Strand-specific single-cell methylomics reveals distinct modes of DNA demethylation dynamics during early mammalian development

DNA methylation (5mC) is central to cellular identity. The global erasure of 5mC from the parental genomes during preimplantation mammalian development is critical to reset the methylome of gametes to the cells in the blastocyst. While active and passive modes of demethylation have both been suggested to play a role in this process, the relative contribution of these two mechanisms to 5mC erasure remains unclear. Here, we report a single-cell method (scMspJI-seq) that enables strand-specific quantification of 5mC, allowing us to systematically probe the dynamics of global demethylation. When applied to mouse embryonic stem cells, we identified substantial cell-to-cell strand-specific 5mC heterogeneity, with a small group of cells displaying asymmetric levels of 5mCpG between the two DNA strands of a chromosome suggesting loss of maintenance methylation. Next, in preimplantation mouse embryos, we discovered that methylation maintenance is active till the 16-cell stage followed by passive demethylation in a fraction of cells within the early blastocyst at the 32-cell stage of development. Finally, human preimplantation embryos qualitatively show temporally delayed yet similar demethylation dynamics as mouse embryos. Collectively, these results demonstrate that scMspJI-seq is a sensitive and cost-effective method to map the strand-specific genome-wide patterns of 5mC in single cells. Erasure of DNA methylation from the parental genomes is critical to reset the methylome of differentiated gametes to pluripotent cells in the blastocyst. Here, the authors present a high-throughput single-cell method that enables strand-specific quantification of DNA methylation and identify distinct modes of DNA demethylation dynamics during early mammalian development

Transcriptional profiling of cells sorted by RNA abundance

We have developed a quantitative technique for sorting cells on the basis of endogenous RNA abundance, with a molecular resolution of 10-20 transcripts. We demonstrate efficient and unbiased RNA extraction from transcriptionally sorted cells and report a high-fidelity transcriptome measurement of mouse induced pluripotent stem cells (iPSCs) isolated from a heterogeneous reprogramming culture. This method is broadly applicable to profiling transcriptionally distinct cellular states without requiring antibodies or transgenic fluorescent proteins

Simultaneous quantification of protein-DNA contacts and transcriptomes in single cells.

Protein-DNA interactions are critical to the regulation of gene expression, but it remains challenging to define how cell-to-cell heterogeneity in protein-DNA binding influences gene expression variability. Here we report a method for the simultaneous quantification of protein-DNA contacts by combining single-cell DNA adenine methyltransferase identification (DamID) with messenger RNA sequencing of the same cell (scDam&T-seq). We apply scDam&T-seq to reveal how genome-lamina contacts or chromatin accessibility correlate with gene expression in individual cells. Furthermore, we provide single-cell genome-wide interaction data on a polycomb-group protein, RING1B, and the associated transcriptome. Our results show that scDam&T-seq is sensitive enough to distinguish mouse embryonic stem cells cultured under different conditions and their different chromatin landscapes. Our method will enable the analysis of protein-mediated mechanisms that regulate cell-type-specific transcriptional programs in heterogeneous tissues

Simultaneous quantification of protein-DNA interactions and transcriptomes in single cells with scDam&T-seq.

Protein-DNA interactions are essential for establishing cell type-specific chromatin architecture and gene expression. We recently developed scDam&T-seq, a multi-omics method that can simultaneously quantify protein-DNA interactions and the transcriptome in single cells. The method effectively combines two existing methods: DNA adenine methyltransferase identification (DamID) and CEL-Seq2. DamID works through the tethering of a protein of interest (POI) to the Escherichia coli DNA adenine methyltransferase (Dam). Upon expression of this fusion protein, DNA in proximity to the POI is methylated by Dam and can be selectively digested and amplified. CEL-Seq2, in contrast, makes use of poly-dT primers to reverse transcribe mRNA, followed by linear amplification through in vitro transcription. scDam&T-seq is the first technique capable of providing a combined readout of protein-DNA contact and transcription from single-cell samples. Once suitable cell lines have been established, the protocol can be completed in 5 d, with a throughput of hundreds to thousands of cells. The processing of raw sequencing data takes an additional 1-2 d. Our method can be used to understand the transcriptional changes a cell undergoes upon the DNA binding of a POI. It can be performed in any laboratory with access to FACS, robotic and high-throughput-sequencing facilities

Integration of multiple lineage measurements from the same cell reconstructs parallel tumor evolution

Summary: Organoid evolution models complemented with integrated single-cell sequencing technology provide a powerful platform to characterize intra-tumor heterogeneity (ITH) and tumor evolution. Here, we conduct a parallel evolution experiment to mimic the tumor evolution process by evolving a colon cancer organoid model over 100 generations, spanning 6 months in time. We use single-cell whole-genome sequencing (WGS) in combination with viral lineage tracing at 12 time points to simultaneously monitor clone size, CNV states, SNV states, and viral lineage barcodes for 1,641 single cells. We integrate these measurements to construct clonal evolution trees with high resolution. We characterize the order of events in which chromosomal aberrations occur and identify aberrations that recur multiple times within the same tumor sub-population. We observe recurrent sequential loss of chromosome 4 after loss of chromosome 18 in four unique tumor clones. SNVs and CNVs identified in our organoid experiments are also frequently reported in colorectal carcinoma samples, and out of 334 patients with chromosome 18 loss in a Memorial Sloan Kettering colorectal cancer cohort, 99 (29.6%) also harbor chromosome 4 loss. Our study reconstructs tumor evolution in a colon cancer organoid model at high resolution, demonstrating an approach to identify potentially clinically relevant genomic aberrations in tumor evolution

Pseudo-RNA-binding domains mediate RNA structure specificity in upstream of N-Ras

RNA-binding proteins (RBPs) commonly feature multiple RNA-binding domains (RBDs), which provide these proteins with a modular architecture. Accumulating evidence supports that RBP architectural modularity and adaptability define the specificity of their interactions with RNA. However, how multiple RBDs recognize their cognate single-stranded RNA (ssRNA) sequences in concert remains poorly understood. Here, we use Upstream of N-Ras (Unr) as a model system to address this question. Although reported to contain five ssRNA-binding cold-shock domains (CSDs), we demonstrate that Unr includes an additional four CSDs that do not bind RNA (pseudo-RBDs) but are involved in mediating RNA tertiary structure specificity by reducing the conformational heterogeneity of Unr. Disrupting the interactions between canonical and non-canonical CSDs impacts RNA binding, Unr-mediated translation regulation, and the Unr-dependent RNA interactome. Taken together, our studies reveal a new paradigm in protein-RNA recognition, where interactions between RBDs and pseudo-RBDs select RNA tertiary structures, influence RNP assembly, and define target specificity

Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction

The epigenetic DNA modification 5-hydroxymethylcytosine (5hmC) has crucial roles in development and gene regulation. Quantifying the abundance of this epigenetic mark at the single-cell level could enable us to understand its roles. We present a single-cell, genome-wide and strand-specific 5hmC sequencing technology, based on 5hmC glucosylation and glucosylation-dependent digestion of DNA, that reveals pronounced cell-to-cell variability in the abundance of 5hmC on the two DNA strands of a given chromosome. We develop a mathematical model that reproduces the strand bias and use this model to make two predictions. First, the variation in strand bias should decrease when 5hmC turnover increases. Second, the strand bias of two sister cells should be strongly anti-correlated. We validate these predictions experimentally, and use our model to reconstruct lineages of two- and four-cell mouse embryos, showing that single-cell 5hmC sequencing can be used as a lineage reconstruction tool