Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

Abstract

Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

References 69

1. 1.

   Lepper C, Partridge TA, Fan CM. An absolute requirement for pax7-positive satellite cells in acute injury-induced skeletal muscle regeneration. Development. 2011;138:3639-3646.

   Not in our library10.1242/dev.067595

2. 2.

   Murphy MM, Lawson JA, Mathew SJ, Hutcheson DA, Kardon G. Satellite cells, connective tissue fibroblasts and their interactions are crucial for muscle regeneration. Development. 2011.

   Not in our library10.1242/dev.064162

3. 3.

   Relaix F, Zammit PS. Satellite cells are essential for skeletal muscle regeneration: The cell on the edge returns centre stage. Development. 2012.

   Not in our library10.1242/dev.069088

4. 4.

   Sambasivan R, et al. Pax7-expressing satellite cells are indispensable for adult skeletal muscle regeneration. Development. 2011;138:3647-3656.

   Not in our library10.1242/dev.067587

5. 5.

   Joe AW, et al. Muscle injury activates resident fibro/adipogenic progenitors that facilitate myogenesis. Nat. Cell Biol. 2010;12:2015.

   Not in our library10.1038/ncb2015

6. 6.

   Kopinke D, Roberson EC, Reiter JF. Ciliary hedgehog signaling restricts injury-induced adipogenesis. Cell. 2017;170:340-351.

   Not in our library10.1016/j.cell.2017.06.035

7. 7.

   Lukjanenko L, et al. Aging disrupts muscle stem cell function by impairing matricellular WISP1 secretion from fibro-adipogenic progenitors. Cell Stem Cell. 2019;24:433-446.

   Not in our library10.1016/j.stem.2018.12.014

8. 8.

   Murphy M, Kardon G. Origin of vertebrate limb muscle: The role of progenitor and myoblast populations. Curr. Top. Dev. Biol. 2011;96:1-32.

   Not in our library10.1016/b978-0-12-385940-2.00001-2

9. 9.

   Santini MP, et al. Tissue-resident PDGFR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α+ progenitor cells contribute to fibrosis versus healing in a context- and spatiotemporally dependent manner. Cell Rep. 2020;30:555-570.

   Not in our library10.1016/j.celrep.2019.12.045

10. 10.

    Uezumi A, Fukada SI, Yamamoto N, Takeda S, Tsuchida K. Mesenchymal progenitors distinct from satellite cells contribute to ectopic fat cell formation in skeletal muscle. Nat. Cell Biol. 2010;12:14.

    Not in our library10.1038/ncb2014

11. 11.

    Uezumi A, et al. Fibrosis and adipogenesis originate from a common mesenchymal progenitor in skeletal muscle. J. Cell Sci. 2011;124:86629.

    Not in our library10.1242/jcs.086629

12. 12.

    Wosczyna MN, et al. Mesenchymal stromal cells are required for regeneration and homeostatic maintenance of article mesenchymal stromal cells are required for regeneration and homeostatic maintenance of skeletal muscle. Cell Rep. 2019;27:2029-2035.

    Not in our library10.1016/j.celrep.2019.04.074

13. 13.
    Fibroadipogenic progenitors are responsible for muscle loss in limb girdle muscular dystrophy 2B · 2019

    No notice
14. 14.

    Liu W, Liu Y, Lai X, Kuang S. Intramuscular adipose is derived from a non-Pax3 lineage and required for efficient regeneration of skeletal muscles. Dev. Biol. 2012.

    Not in our library10.1016/j.ydbio.2011.10.011

15. 15.

    Scott RW, Arostegui M, Schweitzer R, Rossi FM, Underhill TM. Hic1 defines quiescent mesenchymal progenitor subpopulations with distinct functions and fates in skeletal muscle regeneration. Cell Stem Cell. 2019;25:797-813.

    Not in our library10.1016/j.stem.2019.11.004

16. 16.

    Stumm J, et al. Odd skipped-related 1 (Osr1) identifies muscle-interstitial fibro-adipogenic progenitors (FAPs) activated by acute injury. Stem Cell Res. 2018;32:8-16.

