf1r at the onset of alveologenesis in postnatal day 2 (PN2) mouse neonates and analyzed the resulting phenotypes. Inactivation of Igf1r resulted in mutant lungs with simplified and immature alveolar structure resembling that of human BPD at a moderate severity  where their expression during alveologenesis was reported; the cellular expression patterns of these genes were then annotated on lungMAP scRNAseq data for the screen of SCMF genes; the regulatory interactions among these genes were subsequently examined from in vivo and in vitro perturbations; and cellular communications were finally determined by secretome-receptome analysis. Combining this data, we constructed the IGF1 signaling GRN underlying alveologenesis from this mouse model of human BPD phenocopy.We followed the protocol similar to that used in the construction of the sea urchin GRN , regulatOur GRN work on alveologenesis represents a transformative view of how to understand and perhaps even design future preventive and therapeutic strategies for BPD treatment.Igf1 and Igf1r during early postnatal lung development, we performed RT-PCR using total lung RNA from embryonic day E18 to PN30. The analysis showed Igf1 is expressed dynamically during development, peaking at the onset of alveologenesis and progressively decreasing thereafter  is activated at the onset of mesodermal lineage specification  fibroblasts have been rigorously examined through lineage tracing using CreERT2;Rosa26mTmGGli1 in our lab cells were observed primarily localized to the secondary septa and secondarily to parabronchial and perivascular smooth muscle fibers . When Taublished . NonetheCreERT2Gli1, we inactivated floxed alleles of Igf1r on PN2 at the onset of alveologenesis  and CreERT2;Rosa26mTmG;Igf1rflox/floxGli1 (mutant) mice. Using the sorted cells, we compared the gene expression between them to identify genes enriched within SCMFs, those altered within SCMFs, and those altered within non-SCMFs in the mutant lungs (Both GFP+ (SCMF) and Tomato+ (non-SCMF) cells were isolated by FACS from lungs dissected from nt lungs .https://www.lungmap.net/), where they were indicated to be expressed from SCMF between PN3 and PN14 in the mouse developmental window  functionally active in SCMF with its delta cycle threshold (deltaCT) ≤9    or RNAscry (IHC) . Consist of SCMF , and datle cells .Regulation of the latter 19 genes by Igf1 signaling was illustrated on Biotapestry  as displGli1, Foxd1, Tbx2, Sox8, Twist2, Rxra) and signaling molecules . The second group comprised Acta2, Actc1, Bgn, Des, Eln, Cnn1, Fn1, and Tnc, representing genes that encode structural and cellular molecules. Based on the GRN’s hierarchical design  confirming its regulatory control on fibroblast growth and development. This finding in regard to this aspect of Wnt5a’s function resonates well with the clinical data from the study of some idiopathic pulmonary fibrosis (IPF) patients  in this postgenomic era, one pressing task is to identify the function of genes, in particular, how they interact with one another . With GRThe application of the GRN approach in the lung field is novel as it hasn’t been previously reported. Although not all the genes on the network have been examined by perturbation and the links haven’t been determined as direct or indirect, the regulatory connections running along the constructed GRN are already able to provide potential mechanistic explanation as to how the effect of IGF1 signaling is transduced from one gene to a constellation of its downstream genes, and from one cell type to another. Indeed, blocking WNT5A signaling is confirmed to produce a mouse BPD-like phenotype that mimics Igf1r−/− lungs as inferred by the network . SignaliBehind, the overall process of alveologenesis is a much larger signaling GRN where several different cell types are involved. Within the mesenchymal cell type only, it is known that blocking IGF1, WNT5a, PDGFA, and TGFB signaling leads to impairment in development of alveoli, though with varying severities . The sumClinically, BPD is caused predominantly by extrinsic factors which in the first place would interfere with normal cell’s extracellular activities including cell signaling and communication. From the GRN perspective, BPD is a developmental disease when the signaling GRN for alveologenesis is derailed and disrupted by these extrinsic factors .The type and effect of these extrinsic factors must be taken into consideration when studying this disease. There was a reported increase in Wnt5A expression by mesenchymal cells from hyperoxia BPD models —a findinThe aforementioned network disruption may occur within different cell types, onto different signaling pathways, and upon different hierarchies of the network . From thOur GRN view on alveologenesis presents a transformative viewing of BPD and could potentially help in designing novel strategies to prevent and treat it. As for prevention, the GRN suggests a study centered on the network’s periphery with its focus on cutting off the extrinsic factors and blocking their connections to the intrinsic alveologenesis GRN. For treatment, it is a study on the network itself focusing on recovering and rescuing the signaling pathways that have been disrupted. However, all these rely on the foundational construction of the whole alveologenesis GRN—which includes all major signaling pathways involved within and between each cell type and the network assembly of the hierarchical regulatory connections among them. Though the network construction is currently not experimentally accessible in human models, our work shows the alveologenesis GRN is well conserved between mouse and human lungs. With the mouse model, the whole signaling GRN behind alveologenesis can be decoded as we did in this paper. The impact of extrinsic factors/signaling pathways and their manipulation on the network can be modeled and tested as well. The insights collected can then be used to guide health delivery for BPD clinically. The general process would then be as follows: clinical patient exams and tests are used to determine the extrinsic factors; cell type gene expression data from bronchoalveolar lavage and/or biopsy are used to reveal genes altered within the alveolar compartments in patient lungs; implied extrinsic factors and altered genes are mapped onto the alveologenesis GRN where points of network disruptions can thus be defined; therapies proven successful from mouse modeling data can then be clinically pursued to recover/rescue the disruption points in the patient.The GRN is traditionally a network built solely on chemical interactions . Having said that, however, it has been recognized that these interactions can also happen at mechanical and electrical level i.e. . Mechanohttps://sites.google.com/view/the-alveologenesis-grn/home, which we hope can be used as an online repository to promote any further studies and collaborations in this direction.A web resource of the network and the data presented in this paper is made available to the public: CreTwist2, CreERT2, Rosa26mTmG, CAGTomato, Igf1flox/flox, Igf1rflox/floxGli1, and CreERCAG mice were purchased from the Jackson Laboratory. GFPCreERT2Foxd1 mice were generated by McMahon lab at USC. GFPSftpc mice were generated by Wright lab at Duke University. Wnt5aflox/flox mice were provided by Kuruvilla lab at Johns Hopkins University.All animal studies were conducted strictly according to protocols approved by the University of Southern California (USC) Institutional Animal Care and Use Committee . The mice were housed and maintained in pathogen-free conditions