Massively parallel single-cell profiling of transcriptome and multiple epigenetic proteins in cell fate regulation

Sculpting the epigenome with a combination of histone modifications and transcription factor (TF) occupancy determines gene transcription and cell fate specification. Here we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultra-high throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3 and H3K27me3) in 19,860 single cells. We applied uCoTarget to the in vitro generation of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget with high sensitivity per modality reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) along the differentiation trajectory and RUNX1 engagement priming for the H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multi-modal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.

[1]  P. Sethupathy,et al.  Spatial mapping of the total transcriptome by in situ polyadenylation , 2022, Nature Biotechnology.

[2]  Michael P. Meers,et al.  Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag , 2022, Nature Biotechnology.

[3]  C. Lareau,et al.  Single-cell chromatin state analysis with Signac , 2021, Nature Methods.

[4]  G. Tonon,et al.  Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin , 2021, Nature Biotechnology.

[5]  M. Lorincz,et al.  Interplay between chromatin marks in development and disease , 2021, Nature Reviews Genetics.

[6]  Eleni P. Mimitou,et al.  Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro , 2021, Nature Biotechnology.

[7]  W. Greenleaf,et al.  NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells , 2021, Nature Methods.

[8]  Bertrand Z. Yeung,et al.  Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells , 2021, Nature Biotechnology.

[9]  Howard Y. Chang,et al.  ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis , 2021, Nature Genetics.

[10]  Howard Y. Chang,et al.  Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution , 2020, Cell.

[11]  Sagar,et al.  Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis. , 2020, Annual review of biomedical data science.

[12]  B. Gottgens,et al.  Advancing Stem Cell Research through Multimodal Single-Cell Analysis. , 2020, Cold Spring Harbor perspectives in biology.

[13]  S. Teichmann,et al.  Computational methods for single-cell omics across modalities , 2020, Nature Methods.

[14]  S. Preissl,et al.  Single-cell multimodal omics: the power of many , 2020, Nature Methods.

[15]  J. Collins,et al.  Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility , 2019, bioRxiv.

[16]  B. Ren,et al.  An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome , 2019, Nature Structural & Molecular Biology.

[17]  Fabian J Theis,et al.  Generalizing RNA velocity to transient cell states through dynamical modeling , 2019, Nature Biotechnology.

[18]  Kun Zhang,et al.  High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell , 2019, Nature Biotechnology.

[19]  Howard Y. Chang,et al.  Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion , 2019, Nature Biotechnology.

[20]  S. Aerts,et al.  cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data , 2019, Nature Methods.

[21]  Andrew C. Adey,et al.  The accessible chromatin landscape of the murine hippocampus at single-cell resolution , 2019, Genome research.

[22]  Aibin He,et al.  CoBATCH for high-throughput single-cell epigenomic profiling , 2019, bioRxiv.

[23]  Hatice S. Kaya-Okur,et al.  CUT&Tag for efficient epigenomic profiling of small samples and single cells , 2019, Nature Communications.

[24]  Siddharth S. Dey,et al.  scDam&T‐seq combines DNA adenine methyltransferase-based labeling of protein-DNA contact sites with transcriptome sequencing to analyze regulatory programs in single cells , 2019, Nature Biotechnology.

[25]  Fan Guo,et al.  Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes , 2018, Cell Research.

[26]  Long Gao,et al.  RUNX1 and the endothelial origin of blood. , 2018, Experimental hematology.

[27]  Lu Wen,et al.  Single-cell multiomics sequencing and analyses of human colorectal cancer , 2018, Science.

[28]  Andrew C. Adey,et al.  Joint profiling of chromatin accessibility and gene expression in thousands of single cells , 2018, Science.

[29]  Andrew C. Adey,et al.  Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data. , 2018, Molecular cell.

[30]  Lia Chappell,et al.  Single-Cell (Multi)omics Technologies. , 2018, Annual review of genomics and human genetics.

[31]  Erik Sundström,et al.  RNA velocity of single cells , 2018, Nature.

[32]  G. Sanguinetti,et al.  scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells , 2018, Nature Communications.

[33]  Vanessa M. Peterson,et al.  Multiplexed quantification of proteins and transcripts in single cells , 2017, Nature Biotechnology.

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

[35]  H. Swerdlow,et al.  Large-scale simultaneous measurement of epitopes and transcriptomes in single cells , 2017, Nature Methods.

[36]  Sebastian Pott.  Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells , 2017, bioRxiv.

[37]  C. Porcher,et al.  SCL/TAL1: a multifaceted regulator from blood development to disease. , 2017, Blood.

[38]  A. Heger,et al.  UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy , 2016, bioRxiv.

[39]  N. Hubner,et al.  A covalent and cleavable antibody-DNA conjugation strategy for sensitive protein detection via immuno-PCR , 2016, Scientific Reports.

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

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

[42]  C. Ponting,et al.  G&T-seq: parallel sequencing of single-cell genomes and transcriptomes , 2015, Nature Methods.

[43]  William B. Langdon,et al.  Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks , 2015, BioData Mining.

[44]  Shane J. Neph,et al.  A comparative encyclopedia of DNA elements in the mouse genome , 2014, Nature.

[45]  Fidel Ramírez,et al.  deepTools: a flexible platform for exploring deep-sequencing data , 2014, Nucleic Acids Res..

[46]  Jian Sun,et al.  Histone posttranslational modifications and cell fate determination: lens induction requires the lysine acetyltransferases CBP and p300 , 2013, Nucleic Acids Research.

[47]  E. Furlong,et al.  Transcription factors: from enhancer binding to developmental control , 2012, Nature Reviews Genetics.

[48]  Nicola K. Wilson,et al.  GFI1 and GFI1B control the loss of endothelial identity of hemogenic endothelium during hematopoietic commitment. , 2012, Blood.

[49]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration , 2012, Briefings Bioinform..

[50]  Yang Gao,et al.  TGFβ inhibition enhances the generation of hematopoietic progenitors from human ES cell-derived hemogenic endothelial cells using a stepwise strategy , 2011, Cell Research.

[51]  R. Young,et al.  Histone H3K27ac separates active from poised enhancers and predicts developmental state , 2010, Proceedings of the National Academy of Sciences.

[52]  Manolis Kellis,et al.  Discovery and characterization of chromatin states for systematic annotation of the human genome , 2010, Nature Biotechnology.

[53]  Elaine Dzierzak,et al.  Runx1 is required for the endothelial to hematopoietic cell transition but not thereafter , 2009, Nature.

[54]  Jerry L. Workman,et al.  Crosstalk among Histone Modifications , 2008, Cell.

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

[56]  Caroline L. Speck,et al.  Runx1-mediated hematopoietic stem-cell emergence is controlled by a Gata/Ets/SCL-regulated enhancer. , 2007, Blood.

[57]  James A. Cuff,et al.  A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells , 2006, Cell.

[58]  J. Gurdon.  Cell Fate Determination by Transcription Factors. , 2016, Current topics in developmental biology.

[59]  Wolfgang Huber,et al.  Combinatorial effects of four histone modifications in transcription and differentiation. , 2008, Genomics.