Project description:Aging is the primary risk factor for many neurodegenerative diseases, primarily attributed to progressive changes in cellular epigenomes. Despite this known association, the complexity of the mammalian brain complicates the identification of brain regions and cell types that are most susceptible to aging effects. While alterations in gene expression have been observed in aging neural cells, comprehension of the epigenetic regulatory mechanisms underlying the transcriptomic changes remains largely elusive. To unveil these complexities in a comprehensive manner, we utilized single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq) to produce extensive datasets comprising 132,551 methylomes and 72,666 chromatin conformation-methylome joint profiles from eight distinct brain regions of C57BL/6J mice aged 2, 9, and 18 months. These regions include the anterior hippocampus (AHC), posterior hippocampus (PHC), frontal cortex (FC), amygdala (AMY), nucleus accumbens (NACB), PAG/PCG, entorhinal cortex (ENT), and caudate putamen (CP). We utilized this dataset to investigate age-related changes in DNA methylomes and transcription factor motifs potentially affected by methylation alterations. We also examined methylation changes in transposable elements, observing locus-specific retrotransposon activation. At the chromatin conformation level, we analyzed aging-related changes in compartmentalization, topologically associating domains (TADs), and chromatin loops. Furthermore, we explored regional heterogeneity during aging within identical cell types and validated our findings using the MERFISH dataset.
Project description:Tumors show substantial amounts of cellular heterogeneity by forming complex ecosystems of malignant and non-malignant cells. Herein, we present a comprehensive multi-omic cell atlas of matched single-cell transcriptome and single-cell chromatin accessibility profiles spanning over 150,000 cells from 11 human gynecologic tumors. By jointly analyzing these transcriptomic and chromatin accessibility profiles at single-cell resolution, we identify 115,734 total peak-to-gene links representing putative regulatory interactions. We find some of these regulatory interactions explain cell type-specific expression patterns of hallmark cancer pathway regulators such as the mTOR activator RHEB. We also leverage these data to infer differential transcription factor activity, such as ZEB1, across cell type-specific enhancers between two different fractions of the same patient tumor. Our work highlights the importance of precision medicine in the treatment of gynecologic cancers and we show that this resource will deepen our understanding of non-coding genomic regions in the context of tumor biology.