    Not in our library10.1016/j.scr.2018.08.010

17. 17.
    Quantifying fat replacement of muscle by quantitative MRI in muscular dystrophy · 2017

    No notice
18. 18.

    Goodpaster BH, et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch. Intern. Med. 2005;165:777-783.

    Not in our library10.1001/archinte.165.7.777

19. 19.

    Goodpaster BH, et al. Tissue distribution and both type 2 diabetes and impaired glucose. Diabetes Care. 2003;26:372-379.

    Not in our library10.2337/diacare.26.2.372

20. 20.

    Goodpaster BH, Thaete FL, Kelley DE. Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. Am. J. Clin. Nutr. 2000;71:885-892.

    Not in our library10.1093/ajcn/71.4.885

21. 21.

    Goodpaster BH, et al. The loss of skeletal muscle strength, mass, and quality in older adults: The health, aging and body composition study. J. Gerontol. A. 2006;61:1059-1064.

    Not in our library10.1093/gerona/61.10.1059

22. 22.

    Goodpaster BH, Theriault R, Watkins SC, Kelley DE. Intramuscular lipid content is increased in obesity and decreased by weight loss. Metab. Clin. Exp. 2000;49:467-472.

    Not in our library10.1016/s0026-0495(00)80010-4

23. 23.
    Increased plasma lipid levels exacerbate muscle pathology in the mdx mouse model of Duchenne muscular dystrophy · 2017

    Not checked
24. 24.

    Murphy WA, Totty WG, Carroll JE. MRI of normal and pathologic skeletal muscle. Am. J. Roentgenol. 1986;146:565-574.

    Not in our library10.2214/ajr.146.3.565

25. 25.

    Willcocks RJ, et al. Multicenter prospective longitudinal study of magnetic resonance biomarkers in a large Duchenne muscular dystrophy cohort. Ann. Neurol. 2016;79:535-547.

    Not in our library10.1002/ana.24599

26. 26.

    Wokke BH, et al. Quantitative MRI and strength measurements in the assessment of muscle quality in Duchenne muscular dystrophy. Neuromuscular Disord. 2014;24:409-416.

    Not in our library10.1016/j.nmd.2014.01.015

27. 27.
    Comparative Study of Injury Models for Studying Muscle Regeneration in Mice · 2016

    No notice
28. 28.

    Mahdy MA, Lei HY, Wakamatsu JI, Hosaka YZ, Nishimura T. Comparative study of muscle regeneration following cardiotoxin and glycerol injury. Ann. Anat. 2015.

    Not in our library10.1016/j.aanat.2015.07.002

29. 29.

    Miller MS, Bedrin NG, Ades PA, Palmer BM, Toth MJ. Molecular determinants of force production in human skeletal muscle fibers: Effects of myosin isoform expression and cross-sectional area. Am. J. Physiol. 2015;308:C473-C484.

    Not in our library10.1152/ajpcell.00158.2014

30. 30.

    Virgilio KM, Martin KS, Peirce SM, Blemker SS. Agent-based model illustrates the role of the microenvironment in regeneration in healthy and mdx skeletal muscle. J. Appl. Physiol. 2018;125:1424-1439.

    Not in our library10.1152/japplphysiol.00379.2018

31. 31.

    Briguet A, Courdier-Fruh I, Foster M, Meier T, Magyar JP. Histological parameters for the quantitative assessment of muscular dystrophy in the mdx-mouse. Neuromuscular Disord. 2004;14:675-682.

    Not in our library10.1016/j.nmd.2004.06.008

32. 32.
    Muscular dystrophy in the mdx mouse is a severe myopathy compounded by hypotrophy, hypertrophy and hyperplasia · 2015

    No notice
33. 33.

    Flück M. Functional, structural and molecular plasticity of mammalian skeletal muscle in response to exercise stimuli. J. Exp. Biol. 2006;209:2239-2248.

    Not in our library10.1242/jeb.02149

34. 34.

    McDonald AA, Hebert SL, Kunz MD, Ralles SJ, McLoon LK. Disease course in mdx:Utrophin+/- mice: Comparison of three mouse models of duchenne muscular dystrophy. Physiol. Rep. 2015;3:1-22.

