Tracing_quantification_and_analysis

pipelines for detection and analysis of RV+ neurons and AAV+ pixels.

Prerequisites

• AAV tracings imaged with SP5 or SP8 laser scanning confocal microscope
• RV tracings imaged with epifluorescent microscope
• images need to be single sections

Requirements

• FIJI (Fiji is just ImageJ, NIH)
• Python (written in Python 3.6)

Workflow for AAV tracings

1. autonomous_pixel_detection.ijm

2. counting_AAV.ijm and ROIset

• 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

3. summary_AAV.py script needs to be in the folder of counting data

4. analysis_AAV.py

Workflow for RV tracings

1. autonomous_neuron_detection.ijm Before, train ‘Trainable Weka Segmentation’ on 3 – 4 sections and save settings Select path in line 34

2. counting_RV.ijm and ROIset 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

3. summary_RV.py script needs to be in the folder of counting data

4. analysis_RV.py

Outputs

• autonomous_xx_detection: stack of DAPI + GFP + segmented image
• counting_RV: two excel tables
  1. excel sheet containing table of ROI labels, counts, total area, average size and %area.
  2. 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.