Flexible and high-throughput simultaneous profiling of gene expression and chromatin accessibility in single cells
- Friedrich Miescher Laboratory of the Max Planck Society, Germany
- Department of Translational Genomics, University of Cologne, Germany
- University of Groningen, Groningen Institute for Evolutionary Life Sciences, Netherlands
Figures
Figure 1 with 1 supplement
easySHARE-seq enables high-quality and accurate simultaneous scATAC-seq and scRNA-seq profiling.
(A) Schematic workflow of easySHARE-seq. (B) Generation and structure of the cell-specific barcode within Index 1. Total length of the final barcode is just 17 nt compared to 99 nt previously. (C) Fraction of sequenced DNA bases allocated for either barcodes (grey) or genetic information (red) in easySHARE-seq and SHARE-seq using different sequencing kits. (D) Left: Principle of a species-mixing experiment. Murine OP-9 and human HEK cells are mixed prior to easySHARE-seq. After sequencing, sequences associated with each cell barcode are assessed for genome of origin. Middle left: Unique ATAC-seq fragments per cell aligning to the mouse or human genome. Cells are coloured according to their assigned origin (red: human; blue: mouse; orange: doublet). Middle right: Unique RNA-seq transcripts per cell aligning to the mouse or human genome. Right: Percentage of ATAC-seq fragments or RNA-seq transcripts per cell relative to total sequencing reads mapping uniquely to the human genome. 3.17% of all observed cells classified as doublets. Accounting for same-species doublets, this results in a doublet rate of 6.34%. (E) Comparison of unique molecular identifiers (UMIs)/cell across different single-cell technologies. Red shading denotes all multiomic technologies. Datasets are this study, SHARE-seq (Ma et al., 2020) (murine skin cells), sci-CAR (Cao et al., 2018) (murine kidney nuclei), SNARE-seq (Chen et al., 2019) (adult and neonatal mouse cerebral cortex nuclei), 10x Multiome (Bravo González-Blas et al., 2024) (murine liver nuclei), 10x3’ Expression (Su et al., 2021a) (murine liver nuclei), and sci-RNAseq3 (Martin et al., 2023) (E16.5 mouse embryo nuclei). Cells have been downsampled to a common sequencing depth where possible (see Methods). (F) Comparison of fragments per cell across different single-cell technologies. Colouring as in (E). Datasets differing to (A) are 10x scATAC (Nikopoulou et al., 2023) (murine liver nuclei) and sciATAC-seq (Cusanovich et al., 2018) (murine liver nuclei). Cells have been downsampled to a common sequencing depth where possible (see Methods).
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Figure 1—source data 1
Unique molecular identifier (UMI) counts per technique for Figure 1E.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-data1-v1.zip
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Figure 1—source data 2
Fragment counts per technique for Figure 1F.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-data2-v1.zip
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Figure 1—source data 3
Species mixing counts for Figure 1D.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-data3-v1.zip
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Figure 1—source data 4
Data for Figure 1C.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-data4-v1.xlsx
Figure 1—figure supplement 1
Barcode structure and summary of quality control measures in liver nuclei.
(A) Structure of scATAC-seq and scRNA-seq sequencing reads in easySHARE-seq and the original protocol (BC1/2: barcode segment 1/2, UMI: unique molecular identifier). (B) Percentage of total scRNA-seq sequencing reads containing cDNA fragments in murine primary liver nuclei. (C) Percentage of de-duplicated scRNA-seq sequencing reads overlapping an exon, intron, 5’UTR, or 3’UTR. (D) Distribution of fraction of reads in peaks (FRiP) per cell in scATAC-seq data in murine primary liver nuclei (mean FRiP: 0.55). (E) Boxplot depicting the distribution of expressed genes and accessible peaks per cell in murine primary liver nuclei (mean expressed genes: 1798; mean accessible peaks: 1983). (F) Number of mean UMIs per cell recovered in the snRNA-seq when subsampling to different raw sequencing depths. (G) Number of mean fragments in peaks per cell recovered in the snATAC-seq when subsampling to different raw sequencing depths. (H) Mean transcription start site (TSS) enrichment score per cell in relation to distance from nearest TSS in the snATAC-seq data. (I) Histogram of fragment length in snATAC-seq sequencing reads. (J) Reproducibility of easySHARE-seq between sub-libraries shown by comparing the number of UMIs recovered per gene or peak across them. Each dot depicts either a gene (left) or peak (right). (K) Reproducibility of easySHARE-seq between biological replicates shown by comparing the number of UMIs recovered per gene or peak across biological replicates. Each dot depicts either a gene (left) or peak (right). (L) Comparison of genes expressed per cell across different single-cell technologies. Red shading denotes all multiomic technologies. Datasets are the same as in Figure 1E. Cells have been downsampled to a common sequencing depth where possible (see Methods). (M) Comparison of accessible peaks per cell across different single-cell technologies. Colouring as in (L). Datasets are the same as in Figure 1F. Cells have been downsampled to a common sequencing depth where possible (see Methods). (N) Aggregated scRNA- and scATAC-seq tracks of easySHARE-seq at the GAPDH locus. (O) Histogram of fraction of contaminated RNA counts (UMIs) per nuclei as estimated by decontX. For a discussion of ambient RNA contamination, see Appendix 1.
