Influenza vaccines require annual readministration; however, several reports have suggested that repeated vaccination might attenuate the vaccine's effectiveness. We aimed to estimate the reduction in vaccine effectiveness associated with repeated influenza vaccination.In this systematic review and meta-analysis, we searched MEDLINE, EMBASE, and CINAHL Complete databases for articles published from Jan 1, 2016, to June 13, 2022, and Web of Science for studies published from database inception to June 13, 2022. For studies published before Jan 1, 2016, we consulted published systematic reviews. Two reviewers (EJ-G and EJR) independently screened, extracted data using a data collection form, assessed studies' risk of bias using the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) and evaluated the weight of evidence by Grading of Recommendations Assessment, Development, and Evaluation (GRADE). We included observational studies and randomised controlled trials that reported vaccine effectiveness against influenza A(H1N1)pdm09, influenza A(H3N2), or influenza B using four vaccination groups: current season; previous season; current and previous seasons; and neither season (reference). For each study, we calculated the absolute difference in vaccine effectiveness (ΔVE) for current season only and previous season only versus current and previous season vaccination to estimate attenuation associated with repeated vaccination. Pooled vaccine effectiveness and ∆VE were calculated by season, age group, and overall. This study is registered with PROSPERO, CRD42021260242.I2=0%; low certainty) for influenza A(H1N1)pdm09, –18%  for influenza A(H3N2), and –7%  for influenza B, indicating lower protection with consecutive vaccination. However, for all types, A subtypes and B lineages, vaccination in both seasons afforded better protection than not being vaccinated.We identified 4979 publications, selected 681 for full review, and included 83 in the systematic review and 41 in meta-analyses. ΔVE for vaccination in both seasons compared with the current season was –9% (95% CI –16 to –1, Our estimates suggest that, although vaccination in the previous year attenuates vaccine effectiveness, vaccination in two consecutive years provides better protection than does no vaccination. The estimated effects of vaccination in the previous year are concerning and warrant additional investigation, but are not consistent or severe enough to support an alternative vaccination regimen at this time.WHO and the US National Institutes of Health. Influenza vaccines require annual readministration because circulating viruses, especially influenza AH3N2) viruses, viruses,The first study to report reduced vaccine effectiveness in repeat vaccinees came from a 1970s vaccine trial in an English boarding school, which observed that infection rates were higher for boys vaccinated in the current and previous season than for boys receiving their first vaccination.These findings indicate that the capacity of vaccination to update immunity against new influenza viruses might be limited by pre-existing immunity.Evidence before this studyWe searched MEDLINE, Web of Science, CINAHL Complete, and EMBASE with the terms “influenza”, “vaccines”, “immunization”, “efficacy”, and “effectiveness” without language restriction from database inception to June 13, 2022, and the reference lists of previous systematic reviews on repeat influenza vaccine effectiveness. Studies of any design were included if they investigated the vaccine effectiveness of influenza vaccination in consecutive seasons. Two previous related meta-analyses have summarised influenza A subtypes and influenza B with minimal subgroup analysis and quality appraisal. Our literature search revealed substantially more articles for inclusion since these reviews were published.Added value of this studyWe identified 83 observational studies from 30 countries; 41 studies were included in meta-analysis with the earliest season estimates from 2007–08. Our meta-analysis provides up-to-date summary vaccine effectiveness estimates for influenza vaccination in two consecutive seasons for policy planning consideration. We included a critical appraisal of the body of evidence by providing a risk-of-bias assessment for studies included in the meta-analysis. Our protocol is available on Prospero for those wishing to repeat our process. We estimated that vaccine effectiveness against influenza A(H1N1)pdm09 and the influenza B viruses for people vaccinated in both the current and previous seasons were, on average, slightly attenuated compared with effectiveness in people vaccinated in the current season only. Vaccine effectiveness against influenza A(H3N2) was worse overall than for influenza B and displayed a greater loss in effectiveness with repeated vaccination. However, on average, vaccination in both the current and previous seasons afforded better protection than not being vaccinated in either season or vaccination in the previous season only for all types, A subtypes and B lineages examined.Implications of all the available evidenceAnnual seasonal influenza vaccination is currently recommended in several countries. Our study contributes to the growing body of evidence on repeated influenza vaccination. We used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of the body of evidence by type, A subtype and B lineage, which was low for influenza A(H1N1)pdm09 and influenza B, and very low for influenza A(H3N2). The currently available evidence does not warrant a change to policies that recommend annual vaccination. Our results support current season vaccination regardless of previous season vaccination and suggest that vaccination in any combination of current and previous seasons provided better protection than not being vaccinated.Concepts regarding the underlying immunological mechanisms have evolved over many years. The initial concept of original antigenic sin suggests that a person's first influenza infection preferentially orients antibodies towards priming epitopes that remain in subsequent strains, often as subdominant epitopes.Since many people vaccinated in any one season tend to be vaccinated in every season, any loss in effectiveness presents an important policy consideration since it will affect the majority of those vaccinated. To investigate whether vaccine effectiveness is reduced by repeated vaccination, we did a systematic review and meta-analysis of studies reporting vaccine effectiveness by the previous year's vaccination status.. We also searched reference lists of reviews,We followed PRISMA guidelines  throughoWe included observational studies and randomised controlled trials that reported vaccine effectiveness against laboratory-confirmed influenza for four comparison groups: current season only, previous season only, current and previous seasons, and neither season (reference group). For studies not in English, we required an English language abstract.Two reviewers (EJ-G and EJR) independently conducted screening, data extraction, and risk of bias analyses. Titles and abstracts were initially screened, followed by full-text screening according to predefined exclusion and inclusion criteria using Covidence. Conflicts between reviewers were resolved via consensus or consultation with a third reviewer (SGS). Non-English language studies were evaluated by individuals fluent in the relevant language.We extracted data using a standardised form after the removal of duplicates . ExtractWe assessed study quality using Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I).CP) seasons and those vaccinated in the current season only (VEC) was calculated as: ΔVE = VECP– VEC. ΔVE>0 implies higher vaccine effectiveness if vaccinated in the current and previous seasons than in the current season alone. We calculated CIs for ΔVE by bootstrapping 1000 samples for VEC and VECP  for people who were vaccinated in the current and previous pdm09, influenza A(H3N2), and influenza B by season and age group for each vaccination group . If studies did not report estimates for all four comparison groups, we used all available estimates. We did not incorporate studies with estimates or 95% CI values equal to 100% vaccine effectivenessI2 statistic. In addition, we calculated both random-effects and fixed-effect models as discrepancies could indicate instability in the pooled estimates.41The primary analysis for each type, A subtype or B lineage used vaccine effectiveness estimates for the broadest age group . Estimates from studies that only reported vaccine effectiveness for a particular age group  were only included in age group estimates. Southern hemisphere estimates were grouped with northern hemisphere estimates with the same vaccine formulation. Inpatient and outpatient studies were pooled, as these have been found to be broadly consistent.We did several sensitivity analyses to assess the robustness of pooled estimates to study design: inclusion of studies using non-PCR diagnostics tests; restriction of data to the northern hemisphere; restriction of data to outpatient studies; restriction of data to test-negative studies; and exclusion of studies with serious, critical, or no information risk of bias assessed by ROBINS-I.All analyses were done in R (version 3.6.1) using the package metafor for meta-analyses and the robvis package for risk of bias visualisations.PROSPERO (CRD42021260242).This systematic review and meta-analysis was preregistered on The WHO SAGE Working Group on Influenza defined the scope of the review and provided feedback on the final report. The US National Institutes of Health had no role in study design, data collection, analysis, or interpretation.We identified 4979 unduplicated publications and selected 681 for full-text review. 83 publications met the eligibility criteria  studies and by a rapid diagnostic test in four (5%) studies.% studies113Influenza vaccination was confirmed by medical record or registry in 41 49%) studies,% studiesAmong the 83 studies included in the systematic review, 20 (24%) studies provided a total of 27 vaccine effectiveness estimates against generalised influenza for one previous season.Four (5%) of 83 studiesTen (12%) of 83 studiesTwo (2%) of 83 studies41 (49%) of 83 studies were eligible for inclusion in the meta-analysis . These sEstimates for influenza A(H1N1)pdm09 were reported for seven seasons . Across Vaccine effectiveness estimates for influenza A(H3N2) were available for ten seasons but estimates could only be pooled for 7 seasons . Pooled Most studies providing estimates for influenza B did not estimate vaccine effectiveness by lineage. Across eight seasons, irrespective of the infecting lineage and the lineage included in the vaccine, vaccine effectiveness was minimally reduced for people vaccinated in consecutive seasons compared with those vaccinated in the current season only pdm09 and influenza B remained low with subsequent vaccinations; that is, repeat vaccination might reduce vaccine effectiveness . For infThis systematic review of 83 studies did not identify sufficient evidence to warrant a change in annual influenza vaccination recommendations. The meta-analysis of 41 studies observed an average reduction in vaccine effectiveness for people vaccinated in the current and previous seasons compared with the current only season (ΔVE) of –9% (95% CI –16 to –1) for influenza A(H1N1)pdm09, –18% (–26 to –11) for influenza A(H3N2), and –7 (–14 to 0) for influenza B. These estimates suggest that there might be some attenuation of vaccine effectiveness with successive revaccination. However, in most seasons, vaccine effectiveness for the group vaccinated in the current and previous season was positive (vaccine effectiveness >0%) and was higher than vaccine effectiveness for the people vaccinated in the previous season only, indicating that vaccination in successive seasons offers better protection against influenza illness than no vaccination.Estimates for the