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Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells

Nature Biotechnology volume 39, pages 1246–1258 (2021)Cite this article

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Abstract

Recent technological advances have enabled massively parallel chromatin profiling with scATAC-seq (single-cell assay for transposase accessible chromatin by sequencing). Here we present ATAC with select antigen profiling by sequencing (ASAP-seq), a tool to simultaneously profile accessible chromatin and protein levels. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA for clonal tracking, capturing three distinct modalities in single cells. ASAP-seq uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing. Together with DOGMA-seq, an adaptation of CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) for measuring gene activity across the central dogma of gene regulation, we demonstrate the utility of systematic multi-omic profiling by revealing coordinated and distinct changes in chromatin, RNA and surface proteins during native hematopoietic differentiation and peripheral blood mononuclear cell stimulation and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells.

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Fig. 1: ASAP-seq incorporates protein detection in scATAC-seq workflows.
Fig. 2: ASAP-seq enables a modular and versatile multi-omics toolkit.
Fig. 3: Dissection of native human hematopoiesis with multimodal cell state inference and mtDNA-based lineage tracing.
Fig. 4: ASAP-seq and CITE-seq reveal coordinated and distinct changes in chromatin, RNA and protein levels.
Fig. 5: DOGMA-seq enables a high-quality capture of multiple modalities sensitive to biological changes.
Fig. 6: Multiplexed CRISPR perturbations with ASAP-seq in primary human T cells.
Fig. 7: ASAP-seq enables detection of intracellular proteins with barcoded antibodies.

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Data availability

Data are available at the Gene Expression Omnibus under accession number GSE156478.

Code availability

Custom code to reproduce all analyses and figures is available at https://github.com/caleblareau/asap_reproducibility.

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Acknowledgements

We acknowledge support from the Broad Institute and the Whitehead Institute Flow Cytometry Core facilities. This research was supported by National Institutes of Health grants nos. R01 DK103794 (V.G.S.) and R01 HL146500 (V.G.S.); National Institutes of Health/National Human Genome Research Institute grants nos. R21 HG-009748 (P.S.) and RM1 HG0110014 (P.S.); Grants-in-Aid by the Japan Society for Promotion of Science for Specially Promoted Research no. 16H06295 (S.S.); the Japan Agency for Medical Research and Development for Leading Advanced Projects for Medical Innovation (S.S.); a gift from the Lodish Family to Boston Children’s Hospital (V.G.S.); the New York Stem Cell Foundation (NYSCF) (V.G.S.); the Howard Hughes Medical Institute and Klarman Cell Observatory (A.R.); and the Chan Zuckerberg Initiative/Silicon Valley Community Foundation Human Cell Atlas grant no. HCA3-0000000309 (P.S.). V.G.S. is an NYSCF Robertson Investigator. C.A.L. is supported by a Stanford Science Fellowship. L.S.L is supported by an Emmy Noether fellowship by the German Research Foundation (LU 2336/2-1).

Author information

Author notes

  1. Aviv Regev

    Present address: Genentech, South San Francisco, CA, USA

  2. These authors contributed equally: Eleni P. Mimitou, Caleb A. Lareau, Kelvin Y. Chen.

  3. These authors jointly supervised this work: Shimon Sakaguchi, Leif S. Ludwig, Vijay G. Sankaran, Aviv Regev, Peter Smibert.

Authors and Affiliations

  1. Technology Innovation Lab, New York Genome Center, New York, NY, USA

    Eleni P. Mimitou & Peter Smibert

  2. Department of Pathology, Stanford University, Stanford, CA, USA

    Caleb A. Lareau

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Caleb A. Lareau, Wendy Luo, Pratiksha I. Thakore, Leif S. Ludwig, Vijay G. Sankaran & Aviv Regev

  4. Division of Hematology / Oncology, Boston Children’s Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Caleb A. Lareau, Wendy Luo, Leif S. Ludwig & Vijay G. Sankaran

  5. Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan

    Kelvin Y. Chen, Yusuke Takeshima, Tatsuya Kibayashi, James Badger Wing, Mayu Hata & Shimon Sakaguchi

