Journal: The Journal of Biological Chemistry

Article Title: Single-cell RNA-Seq analysis identifies a noncoding interleukin 4 ( IL-4 ) RNA that post-transcriptionally up-regulates IL-4 production in T helper cells

doi: 10.1074/jbc.RA118.004111

Figure Lengend Snippet: Single-cell RNA-Seq reveals transcriptome heterogeneity of in vitro differentiated Th2 cells. Naïve CD4+ T cells (CD4+CD62LhiCD44lowCD25−) isolated from C57BL/6 mice were stimulated with anti-CD3 and anti-CD28 in the presence of IL-4 and anti-IFN-γ. Five days after stimulation, the cells were restimulated with plate-bound anti-CD3 and anti-CD28 for 6 h, and single cells were captured with a Fluidigm C1 system. A total of 56 cells were collected and sequenced with an Illumina Hiseq system, and 10,000 pooled cells were sequenced using a standard protocol (Pooled cells). A, correlations of gene expression between the pooled cells and average of the 56 single cells (left panel), between two randomly selected single cells (center panel), and between the pooled cells and a single cell (right panel). 10,565 genes were detected. Gene expression was determined by TPM, and Pearson correlation coefficient (r) is depicted. B, heatmap depicting Pearson correlation coefficients between global gene expression profiles of each pair of the 56 individual cells. C, cytokine genes were abundantly expressed and variable among Th2 single cells. Average expression (μ, x axis) and standard deviation (σ, y axis) of each gene were calculated and plotted. The blue dashed lines represent the threshold of CV (highly variable, >0.28; less variable, <0.2). The black dashed line represents the threshold of abundantly expressed genes (μ > 8). Red represents cytokine genes, green denotes housekeeping genes, and gray represents other genes. D, gene ontology (GO) analysis of the most variably expressed genes (μ > 8 and CV > 0.28, left panel) and most consistently expressed genes (μ > 8 and CV < 0.2, right panel). The top five most significantly enriched GO terms (biological processes and molecular functions) are shown. The p values were Bonferroni-corrected.

Article Snippet: Five days after stimulation, the cells were restimulated with plate-bound anti-CD3 and anti-CD28 for 6 h, and single cells were captured with a Fluidigm C1 system.

Techniques: RNA Sequencing Assay, In Vitro, Isolation, Expressing, Standard Deviation

Journal: The Journal of Biological Chemistry

Article Title: Single-cell RNA-Seq analysis identifies a noncoding interleukin 4 ( IL-4 ) RNA that post-transcriptionally up-regulates IL-4 production in T helper cells

doi: 10.1074/jbc.RA118.004111

Figure Lengend Snippet: IL4nc is constitutively expressed in Th2 cells. A, IL4nc was specifically expressed in Th2 and Th0 cells. Naïve CD4+ T cells were activated with anti-CD3 and anti-CD28 and differentiated into various lineages under the respective conditions. Five days after differentiation, expression of IL4nc was analyzed by real-time PCR either under steady state or after being activated by anti-CD3 and anti-CD28 or PAM and ionomycin for 5 h. B, IL4nc is primarily located in the cytoplasm. D10.G4.1 cells were fractionated into the nuclear (N) fraction and cytoplasmic (C) fraction, and the presence of the IL4nc isoform was determined by real-time PCR (left panel). The protein lysates from both fractions were further analyzed by immunoblotting for the cytoplasmic marker (B-actin) and nucleus marker (histone H3) (right panel). C, expression of either IL4nc or IL4fl in resting in vitro differentiated Th2 cells was determined by real-time PCR. D, induction of IL4nc RNA precedes IL4fl following TCR stimulation. Th2 cells were differentiated as described and restimulated with anti-CD3/anti-CD28 for the indicated time. Expression of either IL4nc or IL4fl was determined by real-time PCR. Data are representative (B) or the sum (A–D) of at least three independent experiments. Error bars stand for the standard deviation of the mean. **, p < 0.01 in paired Student's t test. ns, not significantly changed.

Article Snippet: Five days after stimulation, the cells were restimulated with plate-bound anti-CD3 and anti-CD28 for 6 h, and single cells were captured with a Fluidigm C1 system.

