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NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells

Nature Methods volume 19, pages 547–553 (2022)Cite this article

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Abstract

In this work, we describe NEAT-seq (sequencing of nuclear protein epitope abundance, chromatin accessibility and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive T cell subsets and identify examples of TFs with regulatory activity gated by transcription, translation and regulation of chromatin binding. We also link a noncoding genome-wide association study single-nucleotide polymorphism (SNP) within a GATA motif to a putative target gene, using NEAT-seq data to internally validate SNP impact on GATA3 regulation.

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Fig. 1: An intra-nuclear staining protocol using oligo-antibodies enables simultaneous profiling of nuclear proteins, chromatin accessibility and RNA transcripts in single cells.
Fig. 2: Profiling of CD4 memory T cells using NEAT-seq reveals modes of regulation across the central dogma.
Fig. 3: Identification of peak–gene linkages associated with master TF protein expression.

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Data availability

Raw and processed sequencing data generated in this study are available through GEO (GSE178707). Published bone marrow and peripheral blood single-cell ATAC-seq and RNA-seq data were obtained from GSE139369. The CISBP database is available at http://cisbp.ccbr.utoronto.ca/. The Transfac database is available at https://genexplain.com/transfac/. Source data are provided with this paper.

Code availability

Code used for analysis and figures are available at https://github.com/GreenleafLab/NEAT-seq_reproducibility.

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Acknowledgements

We thank R. Baskar, S. Pierce, S. Klemm, Y. Kim and members of the Greenleaf laboratory for helpful discussions and suggestions. We also thank the Stanford Fluorescence Activated Cell Sorting facility and Stanford Functional Genomics Facility for technical support. Figure schematics were created with BioRender.com. This work was supported by funding from the National Institutes of Health (NIH) (grant nos. P50HG007735, R01HG008140, R01HG009909, UM1HG011972, U01MH116529, U54HG010426, U01DK127419, U01HG011762, U19AI057266 and UM1HG009442), the Rita Allen Foundation, the Baxter Foundation Faculty Scholar Grant and the Human Frontiers Science Program (grant no. RGY006S) to W.J.G. W.J.G. is a Chan Zuckerberg Biohub investigator (grant nos. 2017-174468 and 2018-182817). Fellowship support was provided by the Stanford School of Medicine Dean’s Fellowship and NIH (grant no. F32GM135996) to A.F.C. and a training grant from the National Institute of Standards and Technology to B.P.

Author information

Authors and Affiliations

  1. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Amy F. Chen, Benjamin Parks, Arwa S. Kathiria, Benjamin Ober-Reynolds & William J. Greenleaf

  2. Department of Computer Science, Stanford University School of Engineering, Stanford, CA, USA

    Benjamin Parks

  3. Department of Medicine, Palo Alto Veterans Administration Healthcare System, Palo Alto, CA, USA

    Jorg J. Goronzy

  4. Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA, USA

    Jorg J. Goronzy

  5. Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA

    William J. Greenleaf

  6. Department of Applied Physics, Stanford University, Stanford, CA, USA

    William J. Greenleaf

  7. Chan-Zuckerberg Biohub, San Francisco, CA, USA

    William J. Greenleaf

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  1. Amy F. Chen
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Contributions

A.F.C. and W.J.G. conceived the project and designed the experiments with input from all authors. A.F.C. led method development and performed experiments with help from A.S.K. A.F.C. and B.P. performed bioinformatic analysis, visualization and interpretation. J.J.G. provided input on the CD4 memory T cell experiment and interpretation of results. A.F.C. and W.J.G. drafted the manuscript. A.F.C., B.P., B.O.-R. and W.J.G. revised and edited the manuscript with input from all authors.

Corresponding author

Correspondence to William J. Greenleaf.

