Jan 22, 2025

Public workspaceImaging and quantification V.2

DOI
https://dx.doi.org/10.17504/protocols.io.yxmvm31mbl3p/v2
  • Tony Hsiao1,2,
  • Felicia Xaveria Suteja1,2,
  • Barkha Kishor Goswami1,2,
  • Anahid Ansari Mahabadian1,2,
  • YuHong Fu1,2,
  • Glenda Halliday1,2
  • 1Brain and Mind Centre & Faculty of Medicine and Health School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia;
  • 22Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
Tony Hsiao
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DOI: https://dx.doi.org/10.17504/protocols.io.yxmvm31mbl3p/v2
Protocol Citation: Tony Hsiao, Felicia Xaveria Suteja, Barkha Kishor Goswami, Anahid Ansari Mahabadian, YuHong Fu, Glenda Halliday 2025. Imaging and quantification. protocols.io https://dx.doi.org/10.17504/protocols.io.yxmvm31mbl3p/v2Version created by Tony Hsiao
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: December 16, 2024
Last Modified: January 22, 2025
Protocol Integer ID: 115713
Keywords: ASAPCRN, using olympus vs200 scanner, olympus vs200 scanner, quantification imaging, quantification by qupath, qupath, imaging
Funders Acknowledgements:
ligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase,
Grant ID: 20815
Abstract
Imaging using Olympus VS200 scanner and quantification by Qupath ver 3.8
Troubleshooting
Cropping (OlyVIA ver 3.8) and Quantification (QuPath ver 0.4.3)
Cropping and section selection
Images are cropped into groups following the Bregma sections (Bregma -2.69mm to -4.03mm):


a. Rostral
b. Intermediate
c. Caudal
Channel separation and Annotation
Channels are renamed based on their antibody (DAPI, TH, NeuN, mCherry, αSyn)
Annotations (categorized as SNC and VTA) is created surrounding TH+ cells clusters.
Cell detection
Cell detection is applied with the parameters as followed:
a. Detection channel: DAPI/TH/NeuN/mCherry/αSyn (depending on what channel we would like to detect)
b. Background radius: 8µm
c. Median filter radius: 0µm
d. Median filter radius: 0µm
e. Sigma: 1.5 to 2 µm (depending on the size of the targeted stain)
f. Minimum area: X µm2 (depending on the size of the targeted stain)
g. Maximum area: 1000 µm2 (depending on the size of the targeted stain)
h. Threshold: X (depending on the average targeted stain intensity on each detection)
Single Measurement Classifier
  1. Add class for the targeted channels of the classifier:
a. TH+
b. TH-
c. NeuN+
d. NeuN-
e. mCherry+
f. mCherry-
g. αSyn+
h. αSyn-
After the necessary detections are made, a new ‘Single measurement classifier’ is created with the parameters as followed:
a. Object filler: Detections (all)
b. Channel filter: TH/NeuN/mCherry/αSyn (depending on the targeted stain, but was not detected using ‘cell detection’)
c. Measurement: Cell/Nucleus/Cytoplasm: DAPI/TH/NeuN/mCherry/αSyn mean (depending on where the targeted channel is concentrated)
Threshold: X (depending on the average targeted stain intensity on each detection)
d. Above Threshold: TH+/NeuN+/mCherry+/αSyn+
e. Below Threshold: TH-/NeuN-/mCherry-/αSyn-

The new classifier was saved after given a name according to the detected channel and threshold.
Export measurement
a. ‘Annotation measurement’ results of each image are then exported to excel and arranged accordingly.
DAPI count
a. Apply ‘cell detection’ using the channel: DAPI
NeuN+ with TH+/TH- and αSyn+/αSyn- count
a. Apply ‘cell detection’ using the channel: NeuN
b. Apply ‘single measurement classifier’ using the channel: TH
c. Apply ‘single measurement classifier’ using the channel: αSyn