Abstract
To better define the control of immune system regulation, we generated an atlas of microRNA (miRNA) expression from 63 mouse immune cell populations and connected these signatures with assay for transposase-accessible chromatin using sequencing (ATAC–seq), chromatin immunoprecipitation followed by sequencing (ChIP–seq) and nascent RNA profiles to establish a map of miRNA promoter and enhancer usage in immune cells. miRNA complexity was relatively low, with >90% of the miRNA compartment of each population comprising <75 miRNAs; however, each cell type had a unique miRNA signature. Integration of miRNA expression with chromatin accessibility revealed putative regulatory elements for differentially expressed miRNAs, including miR-21a, miR-146a and miR-223. The integrated maps suggest that many miRNAs utilize multiple promoters to reach high abundance and identified dominant and divergent miRNA regulatory elements between lineages and during development that may be used by clustered miRNAs, such as miR-99a/let-7c/miR-125b, to achieve distinct expression. These studies, with web-accessible data, help delineate the cis-regulatory elements controlling miRNA signatures of the immune system.
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Data availability
Data that support the findings of this study are available on the ImmGen website (www.immgen.org), and raw and processed miRNA expression data, including sample metadata, are available at the GEO (accession no. GSE144081). Additionally, tables with ATAC–seq signal, P values and peak locations with previously blacklisted peaks included are provided (Supplementary Data 3). Sequence Read Archive ID and other sample information for downloaded ChIP–seq and nascent RNA datasets can be found in Supplementary Table 12. Processed histone mark and nascent RNA data are available in Supplementary Data 1 and 2, respectively. Source data for all figures in this manuscript are provided. External mRNA-seq and ATAC–seq data were downloaded through the ImmGen website. miRNA promoter annotations were downloaded from the supplements of their respective studies cited in the text or the GENCODE database (https://www.gencodegenes.org/mouse/). TAD boundary data were downloaded from Johanson et al.35. miRNA conservation and other information was downloaded from TargetScan v.7 (www.targetscan.org). ENCODE blacklist regions for mm10 were downloaded from https://sites.google.com/site/anshulkundaje/projects/blacklists. rRNA sequence and sca/snoRNA loci were retrieved from iGenomes (https://support.illumina.com/sequencing/sequencing_software/igenome.html) and RFAM v.14.2 (https://rfam.xfam.org/), respectively. CAGE peaks from the FANTOM5 consortium were downloaded from their website (http://fantom.gsc.riken.jp/5/datafiles/latest/extra/CAGE_peaks/). phastCons conservation scores were downloaded from http://hgdownload.cse.ucsc.edu/goldenpath/mm10/phastCons60way. Source data are provided with this paper.
Code availability
Custom code used in analysis will be made available upon request. Code for normalization and batch correction of qPCR data is available at https://github.com/srose89/ImmGen-miRNA.
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Acknowledgements
We thank all members of the ImmGen Consortium for their comments and advice throughout this work and for their critical feedback on the manuscript, especially C. Benoist (Harvard), L. Lanier (UCSF), S. Nutt (WEHI), P. Monach (Boston U), S. Turley (Genetech) and D. Hasson (Mount Sinai). B.D.B. was supported by the NIH (nos. R01AI113221 and R01AT011326), the Cancer Research Institute and the Alliance for Cancer Gene Therapy. M.M. was supported by the NIH (nos. R01CA257195 and R01CA254104), and S.A.R. by no. T32AI007605. J.D.B. acknowledges support from the NIH Director’s New Innovator Award (no. DP2HL151353). This work was also supported by the NIH (no. R24AI072073) to the ImmGen Consortium.
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S.A.R. designed and performed experiments, analyzed data and wrote the manuscript. A.W. and M.D. performed experiments and edited the manuscript. H.Y. analyzed data and edited the manuscript. B.B.-Z. and A.B. provided technical assistance. J.M.S. carried out qPCR profiling. A.R., E.Y.K., B.Y. and Y.L. provided samples. M.M. designed and supervised the research, analyzed data and edited the manuscript. J.D.B. analyzed data and edited the manuscript. B.D.B. designed and supervised the research, analyzed data and wrote the manuscript.
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J.M.S. is an employee and stockholder of Qiagen Sciences. J.D.B. holds patents related to ATAC–seq and is on the scientific advisory boards of Camp4, Seqwell, and Celsee. The remaining authors declare no competing interests.
