Predicting local tissue mechanics using immunohistochemistry

Koser, DE; Moeendarbary, Emad; Küerten, Stefanie; Franze, Kristian

doi:10.1101/358119

Cited by 8 publications

(18 citation statements)

References 37 publications

(55 reference statements)

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“…Nevertheless, the OB was the stiffest among the investigated brain regions. The OB is altogether a cell dense region and cell density is known to correlate with tissue stiffness (Koser et al, 2015;Thompson et al, 2019). However, we also found all lamins of the nuclear matrix to be enriched in the OB, and lamin A correlates with tissue stiffness (Swift et al, 2013) (Figure S2F).…”

Section: Unique Stiffness Of the Neurogenic Nichesmentioning

confidence: 57%

Defining the Adult Neural Stem Cell Niche Proteome Identifies Key Regulators of Adult Neurogenesis

et al. 2020

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Section: Unique Stiffness Of the Neurogenic Nichesmentioning

confidence: 57%

Defining the Adult Neural Stem Cell Niche Proteome Identifies Key Regulators of Adult Neurogenesis

et al. 2020

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“…This conjecture is supported by another recent study showing that the stiffness of brain tissue can not be solely determined by the stiffness of the cells that constitute the tissue [81]. But, it contradicts a previous study on spinal cord tissue, where the stiffness positively correlated with the relative tissue area covered by cell nuclei [94,95]. The latter finding could be attributed to the fact that those measurements were performed using atomic force microscopy indentation on a smaller length scale than the experiments which are the basis for Fig.…”

Section: Is Brain Stiffness Correlated With Cell Density?mentioning

confidence: 74%

Fifty Shades of Brain: A Review on the Mechanical Testing and Modeling of Brain Tissue

Budday

Ovaert

Holzapfel

et al. 2019

Arch Computat Methods Eng

290

288

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Brain tissue is not only one of the most important but also the most complex and compliant tissue in the human body. While long underestimated, increasing evidence confirms that mechanics plays a critical role in modulating brain function and dysfunction. Computational simulations-based on the field equations of nonlinear continuum mechanics-can provide important insights into the underlying mechanisms of brain injury and disease that go beyond the possibilities of traditional diagnostic tools. Realistic numerical predictions, however, require mechanical models that are capable of capturing the complex and unique characteristics of this ultrasoft, heterogeneous, and active tissue. In recent years, contradictory experimental results have caused confusion and hindered rapid progress. In this review, we carefully assess the challenges associated with brain tissue testing and modeling, and work out the most important characteristics of brain tissue behavior on different length and time scales. Depending on the application of interest, we propose appropriate mechanical modeling approaches that are as complex as necessary but as simple as possible. This comprehensive review will, on the one hand, stimulate the design of new experiments and, on the other hand, guide the selection of appropriate constitutive models for specific applications. Mechanical models that capture the complex behavior of nervous tissues and are accurately calibrated with reliable and comprehensive experimental data are key to performing reliable predictive simulations. Ultimately, mathematical modeling and computational simulations of the brain are useful for both biomedical and clinical communities, and cover a wide range of applications ranging from predicting disease progression and estimating injury risk to planning surgical procedures.

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“…The mechanical properties of biological tissues are thought to be determined by the material properties of the constituent cells, ECM and the degree of intercellular adhesion and connectivity [19]. Koser et al have proposed to consider additional criteria to predict nervous tissue stiffness based on fluorescently labeled tissue components such as cell body density, myelin content, collagen content, extra cellular matrix composition and axonal orientation [20, 37]. Our results show that cell body density is not sufficient to explain the mechanical differences between gray and white matter in zebrafish spinal cords nor the spatio-temporal evolution of the apparent Young’s modulus during spinal cord regeneration.…”

Section: Discussionmentioning

confidence: 99%

“…In vivo , this mechanical environment is formed by the surrounding nervous tissue whose mechanical properties are determined by factors such as the combined material properties of neighboring cells, cell density, myelin content, collagen content, extra cellular matrix composition and cell interconnectivity [19, 20]. As these may change during development or after pathological events, concomitant changes of mechanical tissue properties and their direct involvement in a wide range of CNS conditions and diseases becomes apparent [21–24].…”

Section: Introductionmentioning

confidence: 99%

Zebrafish spinal cord repair is accompanied by transient tissue stiffening

Möllmert

Kharlamova

Hoche

et al. 2019

Preprint

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1 Severe injury to the mammalian spinal cord results in permanent loss of function due 2 to the formation of a glial-fibrotic scar. Both the chemical composition and the 3 mechanical properties of the scar tissue have been implicated to inhibit neuronal 4 regrowth and functional recovery. By contrast, adult zebrafish are able to repair 5 spinal cord tissue and restore motor function after complete spinal cord transection 6 owing to a complex cellular response that includes neurogenesis and axon regrowth.7 The mechanical mechanisms contributing to successful spinal cord repair in adult 8 zebrafish are, however, currently unknown. Here, we employ AFM-enabled nano-9 indentation to determine the spatial distributions of apparent elastic moduli of living 10 spinal cord tissue sections obtained from uninjured zebrafish and at distinct time 11 points after complete spinal cord transection. In uninjured specimens, spinal gray 12 matter regions were stiffer than white matter regions. During regeneration after 13 transection, the spinal cord tissues displayed a significant increase of the respective 14 apparent elastic moduli that transiently obliterated the mechanical difference 15 between the two types of matter, before returning to baseline values after completion 16 of repair. Tissue stiffness correlated variably with cell number density, 17 oligodendrocyte interconnectivity, axonal orientation, and vascularization. The 18 presented work constitutes the first quantitative mapping of the spatio-temporal 19 changes of spinal cord tissue stiffness in regenerating adult zebrafish and provides 20 the tissue mechanical basis for future studies into the role of mechanosensing in 21 spinal cord repair. 22 42 recovery in adult zebrafish within 6-8 weeks post-injury [8]. 43Morphological changes, proliferation, migration and differentiation also constitute 44 responses that mechanosensitive neurons and glia exhibit when exposed to distinct 45 mechanical environments [14][15][16][17]. In vitro studies of neural cells reported an 46 increased branching of neurons on compliant, but directed axonal growth on stiff 47 substrates [14, 18]. Astrocytes and microglia display morphological characteristics of 48 an activated phenotype and upregulate inflammatory genes and proteins when 49 exposed to a mechanical stimulus that deviates from their physiological mechanical 50 4 environment both in vitro and in vivo [17]. Oligodendrocyte precursor cells increase 51 their expression of myelin basic protein and display an elaborated myelin membrane 52 on stiffer substrates as compared to more compliant substrates indicating a preferred 53 mechanical environment for differentiation [15]. 54 In vivo, this mechanical environment is formed by the surrounding nervous tissue 55 whose mechanical properties are determined by factors such as the combined 56 material properties of neighboring cells, cell density, myelin content, collagen 57 content, extra cellular matrix composition and cell interconnectivity [19, 20]. As these 58 may change during develop...

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Predicting local tissue mechanics using immunohistochemistry

Cited by 8 publications

References 37 publications

Defining the Adult Neural Stem Cell Niche Proteome Identifies Key Regulators of Adult Neurogenesis

Defining the Adult Neural Stem Cell Niche Proteome Identifies Key Regulators of Adult Neurogenesis

Fifty Shades of Brain: A Review on the Mechanical Testing and Modeling of Brain Tissue

Zebrafish spinal cord repair is accompanied by transient tissue stiffening

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