1. Developmental Biology
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Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes

  1. Allen Wang  Is a corresponding author
  2. Joshua Chiou
  3. Olivier B Poirion
  4. Justin Buchanan
  5. Michael J Valdez
  6. Jamie M Verheyden
  7. Xiaomeng Hou
  8. Parul Kudtarkar
  9. Sharvari Narendra
  10. Jacklyn M Newsome
  11. Minzhe Guo
  12. Dina A Faddah
  13. Kai Zhang
  14. Randee E Young
  15. Justinn Barr
  16. Eniko Sajti
  17. Ravi Misra
  18. Heidie Huyck
  19. Lisa Rogers
  20. Cory Poole
  21. Jeffery A Whitsett
  22. Gloria Pryhuber
  23. Yan Xu
  24. Kyle J Gaulton  Is a corresponding author
  25. Sebastian Preissl  Is a corresponding author
  26. Xin Sun  Is a corresponding author
  27. NHLBI LungMap Consortium
  1. Center for Epigenomics & Department of Cellular & Molecular Medicine, University of California, San Diego, United States
  2. Biomedical Sciences Graduate Program, University of California San Diego, United States
  3. Department of Pediatrics, University of California-San Diego, United States
  4. Division of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, United States
  5. Divisions of Pulmonary Biology and Biomedical Informatics, University of Cincinnati College of Medicine, United States
  6. Vertex Pharmaceuticals, United States
  7. Ludwig Institute for Cancer Research, United States
  8. Laboratory of Genetics, Department of Medical Genetics, University of Wisconsin-Madison, United States
  9. Department of Pediatrics and Clinical & Translational Science Institute, University of Rochester Medical Center, United States
  10. Department of Biological Sciences, University of California-San Diego, United States
  11. NIH, United States
Research Article
Cite this article as: eLife 2020;9:e62522 doi: 10.7554/eLife.62522
5 figures, 1 table and 9 additional files

Figures

Figure 1 with 2 supplements
Single-nucleus atlas of chromatin accessibility and transcriptomes in the human lungs.

(A) UMAP (Uniform Manifold Approximation and Projection) embedding (McInnes et al., 2018) and clustering results of snATAC-seq data from 90,980 single-nucleus chromatin profiles from ten donors: premature born (30 weekGA for gestational age, n = 3), 4-month-old (n = 1), three yo (n = 3) and 30 yo (n = 3). For library quality control see Figure 1—figure supplement 1A–D. (B) Dot plot of marker genes from snRNA-seq used for cluster annotation. For additional genes see Figure 1—figure supplement 2A. (C) UMAP embedding (McInnes et al., 2018) and clustering result of 46,500 snRNA-seq data from nine donors: premature born (30 weekGA), three yo, 30 yo, n = 3 per time point, identifies 31 clusters. Each dot represents a nucleus. Spread-out gray dots correspond to nuclei of unclassified cells. For library quality control see Figure 1—figure supplement 1E–H. (D) Dot plot of marker genes from snRNA-seq used for cluster annotation. For additional genes see Figure 1—figure supplement 2B.

Figure 1—figure supplement 1
Quality control of snATAC-seq and snRNA-seq datasets.

(A) Nuclei with less than 1000 uniquely mapped reads were filtered from snATAC-seq datasets. (B) Number of nuclei selected for snATAC-seq libraries after quality control and removing potential doublets. (C) Average number of reads per nucleus. (D) Fraction of reads in peak regions per dataset. (E) Representative UMI barcode distribution output from CellRanger pipeline (10x Genomics) for snRNA-seq libraries from human lungs. (F) Sequencing saturation of snRNA-seq libraries. (G) Number of nuclei selected for snRNA-seq libraries after quality control and removing potential doublets. (H) Genes detected per nucleus. All data are represented as mean ± SD.

Figure 1—figure supplement 2
Expression and chromatin accessibility at marker gene loci used for annotation.

(A) Dot plot of marker genes from snATAC-seq used for cluster annotation. Extension of dot plot in Figure 1B (B) Dot plot of marker genes from snRNA-seq used for cluster annotation. Extension of dot plot in Figure 1D.

