Key words: neurite outgrowth • cell-by-cell • fluorescent image analysis • IN Cell Analyzer • IN Cell Developer Toolbox
Neurite extension, or neurite outgrowth, plays a fundamental role in embryonic development, neuronal differentiation, and nervous system function. Neurite outgrowth is also critical in some neuropathological disorders as well as neuronal injury and regeneration. A variety of neurite parameters, including length and number of neurites, are sensitive to the extracellular environment and pharmacological agents. The cell-bycell analysis of neurite outgrowth with IN Cell Developer Toolbox allows quantitation of neurite extensions from fluorescently labeled cells imaged by an IN Cell Analyzer 1000 or 3000.
The protocol uses a two-color labeling approach, whereby the biological sample is labeled with two fluorophores that emit light at different wavelengths. One fluorophore is used to mark the cell nuclei and the other is used to identify the entire cytoplasm of each cell. The analysis procedure identifies nuclei, cell bodies, and neurites, discriminates between cellular components, and quantitates spatial characteristics of defined features.
Users can modify the protocol according to the requirements of a particular experiment: channels can be changed based on fluorescent markers used, parameters can be optimized according to cell size and fluorescence intensity, conditions for cell-by-cell segmentation of neurite networks can be defined as required, and parameters for data output can be chosen from the whole range of available measures.
IN Cell Analyzer 3000 25-8010-11
IN Cell Developer Toolbox Seat License 25-8098-26
Neurite Outgrow th Image Analysis Module
Other materials required
Mouse renoblastoma neuro-2a (N2a) cells (ATCC)
Retinoic acid (Sigma)
Monoclonal anti-α-tubulin clone DM1A (Sigma)
Anti-mouse IgG-FITC antibody (Sigma)
Hoechst™33342 (Molecular Probes)
Retinoic acid-induced neurite outgrowth was measured by quantitation of neurite extensions from fluorescently labeled neuronal cells. In the indirect immunoassay used here, FITC-conjugated secondary antibody detects anti-neurofilament 200 kD subunit (NF200) antibody. Cells were counter-stained with Hoechst 33342 to identify nuclei and imaged using the IN Cell Analyzer 3000.
1. Open image stack in the IN Cell Developer Toolbox.
2. Create target set: ‘Nuclei’
a. Select the channel with the image of the nuclei marker (Fig 2).
b. Select Preprocessing operations if required.
c. Select Segmentation option most appropriate for an accurate nuclear segmentation (Fig 3).
d. Use Erode and Sieve Postprocessing operations to refine nuclei bitmap (Fig 4).
e. Choose Count measurement and apply Statistical Function – Count to report total number of nuclei in the population.
3. Create target set: ‘Cell body’.
a. Select channel with the image of the neurite (Fig 5).
b. Select Remove Single Pixel Targets B>option in the Target Details panel.
c. Select Preprocessing operations if required.
d. Select Segmentation option that will identify whole cells (cell bodies and neurites) (Fig 6).
e. Use Erode operation to exclude neurites from the bitmap (Fig 7).
f. Use the Sieve operation to filter out debris mainly remaining from the joint points.
g. Use the Dilate operation with the same kernel settings as the previous Erode step to restore the cell bodies bitmap so that the outer edge of the mask coincides with the cell bodies edge but no neurites are included (Fig 8).
h. Use a second Dilate step with minimal kernel size in order to overlap the cell bodies bitmap with the neurites bitmap.
i. Apply Clump Breaking operation using ‘Nuclei’ as ‘seed’ (Fig 9).
j. Choose Count measurement and apply Statistical Function - Count to report total number of cell bodies in the population.
4. Create target set: ‘Neurites’.
a. Select channel with the image of neurites (Fig 5).
b. Create a macro that includes the following operation: Subtraction of the dilated cell bodies bitmap (Step 3g) from the whole cells bitmap (Step 3d). Include this macro in the protocol as ‘Preprocessing Macro’.
c. Select Intensity Segmentation option to create a mask from the output of the preprocessing operation by setting the lower intensity threshold above 0 (Fig 10).
d. Select Remove Single Pixel Targets and Filled options in the Target Details panel.
5. Create a linked One to Many target set (‘Cells with neurites’) to link ‘Cell body’ (primary target set) with ‘Neurites’ (secondary target set). Set Overlap conditions as required (e.g., any intersection secondary target within primary target). To count each neurite only once, do not choose the option Allow multiple primary targets to share secondary targets.
6. Generate User Defined Measurements to report, for e. Number of neurites per cell
a. Number of cells with neurites: [Count<\cell with neurites\All:Neurites>]
b. Length of individual neurites [Fiber Length<\cell with Neurites\Neurites>]
c. Total length of neurites per cell [Fiber Length<\cell with Neurites\All:Neurites>]
d. Total length of neurites per population [Fiber Length<\cell with Neurites\ Neurites>] Select Statistical function - Sum for this measure.
e. Number of neurites per cell [Count<\cell with neurites\All:Neurites>]
f. Population average number of neurites per cell [Count<\cell with neurites\All:Neurites>] Select Statistical function - Mean for this measure.
g. Population average neurite length [Fiber Length<\cell with Neurites\ Neurites>] Select Statistical function - Mean for this measure.
7. Export the summary table. Analyze the data.
Images from retinoic acid-induced neurite outgrowth were acquired with the IN Cell Analyzer 3000 and analyzed by both the IN Cell Analyzer 3000 Neurite Outgrowth Image Analysis Module and the IN Cell Developer Toolbox.
The IN Cell Analyzer 3000 software analyzes neurite outgrowth on the basis of population (i.e., the parameters are quantitated for the whole population and then reported either as a population total or averaged per nuclei or neurite correspondingly). The IN Cell Developer Toolbox allows users to analyze neurite outgrowth on a cell-by-cell basis. This permits a wider range of parameters, both population and individual cell-based, to be reported.
We have performed Pearson’s correlation analysis to compare results obtained using IN Cell Developer Toolbox and IN Cell Analyzer 3000 software. The retinoic acid dose-response neurite outgrowth experiment included 12 replicate wells for each of the six retinoic acid concentrations. Analysis with IN Cell Developer Toolbox was performed based on the described protocol and average neurite length results obtained using IN Cell Developer Toolbox and IN Cell Analyzer 3000 are shown (Fig 13). The Pearson’s correlation test demonstrates significant correlation (P < 0.001) between data, the data obtained using two analysis methods.
Neurite outgrowth can be quantitated on a cell-by-cell basis using IN Cell Developer Toolbox. The software allows the user to design a flexible protocol that can be adjusted to meet any specific assay requirements. A variety of approaches can be used for definition of cell regions within neurite networks. A wide choice of parameters is available for characterization of neurite outgrowth in analyzed samples. Population related results obtained using the IN Cell Dev eloper Toolbox are comparable with those obtained from the IN Cell Analyzer 3000 Neurite Outgrowth Image Analysis Module.
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