effects of repeated vaccination for influenza A(H1N1)pdm09 immediately after its pandemic appeared to indicate an additive benefit of consecutive vaccination for these viruses and lingering benefits to vaccination in a previous season, an observation shared with previous reviews.Low vaccine effectiveness against influenza A(H1N1)pdm09 in the 2013–14 season has been linked to a change in an epitope conserved between older seasonal influenza A(H1N1) and influenza A(H1N1)pdm09 viruses, on which antibodies were focused.As expected, vaccine effectiveness against influenza A(H3N2) viruses appeared to be reduced most by repeated vaccination. ΔVE estimates for influenza A(H3N2) viruses were the largest observed among types, A subtypes and B lineages, compounded by overall low vaccine effectiveness (mostly <40%). Nevertheless, in most seasons, vaccination in consecutive seasons afforded some protection and this protection exceeded the protection afforded by vaccination in the previous season only. In seasons when repeated vaccination appeared to be helpful, data were sparse and imprecise and the influence of those seasons on overall estimates was limited.Multiple factors might contribute to poor vaccine effectiveness against influenza A(H3N2). First, there is greater heterogeneity among circulating influenza A(H3N2) viruses than other influenza viruses, making it difficult to select antigens capable of eliciting a broad antibody response.Immunological studies have shown that antibody responses to influenza A(H3N2) can be blunted with each additional vaccination received.Our analysis of influenza A(H3N2) vaccine effectiveness by age group was also hampered by insufficient data. Importantly, the data available represented a small number of seasons and should be interpreted with caution because of strong seasonal effects and heterogeneity among seasons. Although attenuation of vaccine effectiveness might be more pronounced in older age groups than in younger age groups, we cannot be certain of this on the basis of the available data. Some of the reduced vaccine effectiveness in older age groups might be associated with immunosenescence. However, modelling studies have suggested that poor vaccine effectiveness in older age groups is better explained by repeated vaccination than with age-associated immunosenescence.For influenza B, the reduction in vaccine effectiveness associated with repeated vaccination was small. Understanding these effects for influenza B is complicated by lineage-mismatched trivalent inactivated influenza vaccine and interseasonal variations in the dominant lineage. Disentangling the effects of repeated vaccination for influenza B lineages will be enabled by increased use of quadrivalent vaccines. However, circulation of influenza B/Yamagata lineage viruses has not been confirmed since early 2020,I2 statistic might not have been a reliable indicator of heterogeneity because most estimates were imprecise and consistency of effect might be more important for interpretation. Nevertheless, interseasonal variability in estimates reinforces hypotheses that the effects of repeated vaccination are not expected to be evident every year and are influenced by interseasonal variations in vaccine formulations, dominant circulating viruses, emergence of antigenically drifted variants, antigenic similarity between vaccine and circulating antigens, and the overall population susceptibility. Pooling data across seasons hides some of these problems but does not indicate the absence of a problem.A key observation for all types, A subtypes or B lineages of influenza was evidence of heterogeneity between, but not necessarily within, seasons. In some seasons, the Our systematic review and meta-analysis followed PRISMA guidelines see  for PRISIn conclusion, the data available currently suggest the vaccine effectiveness for people vaccinated in both the current and previous season is, on average, comparable with vaccine effectiveness for people vaccinated in the current season only and better than vaccine effectiveness for people vaccinated in the previous season only. Vaccine effectiveness against influenza A(H3N2) is worse overall and there is a greater loss of effectiveness with repeated vaccination. Quantifying the relative vaccine effectiveness for people vaccinated in consecutive seasons is difficult if vaccine effectiveness is low and points to the general need for better vaccines against these viruses in particular and against influenza in general.Extracted data are available from the corresponding author on request.SGS and AF report the following funding for influenza vaccination and infection studies: US National Institutes of Health (SGS and AF); US Centers for Disease Control (SGS and AF); the National Health and Medical Research Council of Australia (AF); and OptumLabs research credits (SGS). SGS and AF are employed by the WHO Collaborating Centre for Reference and Research on Influenza, which receives funding from the International Federation of Pharmaceutical Manufacturers and Associations and Seqiris for the development of influenza vaccines. SGS has served in an unpaid capacity on advisory boards for Sanofi and Seqiris. From 2017–21, SGS was a member of the WHO Strategic Advisory Group of Experts (SAGE) on Immunization Working Group on Influenza. SGS serves on the Australian National Influenza Surveillance Committee. EJ-G, EJR, and AJK declare no competing interests.


Sepsis survivors have the second highest readmission rate among Medicare beneficiaries, next to heart failure. I-TRANSFER is an implementation science study to improve transitions and reduce readmissions among sepsis survivors transitioning from acute to home health care (HHC). A total of 63 semi-structured interviews were conducted with stakeholders among 12 hospitals and 5 affiliated home health agencies (HHAs). The purpose of this secondary analysis was to: 1) examine how the sepsis diagnosis is reflected in HHC documentation, 2) identify barriers in sepsis information transfer to HHC, 3) recommend a documentation strategy to enhance information transfer and HHC documentation. We analyzed the diagnosis coding within a national OASIS dataset and eight I-TRANSFER interviews with hospitalists, documentation specialists, and HHC coders. Findings include: a) sepsis diagnoses were nearly invisible in HHC records being documented for only 10% of 165,000 sepsis survivors transitioned to HHC, b) sepsis information in referral documentation can be unclear to HHC coders, c) the lack of sepsis diagnosis documentation might make HHC clinicians unaware of the patient’s risk for readmission, d) HHC coders recommended improved language in the acute care discharge summary to link the need for HHC to sepsis, thereby supporting the use of ICD-10 sepsis ‘A’ codes in HHC. The use of terms, such as “History of Sepsis” or “Sepsis Resolved,” leads to HHC using non-specific codes, thus is not recommended. We highlight the need for new ICD-10 codes for “Sepsis Aftercare” and “Post-Sepsis Syndrome” to clearly communicate and document the needs of sepsis survivors.


Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource ( The SomaGene tool is developed to identify non-coding RNAs (ncRNAs) mutated in breast cancer but can be used for other cancers. Candidate ncRNAs are shown to be enriched for regulatory features and to contain specific trait loci polymorphisms. A deeper understanding of the underlying mechanisms of breast cancer genetics and pathogenesis can be used to detect early-stage cancer to reduce morbidity and mortality due to breast cancer2. Over the last 10 years, high-throughput sequencing has comprehensively investigated the underlying genetic mechanisms that initiate or drive cancer progression and revealed many cancer-associated mutations. The International Cancer Genome Consortium (ICGC)3 has provided an extensive catalog of somatic mutations for various cancer types. This information has enabled researchers to characterize numerous protein-coding genes essential in cancer progression7.Breast cancer is the most common cancer in women that has the highest frequently leading cause of cancer-related mortality amongst females worldwide8, indicating there are many mutations in non-coding regions that need to be investigated. A large portion of non-coding regions operates as regulators of oncogenes9. In addition, around 93% of disease-associated genome-wide association single nucleotide polymorphisms (GWAS SNPs) are located within these regions11, which can significantly influence gene expression of coding and non-coding genes.Although most studies have mainly focused on protein-coding genes in investigating driver mutations in cancer, several lines of evidence imply that about 80% of the genome is biochemically functional17. For example, GAS5 is one of the lncRNAs considerably downregulated in breast cancer18. HOTAIR is another well-known lncRNA upregulated in breast cancer, contributing to aberrant histone H3K27 methylation and cancer metastasis20. Furthermore, lncRNAs contribute to various regulatory activities in the cell, such as regulating gene expression via interaction with other chromatin regulatory proteins21, functioning as active enhancers23, and regulating chromatin structure25. Despite various studies on the impact of non-coding somatic mutations occurring in ncRNAs, the role of such ncRNAs has remained underexplored in breast cancer.Long non-coding RNAs (lncRNAs), an influential class of non-coding transcripts with more than 200 nucleotides, are potential cancer progression indicators and are emerging as diagnostic biomarkers in cancer and other diseasesIn this study, we developed a new tool, SomaGene, to identify 1030 ncRNAs that are significantly and specifically mutated in breast cancer. Using SomaGene, we show that candidate ncRNAs identified in our study are enriched considerably for regulatory features , and enhancer marks). Notably, we show that our breast cancer candidate ncRNAs have a much higher fraction of GWAS SNPs and expression of quantitative trait loci (eQTL) polymorphisms. Finally, our analyses on high-throughput chromosome conformation capture (Hi-C) data from the Human Mammary Epithelial Cell (HMEC) indicate that many of our candidate ncRNAs significantly interact with at least one protein-coding gene, which may suggest a potential enhancer role for these ncRNAs. An overview of the pipeline used in this study is shown in Fig. P value to identify 2nd, 3rd, and last sets of ncRNAs. i.e., ncRNAs that significantly mutated in breast cancer samples are positioned in the top places of the list (referring to the 1030 ncRNAs as the candidate set). We then defined the next 1030 ncRNAs after the candidate ones, in the sorted list, as the 2nd set, and the subsequent 1030 ncRNAs after the 2nd set, as the 3rd set of ncRNAs. We also defined the last 1030 ncRNAs in the sorted list as the last set of ncRNAs .We also compared enrichment of regulatory features, GWAS SNPs, and eQTL polymorphisms in the candidate set of ncRNAs with the 2nd, 3rd and last sets of ncRNAs (each set contains 1030 ncRNAs). We first sorted ncRNAs based on their mutational P value and annotated genomic signals at http://www.ncrna.ictic.sharif.edu and https://www.ihealthe.unsw.edu.au/research. SomaGene as an open-source R package is also available at https://github.com/bcb-sut/SomaGene.We provide a list of non-coding genes with their mutational enrichment 26, Ensembl27 consortia, and an atlas of ncRNAs28 (see method section). This includes ncRNAs from different types inclusive of pseudogene (22.9%), lncRNA intergenic (21.4%), long intergenic ncRNAs (5.6%), lncRNA divergent (13.4%), antisense (3.3%), lncRNA sense intronic (6%), miRNA (5%), misc RNA (3%), lncRNA antisense (4.8%). A full list of ncRNAs is provided in Fig. 29, including 1855 breast cancer samples containing 17,163,482 single point somatic mutations and 10,460 samples with other cancers containing 67,752,271 somatic