Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

A microRNA expression and regulatory element activity atlas of the mouse immune system

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The miRNome of immune cells.
Fig. 2: Population and cell type miRNA signatures.
Fig. 3: miRNA distal element discovery by OCR and expression associations.
Fig. 4: Additive effects of multiple miRNA promoters.
Fig. 5: Chromatin accessibility patterns at multi-promoter and duplicated miRNAs.

Similar content being viewed by others

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.

References

  1. Bartel, D. P. Metazoan microRNAs. Cell 173, 20–51 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Mehta, A. & Baltimore, D. MicroRNAs as regulatory elements in immune system logic. Nat. Rev. Immunol. 16, 279–294 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. O’Connell, R. M., Rao, D. S., Chaudhuri, A. A. & Baltimore, D. Physiological and pathological roles for microRNAs in the immune system. Nat. Rev. Immunol. 10, 111–122 (2010).

    Article  PubMed  Google Scholar 

  4. Montagner, S., Dehó, L. & Monticelli, S. MicroRNAs in hematopoietic development. BMC Immunol. 15, 14 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kuchen, S. et al. Regulation of microRNA expression and abundance during lymphopoiesis. Immunity 32, 828–839 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mildner, A. et al. Mononuclear phagocyte miRNome analysis identifies miR-142 as critical regulator of murine dendritic cell homeostasis. Blood 121, 1016–1027 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Landgraf, P. et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Monticelli, S. et al. MicroRNA profiling of the murine hematopoietic system. Genome Biol. 6, R71 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Basso, K. et al. Identification of the human mature B cell miRNome. Immunity 30, 744–752 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wu, H. et al. miRNA profiling of naïve, effector and memory CD8 T cells. PLoS ONE 2, e1020 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Agudo, J. et al. The miR-126-VEGFR2 axis controls the innate response to pathogen-associated nucleic acids. Nat. Immunol. 15, 54–62 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fehniger, T. A. et al. Next-generation sequencing identifies the natural killer cell microRNA transcriptome. Genome Res. 20, 1590–1604 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fukao, T. An evolutionarily conserved mechanism for microRNA-223 expression revealed by microRNA gene profiling. Cell 129, 617–631 (2007).

    Article  CAS  PubMed  Google Scholar 

  15. Fazi, F. et al. A minicircuitry comprised of microRNA-223 and transcription factors NFI-A and C/EBPα regulates human granulopoiesis. Cell 123, 819–831 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Taganov, K. D., Boldin, M. P., Chang, K. J. & Baltimore, D. NF-κB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc. Natl Acad. Sci. USA 103, 12481–12486 (2006).

  17. Ye, Z. et al. Regulation of miR-181a expression in T cell aging. Nat. Commun. 9, 3060 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kirigin, F. F. et al. Dynamic microRNA gene transcription and processing during T cell development. J. Immunol. 188, 3257–3267 (2012).

  19. Georgakilas, G. et al. microTSS: accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs. Nat. Commun. 5, 5700 (2014).

  20. Chang, T. C., Pertea, M., Lee, S., Salzberg, S. L. & Mendell, J. T. Genome-wide annotation of microRNA primary transcript structures reveals novel regulatory mechanisms. Genome Res. 25, 1401–1409 (2015).

  21. Marson, A. Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells. Cell 134, 521–533 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. de Rie, D. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat. Biotechnol. 35, 872–878 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Suzuki, H. I., Young, R. A. & Sharp, P. A. Super-enhancer-mediated RNA processing revealed by integrative microRNA network analysis. Cell 168, 1000–1014 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mestdagh, P. et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (mirQC) study. Nat. Methods 11, 809–815 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Jayaprakash, A. D., Jabado, O., Brown, B. D. & Sachidanandam, R. Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res. 39, e141 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Giraldez, M. D. et al. Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling. Nat. Biotechnol. 36, 746–757 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Brown, B. D. et al. Endogenous microRNA can be broadly exploited to regulate transgene expression according to tissue, lineage and differentiation state. Nat. Biotechnol. 25, 1457–1467 (2007).

    Article  CAS  PubMed  Google Scholar 

  28. Cho, S. et al. miR-23 approximately 27 approximately 24 clusters control effector T cell differentiation and function. J. Exp. Med. 213, 235–249 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Trifari, S. et al. MicroRNA-directed program of cytotoxic CD8+ T-cell differentiation. Proc. Natl Acad. Sci. USA 110, 18608–18613 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. O’Connell, R. M., Rao, D. S. & Baltimore, D. microRNA regulation of inflammatory responses. Annu. Rev. Immunol. 30, 295–312 (2012).

    Article  PubMed  Google Scholar 

  31. Rodríguez-Galán, A., Fernández-Messina, L. & Sánchez-Madrid, F. Control of immunoregulatory molecules by miRNAs in T cell activation. Front. Immunol. 9, 2148 (2018).

  32. He, M. et al. Cell-type-based analysis of microRNA profiles in the mouse brain. Neuron 73, 35–48 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yoshida, H. et al. The cis-regulatory atlas of the mouse immune system. Cell 176, 897–912 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Harrow, J. et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 22, 1760–1774 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Johanson, T. M. et al. Transcription-factor-mediated supervision of global genome architecture maintains B cell identity. Nat. Immunol. 19, 1257–1264 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Bouvy-Liivrand, M. et al. Analysis of primary microRNA loci from nascent transcriptomes reveals regulatory domains governed by chromatin architecture. Nucleic Acids Res. 45, 12054 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ozsolak, F. et al. Chromatin structure analyses identify miRNA promoters. Genes Dev. 22, 3172–3183 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ribas, J. et al. A novel source for miR-21 expression through the alternative polyadenylation of VMP1 gene transcripts. Nucleic Acids Res. 40, 6821–6833 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ruan, Q. et al. MicroRNA-21 regulates T-cell apoptosis by directly targeting the tumor suppressor gene Tipe2. Cell Death Dis. 5, e1095 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. O’Connell, R. M. et al. MicroRNAs enriched in hematopoietic stem cells differentially regulate long-term hematopoietic output. Proc. Natl Acad. Sci. USA 107, 14235–14240 (2010).