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Peer review information Nature Immunology thanks Musa Mhlanga and Massimiliano Pagani for their contribution to the peer review of this work. Jamie D. K. Wilson and Laurie Dempsey were the primary editors 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 Determination of miRNA abundance thresholds.
a, qPCR standard curve generated for 10 different miRNAs using synthetic RNA mimics. Plotted are copies of synthetic mature miRNA species input into qPCR reaction against the corresponding Ct value. Horizontal grey dashed line marks a Ct value of 28. b, Representative scatterplot of Ct values for two PC macrophage replicates against each other. c, Schematic diagram of the in vivo miRNA sensor assay. Lineage negative cells were isolated from CD45.1+ C57BL/6 mice, transduced with lentiviral-based sensors for miR-24-3p or miR-652-3p, or a control vector, and transplanted in to lethally irradiated CD45.2+ mice. After 8 weeks, cells from the spleen and peritoneal cavity were collected, stained for immune cell markers, and NGFR, and analyzed by flow cytometry. d, Representative flow plots showing logarithmic fluorescence intensities for NGFR and GFP from mice that received a sensor for the indicated microRNA. Values are the percent of NGFR+ GFP+ cells in the total population. Accompanying integer values for each plot represent MFI of GFP of all NGFR+ cells within the plot. e, Correlation between miRNA sensor suppression and miRNA expression in 7 cell types from the 11-cell set. Percent suppression was calculated as one minus the target miRNA GFP/NGFR median fluorescence intensity divided by the average of the same ratio for all 3 non-targeting control replicates in a given cell type. Error bars represent the standard error of the mean (miR-24-3p n = 2 mice; miR-652-3p n = 4 mice). f, Cumulative percent of total linear abundance within a given cell type compared to the number of miRNAs added in decreasing order of expression.
Extended Data Fig. 2 miRNA abundance patterns in T cells and after perturbation.
a, Pearson correlation of the 11-cell immune subset and lymph node stroma cells based on miRNA expression signatures. Expression data was filtered on miRNAs that are high-abundance (>32 AU) in at least one cell type. Only correlations between samples >0.7 are plotted. b, Htr2c read counts in ImmGen cis-Atlas samples. c, Percent linear abundance of the 15 most highly expressed miRNA and miRNA families in T cell subsets and DP thymocytes cells. Bars are shaded by Z-score value of the miRNA family across populations. d, miRNAs changing consistently in 3 or more perturbation conditions not highlighted in Fig. 2d. (limma two-sided unadjusted p<0.05, log2 FC > 1, and expression >4AU in perturbed or >32AU in steady-state population; n = 2 for all activated and stimulated populations except: NK.Sp = 5, NKT = 5, B1ab = 5).
Extended Data Fig. 3 Characterization of miRNA associate cis-elements.
a, Table displaying the number of pre-miRNAs (having an expressed mature isoform) with promoter annotations after aggregation, broken down by TargetScan V7 conservation category. b, The number of annotation sources from compiled studies annotating a particular OCR as a promoter region. c, Table displaying the alignment of cell types from externally downloaded datasets with ImmGen miRNA and ATAC-seq populations for integrative analysis. † = BM monocyte miRNA profile only used for H3K27ac signal to miRNA expression correlation. ‡ = NK cells were not part of 22 overlapping cell types used for miRNA to ATAC-seq correlations. d, Distance from significantly correlated OCRs to an annotated miRNA promoter/TSS. e, Percent of correlated OCRs within the same TAD as the promoter for the same miRNA according to TAD definitions in 4 listed cell types from Johanson et al. f, Frequency of miRNAs with different numbers of significantly correlated OCRs. g, Unaligned additional datasets incorporated in promoter and enhancer analyses. h, log10 ATAC-seq signal compared with log10 H3K27ac signal at associated distal elements across the 6 fully aligned populations in c. i, Fraction of associated DEs in either direction of effect above or below high-abundance miRNAs in the 6 fully aligned populations meeting various molecular criteria of active enhancer elements. Bars from left to right represent the number of accessible putative DEs of total possible for expressed miRNAs, the number of accessible putative DEs marked with H3K4me1 or H3K27ac, and the number of accessible putative DEs marked with H3K27ac and with nascent RNA transcripts detected.
Extended Data Fig. 4 Histone mark and nascent RNA visualization at select miRNA loci.
a,b, IGV plot of layered available molecular information at miR-142 (a) and miR-21a (b) loci in splenic B cells and RAW 264.7 macrophages. Correlated DEs from Fig. 3a and regions selected for luciferase reporter assays displayed in Fig. 3b are labeled. Lanes are normalized individually. Promoter regions are shaded in gray for all panels. c, Representative read pile-up tracks of ATAC-seq signal, all normalized to same scale, showing differential cis-element accessibility at the miR-223 locus in select cells. Peak highlighted in gray is the pri-miR-223 promoter region and peaks highlighted in light blue are correlated elements with miR-223 expression from analysis in Fig. 3a. d, H3K27ac and H3K4me1 read pile-ups in aligned histone mark populations at the miR-223 locus. Tracks are normalized by histone mark. e, IGV plots of histone mark and nascent RNA signatures at associated distal elements in CD4+ T cells at the miR-146a locus. e1 corresponds to the enhancer site targeted by flanking sgRNAs in Fig. 3d. Lanes are normalized individually.