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Competing interests

A.F.C. and W.J.G. are listed as coinventors on a patent related to this work. 10X Genomics holds the license to other patents in which W.J.G. is listed as an inventor. W.J.G. is an equity holder of 10X genomics and a co-founder of Protillion Biosciences. W.J.G. consults for Guardant Health, Quantapore, Protillion Biosciences, and Ultima Genomics. The remaining authors declare no competing interests.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Staining of nuclear and cytosolic proteins using oligo-antibodies blocked with EcoSSB.

a) Sorting of cells expressing low, mid, or high levels of nuclear GFP that have been stained with an anti-GFP oligo-conjugated antibody. b) Quantitative PCR for the conjugated oligo from equal cell numbers of sorted populations in (a) for n = 2 technical replicates. c) Staining of K562 cells and mouse ESCs for endogenous GATA1 protein using an anti-GATA1 antibody linked to an 80 bp oligo with 3’-Cy5. d) Flow cytometry plot of HEK293T cells expressing cytosolic GFP and stained with an anti-GFP antibody linked to a 100 bp single stranded DNA oligo with 3’-Cy5 modification. e) Flow cytometry plot of nuclear GFP-expressing HEK293T cells with a GFP antibody linked to a Cy5-modified ssDNA oligo using the conditions indicated. “NEAT-seq”: NEAT-seq fixation, permeabilization, and staining conditions using oligo-antibodies pre-incubated with EcoSSB. “Dextran sulfate block”: NEAT-seq fixation and permeabilization conditions with inCITE-seq staining conditions (i.e with dextran sulfate blocking agent). “inCITE-seq”: inCITE-seq fixation, permeabilization, and staining conditions. Spearman correlation is shown.

Extended Data Fig. 2 NEAT-seq species mixing experiment data quality.

a-b) Cutoffs for annotating a cell as human, mouse, or mixed based on percentage of reads in a cell mapping to the human genome for ATAC-seq (a) and RNA-seq (b) data. c) Scatterplot of number of ATAC-seq reads mapping to the human vs mouse genome in each cell prior to removing HTO doublets, with each cell colored by its classification as a human cell, mouse cell, mixed species doublet, or an HTO doublet. d) Same as (c) but for RNA-seq reads. e) Fragment length distribution of ATAC-seq data generated using NEAT-seq. f) Average Tn5 insertions across transcriptional start sites normalized to the flanking region + /− 2 kb from the start site (i.e TSS enrichment) from scATAC-seq data generated using NEAT-seq. g) Log-transformed Tn5 insertions in ATAC-seq peaks for NEAT-seq data vs bulk ATAC-seq data in K562 cells. Pearson correlation is shown. h) A comparison of RNA-seq counts from bulk data vs NEAT-seq in K562 cells (left) or standard 10X Multiome data in GM12878 cells (right). Values are log-transformed TPM (for single cell data) or FPKM (for bulk data). Pearson correlation is shown. i) Western blot of mESC and K562 cell lysate using oligo-conjugated SOX2 antibody pre-incubated with EcoSSB and detected with IR800 secondary antibody (Licor). GAPDH was also probed as a loading control with IR700 secondary antibody. Imaging was performed on a Licor Odyssey imaging system. Images shown are representative results of 2 independent experiments.

Source data

Extended Data Fig. 3 CD4 memory T cell subset annotations.

a) scRNA-seq UMAP of CD4 memory T cells colored with scATAC-seq cluster IDs. TCM = central memory, Act. = recently activated cells, Uncom. = uncommitted memory cells. b) RNA expression of master TF drivers and canonical cell surface markers of CD4 memory cell subsets in each scATAC-seq cluster. TBX21 = Tbet transcript, RORC = RORγT transcript, IKZF2 = Helios transcript. c) Gene accessibility for cytokines induced in different CD4 T cell subsets overlayed on the scATAC-seq UMAP. d) RNA levels for the cytokines in (c). e) chromVAR deviation scores for the naïve and CM T cell TFs, LEF1 and TCF7, overlayed on scATAC-seq UMAP. f) RNA expression of the CM marker, CCR7, overlayed on the scATAC-seq UMAP.