at constant room temperature (20–22°C), with a 12 hr light/dark cycle and free access to water and food. cre;Igf1flox/floxTwist2 and cre;Igf1rflox/floxTwist2 mice were generated by breeding cre with Igf1flox/floxTwist2 and flox/floxIgf1r, respectively.CreERT2;Rosa26mTmG miceGli1 were generated by breeding CreERT2Gli1 and mTmGRosa26 mice.CreERT2;Igf1rflox/floxGli1 mice were generated by breeding CreERT2Gli1 mice with the flox/floxIgf1r mice.mTmG;Igf1rflox/floxRosa26 mice were generated by breeding mTmGRosa26 mice with the flox/floxIgf1r mice.CreERT2;Rosa26mTmG;Igf1rflox/floxGli1 mice were generated by breeding CreERT2;Igf1rflox/floxGli1 mice with the mTmG;Igf1rflox/floxRosa26 mice.GFPCreERT2;CAGTomatoFoxd1 mice were generated by breeding GFPCreERT2Foxd1 mice with the TomatoCAG mice.CreER;Wnt5aflox/floxCAC mice were generated by breeding CreERCAG mice with the flox/floxWnt5a mice.Genotyping of the transgenic mice was performed by PCR with genomic DNA isolated from mouse tails. The forward (F) and reverse primers (R) for transgenic mouse genotyping are listed in A single dose of tamoxifen (8 mg/mL in peanut oil) was administered by oral gavage to neonates at PN2 (100 μg per pup) with a plastic feeding needle . Neonatal lungs were collected between PN7 and PN30 for morphological, immunohistochemical, cellular, and molecular biological analyses.2 inhalation at the time of tissue harvest. Chest cavity was exposed, and lungs cleared of blood by perfusion with cold PBS via the right ventricle. Lungs were inflated with 4% formaldehyde under constant 30 cm H2O pressure and allowed to fix overnight at 4°C. Tissue was dehydrated through a series of ethanol washes after which they were embedded in paraffin and sectioned.Mice were euthanized by COH&E staining was performed as usual, and morphometric measurements were made using ImageJ. Immunofluorescent staining was performed as previously described using paraffin-embedded lung sections . In brieSamples were fixed in 10% neutral buffered formalin, dehydrated with ethanol, and embedded in paraffin wax. 5 μm sections from paraffin blocks were processed using standard pretreatment conditions per the RNAscope multiplex fluorescent reagent kit version 2 (Advanced Cell Diagnostics) assay protocol. TSA-plus fluorescein, Cy3, and Cy5 fluorophores were used at different dilutions optimized for each probe. RNAScope probes used are listed in the Key resources table below. Images were made with Leica DMi8 fluorescence microscope and processed with Leica LAS X and ImageJ.2, 10 mg/ml Bacillus licheniformis protease), dissected, and transferred to a petri dish where the heart, thymus, and trachea were removed. The lobes were minced using a razor blade. The minced tissue was then immersed in extra cold active protease solution for 10 min and triturated using a 1 ml pipette. This homogenate was transferred to a Miltenyi C-tube with 5 ml HBSS/DNase , and the Miltenyi gentleMACS (magnetic-activated cell sorting) lung program was run twice on GentleMACs dissociator. Subsequently, this suspension was passed through a 100 um strainer, pelleted at 300 g for 6 min, suspended in 2 ml RBC lysis buffer (BioLegend), and incubated for 2 min. At this point, 8 ml HBSS was added and centrifuged again. The pellet was suspended in HBSS, then filtered through a 30 um strainer. The suspension was pelleted again and finally suspended in MACS separation buffer (Miltenyi Biotec) with 2% FBS  for FACS. Cell separation and viability were examined under the microscope and through Vi-CELL cell counter after staining with trypan blue.Single-cell suspension was prepared as described in FACS was performed on a BD  FACS Aria II at stem cell flow cytometry core at the Keck School of Medicine of USC. The sorting was gated on viability, singlets, GFP, and/or Tomato. GFP+ and/or Tomato+ cells were collected as needed. For cell culture, cells were sorted in DMEM (Dulbecco's Modified Eagle Medium) containing 10% FBS. For RNA, cells of interest were collected in Trizol-LS reagent (ThermoFisher).GFPSftpc mouse at PN14 were dissociated into single-cell suspension, and GFP+ cells were sorted and collected as described above. RNA was extracted using Qiagen RNeasy microkit and then submitted to the Millard and Muriel Jacobs Genetics and Genomics Laboratory at Caltech for sequencing, which was run at 50 bp, single end, and 30 million reading depth. The unaligned raw reads from aforementioned sequencing were processed on the PartekFlow platform. In brief, read alignment, and gene annotation and quantification, were based on mouse genome (mm10) and transcriptome (GENECODE genes-release 7). Tophat2 and upper quartile algorithms were used for mapping and normalization. The RNA-seq data have been deposited with GEO (Gene Expression Omnibus) under the accession number GSE182886.Lungs dissected from The raw scRNAseq datasets  website, and the specificity of their amplification was verified by their melting curve. Sequences of the primers are listed in Neonatal mouse lung cells were collected from FACS or cell culture as described above. The RNA was isolated with Direct-zol RNA MiniPrep kit according to the manufacturer’s protocol (ZYMO Research). Following RNA purification, cDNA was generated using the SuperScript IV First-Strand Synthesis System (ThermoFisher). Expression of selected genes was quantified by quantitative real-time RT-PCR performed on a light cycler (Roche) or 7900HT fast real-time PCR system (Applied Biosystems) using SYBR green reagents (ThermoFisher). The deltaCT method was used to calculate relative ratios of a target gene mRNA in mutant lungs compared to littermate control lungs. BPD and non-BPD postnatal human lung tissues were provided by the International Institute for the Advancement of Medicine and the National Disease Research Interchange and were classified exempt from human subject regulations per the University of Rochester Research Subjects Review Board protocol (RSRB00056775).Cell-to-cell communications were predicted using a published Fantom5 Cell Connectome dataset linking ligands to their receptors (STAR methods) . The ligGapdh, and the final result was presented as deltaCT or fold change. In morphometric quantification and cell counting, four lungs for each experimental group (Ctrl vs Mut) were used. Left lobe and right inferior lobe from each lung were targeted. Five images from each lobe after staining were analyzed for morphometric quantification (at ×10 magnification) and cell counting (at ×40 magnification). A two-tailed Student’s t-test was used for the comparison between two experimental groups, and a one-way ANOVA was used for multiple comparisons. Quantitative data are presented as mean values ± SD. Data were considered significant if p<0.05.In gene expression quantification using RT-PCR, at least three biological replicates (in different cases including lungs/FACS sorted cells/cultured cells) for each experimental group  were used. Measurement for each biological replicate was repeated three times. The Ct (cycle threshold) was normalized to  This is an important paper describing the role of Igf1 and a corresponding 47-gene network in alveologenesis and bronchopulmonary dysplasia. The authors provide compelling computational and experimental evidence of their findings in a mouse model. The authors also provide solid evidence that these processes are recapitulated in human tissue, however there are caveats around the set of control samples. This paper will be of interest to those interested in lung development and disease. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Nicholas Banovich as the Reviewing Editor and Edward Morrisey as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Decoding the IGF1 Signaling Gene Regulatory Network Behind Alveologenesis from A Mouse Model of Bronchopulmonary Dysplasia" for consideration by eLife. In particular, there was enthusiasm for the overall novelty of the approach, with respect to translating publicly available data into meaningful biological insights. However, there were a number of major concerns that must be addressed for this manuscript to be considered for publication. Below I have attached complete reviews from the three reviewers. However, for this paper to be considered for publication the following points should be explicitly addressed.The reviewers were in agreement that this manuscript had strong merit and could be suitable for 1) All three reviewers noted problems with the RNAScope (and to a lesser degree the immunostaining). New RNAScope images with rigorous quantification should be provided.2) Multiple reviewers noted that bulk RT-PCR is not sufficient to validate signals specific to particular cell populations. The authors should analyze their results in the context of existing single-cell datasets to assess the robustness of their claims.3) Multiple reviewers commented on the specificity of Gli1 to SCMFs. This must either be demonstrated with additional KO experiments using are drivers with higher specificity to SCMFs, or alternatively, data needs to be generated to assess whether the phenotype is restricted to SCMFs rather than other mesenchymal cells types.4) There we a number of questions around the results pertaining to WNT5A. These include questions about specificity to SCMFs, canonical and non-canonical Wnt signaling, and conflicting results with other recent publications. These issues should be addressed.If you choose to revise and resubmit this manuscript, please address these concerns as well as those raised below.Reviewer #1 (Recommendations for the authors):While the conclusions are mostly well-supported, a few claims could be better supported.1) Some of the immunostaining looks fairly low quality with possible non-nuclear signals from transcription factors . Perhaps a few close-ups to allow better visualization would be helpful.2) The authors propose that Wnt5a is signaling non-canonically to both the MFB and AT2 cells based on the identification of Ror1 and Ror2 expression. However, the canonical Wnt target gene Axin2 has been identified in a subset of AT2 stem cells by others, and Wnt5a is capable of both canonical and non-canonical Wnt signaling. So, how do the authors know that canonical signaling is not involved, either alone or in conjunction with non-canonical signaling? At least Axin2 levels could be measured upon Wnt5a silencing in MFBs, and perhaps qRT-PCR performed for Axin2 and other Wnt receptors that could mediate canonical signaling. If they cannot experimentally demonstrate the absence of canonical Wnt signaling in MFBs (and AT2 cells), perhaps they should update their GRN to indicate this possibility?2) The ISH for Igf1 in Figure 1b looks almost ubiquitous, yet the GRN proposes only autocrine signaling. I don't think this hurts their conclusion since they deleted Igf1R in MFBs which should abrogate signaling from any source, but this should probably be addressed or their GRN revised to indicate potential paracrine signaling.3) In Supplemental Figure 1g, at E18 there is no statistically significant difference in MLI upon deletion of Igf1r, however, the images presented (a and d) appear to show a major reduction in MLI. Is this image representative?Reviewer #2 (Recommendations for the authors):In this manuscript, Gao et al., claim that they have constructed a gene regulatory network underlying alveologenesis and its significance to bronchopulmonary dysplasia (BPD). Using RT-PCR and in situ hybridization, the authors claim that Igf1 and Igf1r are expressed in secondary crest myofibroblasts (SCMFs) and their loss of function using Gli1-creER results in alveolar simplification, a tissue level disorganization of alveoli that phenocopies BPD. Further, the authors investigate transcriptomic changes in mesenchymal and epithelial populations from control and Igf1r mutant lungs. For this, the authors developed a 47-gene panel that they claim to represent signaling modules within SCMFs and used this panel for RT-PCR analysis. These data are used to generate an interaction network to evaluate signaling partners, co-effectors mediated by IGF1 signaling in SCMFs, other fibroblasts and alveolar epithelial cells. Using this GRN, the authors concluded that Wnt5a is a key signaling molecule downstream of IGF1 signaling that regulates alveologenesis.eLife). Additionally, the genetic loss of function studies performed here are not specific to SCMFs and instead they target broader alveolar and airway fibroblasts. The construction of a gene regulatory network is a potentially exciting tool, but this requires additional perturbations to distinct nodes identified in this work. It would be of particular interest to determine whether there is any redundancy among these nodes and what are the downstream effectors that are specific to each node. While I recognize that this is outside the scope of this work, the authors need to demonstrate the significance of at least one such node.While the authors' claims are salient, some of the conclusions were previously shown by others. For example, a role for Wnt5a driven Ror/Vangl2 has already been shown to be a key mediator of alveologenesis, by virtue of the same signaling effectors identified in this study  or from LungMAP to assess this.3. The authors used Gli1-CreER to delete Igf1r in SCMFs. However, Gli1 has been shown to be expressed in peribronchiolar fibroblasts  and alveolar lipofibroblasts . The cited publication  also shows Gli1-labeled cells around the proximal airways and not just in SCMFs. Therefore, the phenotypes observed in Igf1r KO, as well as all downstream RT-PCR studies, could be a result of loss of IGF1R in other cell types and not specific to just SCMFs. Recent studies have shown that FGF18 is specific to SCMFs . Authors could use FGF18-creER line to delete Igf1r.4. The authors performed RT-PCR for a large panel of genes. However, the cell populations used for these analyses should be more specific. For example, in Figure 3B the authors compared GFP+ vs GFP- bulk populations. I suggest the authors compare between GFP+ SCMFs compared to non-SCMF fibroblasts. Additionally, Figure 5A should be compared with sorted AT1 or AT2 populations as opposed to bulk GFP-negative cells. As the authors show in Figure 2, AT1 and AT2 numbers are reduced in knockout mice, and thus RT-PCR on the bulk "GFP-negative population" does not seem appropriate.5. Criteria for the selection of gene panels used for RT-PCR: The authors need to provide a rationale for the 47 gene panel selected for RT-PCR, as opposed to numerous other signaling molecules and transcription factors? This is particularly important as the downstream GRN constructed is derived from differentially expressed genes among this pre-selected subset. A more unbiased approach (bulk RNA-Seq or single-cell RNA-Seq) would allow for interrogation of all differentially regulated genes, from which a more inclusive GRN could be constructed.6. Figure 3C, D – validation of markers should also be done on Igf1 deleted lungs, not just wild type. Additionally, the images for FOXD1 do not show nuclear localization as expected for a transcription factor.SOX8 are key for SCMF identity/function. Current data are merely based