Extended Data Fig. 5 Histone marks and nascent RNA support promoter additivity.
a, Fraction of mature miRNAs (includes duplicated) with a given number of promoter regions, colored by TargetScan conservation categories. b, miRNA log2 expression compared to its number of ‘active’ promoter regions across the genome using the 6 aligned populations with chromatin mark, nascent RNA, accessibility, and miRNA expression measurements. Active promoters are defined as accessible by ATAC-seq, presence of H3K4me3, and a nascent transcript detected initiating from the promoter region and spanning the miRNA. (n = 6 populations) (c) Aligned dataset read pile-ups and de novo nascent transcript calls at the miR-29a/b-1 locus in BMDMs and MEFs illustrating multiple promoter use. All tracks are normalized independently. d, Number of active promoters for each expressed miRNA across the 6 aligned populations with or without histone mark and nascent RNA criteria.
Extended Data Fig. 6 Step-wise regression at multi-promoter loci.
a, Individual promoter region associations to miRNA expression at multi-promoter loci. Each point represents the strength and direction of association from a promoter region accessibility to expression step-wise regression at a multi-promoter miRNA loci, plotted against the miRNA to promoter region distance relative to the most distal promoter. Gray dotted lines indicate p value of 0.05. Only associations with p<0.1 are labeled with text. b, Stepwise regression associations for each multi-promoter miRNA with a significant association. Each locus is labeled with the miRNA, coordinates, and host-gene if available. Arrows indicate the most distal position for each promoter region in the locus. Boxes indicate an annotated host-transcript isoform TSS. Height of bars over promoter regions represents the signed –log10 p value in the stepwise regression using promoter accessibility as a predictor of miRNA expression.
Extended Data Fig. 7 Promoter accessibility in multi-copy miRNAs.
a, ATAC-seq, GRO-seq, and H3K4me3 ChIP-seq data at miR-199 loci in FRC.SLN cells and MEFs. b, Heatmap of OCR accessibility at TSS/promoter regions for 14 select duplicated miRNAs with promoter annotations at both loci. Clear boxes represent OCRs not detected above background. For each locus, if the miRNA is on the positive strand the promoters are ordered from furthest to closest going left to right. The opposite is true for miRNAs on the negative strand.
Extended Data Fig. 8 miR-128 and miR-125b promoter activities.
a, Number of open merged promoter regions compared to log2 AU miRNA expression for miR-128-3p across 22 overlapping miRNA and ATAC-seq samples. (n = 22 populations) (b) ImmGen miRNA Browser view of miR-128-3p expression across B and T cells. c, GRO-seq read pile-ups in pro-B cells and mature B cells at the miR-128-2 locus. Active promoter in progenitor cells highlighted in gray. d, Heatmap of pairwise Manhattan distance values between promoter regions of miR-125b-1 and miR-125b-2. Promoter numbers correspond to Fig. 3e,f. e, GRO-seq read pile-ups normalized within each row across selected cell types at the miR-125b-2 locus. Promoter regions highlighted in gray.
Supplementary information
Supplementary Information
Representative sort reports for selected myeloid populations.
Supplementary Tables
Supplementary Tables 1–12.
Supplementary Data 1
Histone mark ChIP–seq quantification at ATAC–seq-defined peak locations in public data from immune populations.
Supplementary Data 2
Nascent RNA transcripts called in public data from immune populations.
Supplementary Data 3
ATAC–seq OCR signals, P values and peak locations, including blacklisted peaks.
Source data
Source Data Fig. 1
miRNA expression matrix.
Source Data Fig. 2
miRNA specificity scores and differential expression.
Source Data Fig. 3
ATAC–miRNA correlations, luciferase assays and miR-146a qPCR.
Source Data Fig. 4
miRNA annotations and additivity tests, and miR-21a promoter knockout data.
Source Data Fig. 5
Duplicated miRNA promoter accessibility.
Source Data Extended Data Fig. 1
miRNA qPCR standard curve and miRNA sensor suppression data.
Source Data Extended Data Fig. 2
Immune population correlation based on miRNA expression, Htr2c expression and perturbation differential expression tests.
Source Data Extended Data Fig. 3
miRNA cis-element annotation and characterization with histone mark and TAD boundaries.
Source Data Extended Data Fig. 4
Luciferase assay insert coordinates.
Source Data Extended Data Fig. 5
Integrated histone mark, ATAC–seq and nascent RNA data used to study promoter use.
Source Data Extended Data Fig. 6
Multipromoter loci stepwise regression statistics.
Source Data Extended Data Fig. 7
Duplicated miRNA promoter accessibilities.
Source Data Extended Data Fig. 8
miR-128-3p expression and miR-125b promoter accessibilities.
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Rose, S.A., Wroblewska, A., Dhainaut, M. et al. A microRNA expression and regulatory element activity atlas of the mouse immune system. Nat Immunol 22, 914–927 (2021). https://doi.org/10.1038/s41590-021-00944-y
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DOI: https://doi.org/10.1038/s41590-021-00944-y
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