High-content single-cell combinatorial indexing

Single-cell genomics assays have emerged as a dominant platform for interrogating complex biological systems. Methods to capture various properties at the single-cell level typically suffer a tradeoff between cell count and information content, which is defined by the number of unique and usable reads acquired per cell. We and others have described workflows that utilize single-cell combinatorial indexing (sci)1, leveraging transposase-based library construction2 to assess a variety of genomic properties in high throughput; however, these techniques often produce sparse coverage for the property of interest. Here, we describe a novel adaptor-switching strategy, ‘s3’, capable of producing one-to-two order-of-magnitude improvements in usable reads obtained per cell for chromatin accessibility (s3-ATAC), whole genome sequencing (s3-WGS), and whole genome plus chromatin conformation (s3-GCC), while retaining the same high-throughput capabilities of predecessor ‘sci’ technologies. We apply s3 to produce high-coverage single-cell ATAC-seq profiles of mouse brain and human cortex tissue; and whole genome and chromatin contact maps for two low-passage patient-derived cell lines from a primary pancreatic tumor.

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

[2]  Richard Durbin,et al.  Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..

[3]  Andrew C. Adey,et al.  Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition , 2010, Genome Biology.

[4]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer , 2011, Nature Biotechnology.

[5]  Andrew C. Adey,et al.  Ultra-low-input, tagmentation-based whole-genome bisulfite sequencing , 2012, Genome research.

[6]  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.

[7]  Andrew C. Adey,et al.  In vitro, long-range sequence information for de novo genome assembly via transposase contiguity , 2014, Genome research.

[8]  Andrew C. Adey,et al.  Haplotype-resolved whole-genome sequencing by contiguity-preserving transposition and combinatorial indexing , 2014, Nature Genetics.

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

[10]  J. Shendure,et al.  High-throughput determination of RNA structure by proximity ligation , 2015, Nature Biotechnology.

[11]  Tal Galili,et al.  dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering , 2015, Bioinform..

[12]  Roland Eils,et al.  Complex heatmaps reveal patterns and correlations in multidimensional genomic data , 2016, Bioinform..

[13]  Kun Zhang,et al.  Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay , 2016, Genome Biology.

[14]  Steven J. M. Jones,et al.  Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma. , 2017, Cancer cell.

[15]  S. Agarwal,et al.  Conditional reprogramming and long-term expansion of normal and tumor cells from human biospecimens , 2017, Nature Protocols.

[16]  Nancy R. Zhang,et al.  CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing , 2017, bioRxiv.

[17]  X. Xie,et al.  Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI) , 2017, Science.

[18]  Andrew C. Adey,et al.  Sequencing thousands of single-cell genomes with combinatorial indexing , 2017 .

[19]  Mauricio O. Carneiro,et al.  Scaling accurate genetic variant discovery to tens of thousands of samples , 2017, bioRxiv.

[20]  R. Xu,et al.  The TGF-β/Smad4 Signaling Pathway in Pancreatic Carcinogenesis and Its Clinical Significance , 2017, Journal of clinical medicine.

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

[22]  Andrew C. Adey,et al.  Highly scalable generation of DNA methylation profiles in single cells , 2018, Nature Biotechnology.

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

[24]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.

[25]  D. Dickel,et al.  Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation , 2018, Nature Neuroscience.

[26]  N. Shafie,et al.  Dual-specificity phosphatase 6 (DUSP6): a review of its molecular characteristics and clinical relevance in cancer , 2018, Cancer biology & medicine.

[27]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[28]  X. Xie,et al.  Three-dimensional genome structures of single diploid human cells , 2018, Science.

[29]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection , 2018, J. Open Source Softw..

[30]  S. Agarwal,et al.  AB024. S024. Drug responses of patient-derived cell lines in vitro that match drug responses of patient PDAc tumors in situ , 2018 .

[31]  Stein Aerts,et al.  Cis-topic modelling of single-cell epigenomes , 2018, bioRxiv.

[32]  Hajk-Georg Drost,et al.  Philentropy: Information Theory and Distance Quantification with R , 2018, J. Open Source Softw..

[33]  Jie Liu,et al.  Unsupervised embedding of single-cell Hi-C data , 2018, Bioinform..

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

[35]  Mark Gerstein,et al.  GENCODE reference annotation for the human and mouse genomes , 2018, Nucleic Acids Res..

[36]  William Stafford Noble,et al.  High-Throughput Single-Cell Sequencing with Linear Amplification. , 2019, Molecular cell.

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

[38]  Yuchao Jiang,et al.  SCOPE: a normalization and copy number estimation method for single-cell DNA sequencing , 2019, bioRxiv.

[39]  Allan R. Jones,et al.  Conserved cell types with divergent features in human versus mouse cortex , 2019, Nature.

[40]  Richard A. Moore,et al.  Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing , 2019, Cell.

[41]  Martin J. Aryee,et al.  Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility , 2019, Nature Biotechnology.

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

[43]  J. Bruce,et al.  Plasma Membrane Ca2+ ATPase Isoform 4 (PMCA4) Has an Important Role in Numerous Hallmarks of Pancreatic Cancer , 2020, Cancers.

[44]  Avi Srivastava,et al.  Multimodal single-cell chromatin analysis with Signac , 2020, bioRxiv.

[45]  Andrew J. Hill,et al.  A human cell atlas of fetal chromatin accessibility , 2020, Science.

[46]  Qicai Liu,et al.  PRSS1 genotype is associated with prognosis in patients with pancreatic ductal adenocarcinoma , 2019, Oncology letters.

[47]  Yuchao Jiang,et al.  SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing. , 2020, Cell systems.