on expression and prediction analysis and such claims require additional evidence from loss of function studies.7. In Figure 4, the claim that the predicted GRNs involving FOXD1, TBX2, and 8. On a technical note, the authors should better characterize their SCMF cultures. Is this culture condition optimized for SCMF maintenance, and how similar are cultured cells to their in vivo counterparts?9. Is Wnt5a expression specific to SCMFs? To determine the role of Wnt5a specific to SCMFs, authors need to use a specific creER driver line for loss of function studies similar to Comment-3?Reviewer #3 (Recommendations for the authors):1. The RNA ISH shown in Figure 1 is not convincing of overlap between Pdgfra and Igf1 as specific markers of SCMF. Is SCMF a distinct cell population? Are all Pdgfra cells SCMF?2. At least two large single-cell transcriptomes of the developing mouse lung have been published in the last 12 months  and the examination of the expression of Igf1 and Igfr in specific myofibroblast populations over time should be explored in these publicly available datasets, rather than using whole lung qPCR.3. While Gli1 is a previously published marker of SCMF, does this marker have specificity in the context of newer single-cell transcriptomic datasets?4. There exist significant concerns about the rigor of this study, including a lack of information about the number of technical and biological replicates used. This information should be disclosed.5. Moreover, whole lung qPCR is used on human lung as an attempt to validate these methods, however, there is no significant clinical data given about the patients from who this RNA was obtained . Perhaps FFPE human tissue blocks could be used with RNA ISH as a way to validate the qPCR findings.6. The discussion ignores several recent papers about Wnt5A in chronic lung disease including IPF  and BPD , the later of which showed an increase in Wnt5A expression by mesenchymal sells with hyperoxia injury and in human BPD. How do the authors reconcile their seemingly opposite findings?eLife. Two of the original reviewers have assessed your updated manuscript, and while the manuscript has been improved there are some remaining issues that need to be addressed, as outlined below:Thank you for resubmitting your work entitled "Decoding the IGF1 Signaling Gene Regulatory Network Behind Alveologenesis from A Mouse Model of Bronchopulmonary Dysplasia" for further consideration by While Reviewer 2 felt you addressed all of their concerns, Reviewer 3 still has three concerns that must be addressed.The first concern relates to the RNAscope data. While the quality is much improved, the original review asked for quantification of the RNAscope data. As this was not provided and the colocalization of Pdgfra and Igf1 observed in Figure 1 remains difficult to assess, please provide the quantitation requested or explain why such studies are technically unaddressable.The second concern is around the specificity of Gli1. The Reviewer feels as if the point was not sufficiently addressed. Please see their comment.The final concern is around the new human data. Again, please see the Reviewer's comments.We believe that these issues can be addressed with revisions to the language in the manuscript.The full comments are below.Reviewer #2 (Recommendations for the authors):The authors provided additional data and explanation (ex: SCMF specific markers etc.) to address my prior comments. I have no further comments.Reviewer #3 (Recommendations for the authors):In this revised manuscript, the authors have attempted to address many of the concerns raised by the reviewers. These efforts have addressed nearly all the issues raised by reviewers initially. That said, there remain some outstanding areas of concern, that if addressed, would significantly improve the manuscript.RNAscope data in Figure 1 does not support the claims made by the authors. In Figure 1 C, in the representative image shown, there is not consistent colocalization of Pdgfra and Igf1 expression. Despite requests by reviewers, the RNAscope has not been quantified .This reviewer continues to have concerns about the specificity of Gli1 for SCMFs. The authors note that Gli1 is a suitable marker when combined with other markers of "SCMF"s, but still need to address the specificity of Gli1 and the possibility of off-target effects in other cell types. We appreciate the rigor gained by the downloading and analysis of other recent scRNAseq datasets, but even this analysis suggests that Gli1 does not mark a unique subpopulation of mesenchyme and may be expressed in other cell types. To address this, the authors could revise the text to acknowledge that using Gli1 as a driver may result in off-target effects in other cell types.We thank the authors for including additional details about the clinical data. Review of this has raised some additional concerns about how the human data should be interpreted. Two of the controls in the "non-BPD" group should really not be considered controls-the infants were born preterm (26 and 24 wks) but died at 28 and 26 wks, before a BPD diagnosis could be made . Including these infants in the control group could significantly skew the data as the other control infants were born at term. How do the authors know that the differences in gene expression are not simply due to differences in term vs preterm gestation? A better comparison would be between infants of the same gestational age who did/did not develop BPD. The human BPD data has 4 subjects in control (with 2 of these controls being not true controls as noted above) and 5 in BPD. Given the high degree of variability in human subjects, this appears to be underpowered to detect significant differences between groups, especially since it is not clear if the data are corrected for multiple comparisons. In summary, this human data detracts from an otherwise high-quality manuscript, which provides novel insights into the developing lung. We recognize that obtaining the additional human samples required to expand the dataset is not possible, but feel that the conclusions drawn from the mouse experiments would be stronger without the inclusion of this data. The reviewers were in agreement that this manuscript had strong merit and could be suitable for eLife. In particular, there was enthusiasm for the overall novelty of the approach, with respect to translating publicly available data into meaningful biological insights. However, there were a number of major concerns that must be addressed for this manuscript to be considered for publication. Below I have attached complete reviews from the three reviewers. However, for this paper to be considered for publication the following points should be explicitly addressed.We thank the editors and the reviewers for their recognition of the novelty and importance of deriving meaningful biological insight from the massive, publicly available data using the GRN approach.Our overall goal is to construct a comprehensive developmental GRN behind alveologenesis. Using the IGF1 knockout mice with BPD phenocopy as an example, we built the GRN behind alveologenesis by employing concepts and methods mirrored from the sea urchin GRN’s construction. One primary goal of this paper is to serve as a proof of concept to bring the GRN study into the lung field.(1) All three reviewers noted problems with the RNAScope (and to a lesser degree the immunostaining). New RNAScope images with rigorous quantification should be provided.We have repeated these RNAScopes. New images, taken by confocal microscopy, are now provided in Figures 1 and 3 of the revised manuscript.