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ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells

Nature Methods volume 19, pages 1243–1249 (2022)Cite this article

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Abstract

Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. Here, we develop in situ sequencing hetero RNA–DNA-hybrid after assay for transposase-accessible chromatin-sequencing (ISSAAC-seq), a highly sensitive and flexible single-cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single nucleus. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from over 10,000 nuclei from the mouse cerebral cortex, we uncovered major and rare cell types and cell-type specific regulatory elements and identified heterogeneity at the chromatin level within established cell types defined by gene expression. Finally, we revealed distinct dynamics and relationships of gene expression and chromatin accessibility during an oligodendrocyte maturation trajectory.

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Fig. 1: Joint profiling of chromatin accessibility and gene expression using ISSAAC-seq.
Fig. 2: Simultaneous investigation of chromatin accessibility and gene expression from the mouse cerebral cortex using ISSAAC-seq.
Fig. 3: Joint analysis of RNA and ATAC from the same cell in the mouse cortex.

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Data availability

The raw sequencing data generated in this study has been deposited at ArrayExpress under the accession number E-MTAB-11264. External data from previously published studies are available from the Gene Expression Omnibus: sci-CAR-seq (GSE117089), SNARE-seq (GSE126074), Paired-seq (GSE130399) and SHARE-seq (GSE140203). Source data are provided with this paper.

Code availability

The code used for the data processing and analysis mentioned in the method section is available on the GitHub repository https://github.com/dbrg77/ISSAAC-seq.

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Acknowledgements

We thank all members from the Chen and Jin laboratories for the helpful discussion of the project. We also thank W. Chen and H. Cui from SUSTech for the helpful advice on the experiments. We thank X. Lu from SUSTech Core Research Facilities for the excellent support of FACS. The computational work was supported by Center for Computational Science and Engineering at Southern University of Science and Technology. Figures 1a and 2a were created with BioRender.com. This study was supported by National Key R&D Program of China (grant no. 2021YFF1200900 to W.J., X.C. and N.H.), National Natural Science Foundation of China (grant nos. 32170646 and 81872330 to W.J.), Guangdong Basic and Applied Basic Research Foundation (grant no. 2021B1515120070 to X.C.), Shenzhen Innovation Committee of Science and Technology (grant no. ZDSYS20200811144002008 to Shenzhen Key Laboratory of Gene Regulation and Systems Biology, to K.S., W.J. and X.C.), Shenzhen Science and Technology Program (grant no. KQTD20180411143432337 to W.J.), Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (grant no. 2021SHIBS0002 to K.S.) and Cambridge-SU Joint Research Center for Genomic Resources (grant no. 2017B01012 to X.C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Wei Xu, Weilong Yang.

Authors and Affiliations

  1. Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Wei Xu, Weilong Yang, Yunlong Zhang, Yawen Chen, Ni Hong, Qian Zhang, Xuefei Wang, Yukun Hu, Kun Song, Wenfei Jin & Xi Chen

  2. Brain Research Center and Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Yawen Chen & Kun Song

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Contributions

X.C. and W.J. conceived the project. X.C., W.X. and W.J. designed the protocol and experiments. W.X. performed the experiments with help from Y.C., N.H., Q.Z., X.W. and Y.H. W.Y. carried out the computational analysis with help from Y.Z., W.X., X.C., W.J. and K.S. X.C., W.J., K.S. and N.H. supervised the entire project. All authors contributed to the writing.

Corresponding authors

Correspondence to Wenfei Jin or Xi Chen.

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Competing interests

W.X., N.H., W.J. and X.C. have filed a patent application related to this work. The remaining authors declare no competing interests.

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Peer review information

Nature Methods thanks Ansuman Satpathy, Caleb Lareau, Kun Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 The plate-based (FACS) ISSAAC-seq workflow.

Schematic view of the plate-based (FACS) ISSAAC-seq workflow and adaptor configurations.

Extended Data Fig. 2 The droplet-based ISSAAC-seq workflow.

Schematic view of the droplet-based ISSAAC-seq workflow and adaptor configurations using 10x Chromium.