point mutations inside the ncRNA regions.To have a comprehensive list of ncRNAs, we used a combined list of ncRNAs provided by the FANTOMCATP value for each ncRNA using Fisher’s exact test (see method section). We calculated P values for 1,000,000 random permutations of breast/non-breast labels for each ncRNA to identify significantly mutated ncRNAs. We estimated that an association’s probability emerges by chance (see method section). This provides a threshold for each ncRNA separately  that significantly mutated in breast cancer samples  gene expression from TANRIC32, suggesting non-coding regulatory regions are relevant to disease and genetic mutations in gene regulatory regions is a significant mechanical contributor to diseases. To examine the enrichment of BC-associated GWAS SNPs in the candidate ncRNAs, we extracted the BC-associated SNPs from a pooled list of two GWAS datasets from the EBI GWAS Catalog33 and GWASdb v2 from the Wang Lab34 (for more details see the method section). As Fig. P value 2.3e−27) in the candidate ncRNAs. This enrichment is much higher for those ncRNAs that contain more than 4 GWAS SNPs (>10 times enrichment). Interestingly, when we performed the enrichment analysis for ncRNAs with more than 5 GWAS SNPs, only the candidate list of ncRNAs showed enrichment for GWAS SNPs (>20 times), and there was no enrichment in the 2nd and 3rd sets of ncRNAs  in the candidate ncRNAs. Interestingly, this enrichment is much higher for those ncRNAs that contain more than three eQTL polymorphisms (>2× enrichment), and such an increase has not been seen in the 2nd and 3rd sets of ncRNAs , suggesting breast cancer de novo somatic mutations are enriched in ncRNAs with enhancer and/or promoter like functions.To determine if the somatic mutations are enriched in ncRNAs with enhancer activity, we first examined the enrichment of HMEC-related chromatin states provided by the ENCODE consortium within our significant ncRNAs. As Fig. P values (see method section). As Fig. We also investigated these enrichments in the 2nd and 3rd sets of ncRNAs (each set contains 1030 ncRNAs) and the last set of ncRNAs with worse mutational 39) to study cell-type-specific enhancers. Therefore, we investigated the enrichment of FANTOM5 promoters and enhancers that overlap with the significant ncRNAs identified in this study. Figure P value 3.59e−34 and 3.68e−27) enrichment for FANTOM5 promoters and enhancers, respectively). In other words, 52.4% of candidate ncRNAs overlapped with FANTOM5 promoters, and 36.4% of them overlapped with FANTOM5 enhancers. However, only 34.9% and 23.5% of all ncRNAs overlapped with FANTOM5 promoters and enhancers. The same analysis on FANTOM5 mammary-specific enhancers demonstrated that the proportion of candidate ncRNAs that overlap with differentially expressed enhancers in the mammary epithelial cell is 3.84 times  more than the genome-wide expectation  breast cancer-associated cell lines40 and downregulated in breast cancer41. It is also a biomarker associated with breast cancer prognosis43. Our analysis demonstrated that NKAPP1 is one of the most significant ncRNAs, with a P value of 3.43e−06. This non-coding gene overlapped with both FANTOM5 enhancer and ChromHMM predicted enhancer and promoter. CATG00000062386 is another ncRNA gene that is significantly mutated in breast cancer samples. This FANTOMCAT specific ncRNA overlapped with FANTOM5 and ChromHMM enhancers and FANTOM5 mammary epithelial cell differentially expressed enhancers, indicating a potential enhancer role for this ncRNA in breast cancer.The FANTOM5 consortium has released lists of human transcribed human promoters and tissue-specific transcribed enhancers of humans using CAGE  CTCF binding sites and DHS in the candidate ncRNAs identified in this study. These active chromatin marks are involved in many processes, including transcriptional regulation that regulates gene expression47. Our investigation of histone active marks demonstrated that both histone marks H3K27ac and H3K4me1 are significantly enriched 2.1 times  and 1.6 times (1.90e−42) in the candidate ncRNAs  for histone H3K27me3 within our BC-associated ncRNAs  and three DHS ChIP-seq experiments , all related to the breast cancer. We calculated the enrichment for the four sets of ncRNAs, including significant, 2nd, and 3rd sets of ncRNAs with the best mutational P values and the last set of ncRNAs with the lowest mutational P values. Our assessment of CTCF binding sites and DHS demonstrated that both CTCF  and DNase  are significantly enriched for our candidate ncRNA genes 51. These ncRNAs overlapped with ENCODE-predicted enhancers, histone 27 acetylation, CTCF binding sites, and DHS, suggesting a potential transcriptional regulatory role for these ncRNAs. An annotated list of candidate ncRNAs with these features is provided in Supplementary Data For example, 53. To further understand the genes in which the genomic loci encompassing candidate ncRNAs interact, we analyzed two publicly available Hi-C datasets from HMEC cell lines54. We used MHiC55 and MaxHiC56 to analyze Hi-C raw data and identify statistically significant interactions. We identified 188,982 statistically significant interactions  in the Hi-C library 1. For 6187 of the interactions (%3.3), one side of the interaction overlapped with at least one candidate ncRNA genomic region. Another side of the interaction overlapped with protein-coding genes  based assays have been used to successfully identify regulatory regions and targets of disease-associated variationsP value 4.61e−11) than genomic regions of all ncRNAs that overlapped with one side of the interactions in both the libraries (14%). The overlapping between 251 ncRNAs and regulatory features used in this study is shown in Supplementary fig. We identified 1167 common significant interactions between the two libraries where one side of the interaction overlaps with genomic regions that encompassed at least one candidate ncRNAs , it can induce epigenetic reprogramming of somatic cells to an embryonic pluripotent state57. In both Hi-C libraries, we found significant Hi-C interactions between MYC and two of our candidate ncRNAs, CASC8 and PVT1. PVT1 is a known enhancer for MYC58. However, CASC8 has not yet been identified as a putative enhancer for MYC. There are ~200 kb genomic distances between the CASC8 and transcription start site of MYC in which there is no protein-coding gene that overlaps with CASC8. There are strong signals of histone active marks and ENCODE predicted enhancers, and most importantly, a FANTOM5 breast differentially expressed enhancer overlapped with CASC8, suggesting a potential enhancer region in CASC8 , we identified at least one interaction in either Hi-C library 1 or library 2, resulting in 21,564 interactions. For 19,674 out of 21,564 interactions . There is a significant Hi-C interaction between CATG00000061359 and gene GTPBP8 (a known breast cancer-associated gene59) in both the libraries. We also found a breast tissue-associated eQTL polymorphism in this lncRNA that influences the expression of GTPBP8 in breast tissue. Interestingly, both HMEC specific H3K27ac and HMM predicted enhancers overlap with this lncRNA. This suggests that variation in CATG00000061359 may influence the expression of GTPBP8 in breast cancer. We have provided an annotated list of candidate ncRNAs with Hi-C interactions in Supplementary Data Another example is ncRNA 60. Recent studies on somatic mutation evolution in cancer have identified cancer driver genes61 and mutational cancer signatures64. However, the analysis of somatic mutations has focused mainly on the protein-coding genes of the genome, and their potential impact on the non-coding RNA genes has been far less studied. ncRNAs have long been considered a non-functional part of the human genome65. However, these non-coding elements (majority lncRNAs) have recently opened a new insight into the study of breast cancer, acting as indispensable contributors to cellular activities, including the proliferation, apoptosis, survival, differentiation, and breast cancer metastasis66. In addition, ncRNAs have been used as biomarkers in many cancers, including breast cancer, through various mechanisms, including regulating the expression of protein-coding genes and functions at transcriptional, translational, and post-translational levels25. This indicates that ncRNAs may have the potential for diagnosis, prognosis, and therapeutics of cancers. This study identified ncRNAs that were mutated explicitly in breast cancer patients and then uncovered the connection between somatic point mutations in BC-associated ncRNAs and ncRNA regulatory properties in breast cancer.Somatic point mutations play a key role in tumorigenesis and the development of cancer67; Our study demonstrated that the breast cancer-related ncRNAs have much higher enrichment of breast tissue/cell line-specific features; we have shown that the candidate ncRNAs with enrichment of somatic point mutations have a much higher fraction of regulatory features compared to genome-wide expectation, suggesting the potential impact of somatic mutations on the regulatory function of ncRNAs. Notably, we have shown that most of the candidate ncRNAs interact with promoters of protein-coding genes, again indicating the potential regulatory role of ncRNAs with significant enrichment of somatic point mutations in breast cancer.As the previous study shown, tissue specificity is an important aspect of many genetic diseases, including cancers69 and acquired somatic mutations driving the disease, in which germline mutations may interact with somatic mutations to drive carcinogenesis or involve in tumorigenesis by contributing to critical biological and cellular processes. Our analyses revealed that the ncRNAs with enhancer-like activity are significantly overlapped with breast cancer-associated GWAS SNPs and breast tissue-related eQTL polymorphisms. This highlights the importance of somatic and germline mutations in breast cancer development and progression.Researchers have recently shown that germline mutations are associated with an increased risk of developing BCThe enrichment of somatic mutations in the ncRNAs with enhancer-like activity has not been previously explored. Our Hi-C analyses also provided more evidence of enhancer activity of genomic loci encompassing BC-associated ncRNAs. Interestingly, we showed that the 251 ncRNAs supported by multiple Hi-C-based assays have much higher enrichment of regulatory features, GWAS, and eQTL polymorphisms than the remaining ncRNAs in the candidate list, indicating these ncRNAs are more likely to be involved in the gene regulation process.However, our analyses are limited to the associations identified by pure data-driven analyses. Future experimental validation can reveal finer details about the interdependence of ncRNAs function in breast cancer.We have developed an extensive web-based resource to communicate our results with the research community. Further works could focus on the candidate ncRNAs provided in this resource to check their complex regulatory functions and reveal the novel mechanism underlying carcinogenesis and breast cancer treatment.This study also presented a novel computational method, SomaGene, to prioritize and predict disease-associated ncRNAs by integrating multi-level omics data, including somatic mutations, transcriptomic and epigenetic signals, and chromatin conformation data.https://github.com/bcb-sut/SomaGene.The SomaGene open-source R package, a sample dataset, and instructions on running SomaGene are provided at We used the ICGC dataset, which contains somatic point mutations from 1855 breast cancer samples and 10,419 samples from 17 different types of cancers.39 and Ensembl70 consortia (genome building hg19), enabling us to have a comprehensive list of non-coding RNA genes. We used an in-house script to combine the lists based on gene coordinates and/or gene names. If both consortiums have the