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Abstract
Integrative analyses of transcriptomic and neuroimaging data have generated a wealth of information about biological pathways underlying regional variability in imaging-derived brain phenotypes in humans, but rarely in nonhuman primates due to the lack of a comprehensive anatomically-defined atlas of brain transcriptomics. Here we generate complementary bulk RNA-sequencing dataset of 819 samples from 110 brain regions and single-nucleus RNA-sequencing dataset, and neuroimaging data from 162 cynomolgus macaques, to examine the link between brain-wide gene expression and regional variation in morphometry. We not only observe global/regional expression profiles of macaque brain comparable to human but unravel a dorsolateral-ventromedial gradient of gene assemblies within the primate frontal lobe. Furthermore, we identify a set of 971 protein-coding and 34 non-coding genes consistently associated with cortical thickness, specially enriched for neurons and oligodendrocytes. These data provide a unique resource to investigate nonhuman primate models of human diseases and probe cross-species evolutionary mechanisms.
A comprehensive anatomically-defined atlas of brain transcriptomics in macaques is still lacking. Here, the authors generate complementary bulk RNA-seq and snRNA-seq datasets from cynomolgus macaques to examine the link between brain-wide gene expression and regional variation in morphometry.
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1 Shanghai Jiao Tong University School of Medicine, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293); Shanghai Jiao Tong University School of Medicine, Department of Radiology, Ruijin Hospital, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293); Shanghai Jiao Tong University School of Medicine, Clinical Neuroscience Center, Ruijin Hospital Luwan Branch, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293)
2 University of Chinese Academy of Sciences, Chinese Academy of Sciences, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419)
3 ShanghaiTech University, Shanghai Institute for Advanced Immunochemical Studies, Shanghai, China (GRID:grid.440637.2) (ISNI:0000 0004 4657 8879)
4 Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, Department of Medical Imaging, Zhengzhou, China (GRID:grid.414011.1) (ISNI:0000 0004 1808 090X)
5 Peking University, School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319)
6 Chinese Academy of Sciences, Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Shanghai, China (GRID:grid.9227.e) (ISNI:0000000119573309); University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419)
7 Beijing University of Chemical Technology, College of Life Science and Technology, Beijing, China (GRID:grid.48166.3d) (ISNI:0000 0000 9931 8406)
8 Harbin Engineering University, Qingdao Innovation and Development Center, Qingdao, China (GRID:grid.33764.35) (ISNI:0000 0001 0476 2430); Harbin Engineering University, College of Intelligent Systems Science and Engineering, Harbin, China (GRID:grid.33764.35) (ISNI:0000 0001 0476 2430)
9 Peking University, School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319); Hainan University, School of Biomedical Engineering, Haikou, China (GRID:grid.428986.9) (ISNI:0000 0001 0373 6302)