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Figure 1—figure supplement 1—source data 1
Gene counts per technique for Figure 1—figure supplement 1L.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-figsupp1-data1-v1.zip
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Figure 1—figure supplement 1—source data 2
Peak counts per technique for Figure 1—figure supplement 1M.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-figsupp1-data2-v1.zip
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Figure 1—figure supplement 1—source data 3
Saturation data for Figure 1—figure supplement 1F.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-figsupp1-data3-v1.zip
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Figure 1—figure supplement 1—source data 4
Saturation data for Figure 1—figure supplement 1G.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig1-figsupp1-data4-v1.zip
Figure 2 with 1 supplement
Cell-type classification in primary liver nuclei using joint expression and chromatin accessibility profiles.
(A) UMAP visualisation of WNN-integrated scRNA-seq and scATAC-seq modalities of 19,664 liver nuclei. Nuclei are coloured by their assigned cell-type identity. (B) WNN-UMAPs of 19,664 liver nuclei with nuclei coloured according to the mean expression strength of marker genes for several cell types (Alb: hepatocytes, Clec4g: liver sinusoidal endothelial cells [LSECs], Dcn: hepatic stellate cells [HSCs], Vsig4: Kupffer cells). Plots for further marker genes can be found in Figure 2—figure supplement 1. (C) Violin plots depicting the distribution of the normalised expression level of marker genes in all cells assigned to each cell type. (D) Pseudo-bulk ATAC-seq coverage tracks of normalised chromatin accessibility in all cells of each cell type in open chromatin regions overlapping marker genes.
Figure 2—figure supplement 1
easySHARE-seq robustly separates cell types.
(A) UMAP visualisation of total merged and integrated liver nuclei snRNA-seq data. Nuclei are coloured according to their cell type identified in Figure 2. (B) UMAP visualisation of total merged and integrated liver nuclei snATAC-seq data. Nuclei are coloured according to their cell type identified in Figure 2. (C) Fraction of each recovered cell type relative to all 19,664 nuclei. (D) Violin plots depicting the distribution of unique molecular identifiers (UMIs; transcripts) per cell split by cell type. (E) Violin plots depicting the distribution of unique fragments per cell split by cell type. (F) WNN-UMAPs of 19,664 liver nuclei with nuclei coloured according to the mean expression strength of marker genes for several cell types (Cyp3a25: hepatocytes, Stab2: liver sinusoidal endothelial cells [LSECs], Reln: hepatic stellate cells [HSCs], Clec4f: Kupffer cells, Ptprc: B cells, Oasl1: monocytes, Kcnip1: neurons Spp1: cholangiocytes). Red circles indicate the position of the cell population showing elevated expression for this marker gene. (G) WNN-UMAPs of hepatocytes with nuclei coloured according to the mean expression strength of several genes showing expression gradients.
Figure 3 with 1 supplement
Assigning putative cis-regulatory elements (pCREs) to their target genes by correlating simultaneous measurements of gene expression and chromatin accessibility.
(A) Schematic depicting the concept for linking pCREs to their target genes. For each gene, all open chromatin regions (OCRs) within ±500 bp of its transcription start site (TSS) are tested. A pCRE is linked to a gene if the Spearman correlation of its chromatin accessibility to the gene’s expression falls outside the expected distribution estimated by correlating chromatin accessibility of 100 unrelated peaks (on different chromosomes) to the gene expression. (B) Genes ranked by their number of significantly correlated pCREs (p<0.05, ±500 kbp from TSS) in liver sinusoidal endothelial cells (LSECs). Marked are genes in the top 1% that are either transcription factors or cell-type-specific regulators shown to fulfil a critical role in LSECs. (C) pCREs are enriched for TSS proximity. Normalised density of all open chromatin regions within ±50 kbp of a TSS (red) and of all pCREs within ±50 kbp of a TSS (blue). (D) Aggregate snATAC-seq pileup (red) of LSECs at the Gata4 locus and 500 kbp upstream region. Grey bars indicate open chromatin regions. Loops denote pCREs significantly correlated with Gata4 and are coloured by Spearman correlation of respective pCRE–Gata4 comparison.
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Figure 3—source data 1
Peak–gene associations.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig3-data1-v1.zip
Figure 3—figure supplement 1
Summary of peak–gene correlations.