same gene but different gene coordinates, we considered the FANTOM5 genes as the priority. We also added a recently published list of long non-coding RNAs from an atlas of non-coding RNAs28 into our list of ncRNAs. In total, 65,257 non-coding RNA genes were available in the combined list.We have combined two gene lists from FANTOM573. In this study, we developed SomaGene, which uses a “vcf” file from the whole genome or exome data. We included all samples from ICGC with at least one de novo mutation. We only considered single nucleotide mutations and excluded insertions or deletions from the analyses. To identify ncRNAs that significantly mutated in breast cancer samples compared to other cancers, we used Fisher’s exact test and permutation testing in the following manner:Many computational techniques have recently been used for mutational information to investigate biomarkers in human and viral genomesP value for each ncRNA using a one-sided Fisher’s exact test applied to a 2 × 2 contingency table whose elements are (1) the number of samples in breast cancer that are mutated in an ncRNA, (2) the number of samples in breast cancer that are not mutated in an ncRNA, (3) the number of samples in all cancers other than breast cancer that are mutated in an ncRNA and (4) the number of samples in all cancers other than breast cancer that are not mutated in an ncRNA  of sample IDs across all cancers to estimate the probability that an association emerges by chance at a confidence interval of 99%. We identified a total of 1030 significantly mutated ncRNAs in breast cancer.We calculated a To adequately examine the overlapping of ncRNAs with regulatory features and calculate the overlapping score with each element, each annotation’s overlapping ranges were used in our analysis. In the case of FANTOM5 promoters, enhancers, and tissue-specific enhancers, three binary variables for each ncRNA were calculated, indicating that an ncRNA has overlapped with any enhancer or promoter in the FANTOM5 dataset . The percentage of overlap with a segment was calculated as the sum of proportions of nucleotides in that segment’s ranges overlapped with the ncRNA. Thus, a profile of overlapping chromatin segments was calculated with their corresponding coverage over the ncRNA  of each ncRNA with histone modification ranges was also calculated as the total proportion of nucleotides in the ncRNA covered with histone modification ranges.In the case of DNase annotation, each genomic range is attributed to a group of cell types that show DNase hypersensitivity in that area. Several cell type IDs with corresponding DNase hypersensitivity scores are associated for each range. We combined the overlap annotation ranges to get an aggregated set of cell-type IDs, scores, and overlap percentages by calculating the proportion of nucleotides in the ncRNA covered with these ranges. After this step, several IDs were duplicated in many aggregated sets summed over the overlap percentages to obtain a single overlap measure. Also, it was averaged over its scores by the corresponding overlap percentages as their weights to take a single score for that ID. For each genomic range in transcription factors annotation, a group of transcription factors (from ENCODE) with their corresponding ChIP-Seq peaks are reported. While the schema of transcription factors annotation is similar to that of DNase annotation, the same procedure as described for DNase annotation was performed to obtain an aggregated set of transcription factor IDs, scores, and overlapping percentages for each ncRNA . Thus, the expected value of X equals S×A⁄N which shows that the expected fraction of items having the property of interest in a random set of ncRNAs with size S equalsA⁄N. We then conclude that the defined enrichment can be calculated as (Y⁄S)⁄(A⁄N).Every “enrichment” that is calculated throughout this study is defined as “the fraction of ncRNAs in the significant set that have the trait of interest  divided by genome-wide expectation.” It can be easily justified that the mentioned value equals the fraction of items in the whole set of ncRNAs with the trait of interest. Let’s assume that the total number of ncRNAs is 26 to identify a set of significant ncRNAs that overlapped with FANTOM5 promoters and enhancers. The entire collection of enhancers and promoters found in the FANTOM5 data were downloaded from the FANTOM5 Phase274. We also downloaded FANTOM5 breast differentially expressed enhancers from ref. 75.We used the FANTOM5 CAGE expression atlas8.The Chromatin State Segmentation uses a standard set of states learned by computationally integrating ChIP-Seq data for nine factors plus input using a Hidden Markov Model (HMM) across various cell types. Also, it shows a classification of chromatin, like “enhancer,” “promoter,” or “repressed.” We used this dataset to identify the set of breast-specific candidate ncRNAs that overlapped with chromatin states. The complete set of chromatin state segmentations for the models derived from HMECs, grown in vitro related to breast cancer was downloaded from the ENCODE project8.For this study, we used HMEC-specific ChIP-Seq data in processed peak calls for histone modifications H3K27ac, H3K27me1, H3K4me3, and CTCF from the ENCODE project to identify a set of significant ncRNAs that overlapped with three types of histone modifications76. The transcription factors are responsible for modulating gene transcription bind as assayed by chromatin immunoprecipitation with antibodies specific to the transcription factor followed by sequencing the precipitated DNA (ChIP-Seq). We used this dataset to identify a set of significant ncRNAs that overlapped with transcription factor ChIP-Seq. The set of transcription factor ChIP-Seq data for the HMEC cell line was downloaded from ENCODE project8.This data shows regions of transcription factor binding derived from an extensive collection of ChIP-Seq experiments and DNA binding motifs identified within these regions by the ENCODE Factorbook repositoryRegulatory regions tend to be DNase-sensitive which are accessible chromatin zones and functionally related to transcriptional activity. We used DHS from MCF-7 and T47D from ENCODE project to identify a set of significant ncRNAs that overlapped with DNase clusters.54. We used MHiC55 and Hi-C Pro77 with the default parameters for analyzing and aligning Hi-C data in 5 kb fragment size. We used MaxHiC56 as a background correction model to identify significant Hi-C interactions for true cis-interaction. Here, we included those significant interactions with P value < 0.01, read-count ≥10, with the distance between two sides of interaction more than 5 kb and <20 Mb. We then annotated Hi-C interactions with coding and non-coding genes from our combined genes list. At least 10% overlap between gene and Hi-C fragments has been considered to annotate Hi-C fragments with genes.Hi-C is an experiment for identifying the number of interactions between genomic loci in a 3D space. Our study used two replications related to the HMEC36. We used GTEx v7 eQTLs identified as significant in the HMEC line from the GTEx project.eQTL data were downloaded from the Genotype-Tissue Expression (GTEx) Project85. A decision tree approach and a knowledge-based filtering system technique have also been used to categorize the texts from the literature search87. The search terms included “non-coding RNA” or “lncRNAs” or “genes name + cancer.” “BC” or “breast carcinoma” and “breast neoplasm.”Our literature searches were focused on human studies and English language publications available in PubMed, Scopus, and Web of Science. Both Medical Subject Headings terms and related free words were used to increase the sensitivity of the search. We also used data and text mining techniques to extract additional associated studies33 and GWASdb v2, from Wang Lab34 to identify a set of significant ncRNAs that overlapped with GWAS SNPs. We first converted all GWAS SNP coordinates to UCSC hg19 using UCSC Lift Genome Annotations tools88(www.ncbi.nlm.nih.gov/genome/tools/remap) and then used an in-house script to combine both GWAS datasets based on gene coordinates and/or gene symbols. All GWAS SNPs with P value < e−8 were included in the analyses.We have pooled two GWAS datasets, EBI GWAS CatalogFurther information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-12Reporting Summary


Prenatal alcohol exposure is a major cause of neurobehavioral disabilities. MRI studies in humans have shown that alcohol is associated with white matter microstructural anomalies but these studies focused on myelin abnormalities only after birth. Only one of these studies evaluated oligodendrocyte lineage, but only for a short period during human foetal life. As data are lacking in humans and alcohol is known to impair oligodendrocyte differentiation in rodents, the present study aimed to compare by immunohistochemistry the oligodendrocyte precursor cells expressing PDGFR-α and immature premyelinating/mature oligodendrocytes expressing Olig2 in the ganglionic eminences and the frontal cortex of 14 human foetuses exposed to alcohol from 15 to 37 weeks’ gestation with age-matched controls. The human brains used in this study were obtained at the time of foetal autopsies for medical termination of pregnancy, in utero or post-natal early death. Before birth, PDGFR-α expression was strongly increased in the ganglionic eminences and the cortex of all foetuses exposed to alcohol except at the earliest stage. No massive generation of Olig2 immunoreactive cells was identified in the ganglionic eminences until the end of pregnancy and the density of Olig2-positive cells within the cortex was consistently lower in foetuses exposed to alcohol than in controls. These antenatal data from humans provides further evidence of major oligodendrocyte lineage impairment at specific and key stages of brain development upon prenatal alcohol exposure including defective or delayed generation and maturation of oligodendrocyte precursors.The online version contains supplementary material available at 10.1186/s40478-022-01378-9. Prenatal alcohol exposure (PAE) is a major non genetic cause of central nervous system (CNS) abnormalities resulting in irreversible life-long consequences which associate mental retardation or more specific neurocognitive disorders and behavioural disabilities . Foetal In the mature brain, heavy chronic or binge drinking is responsible for a variety of brain injuries, notably a disproportionate loss of cerebral white matter which accounts for white matter atrophy, as glial cells are major targets of alcohol . AlcoholOLs originate from neural stem cell-derived OPCs which express platelet-derived growth factor receptor-α (PDGFR-α), then differentiate into immature premyelinating OLs (pre-OLs) expressing oligodendrocyte lineage factor 4 and eventually into mature OLs expressing myelin oligodendrocyte glycoprotein, phospholipid protein and myelin basic protein (MBP) which contact axons and begin to produce myelin , 13. ProIn rodents, the first wave of OPCs originates in the anterior entopeduncular area followed by two other waves arising from the lateral and caudal ganglionic eminences (LGE and CGE) . OPCs rePDGF signalling is essential for the control of OPCs’ proliferation and differentiation . These mHuman antenatal data concerning the generation and differentiation of OLs upon PAE are lacking even though alcohol is known to impair OL differentiation in other species. Therefore, we hypothesised that during the foetal period alcohol prevents the differentiation of PDGFR-α+ OPCs into Olig2+ pre-OL or mature OL, which could explain, at least partially, the myelination defects observed on imaging studies. To test this hypothesis, we compared using immunohistochemical techniques PDGFR-α and Olig2 expression in the GE and cortical plate (CP) of foetuses antenatally exposed to alcohol with age-matched controls. The objective was to confirm the pre-existing