Techniques: Expressing, Real-time Polymerase Chain Reaction, Western Blot, Marker, In Vitro, Standard Deviation

Journal: The Journal of Biological Chemistry

Article Title: Single-cell RNA-Seq analysis identifies a noncoding interleukin 4 ( IL-4 ) RNA that post-transcriptionally up-regulates IL-4 production in T helper cells

doi: 10.1074/jbc.RA118.004111

Figure Lengend Snippet: IL4nc RNA promotes IL-4 production in Th2 cells. A, a diagram showing the gene structure of IL4fl (top panel) or IL4nc RNAs (bottom panel). The gray boxes depict noncoding region of transcripts, and the black boxes depict coding regions. B and C, overexpression of IL4nc promotes IL-4 production in Th2 cells. Th2 cells were differentiated in vitro as described in Fig. 1 and transduced with a retrovirus containing either IL4nc RNA or empty vector (Mock). 5 days after stimulation, the cells were restimulated with PMA (50 ng/ml) and ionomycin (500 ng/ml) for 5 h. Expression of IL-4 was determined by intracellular staining. Data shown are representative (B) or summary (C) of IL-4+% or IL-13+% cells from three independent experiments. D, Th2 cells were differentiated and stimulated as in B, and secreted IL-4 in the supernatant was determined by ELISA. Data are summary of three independent experiments. E–G, exon 3 of IL4nc is required for promoting IL-4 production post-transcriptionally. As above, full-length IL4nc (FL) or its mutants depleted the indicated exons (Δexon1, Δexon2, and Δexon3, respectively) and were retrovirally expressed in Th2 cells. Production of IL-4 was analyzed by intracellular staining (E and F) or ELISA (G). H, as in E. Expression of IL-4 and GATA3 mRNAs 2 h after restimulation was analyzed by real-time quantitative PCR. I, as in B. Th2 cells were transduced with a retrovirus overexpressing IL4nc RNA, and the cells were selected with puromycin for 3 days (3 μg/ml). 5 days after differentiation, expression of IL4fl was analyzed by real-time PCR after activation by anti-CD3 (5 μg/ml) and anti-CD28 (1 μg/ml) (left panel) or PMA (50 ng/ml) and ionomycin (500 ng/ml) (right panel) for the indicated time. Data are representative (B and E) or the sum (C, D, and F–I) of at least three independent experiments. Error bars stand for the standard deviation of the mean. *, p < 0.05; **, p < 0.01; paired Student's t test. ns, not significantly changed.

Article Snippet: Five days after stimulation, the cells were restimulated with plate-bound anti-CD3 and anti-CD28 for 6 h, and single cells were captured with a Fluidigm C1 system.

Techniques: Over Expression, In Vitro, Transduction, Plasmid Preparation, Expressing, Staining, Enzyme-linked Immunosorbent Assay, Real-time Polymerase Chain Reaction, Activation Assay, Standard Deviation

Journal: The Journal of Biological Chemistry

Article Title: Single-cell RNA-Seq analysis identifies a noncoding interleukin 4 ( IL-4 ) RNA that post-transcriptionally up-regulates IL-4 production in T helper cells

doi: 10.1074/jbc.RA118.004111

Figure Lengend Snippet: Knockdown of IL4nc RNA represses IL-4 production. Th2 cells were differentiated in vitro as described in Fig. 1 and transduced with a retrovirus containing a scramble sequence (sh-Ctl) or two independent shRNAs against IL4nc RNA. Five days after differentiation, the cells were restimulated with PMA (50 ng/ml) and ionomycin (500 ng/ml) for 5 h. A–C, production of IL-4 was determined by intracellular staining (A and B) and ELISA (C). D, as in A. The cells were restimulated with anti-CD3 and anti-CD28, and expression of IL4fl (left panel) and IL4nc (right panel) RNA was analyzed by real-time PCR at the indicated time after restimulation. E, as in A. 5 days after differentiation, the cells were restimulated with PMA (50 ng/ml) and ionomycin (500 ng/ml) for 3.5 h. The cells were then treated with cycloheximide (100 μg/ml), lysed, and fractionated on a 10–50% sucrose gradient. Distribution of IL4 mRNA at each fraction was analyzed by real-time PCR. Distribution of ribosomal RNA is shown in Fig. S6. F, as in E. In vitro differentiated Th2 cells were transduced with a retrovirus overexpressing IL4nc (IL4nc) or an empty vector (Mock). Distribution of IL4 mRNA at each fraction after sucrose density gradient centrifugation was determined by real-time PCR. Data are representative (A) or the sum (B–F) of at least three independent experiments. Error bars stand for the standard deviation of the mean. *, p < 0.05; **, p < 0.01; paired Student's t test.

