Human cells contain five canonical, replication-dependent somatic histone H1 subtypes (H1.1, H1.2, H1.3, H1.4, and H1.5). Although they are key chromatin components, the genomic distribution of the H1 subtypes is still unknown, and their role in chromatin processes has thus far remained elusive. Here, we map the genomic localization of all somatic replication-dependent H1 subtypes in human lung fibroblasts using an integrative DNA adenine methyltransferase identification (DamID) analysis. We find in general that H1.2 to H1.5 are depleted from CpG-dense regions and active regulatory regions. H1.1 shows a DamID binding profile distinct from the other subtypes, suggesting a unique function. H1 subtypes can mark specific domains and repressive regions, pointing toward a role for H1 in three-dimensional genome organization. Our work integrates H1 subtypes into the epigenome maps of human cells and provides a valuable resource to refine our understanding of the significance of H1 and its heterogeneity in the control of genome function.
Active telomerase is essential for stem cells and most cancers to maintain telomeres. The enzymatic activity of telomerase is related but not equivalent to the expression of TERT, the catalytic subunit of the complex. Here we show that telomerase enzymatic activity can be robustly estimated from the expression of a 13-gene signature. We demonstrate the validity of the expression-based approach, named EXTEND, using cell lines, cancer samples, and non-neoplastic samples. When applied to over 9,000 tumors and single cells, we find a strong correlation between telomerase activity and cancer stemness. This correlation is largely driven by a small population of proliferating cancer cells that exhibits both high telomerase activity and cancer stemness. This study establishes a computational framework for quantifying telomerase enzymatic activity and provides new insights into the relationships among telomerase, cancer proliferation, and stemness.
Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark five such methods, including single sample gene set enrichment analysis (ssGSEA), Gene Set Variation Analysis (GSVA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.
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