Lineage tracing of nuclei in skeletal myofibers uncovers distinct transcripts and interplay between myonuclear populations.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
13
09
2023
accepted:
10
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Multinucleated skeletal muscle cells need to acquire additional nuclei through fusion with activated skeletal muscle stem cells when responding to both developmental and adaptive growth stimuli. A fundamental question in skeletal muscle biology has been the reason underlying this need for new nuclei in cells that already harbor hundreds of nuclei. Here we utilize nuclear RNA-sequencing approaches and develop a lineage tracing strategy capable of defining the transcriptional state of recently fused nuclei and distinguishing this state from that of pre-existing nuclei. Our findings reveal the presence of conserved markers of newly fused nuclei both during development and after a hypertrophic stimulus in the adult. However, newly fused nuclei also exhibit divergent gene expression that is determined by the myogenic environment to which they fuse. Moreover, accrual of new nuclei through fusion is required for nuclei already resident in adult myofibers to mount a normal transcriptional response to a load-inducing stimulus. We propose a model of mutual regulation in the control of skeletal muscle development and adaptations, where newly fused and pre-existing myonuclear populations influence each other to maintain optimal functional growth.
Identifiants
pubmed: 39477931
doi: 10.1038/s41467-024-53510-z
pii: 10.1038/s41467-024-53510-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9372Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
ID : R01AG059605
Informations de copyright
© 2024. The Author(s).
Références
Bachman, J. F. et al. Prepubertal skeletal muscle growth requires Pax7-expressing satellite cell-derived myonuclear contribution. Development 145, dev167197 (2018).
pubmed: 30305290
pmcid: 6215399
doi: 10.1242/dev.167197
Pawlikowski, B., Pulliam, C., Betta, N. D., Kardon, G. & Olwin, B. B. Pervasive satellite cell contribution to uninjured adult muscle fibers. Skelet. muscle 5, 1–13 (2015).
doi: 10.1186/s13395-015-0067-1
White, R. B., Biérinx, A.-S., Gnocchi, V. F. & Zammit, P. S. Dynamics of muscle fibre growth during postnatal mouse development. BMC Dev. Biol. 10, 1–11 (2010).
doi: 10.1186/1471-213X-10-21
Bachman, J. F. & Chakkalakal, J. V. Insights into muscle stem cell dynamics during postnatal development. FEBS J. 289, 2710–2722 (2022).
pubmed: 33811430
doi: 10.1111/febs.15856
Collins, B. C. et al. Cellular dynamics of skeletal muscle regeneration. bioRxiv https://doi.org/10.1101/2023.05.02.538744 (2023).
Dumont, N. A., Bentzinger, C. F., Sincennes, M. C. & Rudnicki, M. A. Satellite cells and skeletal muscle regeneration. Compr. Physiol. 5, 1027–1059 (2015).
pubmed: 26140708
doi: 10.1002/cphy.c140068
Hawke, T. J. & Garry, D. J. Myogenic satellite cells: physiology to molecular biology. J. Appl. Physiol. 91, 534–551 (2001).
pubmed: 11457764
doi: 10.1152/jappl.2001.91.2.534
Kang, J.-S. & Krauss, R. S. Muscle stem cells in developmental and regenerative myogenesis. Curr. Opin. Clin. Nutr. Metab. Care 13, 243–248 (2010).
pubmed: 20098319
pmcid: 2872152
doi: 10.1097/MCO.0b013e328336ea98
Mauro, A. Satellite cell of skeletal muscle fibers. J. Biophys. Biochem Cytol. 9, 493 (1961).
pubmed: 13768451
pmcid: 2225012
doi: 10.1083/jcb.9.2.493
Millay, D. P. Regulation of the myoblast fusion reaction for muscle development, regeneration, and adaptations. Exp. cell Res. 415, 113134 (2022).
pubmed: 35367215
pmcid: 9058940
doi: 10.1016/j.yexcr.2022.113134
Relaix, F. & Zammit, P. S. Satellite cells are essential for skeletal muscle regeneration: the cell on the edge returns centre stage. Development 139, 2845–2856 (2012).
pubmed: 22833472
doi: 10.1242/dev.069088
Blau, H. M., Cosgrove, B. D. & Ho, A. T. The central role of muscle stem cells in regenerative failure with aging. Nat. Med. 21, 854–862 (2015).
pubmed: 26248268
pmcid: 4731230
doi: 10.1038/nm.3918
Porpiglia, E. & Blau, H. M. Plasticity of muscle stem cells in homeostasis and aging. Curr. Opin. Genet. Dev. 77, 101999 (2022).
pubmed: 36308777
doi: 10.1016/j.gde.2022.101999
Prasad, V. & Millay, D. P. In Seminars in Cell & Developmental Biology. 3–10 (Elsevier).
