GAIN: A graphical method to automatically analyze individual neurite outgrowth.
BACKGROUND
Neurite outgrowth is a metric widely used to assess the success of in vitro neural stem cell differentiation or neuron reprogramming protocols and to evaluate high-content screening assays for neural regenerative drug discovery. However, neurite measurements are tedious to perform manually, and there is a paucity of freely available, fully automated software to determine neurite measurements and neuron counting. To provide such a tool to the neurobiology, stem cell, cell engineering, and neuroregenerative communities, we developed an algorithm for performing high-throughput neurite analysis in immunofluorescent images.


METHODS
Given an input of paired neuronal nuclear and cytoskeletal microscopy images, the GAIN algorithm calculates neurite length statistics linked to individual cells or clusters of cells. It also provides an estimate of the number of nuclei in clusters of overlapping cells, thereby increasing the accuracy of neurite length statistics for higher confluency cultures. GAIN combines image processing for neuronal cell bodies and neurites with an algorithm for resolving neurite junctions.


RESULTS
GAIN produces a table of neurite lengths from cell body to neurite tip per cell cluster in an image along with a count of cells per cluster.


METHODS
GAIN's performance compares favorably with the popular ImageJ plugin NeuriteTracer for counting neurons, and provides the added benefit of assigning neurites to their respective cell bodies.


CONCLUSIONS
In summary, GAIN provides a new tool to improve the robust assessment of neural cells by image-based analysis.
Department of Bioengineering, Rice University, Houston, TX 77030 USA. Electronic address: bl6@rice.edu.
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BL
Long
Department of Bioengineering, Rice University, Houston, TX 77030 USA.
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H
Li
Department of Bioengineering, Rice University, Houston, TX 77030 USA.
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Mahadevan
Department of Bioengineering, Rice University, Houston, TX 77030 USA.
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T
Tang
Department of Bioengineering, Rice University, Houston, TX 77030 USA.
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Balotin
Department of Bioengineering, Rice University, Houston, TX 77030 USA.
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Grandel
Department of Bioengineering, University of California, Berkeley, CA 94720 USA.
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Soto
Department of Bioengineering, University of California, Berkeley, CA 94720 USA.
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Wong
Department of Bioengineering, Rice University, Houston, TX 77030 USA.
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Abrego
Department of Bioengineering, University of California, Los Angeles, CA 90095 USA.
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Li
Department of Bioengineering, Rice University, Houston, TX 77030 USA.
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Qutub
D000465:Algorithms; D000818:Animals; D058948:Cell Tracking / Q000379:methods*; D002478:Cells, Cultured; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D051379:Mice; D058953:Neural Stem Cells / Q000166:cytology* / Q000502:physiology*; D016501:Neurites / Q000502:physiology* / Q000648:ultrastructure*; D000071446:Neuronal Outgrowth / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
Image processing; Neural progenitor cells; Neurons; Segmentation; Tracing