• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • br Mass cytometry for analyses of


    2.8. Mass cytometry for analyses of immune cell subpopulations, cytokines and selected biomarkers
    Tumors from each mouse were harvested after 10–12 days of treatment as described above. Single cell suspensions were generated from tumors using the MACS mouse tumor dissociation kit (Miltenyi Biotech Cat. 130-096-730) following manufacturer’s instructions. One million cells per tumor were resuspended in PBS and labeled with Cell-ID Cisplatin (Fluidigm, Cat. 201064) to assess for live/dead cells. For antibody labeling, we used the recommended cell surface staining procedure (Fluidigm) followed by the FoxP3/Transcription Staining Buffer Set protocol (eBiosciences™). Cells were labeled with a panel of
    28 metal-conjugated LY 500307 to determine different immune lineages in addition to memory, trafficking, activation, and exhaustion markers (see Supplementary Table 1 for list of antibodies). After washing and centrifugation, cells were fixed using MaxPar Fix and Perm buffer (Fluidigm, Cat. 201067) and labelled for single cell discrimination with Cell-ID Intercalator-Ir (Fluidigm, Cat. 201192A). Samples were re-suspended with 10% EQ four-element calibration beads (Fluidigm, Cat. 201078), and filtered through a 40 μm mesh filter prior to acquisition on a Helios™ mass cytometer (Fluidigm), at a rate of 300–500 events/s.
    2.9. Dimensionality reduction, cluster analysis and visualization
    Collected mass cytometry data was analyzed as previously described [45]. Briefly, samples were normalized utilizing a bead standard. First, each cytometry file was processed in FlowJo (v10.3), then manually gated for stability of signal over time, followed by exclusion of nor-malization beads, ratio of DNA intercalators (191Ir + vs 193Ir+), with finally single cell events (Ir193 vs event length)(Fig. 9A). After that, viable (195Pt−) CD45+ events were exported and uploaded into the X-shift (VorteX) clustering environment to obtain the k -nearest-neighbor density estimation as described before [45,46]. Dimension-ality reduction of unclustered data was performed using the t-stochastic neighborhood embedding (t-SNE) and PhenoGraph algorithms im-plemented in the Cytofkit library [47], supplied by Bioconductor v.3.4 and run in RStudio v.1.1.463. A fixed number of 10,000 cells were sampled without replacement from each file and combined for analysis. Resulting t-SNE plots were subsequently filtered by marker expression to visualize differences between different treatment groups. Heatmaps were generated using Z-scores based on median marker expression (excel and Prism v7). Then, we used Wei et al. [48] criteria to exclude clusters from analyses that had an expression level lower than 0.5%.
    2.10. Flow cytometry and bone marrow cell analysis
    Human myeloid-derived suppressor cells were expanded from bone marrow (BM) specimens of BC patients after standard Ficoll gradient purification and red blood cell lysis. Briefly, 2 × 106 BM cells were cultured in the presence of 1000 IU/ml of GM-CSF and 40 ng/ml IL-6 in 
    different media conditions including regular RPMI-1640 with 15% FBS or phenol red-free medium with 15% DCC-FBS with or without 100 nM E2 (7). After 6 days of culture, cells were harvested, stained with a 14 antibody panel including anti-phospho-STAT3 (pSTAT3) and analyzed by flow cytometry with an LSRII with a 5 lasers (UV, violet, blue, green-yellow and red). Data was processed using FlowJo (v10.3). De-identi-fied BM specimens were retrospectively-collected and deposited in the UCLA Pathology Tumor Bank according to Human Subject Protection Committee guidelines at our institution.
    2.11. Immunohistochemistry
    Paraffin-embedded LY 500307 sections from 4T1 tumors were cut at 4 μm thickness and paraffin removed with xylene and rehydrated through graded ethanol. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide in methanol for 10 min. Heat-induced antigen re-trieval (HIER) was carried out for all sections in 0.001 M EDTA buffer, pH = 8.00 using a vegetable steamer at 95 °C for 25 min. Sections were incubated with anti mouse CD8a (eBioscience, 14-0808-82) at 1:100 dilution for 1 h at room temperature. After primary antibody incuba-tion, tissues were then incubated with secondary rabbit anti-rat im-munoglobulin for 30 min at 1:200 dilution (Vector, AI-4001) followed by a 30 min incubation with Dakocytomation EnvisionÅ System Labelled Polymer HRP anti rabbit (Agilent, K4003). All sections were visualized with the diaminobenzidine reaction and counterstained with hematoxylin. The number of immune-positive cells were counted in five randomly chosen fields per tumor at 100-fold magnification. 4–6 mice tumors per condition were used for analysis. Results from the five areas/mouse were averaged and used in the statistical analysis.
    For in vitro studies, triplicates of experiments were done to verify results. ANOVA or t-tests were used as appropriate to compare inter-ventions. Analyses of cells were evaluated using bar and scatter graphs with mean, standard deviation (SD) and standard error (SE). Repeated measures ANOVA was used as appropriate to assess time, condition, and time by condition interaction effects. For in vivo studies, mice with similar tumor size were randomized to different treatment groups with controls for up to 28 days. Data analyses by appropriate parametric or nonparametric methods were applied [22,35–37]. Briefly, these ana-lyses use mixed-effects models with tumor size as outcome measure (transformed as needed). Analyses of mass and flow cytometry data were performed using GraphPad Prism version 7.0 (GraphPad, San Diego, CA) using one-way ANOVA followed Bonferroni’s multiple comparisons test or two-tailed unpaired Student’s t-test approaches as described before [45,46,49,50]. Differences were considered significant for P values less than 0.05.