Figure 2 with 1 supplement
snATAC-seq analysis of human lungs reveals candidate cis-regulatory elements for ACE2 and TMPRSS2.

(A) Dot plot illustrating cluster-specific gene expression of candidate SARS-CoV-2 cell entry genes. For violin plots illustrating cluster-specific gene expression please see Figure 2—figure supplement 1A–E. (B) Dot plot illustrating cluster-specific gene body chromatin accessibility of candidate SARS-CoV-2 cell entry genes. (C) Union set of peaks (vertical lines) identified in all clusters surrounding ACE2 and 15 peaks that showed co-accessibility with the ACE2 promoter (red lines, co-accessibility score >0.05) via Cicero (Cusanovich et al., 2018). (D) Zoom into ACE2 locus and genome browser tracks of snATAC-seq signal (Robinson et al., 2011). ACE2 promoter region highlighted by red box. (E) Union set of peaks (vertical lines) identified in all clusters surrounding TMPRSS2 and 73 peaks that showed co-accessibility with the TMPRSS2 promoter (red lines, co-accessibility score >0.05) via Cicero (Cusanovich et al., 2018). (F) Zoom into TMPRSS2 locus and genome browser tracks of snATAC-seq signal (Robinson et al., 2011). TMPRSS2 promoter region highlighted by red box. For genome browser tracks of BSG, FURIN, CTSL please see Figure 2—figure supplement 1F.

Figure 2—figure supplement 1
Gene expression and chromatin accessibility for SARS-COV-2 cell entry genes.

(A-E) Cluster-specific violin plots of gene expression of (A) ACE2, (B) TMPRSS2, (C) CTSL, (D) FURIN and (E) BSG. (F) Genome browser (Robinson et al., 2011) representation of snATAC-seq signal at SARS-COV-2 cell entry gene loci CTSL, BSG, FURIN.

Figure 3 with 1 supplement
Age-increasing gene expression and accessible chromatin in AT2 cells exhibits signatures of immune regulation and harbors TMPRSS2-linked sites of chromatin accessibility.

(A) Differential analysis was performed on AT2 cells between three ages with replicates (n = 3 per stage). (B) Fraction of AT2 cells with expression of ACE2 (left) and TMPRSS2 (right) in 30wkGA, 3yo and 30yo human lung samples. All data are represented as mean ± SD. p values derived from unpaired, two-tailed t-tests. For expression data of BSG, CTSL, FURIN please see Figure 3—figure supplement 1A. (C) Log normalized expression of TMPRSS2 in AT2 cells. Displayed are median expression values for AT2 cells in individual samples with at least 1 UMI (unique molecular identifier). (D) Differential analysis was performed on AT2 cells using pairwise comparisons between three ages with replicates (n = 3 per stage). (E) K-means cluster analysis (K = 5) of relative accessibility scores (see Materials and methods) for 22,845 age-dynamic peaks (FDR < 0.05, EdgeR) (Robinson et al., 2010) in AT2 cells. Clusters III and IV show increasing accessibility with age and contain nine TMPRSS2-co-accessible sites. (F) GREAT (McLean et al., 2010) analysis of elements in group cIII (left panel) and cIV (right panel) shows enrichment of immune-related gene ontology terms. (G) Transcription factor motif enrichment analysis of elements in cIII and cIV. (H) K-means cluster analysis (K = 6) of TMPRSS2-co-accessible sites based on the relative percentage of AT2 cells with at least one fragment overlapping each peak. Red bars indicate dynamic peaks identified from pair-wise differential analysis (FDR < 0.05, EdgeR) (Robinson et al., 2010). (I) Locus restricted differential analysis of TMPRSS2-linked peaks with increased accessibility in AT2 with age (top panel in 3H). Data are represented as mean ± SD. Black asterisk, p<0.05 (independent t-test); Red asterisk, FDR < 0.05 (EdgeR) (Robinson et al., 2010) from dynamic peak analysis. For additional sites and promoter accessibility of TMPRSS2 please see Figure 3—figure supplement 1B,C. (J) Genome browser representation of four TMPRSS2-linked peaks across age groups (Robinson et al., 2011).

