Fig 2: Distinct phenotype and TCR repertoire for human iNKT cells from different anatomical locations.(A) Mean percentage of iNKT cells (CD1d-tet-PBS57+CD3+) from total CD3+B220-CD14- cells in blood or tonsils. (B–C) Flow-cytometry (left) and quantification (right) showing the percentage of CD4+, CD8+ and DN cells (B) or CD69+ and CD25+ cells (C) within the iNKT cell population in the depicted tissues. (D–E) Flow-cytometry (left) and quantification (right) showing the percentage of Vß11- iNKT cells within CD1d-tet-PBS57+Va24+ cells (D); or the percentage of Va24- cells within CD1d-tet-PBS57+Vß11+ cells (E). (A–E) Data are expressed as box-and-whisker diagrams depicting the median ± lower quartile, upper quartile, sample minimum and maximum. *p<0.05, ***p<0.001, two-tailed unpaired t-test. n = 10 samples per tissue from four independent experiments.Figure 7—source data 1.Distinct phenotype and TCR repertoire for human iNKT cells from different anatomical locations.

Fig 3: Gating strategy for MFC-based assessment of minimal residual disease in autologous stem cell grafts. MCL cells were defined as single, viable cells negative for the lineage markers CD3/CD14/CD56 and the stem cell marker CD34, low/negative for CD23 and positive for CD5, CD19 and CD22. (A) Depiction of gating strategy in a control MCL sample (peripheral blood mononuclear cells). (B) The 1:100 000 dilution of control MCL sample in an autologous stem cell graft without measurable residual disease (UPN24). (C) Exemplary displays of positive MRD in autologous stem cell grafts of UPN31 (upper panels) and UPN10 (lower panels). (D) Exemplary displays of negative MRD in autologous stem cell grafts of UPN7 (upper panels) and UPN16 (lower panels). Abbreviations: FSC indicates forward scatter; lin; lineage; MFC, multiparameter flow cytometry; MRD, measurable residual disease; sLC, surface light chain; SSC, side scatter; UPN, uniform patient number.

Fig 4: Gating strategy for identifying CXCR3 expression on monocytes using Kaluza software.a) First, we identified Singlets using forward height vs. area scatter. b) Next, we identified monocytes using a forward side scatter plot. c) Monocyte subsets were identified using the markers CD14 and CD16 (classical monocytes CD14+CD16-, intermediate monocytes CD14+CD16+, and non-classical monocytes CD14-CD16+). d) Finally, we used a negative isotype control (set to 1%) to distinguish the CXCR3 positive monocytes (shown in the overlay plot). The same procedure was done for lymphocytes, using the markers CD4+ and CD8+ for identifying CD4+ and CD8+ T cells.

Fig 5: In vitro induction of DCs from HSPCs. (A) Workflow for the induction of HSPC-DCs. (B) Flow cytometry of pDCs (CD66b-CD14-CD1c-CD123+CD303-) and CD1c+cDCs (CD66b-CD14-CD1c+) populations after 14 days of induction from CD34+HSPCs. On day 14, (C) After stimulated with the TLR4 and TLR7 agonists for 2 hours, mRNA of FACS sorter purified CD1c+cDCs was extracted to measure gene expression using real time RT-PCR. (D) CD123+pDCs and CD303+pDCs were highly purified using FACs sorter. Then both populations were stimulated with the TLR7 and TLR9 for 2 hours, mRNA was collected to measure gene expression using real time RT-PCR. Error bars indicate mean ± SD. **P < 0.01; ***P < 0.001; ****P < 0.0001.