Dynamics and Spatial Genomics of the Nascent Transcriptome by Intron seqFISH

TL;DR: It is shown that during this phase, coexpression of enzymes required for DNA methylation turnover, DNMT3s and TETs, promotes cell-to-cell variability in this epigenetic mark, indicating that dynamic changes inDNA methylation might influence early cell fate decisions.

TL;DR: In this article , the authors focus on the interesting and unique role of non-coding NATs in cancer, paying particular attention to those acting as miRNA sponges.

TL;DR: In this article , a two-layer DNA seqFISH+ dataset was used for simultaneous mapping of 100,049 genomic loci, together with nascent transcriptome for 17,856 genes and a diverse set of immunofluorescently labeled subnuclear structures all in single cells in cell lines and adult mouse cerebellum.

TL;DR: The proposed SpatialMap develops a computational method, which primarily facilitates spatial mapping of unmeasured gene profiles in spatial transcriptomic data via integrating with scRNA-seq data from the same tissue, and directly models the count nature of spatial gene expression data through generalized linear spatial models.

TL;DR: In situ Hi-C is used to probe the 3D architecture of genomes, constructing haploid and diploid maps of nine cell types, identifying ∼10,000 loops that frequently link promoters and enhancers, correlate with gene activation, and show conservation across cell types and species.

TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.

TL;DR: It is found that the boundaries of topological domains are enriched for the insulator binding protein CTCF, housekeeping genes, transfer RNAs and short interspersed element (SINE) retrotransposons, indicating that these factors may have a role in establishing the topological domain structure of the genome.

TL;DR: It is shown that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols, and expected to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.