    Not in our library10.14814/phy2.12391

35. 35.

    Encarnacion-Rivera L, Foltz S, Hartzell HC, Choo H. Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software. PLoS ONE. 2020;15:1-22.

    Not in our library10.1371/journal.pone.0229041

36. 36.

    Kastenschmidt JM, et al. QuantiMus: A machine learning-based approach for high precision analysis of skeletal muscle morphology. Front. Physiol. 2019;10:1-10.

    Not in our library10.3389/fphys.2019.01416

37. 37.
    Automated image-analysis method for the quantification of fiber morphometry and fiber type population in human skeletal muscle · 2019

    Not checked
38. 38.
    SMASH – semi-automatic muscle analysis using segmentation of histology: a MATLAB application · 2014

    No notice
39. 39.

    Bergmeister KD, et al. A rapid automated protocol for muscle fiber population analysis in rat muscle cross sections using myosin heavy chain immunohistochemistry. J. Vis. Exp. 2017;2017:1-10.

    Not in our library10.3791/55441

40. 40.

    Desgeorges T, et al. Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle. Skelet. Muscle. 2019;9:186.

    Not in our library10.1186/s13395-018-0186-6

41. 41.

    Kostrominova TY, Reiner DS, Haas RH, Ingermanson R, McDonough PM. Automated methods for the analysis of skeletal muscle fiber size and metabolic type. Int. Rev. Cell Mol. Biol. 2013;306:7.

    Not in our library10.1016/b978-0-12-407694-5.00007-9

42. 42.
    MuscleJ: a high-content analysis method to study skeletal muscle with a new Fiji tool · 2018

    No notice
43. 43.

    Sertel O, Dogdas B, Chiu CS, Gurcan MN. Microscopic image analysis for quantitative characterization of muscle fiber type composition. Comput. Med. Imaging Graph. 2011;35:9.

    Not in our library10.1016/j.compmedimag.2011.01.009

44. 44.

    Wen Y, et al. Myo Vision: Software for automated high-content analysis of skeletal muscle immunohistochemistry. J. Appl. Physiol. 2018;124:762.

    Not in our library10.1152/japplphysiol.00762.2017

45. 45.

    Kopinke D, Norris AM, Mukhopadhyay S. Developmental and regenerative paradigms of cilia regulated hedgehog signaling. Developmental. 2021.

    Not in our library10.1016/j.semcdb.2020.05.029

46. 46.

    Hilgendorf KI, et al. Omega-3 fatty acids activate ciliary FFAR4 to control adipogenesis article omega-3 fatty acids activate ciliary FFAR4 to control adipogenesis. Cell. 2019;179:1289-1305.

    Not in our library10.1016/j.cell.2019.11.005

47. 47.

    Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: A generalist algorithm for cellular segmentation. Nat. Methods. 2021;18:100-106.

    Not in our library10.1038/s41592-020-01018-x

48. 48.

    Kawai H, et al. Experimental glycerol myopathy: A histological study. Acta Neuropathol. 1990;80:192-197.

    Not in our library10.1007/bf00308923

49. 49.

    Lukjanenko L, Brachat S, Pierrel E, Lach-Trifilieff E, Feige JN. Genomic profiling reveals that transient adipogenic activation is a hallmark of mouse models of skeletal muscle regeneration. PLoS ONE. 2013;8:e71084.

    Not in our library10.1371/journal.pone.0071084

50. 50.

    Pisani DF, Bottema CD, Butori C, Dani C, Dechesne CA. Mouse model of skeletal muscle adiposity: A glycerol treatment approach. Biochem. Biophys. Res. Commun. 2010.

    Not in our library10.1016/j.bbrc.2010.05.021

51. 51.

    Carass A, et al. Evaluating white matter lesion segmentations with refined Sørensen-dice analysis. Sci. Rep. 2020;10:648.

    Not in our library10.1038/s41598-020-64803-w

52. 52.

