Single-nucleus multiome analysis in the human prefrontal cortex identifies gene expression and cis-regulatory elements associated with aging.

A large-scale single-nucleus multiome dataset was generated from 357 human prefrontal cortex samples spanning ages 15 to 100 years.

• Samples were derived from individuals of European and African admixed ancestry.
• The final dataset consisted of paired transcriptomic and epigenomic profiles for over 1.5 million cells.
• Cells were classified into seven major cell types using canonical marker genes.
• Both ATAC (chromatin accessibility) and gene expression modalities were captured simultaneously per nucleus.

Seven major cell types were identified and each was analyzed for features associated with aging.

• Cell type classification was performed using canonical marker genes.
• Each of the seven major cell types was independently analyzed for age-associated transcriptomic and epigenomic features.
• The analysis spanned a broad age range from 15 to 100 years, enabling detection of age-related changes across the lifespan.

Open chromatin regions were correlated with transcription factor expression to identify age-associated regulatory networks.

• Chromatin accessibility data from the ATAC modality was used to identify open chromatin regions.
• Correlations between open chromatin regions and transcription factor expression were computed to define regulatory networks.
• These analyses were performed in a cell-type-specific manner.
• The approach aimed to link epigenomic changes to transcriptional regulation associated with aging.

Co-accessibility analysis identified linked peaks and genes, generating a catalog of putative cis-regulatory elements by cell type.

• Co-accessibility was used to link distal open chromatin peaks to nearby genes.
• The resulting catalog of putative cis-regulatory elements is organized by cell type.
• This resource enables characterization of transcriptional regulation in a cell-type-specific manner.
• The data are intended to generate hypotheses about how cis-regulatory elements influence and are influenced by aging and disease.

The multiomic dataset serves as a resource to characterize transcriptional regulation by cell type and generate hypotheses about aging and disease.

• Data include both transcriptomic and epigenomic profiles for each nucleus.
• The resource covers diverse ancestry backgrounds including European and African admixed individuals.
• The dataset is intended to support investigation of how distinct cell-type profiles both influence and are influenced by aging and disease.
• The broad age range (15–100 years) and large sample size (357 samples, >1.5 million cells) enhance the power to detect age-associated molecular changes.