Journal: eLife

Article Title: EGFR transactivates RON to drive oncogenic crosstalk

doi: 10.7554/eLife.63678

Figure Lengend Snippet: ( A ) HEK RON/EGFR cells were first labeled for RON using ⍺-HA-FITC Fab fragment (green), treated with 10 nM EGF-AF647 (magenta) for 5 min on ice and then fixed. Representative images from three biological replicates show colocalization of RON and EGFR at the plasma membrane. Scale bars, 10 μm (cross-section) and 2 μm (apical membrane). ( B ) Top row: Membrane sheets were prepared from A431 RON cells ± 50 nM EGF for 2 and 5 min. Sheets were labeled on the cytoplasmic face using antibodies to RON (6 nm gold) and EGFR (12 nm gold). Circles indicate co-clusters of RON and EGFR in representative images from three biological replicates; arrowheads indicate clusters containing RON (green) or EGFR (magenta) only. Scale bar, 100 nm. Bottom row: Ripley’s K bivariant function was used to evaluate co-clustering. The experimental values for L(r)-r (corresponding to EM image directly above) are shown in magenta and the 99% confidence window for complete spatial randomness is plotted as dashed lines. In each case, experimental values are seen to fall above the confidence window, indicating co-clustering.

Article Snippet: antibody , Anti-EGFR AF647 (Mouse monoclonal) , Santa Cruz Biotechnology , Cat# sc-101 AF647 , Clone R-1 FACS (5–40 ug/mL).

Techniques: Labeling, Clinical Proteomics, Membrane

Journal: eLife

Article Title: EGFR transactivates RON to drive oncogenic crosstalk

doi: 10.7554/eLife.63678

Figure Lengend Snippet: Control sample of EGF-AF647 and anti-EGFR-AF488 shows the expected high colocalization (top left). Labeling of EGFR by either EGF-AF647 (ligand-bound, top right) or anti-EGFR-AF647 (no ligand, bottom left) both show high colocalization with HA-RON (anti-HA-AF488). Pearson’s Coefficient for the cell shown is indicated in the bottom left corner of the respective image. Five cells were analyzed per condition and averages ± SD are shown in the bar graph. Scale bars, 5 µm. Figure 2—figure supplement 1—source data 1. Source data for colocalization analysis in .

Article Snippet: antibody , Anti-EGFR AF647 (Mouse monoclonal) , Santa Cruz Biotechnology , Cat# sc-101 AF647 , Clone R-1 FACS (5–40 ug/mL).

Techniques: Control, Labeling

Journal: eLife

Article Title: EGFR transactivates RON to drive oncogenic crosstalk

doi: 10.7554/eLife.63678

Figure Lengend Snippet: ( A ) Single particle tracking of QD605-HA-RON was used to quantify RON mobility on A431 RON cells ± ligand. Ensemble mean squared displacement (MSD) shows reduction in slope of the MSD with ligand stimulation, indicating a reduced mobility. Treatment with EGFR kinase inhibitor prevents RON slow down with EGF. The number of jumps fit for each condition range from 42,183 to 898,300. ( B ) Corresponding distribution of diffusion coefficients, D, for individual cells is plotted for arange of 39 to 517 cells per condition; *** p < 0.001. ( C ) HEK RON/EGFR cells were labeled for RON with anti-HA-FITC Fab fragment (green), treated with 10 nM EGF-AF647 (magenta) for 5 min on ice followed by 10 min at 37°C, then fixed and labeled with an antibody to EEA1 (early endosomes, blue). Representative images from three biological replicates show that EGF-positive endosomes (arrows) primarily do not contain RON. Pearson’s coefficient for the image shown and colocalization with EEA1 is shown in the bottom left corner. ( D ) Alternative labeling method for monitoring endosome content where HEK RON/EGFR cells were treated with 50 nM EGF for 10 min at 37°C, fixed and then antibodies were used to label RON (anti-HA, green) or EGFR (magenta). Further quantification for C, D is in . ( E ) Membrane sheets prepared from A431 RON cells ± 50 nM EGF for 5 min were labeled for RON (6 nm gold) or EGFR (12 nm gold). TEM images show clathrin-coated pit lattices on the cell membranes containing EGFR, but not RON. Scale bars, 50 nm. Figure 3—source data 1. Source data for diffusion coefficient distributions in .

