Stem cell biology using human pluripotent stem cells (hPSCs), including embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs), is providing unprecedented opportunities for the study of human development by recapitulating embryogenesis (Tchieu et al., 2017; Irie et al., 2015; van de Leemput et al., 2014; Qi et al., 2017; Guo et al., 2017). Directed specification of hESCs/hiPSCs is especially advantageous for certain cell types because it produces large quantities of otherwise extremely rare cell populations during development (e.g. skeletal muscle stem/progenitor cells). Often such cellular subtypes are critical for the formation of a tissue type, and they can be important for a better understanding of cellular ontogeny from embryonic stage to postnatal stage. In particular, compartmentalization of the serial developmental processes using hPSCs in vitro can deliver unprecedented opportunity to gain insights into the transcriptional continuum of a specific cellular lineage. For example, the analysis of global transcriptional expression in myogenic development in a ‘step-wise’ platform can provide invaluable information regarding the patterns of transcriptional dynamics related to human skeletal muscle specification processes. Furthermore, while there are many rare genetic disorders with musculoskeletal symptoms, we have limited tools to gain a better understanding of the disease mechanisms.

During embryonic myogenesis, skeletal muscle is generated through multiple cellular stages, including pluripotent preimplanted embryos, somite cells, skeletal muscle stem/precursor cells, proliferating myoblasts, and multinucleated myotubes. While previous studies reported the generation of in vitro skeletal muscle cells with overexpression of exogenous myogenic transcription factors (TFs) into fibroblasts and stem cells (Darabi et al., 2012; Tedesco et al., 2012; Albini et al., 2013; Salani et al., 2012; Davis et al., 1987; Magli and Perlingeiro, 2017), they did not provide a step-wise transcriptional blueprint of myogenic specification. Recently, several reports were published that effectively directed hPSCs to human skeletal muscle cell fates (Chal et al., 2015; Choi et al., 2016; Shelton et al., 2014), which can be a reasonable platform to tease out the transcriptional trajectory of human myogenesis processes. However, the heterogenous cellular populations in the myogenic cell culture hamper further cellular isolation of specific myogenic cells for detailed studies.

Identification of the molecular regulators for the generation of human skeletal muscle cells is important for discovering molecular mechanisms of human myogenesis. While OCT4 is one of the POU transcription factors that is essential for maintaining pluripotency and self-renewal of PSCs, MSGN1 has a key function in generation of paraxial mesoderm cells (Loh et al., 2006; Fior et al., 2012). MYOG is a skeletal muscle-specific transcription factor in the basic helix loop helix class of DNA-binding proteins and has a critical role in skeletal muscle differentiation during embryogenesis (Nabeshima et al., 1993; Bentzinger et al., 2012; Hasty et al., 1993; Brunetti and Goldfine, 1990; Magli and Perlingeiro, 2017). Another major gene is PAX7, a member of the paired box (PAX) family of transcription factors, which has important roles in skeletal muscle development, maintenance, and regeneration (Bentzinger et al., 2012; Seale et al., 2000; Kassar-Duchossoy et al., 2005; Parker et al., 2003; Biressi et al., 2007; Buckingham and Relaix, 2007). Previous reports with genetic reporter mice showed the transcriptional profiles of PAX3 or PAX7 expressing cells in embryonic and postnatal muscle tissues (Relaix et al., 2005; Tichy et al., 2018), which presented developmental transcriptional changes in a specific population.

Recently, the CRISPR/Cas9 system has been developed and used widely in genetic modification of hPSCs due to its simplified design and feasibility (Mali et al., 2013; Cong et al., 2013). For instance, a ‘knock-in’ strategy to generate a genetic reporter hPSC line using the CRISPR/Cas9 system is now feasible and enables us to purify a homogenous cell population by prospective isolation (Ganat et al., 2012; Tchieu et al., 2017; Mukherjee et al., 2018). Moreover, multiple genetic reporter hPSC lines for stage-specific transcription factors can provide a step-wise transcriptional landscape for a certain lineage.

In this study, to systematically investigate the transcriptional blueprint for developing human skeletal muscle cells and to gain comprehensive insights into the molecular signatures of putative skeletal muscle stem/progenitors, we conducted step-wise isolations of stage-specific cellular subtypes during muscle differentiation in vitro and performed global gene expression analysis. Using our human genetic reporter PSC lines and a newly devised method for myotube enrichment, we isolated five distinct cell types in human embryonic myogenesis, including OCT4::EGFP+ embryonic stem cells, MSGN1::EGFP+ presomite cells, PAX7::EGFP+ putative skeletal muscle stem/precursor cells, MYOG::EGFP+ myoblast cells, and multinucleated myotubes (Loh et al., 2006; Fior et al., 2012; Nichols et al., 1998; Seale et al., 2000; Hasty et al., 1993). The clustering analysis presented transcriptional changes during in vitro muscle generation, and we identified several transcription factors that have key roles in human myogenesis in vitro. One of the transcription factors we identified is TWIST1, which has not been extensively studied in human muscle biology and relevant genetic diseases.

Here, we present a comprehensive analysis for the transcriptome of hPSC-derived myogenic specification. By utilizing multiple reporting hPSC lines, we reconstructed transcriptionally defined stages of human in vitro myogenesis and uncovered sets of novel genes including transcription factors, followed by validation of a new gene, TWIST1. Our data provide insights into the dynamics of the transcriptome during in vitro myogenesis and demonstrate the ability to study significant mechanisms in a stage specific manner during embryonic myogenesis.

As shown in stem cell research to isolate specific sub-populations during differentiation processes (Ganat et al., 2012; Tchieu et al., 2017), genetic reporter stem cell lines are important tools to study transcriptional regulation and functions in various cell fate determination processes, and they are useful to understand the dynamic fluctuations of expression of key transcription factors. In this study, we successfully utilized six individual ‘knock-in’ genetic reporter hPSC lines targeting key myogenic specification genes. These new reporter cell lines allowed us to prospectively identify and purify specific cellular populations with high purity during in vitro human myogenesis, leading us to delineate a comprehensive transcriptional landscape of human skeletal muscle development.

Early embryonic myogenesis has been investigated in animal models, and key transcription factors, such as PAX7 and MYOG, have been discovered (Bentzinger et al., 2012; Kassar-Duchossoy et al., 2005; Parker et al., 2003); however, the transcriptional similarity and disparity between PAX7 expressing cells (muscle stem/progenitor cells) and MYOG expressing cells (myoblast cells) has not been well characterized. By using PAX7::EGFP and MYOG::EGFP genetic reporter hPSC lines, we were able to capture two distinct but converged cellular populations during human in vitro myogenesis. We believe that the myogenic specification from human pluripotent stem cells are not synchronized events, as we see that gradual increase of PAX7::EGFP expression during the time course. Also the in vitro condition may not be favorable to the maintenance of PAX7 expression, which forces the PAX7::EGFP+ cells to be differentiated into MYOG::EGFP+ cells. Therefore, we believe that in vitro culture condition (favorable for differentiation) and the transcriptional heterogeneity of PAX7::EGFP+ cells are responsible for the slightly increased levels of MYOG::EGFP+ cells in early myogenic specification stage (Day 20–22) in the Figure 1D. However, our data do not necessarily conclude that MYOG+ cells are not derived from PAX7::EGFP+ cells. To understand the transcriptional association and distinctive characteristics between PAX7::EGFP+ cells and MYOG::EGFP+ cells, we compared these two populations to OCT4::EGFP+ cells to tease out detailed transcriptional similarities and examine differential gene expression profiles (Figure 2). Our data indicate that over-represented GO terms of the enriched gene set in PAX7::EGFP+ cells involve DNA binding and/or signaling regulation. For instance, the list of enriched transcription factors in PAX7::EGFP+ cells, but not in MYOG::EGFP+ cells, include genes of the JUN signaling pathway family, such as JUN, JUNB and FOS genes, which are known to prevent the activation of MYOG (Li et al., 1992). ID3, a well-known downstream target gene of PAX7 as reported in mouse skeletal muscle stem cells (Kumar et al., 2009; van Velthoven et al., 2017), is also expressed in the PAX7::EGFP+ cells. The HMG family of DNA binding coding genes, including HMGB1, HMGB2, HMGA1, and HMGB3, are also enriched in the PAX7::EGFP+ population, suggesting their potential roles in skeletal muscle stem/progenitor properties. The HMGB1 gene is heavily involved in regulation of proliferation and differentiation in other stem cell populations (Meng et al., 2018; Wang et al., 2014).

On the other hand, the list of upregulated transcription factors in MYOG::EGFP+ cells compared to PAX7::EGFP+ cells contains previously known myoblast marker genes, such as MYOG, MEF2C, and MYOD1 (Arnold and Braun, 1996). Also, increased FOXO1 and FOXO4 transcription levels in MYOG::EGFP+ cells might be related to the proliferation ability of MYOG::EGFP+ cells, since the FOXO family contributes to proliferation of myoblasts through the AKT signaling pathway (Gross et al., 2008; Xu et al., 2017). Similarly, a list of genes coding for the ERK signaling pathway proteins, such as ATF4, TEAD4, and SNAI1, are enriched in the MYOG::EGFP+ population, suggesting that properties of myoblasts are related to the ERK signaling pathway as previously shown in animal studies (Ayuso et al., 2016; Benhaddou et al., 2012; Horak et al., 2016).

The clustering analysis of gene expression data is beneficial for identification of novel genes, finding specialized functions associated with continuous biological events, and discovering transcriptionally distinct properties of defined stage-specific cell populations. K-mean clustering was applied to group highly correlated gene expression patterns during hPSC-based in vitro myogenesis and classified the genes into 10 clusters. This approach gave us new insights that divergent gene expression patterns contribute to different stages during in vitro myogenesis. Cluster 10 involves a list of genes that have gradually increased expression patterns during myogenesis. Although most of the genes in Cluster 10 have roles in myotube formation during skeletal muscle generation, some of them have functions in non-skeletal muscle lineages. For example, Cluster 10 includes RUNX1, which has critical roles in hematopoiesis and leukemogenesis (Ichikawa et al., 2013). Our data indicate that the RUNX1 gene is transcriptionally and translationally active in our human myotube samples, but not in myoblasts based on our data with the RUNX1::EGFP reporter cell line. While the TMEM8C (MYOMAKER) gene involved in Cluster 10 was studied as a membrane activator of myoblast fusion (Millay et al., 2013), Cluster 3 includes the MYMX (MYOMIXER) gene, which was reported as a key factor to induce myoblast fusion (Bi et al., 2017). MYMX and TMEM8C are known to be expressed in myoblasts and induce myotube formation, and a recent study reported that these genes contribute to distinct steps in myotube formation processes (Leikina et al., 2018). Another interesting cluster is Cluster 4, which includes a gene list related to muscle stem/progenitor markers, such as mouse muscle stem cell markers including PAX7, PAX3, SYND3, and BARX2 (Bentzinger et al., 2012; Yin et al., 2013; Magli and Perlingeiro, 2017). The GO terms represented in Cluster 4 suggest that genes belonging to muscle development categories and signaling pathways are involved in biological functions that are related to the properties of skeletal muscle stem/precursor cells. Interestingly, the CNBP gene, a culprit gene that causes myotonic dystrophy 2 (MD2) (Meola and Cardani, 2015), belongs to Cluster 4, enriched in the PAX7::EGFP+ cell population. In an attempt to further analyze the stage-specific transcriptome regulation during human myogenesis, we applied weighted gene co-expression network analysis (WGCNA) in five different cell types (Figure 6—figure supplement 2). WGCNA with stages-specific transcriptomes allowed us to define modules of genes that are continuously coregulated and to study their stage-specific variation during skeletal muscle differentiation. By including all expressed genes with expression variation, we identified a total of 92 modules defined as groups of genes coordinately expressed across 20 samples. These data imply that cellular dysfunction might be initiated even in the muscle stem/precursor cell stage, which could provide new insights to study MD2 pathogenesis.