    Dice LR. Measures of the amount of ecologic association between species Author(s): Lee R. Dice published by: Ecological Society of America Stable. Ecology. 1945;26:297-302.

    Not in our library10.2307/1932409

53. 53.

    Zou KH, et al. Statistical validation of image segmentation quality based on a spatial overlap index. Acad. Radiol. 2004;11:178-189.

    Not in our library10.1016/s1076-6332(03)00671-8

54. 54.

    Bulfield G, Siller WG, Wight PA, Moore KJ. X chromosome-linked muscular dystrophy (mdx) in the mouse. Proc. Natl. Acad. Sci. USA. 1984;81:1189-1192.

    Not in our library10.1073/pnas.81.4.1189

55. 55.

    Hoffman EP, Brown RH, Kunkel LM. Dystrophin: The protein product of the duchenne muscular dystrophy locus. Cell. 1987;51:919-928.

    Not in our library10.1016/0092-8674(87)90579-4

56. 56.

    Ownby CL, Fletcher JE, Colberg TR. Cardiotoxin 1 from cobra (Naja naja atra) venom causes necrosis of skeletal muscle in vivo. Toxicon. 1993;31:697-709.

    Not in our library10.1016/0041-0101(93)90376-t

57. 57.

    Schiaffino S, Reggiani C. Fiber types in mammalian skeletal muscles. Physiol. Rev. 2011;91:1447-1531.

    Not in our library10.1152/physrev.00031.2010

58. 58.

    Evans WJ, Lexell J. Human aging, muscle mass, and fiber type composition. J. Gerontol. A. 1995;50A:11-16.

    Not in our library10.1093/gerona/50a.special_issue.11

59. 59.

    Handschin C, et al. Skeletal muscle fiber-type switching, exercise intolerance, and myopathy in PGC-1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α muscle-specific knock-out animals. J. Biol. Chem. 2007;282:30014-30021.

    Not in our library10.1074/jbc.m704817200

60. 60.

    Rafael JA, et al. Dystrophin and utrophin influence fiber type composition and post-synaptic membrane structure. Hum. Mol. Genet. 2000;9:1357-1367.

    Not in our library10.1093/hmg/9.9.1357

61. 61.

    Reyes NL, et al. Fnip1 regulates skeletal muscle fiber type specification, fatigue resistance, and susceptibility to muscular dystrophy. Proc. Natl. Acad. Sci. USA. 2015;112:424-429.

    Not in our library10.1073/pnas.1413021112

62. 62.

    Röckl KS, et al. Skeletal muscle adaptation to exercise training: AMP-activated protein kinase mediates muscle fiber type shift. Diabetes. 2007;56:2062-2069.

    Not in our library10.2337/db07-0255

63. 63.
    Rescue of Dystrophic Skeletal Muscle by PGC-1α Involves a Fast to Slow Fiber Type Shift in the mdx Mouse · 2012

    No notice
64. 64.

    Talbot J, Maves L. Skeletal muscle fiber type: Using insights from muscle developmental biology to dissect targets for susceptibility and resistance to muscle disease. Wiley Interdiscipl. Rev. Dev. Biol. 2016;5:518-534.

    Not in our library10.1002/wdev.230

65. 65.

    Thompson LDV. Skeletal muscle adaptations with age, inactivity, and therapeutic exercise. J. Orthop. Sports Phys. Ther. 2002;32:44-57.

    Not in our library10.2519/jospt.2002.32.2.44

66. 66.

    Haycraft CJ, et al. Intraflagellar transport is essential for endochondral bone formation. Development. 2007;134:307-316.

    Not in our library10.1242/dev.02732

67. 67.

    Kang SH, Fukaya M, Yang JK, Rothstein JD, Bergles DE. NG2+ CNS glial progenitors remain committed to the oligodendrocyte lineage in postnatal life and following neurodegeneration. Neuron. 2010;68:668-681.

    Not in our library10.1016/j.neuron.2010.09.009

68. 68.
    The Design of SimpleITK · 2013

    No notice
69. 69.

    Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: Integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics. 10.1093/bioinformatics/btw413 (2016).

    Not in our library10.1093/bioinformatics/btw413

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