Article Snippet: Five days after stimulation, the cells were restimulated with plate-bound anti-CD3 and anti-CD28 for 6 h, and single cells were captured with a Fluidigm C1 system.

Techniques: In Vitro, Transduction, Sequencing, Staining, Enzyme-linked Immunosorbent Assay, Expressing, Real-time Polymerase Chain Reaction, Plasmid Preparation, Gradient Centrifugation, Standard Deviation

Journal: STAR Protocols

Article Title: Protocol to characterize immune cell subpopulations in cerebrospinal fluid of patients with neuroinflammatory diseases using mass cytometry

doi: 10.1016/j.xpro.2024.103038

Figure Lengend Snippet:

Article Snippet: Anti-biotin-(1D4-C5)-170Er (1:100) , Standard BioTools , Cat#3170003B.

Techniques: Recombinant, Labeling, Blocking Assay, Software, Cytometry, Flow Cytometry, Single-cell Analysis, Gene Expression

Journal: iScience

Article Title: A nonhuman primate model of vertical sleeve gastrectomy facilitates mechanistic and translational research in human obesity

doi: 10.1016/j.isci.2021.103421

Figure Lengend Snippet: Macrophage phenotypes, CLSs, and T cell infiltration in AT one year after VSG (A and B) Tissue-resident macrophages (CD64 + CD45 + stromal vascular cells) in VAT were significantly suppressed after VSG (n = 3) compared to sham (n = 3) (A), with less CD206 expression (a classic M2 macrophage marker) and less expression of activation markers CD11c, HLA-DR, and CD169 (B). (C–F) (C) CLS density (expressed as Δchange from D0) trended to slightly increased after sham surgery, but was significantly reduced in VAT after VSG suggested reduced inflammation in the VAT. This was VAT-compartment specific, with SAT (D) demonstrating increases in CLSs after both sham surgery and VSG. Similar trends were seen with CD3 + T cells, with a decrease after VSG in VAT (E) but not SAT (F). ∗p < 0.05, sham vs. VSG; #p = 0.05, sham vs. VSG. Data reported as group average ±SEM. See also Figure S3 .

Article Snippet: CD3 , Fluidigm , Cat#3170007B; Clone: SP34-2.

Techniques: Expressing, Marker, Activation Assay

Journal: iScience

Article Title: A nonhuman primate model of vertical sleeve gastrectomy facilitates mechanistic and translational research in human obesity

doi: 10.1016/j.isci.2021.103421

Figure Lengend Snippet: Diversity of myeloid and lymphoid immune cells one month after VSG (A–C) Our short-term study (A) was designed to characterize early immunocyte changes in a group of animals with matched weight loss following VSG to the long-term study (B). VSG promoted M2-like macrophage polarization, increased the number of regulatory T cells, and decreased the number of proliferating T cells in the adipose tissue. Fold-change from baseline in PBLs, SAT, and VAT in the ratio of: (C) M2/M1 macrophages (M1: CD206 + CD11c + , M2: CD206 + CD11c − of CD14 + CD68 + CD3 - lym), M2/M1 in SAT n = 1, all others n = 2. (D) Tregs (FoxP3 + CD4 + of CD3 + lym). (E) t -SNE projection of 26,400 myeloid cells (CD3 − CD20 - /CD45 + lym) sampled equally from PBLs (n = 2), SAT (n = 2), and VAT (n = 2) at D0 and D28 after VSG. (F) 12 individual FlowSOM-identified myeloid populations from the t -SNE projection in (E) as fold change from D0. (G) Heatmap showing mean expression of indicated markers across the 12 FlowSOM myeloid populations. (H) t -SNE projection of 4860 Tregs (CD25 + CD127 - /CD4 + lym) sampled equally from PBLs (n = 2), SAT (n = 2), and VAT (n = 2) at D0 and D28 after VSG. (I) Six individual FlowSOM-identified Treg populations from the t -SNE projects in (H) as fold change from D0. (J) Heatmap showing mean expression of indicated markers across the six FlowSOM Treg populations. Data reported as group average ±SEM. See also , , and , and and .

Article Snippet: CD3 , Fluidigm , Cat#3170007B; Clone: SP34-2.

Techniques: Expressing

Journal: iScience

Article Title: A nonhuman primate model of vertical sleeve gastrectomy facilitates mechanistic and translational research in human obesity

doi: 10.1016/j.isci.2021.103421

Figure Lengend Snippet:

Article Snippet: CD3 , Fluidigm , Cat#3170007B; Clone: SP34-2.