(2) Multiple reviewers noted that bulk RT-PCR is not sufficient to validate signals specific to particular cell populations. The authors should analyze their results in the context of existing single-cell datasets to assess the robustness of their claims.et al., 2021; Negretti et al., 2021; Zepp et al., 2021) were selected for the suggested analyses. The results of our analyses of the datasets are shown in Figure 4—figure supplement 1 and Figure 4-Source Data 1.The bulk RNAseq data used are all from FACS isolated cells which closely correspond to the cell populations used in our RT-PCR analyses—on which the network was constructed. We are happy to see more single-cell datasets coming out since our work. The two largest scRNAseq datasets from the latest publications  were downloaded from the SRA server. The 10x Genomics’ Cell Ranger pipeline was used to demultiplex raw base call (BCL) files into FASTQ files, perform alignment, filtering, barcode counting, and UMI counting, combine and normalize counts from multiple samples, generate feature-barcode matrices, run the dimensionality reduction, clustering, and gene expression analysis using parameters once they were provided in the original papers. Quality control was done additionally in Partek Flow to filter out cells with excess mitochondrial reads and possible doublets and remove batch effects. Loupe Browser was used for data visualization and analysis including cell clustering, cell counting, cell type classification, gene expression and comparative analysis. For Secretome-Receptome analysis, signaling genes were collected respectively from AT1, AT2, and SCMF/Myofibroblast cell clusters; Cell to cell communications were predicted using a published Fantom5 Cell Connectome dataset linking ligands to their receptors (STAR Methods) . The cellular communications between IGF1, WNT5A, and FGF10 ligands from SCMF and receptors from SCMF, AT2, and AT1 are confirmed to be consistent from bulk RNAseq and scRNAseq data analyses.In brief, the raw scRNAseq datasets (GSE160876 from Schuler (3) Multiple reviewers commented on the specificity of Gli1 to SCMFs. This must either be demonstrated with additional KO experiments using are drivers with higher specificity to SCMFs, or alternatively, data needs to be generated to assess whether the phenotype is restricted to SCMFs rather than other mesenchymal cells types.et al., 1996; Li et al., 2015; Li et al., 2018; Sun et al., 2022; Zepp et al., 2021).Alveologenesis is characterized by secondary septa/crest formation, and the Alveolar Myofibroblasts are recognized as the driving force behind it. Secondary Crest Myofibroblast was derived from this concept and is broadly adopted in independent publications (i.e. Bostrom Gli1+) fibroblasts have been rigorously examined through lineage tracing using CreERT2;Rosa26mTmGGli1 in our lab . Cell lineage analysis shows SHH targets different mesenchymal cell lineages through lung development. There was a window of time in the early postnatal stage during which the derived GFP+ cells were observed primarily localized to the secondary septa, and secondarily to parabronchial and perivascular smooth muscle fibers . When Tamoxifen was titrated down to a certain dosage, the smooth muscle fibers were not labeled by GFP . This very specific regimen which demonstrates the specificity of the Gli1 to SCMFs was employed throughout our current manuscript.The SHH targeted .Our analysis from the two largest scRNAseq datasets recently published, shows that consistent with our observations, Gli1 is predominantly expressed by proliferative SCMF/Myofibroblast cells , Stc1 , Tagln , Fgf18  and Pdgfra Li et al., 2015 have been suggested from previous work. To help resolve this issue, we compiled a comprehensive list of these markers suggested from literature and examined their expression across different mesenchymal cell types as clustered on the two latest scRNAseq datasets . Please see Figure 1—figure supplement 1C and F in revised manuscript. We found: 1. Not all markers are specific to SCMF; 2. Among some of the few good candidate markers agreed upon by both datasets are Pdgfra, Stc1, Gli1, Tgfbi, and P2ry14.Presently, a consensus marker for SCMF hasn’t been established although a list of markers,such as et al., 2015 and Li et al., 2019) cells targeted by CreERT2Gli1 are predominantly SCMFs which are also ACTA2/PDGFRA double positive and, importantly, localized to the secondary crests during alveologenesis. In line with our current understanding of SCMF, we opted to continue our original designation of SCMF to describe them. If an agreement cannot be reached on the using of this term here, we are happy to call them alveolar myofibroblasts or use the term “hedgehog-responsive Pdgfra+ fibroblasts,” as we did in a recent publication .Taken together, we have shown that under conditions described above, in this manuscript, and in several published works (e.g. Li The revised text was highlighted in blue on the revised manuscript and can be found from line 128 to 140 on Page 5 and from line 161 to 171 on Page 6.(4) There we a number of questions around the results pertaining to WNT5A. These include questions about specificity to SCMFs, canonical and non-canonical Wnt signaling, and conflicting results with other recent publications. These issues should be addressed.These issues have been addressed in our Response to the individual reviewer’s specific concerns (Please see below).If you choose to revise and resubmit this manuscript, please address these concerns as well as those raised below.Reviewer #1 (Recommendations for the authors):While the conclusions are mostly well-supported, a few claims could be better supported.(1) Some of the immunostaining looks fairly low quality with possible non-nuclear signals from transcription factors . Perhaps a few close-ups to allow better visualization would be helpful.Foxd1 expression in Figure 3C is labeled by Tomato driven by CreERT2Foxd1, and Tomato is not restricted to nuclei. We have repeated the RNAScope on the other genes, and new images have been taken by Confocal and provided in Figures 1 and 3 of the revised manuscript. We hope this clarifies the situation and alleviates the Reviewer’s concern.(2) The authors propose that Wnt5a is signaling non-canonically to both the MFB and AT2 cells based on the identification of Ror1 and Ror2 expression. However, the canonical Wnt target gene Axin2 has been identified in a subset of AT2 stem cells by others, and Wnt5a is capable of both canonical and non-canonical Wnt signaling. So, how do the authors know that canonical signaling is not involved, either alone or in conjunction with non-canonical signaling? At least Axin2 levels could be measured upon Wnt5a silencing in MFBs, and perhaps qRT-PCR performed for Axin2 and other Wnt receptors that could mediate canonical signaling. If they cannot experimentally demonstrate the absence of canonical Wnt signaling in MFBs (and AT2 cells), perhaps they should update their GRN to indicate this possibility?et al., 2008). The constructed network only illustrates the data we analyzed within this paper at this time. We have revised the manuscript to include this possibility.We agree with the reviewer that WNT5a can signal through canonical WNT signaling directly or indirectly, and our work does not exclude this possibility in our model. In fact, our previous study has shown that the Ror receptor does activate canonical WNT signaling (Li The revised text was highlighted in blue on the revised manuscript and can be found from line 255 to 259 on Page 8.