results in humans and to provide information regarding oligodendroglial lineage development over an extended neurodevelopmental period.As previously reported , the braperpartum or immediate postpartum death with a cause other than cerebral or with no found cause after careful examination of the placenta and foetal organs. In all cases, the brain was macroscopically and microscopically free of detectable abnormalities. Despite the absence of lesions, patients who had been antenatally suspected of central nervous system anomalies or who had been clinically suspected of dying of neurological causes were systematically excluded.Ten foetal control brains ranging from 16 to 36 WG were selected. Main clinical and morphological characteristics are presented in Table Fourteen foetal FAS or FASD brains ranging from 15 to 37 WG whose clinical and morphological characteristics are presented in Table Autopsies had been performed according to standardized protocols . DevelopBrain growth was evaluated according to the biometric data of Guihard-Costa and Larroche . MacroscImmunohistochemical analyses of OPCs and pre-OLs were carried out on six-micrometer sections obtained from paraffin-embedded material according to standardized protocols using antisera directed against Olig2  and anti-PDGFR-α . Noteworthy, some markers of mature oligodendrocytes such as myelin basic protein (MBP), myelin oligodendrocyte glycoprotein (MOG), phospholipid protein (PLP), adenomatous polyposis coli complex (APC) and cyclic nucleotide phosphodiesterase (CNPase) could not be used as they work on frozen tissues.Immunohistochemical procedures included a microwave pre-treatment protocol to aid antigen retrieval . Incubations were performed for 32 min at room temperature using the Ventana Benchmark XT system. After incubation, slides were processed by means of the Ultraview Universal DAB detection kit (Ventana). Semi-quantitative analyses of the density in Olig2 and PDGFR-α positive cells in the GE as well as in the different layers of the frontal CP were evaluated and scored blindly by two neuropathologists (FM and AL), and together reanalyzed in case of discrepant results. Immunolabellings were scored as follows: 0: no cell labelled; +: less than 10% of cells labelled; ++: between 10 and 25% of cells labelled; +++: between 25 and 50% of cells labelled and ++++: more than 50% of the cells labelled.In order to identify more precisely the different Olig2-positive populations, double immunolabelling was performed using Olig2 and either PDGFR-α, GABA, MAP2 or GFAP within the GE at 15–16 WG, a developmental stage in which some Olig2 positive cells could belong to other lineages than OL. The antibodies used for confocal analyses are described in Table Among the 14 cases exposed to alcohol, 3 21%) had intra uterine growth retardation (IUGR). All but 2 cases (86%) had cranio-facial dysmorphism which was characteristic of FAS in half of these cases, associating short palpebral fissures, smooth philtrum and thin vermillion border . Five ca1% had inDaily chronic alcohol intake throughout the pregnancy was self-reported by 7 mothers/ 14 (50%). Three out of the 7 mothers also consumed episodic high doses of alcohol named «binge drinking». In the other 7 cases, maternal alcohol intake was suspected on the basis of reports by their relatives (family and/or friends) or clinically suspected at the time of foetal autopsy according to the criteria established by Riley et al., i.e., craniofacial dysmorphism characteristic of FAS (6/7 suspected cases), microcephaly (3/7 suspected cases), other CNS lesions (5/7 suspected cases) and IUGR (2/7 suspected cases) . Ten motSemi-quantitative evaluation of PDGFR-α and Olig2 immunolabellings are summarised in Fig. PDGFR-α expression was strongly increased in the GE and in the CP of all FASD brains in comparison with controls whatever the developmental stage with the exception of the earliest stage Fig. a, b. IndAt 16 WG, more than 60% Olig2-positive precursors and pre-OLs were identified in the MGE and LGE of the control brain, whereas Olig2-positive cells were less numerous in the FASD brain Fig. a, b. MorAt all developmental stages, the density of Olig2-positive cells within the CP was consistently higher in each control brain in comparison with FASD brains Figs. d, 4, witAt early stages, a diffuse and intense immunolabelling of cells with Olig2 antibody was identified. As Olig2 is known to be also expressed in immature neurons and interneurons as well as in a subpopulation of astrocytes, we performed double immunolabellings using Olig2 and either MAP2, GABA, GFAP or PDGFR-α to evaluate more precisely the percentage of Olig2-positive OPCs , 23, 30.In living children with FASD aged from 10 to 17, imaging studies have revealed lower white matter volumes and sometimes a complete lack of myelination of major white matter tracts in the brain as well as white matter microstructure alterations , 48, 49.To our knowledge, no study concerning the effects of alcohol during human brain development has focused on OPC production from the GE. Nevertheless, using in vivo and in vitro mouse models, it has been demonstrated that alcohol hinders basal progenitor proliferation in the ventricular zone/subventricular zone (VZ/SVZ) by interfering with the cell cycle at G1-S transition from early development . It mighWhereas changes in OL morphology, maturation, differentiation and survival have been reported in third trimester-equivalent preclinical models of FASD , 8, 52, A receptors has been shown either to increase or to decrease proliferation, apoptosis and consequently oligodendrocyte numbers, as well as shortening internode length, which allows for faster saltatory conduction velocities. Similar discrepancies have also been observed regarding glutamate which blocks proliferation and progression of OPCs but also promotes myelin formation [Another mechanism which regulates the formation of myelin around axons consists in interactions between OPCs which receive excitatory and inhibitory inputs mediated by glutamate and GABA, and developing axons. Recent studies have demonstrated that a significant proportion of grey matter myelin in the cortex forms on the axons of local inhibitory interneurons in both rodents and humans , 44. Durormation , 53.It has long been acknowledged that in rodents, Olig2 expressing progenitors in the MGE give rise to GABAergic interneurons at early developmental stages and oligodendrocytes thereafter . In humaThe present study provides further evidence that there is major oligodendrocyte lineage impairment at all stages of brain development upon PAE, consisting in defective/delayed generation, migration and maturation of oligodendrocyte precursors. Since oligodendrocyte development and myelin are a target of alcohol, the disruption of oligodendrocyte differentiation and of myelination process is very likely responsible for inadequate establishment of neuronal networks and inefficient conduction of neuronal signals. Disruption of oligodendrocyte generation and differentiation, together with GABA interneuronopathy that we previously identified , most liAdditional file 1: Figure S1. Confocal analyses in the GE of a normal brain at 16 WG Double immunolabellings using Olig2 (red) and MAP2 (green) (a), Olig2 (red) and GFAP (green) (b), Olig2 (red) and GABA (green) (c) and using Olig2 (red) and PDGFR-α (green) (d) did not reveal any co-expression with neurons, interneurons, astrocytes and oligodendroglial precursor markers


The proper regulation of muscle stem cell (MuSC) fate by cues from the niche is essential for regeneration of skeletal muscle. How pro-regenerative niche factors control the dynamics of MuSC fate decisions remains unknown due to limitations of population-level endpoint assays. To address this knowledge gap, we developed a dual fluorescence imaging time lapse  microscopy approach that leverages machine learning classification strategies to track single cell fate decisions with high temporal resolution. Using two fluorescent reporters that read out maintenance of stemness and myogenic commitment, we constructed detailed lineage trees for individual MuSCs and their progeny, classifying each division event as symmetric self-renewing, asymmetric, or symmetric committed. Our analysis reveals that treatment with the lipid metabolite, prostaglandin E2 (PGE2), accelerates the rate of MuSC proliferation over time, while biasing division events toward symmetric self-renewal. In contrast, the IL6 family member, Oncostatin M (OSM), decreases the proliferation rate after the first generation, while blocking myogenic commitment. These insights into the dynamics of MuSC regulation by niche cues were uniquely enabled by our Dual-FLIT approach. We anticipate that similar binary live cell readouts derived from Dual-FLIT will markedly expand our understanding of how niche factors control tissue regeneration in real time. MuSCs are of great interest as therapeutic targets due to their potent regenerative potential in repairing damaged tissue and restoring tissue function5. The regenerative potential of MuSCs is due to their inherent ability to self-renew, proliferate, and differentiate to form myofibers that contribute to tissue repair. Quiescent MuSCs are defined by the expression of the paired-box transcription factor Pax7 and the absence of markers of “activation,” including cell cycle regulators and the Myf5 and MyoD transcription factors. The MuSC quiescent state is reinforced by Notch-Dll1 signaling, which regulates the expression of Hes/Hey family members, as well as FOXO transcriptional activity6. Secreted paracrine signals from the niche including Wnt4, vascular endothelial growth factor (VEGF), and oncostatin M (OSM) have also been implicated in maintaining MuSC quiescence9. Following activation, additional myogenic transcription factors, such as myogenin (MyoG), govern further myogenic commitment and specialization. Upon activation, MuSCs expand through either asymmetric or symmetric division events, giving rise to self-renewing stem cells and to committed progenitors that undergo myogenic specification10. Progeny of self-renewing division events can retain the capacity to return to a quiescent state upon resolution of muscle injuries, reconstituting the reserve pool of MuSCs.Muscle stem cells  play an essential role in skeletal muscle homeostasis, growth, repair, and regeneration during development and aging. Muscle stem cells (MuSCs) reside in an anatomically distinct compartment, between the myofiber plasma membrane and the basal lamina that surrounds the myofiber, which serves as an instructive microenvironment, or niche3, as seen in the stem cell exhaustion that characterizes Duchenne muscular dystrophy11. Our lab and others have identified a suite of soluble regulatory factors that is responsible for MuSC self-renewal, commitment to myogenic progression, or return to quiescence15. In addition to soluble biochemical cues, insoluble biophysical cues from the MuSC niche, including extracellular matrix stiffness and composition22, have been identified by our lab and others as crucial regulators of MuSC fate. Thus, both soluble biochemical cues and insoluble biophysical cues must be considered when assessing mechanisms regulating MuSC fate.During muscle tissue repair, MuSC fate is tightly controlled by networks of biochemical and biophysical cues. Dysregulation of these networks can lead to progressive loss of tissue function and to pathological conditions23, to explore the regulation of myogenic stem cell fate using a novel binary dynamic fluorescent reporter system and image analysis algorithm. This approach improves upon traditional experimental designs based on population-level analyses, which may obscure the properties of potentially interesting sub-populations and their growth dynamics in the averaged endpoint behaviors of global populations24. To avoid these pitfalls, analyses tracking numerous single cells, which provide