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 <div> <br><table> <tr> <td> <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> <table> <tr> <td> <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> </td> </tr> </table> </div> </td> </tr> </table> </div>