10.25402/BTN.11617872.v1
Ying Zhu
Ying
Zhu
József Mészáros
József
Mészáros
Roman Walle
Roman
Walle
Rongxi Fan
Rongxi
Fan
Ziyi Sun
Ziyi
Sun
Andrew J. Dwork
Andrew
J. Dwork
Pierre Trifilieff
Pierre
Trifilieff
Jonathan A. Javitch
Jonathan A.
Javitch
Supplemental Methods and Materials
Future Science Group
2020
dopamine
D2R
Adenosine
A2AR
GPCR
proximity ligation assay
machine learning
naïve Bayesian classifier
Proteomics and Intermolecular Interactions (excl. Medical Proteomics)
Neurogenetics
Neurosciences not elsewhere classified
2020-01-15 11:19:07
Figure
https://future-science-group.figshare.com/articles/figure/Supplemental_Methods_and_Materials/11617872
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<p><b>Supplemental
Methods and Materials</b></p>
<p><b> </b></p>
<p><b>Supplemental Table 1. Human
Subject information</b></p>
<p><b> </b></p>
<p><b>Supplemental Table 2. ROI, sampling areas and counting areas </b></p>
<p><b>Supplemental
Figure. 1. </b>Sampling procedures for PLA-BF. Luxol fast blue/cresyl violet
staining was performed to discriminate between white matter and grey matter
(A-C). An outline of ventral striatum sub-territories (D) was drawn based on the
stained results of each sample. Overlapping the sampling grid (E) and the
outline (D) divided the brain section into several evenly distributed
sampling areas (F and G). Within the NAcc, the ROI in this study (G), one
counting locus (indicated by * bounded by purple frame in the inset) was
selected in each sampling area (indicated by the blue frame in the inset) and
the 40x image of this counting locus was exported for quantification (H). A
section of (H) is shown at high magnification (I). Scale bar, A-C, 5 mm; H-I, 50 µm.</p><p><b>Supplemental Figure. 2.</b> Quantification of PLA signal with BOPSS and
manual counting. Three randomly selected areas in a full counting image of each
PLA condition, single (A), dual (B) and negative PLA (C) (from PI12277) were
quantified with BOPSS or manually (four times independently). The puncta counted by BOPSS were marked in
red in the representative images of pre-optimization (BOPSS_0, D-F) and
post-optimization analysis (BOPSS, G-I). The blue arrows indicated examples of
reduced non-specific detection in post-optimization analysis. The manually
counted puncta were marked in black and labelled with yellow numbers with Cell
Counter (Image J) (J-L). The orange and white arrows indicate examples of
overcounted and undercounted puncta detected by BOPSS compared with manual counting,
respectively. One-way ANOVA was performed to analyze the results of single PLA.
There is no significant difference among three quantification methods (P
value=0.113) (M). Two-way ANOVA was performed to compare the quantification
results for dual PLA and its negative control, which had the same PLA condition
as dual PLA but omit one primary antibody (N). The interaction accounts for
approximately 2.5 % of the total variance and is considered not significant (P
value=0.069). Both quantification methods (accounts 33.9 % of the total
variance, P value is <0.001) and PLA conditions (accounts 41.6 % of the
total variance, P value is <0.0001) have significant effect on the
variation. Bonferroni’s multiple comparison were performed to compare BOPSS and
other methods (M and N), **** multiplicity adjusted P value <0.0001, **
<0.01.<br></p><p><br></p><div>
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<p><b>Supplemental Figure. 3.</b>
Quantifying the signal of single PLA for A2AR and D2R, and dual PLA for
D2R-A2AR, in the NAcc. The numbers of PLA puncta / mm<sup>2 </sup>were
quantified by BOPSS (A-D, data were plotted as mean ± SEM). The fractions of
D2R-A2AR dual PLA puncta relative to D2R (E) and A2AR (F) single PLA were
calculated with the means in (A-C) and data were plotted as mean ± propagated
error. The error was propagated as described in supplemental methods.</p>
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