Techniques: Recombinant, Blocking Assay, Staining, Antibody Labeling, Multiplex Assay, Software

Journal: Cell

Article Title: Single-cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment

doi: 10.1016/j.cell.2018.05.060

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: SEQC is available on https://github.com/ambrosejcarr/seqc.git and Biscuit is available on https://github.com/sandhya212/BISCUIT_SingleCell_IMM_ICML_2016 . table ft1 table-wrap mode="anchored" t5 REAGENT or RESOURCE SOURCE IDENTIFIER Biological Samples Breast carcinoma Fresh operative tissue samples at MSKCC N/A Normal breast tissue Fresh operative tissue samples at MSKCC N/A Lymph node Fresh operative tissue samples at MSKCC N/A Peripheral blood Patient venipuncture at MSKCC N/A Antibodies anti-CD45 ab Biolegend RRID:AB_2566372 DAPI Stain Calbiochem SCR_014366 anti-Foxp3 ab Thermo Fisher RRID:AB_1834364 anti-CD3 ab Biolegend RRID:AB_1575008 anti-CD4 ab Biolegend RRID:AB_571945 anti-CD16 ab Biolegend RRID:AB_314207 anti-CD56 ab BD Biosciences RRID:AB_396853 anti-CD8 ab Biolegend RRID:AB_528885 anti-CD19 ab Biolegend RRID:AB_2562015 anti-CD11b ab Biolegend RRID:AB_2563395 anti-CD45 Y89 Fluidigm RRID:AB_2261851 anti-CD235ab BioLegend RRID:AB_314620 anti-CD61 BioLegend RRID:AB_1227584 anti-CD196/CCR6 Pr141 Fluidigm Cat:3141014A anti-CD19 142Nd Fluidigm RRID:AB_2651155 anti-CD278/ICOS 143Nd Fluidigm Cat:3143025B anti-CD270 144Nd Fluidigm Cat:3144022B anti-CD4 145Nd Fluidigm RRID:AB_2661789 anti-CD8a 146Nd Fluidigm Cat:3146001B anti-CD11c 147Sm Fluidigm RRID:AB_2687850 anti-CD14 148Nd Fluidigm Cat:3148010B anti-CD127 149Sm Fluidigm RRID:AB_2661792 anti-TIGIT eBioscience Cat:16-9500-82 anti-CD123 151Eu Fluidigm RRID:AB_2661794 anti-CD95/Fas 152Sm Fluidigm Cat:3152017B anti-CD45RA 153Eu Fluidigm Cat:3153001B anti-TIM3 154Sm Fluidigm Cat:3154010B anti-CD39 BioLegend RRID:AB_940438 anti-CD274/PD-L1 156Gd Fluidigm Cat: 3156026B anti-CD27 158Gd Fluidigm Cat: 3158010B anti-CD357/GITR 159Tb Fluidigm Cat: 3159020B anti-CD28 160Gd Fluidigm Cat: 3160003B anti-CD152/CTLA4 161Dy Fluidigm Cat: 3161004B anti-FoxP3 162Dy Fluidigm RRID:AB_2687650 anti-CD33 163Dy Fluidigm RRID:AB_2687857 anti-CD45RO 164Dy Fluidigm Cat: 3164007B anti-CD223/LAG3 165Ho Fluidigm RRID:AB_2687859 anti-Ki67 BD RRID:AB_396287 anti-CD197/CCR7 167Er Fluidigm Cat:3167009A anti-CD154/CD40L 168Er Fluidigm Cat: 3170011B anti-CD25 169Tm Fluidigm RRID:AB_2661806 anti-CD3 170Er Fluidigm RRID:AB_2661807 anti-CD226 171Tb Fluidigm Cat: 3171013B anti-CD38 172Yb Fluidigm Cat: 3172007B anti-CD137/4–1BB 173Yb Fluidigm Cat: 3173015B anti-CD279/PD-1 174Yb Fluidigm Cat: 3174020B anti-CD194/CCR4 175Lu Fluidigm Cat: 3175021A anti-CD56 176Yb Fluidigm RRID:AB_2661813 anti-CD11b 209Bi Fluidigm Cat: 3209003B Critical Commercial Assays inDrop platform Custom-built droplet microfluidics platform; Klein et al., 2015 ; Zilionis et al., 2017 .

Techniques: Staining, Antibody Labeling, Software, Diffusion-based Assay