Semi-automatic quantification of neurite fasciculation in high-density neurite images by the neurite directional distribution analysis (NDDA)

Abstract

Background Bundling of neurite extensions occur during nerve development and regeneration. Understanding the factors that drive neurite bundling is important for designing biomaterials for nerve regeneration toward the innervation target and preventing nociceptive collateral sprouting. High-density neuron cultures including dorsal root ganglia explants are employed for in vitro screening of biomaterials designed to control directional outgrowth. Although some semiautomated image processing methods exist for quantification of neurite outgrowth, methods to quantify axonal fasciculation in terms of direction of neurite outgrowth are lacking. New Method This work presents a semi-automated program to analyze micrographs of high-density neurites; the program aims to quantify axonal fasciculation by determining the orientational distribution function of the tangent vectors of the neurites and calculating its Fourier series coefficients (‘c’ values). Results We found that neurite directional distribution analysis (NDDA) of fasciculated neurites yielded ‘c’ values of ≥ ~0.25 whereas branched outgrowth led to statistically significant lesser values of <~0.2. The ‘c’ values correlated directly to the width of neurite bundles and indirectly to the number of branching points. Comparison with Existing Methods Information about the directional distribution of outgrowth is lost in simple counting methods or achieved laboriously through manual analysis. The NDDA supplements previous quantitative analyses of axonal bundling using a vector-based approach that captures new information about the directionality of outgrowth. Conclusion The NDDA is a valuable addition to open source image processing tools available to biomedical researchers offering a robust, precise approach to quantification of imaged features important in tissue development, disease, and repair.

Silk Hydrogels as Soft Substrates for Neural Tissue Engineering

“…Although literature suggests that FN decreases neurite extension,78 we found that FN‐functionalized silk gels produced cDRG extensions longer than those cultured on substrates functionalized with LN and NT‐3, perhaps due to FN's integrin and growth factor binding sites acting synergistically with NT‐3 ( Figure a). The extent of fasciculation as a function of distance from the explant body was quantified using an orientational distribution function (ODF) that assessed extent of alignment of neurites with a radially aligned vector field 79. We found ECM‐functionalized silk hydrogels exhibited greater fasciculation of neurites as compared to silk hydrogel alone with NT‐3 (Figure 6b,c).…”

“…Neurite Directional Distribution Analysis in Mathematica : Fasciculation of cDRG neurite extensions was assessed through a radial tangent vector profile analysis program 79. Briefly, tangent lines were fit to continuous lines of fluorescence in immunofluorescent images of cDRG extension at day 4 of culture.…”

Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement

“…The development of a disentanglement-focused model will enable users to analyze images featuring multiple neurons, even without 'rainbow' stains like BrainBow or TetBow 21 . This approach would effectively address the limitations inherent in many current methodologies [22][23][24][25] . With the expansive potential of a CNN-based classification system, we can harness is to advance the field of biomedical image analysis and examine individual cells in detail in a high throughput manner.…”

“…Each classifier was generated using "neural architecture search", by randomizing various parameters and then inferring on additional images. The following parameters were randomly assigned: CNN kernel size (2)(3)(4), CNN initial number of filters (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), CNN depth (max(1, log(image width, kernel size))), and filter increase rate (1.25 to 1.75). The number of dense layers after convolution starts with a random size that is no more than half of the flattened size.…”

Extracellular matrix stiffness negatively affects axon elongation, growth cone area and F‐actin levels in a collagen type I 3D culture

“…At 1 or 2 days after seeding, cultures were incubated with a fluorescent vital dye (5carboxyfluorescein diacetate, acetoxymethyl ester; Molecular Probes, Invitrogen) according to our previous protocols from 2D culture studies (Martínez et al, 2016;Richeri et al, 2010) Neurite fasciculation. Manual quantitative analysis of bundles of neurites was done with ImageJ software using the segmented line tool, according to published protocols (Hopkins et al, 2014). 16 neurites per image were selected randomly.…”