(A) Number of significantly correlated putative cis-regulatory elements (pCREs) per gene (p<0.05, FDR ≤ 0.1), considering all peaks ± 500 kbp of the transcription start site (TSS). (B) Number of genes a given pCRE is significantly correlated with (p<0.05, FDR ≤ 0.1), considering all peaks ± 500 kbp of the TSS. (C) Number of significantly correlated pCREs per gene (p<0.05, FDR ≤ 0.1), considering all peaks ± 50 kbp of the TSS. (D) Number of genes a given pCRE is significantly correlated with (p<0.05, FDR ≤ 0.1), considering all peaks ± 50 kbp of the TSS. (E) Histogram of Spearman correlations of all significant peak–gene correlations (p<0.05). (F) Histogram of Spearman correlations of all non-significant peak–gene correlations (p>0.05). (G) Aggregate snATAC-seq track in liver sinusoidal endothelial cells (LSECs) at the Igf1 locus and its upstream region. Grey bars indicate open chromatin regions. Loops denote significantly correlated pCREs with Igf1 and are coloured by their respective Spearman correlation. Shaded grey area denotes a potentially LSEC-specific cis-regulatory element regulating Igf1 expression. (H) Gene ontology enrichment analysis of genes whose associated pCREs are associated with five or more genes. (I) UMIs per gene for genes without and with at least one significant peak–gene correlation (67 vs 221 mean UMIs/gene; p<2.2e–16; two-sided Welch’s t-test). (J) Fragments per peak for peaks with and without a correlated gene (72 vs 158 mean fragments/gene; p<2.2e–16; two-sided Welch’s t-test). (K) Fragments per peak for pCREs associated with genes with 1–5, 6–10, or more than 10 correlated pCREs (Pearson correlation for transcript vs fragment counts of linked pairs: r=0.004; p=0.39).
Figure 4
Zonation profiles in liver sinusoidal endothelial cells (LSECs) across gene expression and chromatin accessibility.
(A) Schematic depiction of a liver lobule. A liver lobule has a ‘central–portal axis’ defined by morphogen gradients starting from the central vein to the portal vein and portal artery. The sinusoidal capillary channels are lined with LSECs. (B) UMAP of 1561 LSECs coloured by pseudotime. (C) Changes along the central–portal axis at the Wnt2 locus. Top: Aggregate snATAC-seq profile (red) of LSECs at the Wnt2 locus. Grey bars denote identified open chromatin regions (OCRs). Bottom: In blue, LOESS trend line of mean normalised Wnt2 gene expression along pseudotime/the central–portal axis (CP axis, central vein, CV; portal vein, PV). In red, LOESS trend line of mean normalised chromatin accessibility in OCRs at the Wnt2 locus along the CP axis. (D) LOESS trend line of mean normalised gene expression (blue) of marker genes and mean normalised chromatin accessibility (red) at OCRs overlapping the marker gene along the CP axis for pericentral markers (top, increased towards the central vein, Kit, Dkk3, and Rspo3) and periportal markers (increased towards the portal vein, Efnb2, Meis1, and Ltbp4). (E) Left: Zonation profiles of 550 genes along the CP axis. Right: Zonation profiles of 744 open chromatin regions along the CP axis. All profiles are normalised by their maximum.
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Figure 4—source data 1
Marker genes across zonation for Figure 4E.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig4-data1-v1.zip
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Figure 4—source data 2
Open chromatin regions across zonation for Figure 4E.
- https://cdn.elifesciences.org/articles/110034/elife-110034-fig4-data2-v1.zip
Tables
Table 1
Comparison between multiomic single-cell technologies.