Figure 3—source data 1

Normalized expression values for TMPRSS2 in AT2 cells.

https://cdn.elifesciences.org/articles/62522/elife-62522-fig3-data1-v2.xlsx
Figure 3—figure supplement 1
Gene expression of additional SARS-COV-2 cell entry genes and chrmatin accessibility of peak linked to TMPRSS2 during aging.

(A) Fraction of AT2 cells with expression of BSG, CTSL and FURIN in 30wkGA,3yo and 30yo human lung samples. All data are represented as mean ± SD. (B) Promoter accessibility of TMPRSS2 in AT2 cells in 30wkGA, 3yo, and 30yo human lung samples. (C) Fraction of cells with accessibility at peaks co-accessible with TMPRSS2, which did not show increase with age. Data are represented as mean ± SD. Red asterisks denote FDR < 0.05 (EdgeR) and black asterisks denote p<0.05 via independent t-test.

Genetic variants predicted to affect age-increasing AT2 accessible chromatin are associated with respiratory phenotypes and TMPRSS2 expression.

(A) Top: genome browser view of AT2 sites linked to TMPRSS2 activity and those with age-dependent increase in accessibility. Middle: Number of non-singleton genetic variants in gnomAD v3 mapping (Karczewski et al., 2019) in each age-dependent site predicted to disrupt binding of AT2-enriched TF motifs. Bottom: Common variants (minor allele frequency >0.05 in at least one population) predicted to bind AT2-enriched TF motifs, color-coded by TF family. Motif-bound variants that also have predicted effects (FDR < 0.10) on AT2 accessible chromatin in deltaSVM models (Lee et al., 2015) highlighted in red. (B) Association of common variants with predicted AT2 effects (motif-disrupting+deltaSVM) with human phenotypes in the UK Biobank (Lab, 2020). The majority of tested variants show at least nominal evidence (p<0.005) for association with phenotypes related to respiratory disease, infection and/or medication. (C) Fine-mapping probabilities for an TMPRSS2 expression QTL in human lung samples from the GTEx project release v8 (GTEx Consortium, 2020). The variant rs35074065 has the highest casual probability (PPA = 0.42) for the eQTL, maps in an age-dynamic AT2 site and is predicted to disrupt binding of IRF and STAT TFs. Variants are colored based on r2 with rs35074065 in 1000 Genomes Project data using all populations (Auton et al., 2015). (D) Estimated cell type proportions for 515 human lung samples from GTEx derived using cell-type-specific expression profiles for cell types with more than 500 cells from snRNA-seq data generated in this study. (E) Association p-values between rs35074065 genotype and TMPRSS2 lung expression after including an interaction term between genotype and estimated cell-type proportions for each sample. We observed stronger eQTL association when including an interaction with AT2 cell proportion as well as macrophage proportion.

Figure 5 with 1 supplement
Fine-mapped risk variants at the 3p21.31 locus associated with respiratory failure in SARS-CoV-2 overlap lung cell-type chromatin sites.

(A) Genome browser view (Robinson et al., 2011) showing posterior probability (PPA) of variants in the fine-mapping credible set at the 3p21.31 association signal and lung cell-type-specific accessible chromatin profiles. Credible set variants that directly overlap lung cell-type chromatin sites are highlighted. Read depth values represent counts per million (CPM). (B) Variant rs17713054 overlaps a site active in AT2 and other epithelial cells and bound by CEBPB among other TFs, and the minor allele A is predicted to bind a CEBP motif. For in-depth analysis of rs76374459 see Figure 5—figure supplement 1. Read depth values represent counts per million (CPM). (C) Co-accessible links between the site harboring rs17713054 (blue box) and other chromatin sites, including the promoter regions of four genes SLC6A20, LIMD1, SACM1L and CCRL2 (gray box). The height of each link represents the strength of co-accessibility (Cusanovich et al., 2018). (D) Expression of SLC6A20 across lung cell types, where each dot represents a nucleus. The highest expression observed was in AT2 cells. (E) Association p-values between rs17713054 genotype and SLC6A20 lung expression after including an interaction term between genotype and estimated cell-type proportions for each sample. We observed strongest eQTL association when including an interaction with AT1/AT2-like and AT2 cell proportion.