snapshots of entire cell populations at the clonal level, are desirable25. However, while lineage tracking, molecular profiling and time lapse imaging are attractive modalities of single-cell fate analysis26, tracking single clones and their fate changes in real time remains a challenge to-date, both due to the absence of accurate fluorescent reporter readouts and of algorithms for subsequent challenging data analyses.Here we leverage two key enabling technologies, bioengineered hydrogel culture substrates and single cell time lapse tracking27. Here, we provide a means of gaining insights into the dynamics of live stem and progenitor cell lineage analysis in conjunction with cell proliferation rate, survival, time to first division, time between divisions and cell migratory trajectories while maintaining genealogical relationships between daughter cells. We employed a dual fluorescence imaging time lapse  microscopy approach to evaluate the effects of prostaglandin E2 (PGE2) and OSM on MuSC fate determination kinetics using two fluorescent reporters that serve as proxies for stemness (Pax7 reporter) and commitment (myogenin reporter) and new machine learning algorithms, which allowed us to classify MuSC fate transitions. The combination of these signals captured by Dual-FLIT enabled the classification of MuSC fate into three distinct types: asymmetric, symmetric self-renewing and symmetric committed. Dual-FLIT revealed that the expansion of the stem cell pool in response to PGE2 occurs by increased numbers of symmetric self-renewing divisions across generations, while OSM elicits a quiescent-like phenotype by markedly reducing both the total number of divisions and the relative fraction of committed divisions, observations uniquely enabled by the ability to track cell fate decisions. We anticipate that our Dual-FLIT approach will be broadly useful to map cell fate trajectories of MuSCs and other stem cell types in response to diverse growth factors and drugs.Here we overcome prior limitations and enable monitoring of stem cell behavior and real-time cell fate transitions in culture, using a new strategy that permits spatial segregation and systematic manipulation of biomechanical and biochemical cues in conjunction with single cell lineage tracing in real time. Previously, we used retrospective analyses based on immunohistochemistry of cells fixed after time lapse imaging is completedE ~ 12kPa) and laminin cell adhesive cues17. Upon activation and initiation of myogenic commitment, characterized by expression of myogenin, the MuSCs harboring the reporter express Sun1-GFP. Sun1 is a nuclear envelope protein, which has been fused with fluorescent GFP in our reporter mouse. Thus, expression of Sun1-GFP marks the nucleus of myogenin-expressing cells with a green fluorescence signal, serving as a tool to identify committed cells. MuSCs from hind limb muscles were isolated by enzymatic digestion and FACS, using our established protocols28. MuSCs were seeded onto hydrogel substrates and cultured for 7 days.We first established a population-level assay for cell fate decisions in response to treatment with pro-regenerative niche factors. For this purpose, we leveraged a myogenin reporter transgenic mouse model and synthetic hydrogels presenting biomimetic stiffness  to the cultured MuSCs at the time of seeding  or vehicle (PBS) control and cultured for an additional 4 days  and the activity of a reporter derived from the promoter of a transcription factor associated with myogenic commitment (myogenin) could yield sufficient information to allow the inference of division types and fate determination processes within the progeny of a single MuSC over time.Population-level analyses are limited by their reliance on averages taken at particular time points Fig. , which o33. However, we reasoned that analysis of only two signals would suffice to delineate myogenesis into 5 distinct cell fates , (ii) asymmetric division to give rise to one Pax7-reporter+ stem cell and one MyoG-reporter+ cell, (iii) symmetric division giving rise to 2 Pax7-reporter+ stem cells, (iv) symmetric division giving rise to two MyoG-reporter+ cells, or (v) no division of a committed cell (MyoG-reporter+).Four known transcription factors are commonly used to demarcate MuSC fate decisions and division kinetics Fig. 6,29–33. tes Fig. : (i) no 34, and infected the MuSCs in culture with a lentiviral-based reporter construct that expresses mCherry under the control of a genetic element derived from the proximal ~1400 bp of the myogenin promoter . Once the image sequence was homogenized, the average fluorescence pixel value was extracted from within the cell borders generated by the tracking algorithm and further normalized by applying a moving average filter. Finally, in order to generate comparable data across colonies and experiments, the quantified fluorescence signals of the Pax7 and MyoG reporters were normalized with respect to the maximum and minimum values observed in each microwell during the imaging period and after differentiation , (ii) asymmetric division (designated as A) to give rise to one Pax7-GFP+ stem cell and one MyoG-mCherry+ cell, (iii) symmetric division giving rise to 2 Pax7-GFP+ stem cells , (iv) symmetric division giving rise to two MyoG-mCherry+ cells (designated as SC for “symmetric commitment”), or (v) death  coupled to a 1-NN algorithm were used to create three classes corresponding to the three division types and to assign new divisions to these classes. A few divisions  with an obvious division type were manually annotated and used as ground-truth values for assignment of weights of the features during clustering , PGE2, or OSM for time lapse imaging. Brightfield and fluorescence images were collected over the following 72 h, as described above. After completion of the time lapse imaging, the expression of the MyoG-mCherry reporter construct was validated by inducing differentiation in the tracked cells. Clones harboring the MyoG reporter were then analyzed by the optimized Dual-FLIT analysis algorithm.We sought to determine how the pro-regenerative niche factors PGE2 and OSM influence clonal proliferation and fate dynamics in MuSCs. We previously showed that both PGE2 and OSM increase the engraftment efficiency of transplanted cells and enhance stem cell-mediated regeneration of tissue damageOur optimized analysis pipeline generated lineage trees for each condition that were overlayed with the relative MyoG reporter signal and cell division classifications Fig. . Two strThe division-type classification associated with each division event enabled facile assessment of the cell fate dynamics in response to PGE2 and OSM treatment over time. PGE2 treatment results in greater overall proliferation than control, while OSM results in decreased proliferation Fig. . Importa31. Our understanding of how these factors impact individual myogenic cell fate decisions in MuSCs is frequently limited by the use of population-level assays taken at specific time points during regeneration in vivo or following MuSC isolation and culture in vitro. To overcome the limitations of population-level fixed time point assays, we developed a method for quantification and analysis of binary fluorescence signals during time lapse imaging of muscle stem cells and applied it to an automated system for classifying identified cell divisions via machine learning strategies. By leveraging K-means clustering approaches applied to 38 distinct parameters derived from our time lapse data and a 1-NN classifier, we were able to robustly classify cell fate decisions without tedious manual and subjective scoring by blinded observers. This approach enabled a full reconstruction and comparison of muscle stem cell fate determination processes.Muscle repair following injury is orchestrated by networks of niche factors that direct muscle stem cell self-renewal, activation, expansion, and differentiationOur approach using live cell time lapse imaging offers substantial benefits as a non-invasive method for elucidating the dynamics of cell fate determination. Unlike endpoint approaches, time lapse imaging does not entail the destruction of cells upon measurement. It allows for lineage tree reconstruction, temporal evaluation of dynamic molecular changes via fluorescent reporters, and identification of cellular heterogeneity. We have validated that the cell tracking by Dual-FLIT is independent of the heterogeneity of the starting population and confirmed the validity of lineage trees generated by Dual-FLIT analysis against the “ground truth” trees generated by manual frame-by-frame tracking of the image sequence. Furthermore, our Dual-FLIT approach yields markedly increased temporal resolution, since it does not rely on time-interval snapshots like other types of single-cell analysis previously used by us and others. Fluorescence time lapse imaging is currently the optimal technique for quantifying dynamic molecular changes in live cells and evaluating short-term single stem cell fate determination in vitro. In conjunction with our machine learning-aided pipeline, fluorescence time lapse imaging maximizes the utility of time lapse data by automating cell fate classification.While we focused our study on muscle stem cells, Dual-FLIT is applicable to the study of many other cell types, including hematopoietic cell types and epithelial cells, where asymmetric/symmetric cell divisions are important determinants of stem cell fate. However, this approach may not be feasible for analysis of fate decisions in cells with short cell cycle durations, such as cancerous or transformed cell lines. This is due to an inherent limitation in leveraging fluorescent signals to predict cell fate; fluorescent images in time lapse analysis cannot be taken at short intervals without inducing considerable phototoxic effects in the cell population. High levels of phototoxicity were observed in our initial measurement setup, where cells underwent blue light exposure every 5 min. Significant improvements in cell survival and proliferative capacity were observed after the exposure time was reduced by implementing image binning and extending the light exposure to 20 min intervals. We were able to reconstruct all cell division events based on image sequences captured at 20 min intervals. By the same token, we have not extended our strategy to include additional myogenic factors including Myf5 and MyoD to mitigate phototoxicity and avoid fluorescent signal overlap. As there is a high degree of expression overlap between Myf5 and MyoD to Pax7 and myogenin, respectively, we reason that it is sufficient to extract cell fate information based on Pax7 and myogenin expression as these factors represent two ends of the myogenic commitment spectrum . While tracking Pax7 and myogenin expression allows us to determine the behavior of each clone during early cell fate decisions, these markers do not reveal behavior beyond the onset of myogenin expression, and therefore we did not pursue image tracking beyond 72 h of live cell imaging . We thus restricted our interpretation to stem cell fate transitions, rather than a broader analysis of long-term stem cell behavior. Nonetheless, as shown here, identification of changes in stem cell fate early during activation and expansion of the cells enables predictions of future regeneration trajectories.12 Treatment with OSM slowed the overall cell division rate, while biasing those divisions that did occur toward symmetric self-renewal, consistent with its role as a pro-quiescence factor. This finding is consistent with our previously published studies of OSM in vivo in mice, which showed this niche factor preserves the stem cell population and improves the regenerative response to muscle injury9. Identification of these temporal changes in proliferation kinetics and cell fate decisions would not be possible without our Dual-FLIT approach. Dynamic fluorescence time lapse is therefore an advantageous