Ralston, E. & Hall, Z. W. Restricted distribution of mRNA produced from a single nucleus in hybrid myotubes. J. cell Biol. 119, 1063–1068 (1992).
pubmed: 1447288
doi: 10.1083/jcb.119.5.1063
Pavlath, G. K., Rich, K., Webster, S. G. & Blau, H. M. Localization of muscle gene products in nuclear domains. Nature 337, 570–573 (1989).
pubmed: 2915707
doi: 10.1038/337570a0
Allen, D. L., Roy, R. R. & Edgerton, V. R. Myonuclear domains in muscle adaptation and disease. Muscle Nerve 22, 1350–1360 (1999).
pubmed: 10487900
doi: 10.1002/(SICI)1097-4598(199910)22:10<1350::AID-MUS3>3.0.CO;2-8
Bruusgaard, J., Liestøl, K., Ekmark, M., Kollstad, K. & Gundersen, K. Number and spatial distribution of nuclei in the muscle fibres of normal mice studied in vivo. J. Physiol. 551, 467–478 (2003).
pubmed: 12813146
pmcid: 2343230
doi: 10.1113/jphysiol.2003.045328
Liu, J. X. et al. Myonuclear domain size and myosin isoform expression in muscle fibres from mammals representing a 100 000-fold difference in body size. Exp. Physiol. 94, 117–129 (2009).
pubmed: 18820003
doi: 10.1113/expphysiol.2008.043877
Van der Meer, S., Jaspers, R. & Degens, H. Is the myonuclear domain size fixed? J. Musculoskelet. Neuro. Interact. 11, 286–297 (2011).
Qaisar, R. & Larsson, L. What determines myonuclear domain size? Indian J. Physiol. Pharmacol. 58, 1–12 (2014).
pubmed: 25464670
Bagley, J. R., Denes, L. T., McCarthy, J. J., Wang, E. T. & Murach, K. A. The myonuclear domain in adult skeletal muscle fibres: past, present, and future. J. Physiol. 601, 723–741 (2023).
pubmed: 36629254
doi: 10.1113/JP283658
Murach, K. A., Englund, D. A., Dupont-Versteegden, E. E., McCarthy, J. J. & Peterson, C. A. Myonuclear domain flexibility challenges rigid assumptions on satellite cell contribution to skeletal muscle fiber hypertrophy. Front. Physiol. 9, 635 (2018).
pubmed: 29896117
pmcid: 5986879
doi: 10.3389/fphys.2018.00635
Arnold, E. M. & Delp, S. L. Fibre operating lengths of human lower limb muscles during walking. Philos. Trans. R. Soc. B: Biol. Sci. 366, 1530–1539 (2011).
doi: 10.1098/rstb.2010.0345
Hansson, K.-A. et al. Myonuclear content regulates cell size with similar scaling properties in mice and humans. Nat. Commun. 11, 6288 (2020).
pubmed: 33293572
pmcid: 7722898
doi: 10.1038/s41467-020-20057-8
Murach, K. A., Dungan, C. M., Von Walden, F. & Wen, Y. Epigenetic evidence for distinct contributions of resident and acquired myonuclei during long-term exercise adaptation using timed in vivo myonuclear labeling. Am. J. Physiol.-Cell Physiol. 322, C86–C93 (2022).
pubmed: 34817266
doi: 10.1152/ajpcell.00358.2021
Pinheiro, H. et al. mRNA distribution in skeletal muscle is associated with mRNA size. J. cell Sci. 134, jcs256388 (2021).
pubmed: 34297126
doi: 10.1242/jcs.256388
Englund, D. A. et al. Satellite cell depletion disrupts transcriptional coordination and muscle adaptation to exercise. Function 2, zqaa033 (2021).
