Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart

Abstract

Sex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex. Sex dependent alterations include pathways such as TGFβ signaling and metabolic shifts by sex, evident in both transcriptional alterations and differing presence of transcription factor (TF) motifs in accessible chromatin. Age was associated with changes such as immune activation-related transcriptional and chromatin accessibility differences, as well as changes in the relative proportion of cardiomyocytes, neurons, and perivascular cells. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs.

CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

“…The required fields represent attributes that are often variable within or across studies and are often identified as strong covariates correlated with gene expression variation within cells. 21,22 Similar to previous experimental data coordination efforts, established ontologies and other community resources are used for standardization wherever possible for consistency and to improve the filtering capabilities of datasets (table S2). 23,24,25 To fully capture gene count information for each dataset, a layer of raw data, meaning nonnormalized, is required for submission of data from all transcriptomic assays to fulfill common computational reuse cases.…”

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

“…We hope that eSVD-DE would be beneficial for inspiring cohort-wide DE tests for future single-cell assays beyond scRNA-seq and single-cell eQTL analyses where abundant individual-level covariate effects must be adequately removed. Additionally, we are curious about broader settings where instead of having case and control individuals within a cohort, we are interested in testing if continuous covariates such as age have a substantial transcriptomic impact on specific cell types, as discussed in [53].…”

eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

“…We hope that eSVD-DE would be beneficial for inspiring cohort-wide DE tests for future single-cell assays beyond scRNA-seq and single-cell eQTL analyses where abundant individual-level covariate effects must be adequately removed. Additionally, we are curious about broader settings where instead of having case and control individuals within a cohort, we are interested in testing if continuous covariates such as age have a substantial transcriptomic impact on specific cell types, as discussed in [51].…”