Similar patterns can be found in DYSTROPHIN, the culprit gene for Duchenne muscular dystrophy (DMD). Recently, the expression of DYSTROPHIN has been studied in mouse satellite cells, demonstrating that DYSTROPHIN is responsible for cell polarity and asymmetric division of satellite cells (Dumont et al., 2015; Chang et al., 2018). Previously, DYSTROPHIN had been known as an important ‘linkage’ protein connecting skeletal muscle fibers of cytoskeleton to the extracellular matrix, and DYSTROPHIN’s functional failure results in severe skeletal muscle degeneration, which is the etiology of DMD (Hoffman et al., 1987). Although DYSTROPHIN is clustered to Cluster 10, we also found minute but detectable levels of DYSTROPHIN expression in PAX7::EGFP+ cells (Figure 3—figure supplement 1B). To confirm our transcriptional analysis data, we performed western blot with DYSTROPHIN antibody (MANDYS1 clone) in isolated PAX7::EGFP+ cells and demonstrated that DYSTROPHIN is also transcriptionally and translationally expressed in hESC-derived PAX7::EGFP+ cells (Figure 3—figure supplement 1C).

CD271 is important for normal development of muscle from mutant mice (Reddypalli et al., 2005). While prior studies were performed using surface markers for the isolation of myoblast (Hicks et al., 2018; Sakai-Takemura et al., 2018), our studies were focused more PAX7+ skeletal muscle stem/progenitor cells during in vitro human skeletal muscle generation. Our data indicate that CD271^bright population overlaps with PAX7::EGFP+ cells in FACS analysis (Figure 4) and shows efficient myotube formation ability according to fusion index during in vitro culture. With the advantages of using a surface marker, CD271 can be a substitute for the PAX7::EGFP reporting system, and it could be applied to other cell types for skeletal muscle disease modeling without tedious and time-consuming steps for generating reporter lines. In addition, other neurotrophin receptors, TRKA and TRKB, were clustered to Cluster 4, which suggests that neurotrophins and their signaling pathways could be one of the key regulatory mechanisms in PAX7 expressing skeletal muscle stem/progenitor cells as shown in previous rodent studies (Griesbeck et al., 1995; Clow and Jasmin, 2010). Collectively, our data suggest that neurotrophin receptor genes, such as CD271, TRKA and TRKB, might have direct/indirect contributions in the process of skeletal muscle development.

TWIST1 has similarity to TWIST2, and TWIST2 was reported as a key transcription factor in adult muscle progenitor cells in mouse (Liu et al., 2017). However, RNA expression of TWIST2 was barely detected in our RNA-seq results, and TWIST1 is contained in Cluster four along with the PAX7 gene expression pattern. It has been demonstrated that Twist1 is actively expressed in mouse adult satellite cells, based on Chip-seq study, marked by H3K4me3 (Woodhouse et al., 2013), but its function has not been clarified. Although TWIST1 has been known for its role cancer progression (Parajuli et al., 2018), two other studies with fly and axolotl system demonstrate that TWIST1 is implicated with skeletal muscle regeneration (Kragl et al., 2013; Baylies and Bate, 1996). In axolotl, as the limb bud elongates, Twist1 expression was found sub-epidermally toward the distal portion of the limb bud and co-localized with some, but not all, of MYF5 and PAX7 expressing muscle cells (Kragl et al., 2013). InDrosophila Twist (Twi) regulates mesodermal differentiation and propels a specific subset of mesodermal cells into somatic myogenesis (Baylies and Bate, 1996). However, TWIST1 expression in human myogenic development and its relevance to genetic diseases has not been studied until now. Saethre-Chotzen syndrome (OMIM # 101400) is caused by TWIST1 mutations, and it is an autosomal dominant craniosynostosis syndrome with uni- or bi-lateral coronal synostosis and mild limb deformities (Musculoskeletal Diseases) (Sharma et al., 2013; Murphy et al., 2018). While the neural crest-related defects in Saethre-Chotzen syndrome patients have been studied, the underlying mechanism of musculoskeletal symptoms is unknown. Importantly, our results show that TWIST1 deletion (three different mutations in each clone) affects the maintenance of PAX7 expression levels in human PAX7::EGFP cells during in vitro expansion. These data suggest that TWIST1 plays a role in putative skeletal muscle stem/progenitor cells and provide experimental evidence to begin to understand pathogenic events responsible for the musculoskeletal symptoms in Saethre-Chotzen syndrome patients. In future studies, it will be important to explore the stage-specific role of TWIST1 and characterize more detailed mechanisms of musculoskeletal symptoms in Saethre-Chotzen syndrome.

The WNT signaling pathway has been reported to play various roles in skeletal muscle development and skeletal muscle stem cell biology (Girardi and Le Grand, 2018; Jones et al., 2015). In addition, it has been shown that Twist1 expression is induced by canonical Wnt signaling and has an important role in inhibiting myotube formation by sequestering DNA binding of MYOD1 (Miraoui and Marie, 2010). Our RNA-seq data show that the lack of TWIST1 protein in human PAX7::EGFP+ cells significantly upregulated a set of genes related to WNT signaling pathways, and treatment with glycogen synthetase kinase 3 (GSK3) inhibitor, a WNT activator, decreased PAX7 gene expression (Figure 6—figure supplement 1B). Moreover, modulating the WNT signaling pathway did not rescue the TWIST1 KO phenotype (Figure 6—figure supplement 1D). These data suggest that TWIST1 could be a mediator to link the canonical WNT signaling pathway to PAX7 gene expression and likely maintenance of putative skeletal muscle stem/progenitor cell identity.

Overall, our study depicts a transcriptional landscape of in vitro myogenesis from hPSCs, which is a valuable platform to deconstruct/reconstruct the dynamic transcriptional activities of multiple stages during human skeletal muscle specification. Our clustering analysis of RNA-seq data will be useful for understanding global gene expression during human in vitro myogenesis and an ideal reference for mining new myogenic regulator genes. In depth understanding of transcriptional dynamics of human myogenesis and identification of a cell intrinsic requirement for maintenance of PAX7 expression should facilitate development of novel strategies enhancing functional repair of many degenerative muscle conditions. We believe that these data will be beneficial for future applications, such as skeletal muscle related disease modeling and genetic engineering for cell replacement therapy.

All the RNA-seq data were deposited to NCBI (GSE129505). The access to the data is available to public.

1. 1. Lee G
   2. Shin J
   3. Choi IY

   (2019) NCBI Gene Expression Omnibus

   ID GSE129505. Transcriptional landscape of human myogenesis reavels a key role of TWIST1 in maintenance of skeletal muscle progenitors.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129505

1. 1. Albini S
   2. Coutinho P
   3. Malecova B
   4. Giordani L
   5. Savchenko A
   6. Forcales SV
   7. Puri PL

   (2013) Epigenetic reprogramming of human embryonic stem cells into skeletal muscle cells and generation of contractile myospheres

   Cell Reports 3:661–670.

   https://doi.org/10.1016/j.celrep.2013.02.012

   • PubMed
   • Google Scholar
2. 1. Arnold HH
   2. Braun T

   (1996)

   Targeted inactivation of myogenic factor genes reveals their role during mouse myogenesis: a review

   The International Journal of Developmental Biology 40:345–353.

   • PubMed
   • Google Scholar
3. 1. Ayuso M
   2. Fernández A
   3. Núñez Y
   4. Benítez R
   5. Isabel B
   6. Fernández AI
   7. Rey AI
   8. González-Bulnes A
   9. Medrano JF
   10. Cánovas Ángela
   11. López-Bote CJ
   12. Óvilo C

   (2016) Developmental Stage, Muscle and Genetic Type Modify Muscle Transcriptome in Pigs: Effects on Gene Expression and Regulatory Factors Involved in Growth and Metabolism

   PLOS ONE 11:e0167858.

   https://doi.org/10.1371/journal.pone.0167858

   • Google Scholar
4. 1. Baylies MK
   2. Bate M

   (1996) Twist: a myogenic switch in Drosophila

   Science 272:1481–1484.

   https://doi.org/10.1126/science.272.5267.1481

   • PubMed
   • Google Scholar
5. 1. Benhaddou A
   2. Keime C
   3. Ye T
   4. Morlon A
   5. Michel I
   6. Jost B
   7. Mengus G
   8. Davidson I

   (2012) Transcription factor TEAD4 regulates expression of myogenin and the unfolded protein response genes during C2C12 cell differentiation

   Cell Death & Differentiation 19:220–231.

   https://doi.org/10.1038/cdd.2011.87

   • PubMed
   • Google Scholar
6. 1. Bentzinger CF
   2. Wang YX
   3. Rudnicki MA

   (2012) Building muscle: molecular regulation of myogenesis

   Cold Spring Harbor Perspectives in Biology 4:a008342.

   https://doi.org/10.1101/cshperspect.a008342

   • PubMed
   • Google Scholar
7. 1. Bentzinger CF
   2. Wang YX
   3. von Maltzahn J
   4. Soleimani VD
   5. Yin H
   6. Rudnicki MA

   (2013) Fibronectin regulates Wnt7a signaling and satellite cell expansion

   Cell Stem Cell 12:75–87.

   https://doi.org/10.1016/j.stem.2012.09.015

   • PubMed
   • Google Scholar
8. 1. Bentzinger CF
   2. von Maltzahn J
   3. Dumont NA
   4. Stark DA
   5. Wang YX
   6. Nhan K
   7. Frenette J
   8. Cornelison DDW
   9. Rudnicki MA

   (2014) Wnt7a stimulates myogenic stem cell motility and engraftment resulting in improved muscle strength

   The Journal of Cell Biology 205:97–111.

   https://doi.org/10.1083/jcb.201310035

   • Google Scholar
9. 1. Bi P
   2. Ramirez-Martinez A
   3. Li H
   4. Cannavino J
   5. McAnally JR
   6. Shelton JM
   7. Sánchez-Ortiz E
   8. Bassel-Duby R
   9. Olson EN

   (2017) Control of muscle formation by the fusogenic micropeptide myomixer

   Science 356:323–327.

   https://doi.org/10.1126/science.aam9361

   • PubMed
   • Google Scholar
10. 1. Biressi S
    2. Molinaro M
    3. Cossu G

    (2007) Cellular heterogeneity during vertebrate skeletal muscle development

    Developmental Biology 308:281–293.

    https://doi.org/10.1016/j.ydbio.2007.06.006

    • Google Scholar
11. 1. Biressi S
    2. Miyabara EH
    3. Gopinath SD
    4. Carlig PM
    5. Rando TA

    (2014) A Wnt-TGF 2 axis induces a fibrogenic program in muscle stem cells from dystrophic mice

    Science Translational Medicine 6:ra176.

    https://doi.org/10.1126/scitranslmed.3008411

    • Google Scholar
12. 1. Brunetti A
    2. Goldfine ID

    (1990)

    Role of myogenin in myoblast differentiation and its regulation by fibroblast growth factor

    The Journal of Biological Chemistry 265:5960–5963.

