Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Integrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generate the first single cell integrated map of chromatin accessibility and transcriptome in early embryos and demonstrate the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states. The ability to obtain these two layers of omics data will help provide more accurate definitions of “single cell state” and enable the deconvolution of regulatory heterogeneity from complex cell populations.Heterogeneity in gene expression and epigenetic states exists across individual cells. Here, the authors develop scCAT-seq, a technique for simultaneously performing ATAC-seq and RNA-seq within the same single cell.

[1]  A. Tanay,et al.  Single cell Hi-C reveals cell-to-cell variability in chromosome structure , 2013, Nature.

[2]  A. Sandelin,et al.  Applied bioinformatics for the identification of regulatory elements , 2004, Nature Reviews Genetics.

[3]  Howard Y. Chang,et al.  Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position , 2013, Nature Methods.

[4]  Steven L Salzberg,et al.  HISAT: a fast spliced aligner with low memory requirements , 2015, Nature Methods.

[5]  Jinyang Zhao,et al.  Genome sequencing of the sweetpotato whitefly Bemisia tabaci MED/Q , 2017, GigaScience.

[6]  F. Tang,et al.  Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing , 2013, Genome research.

[7]  E. Shapiro,et al.  Single-cell sequencing-based technologies will revolutionize whole-organism science , 2013, Nature Reviews Genetics.

[8]  Martin J. Aryee,et al.  Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiation , 2018, Cell.

[9]  William J. Greenleaf,et al.  chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data , 2017, Nature Methods.

[10]  William Stafford Noble,et al.  Massively multiplex single-cell Hi-C , 2016, Nature Methods.

[11]  Siu-Ming Yiu,et al.  SOAP2: an improved ultrafast tool for short read alignment , 2009, Bioinform..

[12]  Hui Jiang,et al.  A reference human genome dataset of the BGISEQ-500 sequencer , 2017, GigaScience.

[13]  Alex A. Pollen,et al.  Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex , 2014, Nature Biotechnology.

[14]  Catalin C. Barbacioru,et al.  mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.

[15]  Magdalena Zernicka-Goetz,et al.  Deconstructing and reconstructing the mouse and human early embryo , 2018, Nature Cell Biology.

[16]  K S Richter,et al.  Quantitative grading of a human blastocyst: optimal inner cell mass size and shape. , 2001, Fertility and sterility.

[17]  Soumen Paul,et al.  GATA3 Is Selectively Expressed in the Trophectoderm of Peri-implantation Embryo and Directly Regulates Cdx2 Gene Expression* , 2009, The Journal of Biological Chemistry.

[18]  Michael Q. Zhang,et al.  Genome-wide map of regulatory interactions in the human genome , 2014, Genome research.

[19]  Aaron R. Quinlan,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2022 .

[20]  Wei Xie,et al.  Epigenome in Early Mammalian Development: Inheritance, Reprogramming and Establishment. , 2017, Trends in cell biology.

[21]  A. M. Arias,et al.  Transition states and cell fate decisions in epigenetic landscapes , 2016, Nature Reviews Genetics.

[22]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.

[23]  D. Weitz,et al.  Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state , 2015, Nature Biotechnology.

[24]  Alvaro Plaza Reyes,et al.  Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos , 2016, Cell.

[25]  Ruiqiang Li,et al.  Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells , 2013, Nature Structural &Molecular Biology.

[26]  Lu Wen,et al.  Reconstructing Complex Tissues from Single-Cell Analyses , 2014, Cell.

[27]  Yuan-Xiao Zhu,et al.  HACS1 encodes a novel SH3-SAM adaptor protein differentially expressed in normal and malignant hematopoietic cells , 2001, Oncogene.

[28]  R. Eils,et al.  Deciphering programs of transcriptional regulation by combined deconvolution of multiple omics layers , 2017, bioRxiv.

[29]  Howard Y. Chang,et al.  Single-cell chromatin accessibility reveals principles of regulatory variation , 2015, Nature.

[30]  J. Aerts,et al.  SCENIC: Single-cell regulatory network inference and clustering , 2017, Nature Methods.

[31]  X. Xie,et al.  Genome-Wide Detection of Single-Nucleotide and Copy-Number Variations of a Single Human Cell , 2012, Science.

[32]  Fei Gong,et al.  Number of biopsied trophectoderm cells is likely to affect the implantation potential of blastocysts with poor trophectoderm quality. , 2016, Fertility and sterility.

[33]  Andrew C. Adey,et al.  Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing , 2015, Science.

[34]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[35]  Claudine Attias-Donfut,et al.  Analyses , 1994, Gérontologie et société.

[36]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.

[37]  Huanming Yang,et al.  Single-Cell Exome Sequencing Reveals Single-Nucleotide Mutation Characteristics of a Kidney Tumor , 2012, Cell.

[38]  William S. DeWitt,et al.  A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility , 2018, Cell.

[39]  Li Teng,et al.  4DGenome: a comprehensive database of chromatin interactions , 2015, Bioinform..

[40]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[41]  Bing Li,et al.  The Role of Chromatin during Transcription , 2007, Cell.

[42]  Huanming Yang,et al.  Single-Cell Exome Sequencing and Monoclonal Evolution of a JAK2-Negative Myeloproliferative Neoplasm , 2012, Cell.

[43]  Z. Weng,et al.  High-Resolution Mapping and Characterization of Open Chromatin across the Genome , 2008, Cell.

[44]  Liuyang Cai,et al.  Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing technology and application , 2014, BMC Genomics.

[45]  Robert Gentleman,et al.  Software for Computing and Annotating Genomic Ranges , 2013, PLoS Comput. Biol..