Multiplexed single cell RNA/AbSeq sequencing of ex vivo Treg and ex-Treg from different time points
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ABSTRACT:
PROVIDER: PRJNA639149 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12005305_1.fastq.gz | Fastqsanger.gz | |||
| SRR12005305_2.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: identify differentially expressed genes between ex-Treg obtained 5, 10 or 15 days post-adoptive transfer into a lymphopenic environment compared to ex vivo Treg in order to identify Treg subset with heightened instability. Method: Treg from Foxp3YFP-Cre Rosa26RFP (PMID: 18387831, PMID: 17171761) were co-injected with congenically-disparated naive T cells in a 1:1 ratio into Rag1KO mice and ex-Treg were sorted at 15 days, 10 days or 5 days post-transfer. As a control, ex vivo Treg were sorted from Foxp3YFP-Cre Rosa26RFP (d0 sample). Next, samples were stained with multiplexing antibodies using the BD Single-Cell Multiplexing Kit (BD biosciences), then pooled and stained with the following oligonucleotide-conjugated antibodies: anti‑CD25 (PC61), anti-CD44 (IM7), anti-CD62L (MEL-14), anti-GITR (DTA-1), anti-CD4 (RM4-5), anti‑TCRb (H57-597), anti‑CD69 (H1.2F3), anti‑TIGIT (1G9), anti‑CD95 (Jo2), anti‑CD122 (TM‑1β), anti‑Tim-3 (5D12), anti‑CCR7 (4B12), anti‑CD103 (M290), anti‑CD279 (J43), anti‑CD274 (MIH5), anti‑CD233 (C9B7W), anti‑CD71 (C2), anti‑CD278 (7E7G9), anti‑ITGB7 (M293), anti‑CD137 (1AH2), anti‑CD40 (3/23), anti‑CD3e (145-2C) from BD Biosciences. Single-cell capture and cDNA library preparation was performed using the BD Rhapsody Single-cell analysis system (BD Biosciences), according to the manufacturer’s instructions, using a custom gene panel (591 genes). Sequencing was performed on an Illumina NextSeq500 instrument using a Mid‑Output kit v2.5 (150 cycles, paired-end). Sample demultiplexing, barcode processing, alignment, filtering, UMI counting were done using the standard BD Biosciences Rhapsody analysis pipeline on Seven Bridges (www.sevenbridges.com). Result: In this study, we showed that ex-Treg downregulate Treg core-specific genes immediatly upon adoptive transfer into a lymphopenic environment. Further, we identified a Treg population enriched for unstable Trg clones. Conclusion: Our study was able to identify a Treg subset enriched for unstable Treg with plastic potential. This Treg subset appears highly enriched for naïve peripheral-induced Treg.
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Project description:Regulatory T (Treg) cells that express the FoxP3 transcription factor are essential for lymphoid homeostasis and immune tolerance to self. Other non-immunological functions of Treg cells, such as controlling metabolic function in adipose tissue, are also emerging. Treg cells originate primarily in the thymus, but can also be elicited from conventional T cells by in vivo exposure to low-dose antigen or homeostatic expansion, or by activation in the presence of TGFβ in vitro. Treg cells are characterized by a distinct transcriptional signature controlled in part, but not solely, by FoxP3. For a better perspective on transcriptional control in Treg cells, we compared gene expression profiles of a broad panel of Treg cells from various origins or anatomical locations. Treg cells generated by different means form different sub-phenotypes identifiable by particular combinations of transcripts, none of which fully encompass the entire Treg signature. Molecules involved in Treg effector function, chemokine receptors, and the transcription factors that control them are differentially represented in these subphenotypes. Treg cells from the gut proved dissimilar to cells elicited by exposure to TGFβ, but instead they resembled a CD103+Klrg1+ subphenotype preferentially generated in response to lymphopenia. All gene expression profiles were obtained from highly purified T cell populations sorted by flow cytometry. To reduce variability, cells from multiple mice were pooled for sorting, and replicates or triplicates were generated for all groups. RNA from 1-5 x 104 cells was amplified, labeled, and hybridized to Affymetrix M430v2 microarrays. Raw data were preprocessed with the RMA algorithm in GenePattern, and averaged expression values were used for analysis.
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Project description:To understand the whole genome transcriptome of in vivo Treg cells and ex vivo TGF-beta induced Treg cells from WT and Aim2 knockout mice, the total RNA was extracted from indicated Treg cells using the Direct-zol miniprep kit (Zymo Research, R2060). The RNA samples were firstly enriched by Oligo(dT) magnetic beads and used to construct BGISEQ-500 libraries. RNA-seq libraries sequenced using the 50bp single-end protocol (in vivo isolated Treg cells) or 100bp paired-end protocol (TGF-β induced Treg cells) via the BGISEQ-500 sequencer per the manufacturer’s protocol. After filtering of adaptors and low quality reads, clean reads (>26 Million reads per sample for in vivo isolated Treg cells and >40 Million reads per sample for TGF-β induced Treg cells) are mapped to mouse reference genome using HISAT /Bowtie2 tool. Mapping results are stored in BAM files using SAMtools. Total read counts in gene level were summarized using featureCounts function in the Rsubread in R environment, with the R package biomaRt for gene and transcripts mapping. The differential expression (DE) genes were analyzed by DESeq2 package with default setting using total read counts as input, and the adjusted p value (padj) less than 0.05.
