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Background: Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices., Results: We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection., Conclusions: Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable., (© 2024. The Author(s).)

Background: Histone lactylation has been recently described as a novel histone post-translational modification linking cellular metabolism to epigenetic regulation., Results: Given the expected relevance of this modification and current limited knowledge of its function, we generate genome-wide datasets of H3K18la distribution in various in vitro and in vivo samples, including mouse embryonic stem cells, macrophages, adipocytes, and mouse and human skeletal muscle. We compare them to profiles of well-established histone modifications and gene expression patterns. Supervised and unsupervised bioinformatics analysis shows that global H3K18la distribution resembles H3K27ac, although we also find notable differences. H3K18la marks active CpG island-containing promoters of highly expressed genes across most tissues assessed, including many housekeeping genes, and positively correlates with H3K27ac and H3K4me3 as well as with gene expression. In addition, H3K18la is enriched at active enhancers that lie in proximity to genes that are functionally important for the respective tissue., Conclusions: Overall, our data suggests that H3K18la is not only a marker for active promoters, but also a mark of tissue specific active enhancers., (© 2022. The Author(s).)

Background: DNA methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse., Results: We have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age which allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the diet., Conclusions: Here we identify and characterise an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.