    • PubMed
    • Google Scholar
13. 1. Buckingham M
    2. Relaix F

    (2007) The role of Pax genes in the development of tissues and organs: pax3 and Pax7 regulate muscle progenitor cell functions

    Annual Review of Cell and Developmental Biology 23:645–673.

    https://doi.org/10.1146/annurev.cellbio.23.090506.123438

    • PubMed
    • Google Scholar
14. 1. Chal J
    2. Oginuma M
    3. Al Tanoury Z
    4. Gobert B
    5. Sumara O
    6. Hick A
    7. Bousson F
    8. Zidouni Y
    9. Mursch C
    10. Moncuquet P
    11. Tassy O
    12. Vincent S
    13. Miyanari A
    14. Bera A
    15. Garnier JM
    16. Guevara G
    17. Hestin M
    18. Kennedy L
    19. Hayashi S
    20. Drayton B
    21. Cherrier T
    22. Gayraud-Morel B
    23. Gussoni E
    24. Relaix F
    25. Tajbakhsh S
    26. Pourquié O

    (2015) Differentiation of pluripotent stem cells to muscle fiber to model duchenne muscular dystrophy

    Nature Biotechnology 33:962–969.

    https://doi.org/10.1038/nbt.3297

    • PubMed
    • Google Scholar
15. 1. Chang AT
    2. Liu Y
    3. Ayyanathan K
    4. Benner C
    5. Jiang Y
    6. Prokop JW
    7. Paz H
    8. Wang D
    9. Li HR
    10. Fu XD
    11. Rauscher FJ
    12. Yang J

    (2015) An evolutionarily conserved DNA architecture determines target specificity of the TWIST family bHLH transcription factors

    Genes & Development 29:603–616.

    https://doi.org/10.1101/gad.242842.114

    • PubMed
    • Google Scholar
16. 1. Chang NC
    2. Sincennes MC
    3. Chevalier FP
    4. Brun CE
    5. Lacaria M
    6. Segalés J
    7. Muñoz-Cánoves P
    8. Ming H
    9. Rudnicki MA

    (2018) The dystrophin glycoprotein complex regulates the epigenetic activation of muscle stem cell commitment

    Cell Stem Cell 22:755–768.

    https://doi.org/10.1016/j.stem.2018.03.022

    • PubMed
    • Google Scholar
17. 1. Chang CN
    2. Kioussi C

    (2018) Location, location, location: signals in muscle specification

    Journal of Developmental Biology 6:11.

    https://doi.org/10.3390/jdb6020011

    • PubMed
    • Google Scholar
18. 1. Chapman DL
    2. Papaioannou VE

    (1998) Three neural tubes in mouse embryos with mutations in the T-box gene Tbx6

    Nature 391:695–697.

    https://doi.org/10.1038/35624

    • Google Scholar
19. 1. Choi IY
    2. Lim H
    3. Estrellas K
    4. Mula J
    5. Cohen TV
    6. Zhang Y
    7. Donnelly CJ
    8. Richard JP
    9. Kim YJ
    10. Kim H
    11. Kazuki Y
    12. Oshimura M
    13. Li HL
    14. Hotta A
    15. Rothstein J
    16. Maragakis N
    17. Wagner KR
    18. Lee G

    (2016) Concordant but varied phenotypes among duchenne muscular dystrophy Patient-Specific myoblasts derived using a human iPSC-Based model

    Cell Reports 15:2301–2312.

    https://doi.org/10.1016/j.celrep.2016.05.016

    • PubMed
    • Google Scholar
20. 1. Chou BK
    2. Mali P
    3. Huang X
    4. Ye Z
    5. Dowey SN
    6. Resar LM
    7. Zou C
    8. Zhang YA
    9. Tong J
    10. Cheng L

    (2011) Efficient human iPS cell derivation by a non-integrating plasmid from blood cells with unique epigenetic and gene expression signatures

    Cell Research 21:518–529.

    https://doi.org/10.1038/cr.2011.12

    • PubMed
    • Google Scholar
21. 1. Clow C
    2. Jasmin BJ

    (2010) Brain-derived neurotrophic factor regulates satellite cell differentiation and skeltal muscle regeneration

    Molecular Biology of the Cell 21:2182–2190.

    https://doi.org/10.1091/mbc.e10-02-0154

    • PubMed
    • Google Scholar
22. 1. Cong L
    2. Ran FA
    3. Cox D
    4. Lin S
    5. Barretto R
    6. Habib N
    7. Hsu PD
    8. Wu X
    9. Jiang W
    10. Marraffini LA
    11. Zhang F

    (2013) Multiplex genome engineering using CRISPR/Cas systems

    Science 339:819–823.

    https://doi.org/10.1126/science.1231143

    • PubMed
    • Google Scholar
23. 1. Connelly JP
    2. Kwon EM
    3. Gao Y
    4. Trivedi NS
    5. Elkahloun AG
    6. Horwitz MS
    7. Cheng L
    8. Liu PP

    (2014) Targeted correction of RUNX1 mutation in FPD patient-specific induced pluripotent stem cells rescues megakaryopoietic defects

    Blood 124:1926–1930.

    https://doi.org/10.1182/blood-2014-01-550525

    • PubMed
    • Google Scholar
24. 1. Darabi R
    2. Arpke RW
    3. Irion S
    4. Dimos JT
    5. Grskovic M
    6. Kyba M
    7. Perlingeiro RC

    (2012) Human ES- and iPS-derived myogenic progenitors restore DYSTROPHIN and improve contractility upon transplantation in dystrophic mice

    Cell Stem Cell 10:610–619.

    https://doi.org/10.1016/j.stem.2012.02.015

    • PubMed
    • Google Scholar
25. 1. Davis RL
    2. Weintraub H
    3. Lassar AB

    (1987) Expression of a single transfected cDNA converts fibroblasts to myoblasts

    Cell 51:987–1000.

    https://doi.org/10.1016/0092-8674(87)90585-X

    • PubMed
    • Google Scholar
26. 1. Dumont NA
    2. Wang YX
    3. von Maltzahn J
    4. Pasut A
    5. Bentzinger CF
    6. Brun CE
    7. Rudnicki MA

    (2015) Dystrophin expression in muscle stem cells regulates their polarity and asymmetric division

    Nature Medicine 21:1455–1463.

    https://doi.org/10.1038/nm.3990

    • PubMed
    • Google Scholar
27. 1. Fior R
    2. Maxwell AA
    3. Ma TP
    4. Vezzaro A
    5. Moens CB
    6. Amacher SL
    7. Lewis J
    8. Saúde L

    (2012) The differentiation and movement of presomitic mesoderm progenitor cells are controlled by mesogenin 1

    Development 139:4656–4665.

    https://doi.org/10.1242/dev.078923

    • PubMed
    • Google Scholar
28. 1. Ganat YM
    2. Calder EL
    3. Kriks S
    4. Nelander J
    5. Tu EY
    6. Jia F
    7. Battista D
    8. Harrison N
    9. Parmar M
    10. Tomishima MJ
    11. Rutishauser U
    12. Studer L

    (2012) Identification of embryonic stem cell-derived midbrain dopaminergic neurons for engraftment

    Journal of Clinical Investigation 122:2928–2939.

    https://doi.org/10.1172/JCI58767

    • PubMed
    • Google Scholar
29. 1. Gianakopoulos PJ
    2. Mehta V
    3. Voronova A
    4. Cao Y
    5. Yao Z
    6. Coutu J
    7. Wang X
    8. Waddington MS
    9. Tapscott SJ
    10. Skerjanc IS

    (2011) MyoD directly Up-regulates premyogenic mesoderm factors during induction of skeletal myogenesis in stem cells

    Journal of Biological Chemistry 286:2517–2525.

    https://doi.org/10.1074/jbc.M110.163709

    • Google Scholar
30. 1. Girardi F
    2. Le Grand F

    (2018) Wnt signaling in skeletal muscle development and regeneration

    Progress in Molecular Biology and Translational Science 153:157–179.

    https://doi.org/10.1016/bs.pmbts.2017.11.026

    • PubMed
    • Google Scholar
31. 1. Griesbeck O
    2. Parsadanian AS
    3. Sendtner M
    4. Thoenen H

    (1995) Expression of neurotrophins in skeletal muscle: quantitative comparison and significance for motoneuron survival and maintenance of function

    Journal of Neuroscience Research 42:21–33.

    https://doi.org/10.1002/jnr.490420104

    • PubMed
    • Google Scholar
32. 1. Gross DN
    2. van den Heuvel AP
    3. Birnbaum MJ

    (2008) The role of FoxO in the regulation of metabolism

    Oncogene 27:2320–2336.

    https://doi.org/10.1038/onc.2008.25

    • PubMed
    • Google Scholar
33. 1. Guo M
    2. Zhang T
    3. Dong X
    4. Xiang JZ
    5. Lei M
    6. Evans T
    7. Graumann J
    8. Chen S

    (2017) Using hESCs to probe the interaction of the Diabetes-Associated genes CDKAL1 and MT1E

    Cell Reports 19:1512–1521.

    https://doi.org/10.1016/j.celrep.2017.04.070

    • PubMed
    • Google Scholar
34. 1. Hasty P
    2. Bradley A
    3. Morris JH
    4. Edmondson DG
    5. Venuti JM
    6. Olson EN
    7. Klein WH

    (1993) Muscle deficiency and neonatal death in mice with a targeted mutation in the myogenin gene

    Nature 364:501–506.

    https://doi.org/10.1038/364501a0

    • PubMed
    • Google Scholar
35. 1. Hicks MR
    2. Hiserodt J
    3. Paras K
    4. Fujiwara W
    5. Eskin A
    6. Jan M
    7. Xi H
    8. Young CS
    9. Evseenko D
    10. Nelson SF
    11. Spencer MJ
    12. Handel BV
    13. Pyle AD

    (2018) ERBB3 and NGFR mark a distinct skeletal muscle progenitor cell in human development and hPSCs

    Nature Cell Biology 20:46–57.

    https://doi.org/10.1038/s41556-017-0010-2

    • PubMed
    • Google Scholar
36. 1. Hoffman EP
    2. Brown RH
    3. Kunkel LM

    (1987) Dystrophin: the protein product of the duchenne muscular dystrophy locus

    Cell 51:919–928.

    https://doi.org/10.1016/0092-8674(87)90579-4

    • PubMed
    • Google Scholar
37. 1. Horak M
    2. Novak J
    3. Bienertova-Vasku J

    (2016) Muscle-specific microRNAs in skeletal muscle development

    Developmental Biology 410:1–13.

    https://doi.org/10.1016/j.ydbio.2015.12.013

    • PubMed
    • Google Scholar
38. 1. Ichikawa M
    2. Yoshimi A
    3. Nakagawa M
    4. Nishimoto N
    5. Watanabe-Okochi N
    6. Kurokawa M

    (2013) A role for RUNX1 in Hematopoiesis and myeloid leukemia

    International Journal of Hematology 97:726–734.

