Massively multiplex single-cell Hi-C

Ramani, Vijay; Deng, Xinxian; Qiu, Ruolan; Gunderson, Kevin L.; Steemers, Frank J.; Distèche, Christine M.; Noble, William Stafford; Duan, Zhijun; Shendure, Jay

doi:10.1038/nmeth.4155

Cited by 475 publications

(379 citation statements)

References 25 publications

(32 reference statements)

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“…Single-cell genomic will provide unparalleled insights into stem cell origin of the different cell lineages, distinguish the different stages of differentiation, characterize cell fate decisions and the regulatory mechanisms that govern the production of different cell types, and elucidate how different cell types work together to form a testis. We also expect that advances in single cell genomics such as Chip-seq, ATAC-seq or again Hi-C (Pott and Lieb, 2015;Ramani et al, 2017;Rotem et al, 2015) will facilitate the identification of epigenetic modifications and regions of open . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”

Section: Wt1mentioning

confidence: 99%

Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing

Stévant

Neirijnck

Borel

et al. 2017

Preprint

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Section: Wt1mentioning

confidence: 99%

Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing

Stévant

Neirijnck

Borel

et al. 2017

Preprint

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“…With the development of new technologies, more diverse chromatin data will be generated for solving unknown biological problems. For examples, single cell Hi-C maps provide an opportunity to study the conservation and heterogeneity from cell to cell, revealing more granular cellular functions and biological processes in cell cycle 37,38 . MSTD is a very flexible and powerful framework, which is expected to be applied to such new data in near future.…”

Section: Discussionmentioning

confidence: 99%

MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps

Gao

Zhang

2018

Preprint

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The chromosome conformation capture (3C) technique and its variants have been employed to reveal the existence of a hierarchy of structures in three-dimensional (3D) chromosomal architecture, including compartments, topologically associating domains (TADs), sub-TADs and chromatin loops. However, existing methods for domain detection were only designed based on symmetric Hi-C maps, ignoring long-range interaction structures between domains. To this end, we proposed a generic and efficient method to identify multi-scale topological domains (MSTD), including cis-and trans-interacting regions, from a variety of 3D genomic datasets.We first applied MSTD to detect promoter-anchored interaction domains (PADs) from promoter capture Hi-C datasets across 17 primary blood cell types. The boundaries of PADs are significantly enriched with one or the combination of multiple epigenetic factors. Moreover, PADs between functionally similar cell types are significantly conserved in terms of domain regions and expression states. Cell type-specific PADs involve in distinct cell type-specific activities and regulatory events by dynamic interactions within them. We also employed MSTD to define multi-scale domains from typical symmetric Hi-C datasets and illustrated its distinct superiority to the-state-of-art methods in terms of accuracy, flexibility and efficiency.

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“…This approach is not readily adaptable to genome-wide DNA methylation profiling due to harsh bisulfite conversion chemistry, which destroys cell and nuclear integrity (preventing conversion prior to droplet encapsulation), and cannot be carried out in a single reaction buffer (preventing both conversion and barcoding in the same droplet). Furthermore, alignment rates for traditional scWGBS libraries are on the order of 25 ± 20%, much lower than for the equivalent bulk protocol [12][13][14][15] , which markedly increases the cost of obtaining sufficient aligned read counts.Recently, we described a platform for combinatorial indexing to acquire long-range sequence information 17 that has been extended to single cell assays for open chromatin (sci-ATAC-seq, scTHS-seq) 5,7 , chromosome conformation (sci-Hi-C) 18 , RNA transcript abundance (sci-RNA-seq) 19 , and whole genome sequencing (sci-DNA-seq) 20 . The key to this combinatorial indexing platform is a two-tier indexing strategy where DNA (or RNA) within nuclei (or cells) is modified with a barcoded adaptor corresponding to one of 96 (or 384) wells while maintaining nuclear integrity.…”

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confidence: 99%

Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing

Mulqueen

Pokholok

Norberg

et al. 2017

Preprint

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Here we present a novel method: single-cell combinatorial indexing for methylation analysis (sci-MET), which is the first highly scalable assay for whole genome methylation profiling of single cells. We use sci-MET to produce 2,697 total single-cell bisulfite sequencing libraries and achieve read alignment rates of 69 ± 7%, comparable to those of bulk cell methods. As a proof of concept, we applied sci-MET to successfully deconvolve the cellular identity of a mixture of three human cell lines.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/157230 doi: bioRxiv preprint first posted online Jun. 28, 2017; MainThe fundamental challenge of identifying and characterizing the molecular properties of every cell type in the body has recently entered the realm of possibility 1 . High-throughput singlecell transcriptome [2][3][4] and chromatin accessibility profiling assays [5][6][7] have dramatically improved our ability to uncover latent cell types within complex tissues and dynamic cell states during differentiation. These same possibilities extend to DNA methylation; a covalent addition of a methyl group to cytosine bases in the genome that largely serves in a repressive role 8 . DNA methylation occurs at a high rate in cytosine-guanine dinucleotides (CG), with cytosine methylation in non-CG sites (CH) occurring rarely and only in select tissues 9 . Both CG and CH methylation have cell type-specificity and are the subject of active modification in developing tissues 8 . DNA methylation can be probed at base-pair resolution using the deaminating chemistry of sodium bisulfite treatment before or after the generation of sequencing libraries (e.g. as in whole genome bisulfite sequencing, WGBS) 10,11 . Despite the comprehensive nature of these assays, key aspects of methylation architecture and dynamics remain elusive, with cell type heterogeneity at the forefront of this challenge.Recent work has optimized bisulfite sequencing to decrease input requirements to the single cell level (scWGBS) [12][13][14][15] . These assays have provided unique insights into environmental effects on methylome dynamics 13 , have uncovered new cell-types that form during hematopoesis 14 , and been coupled with scRNA-seq to directly study the relationship of DNA methylation to transcription 15 . However, these methods use a parallelized library generation strategy in which the WGBS protocol must be carried out on each cell in its own reaction vessel and thus remain low-throughput. Existing platforms to assess the transcriptome of single cells in high-throughput largely rely on droplet-based microfluidics strategies by sequestering single cells into individual droplets along with a substrate harboring barcoded oligonucleotides for cell-level indexing 2,16 . This approach is not readily adaptable to genome-wide DNA methylation pro...

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Massively multiplex single-cell Hi-C

Cited by 475 publications

References 25 publications

Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing

Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing

MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps

Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing

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