  6. Department of Experimental Pathology, Institute for Frontier Medical Sciences, Kyoto University, Kyoto, Japan

    Kelvin Y. Chen & Shimon Sakaguchi

  7. BioLegend, Inc., San Diego, CA, USA

    Andre L. Zorzetto-Fernandes, Tse-Shun Huang, Bertrand Z. Yeung & Kristopher L. Nazor

  8. Center for Genomics and Systems Biology, New York University, New York, NY, USA

    Yuhan Hao, Efthymia Papalexi & Rahul Satija

  9. New York Genome Center, New York, NY, USA

    Yuhan Hao, Efthymia Papalexi, Rahul Satija & Aviv Regev

  10. Laboratory of Human Immunology (Single Cell Immunology), Immunology Frontier Research Center, Osaka University, Osaka, Japan

    James Badger Wing

  11. Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Leif S. Ludwig

  12. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Aviv Regev

  13. Department of Biology and Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aviv Regev

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  1. Eleni P. Mimitou
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Contributions

E.P.M. and P.S. conceived and designed the methods with input from L.S.L., K.Y.C. and C.A.L. E.P.M., K.Y.C., L.S.L. and P.S. designed experiments with input from C.A.L., R.S., V.G.S. and A.R. E.P.M., K.Y.C., E.P., W.L., P.I.T., T.K., M.H. and L.S.L. performed experiments. C.A.L. led data analysis with substantial contributions from E.P.M., K.Y.C., Y.H. and Y.T. A.L.Z.-F., T.-S.H., B.Y. and K.L.N. provided insights and developed key reagents and protocols for protein detection. J.B.W. provided insights and discussions for experimental planning. R.S., S.S., L.S.L., V.G.S., A.R. and P.S. each supervised various aspects of the work. E.P.M., C.A.L., K.Y.C., L.S.L. and P.S. drafted the manuscript with input from all other authors.

Corresponding author

Correspondence to Peter Smibert.

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Competing interests

C.A.L., L.S.L., V.G.S. and A.R. are listed as co-inventors on a patent related to mtscATAC-seq (U.S. provisional patent application 62/683,502). In the past 3 years, R.S. has worked as a consultant for Bristol-Myers Squibb, Regeneron and Kallyope and served as a scientific advisory board member for Immunai and Resolve BioSciences. A.R. is a founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and, until August 31, 2020, was a scientific advisory board member of Syros Pharmaceuticals, Neogene Therapeutics, Asimov and Thermo Fisher Scientific. From August 1, 2020, A.R. is an employee of Genentech. P.S. is listed as co-inventor on a patent related to this work (U.S. provisional patent application 62/515-180).

Additional information

Peer review information Nature Biotechnology thanks Dan Xie, Golnaz Vahedi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Additional technical and computational validation of ASAP-seq workflows.

a. PBMCs and compensation beads were stained with fluorophore-conjugated antibodies and subjected to the ASAP-seq workflow with samples withdrawn at the indicated steps and assessed for fluorophore intensity by flow cytometry. CD19 (staining B cells), CD11c (dendritic cells) and CD4 (lymphocytes and monocytes) signal on fixed cells is hardly affected by permeabilization alone, but after the 37 °C incubation for 1 h to mimic the Tn5 transposition reaction, some signal reduction is observed. b. Barcoding scheme of TSA tags using the bridge oligo for TotalSeqTM-A (BOA). TSA tags do not contain UMIs, so to allow molecule counting, UBIs (N9V) are incorporated via the bridge oligo. c. Species mixing experiment as in Fig. 1c, using the Post-SPRI approach for tag recovery. Points are colored based on species classification using ATAC fragments. d. ATAC library complexity and TSS enrichment for fragments from each species under the two protein-tag library approaches. e. Comparison of protein tag complexity between libraries prepared using the pre- and post-SPRI approach. f. Comparison of ATAC library complexity between mtscATAC-seq and ASAP-seq. Boxplots: center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range. g. Two-dimensional embedding of the PBMC hashing data using t-SNE. The four major clusters (black) correspond to the four hashing antibodies used to stain the PBMCs. 13,772 cells were recovered and 1,396 doublets (red) were detected. h. UMAP embedding resolving PBMC cell types based on chromatin accessibility for cells processed by mtscATAC-seq and ASAP-seq. Data for the two different samples were processed together using cell ranger-atac aggr before dimensionality reduction. i,j. Selected protein markers (i) and corresponding gene score activities (j) superimposed on the ATAC-clustered PBMCs (for the ASAP-seq sample) as in (h).