(2) The ISH for Igf1 in Figure 1b looks almost ubiquitous, yet the GRN proposes only autocrine signaling. I don't think this hurts their conclusion since they deleted Igf1R in MFBs which should abrogate signaling from any source, but this should probably be addressed or their GRN revised to indicate potential paracrine signaling.The reviewer is correct. Following the suggestion, we have now added the paracrine signaling to the GRN  in the revised manuscript. Thank you for pointing that out!(3) In Supplemental Figure 1g, at E18 there is no statistically significant difference in MLI upon deletion of Igf1r, however, the images presented (a and d) appear to show a major reduction in MLI. Is this image representative?By definition, MLI is the mean length of line segments on random test lines spanning the airspace between intersections of the line with the alveolar surface. The number of intercepts determines the value of MLI. Thicker primary septa is the main difference between the mutant and control while the number of saccules exhibits little variation. This may be one of the limitations of using MLI as a measurement. To address this issue, we used ImageJ and analyzed the Airspace Area in these lungs. The new index can better distinguish these structural differences and was appended to Figure 2—figure supplement 1g, in the revised manuscript.Reviewer #2 (Recommendations for the authors):In this manuscript, Gao et al., claim that they have constructed a gene regulatory network underlying alveologenesis and its significance to bronchopulmonary dysplasia (BPD). Using RT-PCR and in situ hybridization, the authors claim that Igf1 and Igf1r are expressed in secondary crest myofibroblasts (SCMFs) and their loss of function using Gli1-creER results in alveolar simplification, a tissue level disorganization of alveoli that phenocopies BPD. Further, the authors investigate transcriptomic changes in mesenchymal and epithelial populations from control and Igf1r mutant lungs. For this, the authors developed a 47-gene panel that they claim to represent signaling modules within SCMFs and used this panel for RT-PCR analysis. These data are used to generate an interaction network to evaluate signaling partners, co-effectors mediated by IGF1 signaling in SCMFs, other fibroblasts and alveolar epithelial cells. Using this GRN, the authors concluded that Wnt5a is a key signaling molecule downstream of IGF1 signaling that regulates alveologenesis.While the authors' claims are salient, some of the conclusions were previously shown by others. For example, a role for Wnt5a driven Ror/Vangl2 has already been shown to be a key mediator of alveologenesis, by virtue of the same signaling effectors identified in this study (Zhang 2020 eLife). Additionally, the genetic loss of function studies performed here are not specific to SCMFs and instead they target broader alveolar and airway fibroblasts. The construction of a gene regulatory network is a potentially exciting tool, but this requires additional perturbations to distinct nodes identified in this work. It would be of particular interest to determine whether there is any redundancy among these nodes and what are the downstream effectors that are specific to each node. While I recognize that this is outside the scope of this work, the authors need to demonstrate the significance of at least one such node.1. Timing of Igf1r deletion. The authors administered tamoxifen to induce deletion of Igfr1r at PN2 and analyzed tissues at PN7, PN14, PN20. It would be valuable to administer tamoxifen at later stages to test the critical time point at which Igf1r signaling is essential for alveologenesis. For example, the authors may consider administering tamoxifen at PN5 and PN10.Igfr1r in this case depends on CreERT2Gli1. The specificity of Gli1 to SCMF cells has been discussed above in our response to General Comment #3. Administering tamoxifen at PN2 in practice was observed to have the highest efficiency in labeling the SCMFs. As a result, we chose to administer tamoxifen at PN2 to induce deletion of Igfr1r from these cells. Less severe phenotype is expected with later administration. We did try Tamoxifen at PN14, and as expected, there was no clear difference between the control and mutant lungs.The specific deletion of 2. In Figure-1, the authors used RNAScope to determine the expression pattern of IGF1 and claim that it is expressed in Pdgfra+ cells. However, it appears that Igf1 transcripts can be seen in other cells. Do the authors need to assess cellular sources of Igf1 transcripts in other cells? Authors could use recently published single-cell transcriptome datasets  or from LungMAP to assess this.CreERT2Gli1 was used to delete Igf1 and Igf1r specifically from these cells. Because of its broader expression, deleting Igf1 ligands from a cell doesn’t necessarily block the signaling inside the cell if extracellular IGF1 from other sources is present. As a result, the deletion of Igf1r became the focus of our study in the paper. Igf1 expression from the entire lung and different types of mesenchymal cells was analyzed on suggested public datasets  and is shown in SFigures 1 A-C and F in the revised manuscript.The reviewer is correct in the sense that IGF1’s expression is broad. Our study was focused on one aspect of IGF1 signal transduction within one specific cell lineage—SCMFs. 3. The authors used Gli1-CreER to delete Igf1r in SCMFs. However, Gli1 has been shown to be expressed in peribronchiolar fibroblasts  and alveolar lipofibroblasts . The cited publication  also shows Gli1-labeled cells around the proximal airways and not just in SCMFs. Therefore, the phenotypes observed in Igf1r KO, as well as all downstream RT-PCR studies, could be a result of loss of IGF1R in other cell types and not specific to just SCMFs. Recent studies have shown that FGF18 is specific to SCMFs . Authors could use FGF18-creER line to delete Igf1r.Gli1 to SCMFs. As it was described in Hagan et al., 2020, Fgf18, aside from its expression in mesothelial, peribronchial, and perivascular cells, is primarily expressed not only in Alveolar Myofibroblast but also in AT1 cells. We hope this addresses the concern expressed by the Reviewer.Please see our response above to General Comment #3 for the specificity of 4. The authors performed RT-PCR for a large panel of genes. However, the cell populations used for these analyses should be more specific. For example, in Figure 3B the authors compared GFP+ vs GFP- bulk populations. I suggest the authors compare between GFP+ SCMFs compared to non-SCMF fibroblasts. Additionally, Figure 5A should be compared with sorted AT1 or AT2 populations as opposed to bulk GFP-negative cells. As the authors show in Figure 2, AT1 and AT2 numbers are reduced in knockout mice, and thus RT-PCR on the bulk "GFP-negative population" does not seem appropriate.et al., 2022). Nonetheless, we agree it is better to compare these cells in a more direct and specific way as the reviewer suggested.These are great suggestions. One way to separate these cell populations is to label them with their surface markers during cell sorting. Unfortunately, good markers are unavailable for us to use at this time, especially for lung fibroblasts and AT1s. To make the comparison possible between GFP+ SCMFs and non-SCMF fibroblasts, we need an antigen which can label all lung fibroblasts. As for isolating AT1s from bulk AT2/AT1epithelial cells, there is lack of a rigorously tested, specific surface antigen for FACS of AT1s. The reviewer is correct in that AT1 and AT2 numbers are reduced in our knockout mice. Our measurement and comparison on the bulk "GFP-negative population" more evidently reflected the difference of AT2 or AT1 cells as a whole population in the control and mutant lungs. Our recent paper reveals how the population effect has an impact on alveologenesis  would allow for interrogation of all differentially regulated genes, from which a more inclusive GRN could be constructed.We apologize for this oversight on our part. In the present work, we used the concept and approaches mirroring the model of sea urchin GRN’s construction. The sea urchin GRN was built predominantly on RT-PCR data as the Next Generation Sequencing was not available at the time. To determine the candidate genes to be examined by RT-PCR in our work, we looked through genes on the LungMAP transcriptomic dataset available at the time and annotated their molecular function, temporal, and spatial expression . The genes considered for analysis were: (1) transcription factors or signaling molecules; (2) actively expressed in the lung during PN3 to PN14; (3) highly enriched in lung myofibroblasts. 