    https://doi.org/10.1007/s12185-013-1347-3

    • PubMed
    • Google Scholar
39. 1. Ikeya M
    2. Takada S

    (2001) Wnt-3a is required for somite specification along the anteroposterior Axis of the mouse embryo and for regulation of cdx-1 expression

    Mechanisms of Development 103:27–33.

    https://doi.org/10.1016/S0925-4773(01)00338-0

    • PubMed
    • Google Scholar
40. 1. Irie N
    2. Weinberger L
    3. Tang WW
    4. Kobayashi T
    5. Viukov S
    6. Manor YS
    7. Dietmann S
    8. Hanna JH
    9. Surani MA

    (2015) SOX17 is a critical specifier of human primordial germ cell fate

    Cell 160:253–268.

    https://doi.org/10.1016/j.cell.2014.12.013

    • PubMed
    • Google Scholar
41. 1. Jones AE
    2. Price FD
    3. Le Grand F
    4. Soleimani VD
    5. Dick SA
    6. Megeney LA
    7. Rudnicki MA

    (2015) Wnt/β-catenin controls follistatin signalling to regulate satellite cell myogenic potential

    Skeletal Muscle 5:14.

    https://doi.org/10.1186/s13395-015-0038-6

    • PubMed
    • Google Scholar
42. 1. Kassar-Duchossoy L
    2. Giacone E
    3. Gayraud-Morel B
    4. Jory A
    5. Gomès D
    6. Tajbakhsh S

    (2005) Pax3/Pax7 mark a novel population of primitive myogenic cells during development

    Genes & Development 19:1426–1431.

    https://doi.org/10.1101/gad.345505

    • PubMed
    • Google Scholar
43. 1. Kim YJ
    2. Lim H
    3. Li Z
    4. Oh Y
    5. Kovlyagina I
    6. Choi IY
    7. Dong X
    8. Lee G

    (2014) Generation of multipotent induced neural crest by direct reprogramming of human postnatal fibroblasts with a single transcription factor

    Cell Stem Cell 15:497–506.

    https://doi.org/10.1016/j.stem.2014.07.013

    • PubMed
    • Google Scholar
44. 1. Kragl M
    2. Roensch K
    3. Nüsslein I
    4. Tazaki A
    5. Taniguchi Y
    6. Tarui H
    7. Hayashi T
    8. Agata K
    9. Tanaka EM

    (2013) Muscle and connective tissue progenitor populations show distinct Twist1 and Twist3 expression profiles during axolotl limb regeneration

    Developmental Biology 373:196–204.

    https://doi.org/10.1016/j.ydbio.2012.10.019

    • PubMed
    • Google Scholar
45. 1. Kumar D
    2. Shadrach JL
    3. Wagers AJ
    4. Lassar AB

    (2009) Id3 is a direct transcriptional target of Pax7 in quiescent satellite cells

    Molecular Biology of the Cell 20:3170–3177.

    https://doi.org/10.1091/mbc.e08-12-1185

    • PubMed
    • Google Scholar
46. 1. Lee MP
    2. Ratner N
    3. Yutzey KE

    (2014) Genome-wide Twist1 occupancy in endocardial cushion cells, embryonic limb buds, and peripheral nerve sheath tumor cells

    BMC Genomics 15:821.

    https://doi.org/10.1186/1471-2164-15-821

    • PubMed
    • Google Scholar
47. 1. Leikina E
    2. Gamage DG
    3. Prasad V
    4. Goykhberg J
    5. Crowe M
    6. Diao J
    7. Kozlov MM
    8. Chernomordik LV
    9. Millay DP

    (2018) Myomaker and myomerger work independently to control distinct steps of membrane remodeling during myoblast fusion

    Developmental Cell 46:767–780.

    https://doi.org/10.1016/j.devcel.2018.08.006

    • PubMed
    • Google Scholar
48. 1. Li L
    2. Chambard JC
    3. Karin M
    4. Olson EN

    (1992) Fos and jun repress transcriptional activation by myogenin and MyoD: the amino terminus of jun can mediate repression

    Genes & Development 6:676–689.

    https://doi.org/10.1101/gad.6.4.676

    • PubMed
    • Google Scholar
49. 1. Liu N
    2. Garry GA
    3. Li S
    4. Bezprozvannaya S
    5. Sanchez-Ortiz E
    6. Chen B
    7. Shelton JM
    8. Jaichander P
    9. Bassel-Duby R
    10. Olson EN

    (2017) A Twist2-dependent progenitor cell contributes to adult skeletal muscle

    Nature Cell Biology 19:202–213.

    https://doi.org/10.1038/ncb3477

    • PubMed
    • Google Scholar
50. 1. Loh Y-H
    2. Wu Q
    3. Chew J-L
    4. Vega VB
    5. Zhang W
    6. Chen X
    7. Bourque G
    8. George J
    9. Leong B
    10. Liu J
    11. Wong K-Y
    12. Sung KW
    13. Lee CWH
    14. Zhao X-D
    15. Chiu K-P
    16. Lipovich L
    17. Kuznetsov VA
    18. Robson P
    19. Stanton LW
    20. Wei C-L
    21. Ruan Y
    22. Lim B
    23. Ng H-H

    (2006) The Oct4 and nanog transcription network regulates pluripotency in mouse embryonic stem cells

    Nature Genetics 38:431–440.

    https://doi.org/10.1038/ng1760

    • Google Scholar
51. 1. Magli A
    2. Perlingeiro RRC

    (2017) Myogenic progenitor specification from pluripotent stem cells

    Seminars in Cell & Developmental Biology 72:87–98.

    https://doi.org/10.1016/j.semcdb.2017.10.031

    • PubMed
    • Google Scholar
52. 1. Mali P
    2. Yang L
    3. Esvelt KM
    4. Aach J
    5. Guell M
    6. DiCarlo JE
    7. Norville JE
    8. Church GM

    (2013) RNA-Guided human genome engineering via Cas9

    Science 339:823–826.

    https://doi.org/10.1126/science.1232033

    • Google Scholar
53. 1. Mascarello F
    2. Toniolo L
    3. Cancellara P
    4. Reggiani C
    5. Maccatrozzo L

    (2016) Expression and identification of 10 sarcomeric MyHC isoforms in human skeletal muscles of different embryological origin. Diversity and similarity in mammalian species

    Annals of Anatomy - Anatomischer Anzeiger 207:9–20.

    https://doi.org/10.1016/j.aanat.2016.02.007

    • PubMed
    • Google Scholar
54. 1. McFarlane C
    2. Hui GZ
    3. Amanda WZ
    4. Lau HY
    5. Lokireddy S
    6. Xiaojia G
    7. Mouly V
    8. Butler-Browne G
    9. Gluckman PD
    10. Sharma M
    11. Kambadur R

    (2011) Human myostatin negatively regulates human myoblast growth and differentiation

    American Journal of Physiology-Cell Physiology 301:C195–C203.

    https://doi.org/10.1152/ajpcell.00012.2011

    • PubMed
    • Google Scholar
55. 1. Meng X
    2. Chen M
    3. Su W
    4. Tao X
    5. Sun M
    6. Zou X
    7. Ying R
    8. Wei W
    9. Wang B

    (2018) The differentiation of mesenchymal stem cells to vascular cells regulated by the HMGB1/RAGE Axis: its application in cell therapy for transplant arteriosclerosis

    Stem Cell Research & Therapy 9:85.

    https://doi.org/10.1186/s13287-018-0827-z

    • PubMed
    • Google Scholar
56. 1. Meola G
    2. Cardani R

    (2015) Myotonic dystrophy type 2: an update on clinical aspects, genetic and pathomolecular mechanism

    Journal of Neuromuscular Diseases 2:S59–S71.

    https://doi.org/10.3233/JND-150088

    • PubMed
    • Google Scholar
57. 1. Millay DP
    2. O'Rourke JR
    3. Sutherland LB
    4. Bezprozvannaya S
    5. Shelton JM
    6. Bassel-Duby R
    7. Olson EN

    (2013) Myomaker is a membrane activator of myoblast fusion and muscle formation

    Nature 499:301–305.

    https://doi.org/10.1038/nature12343

    • PubMed
    • Google Scholar
58. 1. Miraoui H
    2. Marie PJ

    (2010) Pivotal role of twist in skeletal biology and pathology

    Gene 468:1–7.

    https://doi.org/10.1016/j.gene.2010.07.013

    • PubMed
    • Google Scholar
59. 1. Mukherjee S
    2. Zhang T
    3. Lacko LA
    4. Tan L
    5. Xiang JZ
    6. Butler JM
    7. Chen S

    (2018) Derivation and characterization of a UCP1 reporter human ES cell line

    Stem Cell Research 30:12–21.

    https://doi.org/10.1016/j.scr.2018.04.007

    • PubMed
    • Google Scholar
60. 1. Murphy C
    2. Withrow J
    3. Hunter M
    4. Liu Y
    5. Tang YL
    6. Fulzele S
    7. Hamrick MW

    (2018) Emerging role of extracellular vesicles in musculoskeletal diseases

    Molecular Aspects of Medicine 60:123–128.

    https://doi.org/10.1016/j.mam.2017.09.006

    • PubMed
    • Google Scholar
61. 1. Nabeshima Y
    2. Hanaoka K
    3. Hayasaka M
    4. Esumi E
    5. Li S
    6. Nonaka I
    7. Nabeshima Y

    (1993) Myogenin gene disruption results in perinatal lethality because of severe muscle defect

    Nature 364:532–535.

    https://doi.org/10.1038/364532a0

    • PubMed
    • Google Scholar
62. 1. Nichols J
    2. Zevnik B
    3. Anastassiadis K
    4. Niwa H
    5. Klewe-Nebenius D
    6. Chambers I
    7. Schöler H
    8. Smith A

    (1998) Formation of pluripotent stem cells in the mammalian embryo depends on the POU transcription factor Oct4

    Cell 95:379–391.

    https://doi.org/10.1016/S0092-8674(00)81769-9

    • PubMed
    • Google Scholar
63. 1. Niehrs C
    2. Steinbeisser H
    3. De Robertis E

    (1994) Mesodermal patterning by a gradient of the vertebrate homeobox gene goosecoid

    Science 263:817–820.

    https://doi.org/10.1126/science.7905664

    • Google Scholar
64. 1. Parajuli P
    2. Kumar S
    3. Loumaye A
    4. Singh P
    5. Eragamreddy S
    6. Nguyen TL
    7. Ozkan S
    8. Razzaque MS
    9. Prunier C
    10. Thissen JP
    11. Atfi A

    (2018) Twist1 activation in muscle progenitor cells causes muscle loss akin to Cancer cachexia

    Developmental Cell 45:712–725.

    https://doi.org/10.1016/j.devcel.2018.05.026

    • PubMed
    • Google Scholar
65. 1. Parker MH
    2. Seale P
    3. Rudnicki MA

    (2003) Looking back to the embryo: defining transcriptional networks in adult myogenesis

    Nature Reviews Genetics 4:497–507.

