an average of 3,200 per nucleus. We performed standard log-normalization and a variance stabilizing transformation prior to finding anchors and identified variable features individually for each monkeys’ data set using Seurat’s FindVariableFeatures function with number of features set to 7000. Next, we identified anchors using the FindIntegrationAnchors function with default parameters and passed these anchors to the IntegrateData function. This returned a Seurat object with an integrated expression matrix for all nuclei. We scaled the integrated data with the ScaleData function, ran PCA using the RunPCA function, and visualized the results with UMAP. We used Louvain clustering and chose a resolution that reflected the major cell classes of striatum including D1- and D2- MSNs, interneurons and astrocytes. We calculated the differentially expressed genes for each cell class with the FindMarkers function (Data S1). We annotated the clusters based on feature plots of well-known marker genes13,17,22 and verified the identities of cell clusters by using the hypergeometric test to compare differentially expressed genes for each cluster to markers from single cell rodent studies.77 Given the robust conservation of major cell types, the rodent markers were sufficient for annotation.77 We converted rodent genes to rhesus macaque genes by BioMart Ensemble, keeping one-to-one
