Premature Expression of Foxp3 in Double-Negative Thymocytes

<div><p>Peripheral immune regulation depends on the generation of thymic-derived regulatory T (tT_reg) cells to maintain self-tolerance and to counterbalance overshooting immune responses. The expression of the T_reg lineage defining transcription factor Foxp3 in developing tT_reg cells depends on TCR signaling during the thymic selection process of these T cells. In this study, we surprisingly identify Foxp3⁺ immature thymocytes at the double-negative (DN) stage in transcription factor 7 (Tcf7)-deficient mice. These Foxp3⁺ cells did not express a TCR (β or γδ chains), CD3 or CD5 and therefore these cells were true DN cells. Further investigation of this phenomenon in a transgenic TCR model showed that Foxp3-expressing DN cells could not respond to TCR stimulation <i>in vivo</i>. These data suggest that Foxp3 expression in these DN cells occurred independently of TCR signaling. Interestingly, these Foxp3⁺ DN cells were located in a transition state between DN1 and DN2 (CD4^-CD8^-CD3^-TCR^-CD44^highCD25^low). Our results indicate that Tcf7 is involved in preventing the premature expression of Foxp3 in DN thymocytes.</p></div

DataSheet_1_Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4+ T cell clones from murine tissues.pdf

Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.</p

Foxp3 expression at the DN cell stage in Tcf7-deficient mice.

<p>(A) Representative plots and quantification of Foxp3 staining in CD4^-CD8^- (DN) thymocytes from Tcf7^+/+ and Tcf7^-/- mice (n = 8). (B) Left panels: Representative plots showing Foxp3 and intracellular (IC) TCRβ staining in DN thymocytes from Tcf7^+/+ and Tcf7^-/- mice. Middle panels: TCRγδ and CD3 staining on DN Foxp3⁺TCRβ^- cells (gate R1). Right panel: Quantification of DN Foxp3⁺TCRβ^-TCRγδ^-CD3^- cells (gate R2) depicted as the percentage of total DN cells (n = 6). (C) Left panel: Representative histograms showing CD5 staining on Foxp3⁺ DN, Foxp3⁺ DP, and Foxp3⁺ CD4SP cells from Tcf7^-/- mice. Right panel: Quantification of CD5 geometric mean from DN, DP, and CD4SP Foxp3⁺ or Foxp3^- populations (n = 3). Mean + SD are shown for all quantified data. Numbers show percentages of cells within the indicated box. Each symbol represents an individual animal. ** P < 0.01 (unpaired t-test).</p

DataSheet_1_Inflammatory perturbations in early life long-lastingly shape the transcriptome and TCR repertoire of the first wave of regulatory T cells.pdf

The first wave of Foxp3+ regulatory T cells (Tregs) generated in neonates is critical for the life-long prevention of autoimmunity. Although it is widely accepted that neonates are highly susceptible to infections, the impact of neonatal infections on this first wave of Tregs is completely unknown. Here, we challenged newborn Treg fate-mapping mice (Foxp3eGFPCreERT2xROSA26STOP-eYFP) with the Toll-like receptor (TLR) agonists LPS and poly I:C to mimic inflammatory perturbations upon neonatal bacterial or viral infections, respectively, and subsequently administrated tamoxifen during the first 8 days of life to selectively label the first wave of Tregs. Neonatally-tagged Tregs preferentially accumulated in non-lymphoid tissues (NLTs) when compared to secondary lymphoid organs (SLOs) irrespective of the treatment. One week post challenge, no differences in the frequency and phenotypes of neonatally-tagged Tregs were observed between challenged mice and untreated controls. However, upon aging, a decreased frequency of neonatally-tagged Tregs in both NLTs and SLOs was detected in challenged mice when compared to untreated controls. This decrease became significant 12 weeks post challenge, with no signs of altered Foxp3 stability. Remarkably, this late decrease in the frequency of neonatally-tagged Tregs only occurred when newborns were challenged, as treating 8-days-old mice with TLR agonists did not result in long-lasting alterations of the first wave of Tregs. Combined single-cell T cell receptor (TCR)-seq and RNA-seq revealed that neonatal inflammatory perturbations drastically diminished TCR diversity and long-lastingly altered the transcriptome of neonatally-tagged Tregs, exemplified by lower expression of Tigit, Foxp3, and Il2ra. Together, our data demonstrate that a single, transient encounter with a pathogen in early life can have long-lasting consequences for the first wave of Tregs, which might affect immunological tolerance, prevention of autoimmunity, and other non-canonical functions of tissue-resident Tregs in adulthood.</p

Analysis of Foxp3⁺ DN cells in TEa-Tcf7-deficient mice.