Figure 5—figure supplement 1
Molecular characterization of variant rs76374456.

Variant rs76374456 overlaps a cCRE accessible in erythrocytes which is bound by SPI1 among other TFs, and the minor allele C is predicted to disrupt a SPI1 motif. Read depth values represent counts per million (CPM).

Tables

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
 Peptide, recombinant proteinTn5doi: https://doi.org/10.1101/615179
 Chemical compound, drugNEBNext High-Fidelity 2 × PCR Master MixNEBCat# M0541L
 Chemical compound, drugRNasin Ribonuclease InhibitorPromegaCat# N211B
 Chemical compound, drugDRAQ7Cell SignalingCat# 7406
 Commercial assay or kitChromium Single Cell 3′ Library Construction Kit v310x GenomicsCat# 1000075
 Commercial assay or kitChromium Single-Cell B Chip Kit10x GenomicsCat# 1000153
 Commercial assay or kitChromium i7 Multiplex Kit, 96 rxns10x GenomicsCat# 120262
 Chemical compound, drugSPRISelect reagentBeckman CoulterCat# B23319
 Software, algorithmCell Ranger software package v3.0.210x Genomics (https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest)Software
 Software, algorithmSeurat v3.1.4https://satijalab.org/seurat/ doi:10.1016/j.cell.2019.05.031RRID:SCR_016341
 Software, algorithmDoubletFinderhttps://github.com/chris-mcginnis-ucsf/DoubletFinder
doi:10.1016/j.cels.2019.03.003
RRID:SCR_018771
 Software, algorithmGraphPad Prism version 8.0.0www.graphpad.comRRID:SCR_002798
 Software, algorithmTrim galore
(v.0.4.4)
https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/RRID:SCR_011847
 Software, algorithmBWA
(v.0.7.1)
http://bio-bwa.sourceforge.net/ doi:10.1093/bioinformatics/btp324RRID:SCR_010910Software
 Software, algorithmSamtools
(v. 1.10)
http://www.htslib.org/ doi:10.1093/bioinformatics/btp352RRID:SCR_002105Software
 Software, algorithmPicardhttp://broadinstitute.github.io/picard/RRID:SCR_006525Software
 Software, algorithmscanpy
(v.1.4.4.post1)
https://github.com/theislab/scanpyRRID:SCR_018139Software
 Software, algorithmHarmony (v. 0.1.0)https://github.com/immunogenomics/harmony doi:10.1038/s41592-019-0619-0Software
 Software, algorithmCicero (v. 1.4.4)https://github.com/cole-trapnell-lab/cicero-release doi:10.1016/j.molcel.2018.06.044Software
 Software, algorithmliftOverhttps://genome.ucsc.edu/cgi-bin/hgLiftOverRRID:SCR_018160Software
 OthergnomAD v3http://gnomad.broadinstitute.org/ doi:10.1038/s41586-020-2308-7RRID:SCR_014964Database
 OtherJASPAR 2020http://jaspar.genereg.net doi:10.1093/nar/gkz1001RRID:SCR_003030Database
 Software, algorithmFIMO (v. 4.12.0)http://meme-suite.org/ doi:10.1093/bioinformatics/btr064RRID:SCR_001783Software
 Software, algorithmdeltaSVMhttp://www.beerlab.org/deltasvm/ doi:10.1038/ng.3331
 Software, algorithmMuSiC (v.0.1.1)https://github.com/xuranw/MuSiC doi:10.1038/s41467-018-08023-x
 Software, algorithmPythonhttps://www.python.org/RRID:SCR_008394
 Software, algorithmR (v.3.5.1)https://www.r-project.org/RRID:SCR_001905
 Software, algorithmGo (v. 1.12.1)https://golang.org/RRID:SCR_017096
 Software, algorithmNumPy (v.1.16.1)https://numpy.org/RRID:SCR_008633python library
 Software, algorithmScikit-learn (v. 0.20.1)https://scikit-learn.org/stable/RRID:SCR_002577python library
 Software, algorithmseaborn (v. 0.9.0)https://seaborn.pydata.org/api.htmlRRID:SCR_018132python library
 Software, algorithmMatPlotLib (v.0.9.0)http://matplotlib.sourceforge.netRRID:SCR_008624python library
 Software, algorithmATACdemultiplex (v. 0.46.12)https://gitlab.com/Grouumf/ATACdemultiplex/suite of softwares written in GO for snATAC analysis
 Software, algorithmedgeR (v. 3.22.5)http://bioconductor.org/packages/release/bioc/html/edgeR.html doi:10.1093/bioinformatics/btp616RRID:SCR_012802R library
 Software, algorithmMatrix (v.1.2–15)https://cran.r-project.org/web/packages/Matrix/index.htmlR library
 Software, algorithmStringr (v. 1.4.0)https://www.rdocumentation.org/packages/stringr/versions/1.4.0R library
 Software, algorithmCicero (v. 1.0.14)https://www.bioconductor.org/packages/release/bioc/html/cicero.html doi:10.1016/j.molcel.2018.06.044R library
 Software, algorithmHOMER (v4.11.1)http://homer.ucsd.edu/homer/download.html doi:10.1016/j.molcel.2010.05.004RRID:SCR_010881Perl package
 Software, algorithmrGREAT (v. 1.20)https://www.bioconductor.org/packages/release/bioc/html/rGREAT.html for GREAT: doi:10.1038/nbt.1630for GREAT:
RRID:SCR_005807
R library

