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. 2019 Feb 19;50(2):493-504.e7.
doi: 10.1016/j.immuni.2019.01.001. Epub 2019 Feb 5.

VSports手机版 - Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation

Ricardo J Miragaia  1 , Tomás Gomes  2 , Agnieszka Chomka  3 , Laura Jardine  4 , Angela Riedel  5 , Ahmed N Hegazy  3 , Natasha Whibley  6 , Andrea Tucci  6 , Xi Chen  2 , Ida Lindeman  7 , Guy Emerton  2 , Thomas Krausgruber  3 , Jacqueline Shields  5 , Muzlifah Haniffa  4 , Fiona Powrie  3 , Sarah A Teichmann  8
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Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation (VSports在线直播)

Ricardo J Miragaia et al. Immunity. .

"V体育ios版" Abstract

Non-lymphoid tissues (NLTs) harbor a pool of adaptive immune cells with largely unexplored phenotype and development. We used single-cell RNA-seq to characterize 35,000 CD4+ regulatory (Treg) and memory (Tmem) T cells in mouse skin and colon, their respective draining lymph nodes (LNs) and spleen. In these tissues, we identified Treg cell subpopulations with distinct degrees of NLT phenotype. Subpopulation pseudotime ordering and gene kinetics were consistent in recruitment to skin and colon, yet the initial NLT-priming in LNs and the final stages of NLT functional adaptation reflected tissue-specific differences. Predicted kinetics were recapitulated using an in vivo melanoma-induction model, validating key regulators and receptors. Finally, we profiled human blood and NLT Treg and Tmem cells, and identified cross-mammalian conserved tissue signatures. In summary, we describe the relationship between Treg cell heterogeneity and recruitment to NLTs through the combined use of computational prediction and in vivo validation VSports手机版. .

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Figure 1
Figure 1
Steady-State scRNA-Seq Datasets of CD4+ T Cells from LT and NLT (A) Experimental design for scRNA-seq data collection. (B) t-SNE representing all Treg and Tmem cells that passed quality control. (C) Genes defining the identity of Treg and Tmem cells in lymphoid and non-lymphoid tissues. Colon and skin were individually compared with their corresponding draining lymph node and spleen cells. See also Figure S1.
Figure 2
Figure 2
Heterogeneity within LT and NLT Treg Populations (A) t-SNE projections of Treg cells per tissue, colored by subpopulation. cTreg, central Treg; eTreg, effector Treg. (B) Subpopulation marker gene mean expression (Z score). Values greater than 2.5 or lower than −1.5 are colored equally. (C) Relative proportions of Treg cell subpopulations within each tissue that revealed heterogeneity. (D) NLT/LT signature score in each Treg cell subpopulation, measured as the ratio between the number of NLT and LT genes that have been identified as significantly upregulated in each cluster. (E) Percentage of cells expressing each gene in Treg NLT-like cells from mLN and bLN. Genes that are upregulated in the bLN subpopulation are represented by an open circle, and genes upregulated in mLN are represented by a filled circle. (F) Percentage of cells expressing each gene in colon Treg suppressive and Treg NLT subpopulations. (G) Matching of non-colonic Treg cells to colonic Treg cell subpopulations using a logistic regression model (90% accuracy, see Experimental Procedures). Table shows the percentage of each identified subpopulation (y axis) that were labeled by the model as each Treg cell cluster (x axis). (H) Percentage of cells expressing each gene in skin Treg NLT and colon Treg NLT cell subpopulations. See also Figure S2.
Figure 3
Figure 3
Reconstruction of Treg Cell Recruitment from Lymphoid to Non-Lymphoid Tissues in Steady-State (A) Top two latent variables (LV) found with BGPLVM for mLN and colonic Treg cells, with bLN and skin Treg cells mapped over the same coordinates. LV in the x axis is the most relevant one, and mapping of colon and skin subpopulations over it reveals a transition of Treg cell identity across tissues. (B) Gene expression in mLN and colon (top) or bLN and skin (bottom) over LV0 modeled into a sigmoidal curve. Dashed vertical line marks the activation point of each gene. (C) Sequence of activation of GO biological processes across the transition to colon (top) or skin (bottom), evidencing a conservation between both trajectories (Spearman’s rho - 0.61). See also Figure S3.
Figure 4
Figure 4
Recruitment and Adaptation of Treg Cells to the Tumor Environment Recapitulates Steady-State Migration (A) Melanoma induction strategy and sampled tissues. (B) t-SNE depicting Treg and Tmem cells from tumor and steady-state skin, draining brachial lymph nodes, and spleen. (C) Differential expression between skin and tumor Treg cells. Treg cells classified as cycling were excluded. (D) (top) Latent variables found with MRD-BGPLVM representing cell cycle (LV5) and non-lymphoid tissue recruitment/adaptation of Treg cells (LV9). (bottom) Distribution of cells based on Tissue and Condition and Cell-Cycle phase along the recruitment trajectory. (E) Difference in activation time (t0) of genes in control and tumor. Genes are classified as being markers of skin, lymph node, cell cycle, or other. Colored points show mean ± mean SE for each group. Vertical dashed lines represent the mean ± SE for all t0 values. t test between control and melanoma t0 indicates no change (p value = 0.2631), with t0 values having a Spearman correlation coefficient of 0.65 between both conditions. See also Figure S4.
Figure 5
Figure 5
Human-Mouse Comparison of NLT Treg Cell Marker Genes (A) Tissues and cell types sampled from human. (B and C) Top shows overlap between NLT Treg cell markers detected in human and mouse, in either (B) colon or (D) skin datasets. Bottom shows fold-change between gene expression in non-lymphoid and lymphoid tissues in mouse and human. Blood and spleen were used as lymphoid tissues in human and mouse, respectively. (D) NLT paralogs exhibiting opposing expression patterns between human and mouse. See also Figure S5.
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References

    1. Adam M., Potter A.S., Potter S.S. Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development. Development. 2017;144:3625–3632. - PMC - PubMed
    1. Bettelli E., Carrier Y., Gao W., Korn T., Strom T.B., Oukka M., Weiner H.L., Kuchroo V.K. Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature. 2006;441:235–238. - PubMed
    1. Bollrath J., Powrie F.M. Controlling the frontier: regulatory T-cells and intestinal homeostasis. Semin. Immunol. 2013;25:352–357. - PubMed
    1. Campbell D.J., Koch M.A. Phenotypical and functional specialization of FOXP3+ regulatory T cells. Nat. Rev. Immunol. 2011;11:119–130. - PMC (VSports app下载) - PubMed
    1. Campbell K.R., Yau C. switchde: inference of switch-like differential expression along single-cell trajectories. Bioinformatics. 2017;33:1241–1242. - "VSports最新版本" PMC - PubMed

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