High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell

By S. Chen, B. Lake, Kun Zhang. Nature Biotechnology, 2019

699 citations recorded by the source corpus.

Single-cell RNA sequencing can reveal the transcriptional state of cells, yet provides little insight into the upstream regulatory landscape associated with open or accessible chromatin regions. Joint profiling of accessible chromatin and RNA within the same cells would permit direct matching of transcriptional regulation to its outputs. Here, we describe droplet-based single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq), a method that can link a cell’s transcriptome with its accessible chromatin for sequencing at scale. Specifically, accessible sites are captured by Tn5 transposase in permeabilized nuclei to permit, within many droplets in parallel, DNA barcode tagging together with the mRNA molecules from the same cells. To demonstrate the utility of SNARE-seq, we generated joint profiles of 5,081 and 10,309 cells from neonatal and adult mouse cerebral cortices, respectively. We reconstructed the transcriptome and epigenetic landscapes of major and rare cell types, uncovered lineage-specific accessible sites, especially for low-abundance cells, and connected the dynamics of promoter accessibility with transcription level during neurogenesis. SNARE-seq measures expression profiles and chromatin accessibility in the same cell.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b0315ccc55af921535b6faa9a4060659b9671efb

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Citations in the corpus

Methods and applications for single-cell and spatial multi-omics (2023, methodology): “3a,b), which rely on combinatorial indexing; SNARE-seq 39,41 , ASTAR-seq 42 and the commercially available 10x Genomics Multiome technology, which rely on microfluidics for cell barcoding (Fig.”
Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, positive): “The multiomic bridge dataset, which can be generated by a diverse set of technologies 23–32 , helps to translate information between disparate measurements, resulting in robust integration without requiring any limiting biological assumptions.”
Predicting cellular responses to complex perturbations in high‐throughput screens (2023, positive): “…network-based approaches, CPA could be extended to include other modalities, for example via multimodal single-cell CRISPR (Frangieh et al , 2021; Papalexi et al , 2021) combined scRNA-seq and ATAC-seq (Clark et al , 2018; Chen et al , 2019) and CUT&Tag (Kaya-Okur et al , 2019; Wu et al , 2021).”
Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender (2019, background): “Examples include CITE-seq [5], Perturb-Seq [6], scCAT-seq [7], SNARE-seq [8], SHARE-seq [9], ECCITE-seq [10], 10x Multiome, among many other recently introduced droplet-based assays.”
Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “For this reason, and with the introduction of multimodal profiling technologies 26–28 , there has been a recent explosion of novel GRN inference methods.”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding (2022, background): “Recent advances in experimental multi-omics technologies have increased the availability of paired data 8–11 , 34 .”
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, methodology): “SHARE-seq also consistently outperformed previously published joint ATAC-RNA approaches (Cao et al., 2018; Chen et al., 2019; Zhu et al., 2019; Figure 1E).”
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data (2020, background): “As single-cell technologies mature, they are applied to generate data sets with increasingly complex experimental designs [16, 17, 24, 47, 48].”