| Protocol | easySHARE-seq | SCHARE-seq | 10x Multiome |
|---|---|---|---|
| Cost/cell | 0.056$ | 0.043$ | 0.26$ |
| Additional costs? | None | Dedicated sequencing run | Specialised instrument |
| Throughput (cells) | >200,000 | >200,000 | 80,000 |
| Max. sequencing length of insert | 300 bp | 100 bp | 100 bp |
| Processing time | ~12 hr | ~17 hr | ~14.5 hr |
| Convertible to single-channel assay? | Yes | Not described | No |
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background (Mus musculus) | C57BL/6J | Charles River Laboratories | RRID:IMSR_JAX:000664 | |
| Strain, strain background (M. musculus) | PWD/PhJ | Jackson Laboratories | RRID:IMSR_JAX:004660 | |
| Cell line (Homo sapiens) | Embryonic kidney cells HEK293 | RRID:CVCL_0045 | ||
| Cell line (M. musculus) | OP9 Stromal cells | Gift from Juan Carlos Zúñiga-Pflücker | RRID:CVCL_4398 | |
| Sequence-based reagent | Tn5-A | Picelli et al., 2014 | Transposase oligo | TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG |
| Sequence-based reagent | Tn5-B2S | Picelli et al., 2014 | Transposase oligo | CAGGGCATTTGCACCCCATGCC |
| Sequence-based reagent | Tn5-reverse | Picelli et al., 2014 | Transposase oligo | /5Phos/CTGTCTCTTATACACATCT |
| Sequence-based reagent | Reverse-Transcription Primer | This paper | Reverse-Transcription primer | /5Phos/GGGCTCGGAGATGTGTATAAGAGACAGNNNNNNNNNNT/iBiodT/TTTTTTTTTTTTTTTTTTTTTTTTVN |
| Sequence-based reagent | TS oligo | This paper | Template Switch Oligo | rUrUrUTCGTCGGCAGCGTCAGATGTGTATAAGAGACArGrGrG |
| Sequence-based reagent | I7-Tru-Seq-long primer | This paper | I7-Tru-Seq-long primer | CAAGCAGAAGACGGCATACGAGAT |
| Recombinant Protein | Maxima-H Reverse Transcriptase | Thermo Fisher | EP0751 | cDNA generation, template switching |
| Recombinant Protein | Tn5 | This paper | ATAC-seq, library generation | |
| Recombinant Protein | SUPERase | Thermo Fisher | AM2694 | RNase Inhibitor |
| Recombinant Protein | Recombinant RNase Inhibitor | Jena Bioscience | PCR-392S | RNase Inhibitor |
| Recombinant Protein | T4 Ligase | New England Biolabs | M0202L | Ligation during barcoding |
| Recombinant Protein | Q5 Polymerase | New England Biolabs | M0491L | PCR amplification |
| Recombinant Protein | Proteinase K | ITW | A4392,0010 | Reverse crosslinking |
| Recombinant Protein | Dynabeads M280 | Thermo Fisher | 112.05D | cDNA pull-down |
| Recombinant Protein | RNaseIn | QIAGEN | Y9240L | Template switching |
| Chemical compound, drug | DAPI | Thermo Fisher | D1306 | Cell counting |
| Chemical compound, drug | Formaldehyde | Thermo Fisher | 28906 | Fixation |
| Chemical compound, drug | NP-40 | Sigma-Aldrich | 492016 | Permeabilisation |
| Chemical compound, drug | Tween-20 | Bio-Rad | #1706531 | Permeabilisation |
| Chemical compound, drug | TAPS | Carl Roth | 6982.4 | Buffer |
| Chemical compound, drug | DMF | Carl Roth | 6251.1 | Buffer |
| Chemical compound, drug | PEG6000 | Sigma-Aldrich | 528877 | Buffer |
| Chemical compound, drug | Size Selection Beads | Speed Beads | Selection of DNA fragments | |
| Commercial assay or kit | MinElute PCR Cleanup Kit | QIAGEN | 28004 | Purification |
| Commercial assay or kit | Qubit | Thermo Fisher | Q33230 | Quantification of DNA |
| Software | bwa | Li and Durbin, 2009 | bwa v.0.7.17, RRID:SCR_010910 | Read alignment |
| Software | UMI-tools | Smith et al., 2017 | UMI-tools v.1.1.2, RRID:SCR_017048 | Processing RNA-seq |
| Software | featureCounts | Liao et al., 2014 | featureCounts v.2.0.1, RRID:SCR_012919 | Counting transcripts |
| Software | STAR | Dobin et al., 2013 | STAR v.2.7.9a, RRID:SCR_004463 | Aligning RNA-seq |
| Software | Seurat | Hafemeister and Satija, 2019 | Seurat v. 5.0.1, RRID:SCR_016341 | Analysis RNA-seq |
| Software | Signac | Stuart et al., 2021 | Signac v.1.12.9, RRID:SCR_021158 | Analysis ATAC-seq |
| Software | MACS2 | Zhang et al., 2008 | macs2 v.2.2.7.1, RRID:SCR_013291 | Peak calling |
| Software | Monocle | Trapnell et al., 2014 | monocle v.2.22, RRID:SCR_018685 | Pseudotime |
| Software | clusterProfiler – GO enrichment | Yu et al., 2012 | clusterProfiler v3.18.1, RRID:SCR_016884 | GO enrichment |
Additional files
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Supplementary file 1
List of all oligos used in this study.
- https://cdn.elifesciences.org/articles/110034/elife-110034-supp1-v1.xlsx
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Supplementary file 2
Cell type annotations for each barcode.
- https://cdn.elifesciences.org/articles/110034/elife-110034-supp2-v1.zip
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MDAR checklist
- https://cdn.elifesciences.org/articles/110034/elife-110034-mdarchecklist1-v1.docx
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Flexible and high-throughput simultaneous profiling of gene expression and chromatin accessibility in single cells
eLife 15:RP110034.
https://doi.org/10.7554/eLife.110034.4