new tool for stem cell fate elucidation and a resource for drug screening assays. While we demonstrated the feasibility of the Dual-FLIT approach to track muscle stem cell fate changes, the tracking algorithm can be adapted to track any time lapse fluorescent microscopy image sequence for other cell types. Here, the division classification system has been trained specifically on MuSCs and their divisions, but the procedure can easily be reproduced for new cell types using two or more fluorescent markers. In fact, Dual-FLIT has been recently implemented in tracking megakaryocytic erythroid progenitor cell fate39. We anticipate that further application of Dual-FLIT to other cell types and pro-regenerative factors will enable previously inaccessible insight into the dynamics of fate decisions underlying stem cell-mediated regeneration.We applied our Dual-FLIT approach to investigate how two pro-regenerative niche factors, PGE2 and OSM, regulate MuSC fate determination. Cell tracking revealed changes in proliferation rates, while fluorescent reporters and cell division characteristics provided novel insights into the dynamics of the fate determination process. Treatment with PGE2 accelerated the rate of symmetric self-renewing divisions, expanding the stem cell pool, providing insights into the mechanism by which PGE2 boosts MuSC-mediated muscle repair. Indeed, our finding is consistent with our previous publication showing that PGE2-treated MuSC expansion is augmented in vivo, improving muscle regeneration and force output34. MyoG-CreERT2 Sun1-GFP mice were bred by crossing Gt(ROSA)26Sortm5(CAG-Sun1/sfGFP)Nat mice (JAX #021039) with Myogtm1.1(cre/ERT2)Lepr mice (JAX #025668). The hind limb muscles were removed from both legs of each mouse, washed in Phosphate-Buffered Saline (PBS) and placed in C-tubes  and digested with collagenase (1000 U/mL) and dispase (~1 U/mL)28. The resulting mixture was filtered through a 45 µm cell filter .All experiments and protocols were performed in compliance with the institutional guidelines of Stanford University and the Administrative Panel on Laboratory Animal Care. Cells were obtained from either MyoG-CreERT2 Sun1-GFP or Pax7-eGFP mice aged between 2 and 7 monthsThe MyoG-CreERT2 Sun1-GFP MuSCs were stained with PE-Cy7 conjugated antibodies against CD45, CD11b, CD31, and Sca1  to label non-muscle cell types, and fluorescently tagged antibodies to label MuSCs: integrin-α7-PE (Ablab) and CD34-Alexa647 (BD Biosciences). The cell suspension was sorted by FACS to deplete non-muscle lineage cells and enrich MuSCs (Lin-/integrin-α7+/CD34+/GFP−). The Pax7-eGFP reporter cells were sorted by gating on the GFP signal (repeatedly obtaining a purity > 88%) and plated in collagen-coated well plates .−1 basic fibroblast growth factor (bFGF). For lentiviral transduction and reporter construct optimization, MuSCs were cultured in 45% low glucose DMEM, 40% F10 nutrient mix (Thermo Fisher Scientific), 15% FBS, 1% antibiotic/antimycotic, and 2.5 ng ml−1 bFGF. Medium with the same composition but using FluoroBrite DMEM was used for time lapse imaging. MuSCs were maintained at 37 °C in 5% CO2 and medium was changed as noted in figures. To induce differentiation, medium containing 2% horse serum and 1% antibiotic/antimycotic in FluoroBrite DMEM was added.For population-level analyses, MuSCs were cultured in Fluorobrite DMEM with 15% fetal bovine serum (FBS), 1% antibiotic/antimycotic, 1% non-essential amino acids, 1% L-glutamine, 1× sodium pyruvate (Thermo Fisher Scientific), and 2.5 ng mlTo observe myogenic commitment, the cells were infected with a viral construct containing mCherry driven by a ~1400 bp region of the myogenin promoter. The viral vector was based on a commercially-available pEZX-LvPM02 lentiviral construct containing the myogenin promoter . The plasmid map can be found in Supplementary Fig. Viral transduction occurred the day after MuSCs were plated. Polybrene  was added to the wells to increase the efficiency of viral transduction. A concentration of 40 µl per 1 ml of medium was used. The medium was changed 4 h after infection to increase cell viability. After transduction, the cells were cultured for 48 h before being seeded onto the hydrogel microwells.Cell viability was measured 2 days after transduction in cells with and without virus by staining for cleaved caspase-3  and the number of wells in which the colony survived respectively. The dead colonies were not considered for this analysis because such colonies most often result from apoptosis of the initial MuSC, and the MyoG reporter is not expressed at that early time point, making it impossible to distinguish between false and real negatives.Infection rate was calculated as follow:17. A solution of 8-arm 10 kDa PEG-azide was mixed with a solution of 4-arm 10 kDa PEG functionalized with bicyclo[6.1.0]nonyne (BCN) and purified laminin functionalized with 3.5 kDa linear PEG azide at a final polymer concentration of ~5.4% w/v. The gel precursor solution (200 µL) was added to each well, and the plate was centrifuged at 2000 g for 15 min at room temperature to afford a thin layer of hydrogel covering the bottom of the well. Gels were further incubated at 37 °C for 30 min to complete crosslinking, washed with PBS plus 1% antibiotic/antimycotic, and equilibrated in cell culture media at 37 °C prior to cell seeding.Uniform hydrogels with elastic moduli of ~12 kPa were prepared in 24-well glass bottom tissue culture plates using multi-arm poly(ethylene) precursors that crosslink via strain-promoted azide-alkyne cycloaddition16. The hydrogels were prepared from two multi-arm precursors: 4-arm 10 kDa PEG-thiol  and 8-arm 10 kDa PEG-vinyl sulfone . PEG-VS was dissolved in sterile-filtered 0.2 M triethanolamine  to 10% w/v and filtered through a 0.45 µm syringe filter. PEG-SH was dissolved in sterile-filtered distilled water at a concentration of 10% w/v solution and filtered through a 0.45 µm syringe filter. Stock solutions of precursors were stored at −20 °C.Poly(ethylene glycol) (PEG) hydrogels were designed to have an elastic modulus of ~12 kPa16. The resulting PDMS stamps had cylindrical micropillar structures. To functionalize the hydrogel microwell bottoms, the tops of the cylindrical micropillars on the stamps were coated with laminin . The laminin was dialyzed in phosphate-buffered saline (PBS) and deposited on Bis-Tris gels  previously washed in PBS and dried. The PDMS stamps were then deposited with the feature side down onto the dried gels for 30 min to transfer the laminin to the PDMS.To generate microwell structures in the hydrogels, poly(dimethylsiloxane) (PDMS) stamps were fabricated by casting PDMS precursors  on silicon wafers with 300 µm diameter microwells, generated via photolithography, and cut to fit regular 24-wells platesThe hydrogel precursors were degassed and kept on ice. Triethanolamine (TEOA) was used as a buffer for the crosslinking reaction. PEG-SH and PEG-VS were sequentially added to the TEOA to reach a 3.4% wt/vol gel concentration and were vortexed. The laminin-coated PDMS stamps were deposited on glass slides with the features facing up, 200 µl of hydrogel mixture was added to each stamp, and a second glass slide was placed on top of the PEG solution. Spacers with a fixed height located on each side of the PDMS stamps and hydrogel mixture ensured homogeneous thickness of the formed gels. The crosslinking hydrogels were then incubated for 18 h at 37 °C. The hydrogels were then removed from the slides and PDMS and hydrated in PBS for 8 h at 4 °C, washed and stored in PBS at 4 °C.16. PGE2  or OSM  was then administered, and time lapse imaging was started. Every replicate consisted of one treated and one control well.Hydrogels were affixed to the bottom of 24-well plates with a mixture of gel precursors . The gels were washed with PBS and growth medium and allowed to equilibrate in growth medium for 1 h. The cells were detached from their plate with Trypsin-EDTA 0.5%  and diluted to appropriate concentrations (1000 cells/ml for ø 300 µm microwells) prior to seeding. These concentrations ensure that a sufficient number of wells contain a single cell2 module S, Carl Zeiss AG) and was kept at 37.5 °C and 5% CO2. The images were acquired with an AxioCam MRm camera (Carl Zeiss AG) at 1388 × 1040 (brightfield) or 4 × 4 binning 346 × 260 (fluorescent channels) pixel resolution. The microscope software AxioVision (Carl Zeiss AG) was set up to acquire brightfield images in different positions at an interval of 5 min, and a custom-made script was written to acquire fluorescence images of the same positions at an interval of 20 min using Filter Set 44 (Carl Zeiss AG) for GFP and Filter Set 20 (Carl Zeiss AG) for mCherry. The acquisition was conducted for a period of 3 days (72 h). The exposure times for the different channels  and the camera parameters were carefully chosen to reduce phototoxicity. Each single-cell colony was manually selected, and its position was recorded by the software before starting the time lapse, with an equal number of wells imaged for each condition (drug treatment and control). Each time lapse experiment produced about 45 image sequences in total. The resulting images were analyzed with an augmented version of the Baxter Algorithm in Matlab 23. After the time lapse acquisition, differentiation medium was added to the well plate to induce myogenic commitment to validate that the tracked clones were successfully transduced with the MyoG reporter. Validation images of differentiating cells were taken 2 days (48 h) after addition of differentiation medium. Only clones expressing mCherry after the differentiation step were further analyzed.The plates containing the single cells in hydrogel microwells were positioned in the environmental chamber of an inverted microscope with motorized stage and temperature-controlled chamber . The chamber was equipped with a gas exchanger , cell clusters and missed detections. The algorithm successfully keeps track of cell movement, division, and cell death, as well as cells entering and exiting the image window. To do so, the algorithm starts with the hypothesis that no cells are present on the image sequence. It then adds one optimal track at a time to maximize a probabilistically motivated scoring function. The added track’s optimality is guaranteed by solving a dynamic programming problem using the Viterbi algorithm. The algorithm stops once the scoring function cannot be further maximized. The cell tracking component relies entirely on the brightfield images, due to the higher acquisition frequency for higher tracking precision. The computer-generated tracks of every selected well (MyoG reporter positive after differentiation) were corrected by hand as needed to ensure the most reliable quantified data.The existing algorithm used in the Baxter Laboratory is a batch algorithm that uses all available images to perform individual track linking by using temporal information in a probabilistic mannerhttps://github.com/klasma/BaxterAlgorithms) and (https://www.youtube.com/playlist?list=PLWNt_wdNK0Rp3RoAuUG3SK2B-CejdDrW6), respectively.The existing version of the algorithm was augmented with a fluorescence data analysis plug-in. The Baxter Algorithm was updated to handle fluorescence channels and to integrate data derived from these channels into the final analysis. The new plug-in allows for extraction of fluorescence data from the cell regions identified by the tracking algorithm, as described in more detail below. We have provided the Dual-FLIT analysis algorithm as an open-source code in the GitHub repository; the algorithm and a video tutorial can be obtained at : the average signal of every fluorescence image was computed and used to fit the sequence average intensity throughout time. The average of every image was then brought to their computed value by single-value subtraction (or addition). A power law was chosen to fit the average signal of the GFP fluorescent channel