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- Bajic, Andrej, et al.
(author)
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Physically and Chemically Crosslinked Hyaluronic Acid-Based Hydrogels Differentially Promote Axonal Outgrowth from Neural Tissue Cultures
- 2024
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In: Biomimetics. - : MDPI. - 2313-7673. ; 9:3
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Journal article (peer-reviewed)abstract
- Our aim was to investigate axonal outgrowth from different tissue models on soft biomaterials based on hyaluronic acid (HA). We hypothesized that HA-based hydrogels differentially promote axonal outgrowth from different neural tissues. Spinal cord sliced cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) were maintained on a collagen gel, a physically crosslinked HA-based hydrogel (Healon 5®) and a novel chemically crosslinked HA-based hydrogel, with or without the presence of neurotrophic factors (NF). Time-lapse microscopy was performed after two, five and eight days, where axonal outgrowth was assessed by automated image analysis. Neuroprotection was investigated by PCR. Outgrowth was observed in all groups; however, in the collagen group, it was scarce. At the middle timepoint, outgrowth from SCSCs was superior in both HA-based groups compared to collagen, regardless of the presence of NF. In DRGCs, the outgrowth in Healon 5® with NF was significantly higher compared to the rest of the groups. PCR revealed upregulation of NeuN gene expression in the HA-based groups compared to controls after excitotoxic injury. The differences in neurite outgrowth from the two different tissue models suggest that axons differentially respond to the two types of biomaterials.
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| 2. |
- Ossinger, Alexander, et al.
(author)
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A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures : Two image analytic approaches using adaptive thresholds or machine learning
- 2020
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In: Journal of Neuroscience Methods. - : Elsevier BV. - 0165-0270 .- 1872-678X. ; 331
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Journal article (peer-reviewed)abstract
- BACKGROUND: Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.NEW METHOD: We developed and validated algorithms to quantitatively assess neurite outgrowth from living and unstained spinal cord slice cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) based on an adaptive thresholding approach called NeuriteSegmantation. We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures.RESULTS: NeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs. The temporal pattern of axonal growth was successfully assessed. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning.COMPARISON WITH EXISTING METHODS: The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 minutes. In addition, NeuriteSegmentation was compared to NeuriteJ®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.CONCLUSION: The developed image analysis methods were more time-saving and user-independent than established approaches. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience.
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