The accessible chromatin landscape of the murine hippocampus at single-cell resolution.

Here we present a comprehensive map of the accessible chromatin landscape of the mouse hippocampus at single-cell resolution. Substantial advances of this work include the optimization of a single-cell combinatorial indexing assay for transposase accessible chromatin (sci-ATAC-seq); a software suite, scitools, for the rapid processing and visualization of single-cell combinatorial indexing data sets; and a valuable resource of hippocampal regulatory networks at single-cell resolution. We used sci-ATAC-seq to produce 2346 high-quality single-cell chromatin accessibility maps with a mean unique read count per cell of 29,201 from both fresh and frozen hippocampi, observing little difference in accessibility patterns between the preparations. By using this data set, we identified eight distinct major clusters of cells representing both neuronal and nonneuronal cell types and characterized the driving regulatory factors and differentially accessible loci that define each cluster. Within pyramidal neurons, we identified four major clusters, including CA1 and CA3 neurons, and three additional subclusters. We then applied a recently described coaccessibility framework, Cicero, which identified 146,818 links between promoters and putative distal regulatory DNA. Identified coaccessibility networks showed cell-type specificity, shedding light on key dynamic loci that reconfigure to specify hippocampal cell lineages. Lastly, we performed an additional sci-ATAC-seq preparation from cultured hippocampal neurons (899 high-quality cells, 43,532 mean unique reads) that revealed substantial alterations in their epigenetic landscape compared with nuclei from hippocampal tissue. This data set and accompanying analysis tools provide a new resource that can guide subsequent studies of the hippocampus.

Single-cell sequencing of primate preimplantation embryos reveals chromosome elimination via cellular fragmentation and blastomere exclusion.

Aneuploidy that arises during meiosis and/or mitosis is a major contributor to early embryo loss. We previously showed that human preimplantation embryos encapsulate missegregated chromosomes into micronuclei while undergoing cellular fragmentation and that fragments can contain chromosomal material, but the source of this DNA was unknown. Here, we leveraged the use of a nonhuman primate model and single-cell DNA-sequencing (scDNA-seq) to examine the chromosomal content of 471 individual samples comprising 254 blastomeres, 42 polar bodies, and 175 cellular fragments from a large number (N = 50) of disassembled rhesus cleavage-stage embryos. Our analysis revealed that the aneuploidy and micronucleation frequency is conserved between humans and macaques, and that fragments encapsulate whole and/or partial chromosomes lost from blastomeres. Single-cell/fragment genotyping showed that these chromosome-containing cellular fragments (CCFs) can be maternally or paternally derived and display double-stranded DNA breaks. DNA breakage was further indicated by reciprocal subchromosomal losses/gains between blastomeres and large segmental errors primarily detected at the terminal ends of chromosomes. By combining time-lapse imaging with scDNA-seq, we determined that multipolar divisions at the zygote or two-cell stage were associated with CCFs and generated a random mixture of chromosomally normal and abnormal blastomeres with uniparental or biparental origins. Despite frequent chromosome missegregation at the cleavage-stage, we show that CCFs and nondividing aneuploid blastomeres showing extensive DNA damage are prevented from incorporation into blastocysts. These findings suggest that embryos respond to chromosomal errors by encapsulation into micronuclei, elimination via cellular fragmentation, and selection against highly aneuploid blastomeres to overcome chromosome instability during preimplantation development.

Sequencing thousands of single-cell genomes with combinatorial indexing.

Single-cell genome sequencing has proven valuable for the detection of somatic variation, particularly in the context of tumor evolution. Current technologies suffer from high library construction costs, which restrict the number of cells that can be assessed and thus impose limitations on the ability to measure heterogeneity within a tissue. Here, we present single-cell combinatorial indexed sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single-cell libraries for detection of somatic copy-number variants. We constructed libraries for 16,698 single cells from a combination of cultured cell lines, primate frontal cortex tissue and two human adenocarcinomas, and obtained a detailed assessment of subclonal variation within a pancreatic tumor.

Spatially mapped single-cell chromatin accessibility.

High-throughput single-cell epigenomic assays can resolve cell type heterogeneity in complex tissues, however, spatial orientation is lost. Here, we present single-cell combinatorial indexing on Microbiopsies Assigned to Positions for the Assay for Transposase Accessible Chromatin, or sciMAP-ATAC, as a method for highly scalable, spatially resolved, single-cell profiling of chromatin states. sciMAP-ATAC produces data of equivalent quality to non-spatial sci-ATAC and retains the positional information of each cell within a 214 micron cubic region, with up to hundreds of tracked positions in a single experiment. We apply sciMAP-ATAC to assess cortical lamination in the adult mouse primary somatosensory cortex and in the human primary visual cortex, where we produce spatial trajectories and integrate our data with non-spatial single-nucleus RNA and other chromatin accessibility single-cell datasets. Finally, we characterize the spatially progressive nature of cerebral ischemic infarction in the mouse brain using a model of transient middle cerebral artery occlusion.

Highly scalable generation of DNA methylation profiles in single cells.

We present a highly scalable assay for whole-genome methylation profiling of single cells. We use our approach, single-cell combinatorial indexing for methylation analysis (sci-MET), to produce 3,282 single-cell bisulfite sequencing libraries and achieve read alignment rates of 68 ± 8%. We apply sci-MET to discriminate the cellular identity of a mixture of three human cell lines and to identify excitatory and inhibitory neuronal populations from mouse cortical tissue.