due to the residual medium auto-fluorescence caused by ambient illumination during the time lapse setup, whereas a linear polynomial fit was used for the mCherry signal, since auto-fluorescence was not observed in that channel. Image homogenization was preferred over simple background subtraction to account for the varying nature of the fluorescence image averages. Furthermore, adding a single value to every pixel was preferred over multiplying them by a constant in order not to excessively amplify noise.The average signal for every cell was then extracted from the respective cell border  in every image of the image sequence and further smoothed with a moving average filter (with window size of 5). The few overexposed images taken on the mCherry channel, due to the delay of the microscope shutter, were discarded as source of fluorescence signal. Finally, the signal was normalized with respect to the minimum and the maximum signal of the image sequence to have comparable data among colonies and across experiments. The maximum value was taken as either the 99 percentile of the differentiated cells image taken after 2 days in differentiation medium or the maximum value observed during the time lapse, whichever was higher.The normalized fluorescence signal for a cell was classified in five quintiles for representation purposes. This representation was chosen since cells tend to have continuous levels of expression for the two signals and do not follow a simple on/off type of behavior.To validate the fidelity of mCherry fluorescent signal in cells expressing myogenin, we performed immunofluorescence staining for myogenin by fixing samples at the end of the time lapse experiment and overlaying the fluorescence image on the last image frame of the time lapse experiment  were extracted and normalized so as to have similar variances and values across features. Weights were then assigned to each feature to match their importance in characterizing a cellular division. Weights were decided empirically and validated with ground truth lineage trees. In total, 38 features were collected from every division. A complete list can be found in Supplementary Table ® combined with the 1 NN classifier. K-means clustering is a method used in unsupervised or semi-supervised learning to partition n observations in k clusters. The method finds k centroids so as to minimize the distance of every observation to its cluster centroid. It allows regrouping of similar observations together without the need for labeled samples . According to the elbow method—commonly used to identify the best suited number of clusters for a dataset—4 clusters would represent the overall data better than 3, but the only further distinction of a fourth class would be to indicate new divisions that do not yield dividing cells at the end of a time lapse (where it is not possible to know whether cells will undergo further divisions). The clusters were therefore obtained on a specific set of sequences .Divisions of cells were classified using the k-means clustering algorithm in MatlabAll new divisions that were not used in the initial clustering were then classified using a 1 NN algorithm that assigned every division to its closest cluster centroid. The features for these new divisions underwent the same normalization steps that the features of the division used in the clustering part. It is important to note that this classification is not perfectly accurate for borderline cases but generates comparable results across clones.®. New settings were added to the list of settings in the Baxter Algorithms to accommodate parameters such as mismatching intervals between brightfield and fluorescent image acquisition and measurements after differentiation. The new user interface allows for direct output of statistics about single cell lineage tree or overall population statistics.All the above-mentioned image processing and data analysis steps were integrated in a new analysis window under the Baxter Algorithm software. Seamless integration of the different software components was ensured in MatlabVideos were compiled in Photoshop  using the frames generated by the algorithm. The fluorescence signal was enhanced for visualization and quantification  and 0.05% Triton X-100 in PBS was applied, and then cells were stained with rabbit cleaved caspase-3 primary antibody  and Alexa Fluor 647-conjugated goat anti-rabbit IgG secondary antibody  after viral transfection to assess any potential viral toxicity. For population-level analysis of cell fate, cells were fixed with 4% paraformaldehyde in PBS for 30 min, washed three times with PBS, permeabilized with PBS plus 0.25% (v/v) Triton X-100 (PBST) and blocked with 5% BSA and 5% goat serum (GS) in PBST. Cells were subsequently incubated with primary antibodies against Pax7  and MyoD  diluted in 2.5% BSA and 2.5% GS in PBST. Samples were washed three times with PBST and then incubated with secondary antibodies (Cy3 goat anti-mouse IgG2b (115-165-207) and AF647 goat anti-mouse IgG1 (115-605-205), Jackson ImmunoResearch, 1:500) diluted in 2.5% BSA and 2.5% GS in PBST. In all cases, nuclei were counter-stained with Hoechst 33342 . Images were acquired on an Axio Observer Z1 inverted fluorescence microscope. Specificity of the cleaved caspase-3 antibody was validated by staining control samples alongside the virus-treated samples (Supplementary Fig. Further information on research design is available in the Supplementary InformationReporting SummaryEditorial Policy ChecklistSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3


Tissue microenvironments formed by extracellular matrix (ECM) networks play a crucial role in regulating tissue structure and function. Extracellular microfibrillar networks composed of large multidomain glycoproteins such as collagens and fibrillins are of particular interest in this regard since they surround cells in close proximity to basement membranes, and thereby guide proper morphology and functional behavior of specialized cell types. Genetic defects in essential building blocks of the ECM architecture lead to multi-system connective tissue disorders, for instance affecting the musculoskeletal, dermal, vascular, ocular, and renal system, collectively called heritable connective tissue disorders (HCTD). Tissue weakness caused by ECM protein defects often correlates with dysregulated growth and differentiation processes due to an abnormal growth factor bioavailability.Due to the striking clinical features of patients affected by HCTD, investigating the underlying molecular mechanisms allows to reveal fundamental pathways in embryonic development and postnatal tissue homeostasis. This research is not only required for the development of new therapeutic avenues for affected individuals, but also to tackle challenges of general medicine in age-related diseases accompanied by connective tissue degeneration and failed remodelling, and increases our insight into fundamental molecular mechanisms of cell and tissue function.This Research Topic issue highlights molecular pathways underlying HCTD. Supramolecular networks consisting of collagen and elastic fibers not only provide tissue integrity to connective tissues, but also define their biomechanical properties. This mutual interdependence is also required for the proper control of ECM-cell communication that is facilitated by specific ECM receptors or growth factors. The structural and functional interdependence of both major networks is reflected by common clinical features of patients with deficiencies in collagen or elastic fiber network components, that are often accompanied by similar dysregulated signaling pathways. For instance, aberrant TGF-β and integrin signaling have been shown to be involved in several types of osteogenesis imperfecta (OI)  epidermoCardinal et al. report on a therapeutic intervention for osteogenesis imperfecta (OI), a disorder characterized by brittle bones, often due to a collagen type I gene mutation, in a preclinical murine model (oim/oim) by anti-sclerostin antibody administration.Shaw et al., with a particular emphasis on the lessons that can be learned from analysing laminin mutations in terms of laminin biology in health and disease. Nyström et al. focus on the basement membrane protein collagen VII and its role in dystrophic epidermolysis bullosa, a skin blistering disease that is caused by mutations in COL7A1. The skin features of patients affected by dystrophic epidermolysis bullosa vary from mild blistering primarily affecting hands, feet, knees, and elbows to severe cases with widespread blistering that can lead to vision loss, and scarring. Nyström et al. cover genetic modifiers and secondary mechanisms as potential therapeutic targets.Inherited disorders characterized by deficiencies in basement membrane structure and function caused by laminin mutations are the focus of a review by Vroman et al. comprehensively review generated animal models resembling the Ehlers–Danlos syndromes (EDS), a group of HCTD mainly characterized by skin hyperextensibility, joint hypermobility and generalized tissue fragility. Most EDS types are caused by genetic defects that affect connective tissue biosynthesis, thereby compromising collagen biosynthesis or fibrillogenesis and resulting in a disorganized extracellular matrix. For many of the EDS types animal models are available, providing a key advancement for studying the pathomechanisms underlying EDS and for developing new therapeutic avenues for EDS in preclinical trials.ELN, FBLN4, LTBP4, as well as in ATP6V1E1 or ATP6V1A, which encode the E1 and A subunits, respectively, of the V1 domain of the heteromultimeric V-ATPase complex. Mutations affecting the B1 (ATP6V1B1 and B2 (ATP6V1B2) subunits on the other hand cause autosomal recessive distal renal tubular acidosis with early-onset hearing loss and Zimmermann-Laband syndrome, respectively. Findings of Li et al. expand the variant spectrum of hearing-loss-associated genes and provide new insights on understanding of hearing-loss candidate genes ATP6V1B2, but also TJP2, and KIF11.Cutis laxa is a general term for a group of disorders that are characterized by lax, wrinkled skin that is sagging and lacks elasticity. Known causative mutations were reported in LTBP4) which, when mutated leads to autosomal recessive cutis laxa type C (ARCL1C). Alanazi et al. show, using biochemical and biophysical approaches, that LTBP4 mutations contribute to ARCL1C by disrupting the structure and interactions of LTBP-4 which are essential for elastogenesis in a range of mammalian connective tissues.Fibrillin microfibrils represent the extracellular scaffolds onto which tropoelastin deposition occurs, followed by lysyl oxidase mediated crosslinking required for proper elastic fiber formation. Thereby, the targeting of tropoelastin is facilitated by adaptor proteins such as latent TGF-β binding protein-4 . They provide an overview of the human genetic data and generated knockout mouse models targeting the involved genes, pointing towards a potential biological cooperation between A disintegrin and metalloprotease with thrombospondin-1-like domains (ADAMTS) enzymes or ADAMTS-like adaptor proteins and fibrillin-1 in the development of each other opposing clinical phenotypes.Mutations in fibrillin often lead to opposing clinical features ranging from tall to short stature, hyperflexible to stiff joints, or hyperextensible to stiff and fibrotic skin. The manuscript by Mizumoto and Yamada summarize glycobiological aspects of congenital disorders caused by defects in glycosaminoglycan-biosynthetic enzymes including specific glysocyltransferases, epimerases, and sulfotransferases, in addition to core proteins of proteoglycans.Finally Overall, this Research Topic issue provides a broad picture of the intricate molecular pathways underlying HCTD which serve as model diseases to better understand how fundamental the extracellular microenvironment regulates cellular behavior.