    https://doi.org/10.1038/nrg1109

    • PubMed
    • Google Scholar
66. 1. Qi Y
    2. Zhang X-J
    3. Renier N
    4. Wu Z
    5. Atkin T
    6. Sun Z
    7. Ozair MZ
    8. Tchieu J
    9. Zimmer B
    10. Fattahi F
    11. Ganat Y
    12. Azevedo R
    13. Zeltner N
    14. Brivanlou AH
    15. Karayiorgou M
    16. Gogos J
    17. Tomishima M
    18. Tessier-Lavigne M
    19. Shi S-H
    20. Studer L

    (2017) Combined small-molecule inhibition accelerates the derivation of functional cortical neurons from human pluripotent stem cells

    Nature Biotechnology 35:154–163.

    https://doi.org/10.1038/nbt.3777

    • Google Scholar
67. 1. Reddypalli S
    2. Roll K
    3. Lee HK
    4. Lundell M
    5. Barea-Rodriguez E
    6. Wheeler EF

    (2005) p75NTR-mediated signaling promotes the survival of myoblasts and influences muscle strength

    Journal of Cellular Physiology 204:819–829.

    https://doi.org/10.1002/jcp.20330

    • PubMed
    • Google Scholar
68. 1. Relaix F
    2. Rocancourt D
    3. Mansouri A
    4. Buckingham M

    (2005) A Pax3/Pax7-dependent population of skeletal muscle progenitor cells

    Nature 435:948–953.

    https://doi.org/10.1038/nature03594

    • Google Scholar
69. 1. Sakai-Takemura F
    2. Narita A
    3. Masuda S
    4. Wakamatsu T
    5. Watanabe N
    6. Nishiyama T
    7. Nogami K
    8. Blanc M
    9. Takeda S
    10. Miyagoe-Suzuki Y

    (2018) Premyogenic progenitors derived from human pluripotent stem cells expand in floating culture and differentiate into transplantable myogenic progenitors

    Scientific Reports 8:6555.

    https://doi.org/10.1038/s41598-018-24959-y

    • PubMed
    • Google Scholar
70. 1. Salani S
    2. Donadoni C
    3. Rizzo F
    4. Bresolin N
    5. Comi GP
    6. Corti S

    (2012) Generation of skeletal muscle cells from embryonic and induced pluripotent stem cells as an in vitro model and for therapy of muscular dystrophies

    Journal of Cellular and Molecular Medicine 16:1353–1364.

    https://doi.org/10.1111/j.1582-4934.2011.01498.x

    • PubMed
    • Google Scholar
71. 1. Schiaffino S
    2. Rossi AC
    3. Smerdu V
    4. Leinwand LA
    5. Reggiani C

    (2015) Developmental myosins: expression patterns and functional significance

    Skeletal Muscle 5:22.

    https://doi.org/10.1186/s13395-015-0046-6

    • PubMed
    • Google Scholar
72. 1. Schwander M
    2. Leu M
    3. Stumm M
    4. Dorchies OM
    5. Ruegg UT
    6. Schittny J
    7. Müller U

    (2003) ??1 integrins regulate myoblast fusion and sarcomere assembly

    Developmental Cell 4:673–685.

    https://doi.org/10.1016/S1534-5807(03)00118-7

    • Google Scholar
73. 1. Seale P
    2. Sabourin LA
    3. Girgis-Gabardo A
    4. Mansouri A
    5. Gruss P
    6. Rudnicki MA

    (2000) Pax7 is required for the specification of myogenic satellite cells

    Cell 102:777–786.

    https://doi.org/10.1016/S0092-8674(00)00066-0

    • PubMed
    • Google Scholar
74. 1. Sharma VP
    2. Fenwick AL
    3. Brockop MS
    4. McGowan SJ
    5. Goos JA
    6. Hoogeboom AJ
    7. Brady AF
    8. Jeelani NO
    9. Lynch SA
    10. Mulliken JB
    11. Murray DJ
    12. Phipps JM
    13. Sweeney E
    14. Tomkins SE
    15. Wilson LC
    16. Bennett S
    17. Cornall RJ
    18. Broxholme J
    19. Kanapin A
    20. Johnson D
    21. Wall SA
    22. van der Spek PJ
    23. Mathijssen IM
    24. Maxson RE
    25. Twigg SR
    26. Wilkie AO
    27. 500 Whole-Genome Sequences (WGS500) Consortium

    (2013) Mutations in TCF12, encoding a basic helix-loop-helix partner of TWIST1, are a frequent cause of coronal craniosynostosis

    Nature Genetics 45:304–307.

    https://doi.org/10.1038/ng.2531

    • PubMed
    • Google Scholar
75. 1. Shelton M
    2. Metz J
    3. Liu J
    4. Carpenedo RL
    5. Demers SP
    6. Stanford WL
    7. Skerjanc IS

    (2014) Derivation and expansion of PAX7-positive muscle progenitors from human and mouse embryonic stem cells

    Stem Cell Reports 3:516–529.

    https://doi.org/10.1016/j.stemcr.2014.07.001

    • PubMed
    • Google Scholar
76. 1. Stuart CA
    2. Stone WL
    3. Howell ME
    4. Brannon MF
    5. Hall HK
    6. Gibson AL
    7. Stone MH

    (2016) Myosin content of individual human muscle fibers isolated by laser capture microdissection

    American Journal of Physiology-Cell Physiology 310:C381–C389.

    https://doi.org/10.1152/ajpcell.00317.2015

    • PubMed
    • Google Scholar
77. 1. Tchieu J
    2. Zimmer B
    3. Fattahi F
    4. Amin S
    5. Zeltner N
    6. Chen S
    7. Studer L

    (2017) A modular platform for differentiation of human PSCs into all major ectodermal lineages

    Cell Stem Cell 21:399–410.

    https://doi.org/10.1016/j.stem.2017.08.015

    • PubMed
    • Google Scholar
78. 1. Tedesco FS
    2. Gerli MFM
    3. Perani L
    4. Benedetti S
    5. Ungaro F
    6. Cassano M
    7. Antonini S
    8. Tagliafico E
    9. Artusi V
    10. Longa E
    11. Tonlorenzi R
    12. Ragazzi M
    13. Calderazzi G
    14. Hoshiya H
    15. Cappellari O
    16. Mora M
    17. Schoser B
    18. Schneiderat P
    19. Oshimura M
    20. Bottinelli R
    21. Sampaolesi M
    22. Torrente Y
    23. Broccoli V
    24. Cossu G

    (2012) Transplantation of genetically corrected human iPSC-Derived progenitors in mice with Limb-Girdle muscular dystrophy

    Science Translational Medicine 4:ra89.

    https://doi.org/10.1126/scitranslmed.3003541

    • Google Scholar
79. 1. Thomson JA
    2. Itskovitz-Eldor J
    3. Shapiro SS
    4. Waknitz MA
    5. Swiergiel JJ
    6. Marshall VS
    7. Jones JM

    (1998) Embryonic stem cell lines derived from human blastocysts

    Science 282:1145–1147.

    https://doi.org/10.1126/science.282.5391.1145

    • PubMed
    • Google Scholar
80. 1. Tichy ED
    2. Sidibe DK
    3. Greer CD
    4. Oyster NM
    5. Rompolas P
    6. Rosenthal NA
    7. Blau HM
    8. Mourkioti F

    (2018) A robust Pax7EGFP mouse that enables the visualization of dynamic behaviors of muscle stem cells

    Skeletal Muscle 8:27.

    https://doi.org/10.1186/s13395-018-0169-7

    • PubMed
    • Google Scholar
81. 1. van de Leemput J
    2. Boles NC
    3. Kiehl TR
    4. Corneo B
    5. Lederman P
    6. Menon V
    7. Lee C
    8. Martinez RA
    9. Levi BP
    10. Thompson CL
    11. Yao S
    12. Kaykas A
    13. Temple S
    14. Fasano CA

    (2014) CORTECON: a temporal transcriptome analysis of in vitro human cerebral cortex development from human embryonic stem cells

    Neuron 83:51–68.

    https://doi.org/10.1016/j.neuron.2014.05.013

    • PubMed
    • Google Scholar
82. 1. van Velthoven CTJ
    2. de Morree A
    3. Egner IM
    4. Brett JO
    5. Rando TA

    (2017) Transcriptional profiling of quiescent muscle stem cells in Vivo

    Cell Reports 21:1994–2004.

    https://doi.org/10.1016/j.celrep.2017.10.037

    • PubMed
    • Google Scholar
83. 1. Wang X
    2. Blagden C
    3. Fan J
    4. Nowak SJ
    5. Taniuchi I
    6. Littman DR
    7. Burden SJ

    (2005) Runx1 prevents wasting, myofibrillar disorganization, and autophagy of skeletal muscle

    Genes & Development 19:1715–1722.

    https://doi.org/10.1101/gad.1318305

    • PubMed
    • Google Scholar
84. 1. Wang L
    2. Yu L
    3. Zhang T
    4. Wang L
    5. Leng Z
    6. Guan Y
    7. Wang X

    (2014) HMGB1 enhances embryonic neural stem cell proliferation by activating the MAPK signaling pathway

    Biotechnology Letters 36:1631–1639.

    https://doi.org/10.1007/s10529-014-1525-2

    • PubMed
    • Google Scholar
85. 1. Woodhouse S
    2. Pugazhendhi D
    3. Brien P
    4. Pell JM

    (2013) Ezh2 maintains a key phase of muscle satellite cell expansion but does not regulate terminal differentiation

    Journal of Cell Science 126:565–579.

    https://doi.org/10.1242/jcs.114843

    • PubMed
    • Google Scholar
86. 1. Xu M
    2. Chen X
    3. Chen D
    4. Yu B
    5. Huang Z

    (2017) FoxO1: a novel insight into its molecular mechanisms in the regulation of skeletal muscle differentiation and fiber type specification

    Oncotarget 8:10662–10674.

    https://doi.org/10.18632/oncotarget.12891

    • PubMed
    • Google Scholar
87. 1. Yin H
    2. Price F
    3. Rudnicki MA

    (2013) Satellite cells and the muscle stem cell niche

    Physiological Reviews 93:23–67.

    https://doi.org/10.1152/physrev.00043.2011

    • PubMed
    • Google Scholar
88. 1. Zhu X
    2. Yeadon JE
    3. Burden SJ

    (1994) AML1 is expressed in skeletal muscle and is regulated by innervation

    Molecular and Cellular Biology 14:8051–8057.

    https://doi.org/10.1128/MCB.14.12.8051

    • Google Scholar

Acceptance summary:

This study characterises distinct stages of myogenic cell production from human pluripotent stem cells from OCT4+ stage to the generation of differentiated MYOGENIN-expressing cells. The authors provide detailed transcriptomics data of the dynamics of lineage progression in this system. In doing so, they identify TWIST1 as a key regulator for maintenance of PAX7+ skeletal muscle progenitors. Therefore, this study provides important information of stage-specific generation of skeletal muscles from human pluripotent cells.

Decision letter after peer review:

Thank you for submitting your article "Transcriptional landscape of human myogenesis reveals a key role of TWIST1 in maintenance of skeletal muscle progenitors" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Didier Stainier as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Choi et al. utilize an in vitro differentiation system of human pluripotent stem cells (PSC) to recapitulate key events of skeletal myogenesis. Differentiation protocols for human iPSCs are available in the literature, however a detailed step-wise analysis is lacking. To do so, the authors generated several human eGFP reporter ES cell lines, which allowed for the purification of specific cell populations at different stages of development: these include pluripotent (OCT4), presomitic mesoderm (MSGN1), skeletal muscle progenitors (PAX7), "myoblasts" (MYOG) and multinucleated myotubes. Whole transcriptome analysis using these reporter lines identified selected genes. Validations of some were done (CD271, RUNX1, and TWIST1). Overall the study does not provide major insights into the myogenic process. However, this in vitro PSC differentiation system can be considered to be a useful resource. In this light, there are some major gaps in the analysis that would need to be addressed.