Article Snippet: antibody , Anti-EGFR AF647 (Mouse monoclonal) , Santa Cruz Biotechnology , Cat# sc-101 AF647 , Clone R-1 FACS (5–40 ug/mL).

Techniques: Single-particle Tracking, Diffusion-based Assay, Labeling, Membrane

Journal: eLife

Article Title: EGFR transactivates RON to drive oncogenic crosstalk

doi: 10.7554/eLife.63678

Figure Lengend Snippet: ( A ) Colocalization analysis for cells corresponding to treatment in , where RON is labeled live with anti-HA-Fab-FITC before addition of EGF-AF647. After 10 min of EGF-AF647 treatment, EGF is more colocalized with endosomes (EGF/EEA1) than RON (HAFab/EEA1). ( B ) Colocalization analysis for cells corresponding to . Here, cells are treated with ±50 nM EGF, then fixed and labeled with fluorescently-labeled primary antibodies to RON (anti-HA) or EGFR. Colocalization with EEA1 is low for both receptors in the resting state (No Tx). After EGF treatment, a significant increase in Pearson’s Correlation coefficient is seen for EGFR/EEA1 colocalization, but not for RON/EEA1. ( C ) Calculation of the fraction of endosomes containing either EGFR or RON or both, corresponding to ( B ). The fraction of RON-positive endosomes does not increase with EGF stimulation. 180 (No treatment) and 174 (50 nM EGF) endosomes were analyzed across 14 cells for each condition. Figure 3—figure supplement 2—source data 1. Source data for colocalization analysis in .

Article Snippet: antibody , Anti-EGFR AF647 (Mouse monoclonal) , Santa Cruz Biotechnology , Cat# sc-101 AF647 , Clone R-1 FACS (5–40 ug/mL).

Techniques: Labeling

Journal: eLife

Article Title: EGFR transactivates RON to drive oncogenic crosstalk

doi: 10.7554/eLife.63678

Figure Lengend Snippet:

Article Snippet: antibody , Anti-EGFR AF647 (Mouse monoclonal) , Santa Cruz Biotechnology , Cat# sc-101 AF647 , Clone R-1 FACS (5–40 ug/mL).

Techniques: Stable Transfection, Transfection, Plasmid Preparation, Construct, Generated, Cytometry, Kinase Assay, Recombinant, Sequencing, Ligation, Mutagenesis, Bicinchoninic Acid Protein Assay, Expressing, Software, Magnetic Beads

Journal: Molecular cell

Article Title: Rad52 Restrains Resection at DNA Double-Strand Break Ends in Yeast

doi: 10.1016/j.molcel.2019.08.017

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: Lipid bilayers were prepared with 91.5% DOPC (Avanti Polar Lipids), 0.5% biotinylated–PE (Avanti Polar Lipids), and 8% mPEG 2000–DOPE (Avanti Polar Lipids), and deposited onto to the surface of a flowcell sample chamber containing nanofabricated barriers to lipid diffusion prepared by electron beam lithography ( De Tullio et al., 2018 ; Ma et al., 2017 ).

Techniques: Produced, Recombinant, Protease Inhibitor, Clone Assay, DNA Labeling, SYBR Green Assay, Western Blot, Fluorescence, Microscopy, Software, Real-time Polymerase Chain Reaction, Imaging, Hybridization

