20,23 The high costs and the toxicity effects hinder the possibility to conduct massive analysis for long observation times (days) at a very high frame rate (minutes) with the immediate consequences of losing information on the dynamics of the process underlying neurite outgrowth and degradation, along with the possibility to correlate their mutual relationship. If on one hand, cell labeling allows the user to easily segment the soma and the neurites from the background and further analyze their morphological evolution, according to the limitations highlighted above, the construction of a dynamic modeling of the degradation process of neurites is prevented, and most of the existing tools end up providing only an improved view of the neuronal cell 21 or at most a few static measurements at time points of a few hours. Due to the very high complexity in motor neuron structure and temporal degradation processes, state-of-the-art methods for the analysis of living motor neuron in culture are based on cellstaining techniques. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. The developed image analysis methods were more time-saving and user-independent than established approaches. In addition, NeuriteSegmentation was compared to NeuriteJ®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.
#Neurite outgrowth cellprofiler pipeline manual
The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 minutes. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning. The temporal pattern of axonal growth was successfully assessed.
NeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs.
We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures. 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. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.
#Neurite outgrowth cellprofiler pipeline software
Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience.
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