Major points:

General:

A) Protocols for iPSC differentiation still generate heterogenous cell populations, including non-myogenic cells. This point needs to be addressed especially concerning PAX7 which is expressed in neural crest and neuronal cells. Further, there are major gaps in the developmental sequence covered by the study (15-20 days). MSGN1 reporter positive cells are MSGN1+:TBX6+ cells, which represent the paraxial mesoderm before the formation of somites. Therefore, in between the MSGN1+ stage and the PAX7+ stage, the intermediate somite stage is missing. Moreover, although dermomyotome progenitors are PAX7+ and negative for muscle determination gene (MYF5, MYOD) expression – the authors need to consider that at least in the mouse Pax3 and MYF5 expression precede that of PAX7. After a primary myotome is formed, PAX7 then contributes to further myogenesis.

A careful time course analysis is required to assess if this is also the case in human and if Pax3 could provide an intermediate link. Also, authors should consider the expression of other genes (see below) that might bridge the gap. Based on the RNA expression data, showing MYF5 and MYOD expression, it is possible that PAX7 reporter+ cells are a mixed population. A higher resolution analysis could resolve the missing transition states and clarify some of these issues.

Specifically, the authors should:

– report the proportion of PAX7 reporter+ cells expressing MYOD protein.

– characterise cultures between MSGN1+ and PAX7+ states, including Meox1, FoxC 1/2, Paraxis (scleraxis – dermomyotome derivative) and Pax3 expression. While including these stages will make the study comprehensive, the lack of the somite stage may not seriously limit the usefulness of study as a resource. It is important to thoroughly characterise the steps in between and explicitly state the steps missing in the data.

B) For this study to serve the community as a resource, the authors should demonstrate that mining their data will yield insights. In this report, the data on CD271 and Twist1 are presented as examples of findings that could be made using the data. However, deeper characterisation is needed. In addition, organising the data with a user interface would be useful for exploitation of the data by the research community.

C) The study is largely descriptive, focusing on genes previously reported to be involved in myogenesis (PLoS Genet. 2015, 11(8):e1005457; Sci Rep. 2018, 8(1):6555; Dev Cell. 2018, 45(6):712-725). Therefore, despite the potential for discovery, the study does not provide significant conceptual advance for the field as the data largely fits with our current understanding of murine myogenesis. For example, the GO analyses performed in the study to identify functionally-related genes expressed in the distinct developmental stages does not seem to reveal any novel information from the time points chosen.

D) While the study highlights transcription factors expressed differentially in the discrete stages, analysing whether they act in the network to control the different processes associated with each stage will add useful information.

Specific:

1) Figure 4A-F, CD271 (mesenchymal stromal marker) was identified as a cell surface marker to isolate PAX7+ cells suggesting that it is a new marker for muscle progenitor cells in this manuscript. However, CD271(NGFR) was previously identified as a marker for skeletal muscle progenitors in hPSCs. Please cite and discuss the paper published by Hicks et al., 2018; Sakai-Takemura et al., 2018.

Also, the authors clearly show that CD271 is highly expressed in the PAX7+ fraction, but they do not show the expression pattern in the PAX7neg fraction. It is important to further confirm the usefulness of this antibody for the purification of myogenic progenitors. Please compare unsorted, PAX7+, and PAX7neg cell fractions. The authors show that CD271 bright can be expanded without losing myogenic capability. For this finding to be meaningful, the authors should show that other cell fractions (unsorted, CD271low and CD271inter) do lose myogenic capability upon expansion.

2) The authors attempt to provide more mechanistic insight on the role of TWIST1 in the specification, maintenance and differentiation of PAX7+ cells, but the analysis, unfortunately, remains superficial. Confirmation of TWIST1 deletion and subsequent lower levels of PAX7 should be supported by western blot. The authors should comment on the publication on TWIST1 that suggests a different role for this gene (Dev Cell. 2018, 45(6):712-725). This should be discussed.

3) Figure 1C – FACS analysis shows that MSGN1-EGFP+ reached 94% on day4. But the MSGN1+ and MSGN1- cells population are not well distinguished. Please check gating conditions and use hPSC cell line (without EGFP) as a negative control. Also, how was the FACS done for MF20? This is a late differentiation marker. Was the FACS done on single cells and doublets/triplets? Further, MYOG-EGFP+ cells is around 6% in Figure 1D, but stated as 11.81% in the text (paragraph two subsection “Generation of stage-specific genetic reporter hPSC lines to stimulate human embryonic myogenesis in vitro”). Please explain discrepancy.

4) Figure 1D – shows a time-course expression of each marker gene. However, differently from the authors' hypothesis and Figure 1A, PAX7 expression is not higher and does not appear before MYOG expression, indicating that these myocytes (MYOG+, Day30) were not derived from the PAX7+ cells. Also, contrary to these data, Figure 1B shows PAX7 protein highly expressed on Day 20. Please explain these discrepancies and comment on the variability in experiments.

5) Figure 4G-H – authors claim that RUNX1 is a marker for myotube formation, but not the marker for "myoblasts" (MYOG+). However, in Figure 3B cluster 10, both MYOG and RUNX1 are relatively highly expressed in muscle progenitors (PAX7+) and "myoblasts" (MYOG+). However, in Figure S4D, there is no co-localization between PAX7 and RUNX1 antibody staining. Please explain the difference between Figure 4G and Figure S4D. The authors should check the co-localization of MYOG and RUNX1 by immunostaining to clarify their expression in myoblasts and myotubes.

6) Figure 5D – the population of PAX7+ cells is approximately 10% in both of WT and TWIST1-KO cell lines, which is different from 20% of PAX7+ cell shown in Figure 1C and 17.8% in the Results section of Figure 1. Please comment on the variations between experiments and indicate ranges of expression. Perhaps the WT cell line could reach 20% for PAX7 population (Figure 1C), but TWIST1-KO might have decreased levels of PAX7. Considering TWIST1 is a transcription factor essential for mesoderm development, the expression of MSGN1 (day4), TBX6 (day4), PAX7(day20), MYF5 (day20) should be verified.

7) Figure 5E – please show in detail the culture protocol for muscle progenitor maintenance and images of cultured cells and cell number count from 0P to 4P. If the interpretation is correct, in Figure 5E both WT and KO PAX7+ cell population are remarkably decreased from 0P to 1P; KO even more so (from 100% to 23%) compared to WT (from 100% to 37%). However, by 4P, both WT and KO slightly decreased in PAX7+ cells, and KO have approximately 1/2 PAX7+ compared to WT from 2P-4P. These results suggest that the effect of TWIST1-KO is in the period after sorting (0P-1P), but might not be required later. Please also check the immunostaining with MYOD or MYOG in the maintenance 0P-1P to assess the committed status of PAX7+ cells. It remains possible that TWIST1 plays an indirect role in PAX7 regulation; three is a trend to lower proliferation as seen in Figure 5—figure supplement 1D-E.

8) Figure 5—figure supplement 1A – it is hard to see if TWIST1+ staining co-localizes with DAPI. Please show clearer images.

9) Saethre-Chotzen syndrome patients have prematurely fused skull bones along the coronal suture and significant phenotypes in skeletal muscle are not noted. It is therefore not clear how the data provided in this manuscript explains the mechanism of Saethre-Chotzen syndrome. Furthermore, mutations of FGFR2 or FGFR3 are also reported to be associated with Saethre-Chotzen syndrome. The authors should omit the reference to Saethre-Chotzen syndrome in the Abstract, and speculate in the Discussion, if this is of relevance.

10) A "myoblast" is generally referred to as a proliferating cell. It is likely that MYOG+ cells are no longer proliferating, and it is a differentiation marker, hence these cells should be referred to as differentiated cells, and staining needs to be done with this reporter gene and a proliferation marker (ex. Ki67, BrdU).

Major points:

General:

A) Protocols for iPSC differentiation still generate heterogenous cell populations, including non-myogenic cells. […] A higher resolution analysis could resolve the missing transition states and clarify some of these issues.

Specifically, the authors should:

– report the proportion of PAX7 reporter+ cells expressing MYOD protein.

– characterise cultures between MSGN1+ and PAX7+ states, including Meox1, FoxC 1/2, Paraxis (scleraxis – dermomyotome derivative) and Pax3 expression. While including these stages will make the study comprehensive, the lack of the somite stage may not seriously limit the usefulness of study as a resource. It is important to thoroughly characterise the steps in between and explicitly state the steps missing in the data.

We thank the reviewer for constructive comments. To confirm the cellular identity of the PAX7::EGFP+ cells generated by our skeletal muscle differentiation protocol, we performed RT-PCR with primer sets specific for neural and neural crest lineages such as SOX10, PAX6, and antibody staining of SOX10 and AP2 (Figure 1—figure supplement 2A-C). PAX7::EGFP+ cells showed significantly low levels of gene expression, compared to control neural crest cells (as a positive control). In the protein level, we did not detect any SOX10+ cells, or AP2+ cells in the PAX7::EGFP+ cells. With these results, we demonstrate that PAX7::EGFP+ cells derived from our skeletal muscle protocol are mostly skeletal muscle lineage cells.

As the reviewer pointed out, we confirmed the levels of PAX7, MYOD1, and MYOG protein expression in PAX7::EGFP+ cells (Figure 1—figure supplement 2D) via antibody staining. Most of the PAX7::EGFP+ cells are expressing PAX7 protein, but not MYOD1 and MYOG, although we cannot rule out their possible transcriptional heterogeniety (we performed single cell qRT-PCR with PAX7::EGFP+ cells, please see Figure 1—figure supplement 2F). These data were added in the Results section and Figure 1—figure supplement 2.

For the characterization between MSGN1 and PAX7, we performed the gene expression profiles of T(Brachyury), MIXL1, PAX3, MEOX1, FOXC1, FOXC2, PARAXIS, and SCLERAXIS during in vitro myogenesis (Author response image 1). PAX3, MEOX1, FOXC1, and FOXC2 gene started their gene expression at Day 4, and had a peak between Day 6 and Day 8 which imply that intermediate somite stage fills the gap between MSGN1+ stage and PAX7+ stage. Furthermore, PAX3 gene expression was confirmed through PAX3::EGFP reporter line (Author response image 2). PAX3 gene expression through EGFP protein started at Day 4 and had a peak at Day 8, and disappear gene expression from Day 10. And PAX3 protein expression was also validated during in vitro skeletal muscle specification. We include these new data in the Results and Figure 1—figure supplement 1A.

Author response image 1

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Author response image 2

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B) For this study to serve the community as a resource, the authors should demonstrate that mining their data will yield insights. In this report, the data on CD271 and Twist1 are presented as examples of findings that could be made using the data. However, deeper characterisation is needed. In addition, organising the data with a user interface would be useful for exploitation of the data by the research community.

