Fig 2: Integration of protein surface profiling for detailed mapping of canonical immune cell subtypes in the transcriptomic space(A and B) Exemplary cell population gating based on sequenced surface marker profiles and imputed mRNA markers of n = 355,433 PBMCs (refer to Figure S1 for the complete gating strategy). Antibody-derived tags (ADTs) indicate that protein surface markers are shown, whereas “RNA” indicates imputed expression values based on the transcriptome.(C and D) Projection of exemplary gated populations on umap (cohorts MS1–3 and HI 1–3). Refer to Figure S2 for projections of all gates. Lines point to the mean coordinates of the indicated populations.(E) Column-scaled heatmap of gate distributions in unsupervised clusters. mono_c, classical monocytes (CD14+ monocytes); mono_i, intermediate monocytes; mono_nc, non-classical monocytes (CD16+ monocytes); NK_CD56bright, CD56-bright NK cells; NK_im, immature NK cells; NK_m, mature NK cells; NK_NKT, NK T cells within NK cell clusters; T_actv, activated T cells; T_CD4, CD4+ T cells; T_CD4_Tcm, CD4+ central memory T cells; T_CD4_Tem, CD4+ effector memory T cells; T_CD4_Tn, naive CD4+ T cells; T_CD4_Tte, CD4+ terminal effector T cells; T_CD4_Ttm, CD4+ T transitional memory cells; T_CD4_Ttm_Tem, CD4+ T transitional memory/CD4+ T effector memory cells; T_CD8, CD8+ T cells; T_CD8_Tcm, CD8+ central memory T cells; T_CD8_Tem, CD8+ T effector memory cells; T_CD8_Tn, naive CD8+ T cells; T_CD8_Tte, CD8+ terminal effector T cells; T_CD8_Ttm, CD8+ T transitional memory cells; T_CD8_Ttm_Tem, CD8+ T transitional memory/CD8+ T effector memory cells; T_CD29hi, CD29-high T cells; T_CD38hi, CD38-high T cells; T_CD49dhi, CD49d-high T cells; T_CD57hi, CD57-high T cells; T_CD161hi_non_MAIT, CD161-high/non-MAIT cells; T_DN, CD4−/CD8− = double negative T cells; T_DP, CD4+/CD8+ = double positive T cells; T_gd, gamma-delta T cells; T_HLADRhi, HLADR-high T cells; T_INKT, invariant NK T cells; T_MAIT, mucosal-associated invariant T cells; T_NKT, NK T cells within T cell clusters; T_PD1hi, PD1-high T cells; T_reg_protein, T regulatory cells based on protein expression; T_reg_RNA, T regulatory cells based on RNA expression; T_VLA4, VLA4+ T cells.

Fig 3: Human skin fibroblast-derived tumors are recognized by autologous immune cells in Hu-BLT mice(A) Growth curves for repeated experiments showing fetal skin fibroblast-derived tumors from two different donors exposed to allogeneic (top and bottom) and autologous (middle) Hu-BLT immune reconstitution. Shown as mean ± SEM; n = number of tumors, two tumors per mouse.(B) Endpoint tumor volume assessment in BLT mice for each condition presented in (A) showing Auto-BLT to be less proficient at rejecting tumors than Allo-BLT.(C) tSNE dimensional-reduction plots of human blood (left) or tumor-infiltrating immune cells (hTIIC, right) for Allo-BLT (top) and Auto-BLT (bottom) flow cytometry samples. All immune cells are from the same donor.(D) Population annotation of human immune populations in BLT-humanized mice. Combined results and population annotation from (C) (top) and expression of exhaustion markers PD-1 and Tim3 (bottom left and middle) and Treg-associated marker CD25 (bottom right). All these markers are enriched in the hTIIC samples.(E) quantification of effector populations (top row) in blood and hTIIC samples of Auto-BLT (gray bars, filled circles) and Allo-BLT (empty bars and circles) samples. Enrichment of CD8+ cells and concomitant decrease in hCD4+ T cells in Auto-BLT was observed. Quantification of immunosuppressive Treg (bottom far left) and dysfunctional T cells (bottom center-left), and expression levels of PD-1 (bottom center-right) and Tim3 (bottom far right). No significant variation between immunosuppressive and dysfunction markers between Auto- and Allo-BLT samples was observed.

Fig 4: Engineered human skin fibroblast-derived tumors are recognized by autologous immune cells in Auto-AT mice(A) Growth curves for 4T transformed adult dermal skin fibroblasts (left) and individual growth for all tumors without immune humanization (middle, no-AT, blue) and with autologous Hu-AT (right, Auto-AT, green) expressed in radiance integrated density. Shown is the mean ± SEM.(B) End point tumor volume assessment in no-AT (n = 24 tumors) and Auto-AT (n = 10 tumors) conditions.(C) Characterization of the human immune infiltrate by flow cytometry. tSNE dimensional reduction visualization with unsupervised clustering using FlowSOM module for FlowJo and manual labeling of subtypes (left). Differential clustering between hTIIC and blood human CD45+ cells shows little overlap, signifying differential marker expression levels (right).(D) Manual quantification of differentially represented human immune populations between blood and tumor samples.(E) Exhaustion/dysfunction gating strategy (left) and quantification (right) showing no significant change in total CD3, CD8+, and CD4+ T cell population dysfunction frequency between blood and tumor.(F) Differential expression levels of dysfunction markers Tim3 and PD-1 on human T cell populations in blood vs. tumor samples shown by mean fluorescence intensity quantification. In (C, right), (D), (E), and (F), red indicates blood human immune cells, and light blue indicates hTIIC; n = number of tumors, two tumors per mouse.