A simple method for morphological assessment of astrocytes: sexual dimorphism in the maturation dynamics of astrocytes in the rat amygdala

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Acesso é pago ou somente para assinantes

Resumo

Simple, affordable and reliable methods for assessing the status of brain structures maturation are vital for preclinical studies related to the effects of early-life stress. These methods make it possible to evaluate the effectiveness of specific therapies or the prevention of stress-related pathological changes. The morphology of astrocytes is one of the markers representing functional state of synapses and thus it is indicative of maturation state of neuronal networks. We performed the method for evaluating the morphological characteristics of astrocytes using epifluorescence microscopy and the ImageJ program. Application of the method to brain sections of rats on postnatal days 18 and 30 revealed the dynamics of morphological changes in the astrocytes of the basolateral nucleus of the amygdala during normal ontogenesis. The proposed method makes it possible to evaluate not only the density of the cell population, but also their morphological parameters associated with the degree of branching and the length of the astrocyte processes. The approach used revealed sexual dimorphism in the ontogenesis: the length of the astrocytic processes increased during maturation from juvenile to pubertal period in the basolateral nucleus of the amygdala only in female rats, but not in males.

Texto integral

Acesso é fechado

Sobre autores

А. Manolova

Federal state budget institution Institute of Higher Nervous Activity and Neurophysiology RAS

Autor responsável pela correspondência
Email: anna.manolova@ihna.ru
Rússia, Moscow

N. Lazareva

Federal state budget institution Institute of Higher Nervous Activity and Neurophysiology RAS

Email: anna.manolova@ihna.ru
Rússia, Moscow

A. Paramonova

Federal state budget institution Institute of Higher Nervous Activity and Neurophysiology RAS

Email: anna.manolova@ihna.ru
Rússia, Moscow

A. Kvichansky

Federal state budget institution Institute of Higher Nervous Activity and Neurophysiology RAS

Email: anna.manolova@ihna.ru
Rússia, Moscow

М. Odrinskaya

Federal state budget institution Institute of Higher Nervous Activity and Neurophysiology RAS

Email: anna.manolova@ihna.ru
Rússia, Moscow

M. Stepanichev

Federal state budget institution Institute of Higher Nervous Activity and Neurophysiology RAS

Email: anna.manolova@ihna.ru
Rússia, Moscow

N. Gulyaeva

Federal state budget institution Institute of Higher Nervous Activity and Neurophysiology RAS