Extended Data Fig. 3 Experimental tests and benchmarking of ISSAAC-seq.

a and b, Assessment of ISSAAC-seq RNA data. Distributions of total UMIs (a) and numbers of detected genes (b) from indicated conditions performed on the E14 mouse embryonic stem cells. Pro, Protector RNase inhibitor. Ribo, RiboLock RNase inhibitors. c-e, Assessment of ISSAAC-seq ATAC data. Distributions of unique nuclear fragments (c), fraction of reads in peaks (FRiP) (d) and mitochondrial content (e) from indicated conditions performed on the E14 mouse embryonic stem cells. Boxplots show median with the top and bottom edges of the box representing the 75th and 25th percentiles, respectively. The whiskers represent the most extreme values that are within 1.5 times the interquartile range of the 25th and 75th percentiles. Outliers outside the whiskers are not shown. f, UCSC genome browser tracks of aggregated scATAC-seq signal from indicated conditions. g, Species mixing experiments of ISSAAC-seq using HEK293T and NIH3T3 cells.

Extended Data Fig. 4 ISSAAC-seq experimental results from different cell lines and PBMCs.

a, Examples of a typical FACS based workflow using K562 cells. FSC-A and SSC-A were used to select intact nuclei (P1). FSC-A and FSC-W were used to select singlets (P2). DAPI-A was used to select DAPI positive nuclei (P3). b, DNA electrophoresis of typical ISSAAC-seq libraries from the FACS (left) and droplet (right) workflow using K562 cells. c and d, Fragment size (c) and Tn5 insertion frequency (d) of ATAC data using different methods in the indicated cell lines. e, Quality metrics of the ISSAAC-seq PBMC data. f, Genomic distribution of reads from RNA data using different methods. g, Distributions of the numbers of UMIs using different methods performed in the indicated cell lines and PBMCs. Boxplots show median with the top and bottom edges of the box representing the 75th and 25th percentiles, respectively. The whiskers represent the most extreme values that are within 1.5 times the interquartile range of the 25th and 75th percentiles. Outliers outside the whiskers are not shown. The exact number of cells can be found in the Source Data.

Extended Data Fig. 5 UCSC genome browser tracks of single cell aggregates from ISSAAC-seq.

a, Visual comparisons of SHARE-seq, ISSAAC-seq and the 10x Multiome kit from K562 cells. Cell numbers are the same as in Fig. 1b. b, Visualisation of ATAC and RNA signals from single cell aggregates of ISSAAC-seq (FACS) in H1 (Day 3 upon mesoderm differentiation) and E14 (Day 4 upon EB differentiation) cells around the BRACHYURY and MIXL1 genes. c, UMAP projection of ISSAAC-seq PBMCs.

Extended Data Fig. 6 Computational pipeline for the data processing of droplet-based ISSAAC-seq.

a, Flow chart of the computational pipeline to process mouse cortex data. b, Cell barcode qualities and the number of nuclei returned by the pipeline in each step.

Extended Data Fig. 7 ISSAAC-seq ATAC and RNA data comparison.

a, UMAP projection of mouse cortex cells with ISSAAC-seq ATAC data colored by ATAC clusters (left) and batches (right). b, Ribbon plot showed the relationship between all RNA clusters and all ATAC clusters. c, UMAP projection of mouse cortex cells with ISSAAC-seq RNA data, highlighted by RNA cluster R2 Ex-L4/5 IT neurons (left) and ATAC cluster A2, A3 and A9 (right). d, UMAP projection of Ex-L4/5 IT neurons (RNA cluster R2) colored by RNA subclusters (left) and ATAC clusters. e, Gene ontology of the top 50 differentially expressed genes among A2, A3 and A9 clusters within the Ex-L4/5 IT neurons. Hypergeometric tests were used to derive the p-values, and the Benjamini-Hochberg Procedure was used to adjust multiple comparisons. f, SCENIC analysis of regulon (TF) activity in A2, A3 and A9 cells.

Extended Data Fig. 8 The prediction probability of each single cell based on the Allen Brain reference data using the label transfer function from Seurat.

Each row is a predicted label. Each column is a cell. The cluster identify of each cell is indicated at the bottom using the same colour code as in Fig. 2c.

Extended Data Fig. 9 UMAP projection of ISSAAC-seq RNA data coloured by the expression levels of indicated genes or batches.

A total of 24 marker genes are shown. Batch (biological replicates) information is shown in the bottom right plot.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, Notes 1–4, References and Protocols 1 and 2.

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Supplementary Tables

Supplementary Tables 3 and 4.

Source data

Source Data Fig. 1

Source data for the boxplot and the violinplot in Fig. 1c,d.

Source Data Fig. 2

Source data for the boxplot and the violinplot in Fig. 2b.

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Xu, W., Yang, W., Zhang, Y. et al. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat Methods 19, 1243–1249 (2022). https://doi.org/10.1038/s41592-022-01601-4

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