  41. Emmrich, S. et al. miR-99a/100~125b tricistrons regulate hematopoietic stem and progenitor cell homeostasis by shifting the balance between TGFβ and Wnt signaling. Genes Dev. 28, 858–874 (2014).

  42. Mullokandov, G. et al. High-throughput assessment of microRNA activity and function using microRNA sensor and decoy libraries. Nat. Methods 9, 840–846 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bosson, A. D., Zamudio, J. R. & Sharp, P. A. Endogenous miRNA and target concentrations determine susceptibility to potential ceRNA competition. Mol. Cell 56, 347–359 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gasperini, M., Tome, J. M. & Shendure, J. Towards a comprehensive catalogue of validated and target-linked human enhancers. Nat. Rev. Genet. 21, 292–310 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Baccarini, A. et al. Kinetic analysis reveals the fate of a microRNA following target regulation in mammalian cells. Curr. Biol. 21, 369–376 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kingston, E. R. & Bartel, D. P. Global analyses of the dynamics of mammalian microRNA metabolism. Genome Res. 29, 1777–1790 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Treiber, T., Treiber, N. & Meister, G. Regulation of microRNA biogenesis and its crosstalk with other cellular pathways. Nat. Rev. Mol. Cell Biol. 20, 5–20 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Baccarini, A. & Brown, B. D. Monitoring microRNA activity and validating microRNA targets by reporter-based approaches. Methods Mol. Biol. 667, 215–233 (2010).

    Article  CAS  PubMed  Google Scholar 

  50. Prescott, S. L. et al. Enhancer divergence and cis-regulatory evolution in the human and chimp neural crest. Cell 163, 68–83 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Cheung, S. T., Shakibakho, S., So, E. Y. & Mui, A. L. F. Transfecting RAW264.7 cells with a luciferase reporter gene. J. Vis. Exp. 100, 52807 (2015).

    Google Scholar 

  52. Nüssing, S. et al. Efficient CRISPR/Cas9 gene editing in uncultured naive mouse T cells for in vivo studies. J. Immunol. 204, 2308–2315 (2020).

    Article  PubMed  Google Scholar 

  53. Wroblewska, A. et al. Protein barcodes enable high-dimensional single-cell CRISPR screens. Cell 175, 1141–1155 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. van Buuren, S. & Groothuis-Oudshoorn, K. Mice: multivariate imputation by chained equations in R. J. Stat. Softw. http://hdl.handle.net/10.18637/jss.v045.i03 (2011).

  55. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  57. Agarwal, V., Bell, G. W., Nam, J. W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, e05005 (2015).

    Article  PubMed Central  Google Scholar 

  58. Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: efficient manipulation of biological strings. R package v2.46.0 (2017); https://bioconductor.org/packages/Biostrings

  59. Yanai, I. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650–659 (2005).

    Article  CAS  PubMed  Google Scholar 

  60. Dore, L. C. et al. A GATA-1-regulated microRNA locus essential for erythropoiesis. Proc. Natl Acad. Sci. USA 105, 3333–3338 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Bönelt, P. et al. Precocious expression of Blimp1 in B cells causes autoimmune disease with increased self‐reactive plasma cells. EMBO J. 38, e100010 (2019).

    Article  PubMed  Google Scholar 

  62. Danko, C. G. et al. Dynamic evolution of regulatory element ensembles in primate CD4+ T cells. Nat. Ecol. Evol. 2, 537–548 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hah, N. et al. Inflammation-sensitive super enhancers form domains of coordinately regulated enhancer RNAs. Proc. Natl Acad. Sci. USA 112, E297–E302 (2015).

  64. Kaikkonen, M. U. et al. Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol. Cell 51, 310–325 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Nair, S. J. Phase separation of ligand-activated enhancers licenses cooperative chromosomal enhancer assembly. Nat. Struct. Mol. Biol. 26, 193–203 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Nelson, V. L. PPARγ is a nexus controlling alternative activation of macrophages via glutamine metabolism. Genes Dev. 32, 1035–1044 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Wei, C. Repression of the central splicing regulator RBFox2 is functionally linked to pressure overload-induced heart failure. Cell Rep. 10, 1521–1533 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zhu, Y. Comprehensive characterization of neutrophil genome topology. Genes Dev. 31, 141–153 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Mostafavi, S. Parsing the interferon transcriptional network and its disease associations. Cell 164, 564–578 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Escoubet-Lozach, L. et al. Mechanisms establishing TLR4-responsive activation states of inflammatory response genes. PLoS Genet. 7, e1002401 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Quinlan, A. R. & Hall, I. M. The BEDTools manual. (2010); https://github.com/arq5x/bedtools2

  72. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  73. Lara-Astiaso, D. et al. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  76. Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S-Plus (Springer, 2002).

  77. Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Langmead, B. & Salzberg, S. Bowtie2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  80. Kalvari, I. et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res. 49, D192–D200 (2021).

    Article  CAS  PubMed  Google Scholar 

  81. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

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.

Corresponding author

Correspondence to Brian D. Brown.

Ethics declarations

Competing interests

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.

Additional information

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.

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

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.

Source data

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).

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

Supplementary information

Supplementary Information

Representative sort reports for selected myeloid populations.

Reporting Summary

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-021-00944-y

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing