Slide-tags enables single-nucleus barcoding for multimodal spatial genomics.

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 µm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics.

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are 'tagged' with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.

Cross-Organ Transcriptomic Comparison Reveals Universal Factors During Maturation.

Various cell types can be derived from stem cells. However, these cells are immature and do not match their adult counterparts in functional capabilities, limiting their use in disease modeling and cell therapies. Thus, it is crucial to understand the mechanisms of maturation in vivo. However, it is unknown if there are genes and pathways conserved across organs during maturation. To address this, we performed a time-series analysis of the transcriptome of the mouse heart, brain, liver, and kidney and analyzed their trajectories over time. In addition, gene regulatory networks were reconstructed to determine overlapping expression patterns. Based on these, we identified commonly upregulated and downregulated pathways across all four organs. Key upstream regulators were also predicted based on the temporal expression of downstream genes. These findings suggest the presence of universal regulators during organ maturation, which may help us develop a general strategy to mature stem cell-derived cells in vitro.

Estimating DNA methylation potential energy landscapes from nanopore sequencing data.

High-throughput third-generation nanopore sequencing devices have enormous potential for simultaneously observing epigenetic modifications in human cells over large regions of the genome. However, signals generated by these devices are subject to considerable noise that can lead to unsatisfactory detection performance and hamper downstream analysis. Here we develop a statistical method, CpelNano, for the quantification and analysis of 5mC methylation landscapes using nanopore data. CpelNano takes into account nanopore noise by means of a hidden Markov model (HMM) in which the true but unknown ("hidden") methylation state is modeled through an Ising probability distribution that is consistent with methylation means and pairwise correlations, whereas nanopore current signals constitute the observed state. It then estimates the associated methylation potential energy function by employing the expectation-maximization (EM) algorithm and performs differential methylation analysis via permutation-based hypothesis testing. Using simulations and analysis of published data obtained from three human cell lines (GM12878, MCF-10A, and MDA-MB-231), we show that CpelNano can faithfully estimate DNA methylation potential energy landscapes, substantially improving current methods and leading to a powerful tool for the modeling and analysis of epigenetic landscapes using nanopore sequencing data.

PGC1/PPAR drive cardiomyocyte maturation at single cell level via YAP1 and SF3B2.

Cardiomyocytes undergo significant structural and functional changes after birth, and these fundamental processes are essential for the heart to pump blood to the growing body. However, due to the challenges of isolating single postnatal/adult myocytes, how individual newborn cardiomyocytes acquire multiple aspects of the mature phenotype remains poorly understood. Here we implement large-particle sorting and analyze single myocytes from neonatal to adult hearts. Early myocytes exhibit wide-ranging transcriptomic and size heterogeneity that is maintained until adulthood with a continuous transcriptomic shift. Gene regulatory network analysis followed by mosaic gene deletion reveals that peroxisome proliferator-activated receptor coactivator-1 signaling, which is active in vivo but inactive in pluripotent stem cell-derived cardiomyocytes, mediates the shift. This signaling simultaneously regulates key aspects of cardiomyocyte maturation through previously unrecognized proteins, including YAP1 and SF3B2. Our study provides a single-cell roadmap of heterogeneous transitions coupled to cellular features and identifies a multifaceted regulator controlling cardiomyocyte maturation.