This code is associated with the paper from Gehrlach et al., "A whole-brain connectivity map of mouse insular cortex". eLife, 2020. http://doi.org/10.7554/eLife.55585
pipelines for detection and analysis of RV+ neurons and AAV+ pixels.
- AAV tracings imaged with SP5 or SP8 laser scanning confocal microscope
- RV tracings imaged with epifluorescent microscope
- images need to be single sections
- FIJI (Fiji is just ImageJ, NIH)
- Python (written in Python 3.6)
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autonomous_pixel_detection.ijm
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counting_AAV.ijm
andROIset
- ROIset needs to be re-adjusted for every image separately. If section cutting is uneven, a mix between ROIsets needs to be used. If ROIset does not fit over the section even after adjustments, new ROIs need to be drawn
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summary_AAV.py
script needs to be in the folder of counting data -
analysis_AAV.py
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autonomous_neuron_detection.ijm
Before, train ‘Trainable Weka Segmentation’ on 3 – 4 sections and save settings Select path in line 34 -
counting_RV.ijm
andROIset
ROIset needs to be re-adjusted for every image separately. If section cutting is uneven, a mix between ROIsets needs to be used. If ROIset is off, new ROIs need to be drawn -
summary_RV.py
script needs to be in the folder of counting data -
analysis_RV.py
- autonomous_xx_detection: stack of DAPI + GFP + segmented image
- counting_RV: two excel tables
- excel sheet containing table of ROI labels, counts, total area, average size and %area.
- excel sheet contains a table with area and X and Y values of for single cells.
- counting_AAV: excel sheet containing table of ROI labels, counts, total area and pixel density [Pixel/um²]
- summary_xx: csv. file of all counting data combined analysis_xx: csv. files with data for pivot tables for plots along rostro-caudal axis
Author of macros and scripts Daniel Gehrlach of the Gogolla Lab at Max-Planck-Institute of Neurobiology.
Repository created by Caroline Weiand of the Gogolla Lab at Max-Planck-Institute of Neurobiology.