This document describes a pipeline for identifying neurons from cell-based drug screening images using CellProfiler. A training set was created by labeling healthy neurons as positive examples and macrophages as negative examples. A CellProfiler rule-based classifier achieved 77% accuracy. The author also built their own classifier in R using feature transformation and nonlinear algorithms like MARS, achieving 85% accuracy. The neuron detection pipeline was validated using known toxic compounds and identified protective compounds without specific pharmacological classes.

1. Neuron count from cell-based
drug screen images
Vladimir Morozov, ALS-TDI
October , 2014

2. Cell Profiler pipeline
1. Thresholding
2. Nuclei
identification
3. Neurite
attachment
4. Final
neurons

3. Making training set for detecting healthy neurons
•We consider good looking cells as
positive
•Microphages were identified as
negative examples
•Other non-neuron cells (e.g. astrocytes)
were NOT specified as negative

4. CellProfiler rule-based classifier, ~77% accuracy
IF (Nuclei_Intensity_IntegratedIntensity_ch1 >
6.1516400000000004, [0.36232803790685297, -
0.36232803790685297], [-0.80075679385436416,
0.80075679385436416])
IF (Neurite_AreaShape_Solidity > 0.52507099999999995, [-
0.68639735377870881, 0.68639735377870881],
[0.25029235002592609, -0.25029235002592609])
IF (Nuclei_Intensity_StdIntensity_ch2 > 0.00113378,
[0.11906406640858008, -0.11906406640858008], [-
0.65599426031103425, 0.65599426031103425])
IF (Neurite_Intensity_StdIntensity_ch1 > 0.0028765700000000002,
[0.042546288651953375, -0.042546288651953375], [-
0.88894769206543311, 0.88894769206543311])
IF (Neurite_Intensity_MassDisplacement_ch1 >
3.5285700000000002, [0.08250012593027499, -
0.08250012593027499], [-0.50046974611076089,
0.50046974611076089])

5. CellProfiler classifier on the picked image

6. Manually created training set
•We consider good looking cells as
positive
•Microphages were identified as
negative examples
•Other non-neuron cells (e.g. astrocytes)
were NOT specified as negative

7. Built own classifier in R
Using the training set I tried
• Yeo-Johnson feature transformation (can
handle negative values) to make them more
normally distributed
• 4-5 non-linear (rules, trees, boosting) classifier
algorithms
• The best performance ,85% accuracy with the
Multivariate Adaptive Regression Splines
(MARS,”earth”) algorithm. This model was
used for the final neuron classification

8. Toxic compounds
•Validity of the neuron detection pipeline is confirmed by compounds
with the largest cell toxic effect
•These compounds are known cytotoxic agents

9. Protective compounds
•Statically significant protective compounds were identified
•These compounds don’t show statistical enrichment for specific
pharmacological or structural classes or mechanism of action