Thank you for the constructive comments and concerns. We agree with the comment that deeper characterization will be more informative to identify novel insights. As alluded in the above comment, our current study for human myogenesis has possibilities to identify regulators of human myogenic ontogeny. First, we present new transcription analysis data of differential gene expression between PAX7::EGFP+ cells and MYOG::EGFP+ cells (Figure 2—figure supplement 2A-B). Volcano plot indicated significant change in PAX7::EGFP+ cells and MYOG::EGFP+ cells. Furthermore, we classified highly enriched genes specifically in PAX7::EGFP+ cells, MYOG::EGFP+ cells, and Myotubes. Venn diagram showed clear separation between PAX7::EGFP+ cells and Myotubes, while there are overlapped genes in PAX7::EGFP+ cells vs. MYOG::EGFP+ cells, and MYOG::EGFP+ cells vs. Myotubes. Out of 2336 genes upregulated over all samples, 420 genes have specific expression patterns only in PAX7::EGFP+ cells, which were classified into GO terms involved in some signaling pathways including ‘PI3K-Akt signaling pathway’, ‘Wnt signaling’, and ‘Wnt signaling and pluripotency’. These data were confirmed via a phosphorylation antibody blot (R&D, ARY003B), presenting that significantly increased levels of phosphorylation in the CREB and β-catenin in the PAX7::EGFP+ cells over the MYOG::EGFP+ cells (Figure 2—figure supplement 2C). Thanks to the reviewer, we identified ‘Wnt signaling’ pathway is a close association in PAX7::EGFP+ cells, which is consistent with our RNA-seq analysis with TWIST1 KO PAX7::EGFP+ cells (Figure 6D). The detailed experiments for the ‘Wnt signaling’ pathway in early human myogenesis will be pursued in future studies. We include these data in the Results and Figure 2—figure supplement 2A-C.

As suggested in Question B, we organized the data with a user-friendly interface and created a website with an easily searchable interface to act as a companion to this resource paper (www.myogenesis.net) (a screenshot of the website is in Author response image 3). This website will be maintained and updated as new information becomes available. This information was added in the Abstract.

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C) The study is largely descriptive, focusing on genes previously reported to be involved in myogenesis (PLoS Genet. 2015, 11(8):e1005457; Sci Rep. 2018, 8(1):6555; Dev Cell. 2018, 45(6):712-725). Therefore, despite the potential for discovery, the study does not provide significant conceptual advance for the field as the data largely fits with our current understanding of murine myogenesis. For example, the GO analyses performed in the study to identify functionally-related genes expressed in the distinct developmental stages does not seem to reveal any novel information from the time points chosen.

This is a valid point. In this study, our primary goal was realizing comprehensive characterization of the myogenic population derived from human pluripotent stem cells. One of the advantages of our system is time-course analysis of the transcriptional landscape mimicked in vivo human myogenesis, and K-mean clustering grouped genes with significant changes in the time dependent expressions with distinct transcriptional variations during in vitro human skeletal muscle specification. These data defined specialized signature expression profiles for each isolated cell type, constituted of genes with the potential to control cell fates during human skeletal muscle specification. As recommended, to investigate the changes in gene expression associated with the myotube formation, we additionally compared global expression profiles of MYOG::EGFP+ cells and Myotubes (Author response image 4). Ras Pathway, FGF signaling pathway, and EGF receptor signaling pathway were involved in MYOG::EGFP+ cells. Furthermore, we performed differential gene expression analysis between PAX7::EGFP+ cells and MYOG::EGFP+ cells (Figure 2—figure supplement 2A-B). In these data, PI3K-Akt signaling pathway, Wnt signaling, and Wnt signaling and pluripotency were involved in PAX7::EGFP+ cells. With the increased levels of phosphorylation in PAX7::EGFP+ cells via MYOG::EGFP+ cells (Figure 2—figure supplement 2C), we imply that phosphorylation of CREB and β-catenin in PAX7::EGFP+ cells is important to activate CREB during skeletal muscle specification (Stewart, 2011 #127).

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D) While the study highlights transcription factors expressed differentially in the discrete stages, analysing whether they act in the network to control the different processes associated with each stage will add useful information.

We appreciate the reviewer’s comment. In an attempt to further analyze the stage-specific transcriptome regulation during human myogenesis, we applied weighted gene co-expression network analysis (WGCNA) with five different cell types (Figure 6—figure supplement 2). WGCNA with stages-specific transcriptomes allowed us to define modules of genes that are continuously coregulated and to study their stage-specific variation during skeletal muscle differentiation. By including all expressed genes with expression variation, we identified a total of 96 modules defined as groups of genes coordinately expressed across 20 samples (5 cell types and 4 different repeated samples) (Figure 6—figure supplement 2). In the overall network, we found that CD271 belongs to Module 22, TWIST1 to Module 43, and RUNX1 to Module 3. As a key strength of WGCNA is the ability to query the network around genes known to be associated with a trait, we examined the genetic associations within the context of their module, as a network with proteins (genes in each module) as nodes and interaction scores as edges. Our data indicated that TWIST1 has direct interactions with 8 well known transcription factors including MYOD1, TCF4, and TP53, suggesting that TWIST1 may play significant roles in the transcriptional network to control the myogenic specification processes (Figure 6—figure supplement 2B). CD271 is a genetic hub for directing 26 genes to over 10 different genetic hubs (Figure 6—figure supplement 2B). RUNX1 showed massive interactions with 32 transcription factors of many different pathways including epigenetic changes and NOTCH signaling related pathway (Figure 6—figure supplement 2B). Taken together, these data demonstrate that TWIST1, CD271 and RUNX1 play significant roles in the transcriptional network to control the myogenic specification processes. These results were added in Discussion and Figure 6—figure supplement 2.

Specific:

1) Figure 4A-F, CD271 (mesenchymal stromal marker) was identified as a cell surface marker to isolate PAX7+ cells suggesting that it is a new marker for muscle progenitor cells in this manuscript. However, CD271(NGFR) was previously identified as a marker for skeletal muscle progenitors in hPSCs. Please cite and discuss the paper published by Hicks M, et al. Nat. Cell Biol. 2018; Sakai-Takemura et al. Sci. Reports 2018.

Also, the authors clearly show that CD271 is highly expressed in the PAX7+ fraction, but they do not show the expression pattern in the PAX7neg fraction. It is important to further confirm the usefulness of this antibody for the purification of myogenic progenitors. Please compare unsorted, PAX7+, and PAX7neg cell fractions. The authors show that CD271 bright can be expanded without losing myogenic capability. For this finding to be meaningful, the authors should show that other cell fractions (unsorted, CD271low and CD271inter) do lose myogenic capability upon expansion.

We thank the reviewer for the invaluable comments. As mentioned in Question 1, we include two references (Hicks et al., 2018 and Sakai-Takemura et al., 2018) in Discussion.

In the FACS plot, we found three distinctively separate populations in CD271 stained sample (APC only control is in left), and named them as CD271low, CD271inter and CD271bright. Then, we subgated the populations in the PAX7::EGFP channel. As shown in the bottom panels in Figure 4—figure supplement 1A, CD271bright cells included PAX7::EGFP+ cells (77.8% ), compared to the CD271low (2.43%) and CD271inter(24.6% ). With this reason, we only used CD271bright cell populations as a substitute for the PAX7::EGFP+ cells. In the study, we analyzed myogenic capabilities of sorted cells based on CD271 expression levels. As shown Figure 4—figure supplement 1C-E, all three populations showed comparable levels of growth rates, based on the number of cells during cell expansion culture (over two weeks). Importantly, the fusion ability of CD271bright, CD271inter, and CD271low cell populations are quite different, showing that CD271bright cells showed robust fusion capability (comparable to that of PAX7::EGFP+ cells), but not CD271low and CD271inter cells. These data were included in the Results section and Figure 4—figure supplement1.

2) The authors attempt to provide more mechanistic insight on the role of TWIST1 in the specification, maintenance and differentiation of PAX7+ cells, but the analysis, unfortunately, remains superficial. Confirmation of TWIST1 deletion and subsequent lower levels of PAX7 should be supported by western blot. The authors should comment on the publication on TWIST1 that suggests a different role for this gene (Dev Cell. 2018, 45(6):712-725). This should be discussed.

Thank you for pointing this out. As suggested, we confirmed TWIST1 depletion by western blot (Figure 5—figure supplement 1A).

As shown in Author response image 5, PAX7 showed comparable expression level at Day 30 of myogenic specification. After cell sorting, PAX7 expression was decreased in TWIST1 KO PAX7::GFP+ cell clones (#3 and #7), which is a similar pattern to FACS analysis data during expansion of PAX7::GFP+ cells. As mentioned in Question 2, we include the reference (Parajuli et al., 2018) in Discussion. The transcription factors, JUN, ZEB1, ATF-2, MYOD, p53, Pax- 3, STAT3, are known to be bound to transcription factor binding sites in the TWIST1 locus, suggesting that TWIST1 may play a role in myogenic specification processes. This can be one of plausible explanations for our TWIST1 KO experiment results.

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3) Figure 1C – FACS analysis shows that MSGN1-EGFP+ reached 94% on day4. But the MSGN1+ and MSGN1- cells population are not well distinguished. Please check gating conditions and use hPSC cell line (without EGFP) as a negative control. Also, how was the FACS done for MF20? This is a late differentiation marker. Was the FACS done on single cells and doublets/triplets? Further, MYOG-EGFP+ cells is around 6% in Figure 1D, but stated as 11.81% in the text (paragraph two subsection “Generation of stage-specific genetic reporter hPSC lines to stimulate human embryonic myogenesis in vitro”). Please explain discrepancy.

We apologize for the lack of clarity. We included FACS plots of non-genetically modified parental cell line (H9 cell line) as a negative control (Author response image 6). And our FACS experiment protocol was added in the Materials and methods section.

The data in Figure 1D were measured till Day 30 of myogenic specification (Author response image 6), while the percentages of MYOG::EGFP+ cells in subsection “Generation of stage-specific genetic reporter hPSC lines to simulate human embryonic myogenesis in vitro” were measured at Day 35 (Author response image 6), so there is some temporal gap between the figure panel and the text. We ensured that the dates are included in figure legend and main text. As reviewer pointed out, we added protocol for FACS analysis in the Materials and methods.

“For the MF20 FACS analysis, cells were dissociated with Accutase and MF20 was applied and incubated 20 min at 25°C. Secondary antibody was used for the detecting by BD FACS Calibur (Becton Dickinson), and data were visualized using FlowJo software (Tree Star Inc).”

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4) Figure 1D – shows a time-course expression of each marker gene. However, differently from the authors' hypothesis and Figure 1A, PAX7 expression is not higher and does not appear before MYOG expression, indicating that these myocytes (MYOG+, Day30) were not derived from the PAX7+ cells. Also, contrary to these data, Figure 1B shows PAX7 protein highly expressed on Day 20. Please explain these discrepancies and comment on the variability in experiments.

