The Lacunocanalicular Network is Denser in C57BL/6 Compared to BALB/c Mice.

Bone Image analysis Mechanobiology Networks Osteocytes

Journal

Calcified tissue international
ISSN: 1432-0827
Titre abrégé: Calcif Tissue Int
Pays: United States
ID NLM: 7905481

Informations de publication

Date de publication:
16 Oct 2024
Historique:
received: 07 02 2024
accepted: 10 09 2024
medline: 17 10 2024
pubmed: 17 10 2024
entrez: 16 10 2024
Statut: aheadofprint

Résumé

The lacunocanalicular network (LCN) is an intricate arrangement of cavities (lacunae) and channels (canaliculi), which permeates the mineralized bone matrix. In its porosity, the LCN accommodates the cell network of osteocytes. These two nested networks are attributed a variety of essential functions including transport, signaling, and mechanosensitivity due to load-induced fluid flow through the LCN. For a more quantitative assessment of the networks' function, the three-dimensional architecture has to be known. For this reason, we aimed (i) to quantitatively characterize spatial heterogeneities of the LCN in whole mouse tibial cross-sections of BALB/c mice and (ii) to analyze differences in LCN architecture by comparison with another commonly used inbred mouse strain, the C57BL/6 mouse. Both tibiae of five BALB/c mice (female, 26-week-old) were stained using rhodamine 6G and whole tibiae cross-sections were imaged using confocal laser scanning microscopy. Using image analysis, the LCN was quantified in terms of density and connectivity and lacunar parameters, such as lacunar degree, volume, and shape. In the same tibial cross-sections, the calcium content was measured using quantitative backscattered electron imaging (qBEI). A structural analysis of the LCN properties showed that spatially denser parts of the LCN are mainly due to a higher density of branching points in the network. While a high intra-individual variability of network density was detected within the cortex, the inter-individual variability between different mice was low. In comparison to C57BL/6J mice, BALB/c mice showed a distinct lower canalicular density. This reduced network was already detectable on a local network level with fewer canaliculi emanating from lacunae. Spatial correlation with qBEI images demonstrated that bone modeling resulted in disruptions in the network architecture. The spatial heterogeneity and differences in density of the LCN likely affects the fluid flow within the network and therefore bone's mechanoresponse to loading.

Identifiants

pubmed: 39414712
doi: 10.1007/s00223-024-01289-y
pii: 10.1007/s00223-024-01289-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : CIHR
ID : PJT-165939
Pays : Canada
Organisme : Deutsche Forschungsgemeinschaft
ID : SFB1444

Informations de copyright

© 2024. The Author(s).

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Auteurs

Maximilian Rummler (M)

Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.

Alexander van Tol (A)

Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.

Victoria Schemenz (V)

Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany.
Department of Operative and Preventive Dentistry, Charité-Universitätsmedizin - Berlin, Berlin, Germany.

Markus A Hartmann (MA)

1st Medical Department Hanusch Hospital, Ludwig Boltzmann Institute of Osteology at Hanusch Hospital of OEGK and AUVA Trauma Centre Meidling, Vienna, Austria.

Stéphane Blouin (S)

1st Medical Department Hanusch Hospital, Ludwig Boltzmann Institute of Osteology at Hanusch Hospital of OEGK and AUVA Trauma Centre Meidling, Vienna, Austria.

Bettina M Willie (BM)

Research Center, Shriners Hospital for Children, Montreal, Canada.
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.

Richard Weinkamer (R)

Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, Am Mühlenberg 1, 14476, Potsdam, Germany. Richard.Weinkamer@mpikg.mpg.de.

Classifications MeSH