Fig 2: Identification and confirmation of the presence of CD9+ sEVs in ultracentrifugation and tangential flow filtration samples, as detected by image flow cytometry. SEVs were isolated from the murine breast cancer cell line EO771. Samples were labelled with lipophilic fluorescent dye PKH67, and tagged with fluorescent CD9 (APC) antibody. (A) Dot plot of small, single, PKH67+, CD9+ EVs. (B) Frequency of CD9+ single sEVs. Fluorescence intensity of (C) CD9 and (D) PKH67 in CD9+ single sEVs. (E) Images of PKH67‐labelled CD9+ single sEVs in brightfield (CH01), PKH67‐related fluorescence (CH02) and CD9‐related fluorescence (CH11) channels. Data are presented as n = 3 ± SEM. *p < 0.05, **p < 0.01. Statistical analyses were performed using quantile regression with q = 0.5. UC‐SEC: ultracentrifugation‐size exclusion chromatography; TFF‐SEC: tangential flow filtration‐size exclusion chromatography

Fig 3: After size exclusion chromatography, the purity of particles isolated by ultracentrifugation and tangential flow filtration is comparable. (A) Protein quantification as determined by Bradford Assay. (B) Western blot analysis of sEV markers HSP70, TSG101 and CD9 in cell lysate (CL) and sEVs isolated by ultracentrifugation and tangential flow filtration. The endoplasmic reticulum marker Calnexin, and the FBS‐contaminant Albumin, were also assessed. Equal particle amounts (3 × 108 particles) were loaded for each sEV sample. Particle purity expressed as particles per microgram of protein as determined by (C) Tunable Resistive Pulse Sensing (TRPS), (D) Zetaview and (E) Nanosight. Data are presented as n = 3 ± SEM. *p < 0.05. Statistical analyses were performed using quantile regression or two‐way ANOVA. UC‐SEC: ultracentrifugation‐size exclusion chromatography; TFF‐SEC: tangential flow filtration‐size exclusion chromatography

