1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Condensed Matter Physics
  4. Volume 12, 2021
  5. Article

Annual Review of Condensed Matter Physics

Volume 12, 2021

Stem Cell Populations as Self-Renewing Many-Particle Systems

Abstract

This article reviews the physical principles of stem cell populations as active many-particle systems that are able to self-renew, control their density, and recover from depletion. We illustrate the dynamical and statistical hallmarks of homeostatic mechanisms, from stem cell density fluctuations and transient large-scale oscillation dynamics during recovery to the scaling behavior of clonal dynamics and front-like boundary propagation during regeneration.

Keyword(s): active matter, cell dynamics, many-particle dynamics, self-renewal
Loading

Article metrics loading...

/content/journals/10.1146/annurev-conmatphys-041720-125707
2021-03-10
2025-03-25
Loading full text...

Full text loading...

/deliver/fulltext/conmatphys/12/1/annurev-conmatphys-041720-125707.html?itemId=/content/journals/10.1146/annurev-conmatphys-041720-125707&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Ramaswamy S. 2010. Annu. Rev. Condens. Matter Phys. 1:323–45
     [Google Scholar]
  2. 2. 
    Hyman AA, Weber CA, Jülicher F 2014. Annu. Rev. Cell Dev. Biol. 30:39–58
     [Google Scholar]
  3. 3. 
    Maini PK, Othmer HG 2001. Mathematical Models for Biological Pattern Formation New York: Springer
     [Google Scholar]
  4. 4. 
    Siminovitch L, McCulloch EA, Till JE 1963. J. Cell. Comp. Physiol. 62:327–36
     [Google Scholar]
  5. 5. 
    Tsimring LS. 2014. Rep. Prog. Phys. 77:026601–30
     [Google Scholar]
  6. 6. 
    Klein AM, Simons BD. 2011. Development 138:3103–11
     [Google Scholar]
  7. 7. 
    Rulands S, Lescroart F, Chabab S, Hindley CJ, Prior N et al. 2018. Nat. Phys. 14:469–74
     [Google Scholar]
  8. 8. 
    Yamaguchi H, Kawaguchi K. 2019. arXiv:1903.02985
  9. 9. 
    Xie T, Spradling AC. 2000. Science 290:328–30
     [Google Scholar]
  10. 10. 
    Simons BD, Clevers H. 2011. Cell 145:851–62
     [Google Scholar]
  11. 11. 
    Potten CS, Loeffler M. 1990. Development 110:1001–20
     [Google Scholar]
  12. 12. 
    Loeffler M, Roeder I. 2002. Cells Tissues Organs 171:8–26
     [Google Scholar]
  13. 13. 
    Clayton E, Doupé DP, Klein AM, Winton DJ, Simons BD, Jones PH 2007. Nature 446:185–89
     [Google Scholar]
  14. 14. 
    Klein AM, Nakagawa T, Ichikawa R, Yoshida S, Simons BD 2010. Cell Stem Cell 7:214–24
     [Google Scholar]
  15. 15. 
    Blanpain C, Simons BD. 2013. Nat. Rev. Mol. Cell Biol. 14:489–502
     [Google Scholar]
  16. 16. 
    Lescroart F, Chabab S, Lin X, Rulands S, Paulissen C et al. 2014. Nat. Cell Biol. 16:829–40
     [Google Scholar]
  17. 17. 
    Sánchez-Danés A, Hannezo E, Larsimont JC, Liagre M, Youssef KK et al. 2016. Nature 536:1–22
     [Google Scholar]
  18. 18. 
    Lan X, Jörg DJ, Cavalli FMG, Richards LM, Nguyen LV et al. 2017. Nature 549:227–32
     [Google Scholar]
  19. 19. 
    Roeder I, Loeffler M. 2002. Exp. Hematol. 30:8853–61
     [Google Scholar]
  20. 20. 
    Roeder I, Kammina LM, Braesel K, Dontje B, de Haan G, Loeffler M 2002. Blood 102:2609–16
     [Google Scholar]
  21. 21. 
    Klein AM, Doupé DP, Jones PH, Simons BD 2008. Phys. Rev. E 77:031907
     [Google Scholar]
  22. 22. 
