AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics.

With the current surge of spatial transcriptomics (ST) studies, researchers are exploring the deep interactive cell-play directly in tissues, in situ. However, with the current technologies, measurements consist of mRNA transcript profiles of mixed origin. Recently, applications have been proposed to tackle the deconvolution process, to gain knowledge about which cell types (SC) are found within. This is usually done by incorporating metrics from single-cell (SC) RNA, from similar tissues. Yet, most existing tools are cumbersome, and we found them hard to integrate and properly utilize. Therefore, we present AntiSplodge, a simple feed-forward neural-network-based pipeline designed to effective deconvolute ST profiles by utilizing synthetic ST profiles derived from real-life SC datasets. AntiSplodge is designed to be easy, fast and intuitive while still being lightweight. To demonstrate AntiSplodge, we deconvolute the human heart and verify correctness across time points. We further deconvolute the mouse brain, where spot patterns correctly follow that of the underlying tissue. In particular, for the hippocampus from where the cells originate. Furthermore, AntiSplodge demonstrates top of the line performance when compared to current state-of-the-art tools. Software availability: https://github.com/HealthML/AntiSplodge/.

Age-dependent DNA methylation patterns on the Y chromosome in elderly males.

The Y chromosome, a sex chromosome that only exists in males, has been ignored in traditional epigenetic association studies for multiple reasons. However, sex differences in aging-related phenotypes and mortality could suggest a critical role of the sex chromosomes in the aging process. We obtained blood-based DNA methylation data on the Y chromosome for 624 men from four cohorts and performed a chromosome-wide epigenetic association analysis to detect Y-linked CpGs differentially methylated over age and cross-validated the significant CpGs in the four cohorts. We identified 40-219 significant CpG sites (false discovery rate <0.05) with >82% of them hypermethylated with increasing age, which is in strong contrast to the patterns reported on the autosomal chromosomes. Comparing the rate of change in the Y-linked DNA methylation across cohorts that represent different age intervals revealed a trend of acceleration in DNA methylation with increasing age. The age-dependent DNA methylation patterns on the Y chromosome were further examined for their association with all-cause mortality with results suggesting that the predominant pattern of age-related hypermethylation on the Y chromosome is associated with reduced risk of death.

Weighted Gene Coregulation Network Analysis of Promoter DNA Methylation on All-Cause Mortality in Old-Aged Birth Cohorts Finds Modules of High-Risk Associated Biomarkers.

Overall or all-cause mortality is a key measure of health in a population. Multiple epigenome-wide association studies have been conducted on all-cause mortality with limited significant findings and low replication. To elucidate the coregulated DNA methylation patterns associated with all-cause mortality, we conducted a weighted DNA methylation coregulation network analysis on whole-blood samples of 1,425 older individuals from the Lothian Birth Cohorts of 1921 and 1936. Our network-based analysis defined coregulated DNA methylation patterns in gene promoters into clusters or modules whose correlation with all-cause mortality was assessed by survival analysis. We found two significant modules or gene clusters associated with all-cause mortality in LBC1921 based on their eigengenes; one negatively correlated (p = 8.14E-03, 698 genes) and one positively correlated (p = 4.26E-02, 1,431 genes) with the risk of death. The two modules were replicated in LBC1936 with the same directions of correlation (p = 6.35E-02 and p = 3.64E-02, respectively). Furthermore, the modules revealed 32 genes associated with all-cause mortality (FDR < 0.05) linked to various diseases, including cancer and diabetes. Additionally, we performed pathway analysis and found 22 pathways (FDR < 0.05), including a pathway for taste transduction, which has been shown to be associated with poor prognosis in acutely hospitalized patients, and several pathways were linked to different types of cancer. The results from our network analysis show that DNA methylation of multiple genes could have been coregulated in an association with the overall risk of death. The identified epigenetic markers might help with our understanding of the molecular basis of all-cause mortality and general health.

Exploratory analysis of age and sex dependent DNA methylation patterns on the X-chromosome in whole blood samples.

BACKGROUND: Large numbers of autosomal sites are found differentially methylated in the aging genome. Due to analytical difficulties in dealing with sex differences in X-chromosome content and X-inactivation (XCI) in females, this has not been explored for the X chromosome. METHODS: Using data from middle age to elderly individuals (age 55+ years) from two Danish cohorts of monozygotic twins and the Scottish Lothian Birth Cohort 1921, we conducted an X-chromosome-wide analysis of age-associated DNA methylation patterns with consideration of stably inferred XCI status. RESULTS: Through analysing and comparing sex-specific X-linked DNA methylation changes over age late in life, we identified 123, 293 and 55 CpG sites significant (FDR < 0.05) only in males, only in females and in both sexes of Danish twins. All findings were significantly replicated in the two Danish twin cohorts. CpG sites escaping XCI are predominantly de-methylated with increasing age across cohorts. In contrast, CpGs highly methylated in both sexes are methylated even further with increasing age. Among the replicated sites in Danish samples, 16 (13%), 24 (8.2%) and 3 (5.5%) CpGs were further validated in LBC1921 (FDR < 0.05). CONCLUSIONS: The X-chromosome of whole blood leukocytes displays age- and sex-dependent DNA methylation patterns in relation to XCI across cohorts.