Fig 4: UTX alters gene expression in virus-specific CD8+ T cells(A) An illustration of the approach. WT and UTX-deficient P14+CD8+ T cells were introduced into the separate hosts, followed by CD4 depletion and infection with LCMV-A22. Donor cells from 3–4 mice were isolated by FACS at day 15 for H3K27me3 CUT&RUN or UTX CUT&Tag analyses or isolated at day 22 for RNA-seq analyses.(B) EdgeR analysis of RNA-seq data showing genes with significantly changed expression (FDR < 0.05; log2 fold change > |1.0|), comparing genes significantly downregulated in Utx-/- donor P14+ T cells (purple) to those upregulated (green). RNA-seq data are derived from FACS donor cells from 3–4 recipient mice per group.(C) GSEA of effector genes, ranked according to their relative expression in WT and Utx-/- cells. The genes in this gene set are known to be expressed in CTLs, but not naive cells.(D) Genes showing significant differences in expression were subjected to GOrilla analysis followed by REVIGO analysis. The analysis identified several biological processes that are predicted to be significantly altered between WT and Utx-/- T cells based on differential gene expression.(E) Isolated cells were subjected to H3K27me3 CUT&RUN analysis. The anti-H3K27me3-bound DNA fragments were eluted, sequenced, and quantified across the genome. The graph depicts the relative change in Utx-/- H3K27me3 density (log2 fold change) compared to WT for individual genes that were grouped based on changes in Utx-/- gene expression (RNA-seq in B). H3K27me3 changes are shown for genes having equal (black), significantly downregulated (purple), or significantly upregulated (green) RNA expression in Utx-/- P14s. The H3K27me3 CUT&RUN data are from four recipients per group.(F) UCSC genome browser images of normalized H3K27me3 tracks at the Gzma, Thy1, Cd9, Crtam, Ubash3b, and Sema7a loci, showing mean values from four independent replicates of WT (black) or Utx-/- (red). The heavy bars depict regions with significantly enriched peaks of H3K27me3 in UTX-deficient P14 cells, compared to WT cells.(G–I) UTX CUT&Tag was performed on WT CD8+ T cells at day 0 (n = 2) and day 15 post-infection (n = 4) and at day 15 for Utx-/- (KO) cells (n = 2).(G and H) The average profile of UTX coverage is plotted based on normalized read counts for all UTX peaks of enrichment that occur at transcription start sites (TSSs; G) or enhancers (H). Enhancers were annotated based on published ATAC-seq datasets (Beltra et al., 2020). Start and stop define the boundary of the UTX peaks. Line colors indicate the profile for WT uninfected P14 (D0; light blue), WT at day 15 of LCMV infection (dark blue), or Utx-/- at day 15 (black).(I) The pie graph depicts the proportion of UTX binding at TSSs, enhancers, active enhancers, and other locations (neither TSS nor enhancer) that are within 20 kb of TSSs. Active enhancer annotation was based on published H3K27ac datasets (Yao et al., 2019).(J) H3K27me3 CUT&RUN changes were correlated with UTX binding at day 15 post-infection at genes showing reduced expression in Utx-/- T cells. Utx-/- misregulated genes were categorized as UTX bound or unbound, then contrasted for the percentage of these genes that experienced increased H3K27me3 in Utx-/- P14 cells.(K and L) All H3K27me3 peaks of enrichment that failed to overlap with UTX peaks (UTX-unbound; K) are illustrated for comparison to UTX peaks near genes with reduced expression in Utx-/- cells (UTX-bound; L). The average profile of H3K27me3 coverage is plotted based on normalized read counts for WT (red) or Utx-/- P14 (dark red) samples.(M and N) The UTX-bound H3K27me3 profiles in (L) were further subdivided into UTX peaks that overlap TSSs (M) or putative enhancers (N).(O and P) Profiles of ATAC-seq normalized reads (Beltra et al., 2020) identify the open chromatin status of enhancers in “progenitor exhausted„ (Texprog1; O) or intermediate exhausted (Texint; P) T cells. These putative enhancers were subdivided based on overlap with UTX peaks of enrichment (UTX-bound; light blue profile) or peaks lacking UTX association (unbound; black profile).(Q) The profile of H3K27ac ChIP-seq normalized reads (Yao et al., 2019) demonstrates enhancer activation status at regions that overlap with UTX binding (pink) compared to enhancers not bound by UTX (black).See also Figure S6 and Tables S1, S2, S3, and S4.

Fig 5: High-throughput single-cell assay for monitoring EV secretion. (A) The overall workflow of the single-cell assay. Cells and anti-CD81 conjugated beads are loaded on the nanowell and incubated for 2–6 h. The entire array is incubated with fluorescently labeled antibodies against CD63 and imaged using microscopy. (B) Schematic of immunoassays for profiling pan EVs using a cocktail of CD63, CD81, and CD9 antibodies and subset of CD81+CD63+EVs using CD81 for capture and CD63 for detection. Each dot represents a single cell with its mean fluorescence which is the intensity of PE anti-CD63 antibody for CD81+CD63+EVs or intensity of PE anti-CD63, PE anti-CD81, and PE anti-CD9 antibodies for pan EVs. **** p < 0.00001. (C) Representative images of individual nanowells containing 67NR cells with different EV secretion capacities. The insets show single cells and the contour map of CD63 (EVs) intensity. (D) Comparison of the frequency of CD81+CD63+EVs secreting cells between 67NR (non-metastatic) and 4T1 (metastatic) breast cancer cells (mean ± SEM). (E) The rate of secretion of CD81+CD63+EVs by 67NR cells is higher than that of 4T1 cells at two, four, and six hours. Each dot represents a single cell with the median and quartiles of CD63 (EVs) intensity shown across all cells. A representative example from three independent repeats is shown. ** p < 0.001 and **** p < 0.00001. (F) The kinetics of EV secretion from single cells. The three subpopulations corresponding to (1) continuous secretion (green), (2) secretion initiated at two hours (blue), and (3) delayed secretion at four hours (red) are shown as trend lines (mean ± SEM). Representative images and contour maps of a single cell showing a continuous increase of CD63 intensity on the surface of the bead.