    Lopez-Garcia C, Klein AM, Simons BD, Winton DJ 2010. Science 330:822–25
     [Google Scholar]
  23. 23. 
    Hara K, Nakagawa T, Enomoto H, Suzuki M, Yamamoto M et al. 2014. Cell Stem Cell 14:658–72
     [Google Scholar]
  24. 24. 
    Yamaguchi H, Kawaguchi K, Sagawa T 2017. Phys. Rev. E 96:012401
     [Google Scholar]
  25. 25. 
    Hufnagel L, Teleman AA, Rouault H, Cohen SM, Shraiman BI 2007. PNAS 104:3835–40
     [Google Scholar]
  26. 26. 
    Ranft J, Basan M, Elgeti J, Joanny JF, Prost J, Jülicher F 2010. PNAS 107:20863–68
     [Google Scholar]
  27. 27. 
    Hannezo E, Coucke A, Joanny JF 2016. Phys. Rev. E 93:022405
     [Google Scholar]
  28. 28. 
    Lander AD, Gokoffski KK, Wan FYM, Nie Q, Calof AL 2009. PLOS Biol 7:e1000015
     [Google Scholar]
  29. 29. 
    Lo WC, Chou CS, Gokoffski K, Wan F, Lander A et al. 2009. Math. Biosci. Eng. 6:59–82
     [Google Scholar]
  30. 30. 
    Marciniak-Czochra A, Stiehl T, Ho AD, Jäger W, Wagner W 2009. Stem Cells Dev 18:377
     [Google Scholar]
  31. 31. 
    Nakata Y, Getto P, Marciniak-Czochra A, Alarcón T 2012. J. Biol. Dyn. 6:Suppl. 12–18
     [Google Scholar]
  32. 32. 
    Manesso E, Teles J, Bryder D, Peterson C 2013. J. R. Soc. Interface 10:20120817
     [Google Scholar]
  33. 33. 
    Rodriguez-Brenes IA, Wodarz D, Komarova NL 2013. Front. Oncol. 3:82
     [Google Scholar]
  34. 34. 
    Buzi G, Lander AD, Khammash M 2015. BMC Biol 13:13
     [Google Scholar]
  35. 35. 
    Klose M, Florian MC, Gerbaulet A, Geiger H, Glauche I 2019. Stem Cells 37:7948–57
     [Google Scholar]
  36. 36. 
    Kunche S, Yan H, Calof AL, Lowengrub JS, Lander AD 2016. PLOS Comp. Biol. 12:e1004814–34
     [Google Scholar]
  37. 37. 
    Taillefumier T, Posfai A, Meir Y, Wingreen NS 2017. eLife 6:e22644
     [Google Scholar]
  38. 38. 
    Posfai A, Taillefumier T, Wingreen NS 2017. Phys. Rev. Lett. 118:028103
     [Google Scholar]
  39. 39. 
    Adler M, Mayo A, Zhou Y, Franklin RA, Jacox JB et al. 2018. PNAS 115:8E1926–35
     [Google Scholar]
  40. 40. 
    Zhou X, Franklin RA, Adler M, Jacox JB, Bailis W et al. 2018. Cell 172:744–57
     [Google Scholar]
  41. 41. 
    Becker NB, Günther M, Li C, Jolly A, Höfer T 2019. J. Theor. Biol. 481:100–9
     [Google Scholar]
  42. 42. 
    Kitadate Y, Jörg DJ, Tokue M, Maruyama A, Ichikawa R et al. 2019. Cell Stem Cell 24:79–92
     [Google Scholar]
  43. 43. 
    Marchetti MC, Joanny JF, Ramaswamy S, Liverpool TB, Prost J et al. 2013. Rev. Mod. Phys. 85:1143–89
     [Google Scholar]
  44. 44. 
    Bailey NTJ. 1990. The Elements of Stochastic Processes with Applications to the Natural Sciences New York: Wiley Class. Libr.
     [Google Scholar]
  45. 45. 
    Keener J, Sneyd J. 2008. Mathematical Physiology. I: Cellular Physiology New York: Springer. , 2nd. ed.
     [Google Scholar]
  46. 46. 
    Monod J. 1949. Annu. Rev. Microbiol. 3:371–94
     [Google Scholar]
  47. 47. 
    Herbert D, Elsworth R, Telling RC 1956. J. Gen. Microbiol. 14:601–22
     [Google Scholar]
  48. 48. 
    Smith HL, Waltman P. 1995. The Theory of the Chemostat: Dynamics of Microbial Competition Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  49. 49. 
    Campillo F, Joannides M, Larramendy-Valverde I 2011. Ecol. Model. 222:2676–89
     [Google Scholar]
  50. 50. 
    Campillo F, Fritsch C. 2015. Appl. Math. Optim. 72:37–73