Thank you for the constructive comments. We believe that the myogenic specification from human pluripotent stem cells are not synchronized events, as we found a gradual increase of PAX7::EGFP expression during the time course. Also the in vitro condition may not be favourable to the maintenance of PAX7 expression, which forces the PAX7::EGFP+ cells to be differentiated into MYOG::EGFP+ cells. In addition, as shown in Figure 1—figure supplement 2F, single cell transcription analysis data show that some of PAX7::EGFP+ cells already have transcription of MYOG as well as other marker genes, including MYF5 and MYOD1, while most of the PAX7::EGFP+ cells have high levels of PAX7 expression. Therefore, we believe that in vitro culture condition (favorable for differentiation) and the transcriptional heterogeneity of PAX7::EGFP+ cells are responsible for the slightly increased levels of MYOG::EGFP+ cells in early myogenic specification stage (Day 20-22) in the Figure 1D. In other words, our data do not necessarily conclude that MYOG+ cells are not derived from PAX7::EGFP+ cells. We include these data in the Results section and Figure 1—figure supplement 2F, and interpretation in Discussion as below.

“We believe that the myogenic specification from human pluripotent stem cells are not synchronized events, as we see that gradual increase of PAX7::EGFP expression during the time course. Also the in vitro condition may not be favorable to the maintenance of PAX7 expression, which forces the PAX7::EGFP+ cells to be differentiated into MYOG::EGFP+ cells. Therefore, we believe that in vitro culture condition (favorable for differentiation) and the transcriptional heterogeneity of PAX7::EGFP+ cells are responsible for the slightly increased levels of MYOG::EGFP+ cells in early myogenic specification stage (Day 20-22) in the Figure 1D. However, our data do not necessarily conclude that MYOG+ cells are not derived from PAX7::EGFP+ cells.“

5) Figure 4G-H – authors claim that RUNX1 is a marker for myotube formation, but not the marker for "myoblasts" (MYOG+). However, in Figure 3B cluster 10, both MYOG and RUNX1 are relatively highly expressed in muscle progenitors (PAX7+) and "myoblasts" (MYOG+). However, in Figure S4D, there is no co-localization between PAX7 and RUNX1 antibody staining. Please explain the difference between Figure 4G and Figure S4D. The authors should check the co-localization of MYOG and RUNX1 by immunostaining to clarify their expression in myoblasts and myotubes.

This is a valid point. RUNX1 gene is included in the Cluster 10. Cluster 10 is a collection of genes that are not detected in the undifferentiated pluripotent stem cell stage, then start to express during skeletal muscle specification, and show maximum levels of gene expression in the Myotube stage. Indeed, our RNA-seq analysis show that RUNX1 has a gradual expression pattern, detected in PAX7::EGFP+ cells and MYOG::EGFP+ cells, and then highest levels in Myotube group. Furthermore, we tested if protein expression of RUNX1 can be detected in MYOG+ cells, by performing RUNX1 and MYOG antibody staining. As shown in Figure 4—figure supplement 1H, RUNX1 expression was found in multinucleated myotube, and we found that RUNX1 is colocalized MYOG+ cells. These data may imply that there might be a posttranscriptional regulation of RUNX1 during human myogenesis, which results in lack of PAX7 co-localization in myotube stages. The result was added in Figure 4—figure supplement 1H.

6) Figure 5D – the population of PAX7+ cells is approximately 10% in both of WT and TWIST1-KO cell lines, which is different from 20% of PAX7+ cell shown in Figure 1C and 17.8% in the Results section of Figure 1. Please comment on the variations between experiments and indicate ranges of expression. Perhaps the WT cell line could reach 20% for PAX7 population (Figure 1C), but TWIST1-KO might have decreased levels of PAX7. Considering TWIST1 is a transcription factor essential for mesoderm development, the expression of MSGN1 (day4), TBX6 (day4), PAX7(day20), MYF5 (day20) should be verified.

We apologize for the lack of clarity. Usually, we found that PAX7::EGFP+ cells show around 20% of EGFP+ cells at Day 35 of myogenic specification. But we performed the comparison between the wild type (WT) and TWIST1 KO cells in much earlier time points (Day 25 to Day 30) to avoid any possible misinterpretation and/or unexpected effects caused by depleted TWIST1 expression, for example, spontaneous/precocious differentiation to multinucleated myotubes, lack of PAX7 maintenance, or gradual cell death. We presented the data of individual clones of TWIST1 KO lines in Figure 5—figure supplement 1C, showing there is no significant difference in terms of the levels of PAX7::EGFP+ cells.

As reviewer’s recommendation, we performed transcriptional analysis during early development of the TWIST1 KO clones. As shown in Figure 5—figure supplement 2A, MSGN1 and TBX6 gene expression at Day 2 and Day 4 showed comparable pattern to WT and KO cell lines. However, TWIST1 KO clones show aberrant expression patterns in the PAX3, PAX7, MYF5, MYOD1, and MYOG. We included a new sentence in the Results as “there are aberrant expression patterns of PAX3, PAX7, MYF5, and MYOD1 during the myogenic specification, although the levels of PAX7::EGFP+ cells in WT and TWIST1 KO clones (at Day 25 to Day 30 of myogenic specification) are comparable.” We include these data in Figure 5—figure supplement 1C and Figure 5—figure supplement 2A.

7) Figure 5E – please show in detail the culture protocol for muscle progenitor maintenance and images of cultured cells and cell number count from 0P to 4P. If the interpretation is correct, in Figure 5E both WT and KO PAX7+ cell population are remarkably decreased from 0P to 1P; KO even more so (from 100% to 23%) compared to WT (from 100% to 37%). However, by 4P, both WT and KO slightly decreased in PAX7+ cells, and KO have approximately 1/2 PAX7+ compared to WT from 2P-4P. These results suggest that the effect of TWIST1-KO is in the period after sorting (0P-1P), but might not be required later. Please also check the immunostaining with MYOD or MYOG in the maintenance 0P-1P to assess the committed status of PAX7+ cells. It remains possible that TWIST1 plays an indirect role in PAX7 regulation; three is a trend to lower proliferation as seen in Figure 5—figure supplement D-E.

We appreciate comments and concerns. Maintaining skeletal muscle stem cells provide a unique opportunity to understand mechanism of skeletal muscle generation and can be used for further studies, such as in vitro manipulation and disease modeling. In this study, we developed a new culture condition for maintaining putative human PAX7::EGFP+ skeletal muscle stem cells. Although often in vitro culture conditions are permeable to be differentiated into multinucleated myotubes, we found a condition to maintain the around 50% of PAX7::EGFP+ cells with great myogenic capabilities until passage 5 (over a month). In this condition, we found that the EGFP+ levels of TWIST1 KO PAX7::EGFP+ cells could not be maintained. To quantify this trend, we measured percentages of PAX7::EGFP+ cells during the passaging in WT and the KO clones. We present the data of each clone individually (Figure 5—figure supplement 2B) (the data in Figure 5E are combined one of two different KO clones). As shown in Figure 5—figure supplement 2B, both KO clones (clone #1 and #3) gradually lost the GFP levels of PAX7::EGFP+ cells during the passages. In addition, we looked into the cellular aspects. As shown in Figure 5—figure supplement 2C-E, we could not find any significantly difference between the WT and the KO clones in terms of cell morphology, proliferation rates and the proportions of PAX7+ and MYOG+ cells (Figure 5—figure supplement 2C-E). These results were added in the Results and Figure 5—figure supplement 2B-G. As reviewer recommended, we added protocol for cell culture after sorting in the Materials and methods as below.

“For the maintinaing EGFP+/- cells after cell sorting, EGFP+/- cells were plated in 1% Geltrexcoated dish with N2 media containing 5% FBS, 10ng/ml FGF-2 and 100ng/ml FGF-8. Media were changed at every other day. For passaging, cells were dissociated with Accutase for 20 min, washed once with N2 media.”

8) Figure 5—figure supplement 1A – it is hard to see if TWIST1+ staining co-localizes with DAPI. Please show clearer images.

Thank you for the pointing this out. As reviewer suggested, we changed the images of TWIST1+ cells (Figure 5—figure supplement 1A), showing that nuclear staining of TWIST1 is obvious in wildtype (WT), but not in knock-out (KO) clones. And these data were included in Figure 5—figure supplement 1A.

9) Saethre-Chotzen syndrome patients have prematurely fused skull bones along the coronal suture and significant phenotypes in skeletal muscle are not noted. It is therefore not clear how the data provided in this manuscript explains the mechanism of Saethre-Chotzen syndrome. Furthermore, mutations of FGFR2 or FGFR3 are also reported to be associated with Saethre-Chotzen syndrome. The authors should omit the reference to Saethre-Chotzen syndrome in the Abstract, and speculate in the Discussion, if this is of relevance.

As recommended, we omit the Saethre-Chotzen syndrome in the Abstract and moved to Discussion part.

10) A "myoblast" is generally referred to as a proliferating cell. It is likely that MYOG+ cells are no longer proliferating, and it is a differentiation marker, hence these cells should be referred to as differentiated cells, and staining needs to be done with this reporter gene and a proliferation marker (ex. Ki67, BrdU).

Thank you for the reviewer’s concern. We checked the proliferation rates by performing Ki67, PAX7, and MYOG antibody staining. As shown in Figure 1—figure supplement 2E, the percentages of Ki67+ cells in PAX7+ and MYOG+ cells are ~35% and ~5% , respectively. As reviewer recommended, we changed the terminology from myoblast to ‘MYOG::EGFP+ cell’.

Author details

1. In Young Choi

   1. The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States
   2. Department of Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Hotae Lim

   Competing interests

   No competing interests declared

2. Hotae Lim

   1. The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States
   2. College of Veterinary Medicine, Chungbuk National University, Chungbuk, Republic of Korea

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   In Young Choi

   Competing interests

   No competing interests declared

3. Hyeon Jin Cho

   Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States

   Present address

   University of Maryland, College Park, United States

   Contribution

   Software, Formal analysis, Investigation, Visualization

   Competing interests

   No competing interests declared

4. Yohan Oh

   The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States

   Present address

   Department of Medicine, College of Medicine, Hanyang University, Seoul, Republic of Korea

   Contribution

   Resources, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9249-8664

5. Bin-Kuan Chou

   1. The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States
   2. Division of Hematology, Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6022-4127

6. Hao Bai

   1. The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States
   2. Division of Hematology, Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, United States

   Contribution

   Resources, Methodology

   Competing interests

   No competing interests declared

7. Linzhao Cheng

   Division of Hematology, Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, United States

   Contribution

   Resources, Supervision

   Competing interests

   No competing interests declared

8. Yong Jun Kim

   Department of Pathololgy, College of Medicine, Kyung Hee University, Seoul, Republic of Korea

   Contribution

   Supervision, Investigation

   Competing interests

   No competing interests declared

9. SangHwan Hyun

   1. The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States
   2. College of Veterinary Medicine, Chungbuk National University, Chungbuk, Republic of Korea

   Contribution

   Supervision, Investigation

   Competing interests

   No competing interests declared

10. Hyesoo Kim

    1. The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States
    2. Department of Neurology, Johns Hopkins University, School of Medicine, Baltimore, United States

    Contribution

    Data curation, Supervision, Investigation, Visualization, Writing - original draft, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4314-1327

11. Joo Heon Shin

    Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, United States

    Contribution

    Software, Supervision, Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

12. Gabsang Lee

    1. The Institute for Cell Engineering, Johns Hopkins University, School of Medicine, Baltimore, United States
    2. Department of Neurology, Johns Hopkins University, School of Medicine, Baltimore, United States
    3. The Solomon H. Synder Department of Neuroscience, Johns Hopkins University, School of Medicine, Baltimore, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    glee48@jhmi.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5052-5927

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