Single nucleus multi-omics identifies human cortical cell regulatory genome diversity

Luo, Chongyuan; Liu, Hanqing; Xie, Fangming; Armand, Ethan J.; Siletti, Kimberly; Bakken, Trygve E.; Fang, Rongxin; Doyle, Wayne I.; Stuart, T.A.; Hodge, Rebecca D.; Hu, Lijuan; Wang, Bang-An; Zhang, Zhuzhu; Preissl, Sebastian; Lee, Dong-Sung; Zhou, Jingtian; Niu, Sheng-Yong; Castanon, Rosa; Bartlett, A.; Rivkin, Angeline; Wang, Xinxin; Lucero, Jacinta; Nery, Joseph R.; Da, Davis; Mash, Deborah C.; Satija, Rahul; Dixon, Jesse R.; Linnarsson, Sten; Lein, Ed; Behrens, M. Margarita; Ren, Bing; Mukamel, Eran A.; Ecker, Joseph R.

doi:10.1016/j.xgen.2022.100107

Cited by 85 publications

(91 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although epigenomic analyses of adult mammalian brains have identified lineage-specific TF activities ( 26, 32, 33 ), the developmental dataset generated in this study allows us to infer the temporal sequence of TF activity. We have identified dynamic DMRs across the stages of cell-type specification (trajectory-DMRs, Fig.…”

Section: Main Textmentioning

confidence: 99%

Epigenomic and chromosomal architectural reconfiguration in developing human frontal cortex and hippocampus

Heffel

Zhou

Zhang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The human frontal cortex and hippocampus play critical roles in learning and cognition. We investigated the epigenomic and 3D chromatin conformational reorganization during the development of the frontal cortex and hippocampus, using more than 53,000 joint single-nucleus profiles of chromatin conformation and DNA methylation (sn-m3C-seq). The remodeling of DNA methylation predominantly occurs during late-gestational to early-infant development and is temporally separated from chromatin conformation dynamics. Neurons have a unique Domain-Dominant chromatin conformation that is different from the Compartment-Dominant conformation of glial cells and non-brain tissues. We reconstructed the regulatory programs of cell-type differentiation and found putatively causal common variants for schizophrenia strongly overlap with chromatin loop-connected, cell-type-specific regulatory regions. Our data demonstrate that single-cell 3D-regulome is an effective approach for dissecting neuropsychiatric risk loci.

show abstract

Section: Main Textmentioning

confidence: 99%

Epigenomic and chromosomal architectural reconfiguration in developing human frontal cortex and hippocampus

Heffel

Zhou

Zhang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…UMAP visualization of the Fast-Higashi embeddings for the neuron cells in the Lee et al dataset (cells in the red box in (b) ). Cell type information is from Luo et al [23]. See also Fig .…”

Section: Resultsmentioning

confidence: 99%

“…2f). We obtained a new cell type annotation from [23], where the methylation profiles of the Lee et al dataset were jointly embedded with single-cell methylation profiles from snmC-seq, snmCT-seq, and snmC2T-seq on human PFC to annotate cell types. This joint analysis allows the characterization of neuron subtypes in the Lee et al dataset at a much more refined resolution.…”

Section: Resultsmentioning

confidence: 99%

Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi

Zhang

Zhou

2022

Preprint

View full text Add to dashboard Cite

Single-cell Hi-C (scHi-C) technologies can probe three-dimensional (3D) genome structures in single cells and their cell-to-cell variability. However, existing scHi-C analysis methods are hindered by the data quality and the complex 3D genome patterns. The lack of computational scalability and interpretability poses further challenges for large-scale scHi-C analysis. Here, we introduce Fast-Higashi, an ultrafast and interpretable method based on tensor decomposition that can jointly identify cell identities and chromatin meta-interactions. Fast-Higashi is able to simultaneously model multiple tensors with unmatched features of different sizes. A new partial random walk with restart (Partial RWR) algorithm in Fast-Higashi efficiently mitigates data sparseness. Extensive evaluations on real scHi-C datasets demonstrate the advantage of Fast-Higashi over existing methods for embedding, leading to improved delineation of rare cell types and better reconstruction of developmental trajectories. Fast-Higashi can directly infer chromatin meta-interactions, identify 3D genome features that define distinct cell types, and help elucidate cell type-specific connections between genome structure and function. Moreover, Fast-Higashi can be generalized to incorporate other single-cell omics data. Fast-Higashi provides a highly efficient and interpretable scHi-C analysis solution that is applicable to a broad range of biological contexts.

show abstract

“…Yan et al showed that the joint profile of DNA methylation, RNA expression, and chromatin accessibility of human and mouse oocytes revealed the evolution of gene body methylation 8 . Luo et al discovered the relations between DNA methylation and gene expression for neuronal cells with multiomic measurement 9 .…”

Section: Introductionmentioning

confidence: 99%

“…Despite advances in sequencing technology [7][8][9][10][11][12][13][14] , the integrated analysis of single-cell multiomic data is statistically challenging. The challenges come from two aspects.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data

WANG

Wang

2022

Preprint

View full text Add to dashboard Cite

Single-cell sequencing technology enables the simultaneous capture of multiomic data from multiple cells. The captured data can be represented by tensors, i.e., the higher-rank matrices. However, the proposed analysis tools often take the data as a collection of two-order matrices, renouncing the correspondences among the features. Consequently, we propose a probabilistic tensor decomposition framework, SCOIT, to extract embeddings from single-cell multiomic data. To deal with sparse, noisy, and heterogeneous single-cell data, we incorporate various distributions in SCOIT, including Gaussian, Poisson, and negative binomial distributions. Our framework can decompose a multiomic tensor into a cell embedding matrix, a gene embedding matrix, and an omic embedding matrix, allowing for various downstream analyses. We applied SCOIT to seven single-cell multiomic datasets from different sequencing protocols. With cell embeddings, SCOIT achieves superior performance for cell clustering compared to seven state-of-the-art tools under various metrics, demonstrating its ability to dissect cellular heterogeneity. With the gene embeddings, SCOIT enables cross-omics gene expression analysis and integrative gene regulatory network study. Furthermore, the embeddings allow cross-omics imputation simultaneously, outperforming conventional imputation methods with the Pearson correlation coefficient increased by 0.03-0.28.

show abstract

Single nucleus multi-omics identifies human cortical cell regulatory genome diversity

Cited by 85 publications

References 100 publications

Epigenomic and chromosomal architectural reconfiguration in developing human frontal cortex and hippocampus

Epigenomic and chromosomal architectural reconfiguration in developing human frontal cortex and hippocampus

Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi

Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data

Contact Info

Product

Resources

About