     [Google Scholar]
  51. 51. 
    Wade MJ, Harmand J, Benyahia B, Bouchez T, Chaillou S et al. 2016. Ecol. Model. 321:64–74
     [Google Scholar]
  52. 52. 
    Lowengrub JS, Frieboes HB, Jin F, Chuang YL, Li X et al. 2009. Nonlinearity 23:R1–91
     [Google Scholar]
  53. 53. 
    Chauviere A, Hatzikirou A, Kevrekidis IG, Lowengrub JS, Cristini V 2012. AIP Adv 2:011210–14
     [Google Scholar]
  54. 54. 
    Al-Saedi HM, Archer AJ, Ward J 2018. Phys. Rev. E 98:022407
     [Google Scholar]
  55. 55. 
    Miller MB, Bassler BL. 2001. Annu. Rev. Microbiol. 55:165–99
     [Google Scholar]
  56. 56. 
    Doğaner BA, Yan LKQ, Youk H 2016. Trends Cell Biol 26:262–71
     [Google Scholar]
  57. 57. 
    Dean DS. 1996. J. Phys. A 29:L613–17
     [Google Scholar]
  58. 58. 
    van Kampen NG. 2007. Stochastic Processes in Physics and Chemistry Amsterdam, Neth: Elsevier. , 3rd. ed.
     [Google Scholar]
  59. 59. 
    DeWys WD, Goldin A, Mantel N 1970. Cancer Res 30:1692–97
     [Google Scholar]
  60. 60. 
    Zhang YC, Serva M, Polikarpov M 1990. J. Stat. Phys. 58:849–61
     [Google Scholar]
  61. 61. 
    Young WR, Roberts AJ, Stuhne G 2001. Nature 412:328–31
     [Google Scholar]
  62. 62. 
    Houchmandzadeh B. 2002. Phys. Rev. E 66:052902
     [Google Scholar]
  63. 63. 
    Houchmandzadeh B. 2008. Phys. Rev. Lett. 101:078103
     [Google Scholar]
  64. 64. 
    Houchmandzadeh B. 2009. Phys. Rev. E 80:051920
     [Google Scholar]
  65. 65. 
    Clifford P, Sudbury A. 1973. Biometrika 60:581
     [Google Scholar]
  66. 66. 
    Doupé DP. 2009. Quantitative analysis of epithelial homeostasis Ph.D. Thesis, Univ. Cambridge Cambridge, United Kingdom:
     [Google Scholar]
  67. 67. 
    Doupé DP, Alcolea MP, Roshan A, Zhang G, Klein AM et al. 2012. Science 337:1091–93
     [Google Scholar]
  68. 68. 
    Sudbury A. 1976. J. Appl. Prob. 13:355–56
     [Google Scholar]
  69. 69. 
    Sawyer S. 1979. J. Appl. Prob. 16:482–95
     [Google Scholar]
  70. 70. 
    Bramson M, Griffeath D. 1980. Z. Wahrscheinlichkeitstheor. Verw. Geb. 53:183–96
     [Google Scholar]
  71. 71. 
    Cencini M, Lopez C, Vergni D 2003. The Kolmogorov Legacy in Physics R Livi, A Vulpiani 187–210 Berlin/Heidelberg: Springer-Verlag
     [Google Scholar]
  72. 72. 
    Brunet E, Derrida B. 1997. Phys. Rev. E 56:2597–604
     [Google Scholar]
  73. 73. 
    Hallatschek O, Korolev KS. 2009. Phys. Rev. Lett. 103:108103
     [Google Scholar]
  74. 74. 
    Goriely A, McVean GAT, Röjmyr M, Ingemarsson B, Wilkie AOM 2003. Science 301:643–46
     [Google Scholar]
  75. 75. 
    Goriely A, Wilkie AOM. 2012. Am. J. Hum. Genet. 90:175–200
     [Google Scholar]
  76. 76. 
    Slaughter DP, Southwick HW, Smejkal W 1953. Cancer 6:963–68
     [Google Scholar]
  77. 77. 
    Williams T, Bjerknes R. 1972. Nature 236:19–21
     [Google Scholar]
  78. 78. 
    Snippert HJ, Schepers AG, van Es JH, Simons BD, Clevers H 2013. EMBO Rep 15:62–69
     [Google Scholar]
  79. 79. 
    Kitadate Y, Shigenobu S, Arita K, Kobayashi S 2007. Dev. Cell 13:151–59
     [Google Scholar]
  80. 80. 
    Morrison SJ, Spradling AC. 2008. Cell 132:598–611
     [Google Scholar]
  81. 81. 
    Johnston LA. 2009. Science 324:1679–82
     [Google Scholar]
  82. 82. 
    Stine RR, Matunis EL. 2013. Trends Cell Biol 23:357–64
     [Google Scholar]
  83. 83. 
