2026-10-12T20:09:23Z https://www.scidb.cn/oai

10.57760/sciencedb.41612 2026-09-24T14:22:47Z

2026-09-24 A human brain single-cell and single-nucleus transcriptomic resource across age intervals doi:10.57760/sciencedb.41612 en This dataset is an integrated single-cell and single-nucleus transcriptomic resource of the human brain across age intervals. It was constructed from experimentally generated scRNA-seq/snRNA-seq data derived from non-disease human brain samples and does not include disease samples, disease-model samples, organoid-derived datasets, simulated datasets, or statistically inferred single-cell expression profiles. The resource harmonizes 22 published human brain scRNA-seq/snRNA-seq datasets and spans 15 age intervals from embryonic and fetal development to childhood, adolescence, adulthood, and late adulthood. The dataset contains 2,602,031 cells or nuclei, 24,659 genes, 286 donors, 448 biological samples, 3,744 library- or source-level records, and 48 brain regions. All records were processed through unified metadata curation, age-interval mapping, brain-region harmonization and sex-field standardization, and four integration representations were built (raw PCA, Harmony, scVI and RPCA), resulting in 75 clusters and 12 major cell types.The released package includes 10X-compatible expression matrices split by source dataset, cell-level metadata (21 fields), 50-dimensional coordinates and matching 2D UMAPs for the four representations, per-cell LISI validation, source-dataset provenance and licence information, file manifests and MD5 checksums, and example R and Python reader scripts (bundled in scripts/readers.zip). The cell-level metadata include source dataset, sequencing technology, sequencing modality, sample identifier (including the original sample identifier), donor identifier, library identifier, brain region, age, sex, cluster, cell type, age interval, mitochondrial percentage, UMI count, and gene count. This resource can support studies of cellular composition across human brain development and aging, brain-region-specific expression patterns, cross-dataset integration methods, cell-type annotation validation, and reuse of public human brain single-cell transcriptomic data. human brain; single-cell RNA sequencing; single-nucleus RNA sequencing; transcriptomic data resource; age intervals Xu Meng Qi Zhang Yuan Quan Hong-Yu Zhang PUBLIC https://mit-license.org dataset Science Data Bank