Extended Data Fig. 2 Additional validation and comparison of modular ASAP-seq workflows.

a. Barcoding scheme of TSB tags using the bridge oligo for TotalSeqB (BOB). TSB tags contain UMIs (encompassing the antibody barcode), negating the requirement for a UBI on the bridge oligo. b. Pairwise comparison of centered log-ratio (CLR) normalized TSA and TSB counts under OMNI lysis conditions (n = 5,236 cells). Counts were collapsed for unique molecules using UBIs (TSA panel) or UMIs (TSB panel). c. Comparison of CLR normalised TSB counts under the two lysis conditions. Statistical comparisons are two-sided Wilcoxon rank sum test with Bonferroni adjusted p-values (ns = not significant; * padj = 0.0002; ** padj = 2.2×10–16). d. UMAP embedding and cluster annotation of the LLL (n = 5,236) and OMNI (n = 4,748) processed cells. Data for the two different samples were processed together using cell ranger-atac aggr before dimensionality reduction. e. TSA and TSB CLR counts projected on the LLL embeddings. f. TSA and TSB CLR counts projected on the OMNI embeddings.

Extended Data Fig. 3 Supporting information for ASAP-seq bone marrow analyses.

a. Annotation of reduced dimension space with the Doublet Enrichment score from ArchR. Arrow indicates the monocytic progenitor population. b. Histogram of scores from panel (a). c. Feature plots for six additional antibody tags in the reduced dimension space. d. Correlation heatmap between 25 most variable TF activities and surface markers. e. Percent of cells in each ArchR cluster (y axis) mapping to the indicated Seurat cluster (x axis) after label transfer using the protein tags only f. Substitution rate (observed over expected) of mgatk-identified heteroplasmic mutations (y axis) in each class of mononucleotide and trinucleotide change resolved by the heavy (H) and light (L) strands of the mitochondrial genome. g. Projection of 13711 G > A in single cells; threshold for + was 5% heteroplasmy. h. Distribution of observed mtDNA mutations in cells among major cell lineages. i. Association of antibody tag abundance to cell clones determined by mtDNA genotypes, highlighting the erythroid marker CD71. j. Developmental trajectory of erythroid differentiation using semi-supervised pseudotime analysis. k. Expression of select cell surface markers along the erythroid developmental trajectory highlighted in (j). Rows are min-max normalized. l. Expression of chromatin activity scores along the monocytic developmental trajectory for genes encoding proteins shown in Fig. 3h. m. Expression of chromatin activity scores along the erythroid developmental trajectory for genes encoding proteins shown in (k).

Extended Data Fig. 4 Supporting information for combined ASAP-seq and CITE-seq readouts.

a. Antibody tag complexity per condition and technology. Median tag complexity is 1.7-2x higher in CITE-seq compared to ASAP-seq and 1.3-1.6x higher in stimulation compared to control sample. Boxplots: center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range. The lower panels show the per-cluster mean tag abundance for the 50 most variable antibodies and corresponding Pearson correlations. b,c. Cellular distribution of protein tags measured by ASAP-seq (left) and CITE-seq (right) for control (top) and stimulated conditions (bottom) for, (b) CD278 (ICOS) and (c) CD71 (TFRC). d. Protein tag measurement importance in predicting cell cluster and stimulation from two different Random Forest models. Negative controls (rat epitopes) are shown in red. e-g. ASAP-seq and CITE-seq data co-embedding utilizing protein abundances. Cells are highlighted by (e) chromatin/RNA cluster identity, (f) stimulation condition and (g) technology assayed. h-j. UMAPs of chromatin accessibility, mRNA expression, and surface protein levels for (h) CD28, (i) CD4, and (j) CD52. k. Summary of changes in chromatin accessibility, gene expression and surface protein abundance for 103 expressed genes in B cells following T cell stimulation. l,m. UMAPs of chromatin accessibility, mRNA expression, and surface protein levels for genes with differential expression in B cells, including (l) CD184 (CXCR4) and (m) CD25 (IL2RA).

