Sixteen regions of the macaque brain spanning from early prenatal to adulthood were dissected using the same standardized protocol used for human specimens and described in the accompanying study by Li et al ((33 ); see also (32 )). The macaque brain regions and developmental timepoints matched human brain regions and timepoints analyzed in the study by Li et al ((33 )). The sampled homologous brain regions were identified using anatomical landmarks provided in the macaque brain atlas (74 ). An overview of dissected brain regions is provided in fig. S1. Translating Time model (38 (link)) was used to identify equivalent timepoints between macaque and human prenatal development. The list of macaque brains used in this study and relevant metadata are provided in tables S1–2. Macaque studies were carried out in accordance with a protocol approved by Yale University’s Committee on Animal Research and NIH guidelines.
We performed tissue-level RNA extraction and sequencing of all 16 regions, single-cell RNA-Seq of dorsolateral prefrontal cortex [DFC], hippocampus [HIP], amygdala [AMY], striatum [STR], mediodorsal nucleus of the thalamus [MD], and cerebellar cortex [CBC] of mid-fetal macaque, and single-nucleus RNA-Seq of DFC of adult macaque. Single cell/nucleus sample processing was done with 10X Genomics and sequencing was done with Illumina platforms.
For tissue-level analysis, we generated annotation of human-macaque orthologs using the XSAnno pipeline, and matched the developmental age of human and macaque samples based on their respective transcriptome using our algorithm TranscriptomeAge. We also developed TempShift, a method based on Gaussian process model, to reveal the inter-regional differences, inter-species divergence, and genes with heterotopic and heterochronic expression. We also queried differentially expressed genes for enrichment in transcription factor binding sites using findMotifs.pl, and analyzed inter-species differential exon usage using the R package DEXSeq.
The single cell/nucleus data was first analysed by cellranger for decoding, alignment, quality filtering, and UMI counting. After that, data was further analyzed with Seurat according to its guidelines, and cell types were clustered for classification with SpecScore.R. In order to perform direct comparisons between human and macaque at the single-cell level, we focused on the homologous genes between these species and aligned monkey and human cells together to further analyze inter-species divergence of homologous cell types (fig. S47). We used MetageneBicorPlot function to examine the correlation of neuronal and glial cell subtypes, and we employed the correlation analysis to detect the correspondence of excitatory neuron and interneuron subtypes. Finally, we did functional enrichment of disease-associated genes in both tissue-level and single-cell datasets.