Journal: iScience

Article Title: TIGIT contributes to the regulation of 4-1BB and does not define NK cell dysfunction in glioblastoma

doi: 10.1016/j.isci.2023.108353

Figure Lengend Snippet: Exhaustion markers PD-1 and LAG-3 are upregulated in GBM patients, but not TIGIT (A) Diagram depicting interactions between TIGIT, DNAM-1, CD96 and CD155, CD112, as well as intracellular cross-talk between TIGIT and other NK cell receptors (PD-1, 4-1BB, CD69, LAG-3. (B) TCGA Kaplan-Meier survival plot indicating that, together, CD155 and TIGIT are prognostic factors in GBM. High CD155 + TIGIT = 81 patients; Low CD155 + TIGIT = 81 patients. (C and D) Groups were compared by Kaplan-Meier survival analysis. Bar plots depicting (C) MFI ( left ) and (D) percentage ( right ) expression of NK cell activating (CD16, DNAM-1, NKG2D, CD69, NKp30, CD57, 4-1BB) and inhibitory (CD158e1, CD96, CD158b, TIGIT, NKG2A, PD-1, LAG-3, CD94) receptors on GBM patient cNK and tiNK cells, as well as NK cells from healthy donors (n = 8 patients ). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (E) Histograms depicting differences in select NK ligands between healthy donors, GBM patient cNK, and GBM patient tiNK cells. (F and G) Correlation between TIGIT expression (Fold MFI) and 4-1BB expression (Fold MFI) on (F) cNK (R 2 = 0.9181) and (G) tiNK (R 2 = 0.9649) cells harvested from GBM patients (n = 6). R 2 was calculated by simple linear regression. ∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 .

Article Snippet: Antibodies used for flow cytometry and cell sorting were CD3-PeCy7 (BD, UCHT1, Franklin Lakes, NJ), CD56-PeCy5.5 (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-APC (Biolegend, A15153G, San Diego, CA), DNAM-1-BV510 (Biolegend, 11A8, San Diego, CA), Sytox Green (Thermo Fisher Scientific, Franklin Lakes, NJ), Sytox Blue (Thermo Fisher Scientific, Waltham, MA), CD16-BUV395 (BD, 3G8, Franklin Lakes, NJ), NKG2A-FITC (Miltenyi Biotec, REA110, Gaithersburg, MD), CD158e1-BV421 (BD Biosciences, DX9, Franklin Lakes, NJ), PD-1-BV510 (Biolegend, NAT105, San Diego, CA), NKG2D-BV605 (Biolegend, 1D11, San Diego, CA), CD57-BV605 (Biolegend, QA17A04, San Diego, CA), CD69-BV650 (Biolegend, FN50, San Diego, CA), LAG-3-BV650 (Biolegend, 11C3C65, San Diego, CA), NKp30-BV711 (Biolegend, P30-15, San Diego, CA), TIM-3-BV711 (Biolegend, F38-2E2, San Diego, CA), A2AR-PE (R&D Systems, 599717, Minneapolis, MN), CD94-PE (Biolegend, DX22, San Diego, CA), CD158b-APC/Fire750 (Biolegend, DX27, San Diego, CA), 41BB-APC/Fire750 (Biolegend, 4B4-1, San Diego, CA), and KLRG1-APC (Biolegend, SA231A2, San Diego, CA), CD96-PE (Biolegend, NK92.39, San Diego, CA), NKp46-BV785 (Biolegend, 9E2, San Diego, CA), CD56-APC (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-BV421 (Biolegend, A15153G), IFN-γ-PerCP-Cy5.5 (Biolegend, 4S.B3, San Diego, CA), CD107a-PE (Biolegend, H4A3, San Diego, CA), m4-1BB-APC (Thermo Fisher Scientific, 17B5, Waltham, MA), mCD16/32-BV421 (Biolegend, 93, San Diego, CA), mNK1.1-BV510 (Biolegend, PK136, San Diego, CA), mCD11b-BV605 (Biolegend, M1/70, San Diego, CA), mCD27-BV650 (Biolegend, LG.3A10, San Diego, CA), mCD3-BV711 (Biolegend, 145-2C11, San Diego, CA), mLAG-3-BV785 (Thermo Fisher Scientific, C9B7W, Waltham, MA), mTIGIT-PE-Cy7 (Biolegend, 1G9, San Diego, CA), mDNAM-1-APC-Fire 750 (Biolegend, 10E5, San Diego, CA), and mCD96-PE (Biolegend, 3.3, San Diego, CA).