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Project description:Regulatory T (Treg) cells that express the FoxP3 transcription factor are essential for lymphoid homeostasis and immune tolerance to self. Other non-immunological functions of Treg cells, such as controlling metabolic function in adipose tissue, are also emerging. Treg cells originate primarily in the thymus, but can also be elicited from conventional T cells by in vivo exposure to low-dose antigen or homeostatic expansion, or by activation in the presence of TGFβ in vitro. Treg cells are characterized by a distinct transcriptional signature controlled in part, but not solely, by FoxP3. For a better perspective on transcriptional control in Treg cells, we compared gene expression profiles of a broad panel of Treg cells from various origins or anatomical locations. Treg cells generated by different means form different sub-phenotypes identifiable by particular combinations of transcripts, none of which fully encompass the entire Treg signature. Molecules involved in Treg effector function, chemokine receptors, and the transcription factors that control them are differentially represented in these subphenotypes. Treg cells from the gut proved dissimilar to cells elicited by exposure to TGFβ, but instead they resembled a CD103+Klrg1+ subphenotype preferentially generated in response to lymphopenia.
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Project description:Whole genome transcriptome of in vivo Treg cells and ex vivo TGF-beta induced Treg cells from WT and Aim2 knockout mice
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Project description:Background: CD4+CD25hiCD127lo/-FOXP3+ regulatory T cells (Tregs) play a key role in preventing autoimmunity. In type 1 diabetes, adoptive transfer of autologous polyclonal Tregs has been shown to be safe in adults in Phase I clinical trials. Methods: We explore factors contributing to efficacy in autologous polyclonal expanded Tregs (expTregs) Phase 2 clinical trial in 111 children and adolescents with new-onset type 1 diabetes (Sanford/Lisata Therapeutics Trex Phase 2 clinical trial) randomized 1:1:1 high and low treatment to placebo. Cytometry, bulk and single cell RNA-seq were perfomed on selected expTregs and PBMC samples from subjects. Results: A single dose of expTreg therapy (1-24x106cells/kg) was safe but did not prevent the decline in residual beta cell function over one year compared to placebo (p=0.39), regardless of age, baseline C-peptide, or dose of expTregs. ExpTregs were highly activated and suppressive, and a transient increase of activated memory Tregs was detectable one week after infusion in the high dose cohort, suggesting effective transfer of expTregs. However, in vitro fold expansion on expTregs varied across subjects even when accounting for age. Lower fold expansion and its’ associated gene signature was linked with better outcome, regardless of Treg dose. Conclusion: These results suggest that expTregs quantity alone does not alter outcome; instead, expTreg quality may be an important factor contributing to the efficacy of adoptive Treg therapy to prevent type 1 diabetes progression.
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Project description:Human naïve CD4+ T cells (CD4+ CD45RA+ CD25- CD45RO- CD8- CD14- CD15- CD16- CD19- CD34- CD36- CD56- CD123- TCRγ/δ- HLA-DR- and CD235a-) were magnetically negatively isolated from peripheral blood. Cells were stimulated with anti-CD3/anti-CD28 antibodies plus IL-2, and samples were taken at 6h, 24h, 48h and 6d of stimulation. Mock stimulation control cells (sample group G02) received no further compounds, whereas induced regulatory T cells (iTregs) were either differentiated under addition of TGF-b (sample group G03) or TGF-b + retinoic acid + rapamycin (sample group G05). As control, naïve CD4+ T cells were left unstimulated (0h; sample group G01). Ex vivo isolated CD25-high cells were included as positive control for the Treg signature (“nTreg”; sample group G07). Tregs were defined by expression of FOXP3, the “master” transcription factor of Tregs. Samples from 3 male healthy donors (age 34 to 38 years) were prepared with the Qiagen Allprep kit and protein precipitate was solubilized (5 min, 95°C) in freshly prepared buffer containing 4% (w/v) SDS, 25 mM HEPES pH 7.6, 1mM DTT. Samples were prepared using the FASP assay and peptides were labeled with TMT 10-plex reagents and MS data acquired on a Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer.
Phenotyping, stability and functional analyses for iTregs induced under these conditions are available in Schmidt A et al., PLoSONE 2016, PMID: 26886923). In the publication associated to this dataset, the time-course proteomic profiling during human Treg differentiation is presented and integrated with RNA-Seq data from the same cells (including additional iTreg culture conditions and 2h time points for RNA-Seq). The data underwent clustering, network analysis and disease enrichment, which revealed many known regulators of Tregs along with novel candidate genes putatively involved in FOXP3 induction, the biological importance of which was validated with a targeted shRNA screen.
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