pubmed: 34109314
doi: 10.1093/function/zqaa033
Englund, D. A. et al. Depletion of resident muscle stem cells negatively impacts running volume, physical function, and muscle fiber hypertrophy in response to lifelong physical activity. Am. J. Physiol.-Cell Physiol. 318, C1178–C1188 (2020).
pubmed: 32320286
pmcid: 7311742
doi: 10.1152/ajpcell.00090.2020
Fukuda, S. et al. Sustained expression of HeyL is critical for the proliferation of muscle stem cells in overloaded muscle. Elife 8, e48284 (2019).
pubmed: 31545169
pmcid: 6768661
doi: 10.7554/eLife.48284
Goh, Q. & Millay, D. P. Requirement of myomaker-mediated stem cell fusion for skeletal muscle hypertrophy. elife 6, e20007 (2017).
pubmed: 28186492
pmcid: 5338923
doi: 10.7554/eLife.20007
Goh, Q. et al. Myonuclear accretion is a determinant of exercise-induced remodeling in skeletal muscle. Elife 8, e44876 (2019).
pubmed: 31012848
pmcid: 6497442
doi: 10.7554/eLife.44876
Randrianarison-Huetz, V. et al. Srf controls satellite cell fusion through the maintenance of actin architecture. J. Cell Biol. 217, 685–700 (2018).
pubmed: 29269426
pmcid: 5800804
doi: 10.1083/jcb.201705130
Egner, I. M., Bruusgaard, J. C. & Gundersen, K. Satellite cell depletion prevents fiber hypertrophy in skeletal muscle. Development 143, 2898–2906 (2016).
pubmed: 27531949
doi: 10.1242/dev.134411
Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737–746 (2020).
pubmed: 32341560
pmcid: 7289686
doi: 10.1038/s41587-020-0465-8
Chemello, F. et al. Degenerative and regenerative pathways underlying duchenne muscular dystrophy revealed by single-nucleus RNA sequencing. Proc. Natl Acad. Sci. USA 117, 29691–29701 (2020).
pubmed: 33148801
pmcid: 7703557
doi: 10.1073/pnas.2018391117
Dos Santos, M. et al. Single-nucleus RNA-seq and FISH identify coordinated transcriptional activity in mammalian myofibers. Nat. Commun. 11, 5102 (2020).
pubmed: 33037211
pmcid: 7547110
doi: 10.1038/s41467-020-18789-8
Kim, M. et al. Single-nucleus transcriptomics reveals functional compartmentalization in syncytial skeletal muscle cells. Nat. Commun. 11, 6375 (2020).
pubmed: 33311457
pmcid: 7732842
doi: 10.1038/s41467-020-20064-9
Petrany, M. J. et al. Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers. Nat. Commun. 11, 6374 (2020).
pubmed: 33311464
pmcid: 7733460
doi: 10.1038/s41467-020-20063-w
Dos Santos, M. et al. Opposing gene regulatory programs governing myofiber development and maturation revealed at single nucleus resolution. Nat. Commun. 14, 4333 (2023).
pubmed: 37468485
pmcid: 10356771
doi: 10.1038/s41467-023-40073-8
Kurland, J. V. et al. Aging disrupts gene expression timing during muscle regeneration. Stem Cell Rep. 18, 1325–1339 (2023).
doi: 10.1016/j.stemcr.2023.05.005
Masschelein, E. et al. Exercise promotes satellite cell contribution to myofibers in a load-dependent manner. Skelet. muscle 10, 1–15 (2020).
doi: 10.1186/s13395-020-00237-2
Foudi, A. et al. Analysis of histone 2B-GFP retention reveals slowly cycling hematopoietic stem cells. Nat. Biotechnol. 27, 84–90 (2009).
pubmed: 19060879
doi: 10.1038/nbt.1517
Morcos, M. N. et al. Continuous mitotic activity of primitive hematopoietic stem cells in adult mice. J. Exp. Med. 217, e20191284 (2020).
pubmed: 32302400
pmcid: 7971128
doi: 10.1084/jem.20191284
Winje, I. et al. Specific labelling of myonuclei by an antibody against pericentriolar material 1 on skeletal muscle tissue sections. Acta Physiol. 223, e13034 (2018).