47 genes met these criteria best.We acknowledge that when using RT-PCR there is always a limitation on the number of genes to be detected. Our present plans to make the GRN more comprehensive, includes exactly what the Reviewer suggested as more reliable and unbiased approaches (bulk RNA-Seq or single-cell RNA-Seq). We thank the Reviewer for this valuable suggestion.6. Figure 3C, D – validation of markers should also be done on Igf1 deleted lungs, not just wild type. Additionally, the images for FOXD1 do not show nuclear localization as expected for a transcription factor.CreERT2Foxd1, and Tomato is not restricted to nuclei.The validation of some markers was done on Igf1 deleted lungs as shown in Figure 3—figure supplement 1B. In general, it is hard to observe any additional differences between the control and mutant lungs from staining alone other than the morphological and structural defects already known to us. The main goal of Figure 3C,D is to show where these markers are expressed in the wild type lung. Foxd1 expression in Figure 3C is labeled by Tomato driven behind 7. In Figure 4, the claim that the predicted GRNs involving FOXD1, TBX2, and SOX8 are key for SCMF identity/function. Current data are merely based on expression and prediction analysis and such claims require additional evidence from loss of function studies.The reviewer is correct from this perspective. One use of the constructed GRN is to make predictions, and its construction indeed, in a way, is a continuous back and forth process between testing and such predictions. As for the claim itself, we agree that it is largely a prediction at the current time.8. On a technical note, the authors should better characterize their SCMF cultures. Is this culture condition optimized for SCMF maintenance, and how similar are cultured cells to their in vivo counterparts?We agree with the Reviewer that it is always better to use the culture condition optimized to a specific cell type. Such conditions have not been established for SCMF. In our study, culture conditions for fibroblasts were used for SCMFs. The culture was checked under the microscope every day during the experiments. The growth and morphology of these cells with undulating membranes, multiple processes, and the occurrence of very few dead cells led us to believe the culture conditions were appropriate.In support of that we examined and compared the response of SCMFs in culture to those in vivo, to IGF1R inhibitor. We found majority of genes tested in vitro were altered in the same direction as observed in vivo , indicating that the cultured conditions are appropriate for maintaining the intrinsic characteristics of SCMFs.9. Is Wnt5a expression specific to SCMFs? To determine the role of Wnt5a specific to SCMFs, authors need to use a specific creER driver line for loss of function studies similar to Comment-3?Wnt5a is expressed in SCMF and smooth muscle cells as evidenced by the latest scRNAseq datasets , LungMAP Drop-seq data (https://research.cchmc.org/pbge/lunggens/tools/quickview.html?geneid=Wnt5a), and our staining . Wnt5a is inactivated ubiquitously via CreERCAG. Using CreERT2Gli1, we inactivated Ro1/Ro2—the predicted WNT5A receptors on SCMFs. Comparison of the Wnt5a inactivation mutants using CreERCAG vs CreERT2Gli1 shows identical phenotypes which further suggests that functional Wnt5a is predominantly expressed by targets of CreERT2Gli1, i.e. SCMF . Please see our Response above to General Comment #3 for the specificity of Gli1 to SCMFs.Reviewer #3 (Recommendations for the authors):1. The RNA ISH shown in Figure 1 is not convincing of overlap between Pdgfra and Igf1 as specific markers of SCMF. Is SCMF a distinct cell population? Are all Pdgfra cells SCMF?Acta2+ and Pdgfra+ fibroblasts, providing a transient niche indispensable for the alveologenesis process .We have repeated the RNA ISH of Igf1. New images have been taken by confocal microscopy and provided in Figure 1C in the revised manuscript. While definitive criteria for the term SCMF have not been widely agreed upon, it is nevertheless accepted that at a minimum, they are Hedgehog-targeted-Pdgfra is identified as a signature marker for SCMF based on the literatures above and the latest scRNAseq datasets . Please refer to our Response above to General Comment #3 for more information about SCMF and its markers.2. At least two large single-cell transcriptomes of the developing mouse lung have been published in the last 12 months  and the examination of the expression of Igf1 and Igfr in specific myofibroblast populations over time should be explored in these publicly available datasets, rather than using whole lung qPCR.Igf1 and Igfr from the entire lung and in myofibroblast cells through lung development is presented as Figure 1—figure supplement 1 A and B in the revised manuscript.We thank the reviewer for this suggestion. We downloaded these datasets, recapitulated the analyses the papers performed, and examined our data and results in the context of these public data sources. The expression of 3. While Gli1 is a previously published marker of SCMF, does this marker have specificity in the context of newer single-cell transcriptomic datasets?Gli1 is found as a good SCMF marker in the context of newer single-cell transcriptomic datasets suggested by the reviewer .4. There exist significant concerns about the rigor of this study, including a lack of information about the number of technical and biological replicates used. This information should be disclosed.We apologize for this oversight. The Information the Reviewer is asking for can now be found in Quantification and Statistical Analysis under the MATERIALS AND METHODS section. It is copied here for your reference:“In gene expression quantification using RT-PCR, at least three biological replicates (in different cases including lungs/FACS sorted cells/cultured cells) for each experimental group  were used. Measurement for each biological replicate was repeated three times. The Ct (cycle threshold) was normalized to Gapdh, and the final result was presented as deltaCT or fold change. In morphometric quantification and cell counting, four lungs for each experimental group (Ctrl vs Mut) were used. Left lobe and right inferior lobe from each lung were targeted. Five images from each lobe after staining were analyzed for morphometric quantification (at 10x magnification) and cell counting (at 40x magnification). A two-tailed Student's T-Test was used for the comparison between two experimental