<p>(A) Representative plots showing TCRVβ6 and TCRVα2 expression on CD4SP thymocytes from TEa-Tcf7^+/+ and TEa-Tcf7^-/- mice in the presence or absence of cognate antigen (Ag). The Tg TCR population is divided into TCR^high and TCR^low populations. (B-C) Quantification of the percentage of total (B) or TCR^high (C) TCRVβ6⁺TCRVα2⁺ cells among CD4SP thymocytes (n = 8). (D) Representative plots showing Foxp3 expression in DN TCRVβ6⁺TCRVα2⁺ thymocytes from TEa-Tcf7^+/+ and TEa-Tcf7^-/- mice in the absence of Ag. (E) Quantification of Foxp3⁺ DN TCRVβ6⁺TCRVα2⁺ thymocytes from TEa-Tcf7^+/+ and TEa-Tcf7^-/- mice in the presence or absence of Ag (n = 8). (F-G) Representative plots showing TCRVβ6 and TCRVα2 expression on DN Foxp3⁺ (F) or CD4SP Foxp3⁺ (G) thymocytes from TEa-Tcf7^+/+ and TEa-Tcf7^-/- mice in the presence or absence of Ag. Cells are pre-gated on TCRVβ6⁺TCRVα2⁺. Each dot represents one individual animal and mean is shown for all quantified data. Numbers show percentages of cells within the indicated box. NS, not significant, *** P < 0.001, **** P < 0.0001 (unpaired t-test).</p

Inflammatory status of adipose tissue.

<p>Real-time RT-PCR analysis of (A) brown adipose tissue (BAT) and (B) subcutaneous white adipose tissue of T_reg cell-proficient (PBS) and T_reg cell-deficient (DT) mice after cold exposure. Ucp1, uncoupling protein 1; Cidea,cell death-inducing DNA fragmentation factor, alpha subunit-like effector A; Dio2,deiodinase, iodothyronine, type II; Pparg, peroxisome proliferator-activated receptor gamma; Prdm16, PR domain containing 16; Cd68, Cd68 antigen; Ccl2,chemokine (C-C motif) ligand 2; Tnfa, tumor necrosis factor alpha; Ifng, interferon, gamma; Mrc1, mannose receptor, C type 1; Mgl1, macrophage galactose-type C-type lectin 1; Arg1,arginase 1; Il-10, interleukin 10; Il-4, interleukin 4. Data are mean ± SD (n = 9–10); *p<0.05 (Student’s t-test). (C) Representative hematoxylin and eosin (H&E) staining (left) and immunohistochemical anti-MAC-2 staining (right; brown color) in BAT from PBS and DT mice. Scale bar 100 μm. Quantification of MAC-2 positive area (panel below MAC-2 staining) as a percentage of total area.</p

Genotypical comparison of T_reg and T_conv cells isolated from brown adipose tissue (BAT) and spleen tissue (SPL) in cold- and warm-conditioned animals generated with an Illumina Mouse Expression Array.

<p>(A) Gene expression profiles comparing T_reg (top) or T_conv (bottom) cell populations between spleen and adipose tissue samples isolated from warm-conditioned animals (left) or between cells isolated from cold vs warm-conditioned animals (right). Numbers indicate genes either up- or downregulated more than 2-fold (cut-off: dotted line), with the number of significantly different (p<0.05) genes shown in brackets with an asterisk. (B) Volcano plot comparing gene expression and significance values between T_reg and T_conv genes isolated from BAT in warm-conditioned animals. Key up- or downregulated genes in T_reg cells are annotated (Foxp3, Il10, Cxcl1/2, Tcf7, Ifng) and serve as quality control to the published consensus T_reg-cell signature. (C) Hierarchical clustering of the top-30 upregulated genes and the top-10 downregulated genes in warm-conditioned brown adipose tissue T_reg cells versus spleen T_reg cells. (D) Comparison of BAT-T_reg-specific genes with visceral adipose tissue (VAT)-specific genes. We first determined 430 genes to upregulated in BAT warm-conditioned T_reg cells, with 222 genes being significantly altered (p<0.05). We then overlaid BAT T_reg-upregulated genes with VAT T_reg tissue specific expression gene data. 181 genes were matched between both microarary chips, with 169 genes also upregulated in VAT, and only 12 genes specific for BAT (left). The corresponding analysis of the 516 genes upregulated in cold BAT T_reg cells versus warm spleen T_reg cells revealed 194 genes to be significantly upregulated. 158 could be matched to VAT T_reg-specific genes, of which 148 were VAT-specific, whereas only 10 were specific for BAT. P-values indicate the significance of overrepresentation of BAT T_reg-specific genes in the VAT T_reg signature. (E) Comparison of VAT-T_reg specific genes on BAT warm (left) or BAT cold (right) gene signatures. Of 1839 genes specifically overexpressed in VAT T_reg cells, 1059 were statistically significantly (p<0.05) upregulated. Of these 1059 genes, 829 were also detectable in the BAT T_reg microarray. When comparing the VAT T_reg signature to warm BAT T_reg cells, 660 genes were overrepresented in VAT, whereas cold BAT T_reg cells show 685 genes to be overrepresented in VAT.</p

Principal biological processes affected by T_reg cell depletion in BAT.

<p>Principal biological processes affected by T_reg cell depletion in BAT.</p

Physiological parameters.

<p>(A) Body weight (BW) and (B) adipose tissues weights of T_reg cell-proficient (PBS) and T_reg cell-deficient (DT) mice after cold exposure. BAT, brown adipose tissue; scWAT, subcutaneous white adipose tissue, aWAT, abdominal white adipose tissue. (C) Blood glucose, (D) serum non-estherified fatty acids (NEFA) and (E) serum triglycerides in PBS and DT mice. Values are mean ± SD (n = 9–10); *P<0.05 (Student’s t-test).</p