Techniques: Expressing

Journal: iScience

Article Title: TIGIT contributes to the regulation of 4-1BB and does not define NK cell dysfunction in glioblastoma

doi: 10.1016/j.isci.2023.108353

Figure Lengend Snippet: TIGIT blockade correlates with low CD16, high 4-1BB, and higher inflammatory cytokines (A) Heatmap depicting normalized MFI expression of NK cell activating (CD16, DNAM-1, NKG2D, CD69, NKp30, CD57, 4-1BB) and inhibitory (CD158e1, CD158b, NKG2A, PD-1, LAG-3, CD94, A2AR, TIM-3, KLRG1) receptors on NK cells cocultured with GBM43 cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). (B) CD16, (C) 4-1BB, (D) and LAG-3 percentage ( left ) and MFI ( right ) expression on NK cells co-cultured with GBM43-WT cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (E) Density plots depicting CD16, 4-1BB and LAG-3 expression on NK cells co-cultured with GBM43 cells, with or without CD155 and/or TIGIT mAb blockade. Levels of proinflammatory cytokines (F) IFN-γ, TNF-α, IL-8, IL-1β (G) IL-10, IL-5, GM-CSF, and IL-4 in cell supernatant of NK cells co-cultured with GBM43-WT cells, with or without CD155 and/or TIGIT mAb blockade (n = 3). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. (H) Change in expression (measured as MFI fold over isotype control) of 4-1BB on TIGIT KO and WT NK cells generated via CRISPR/Cas9 editing in the absence and presence of GBM43 cells (incubated as co-culture at an E:T 2.5:1). Groups were compared using ordinary one-way ANOVA and Tukey’s post-hoc test. Data are represented as mean ± SEM. ∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001 .

Article Snippet: Antibodies used for flow cytometry and cell sorting were CD3-PeCy7 (BD, UCHT1, Franklin Lakes, NJ), CD56-PeCy5.5 (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-APC (Biolegend, A15153G, San Diego, CA), DNAM-1-BV510 (Biolegend, 11A8, San Diego, CA), Sytox Green (Thermo Fisher Scientific, Franklin Lakes, NJ), Sytox Blue (Thermo Fisher Scientific, Waltham, MA), CD16-BUV395 (BD, 3G8, Franklin Lakes, NJ), NKG2A-FITC (Miltenyi Biotec, REA110, Gaithersburg, MD), CD158e1-BV421 (BD Biosciences, DX9, Franklin Lakes, NJ), PD-1-BV510 (Biolegend, NAT105, San Diego, CA), NKG2D-BV605 (Biolegend, 1D11, San Diego, CA), CD57-BV605 (Biolegend, QA17A04, San Diego, CA), CD69-BV650 (Biolegend, FN50, San Diego, CA), LAG-3-BV650 (Biolegend, 11C3C65, San Diego, CA), NKp30-BV711 (Biolegend, P30-15, San Diego, CA), TIM-3-BV711 (Biolegend, F38-2E2, San Diego, CA), A2AR-PE (R&D Systems, 599717, Minneapolis, MN), CD94-PE (Biolegend, DX22, San Diego, CA), CD158b-APC/Fire750 (Biolegend, DX27, San Diego, CA), 41BB-APC/Fire750 (Biolegend, 4B4-1, San Diego, CA), and KLRG1-APC (Biolegend, SA231A2, San Diego, CA), CD96-PE (Biolegend, NK92.39, San Diego, CA), NKp46-BV785 (Biolegend, 9E2, San Diego, CA), CD56-APC (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-BV421 (Biolegend, A15153G), IFN-γ-PerCP-Cy5.5 (Biolegend, 4S.B3, San Diego, CA), CD107a-PE (Biolegend, H4A3, San Diego, CA), m4-1BB-APC (Thermo Fisher Scientific, 17B5, Waltham, MA), mCD16/32-BV421 (Biolegend, 93, San Diego, CA), mNK1.1-BV510 (Biolegend, PK136, San Diego, CA), mCD11b-BV605 (Biolegend, M1/70, San Diego, CA), mCD27-BV650 (Biolegend, LG.3A10, San Diego, CA), mCD3-BV711 (Biolegend, 145-2C11, San Diego, CA), mLAG-3-BV785 (Thermo Fisher Scientific, C9B7W, Waltham, MA), mTIGIT-PE-Cy7 (Biolegend, 1G9, San Diego, CA), mDNAM-1-APC-Fire 750 (Biolegend, 10E5, San Diego, CA), and mCD96-PE (Biolegend, 3.3, San Diego, CA).