Email: anna.manolova@ihna.ru
Rússia, Moscow

Bibliografia

  1. Dennison M., Whittle S., Yücel M., Vijayakumar N., Kline A., Simmons J., Allen N.B. // Dev. Sci. 2013. V. 16. P. 772–791. doi: 10.1111/desc.12057.
  2. Fish A.M., Nadig A., Seidlitz J., Reardon P.K., Mankiw C., McDermott C.L., Blumenthal J.D., Clasen L.S., Lalonde F., Lerch J.P., Chakravarty M.M., Shinohara R.T., Raznahan A. // NeuroImage. 2020. V. 204. P. 116122. doi: 10.1016/j.neuroimage.2019.116122.
  3. Verwer R.W.H., Van Vulpen E.H.S., Van Uum J.F.M. // J. Comp. Neurol. 1996, 376, 75–96. doi: 10.1002/(SICI)1096-9861(19961202)376:1<75::AID-CNE5>3.0.CO,2-L.
  4. Arruda-Carvalho M., Wu W.-C., Cummings K.A., Clem R.L. // J. Neurosci. 2017. V. 37. P. 2976–2985. doi: 10.1523/JNEUROSCI.3097-16.2017.
  5. Wierenga L.M., Bos M.G.N., Schreuders E., Vd Kamp F., Peper J.S., Tamnes C.K., Crone E.A. // Psychoneuroendocrinology. 2018. V. 91. P. 105–114. doi: 10.1016/j.psyneuen.2018.02.034.
  6. Frere P.B., Vetter N.C., Artiges E., Filippi I., Miranda R., Vulser H., Paillère-Martinot M.-L., Ziesch V., Conrod P., Cattrell A., Walter H., Gallinat J., Bromberg U., Jurk S., Menningen E., Frouin V., Papadopoulos Orfanos D., Stringaris A., Penttilä J., Van Noort B., Grimmer Y., Schumann G., Smolka M.N., Martinot J.-L., Lemaître H. // NeuroImage. 2020. V. 210. P. 116441. doi: 10.1016/j.neuroimage.2019.116441.
  7. Simerly R.B., Swanson L.W., Chang C., Muramatsu M. // J. Comp. Neurol. 1990. V. 294. P. 76–95. doi: 10.1002/cne.902940107.
  8. Cahill L., Uncapher M., Kilpatrick L., Alkire M.T., Turner J. // Learn. Mem. 2004. V. 11. P. 261–266. doi: 10.1101/lm.70504.
  9. Cooke B.M., Stokas M.R., Woolley C.S. // J. Comp. Neurol. 2007. V. 501. P. 904–915. doi: 10.1002/cne.21281.
  10. Kilpatrick L.A., Zald D.H., Pardo J.V., Cahill L.F. // NeuroImage. 2006. V. 30. P. 452–461. doi: 10.1016/j.neuroimage.2005.09.065.
  11. Clarke L.E., Barres B.A. // Nat. Rev. Neurosci. 2013. V. 14. P. 311–321. doi: 10.1038/nrn3484.
  12. Nägler K., Mauch D.H., Pfrieger F.W. // J. Physiol. 2001. V. 533. P. 665–679. doi: 10.1111/j.1469-7793.2001.00665.x.
  13. Pfrieger F.W., Barres B.A. // Science. 1997. V. 277. P. 1684–1687. doi: 10.1126/science.277.5332.1684.
  14. Johnson R.T., Breedlove S.M., Jordan C.L. // Astrocytes in the Amygdala / In Vitamins & Hormones. Elsevier, 2010. Vol. 82. P 23–45. doi: 10.1016/S0083-6729(10.82002-3.
  15. Mong J.A., Kurzweil R.L., Davis A.M., Rocca M.S., McCarthy M.M. // Horm. Behav. 1996. V. 30. P. 553–562. doi: 10.1006/hbeh.1996.0058.
  16. Milner T.A., McEwen B.S., Hayashi S., Li C.J., Reagan L.P., Alves S.E. // J. Comp. Neurol. 2001. V. 429. P. 355–371.
  17. Johnson R.T., Breedlove S.M., Jordan C.L. // J. Comp. Neurol. 2013. V. 521. P. 2298–2309. doi: 10.1002/cne.23286.
  18. Khazipov R., Zaynutdinova D., Ogievetsky E., Valeeva G., Mitrukhina O., Manent J.-B., Represa A. // Front. Neuroanat. 2015. V. 9. doi: 10.3389/fnana.2015.00161.
  19. Paxinos G., Watson C. // The Rat Brain in Stereotaxic Coordinates, 3. ed. / Academic Press: San Diego, Calif., 1997.
  20. Martinez F.G., Hermel E.E.S., Xavier L.L., Viola G.G., Riboldi J., Rasia-Filho A.A., Achaval M. // Brain Res. 2006. V. 1108. P. 117–126. doi: 10.1016/j.brainres.2006.06.014.
  21. Conejo N.M., González‐Pardo H., Cimadevilla J.M., Argüelles J.A., Díaz F., Vallejo‐Seco G., Arias J.L. // J. Neurosci. Res. 2005. V. 79. P. 488–494. doi: 10.1002/jnr.20372.
  22. Immenschuh J., Thalhammer S.B., Sundström-Poromaa I., Biegon A., Dumas S., Comasco E. // Biol. Sex Differ. 2023. V. 14. P. 54. doi: 10.1186/s13293-023-00541-8.
  23. Brenner M., Messing A. // ASN Neuro. 2021. V. 13. P. 175909142098120. doi: 10.1177/1759091420981206.
  24. Khan M.M., Hadman M., Wakade C., De Sevilla L.M., Dhandapani K.M., Mahesh V.B., Vadlamudi R.K., Brann D.W. // Endocrinology. 2005. V. 146. P. 5215–5227. doi: 10.1210/en.2005-0276.
  25. Elmariah S.B., Hughes E.G., Oh E.J., Balice-Gordon R.J. // Neuron Glia Biol. 2004. V. 1. P. 339–349. doi: 10.1017/S1740925X05000189.
  26. Bushong E.A., Martone M.E., Jones Y.Z., Ellisman M.H. // J. Neurosci. 2002. V. 22. P. 183–192. doi: 10.1523/JNEUROSCI.22-01-00183.2002.
  27. Reeves A.M.B., Shigetomi E., Khakh B.S. // J. Neurosci. 2011. V. 31. P. 9353–9358. doi: 10.1523/JNEUROSCI.0127-11.2011.
  28. Bondi H., Bortolotto V., Canonico P.L., Grilli M. // Neurobiol. Aging. 2021. V. 100. P. 59–71. doi: 10.1016/j.neurobiolaging.2020.12.018.
  29. Tavares G., Martins M., Correia J.S., Sardinha V.M., Guerra-Gomes S., Das Neves S.P., Marques F., Sousa N., Oliveira J.F. // Brain Struct. Funct. 2017. V. 222. P. 1989–1999. doi: 10.1007/s00429-016-1316-8.
  30. Baldwin K.T., Murai K.K., Khakh B.S. // Trends Cell Biol. 2023. S0962892423002040. doi: 10.1016/j.tcb.2023.09.006.
  31. Nedergaard M., Ransom B., Goldman S.A. // Trends Neurosci. 2003. V. 26. P. 523–530. doi: 10.1016/j.tins.2003.08.008.
  32. Krebs-Kraft D.L., Hill M.N., Hillard C.J., McCarthy M.M. // Proc. Natl. Acad. Sci. 2010. V. 107. P. 20535–20540. doi: 10.1073/pnas.1005003107.
  33. Mohr M.A., Michael N.S., DonCarlos L.L., Sisk C.L. // Dev. Cogn. Neurosci. 2022. V. 57. P. 101141. doi: 10.1016/j.dcn.2022.101141.
  34. Johnson R.T., Schneider A., DonCarlos L.L., Breedlove S.M., Jordan C.L. // J. Comp. Neurol. 2012. V. 520. P. 2531–2544. doi: 10.1002/cne.23061.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar
2. Fig. 1. Stages of astrocyte image processing from micrograph to skeleton graph using the example of more (a–c) and less (d–e) branched cells. a, g – micrograph of astrocyte, b, d – binarized image, c, e – skeletonized image. Scale bar – 10 µm.

Baixar (160KB)
Metadados
3. Fig. 2. Results of statistical processing of the obtained morphological characteristics. a – increase in the astrocyte population density in the basolateral nucleus of the amygdala with age: F(1, 26) = 5.46, p = 0.027, no gender effect. b – increase in the average length of the skeleton graph edge was found in females, but not in males: “gender” * “age” – (F(1, 22) = 4 .52, p = 0.045), males PD18 vs females PD18 – p = 0.040, females PD18 vs females PD30 ‒ (p = 0.011) (Tukey post hoc). The average value for the group is indicated by a horizontal bar.

Baixar (101KB)
Metadados

Declaração de direitos autorais © Russian Academy of Sciences, 2024