    Yoshida S. 2018. Dev. Growth Differ. 60:542–52
     [Google Scholar]
  84. 84. 
    Doupé DP, Klein AM, Simons BD, Jones PH 2010. Dev. Cell 18:317–23
     [Google Scholar]
  85. 85. 
    Rompolas P, Mesa KR, Kawaguchi K, Park S, Gonzalez D et al. 2016. Science 352:1471–74
     [Google Scholar]
  86. 86. 
    Murai K, Skrupskelyte G, Piedrafita G, Hall M, Kostiou V et al. 2018. Cell Stem Cell 23:687–99
     [Google Scholar]
  87. 87. 
    Jones KB, Furukawa S, Marangoni P, Ma H, Pinkard H et al. 2019. Cell Stem Cell 24:183–92
     [Google Scholar]
  88. 88. 
    Leushacke M, Ng A, Galle J, Loeffler M, Barker N 2013. Cell Rep 5:349–56
     [Google Scholar]
  89. 89. 
    Han S, Fink J, Jörg DJ, Lee E, Yum MK et al. 2019. Cell Stem Cell 25:342–56
     [Google Scholar]
  90. 90. 
    Tai K, Cockburn K, Greco V 2019. Curr. Opin. Cell Biol. 60:84–91
     [Google Scholar]
  91. 91. 
    Aragona M, Dekoninck S, Rulands S, Lenglez S, Mascré G et al. 2017. Nat. Commun. 8:14684
     [Google Scholar]
  92. 92. 
    Ito M, Liu Y, Yang Z, Nguyen J, Liang F et al. 2005. Nat. Med. 11:1351–54
     [Google Scholar]
  93. 93. 
    Morrison SJ, Kimble J. 2006. Nature 441:1068–74
     [Google Scholar]
  94. 94. 
    Kretzschmar K, Watt FM. 2012. Cell 148:33–45
     [Google Scholar]
  95. 95. 
    Schepers AG, Snippert HJ, Stange DE, van den Born M, van Es JH et al. 2012. Science 337:730–35
     [Google Scholar]
  96. 96. 
    Schepers K, Swart E, van Heijst JWJ, Gerlach C, Castrucci M et al. 2008. J. Exp. Med. 205:2309–18
     [Google Scholar]
  97. 97. 
    Gerrits A, Dykstra B, Kalmykowa OJ, Klauke K, Verovskaya E et al. 2010. Blood 115:2610–18
     [Google Scholar]
  98. 98. 
    Lu R, Neff NF, Quake SR, Weissman IL 2011. Nat. Biotech. 29:928–33
     [Google Scholar]
  99. 99. 
    Verovskaya E, Broekhuis MJC, Zwart E, Ritsema M, van Os R et al. 2013. Blood 122:523–32
     [Google Scholar]
  100. 100. 
    Nguyen LV, Cox CL, Eirew P, Knapp DJHF, Pellacani D et al. 2014. Nat. Commun. 5:5871
     [Google Scholar]
  101. 101. 
    Thielecke L, Aranyossy T, Dahl A, Tiwari R, Roeder I et al. 2017. Sci. Rep. 7:43249
     [Google Scholar]
  102. 102. 
    Nakagawa T, Sharma M, Nabeshima YI, Braun RE, Yoshida S 2010. Science 328:62–67
     [Google Scholar]
  103. 103. 
    Rompolas P, Deschene ER, Zito G, Gonzalez DG, Saotome I et al. 2012. Nature 487:496–99
     [Google Scholar]
  104. 104. 
    Ritsma L, Ellenbroek SIJ, Zomer A, Snippert HJ, de Sauvage FJ et al. 2014. Nature 507:362–65
     [Google Scholar]
  105. 105. 
    Pilz GA, Bottes S, Betizeau M, Jörg DJ, Carta S et al. 2018. Science 359:658–62
     [Google Scholar]
  106. 106. 
    Clevers H. 2016. Cell 165:1586–97
     [Google Scholar]
  107. 107. 
    Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC et al. 2020. Genome Biol 21:31
     [Google Scholar]
  108. 108. 
    Eng CHL, Lawson M, Zhu Q, Dries R, Koulena N et al. 2019. Nature 568:235–39
     [Google Scholar]
/content/journals/10.1146/annurev-conmatphys-041720-125707
Loading
Stem Cell Populations as Self-Renewing Many-Particle Systems
Annual Review of Condensed Matter Physics 12, 135 (2021); https://doi.org/10.1146/annurev-conmatphys-041720-125707
/content/journals/10.1146/annurev-conmatphys-041720-125707
/content/journals/10.1146/annurev-conmatphys-041720-125707
Loading