Extended Data Fig. 5 Supporting information for DOGMA-seq.

a-e. QC metrics of indicated modalities captured by DOGMA-seq applied on the stimulated PBMC sample. (a) TSS score, (b) ATAC fragment complexity, (c) % mtDNA content, (d) number of genes/cell and (e) protein tag complexity in the two different cell preparations compare similarly to the control PBMC sample in Fig. 5b-f. f. Percent of UMIs detected in the GEX library that map to mtRNA is higher in the digitonin-treated cells. g-h. Percent of UMIs mapping to exons is higher in the digitonin-treated (DIG) compared to LLL-treated cells (g), but similar when mitochondrial transcripts are excluded (h). i. CD138 tag counts projected on the three modality WNN stimulation clusters. j. Gene activity scores, transcript and protein tag counts projected for the indicated markers on the control and stimulated 3WNN clusters. k. Heatmaps showing percent overlap between clusters detected by 3WNN compared to 2WNN variations applied on the control PBMC dataset. l. Mean coverage along the mtDNA genome in control and stimulated PBMCs. m. Substitution rate (observed over expected) of mgatk-identified heteroplasmic mutations (y axis) in each class of mononucleotide and trinucleotide change resolved by the heavy (H) and light (L) strands of the mitochondrial genome for all cells in the PBMC-LLL condition. n. Observed (red) and permuted (gray) log2 heteroplasmy changes across the 106 identified variants. Statistical test: two-sided Kolmogorov–Smirnov Test. o. 3WNN UMAP embedding of control and stimulated PBMC samples under LLL and DIG processing. Dashed box indicates activated T cell clusters. p. Comparison of peak to gene linkage for genes detected in both protein and RNA modalities. Each dot is a peak to gene link with the z score representing the magnitude of the association. Boxplots: center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range.

Extended Data Fig. 6 Supporting information for ASAP-seq based decoding of perturbations in primary T cells.

a. Schematic for CRISPR perturbation experiment in primary human T cells. CD4 + T cells from healthy donors were stimulated for 72 hours, followed by a resting period of four days to enable expansion. On Day 7, cells were electroporated with Cas9 RNPs and then rested for an additional 8 days before secondary stimulation. b. Heatmap of cell demultiplexing with hashing antibodies, indicating normalized abundance of each hashtag. c. Assessment of the effect of CRISPR perturbations on three indicated protein surface markers. d. UMAP embedding overlaid with expression of the eight indicated surface protein markers. e. Allele-specific CRISPR editing outcomes for ZAP70 gRNA1 (left) and ZAP70 gRNA2 (right). The wildtype allele is indicated by **. f. Volcano plots showing TF motifs with significantly changed chromatin accessibility profiles between NTC cells and the indicated gRNAs (FDR < = 0.05, chromVAR accessibility change > = 0.25). g. Correlation of chromVAR median accessibility changes or FDR (bottom right panel) between the indicated gRNAs. h. Genomic tracks of TNFRSF18 and HAVCR2 loci with corresponding CLR-normalized protein abundance ridge plots. CLR-normalized protein abundance from the PBMC stimulation experiment is indicated by the corresponding boxplots. Differentially accessible regions are highlighted in blue.

Extended Data Fig. 7 Supporting information for intracellular ASAP-seq workflow.

a,b. Selected protein markers (a) and corresponding gene activity scores (b) superimposed on the ATAC-clustered PBMCs from the intracellular staining experiment (see Fig. 3a). c. Heatmap of cell demultiplexing with hashing antibodies, indicating normalized abundance of each hashtag for 24 different perturbation conditions. d. Violin plots showing distribution of CLR normalized protein counts for indicated proteins and their associated gRNA. e. Genomic tracks of IFNG and GZMB loci, indicating pseudo-bulk ATAC signal tracks across six Louvain clusters with corresponding log-normalized gene activity score violin plots shown to the right. Differentially accessible regions are highlighted in blue.

Supplementary information

Supplementary Information

ASAP-seq and DOGMA-seq step-by-step protocols.

Reporting Summary

Supplementary Tables 1–5

Lists of antibodies, oligos, cluster annotations and stimulation-induced modality changes.

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Mimitou, E.P., Lareau, C.A., Chen, K.Y. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat Biotechnol 39, 1246–1258 (2021). https://doi.org/10.1038/s41587-021-00927-2

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