Compared to enhancers, silencers are notably difficult to identify and validate experimentally. In search for human silencers, we utilized H3K27me3-DNase I hypersensitive site (DHS) peaks with tissue specificity negatively correlated with the expression of nearby genes across 25 diverse cell lines. These regions are predicted to be silencers since they are physically linked, using Hi-C loops, or associated, using expression quantitative trait loci (eQTL) results, with a decrease in gene expression much more frequently than general H3K27me3-DHSs. Also, these regions are enriched for the binding sites of transcriptional repressors (such as CTCF, MECOM, SMAD4, and SNAI3) and depleted of the binding sites of transcriptional activators. Using sequence signatures of these regions, we constructed a computational model and predicted approximately 10,000 additional silencers per cell line and demonstrated that the majority of genes linked to these silencers are expressed at a decreased level. Furthermore, single nucleotide polymorphisms (SNPs) in predicted silencers are significantly associated with disease phenotypes. Finally, our results show that silencers commonly interact with enhancers to affect the transcriptional dynamics of tissue-specific genes and to facilitate fine-tuning of transcription in the human genome.

Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and non-uniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial auto-correlation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell-cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomics data. We applied MERINGUE to a variety of spatially resolved transcriptomics datasets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-Seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomics data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease. Cold Spring Harbor Laboratory Press on May 30, 2021 -Published by genome.cshlp.org Downloaded from tissue, thereby losing valuable spatial context (Crosetto et al. 2015). Thus, how these subpopulations of cells are organized in space and how they may interact with each other remains an open question in many systems.To preserve informative spatial context, recent advances in imaging-based approaches have enabled in situ, spatially resolved transcriptomic profiling with single-cell resolution (Zhuang 2021). In addition, approaches based on spatially resolved RNA capture followed by sequencing, such as spatial transcriptomics and Slide-seq provide spatially resolved, untargeted transcriptomic profiling at the pixel level, with pixel size of 10-100µm (Larsson et al. 2021). Such high throughput data generation, both in terms of the number of genes and number of cells assayed, demands scalable computational methods that take advantage of this new spatial dimension to efficiently identify statistically significant spatial patterns and relationships. In addition, as these methods are applied to increasingly complex tissues, statistical analyses must be able to accommodate the non-uniform cell density induced by biological factors, such as the presence of multiple, often spatially organized, cell-types inherent to tissues, as well as technical factors, such as distortions from tissue sectioning.Three statistical methods, SpatialDE, Trendsceek, and SPARK have previously been developed to identify spatial gene expression heterogeneity, defined as an uneven, aggregated or patterned, spatial distribution of gene expression magnitudes (Svensson et al. 2018;Edsgärd et al. 2018;Sun et al. 2020).Briefly, SpatialDE identifies spatial gene expression heterogeneity by decomposing a gene's expression variance into a spatial and a non-spatial c...

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve.

‘Normal’ genomic DNA contains hundreds of mismatches that are generated daily by the spontaneous deamination of C (U/G) and methyl-C (T/G). Thus, a mutagenic effect of their repair could constitute a serious genetic burden. We show here that while mismatches introduced into human cells on an SV40-based episome were invariably repaired, this process induced mutations in flanking DNA at a significantly higher rate than no mismatch controls. Most mutations involved the C of TpC, the substrate of some single strand-specific APOBEC cytidine deaminases, similar to the mutations that can typify the ‘mutator phenotype’ of numerous tumors. siRNA knockdowns and chromatin immunoprecipitation showed that TpC preferring APOBECs mediate the mutagenesis, and siRNA knockdowns showed that both the base excision and mismatch repair pathways are involved. That naturally occurring mispairs can be converted to mutators, represents an heretofore unsuspected source of genetic changes that could underlie disease, aging, and evolutionary change.DOI: http://dx.doi.org/10.7554/eLife.02001.001