Data & Media loading...

Supplementary Data

  • Supplemental Video 1: Simulation of the individual cell-based model in the active phase with nonoscillatory recovery, showing equilibration toward a homeostatic steady state from a subhomeostatic initial state. Parameters and initial conditions are as in Figure 2e (subpanel iii).

    Supplemental Video 2: Simulation of the individual cell-based model in the active phase with oscillatory recovery, showing equilibration toward a homeostatic steady state from a subhomeostatic initial state with large-amplitude density oscillations. Parameters and initial conditions are as in Figure 2e (subpanel ii).

    Supplemental Video 3: Simulation of the individual cell-based model in the dead phase, showing equilibration toward the dead state from an initially populated state. Parameters and initial conditions are as in Figure 2e (subpanel i).

    Supplemental Video 4: Simulation of the individual cell-based model showing neutral competition of single-cell derived clones when the system is in equilibrium. The initial population of cells is marked in different colors, which are inherited by their progeny. Parameters are as in Figure 2e (subpanel ii).

    Supplemental Video 5: Simulation of the individual cell-based model in the nonoscillatory recovery regime, showing colonization of the system by an initially localized population of cells through a propagating density front. Parameters and initial conditions are as in Figure 5a.

    Supplemental Video 6: Simulation of the individual cell-based model in the oscillatory recovery regime, showing colonization of the system by an initially localized population of cells through a propagating density front with elevated density in the front region. Parameters and initial conditions are as in Figure 5b.

    Supplemental Video 7: Simulation of the individual cell-based model with a subpopulation of stem cells with a competitive advantage (black), showing the gradual takeover of the system and extinction of the "wildtype" population (white). Parameters and initial conditions are as in Figure 6.

    Supplemental Video 8: Simulation of the individual cell-based model with localized sources of the fate determinant. Parameters and initial conditions are as in Figure 7.

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error