Extended Data Fig. 4 Enrichment of TF ADTs in the T cell subtype driven by the TF.

a) Unsmoothed ADT counts for each TF overlayed on the scATAC-seq UMAP after the indicated normalization method (raw counts, NPC normalization, and total ADT counts normalization). b) Log2-transformed, NPC-normalized ADT counts for each TF separated by scATAC-seq cluster for cells stained with antibody concentration 1 (see methods). c) Same as (b) but for antibody concentration 2. d) Scatterplots with marginal histograms of log2-transformed read-normalized RNA vs log2-transformed NPC-normalized ADT counts for each TF. Colored data points represent cells belonging to the scATAC-seq cluster most enriched in expression of the indicated TF. e) Unsmoothed, normalized RNA counts of the indicated TFs overlayed on the scATAC-seq UMAP.

Extended Data Fig. 5 Correlation of ADT levels with gene locus accessibility, RNA, and motif accessibility for each TF.

a) Correlation across all cells for each measurement. Values were first smoothed across neighboring cells using MAGIC imputation to account for dropouts. Pearson correlations are shown. b) The data in (a) but averaged across cells within each scATAC-seq cluster.

Extended Data Fig. 6 Post-transcriptional regulation of GATA3.

a) Flow cytometry plots of GATA3-transfected and WT HEK293T cells stained with an EcoSSB-bound oligo-conjugated GATA3 antibody, using a fluorescent secondary antibody for detection. b) Log2-transformed RNA levels of the indicated gene in cells expressing high RNA and low protein (“low”) vs high RNA and high protein for GATA3 (“high”). The mean RNA expression for each group is shown above the violin plot. N = 140 cells examined over one independent experiment for both “high” and “low” populations. Boxplots show median with bounds of the box representing the 25th and 75th percentiles and the whiskers extending to the value closest to but not exceeding 1.5 times the interquartile range. Data extending beyond the whiskers are plotted individually as outliers.

Extended Data Fig. 7 Identification of DNA regulatory elements and genes correlated with master TF protein expression.

a) Spearman correlations between ATAC-seq peak accessibility and NPC-normalized TF ADT counts across single cells. Cutoffs for significant correlations are indicated by dashed lines (see Methods). Points in red indicate peaks containing a binding motif for the TF. TF motif enrichment in significantly correlated peaks was calculated using a hypergeometric test. b) Spearman correlations between read-normalized RNA counts and NPC-normalized TF ADT counts across single cells. Cutoffs for significant correlations are indicated by dashed lines (see Methods). Significantly correlated genes known to be enriched or play a functional role in the relevant T cell subset are labeled. c) CCR4 ATAC-seq tracks in CD4 memory cells separated into quintiles by GATA3 ADT levels, along with significantly correlated peak-gene linkages (adj. p < 0.05). Spearman correlations are shown. Peaks containing a GATA3 motif are indicated. d-e) CCR4 RNA expression (d) and accessibility at the highlighted GATA3 motif-containing peak (e) as a function of GATA3 ADT levels. Mean is shown with standard error of the mean of n = 768 + /− 1 cells per group.

Extended Data Fig. 8 GO term enrichment for candidate target genes in TF-driven peak-gene linkages.

Enriched GO terms in the target gene list were identified by hypergeometric test using enrichGO in the clusterProfiler R package, using all genes with at least one RNA count in the dataset as a background gene list. Adjusted p-values were calculated using the Benjamini-Hochberg procedure.

Extended Data Fig. 9 Single-tissue eQTL analysis for the rs62088464-TSEN54 variant-gene pair from GTEx portal.

a) Normalized effect size (NES) across various tissues for the protective (G) vs risk (A) allele on TSEN54 expression. The risk allele preserves the GATA3 motif. A negative NES value indicates a gene with expression that is associated with the risk allele. Error bars indicate 95% confidence intervals. b) Normalized TSEN54 expression grouped by rs62088464 genotype for lung and esophagus mucosa. c) Genes ordered by NES across tissues for rs62088464. P-values for (b) and (c) are outputs from the GTEx portal (release v8) and are calculated from a two-sided t-test comparing the observed NES in a tissue to a null NES of 0.

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Supplementary Notes 1–3 and Protocol.

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Source Data Extended Data Fig. 2

Unprocessed western blot for panel Fig. 2i.

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Chen, A.F., Parks, B., Kathiria, A.S. et al. NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nat Methods 19, 547–553 (2022). https://doi.org/10.1038/s41592-022-01461-y

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