doi: 10.1111/apha.13034
Cramer, A. A. et al. Nuclear numbers in syncytial muscle fibers promote size but limit the development of larger myonuclear domains. Nat. Commun. 11, 6287 (2020).
pubmed: 33293533
pmcid: 7722938
doi: 10.1038/s41467-020-20058-7
Martinet, C. et al. H19 controls reactivation of the imprinted gene network during muscle regeneration. Development 143, 962–971 (2016).
pubmed: 26980793
doi: 10.1242/dev.131771
Li, J. et al. Long non-coding RNA H19 promotes porcine satellite cell differentiation by interacting with TDP43. Genes (Basel) 11, 259 (2020).
Katoku-Kikyo, N. et al. Per1/Per2-Igf2 axis-mediated circadian regulation of myogenic differentiation. J. Cell Biol. 220, e202101057 (2021).
Zhang, Y. et al. The lncRNA H19 alleviates muscular dystrophy by stabilizing dystrophin. Nat. Cell Biol. 22, 1332–1345 (2020).
pubmed: 33106653
pmcid: 7951180
doi: 10.1038/s41556-020-00595-5
Yue, Y. et al. The long noncoding RNA lnc-H19 is important for endurance exercise by maintaining slow muscle fiber types. J. Biol. Chem. 299, 105281 (2023).
pubmed: 37742921
pmcid: 10598739
doi: 10.1016/j.jbc.2023.105281
Bella, P. et al. Blockade of IGF2R improves muscle regeneration and ameliorates duchenne muscular dystrophy. EMBO Mol. Med. 12, e11019 (2020).
pubmed: 31793167
doi: 10.15252/emmm.201911019
Millay, D. P., Sutherland, L. B., Bassel-Duby, R. & Olson, E. N. Myomaker is essential for muscle regeneration. Genes Dev. 28, 1641–1646 (2014).
pubmed: 25085416
pmcid: 4117939
doi: 10.1101/gad.247205.114
Mademtzoglou, D., Geara, P., Mourikis, P. & Relaix, F. Pax7 haploinsufficiency impairs muscle stem cell function in Cre-recombinase mice and underscores the importance of appropriate controls. Stem Cell Res. Ther. 14, 294 (2023).
pubmed: 37833800
pmcid: 10576335
doi: 10.1186/s13287-023-03506-1
Timson, B. F. Evaluation of animal models for the study of exercise-induced muscle enlargement. J. Appl. Physiol. 69, 1935–1945 (1990).
pubmed: 2076987
doi: 10.1152/jappl.1990.69.6.1935
Murach, K. A. et al. Multi-transcriptome analysis following an acute skeletal muscle growth stimulus yields tools for discerning global and MYC regulatory networks. J. Biol. Chem. 298, 102515 (2022).
pubmed: 36150502
pmcid: 9583450
doi: 10.1016/j.jbc.2022.102515
Iwata, M. et al. A novel tetracycline-responsive transgenic mouse strain for skeletal muscle-specific gene expression. Skelet. muscle 8, 1–8 (2018).
doi: 10.1186/s13395-018-0181-y
Roman, W. et al. Muscle repair after physiological damage relies on nuclear migration for cellular reconstruction. Science 374, 355–359 (2021).
pubmed: 34648328
doi: 10.1126/science.abe5620
Rochlin, K., Yu, S., Roy, S. & Baylies, M. K. Myoblast fusion: when it takes more to make one. Dev. Biol. 341, 66–83 (2010).
pubmed: 19932206
doi: 10.1016/j.ydbio.2009.10.024
Schiaffino, S., Rossi, A. C., Smerdu, V., Leinwand, L. A. & Reggiani, C. Developmental myosins: expression patterns and functional significance. Skelet. Muscle 5, 1–14 (2015).
doi: 10.1186/s13395-015-0046-6
Fukada, S.-I., Higashimoto, T. & Kaneshige, A. Differences in muscle satellite cell dynamics during muscle hypertrophy and regeneration. Skelet. Muscle 12, 1–10 (2022).
doi: 10.1186/s13395-022-00300-0
Jiao, S. et al. Differential regulation of IGF-I and IGF-II gene expression in skeletal muscle cells. Mol. Cell Biochem 373, 107–113 (2013).