Additional files

Supplementary file 1

Donor metadata tables.

Sheet 1: 30wkGA - 30yo: Donor ID, age, sex, race, clinical pathology diagnosis (clinPathDx), gestational age, overall quality of the lung tissue assessment, type of death and cause of death were listed. Not shown are data on body weight, body height, total lung weight and radial alveolar count assessment of alveolarization. All were all within normal limits for age. Abbreviations: DCD: donor after cardiac death; DBD: donor after brain death; GA: gestational age; RDS: respiratory distress syndrome.

https://cdn.elifesciences.org/articles/62522/elife-62522-supp1-v2.xlsx
Supplementary file 2

Summary statistics for sequencing libraries.

https://cdn.elifesciences.org/articles/62522/elife-62522-supp2-v2.xlsx
Supplementary file 3

Cluster composition and number and fraction of nuclei expressing candidate for SARS-CoV2 cell entry.

https://cdn.elifesciences.org/articles/62522/elife-62522-supp3-v2.xlsx
Supplementary file 4

Annotation of peaks co-accessible with candidate genes for SARS-CoV2 cell entry and age-associated changes of chromatin accessibility of peaks co-accessible with TMPRSS2 promoter.

https://cdn.elifesciences.org/articles/62522/elife-62522-supp4-v2.xlsx
Supplementary file 5

GREAT analysis of peaks increasing with age in AT2 cells (groups cIII and cIV in Figure 3F).

https://cdn.elifesciences.org/articles/62522/elife-62522-supp5-v2.xlsx
Supplementary file 6

De novo motif enrichment analysis of peaks increasing with age in AT2 cells (groups cIII and cIV in Figure 3F).

https://cdn.elifesciences.org/articles/62522/elife-62522-supp6-v2.xlsx
Supplementary file 7

Genetic variants with predicted functional effects on sites linked to TMPRSS2.

https://cdn.elifesciences.org/articles/62522/elife-62522-supp7-v2.xlsx
Supplementary file 8

Indexes and primer sequences for snATAC-seq libraries.

https://cdn.elifesciences.org/articles/62522/elife-62522-supp8-v2.xlsx
Transparent reporting form
https://cdn.elifesciences.org/articles/62522/elife-62522-transrepform-v2.docx

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