groups and a one-way ANOVA was used for multiple comparisons. Quantitative data are presented as mean values +/-SD. Data were considered significant if p < 0.05.”5. Moreover, whole lung qPCR is used on human lung as an attempt to validate these methods, however, there is no significant clinical data given about the patients from who this RNA was obtained . Perhaps FFPE human tissue blocks could be used with RNA ISH as a way to validate the qPCR findings.The Human neonatal lung samples used in the paper were provided by the International Institute for the Advancement of Medicine and the National Disease Research Interchange and were classified exempt from human subject regulations per the University of Rochester Research Subjects Review Board protocol (RSRB00056775).We thank the reviewer for pointing out the missing clinical data about these patients. This information is now presented in Figure 6-Source Data 1 in the revised manuscript.We also thank the Reviewer for the suggestion to examine our qPCR findings with RNA ISH on FFPE human tissue blocks. We plan to consider this additional approach in further studies.6. The discussion ignores several recent papers about Wnt5A in chronic lung disease including IPF  and BPD , the later of which showed an increase in Wnt5A expression by mesenchymal sells with hyperoxia injury and in human BPD. How do the authors reconcile their seemingly opposite findings?The reviewer is correct. While we are aware of the two important works, lack of reference to them was simply an oversight on our part. We have now compared their data with ours and discussed them in the revised manuscript.Wnt5a and fibroblast effector genes , confirming its role in fibroblast growth and development. From this aspect of WNT5A’s function, this finding is consistent with the clinical data from the study of certain IPF patients .Our perturbation data has built connections—as shown on the network—between et al., 2020. There are several potential causes for the seemingly conflicting findings between Dr. Sucre’s and ours. For example, etiology of BPD is multifactorial. It includes lung immaturity, hyperoxia, barotrauma and inflammation. Therefore, individual patient may develop BPD from different causes. In support of this, both hyperoxia and hypoxia, two opposite conditions, can both cause lung injury and lead to BPD-like phenotypes. There are also differences in the experimental approaches, which may account for the seemingly different observations that reflect the complexity of Wnt5a signaling and its associated GRN.An increase in Wnt5A expression by mesenchymal cells from hyperoxia BPD models was reported by Sucre The revised text was highlighted in blue on the revised manuscript and can be found from line 278 to 282 on page 8 and from line 362 to 375 on Page 10.Reviewer #3 (Recommendations for the authors):In this revised manuscript, the authors have attempted to address many of the concerns raised by the reviewers. These efforts have addressed nearly all the issues raised by reviewers initially. That said, there remain some outstanding areas of concern, that if addressed, would significantly improve the manuscript.We deeply appreciate these critical points from reviewer #3 for the purpose to improve our manuscript.RNAscope data in Figure 1 does not support the claims made by the authors. In Figure 1 C, in the representative image shown, there is not consistent colocalization of Pdgfra and Igf1 expression. Despite requests by reviewers, the RNAscope has not been quantified .Pdgfra, Igf1 and Igf1r from RNAscope was analyzed and quantified as presented in Figure 1—figure supplemental 2.Cellular localization of Pdgfra+ cells are Igf1+ (A) and Igf1r+ (B). Monochrome DAPI staining was imported into ImageJ where the outline of each cell was traced. Under this outline, Cellular localization of Pdgfra, Igf1 and Igf1r was analyzed and quantified. 10 images from each group were used and data was presented as box plots. Scale bar: 20um for all images.Legend for Figure Supplemental 2: Majority of Pdgfra, Igf1 and Igf1r at the cellular level from RNAscope as we have not made any conclusions in the paper which required such data directly. Also, our RNAscope was done on 5um tissue sections. Given a typical mammalian cell diameter at 10um, stained signals collected for each cell are actually from one segmented slice of the whole cell. The relative location of these cellular slices and their thickness may vary cell by cell on the tissue section, which makes their quantification not the reflection of the whole cell and their comparison to each other not so relevant.We didn’t quantify expression of The revised text was highlighted in blue on the revised manuscript and can be found from line 140 to 142 on Page 5 and from line 820 to 823 on Page 25.This reviewer continues to have concerns about the specificity of Gli1 for SCMFs. The authors note that Gli1 is a suitable marker when combined with other markers of "SCMF"s, but still need to address the specificity of Gli1 and the possibility of off-target effects in other cell types. We appreciate the rigor gained by the downloading and analysis of other recent scRNAseq datasets, but even this analysis suggests that Gli1 does not mark a unique subpopulation of mesenchyme and may be expressed in other cell types. To address this, the authors could revise the text to acknowledge that using Gli1 as a driver may result in off-target effects in other cell types.Gli1 as a driver has demonstrated its high degree of specificity for SCMF, we acknowledge that its off-target effects in other cell types haven’t been fully excluded.Nonetheless, though our experimental scheme on using The revised text was highlighted in blue on the revised manuscript and can be found from line 173 to 175 on Page 6.We thank the authors for including additional details about the clinical data. Review of this has raised some additional concerns about how the human data should be interpreted. Two of the controls in the "non-BPD" group should really not be considered controls-the infants were born preterm (26 and 24 wks) but died at 28 and 26 wks, before a BPD diagnosis could be made . Including these infants in the control group could significantly skew the data as the other control infants were born at term. How do the authors know that the differences in gene expression are not simply due to differences in term vs preterm gestation? A better comparison would be between infants of the same gestational age who did/did not develop BPD. The human BPD data has 4 subjects in control (with 2 of these controls being not true controls as noted above) and 5 in BPD. Given the high degree of variability in human subjects, this appears to be underpowered to detect significant differences between groups, especially since it is not clear if the data are corrected for multiple comparisons. In summary, this human data detracts from an otherwise high-quality manuscript, which provides novel insights into the developing lung. We recognize that obtaining the additional human samples required to expand the dataset is not possible, but feel that the conclusions drawn from the mouse experiments would be stronger without the inclusion of this data.The mark of 36 weeks corrected gestational age for BPD diagnosis is applied to live patients in clinics. The human samples used in the paper were collected from patients who unfortunately died. The diagnosis of these patients was confirmed by the Histopathological sections of their lungs where BPD is characterized by arrested alveologenesis and peripheral vascular dysmorphia.