pubmed: 23054195
doi: 10.1007/s11010-012-1479-4
Erbay, E., Park, I. H., Nuzzi, P. D., Schoenherr, C. J. & Chen, J. IGF-II transcription in skeletal myogenesis is controlled by mTOR and nutrients. J. Cell Biol. 163, 931–936 (2003).
pubmed: 14662739
pmcid: 2173600
doi: 10.1083/jcb.200307158
Li, Y. et al. Functional significance of gain-of-function H19 lncRNA in skeletal muscle differentiation and anti-obesity effects. Genome Med 13, 137 (2021).
pubmed: 34454586
pmcid: 8403366
doi: 10.1186/s13073-021-00937-4
Dey, B. K., Pfeifer, K. & Dutta, A. The H19 long noncoding RNA gives rise to microRNAs miR-675-3p and miR-675-5p to promote skeletal muscle differentiation and regeneration. Genes Dev. 28, 491–501 (2014).
pubmed: 24532688
pmcid: 3950346
doi: 10.1101/gad.234419.113
Hansson, K.-A. & Eftestøl, E. Scaling of nuclear numbers and their spatial arrangement in skeletal muscle cell size regulation. Mol. Biol. Cell 34, pe3 (2023).
pubmed: 37339435
pmcid: 10398882
doi: 10.1091/mbc.E22-09-0424
Blau, H. M., Chiu, C. P. & Webster, C. Cytoplasmic activation of human nuclear genes in stable heterocaryons. Cell 32, 1171–1180 (1983).
pubmed: 6839359
doi: 10.1016/0092-8674(83)90300-8
Pomerantz, J. H., Mukherjee, S., Palermo, A. T. & Blau, H. M. Reprogramming to a muscle fate by fusion recapitulates differentiation. J. Cell Sci. 122, 1045–1053 (2009).
pubmed: 19295131
pmcid: 2720934
doi: 10.1242/jcs.041376
Relaix, F. et al. Perspectives on skeletal muscle stem cells. Nat. Commun. 12, 692 (2021).
pubmed: 33514709
pmcid: 7846784
doi: 10.1038/s41467-020-20760-6
Dumont, N. A., Wang, Y. X. & Rudnicki, M. A. Intrinsic and extrinsic mechanisms regulating satellite cell function. Development 142, 1572–1581 (2015).
pubmed: 25922523
pmcid: 4419274
doi: 10.1242/dev.114223
Lepper, C., Conway, S. J. & Fan, C.-M. Adult satellite cells and embryonic muscle progenitors have distinct genetic requirements. Nature 460, 627–631 (2009).
pubmed: 19554048
pmcid: 2767162
doi: 10.1038/nature08209
Wu, S., Ying, G., Wu, Q. & Capecchi, M. R. A protocol for constructing gene targeting vectors: generating knockout mice for the cadherin family and beyond. Nat. Protoc. 3, 1056–1076 (2008).
pubmed: 18546598
doi: 10.1038/nprot.2008.70
Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).
pubmed: 32055031
doi: 10.1038/s41587-020-0439-x
Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).
pubmed: 28398311
doi: 10.1038/nbt.3820
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
doi: 10.14806/ej.17.1.200
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
pubmed: 23071270
doi: 10.1093/bioinformatics/bts611
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).
pubmed: 33590861
pmcid: 7931819
doi: 10.1093/gigascience/giab008
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. methods 14, 417–419 (2017).
pubmed: 28263959
pmcid: 5600148
doi: 10.1038/nmeth.4197
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
doi: 10.1186/s13059-014-0550-8
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521 (2015).
pubmed: 26925227
doi: 10.12688/f1000research.7563.1
Fleming, S. J., Marioni, J. C. & Babadi, M. CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets. BioRxiv https://doi.org/10.1101/791699 (2019).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 27124452
pmcid: 4944528
doi: 10.1126/science.aad0501
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179
pmcid: 6700744
doi: 10.1038/nbt.4096
Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).
pubmed: 31796933
pmcid: 7073148
doi: 10.1038/s41587-019-0336-3
Wickham, H. & Wickham, H. Data analysis. Vol. 864 (Springer, 2016).
Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic acids Res. 37, W305–W311 (2009).
pubmed: 19465376
pmcid: 2703978
doi: 10.1093/nar/gkp427