Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.

Recent reports on the characteristics of naive human pluripotent stem cells (hPSCs) obtained using independent methods differ. Naive hPSCs have been mainly derived by conversion from primed hPSCs or by direct derivation from human embryos rather than by somatic cell reprogramming. To provide an unbiased molecular and functional reference, we derived genetically matched naive hPSCs by direct reprogramming of fibroblasts and by primed-to-naive conversion using different naive conditions (NHSM, RSeT, 5iLAF and t2iLGöY). Our results show that hPSCs obtained in these different conditions display a spectrum of naive characteristics. Furthermore, our characterization identifies KLF4 as sufficient for conversion of primed hPSCs into naive t2iLGöY hPSCs, underscoring the role that reprogramming factors can play for the derivation of bona fide naive hPSCs.

The regulation of higher-order chromatin structure is complex and dynamic, and a full understanding of the suite of mechanisms governing this architecture is lacking. Here, we reveal the noncanonical SMC protein Smchd1 to be a novel regulator of long-range chromatin interactions in mice, and we add Smchd1 to the canon of epigenetic proteins required for Hox-gene regulation. The effect of losing Smchd1-dependent chromatin interactions has varying outcomes that depend on chromatin context. At autosomal targets transcriptionally sensitive to Smchd1 deletion, we found increased short-range interactions and ectopic enhancer activation. In contrast, the inactive X chromosome was transcriptionally refractive to Smchd1 ablation, despite chromosome-wide increases in short-range interactions. In the inactive X, we observed spreading of trimethylated histone H3 K27 (H3K27me3) domains into regions not normally decorated by this mark. Together, these data suggest that Smchd1 is able to insulate chromatin, thereby limiting access to other chromatin-modifying proteins.