Techniques: Expressing, Cell Culture, Control, Generated, CRISPR, Incubation, Co-Culture Assay

Journal: iScience

Article Title: TIGIT contributes to the regulation of 4-1BB and does not define NK cell dysfunction in glioblastoma

doi: 10.1016/j.isci.2023.108353

Figure Lengend Snippet:

Article Snippet: Antibodies used for flow cytometry and cell sorting were CD3-PeCy7 (BD, UCHT1, Franklin Lakes, NJ), CD56-PeCy5.5 (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-APC (Biolegend, A15153G, San Diego, CA), DNAM-1-BV510 (Biolegend, 11A8, San Diego, CA), Sytox Green (Thermo Fisher Scientific, Franklin Lakes, NJ), Sytox Blue (Thermo Fisher Scientific, Waltham, MA), CD16-BUV395 (BD, 3G8, Franklin Lakes, NJ), NKG2A-FITC (Miltenyi Biotec, REA110, Gaithersburg, MD), CD158e1-BV421 (BD Biosciences, DX9, Franklin Lakes, NJ), PD-1-BV510 (Biolegend, NAT105, San Diego, CA), NKG2D-BV605 (Biolegend, 1D11, San Diego, CA), CD57-BV605 (Biolegend, QA17A04, San Diego, CA), CD69-BV650 (Biolegend, FN50, San Diego, CA), LAG-3-BV650 (Biolegend, 11C3C65, San Diego, CA), NKp30-BV711 (Biolegend, P30-15, San Diego, CA), TIM-3-BV711 (Biolegend, F38-2E2, San Diego, CA), A2AR-PE (R&D Systems, 599717, Minneapolis, MN), CD94-PE (Biolegend, DX22, San Diego, CA), CD158b-APC/Fire750 (Biolegend, DX27, San Diego, CA), 41BB-APC/Fire750 (Biolegend, 4B4-1, San Diego, CA), and KLRG1-APC (Biolegend, SA231A2, San Diego, CA), CD96-PE (Biolegend, NK92.39, San Diego, CA), NKp46-BV785 (Biolegend, 9E2, San Diego, CA), CD56-APC (Thermo Fisher Scientific, CMSSB, Waltham, MA), TIGIT-BV421 (Biolegend, A15153G), IFN-γ-PerCP-Cy5.5 (Biolegend, 4S.B3, San Diego, CA), CD107a-PE (Biolegend, H4A3, San Diego, CA), m4-1BB-APC (Thermo Fisher Scientific, 17B5, Waltham, MA), mCD16/32-BV421 (Biolegend, 93, San Diego, CA), mNK1.1-BV510 (Biolegend, PK136, San Diego, CA), mCD11b-BV605 (Biolegend, M1/70, San Diego, CA), mCD27-BV650 (Biolegend, LG.3A10, San Diego, CA), mCD3-BV711 (Biolegend, 145-2C11, San Diego, CA), mLAG-3-BV785 (Thermo Fisher Scientific, C9B7W, Waltham, MA), mTIGIT-PE-Cy7 (Biolegend, 1G9, San Diego, CA), mDNAM-1-APC-Fire 750 (Biolegend, 10E5, San Diego, CA), and mCD96-PE (Biolegend, 3.3, San Diego, CA).

Techniques: Recombinant, Lactate Dehydrogenase Assay, Cell Based Assay, Gene Knockout, Enzyme-linked Immunosorbent Assay, shRNA, Sequencing, Software, Gene Expression