Fig 2: PPAR? antagonist GW9662 represses adipose PD-L1 expression. (a) Oil Red O staining and Western blotting of aP2, PD-L1, and ß-actin in 3T3-L1 during adipogenesis, with or without GW9662 treatment. (b) Representative PD-L1 immunofluorescent staining of subcutaneous WAT from DMSO- and GW9662-treated mice (left). Fold change (normalized to vehicle-treated group) of fluorescence intensity of PD-L1 is shown on the right. (c-f) Fold changes of mean fluorescence intensity (MFI) from specific cells types isolated from either sham (vehicle) or GW9662-treated mice are shown for splenocytes (c), lung cells (d), thymus cells (e), fat tissue macrophages (CD45+ CD3-CD11b+ F4/80+) (f). Values represent mean ± SD. N = 5 per group.

Fig 3: Injury and proliferation of ECs after influenza infection in vivo.(A) Representative immunofluorescence images of capillaries (red) and leukocytes (green) in lung tissue with or without influenza injury. Scale bars, 50 µm. D10, day 10. (B) Quantification of ECs (CD31+) in the nonleukocyte fraction (exclusion of CD45+ cells) via flow cytometric analysis. (C) Representative gating scheme for identification of proliferating EC analysis via EdU incorporation at day 19 after influenza. (D) Intracellular flow cytometry quantification of proliferative ECs (CD45-/EpCAM-/CD31+/EdU+) as a percentage of total lung ECs on days 0, 7, 19, and 27 after influenza infection. n = 3 to 4 per group. (E) Cumulative endothelial proliferation on day 27 after infection and mock-infected controls. (F) Typical image of lungs at day 19 after influenza. Red dashes represent the lung epithelial lesion area (RAGE-), and the white dashes area represents the VECad low-expression area. Scale bar, 1 mm. (G) The enlarged area from (F) shows the vascular endothelium across normal and injured epithelial areas (white, VECad). Scale bar, 200 µm. (H) Representative immunostaining of proliferative ECs from peripheral (#1) and central (#2) parts of epithelial lesion on day 19. Arrows indicate proliferative ECs (colocalization of ERG and Ki67). Scale bars, 25 µm. (I) Methodology to determine whether regenerated ECs are derived from preexisting endothelium or transdifferentiation of another cell type. (J) Statistical analysis of the level of lineage-traced ECs in uninjured and day 30 after infection mice. Each point in (B), (E), and (J) represents one mouse. Data in (B) were calculated using one-way analysis of variance (ANOVA), followed by Dunnett’s multiple comparison test; data in (E) and (J) were calculated using unpaired two-tailed t test. Data are presented as means ± SEM. *P < 0.05, **P < 0.01, and ***P < 0.001. ns, not significant.

Fig 4: Presence of renal immune cells in kidney tissue in Clr-f-/- mice. (A) Flow cytometry analysis of neutrophil, macrophage, NK1.1+ cell, NKp46+ cell, T cell, and B cell numbers per the whole kidney of Clr-f-/- mice relative to WT mice at 12 weeks of age. Horizontal bars represent mean cell numbers. (B) IF staining of WT and Clr-f-/- mouse kidneys with anti-CD45 antibody or (C) anti-CD11c antibody and DAPI nuclear staining (in red). Glomeruli are indicated by white-dashed lines. Brightfield panels of select glomeruli are shown with CD45+ or CD11c+ cells shown (in green). Scale bars represent 40 µm. (D) IHC staining of WT and Clr-f-/- mouse kidneys with antibodies against F4/80, NKp46, and CD3. Black arrowheads indicate select IHC positive cells. Images are captured at 20 × magnification. Scale bars represent 40 µm. (E) Visual quantification of indicated immune cell type per field from kidney sections of two WT and two Clr-f-/- mice. Each dot represents an individual field count and horizontal bars represent mean cell numbers. (F) IF staining of kidney sections for IL-12 and (G) IFN? cytokines in WT and Clr-f-/- mice. Scale bars represent 40 µm. (H) NKR-P1G mean-fluorescence intensity (MFI) on kidney TCRß+ T cells and NK1.1+ NK cells measured by flow cytometry analysis. Horizontal bars represent mean cell numbers and error bars represent standard deviation.

Fig 5: Location-dependent features are imprinted within selected SC subsets. Single cell suspensions from mLNs and pLNs were sorted for CD24-CD45- cells and subjected to scRNA-seq. Non-endothelial SCs were identified as non-LECs, non-BECs, and Pecam1- and Ackr4-. Data pooled from two independent experiments. a t-SNE plot of merged pLN SCs and mLN SCs showing cluster segregation. b Expression of subset-defining DEGs across all non-endothelial SCs on t-SNE plot. c Heatmap of 272 DEGs (up to 20 per cluster) with the most significant differential expression (minimal FDR-adjusted p-value). Marker genes for respective clusters are indicated with arrows. d Heatmap (left) depicts average expression per cluster of genes persistently up-regulated in SCs from transplanted mLN-SPF. Bargraph (right) shows cumulative Z-score (cZscore) of average expression per cluster for the genes persistently up-regulated in SCs from transplanted mLN-SPF. e Expression of indicated genes persistently up-regulated in non-endothelial SCs from transplanted mLN-SPF on t-SNE plot for mLN-SPF and pLN-SPF scRNA-seq. BEC blood endothelial cell, DEG differentially expressed gene, mSC metabolic stromal cells, pSC proliferating stromal cells, PvC perivascular cells, SC stromal cell, LEC lymphatic endothelial cell, t-SNE t-distributed stochastic neighbor embedding