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. 2020 Jun 24;12:55. doi: 10.1186/s13073-020-00756-z

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Integrated single-cell targeted multi-omics approach identifies a trajectory of human CD4⁺ regulatory T cell (Treg) activation. a UMAP plot depicting the expression of the canonical Treg transcription factor FOXP3 in the identified resting CD4⁺ T cell clusters. Naive and memory Treg clusters are annotated as shown in Fig. 1c. b Heatmap displaying the top 10 differentially expressed genes within the three identified resting Treg clusters, depicted in Fig. 1d. c UMAP plot depicting the overlaid expression of the CD4⁺ T cell transcription factors BACH2 (black to green) and PRDM1 (encoding BLIMP-1; black to red). d Illustrative examples of the expression of highly differentially expressed genes within the cluster of activated Tregs (cluster 3), including HLA-DRA and DUSP4 at the mRNA level and CD39, CCR4, CD80 and CD86 at the protein level