To elucidate the complex immunological responses elicited by triple-negative breast cancer (TNBC), we performed a comprehensive transcriptional analysis of T cells exposed to exosomes derived from 17 distinct TNBC cell lines, in comparison to mock-treated controls. This investigation yielded a high-resolution single-cell reference atlas, providing unprecedented insight into the transcriptional and phenotypic landscapes of human T cells in response to TNBC-derived exosomal stimuli. Exosomes were isolated by size exclusion chromatography and characterized via nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM).
Leveraging a multi-omic framework, we integrated single-cell RNA sequencing (scRNA-seq), miRNA-mRNA network analysis, high-throughput bulk and single-cell cytokine profiling, and T cell receptor (VDJ-seq) sequencing. This multifaceted approach enabled robust identification of recurrently dysregulated genes and pathways, offering mechanistic insights into the immunomodulatory effects of TNBC exosomes on distinct T cell subsets (Fig. 1).
Fig. 1
Schematic overview of the multi-omic framework to assess TNBC exosome-mediated T cell modulation. Exosomes were isolated from 17 molecularly distinct TNBC cell lines and used to treat primary human T cells; untreated T cells served as mock controls. Following exposure, single-cell RNA and surface protein (ADT) profiling, as well as T cell receptor (TCR) V(D)J sequencing, were performed using the 10x Genomics platform. Transcriptomic data underwent normalization, clustering, differential expression, and pathway enrichment analysis. TCR clonotype data were analyzed for CDR3 sequence and V-J-C gene usage to identify tumor-associated T cell expansions. In parallel, exosomal miRNA and lncRNA expression profiles were used for regulatory network analysis. Functional cytokine responses were assessed using Luminex and IsoCode single-cell cytokine analysis chips, highlighting exosome-driven immune modulation
Subset-specific uptake of TNBC-derived exosomes by human T cells
Exosomes were isolated via size exclusion chromatography from the conditioned media of 17 molecularly and genetically distinct triple-negative breast cancer (TNBC) cell lines, obtained from the American Type Culture Collection (ATCC). This panel included both basal-like (HCC1599, HCC1937, HCC1143, MDA-MB-468, HCC38, HCC70, HCC1806, HCC1187, DU4475), mesenchymal (BT-549, MDA-MB-231, MDA-MB-436, MDA-MB-157, Hs 578T), luminal androgen receptor (LAR) (MDA-MB-453), and Unclassified subtypes (HCC1395, BT-20), thereby encompassing the clinical and molecular heterogeneity of TNBC.
Exosome purity was confirmed by nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM), with mean particle sizes ranging from ~80–130 nm, consistent with exosomal dimensions. Representative size distributions and morphological profiles are shown in Supplementary Fig. 1a, b. Additionally, exosomes were isolated from 35 human plasma samples, comprising 7 healthy donors and 28 TNBC patients. The average particle sizes of these plasma-derived exosomes ranged from approximately 60 to 140 nm, consistent with the expected size distribution of plasma extracellular vesicles (Supplementary Fig. 1c, d).
We next investigated differential uptake and intracellular localization of TNBC-derived exosomes across human T cell subpopulations. Single-cell imaging flow cytometry (ImageStream) revealed exosome uptake by CD4⁺, CD8⁺, and regulatory T cell (Treg; CD4⁺, CD127^low/−, CD25⁺) subsets (Supplementary Fig. 1e). High-resolution image analysis confirmed that exosomes were not only bound to the cell surface but were also internalized into the cytoplasm and perinuclear regions, consistent with active uptake. Quantitative analysis of internalization features demonstrated a significantly higher rate of intracellular exosome accumulation in Tregs compared to conventional CD4⁺ and CD8⁺ T cells (Supplementary Fig. 1h), suggesting preferential uptake by immunosuppressive subsets.
Single-cell multiomics reveals exosome-induced immunosuppressive T cell states
Following 48-h treatment with TNBC-derived exosomes, we profiled the transcriptomic and surface protein landscapes of T cell subpopulations using CITE-seq, integrating gene expression with a 137-plex panel of oligo-conjugated antibodies. This high-dimensional single-cell dataset includes 179,169 high-quality human T cells exposed to exosomes isolated from 17 genomically diverse TNBC cell lines. The combination of transcriptomic and phenotypic profiling enabled fine-grained delineation of T cell states and functional programs modulated by tumor-derived exosomal cues. To systematically classify human peripheral blood T cells, we analyzed a high-dimensional CITE-seq dataset using an approach analogous to that applied in tumor-infiltrating T cell profiling. H5 files generated by Cell Ranger from 17 TNBC samples were processed, resulting in 179,169 high-quality cells that passed stringent filtering criteria. Subsequent clustering and downstream analyses were conducted using established R packages. To improve cluster resolution, antibody-derived tags (ADTs) were integrated with gene expression data, enabling finer discrimination of T cell subsets.
The success of data integration was quantitatively assessed using kBET, which measures the degree of batch mixing within the integrated dataset. A low rejection rate indicates that cells from different experimental batches are well interspersed, thereby reflecting effective batch correction.
To evaluate the robustness of integration across the dataset, the data were randomly partitioned into 20 subsets, and the average kBET p value was computed for each. The average p values remained consistently close to 1.0 across all subsets, indicating that batch effects were effectively mitigated and that the integration was stable across different partitions of the data (Supplementary Fig. 2a).
Comparable results were obtained when the same analysis was conducted at the cell-type level, where average p values across clusters were reported (Supplementary Fig. 2b) and aggregated subset results were visualized with boxplots (Supplementary Fig. 2c). The Supplementary Fig. 2c presents a box plot comparing the expected rejection rates with the observed rejection rates across these clusters. The results provide compelling evidence of successful batch correction. The observed median rejection rate is significantly lower than the expected median, which is near 1.0. The low observed rejection rates demonstrate that within each identified cell type, cells from different batches are well-interspersed. The high expected rejection rate represents the baseline rejection rate when cells are not corrected for batch effects, which would be close to 1.0. The substantial difference between these two distributions confirms that our integration method effectively removed technical variability while preserving the biological integrity of each cell type.
Furthermore, UMAP projections colored by the original GEM well (to evaluate batch mixing) and by the identified cell types (to evaluate biological structure) provided complementary visual confirmation of the successful integration (Supplementary Fig. 2d). These findings confirm that the integration successfully removed batch effects while preserving biological variation and avoiding spurious batch-driven structure.
Marker-based annotation was performed using a curated panel of canonical T cell markers derived from the literature, incorporating both transcriptomic and protein-level features to ensure robust classification.
We systematically identified and profiled 11 major T cell subsets, including CD4⁺, CD8⁺, NKT, T helper, Treg, and γδ T cells. These subsets were distinguished based on their characteristic marker expression patterns to provide a comprehensive overview of the T cell landscape (Supplementary Fig. 2e). We first assessed IFNG expression, a key effector cytokine indicative of T cell activation (Fig. 2a), followed by a comprehensive evaluation of 43 additional markers associated with T cell identity and functional states across all clusters (Supplementary Fig. 3). This integrative analysis delineated 14 transcriptionally distinct clusters, each defined by a unique constellation of marker expression signatures (Fig. 2b). Analysis of the single-cell RNA sequencing data revealed that the NKT cell cluster exhibited basal IFNG expression but showed upregulated levels of TNF and PD-L1 relative to other clusters. This expression profile distinguishes the NKT cluster as a small, phenotypically discrete subset within the broader T cell population. Among the five T follicular helper (Tfh)-associated clusters, two were predominantly defined by canonical Tfh markers. In contrast, two other clusters demonstrated a co-expression of Tfh and Th1 markers, while the final cluster displayed a mixed Tfh and Treg phenotype. Within the two Tfh.Th1 clusters, a specific subgroup, was notable for their co-expression of the immune checkpoint markers TIGIT, PD-1, and PD-L1. This subgroup formed a distinct island on the UMAP projection, suggesting unique transcriptional and functional characteristics, potentially indicative of an exhausted or regulatory state (Fig. 2b).
Fig. 2
Transcriptional and phenotypic reprogramming of human T cells in response to TNBC-derived exosomes. a UMAP plot of IFNG expression across the T cell landscape. b UMAP plot illustrating 14 transcriptionally distinct T cell clusters defined by combined gene and protein expression signatures. c Line plot depicting cell count distributions per cluster across exosome-treated samples. Incucyte live-cell imaging of TNBC cell killing by control and exosome-treated T cells. Activated primary human T cells were left untreated or pre-incubated with TNBC-derived exosomes (Exo-T cells) for 48 h and then co-cultured with d HCC1395 or e HCC70 target cells at an effector-to-target ratio of 1:5. Apoptotic tumor cells were quantified every 2 h for 48 h as caspase-3/7-positive objects using the Incucyte system. Data represent the mean ± SEM from three independent replicates. Statistical significance was determined by a two-tailed Mann–Whitney U test (****p < 0.0001; **p < 0.01). f–i Flow cytometry analysis of Treg (CD4+CD25⁺CD127−/low), Tfh (CD4+CXCR5+), TEX (CD8+PD−1⁺CD127−), γδ T cells (CD4−CD8−CD56−TCR γδ+), NKT (CD4−CD8−TCR γδ-CD56⁺). Antibodies included: PE anti-human CD4, APC anti-human CD8α, PE/Dazzle™ 594 anti-human CD127, PerCP/Cyanine5.5 anti-human TCR γ/δ, Brilliant Violet 421™ anti-human CD56, APC/Cyanine7 anti-human CD25, FITC anti-human CXCR5, and FITC anti-human PD-1. Data represent the mean ± SEM from three independent biological replicates. Statistical significance was determined by one-way ANOVA followed by Dunnett’s multiple comparisons test (****p < 0.0001; ***p < 0.001; **p < 0.01; *p < 0.05)
The overall composition of T-cell subsets was largely conserved between treated and untreated samples (Supplementary Fig. 4a). Similarly, the distribution of tumor cell subtypes of origin within treated T cells mirrored that observed in untreated mock controls (Supplementary Fig. 4b). Analysis of T-cell subset abundances across TNBC-treated and untreated samples revealed that BT549 displayed the highest overall T-cell counts (Supplementary Fig. 4c), particularly within the Th17.Treg and Tfh(IFNG+) populations (Supplementary Fig. 4d). Furthermore, basal-like TNBC samples exhibited consistently higher T-cell numbers than other tumor cell–of-origin subtypes across multiple T-cell categories (Supplementary Fig. 4e). These findings indicate that exosome exposure, while capable of eliciting transcriptional alterations, does not markedly perturb the proportional representation of major immune subsets.
By contrast, the distribution of DEGs varied substantially across conditions (Supplementary Fig. 4f), underscoring that the principal impact of exosome exposure is the transcriptional reprogramming of pre-existing cell types rather than broad shifts in cellular composition. This emphasizes the necessity of single-cell transcriptomic resolution to reveal nuanced but functionally consequential changes.
Quantitative comparisons revealed that the Th17.Treg and Tfh (IFNG⁺, CCR4⁺) clusters comprised the highest number of cells, whereas the NKT cluster contained the fewest (Fig. 2c). Within the γδ T cell population, two subclusters were identified, γδ T and γδ T.CD8⁺, with the latter predominating. The relative proportions of CD4⁺ and CD8⁺ T cell subclusters were broadly consistent across samples. However, samples from HCC1395 and MB231 displayed unique distributions of T cell cluster abundance, highlighting inter-sample heterogeneity (Fig. 2c).
To determine whether TNBC-derived exosomes alter the cytotoxic capacity of T cells, we performed Incucyte live-cell imaging of tumor cell apoptosis in co-cultures of primary human T cells and HCC1395 or HCC70 cells, with or without prior exposure of T cells to TNBC exosomes (Exo–T). In both TNBC models, T cells alone induced a progressive increase in caspase-3/7-positive tumor cells over 48 h, whereas Exo-T cells showed a consistently blunted apoptotic response. For HCC1395 targets, caspase-3/7 object counts in the Exo-T condition remained markedly lower than in control T cells throughout the assay (Fig. 2d). A similar, though somewhat less pronounced, impairment of killing was observed against HCC70 cells, where Exo-T cells again generated significantly fewer apoptotic events than control T cells (Fig. 2e). Representative Incucyte images at 48 h illustrate the decreased density of caspase-3/7-positive (green) tumor cells in wells containing Exo-T cells compared with wells containing untreated T cells (Supplementary Fig. 4g).
Flow cytometric validation of CITE-seq predictions showed that TNBC-derived exosomes remodel the T cell compartment (Supplementary Fig. 5a). Across multiple cell-line-derived exosome conditions, we observed a significant expansion of CD4⁺ Treg (CD25⁺CD127−/low) frequencies relative to untreated controls (Fig. 2f). Tfh cells also increased, albeit heterogeneously across lines, and were most pronounced outside the basal-like subtype (Fig. 2g). Within the CD8⁺ compartment, exosomes consistently elevated TEX (PD-1⁺CD127−) cells (Fig. 2h), aligning with the exhaustion signatures seen by CITE-seq. Concomitantly, CD56⁺ NKT-like frequencies declined under all exosome conditions (Supplementary Fig. 5b). In contrast, TCR γδ⁺ cells were selectively enriched, most notably in basal-like and mesenchymal contexts, mirroring the γδ T-cell cluster expansion in the UMAP projections (Fig. 2i). We also observed that the response of T cells, particularly within the Treg and TEX populations, was concentration dependent (5–160 µg), with higher exosome concentrations eliciting progressively stronger effects that eventually plateaued (Supplementary Fig. 5c). Collectively, these orthogonal datasets indicate that TNBC exosomes promote immunoregulatory skewing (Treg and Tfh gains), augment CD8⁺ TEX, suppress NKT-like representation, and expand γδ T cells, providing a mechanistic bridge between the single-cell transcriptional shifts and functional immunophenotypes.
T cell subset-specific transcriptomic responses to TNBC exosomes reveal functional heterogeneity
The pattern of DEGs exhibited substantial divergence from the distribution of cell numbers across clusters and samples. The highest DEG counts were observed in the MB453 sample within the Tfh (IFNG+, CCR4+) cluster and in the HCC1143 sample within the NKT cluster. Notably, no DEGs were detected in the Tfh.Th1 (IL4+, IL17+, IL7R−, AHR+) or γδ T cells (IFNG+, LAG3+, TBX21+) clusters (Fig. 3a).
Fig. 3
Transcriptional reprogramming and pathway enrichment across T cell subsets following TNBC exosome exposure. a Number of differentially expressed genes (DEGs) per sample within each detailed cluster, relative to the Mock sample; b Heatmap displaying the log₂ fold change of genes dysregulated in at least four samples within the same T cell type, compared to the Mock sample; c Enriched biological pathways identified in CD4/CD8 transitional clusters, based on DEG profiles; d Heatmap of log₂ fold changes for DEGs contributing to enriched pathways in CD4/CD8 cluster group samples; e Dot plot illustrating significantly enriched pathways in NKT and γδ T cell populations; f Heatmap of functional DEGs associated with enriched pathways in NKT and γδ T cell populations
A detailed compilation of all DEGs identified for each sample within each cluster is presented in Table S1. Genes dysregulated in at least four samples per cluster are depicted in a heatmap (Fig. 3b), with cluster-specific annotations indicated by numeric suffixes. These numeric designations, ranging from 1 to 9, correspond to the following T cell subtypes: (1) CD4.central memory (IFNG−, BCL6+), (2) CD4.Naive (IFNG−), (3) CD8.Naive (IFNG−), (4) CD8.Naive (IFNG−, TRGV10+, CD127−), (5) NKT (TNF+, PD-L1+, SPI1+, CXCR5+), (6) Tfh (IFNG+, CCR4+), (7) Th17.Treg (IFNG+, TNF+, IL22+, IL4+, LAMP-1+), (8) Treg (IFNG−, CXCR5+), (9) γδ T cells.CD8+ (IFNG+, LAG3+, TBX21+). The heatmap clustering of the 17 T cell samples treated with TNBC-derived exosomes revealed two distinct gene expression profiles: eight samples grouped in the left columns, including BT20, HCC70, MB436, HCC1395, BT549, HCC1599, HCC1187, MB453, and nine in the right columns encompassing HCC1937, HCC1143, HCC38, HCC1806, DU4475, Hs578T, MB231, MB157, and MB468 (Fig. 3b).
Among all subsets, NKT cells displayed the strongest transcriptional activation, marked by coordinated upregulation of immune regulatory genes, signaling molecules, and histone-associated transcripts. This profile suggests enhanced chromatin accessibility, elevated immune activation, and increased vesicular and metabolic activity, collectively supporting the heightened responsiveness of this population. NKT cells from the HCC1143 sample contributed most prominently to these changes. In contrast, CD4⁺ central memory T cells exhibited broad downregulation of genes involved in metabolic homeostasis, receptor signaling, vesicular transport, and cell-cycle control. These alterations indicate reduced activation potential under exosome exposure. However, simultaneous suppression of inhibitory regulators such as CISH and FOS suggests partial release of negative feedback constraints, which may facilitate compensatory activation. A similar pattern was observed in CD8⁺ naïve T cells, where gene downregulation appears to lift inhibitory signaling and may enhance proliferative competence. In a distinct CD8⁺ naïve subset lacking IFNG and CD127 but expressing TRGV10, downregulation of MIR155HG indicates reduced miR-155-mediated activation, suggesting impaired recruitment and cytokine responsiveness (Fig. 3b).
Within the Tfh(IFNG⁺) compartment, exosome exposure altered key signaling and metabolic pathways. Reduced expression of CCL3, LDLR, and RSAD2 points to diminished chemotactic capacity, disrupted lipid metabolism, and weakened antiviral defense, whereas upregulation of RGL4 reflects compensatory proliferative or signaling changes. Th17.Treg cells underwent notable regulatory reprogramming. Upregulation of ABCA1 suggests enhanced cholesterol efflux, while coordinated downregulation of inflammatory mediators and prosurvival genes indicates attenuated effector capacity and a shift toward a more quiescent or regulatory state. Classical Tregs demonstrated increased CXCR5 expression, consistent with enhanced follicular homing, together with broad suppression of effector-associated transcripts, reinforcing a tolerogenic phenotype. Finally, γδ CD8⁺ T cells showed decreased expression of cytoskeletal and interferon-induced genes, suggesting reduced motility and antiviral competence (Fig. 3b).
Among these DEGs, HCC70 and MB157 exhibited the highest overlap, with 49 common DEGs each, indicating significant contributions to the observed profiles. Conversely, HCC1187 displayed the least impact, with only five shared DEGs.
In conclusion, exposure to tumor-derived exosomes elicits profound immunomodulatory effects relative to untreated mock controls. The downregulation of key mediators such as CCL3, FUT7, and OSM attenuates T-cell trafficking and cytokine signaling, thereby compromising anti-tumor inflammatory responses. Concomitant suppression of transcriptional feedback regulators, including DUSP1, EGR1, and FOS, is indicative of sustained yet dysregulated T-cell activation. In parallel, reduced expression of co-stimulatory and inhibitory molecules such as CTLA4, HBEGF, and TNF highlights a multifaceted perturbation of immune synapse integrity. The upregulation of RTN4 further implicates endoplasmic reticulum stress pathways in impairing T-cell homeostasis and survival. Collectively, these transcriptional alterations delineate an immune-evasive phenotype that facilitates tumor progression.
Immunoregulatory and hormone-linked pathways define T cell subset responses to TNBC exosomes
To investigate the functional significance of DEGs within T cell subpopulations, we conducted enrichment analyses. The identified DEGs were systematically mapped to the MSigDB Hallmark database, enabling the characterization of enriched biological pathways. This analysis delineated both shared functional networks and subset-specific specializations across T cell populations, providing deeper insights into their molecular and immunological roles. Notably, significant pathway enrichment (adjusted p value < 0.05) was observed across three major cluster groups: CD4/CD8 clusters, Th/Treg clusters, and NKT/γδ clusters, with functionally convergent pathways enriched within each group.
Within the CD4/CD8 cluster group, the most prominent enriched pathways included ‘Interferon Gamma Response’, ‘TNF-α Signaling via NF-κB’, ‘Estrogen Response Late’, and ‘Estrogen Response Early.’ Among these, ‘Cholesterol Homeostasis’ exhibited the strongest enrichment in the MB157 sample, demonstrating the lowest adjusted p value across CD4/CD8.Naive (IFNG−) clusters (Fig. 3c).
To identify the most functionally impactful genes within the enriched pathways across cell types, we selected DEGs present in all pathways enriched in more than 10 sample-cluster combinations (Fig. 3d). Among the 35 identified genes, FOS was the only gene dysregulated across all four clusters, exhibiting upregulation in the CD4.central memory (IFNG−, BCL6+) subset while being downregulated in the remaining clusters.
Another upregulated gene, DUSP1, is upregulated in CD4.central memory (IFNG−, BCL6+) while downregulated in CD4.Naive (IFNG−) and CD8.Naive (IFNG−). The remaining influential genes were predominantly downregulated. Additionally, OSM, SOCS3, CD69 (a well-established early T-cell activation marker), and CISH were among the most frequently dysregulated DEGs, exhibiting downregulation across three CD4/CD8 clusters (Fig. 3d).
In the NKT-γδ cluster group, a distinctly different functional profile was observed, characterized by the enrichment of unique biological pathways. In the γδ T cells.CD8+ (IFNG+, LAG3+, TBX21+) cluster, the most significantly enriched pathways included ‘Androgen Response,’ ‘TNF-α Signaling via NF-κB,’ ‘p53 Pathway,’ and ‘Apical Junction.’ Conversely, in the NKT (TNF+, PD-L1+, SPI1+, CXCR5+) cluster, ‘IL-2/STAT5 Signaling’ was the most frequently enriched pathway across three samples (Fig. 3e).
Interestingly, ‘Cholesterol Homeostasis’ emerged as the most statistically enriched pathway in the MB157 sample; however, this enrichment was exclusively observed within the γδ T cells.CD8+ (IFNG+, LAG3+, TBX21+) cluster. Additionally, ‘Interferon Gamma Response,’ ‘Interferon Alpha Response,’ and ‘Fatty Acid Metabolism’ were distinctively enriched across both clusters. The pathways ‘Estrogen Response Late’ and ‘Estrogen Response Early’ were not only consistently enriched, as observed in the previous cluster groups, but also contributed to the unique functional signature of the γδ T cells.CD8+ (IFNG+, LAG3+, TBX21+) cluster (Fig. 3e).
Moreover, a total of 24 DEGs were identified within pathways enriched in more than five sample-cluster combinations across both γδ T cells and NKT cells. The color scale in the heatmap (Fig. 3f) reflects the mean log₂(fold-change) values calculated across all samples within each cell type. The majority of these genes were downregulated; however, seven genes, ANXA4, CD83, CTSZ, GLIPR2, IL15, RIPK2, and SYNGR2, were upregulated within the NKT (TNF+, PD-L1+, SPI1+, CXCR5+) cluster. Notably, among the 24 identified DEGs, no genes were shared between the two clusters, underscoring their functionally distinct transcriptional landscapes.
Pathway enrichment analysis in the Th/Treg compartment revealed several consistently overrepresented pathways, including TNF-α signaling via NF-κB, p53 signaling, inflammatory response, and IL-2/STAT5 signaling. Although these pathways appeared broadly across samples, marked variability in sample contribution was observed. Notably, the MB157 sample showed strong enrichment of cholesterol homeostasis, and all 17 samples contributed to pathway activation within the Treg (IFNG−, CXCR5+) cluster. In contrast, only the HCC70 sample showed significant pathway involvement within the Tfh/Treg (IFNG−, CCR10+, GATA3+) subset (Fig. 4a).
Fig. 4
Pathway enrichment analysis and exosome-mediated regulatory networks in Treg cell subsets. a Dot plot showing significantly enriched pathways in T helper (Th) and regulatory T cell (Treg) populations following exosomal exposure; b heatmap of functionally relevant differentially expressed genes (DEGs) contributing to enriched pathways within the Th-Treg T cell populations; c the alluvial plot visualizes the relationships between downregulated DEGs, specific T-cell populations (Treg, Th17.Treg, and Tfh.Treg), and the significantly enriched MSigDB Hallmark pathways across basal-like, mesenchymal, unclassified, and LAR TNBC subtypes
Across Th/Treg clusters, 120 genes were recurrently differentially expressed in pathways enriched in more than ten sample–cluster combinations. Most were downregulated, whereas a small subset, including ABCA1, CD79B, CLIC3, and MT1E, showed upregulation. FOSB demonstrated cluster-specific divergence, with upregulation in the Tfh/Th1 subset but significant suppression in multiple Treg and Th17/Treg clusters. Additional frequently downregulated genes included TNF, HBEGF, HMGCS1, IER3, IFIT3, and EGR1, indicating a predominant transcriptional repression signature across Th/Treg populations (Fig. 4b).
Pathway enrichment analysis underscored key biological processes across clusters, with pathways such as ‘Cholesterol Homeostasis’ and ‘TNF-α Signaling via NF-κB’ prominently featured. Furthermore, hormone-related pathways were enriched in most of the clusters, reflecting their potential role in modulating T cell function within the tumor microenvironment and highlighting the influence of endocrine signaling on immune responses in TNBC. These findings advance our understanding of T cell functionality, shedding light on the molecular underpinnings of their behavior and potential roles in immune modulation.
Suppressive role of Treg cells in the TNBC exosome-conditioned T-cell compartment
To test whether TNBC-derived exosomes skew T cells toward immunoregulatory states, we examined Treg-associated transcriptional programs and validated phenotypes by flow cytometry. TNBC-Exo exposure increased the frequency of CD4⁺CD25⁺CD127−/low Treg cells (Fig. 2f) and enriched Treg/Th17.Treg clusters by CITE-seq, indicating subset expansion rather than broad compositional drift (Figs. 2a–e and S4).
At the transcript level, ITGA4 and TSPAN2 were upregulated in Treg cells, suggesting enhanced trafficking and tissue retention consistent with recruitment into tumor sites. ABCG1 was elevated in both Treg and Th17.T_reg clusters, pointing to shared cholesterol handling programs often linked to immunoregulatory function. Upregulation of LAIR1 and TOX/TOX2 genes associated with inhibitory signaling and chronic stimulation supports a shift toward suppressive/exhaustion-related states within the Treg axis.
We next assessed downregulated DEGs across TNBC subtypes (basal-like, mesenchymal, LAR and unclassified) in Treg, Th17.Treg and Tfh.Treg populations (Fig. 4c). Processes reduced across these cells included lymphocyte/T-cell proliferation, cytokine production, and cell–cell adhesion/migration pathways. Rather than implying loss of Treg identity, these coordinated decreases are most consistent with tonic dampening of activation and motility programs under exosomal conditioning, i.e., a transcriptional state compatible with sustained immune regulation in the tumor milieu, not effector activation. Concordantly, metabolic pathways important for T-cell activation (acetyl-CoA and cholesterol biosynthesis) were also reduced, suggesting reprogramming toward lipid efflux and lower anabolic tone, a known feature of regulatory and exhaustion-prone states. Decreased DNA-templated and miRNA transcription further indicates global transcriptional restraint.
Apoptosis-related annotations were also diminished (e.g., extrinsic apoptotic signaling). We interpret this cautiously: reduced apoptotic signatures may reflect enhanced Treg persistence or general signaling quiescence, both of which would favor accumulation of immunoregulatory cells in the tumor microenvironment. Taken together, these data indicate that TNBC-Exosomes promote Treg expansion and imprint an immunosuppressive, metabolically restrained transcriptional state that is well positioned to dampen anti-tumor immunity (Fig. 4c).
Non-coding RNA networks mediate coordinated downregulation of T cell immune genes in TNBC
To enhance our understanding of gene expression dynamics in response to distinct TNBC types in T cell samples, we expanded our analysis to integrate exosomal non-coding RNA (ncRNA) data extracted from 17 TNBC cell lines, with T cell coding RNA profiles.
This approach was motivated by the hypothesis that tumor-derived ncRNAs significantly influence T cell gene expression. To improve analytical precision, we incorporated data from 35 exosome samples extracted from human plasma, including 7 healthy controls and 28 patient samples.
Differentially expressed miRNAs (DEMs) were identified from annotated datasets in both the TNBC cell line exosome samples and the patient plasma exosome samples. We applied a stringent filtering strategy to ensure the biological relevance of our findings. Only miRNAs that were significantly up- or downregulated in both the cell line-derived exosomes (relative to primary breast cell line exosomes) and the patient plasma exosomes (relative to healthy controls) were retained for further analysis. This dual-validation approach increased our confidence that the observed in vitro expression patterns in the cell lines were consistent with the in vivo expression profiles in human patients. In parallel, we identified dysregulated mRNAs from T cell samples that had been previously exposed to tumor-derived exosomes. These DEGs were obtained by comparing gene expression in each cancer sample to a mock-treated control, independent of clustering. The integrated analysis of these dysregulated mRNAs with the validated DEMs allowed us to construct a comprehensive network of exosome-mediated regulation, providing deeper insight into the molecular mechanisms governing T cell-tumor interactions in TNBC.
The dysregulated mRNAs and miRNAs that exhibited concordant expression patterns within the same samples and had documented interactions in the miRTarBase database were subsequently utilized to construct an miRNA-mRNA interaction network.
A total of ten downregulated genes, CDC42EP1, FOS, HBEGF, HMGCS1, IER3, OLAH, PRSS23, PTGIS, SLC6A9, and TNFSF9, were consistently observed across multiple samples. Concurrently, 20 significantly upregulated miRNAs were identified within the same samples, with their regulatory interactions previously validated in established databases (Fig. 5a, d).
Fig. 5
The miRNA expression and cytokine profiling reveal immunosuppressive remodeling of T cell function by TNBC-derived exosomes. a miRNA–mRNA interaction network depicting validated inhibitory relationships between upregulated miRNAs identified in TNBC cell line-derived exosomes and their corresponding downregulated mRNA targets in T cells exposed to TNBC exosomes, based on established interaction databases; flow cytometric analysis of CD4⁺CD25⁺CD127− Tregs following transfection with hsa-miR-98-5p mimic or control showing b percentage of CD4⁺CD25⁺CD127− Tregs among CD4⁺ T cells and c percentage of PD-1⁺ cells within the CD4⁺CD25⁺CD127− Treg population. Data are presented as mean ± SEM from independent experiments. Statistical significance was determined using an unpaired two-tailed Student’s t test. *p < 0.05; **p < 0.01. d Heatmap showing significantly downregulated mRNA targets in T cells, associated with the regulatory miRNAs; e Dot plot summarizing the integrated Luminex-derived cytokine profiles across all cancer-exosome-treated samples; Bar plots showing differential expression levels of key cytokines; f TNFα, g IL-13, h IL-1β, i IL-17α, j IL-2R, and k IL-6 across T cell samples treated with TNBC-derived exosomes, as measured by Luminex, relative to the untreated Mock control. Data are presented as mean ± SEM from 3 replicates. Statistical significance was determined using an ordinary one-way ANOVA. **p < 0.01; ****p < 0.0001; l UMAP plots displaying single-cell cytokine secretion profiles for control and exosome-treated samples
Downregulation of multiple genes by miRNAs contained in TNBC-derived exosomes impairs T cell function by disrupting essential signaling pathways that govern activation, proliferation, and anti-tumor immune responses. These exosomes deliver an immunosuppressive cargo that reprograms recipient T cells within the tumor microenvironment, thereby facilitating immune escape.
For example, TNFSF9, a co-stimulatory ligand normally expressed by antigen-presenting cells, enhances T cell proliferation, survival, and IFN-γ production through receptor engagement. Its downregulation diminishes this critical co-stimulatory signal. Similarly, repression of FOS, a key component of the AP-1 transcription factor complex, interferes with TCR downstream signaling and disrupts NFAT and NF-κB activation, leading to impaired cytokine production.
Alterations in metabolic pathways further contribute to dysfunction. Suppression of HMGCS1 and PTGIS perturbs lipid metabolism, shifting T cells toward a metabolic profile associated with exhaustion. Downregulation of SLC6A9, a glycine transporter, compromises amino acid availability and energy production, while reduced IER3 expression impairs cytokine regulation and increases susceptibility to apoptosis.
Additional targets reinforce this immunosuppressive phenotype. HBEGF downregulation disrupts survival and proliferative signaling; repression of PRSS23 interferes with cytokine processing or T cell mobility; and inhibition of CDC42EP1, a regulator of CDC42-dependent cytoskeletal remodeling, compromises immune synapse formation and T cell migration, thereby limiting tumor cell engagement.
Network topology analysis identified hsa-miR-98-5p as a highly connected node, targeting several of the downregulated genes, including HBEGF, TNFSF9, and IER3 (Fig. 5a). Based on this central positioning, we further investigated the functional consequences of hsa-miR-98-5p modulation on T cell phenotype. Transfection of T cells with a hsa-miR-98-5p mimic resulted in a significant increase in the proportion of CD4⁺CD25⁺CD127− Tregs compared to control-treated cells (Fig. 5b). In addition, a higher frequency of these Tregs expressed PD-1 (CD4⁺CD25⁺CD127−PD-1⁺) (Fig. 5c). While PD-1 expression has been associated with enhanced suppressive capacity in certain contexts, it can also reflect activation or exhaustion depending on the microenvironment. Therefore, our data indicate an expansion of a PD-1⁺ Treg subset, consistent with a potentially more suppressive phenotype, but functional suppression assays would be required to confirm this directly.
At the molecular level, overexpression of hsa-miR-98-5p was associated with reduced expression of HBEGF and TNFSF9, consistent with their predicted targeting (Fig. 5a). We also observed increased phosphorylation of STAT3 (Y705) without marked changes in total STAT3 levels (Supplementary Fig. 6a), suggesting activation of STAT3 signaling under these conditions. However, based on the current data, we cannot definitively conclude that STAT3 activation is directly mediated through suppression of HBEGF and/or TNFSF9. Rather, these findings demonstrate a correlation between hsa-miR-98-5p overexpression, modulation of its predicted targets, and enhanced STAT3 phosphorylation.
Collectively, these results support a model in which hsa-miR-98-5p functions as a regulatory hub associated with coordinated gene downregulation and altered T cell phenotype, including expansion of a PD-1⁺ Treg population and increased STAT3 activation. Further mechanistic studies will be required to establish direct causal links between specific target suppression and STAT3-driven Treg modulation.
TNBC exosomes harbor consistently upregulated lncRNAs with potential immunomodulatory roles
Similar to the microRNA analysis, the expression profiles of long non-coding RNAs (lncRNAs) were assessed to identify potential regulators in the tumor microenvironment. This was achieved by utilizing data from both TNBC cell line-derived exosomes and a cohort of 35 human plasma samples, which included seven healthy controls and 28 patient samples.
For further analysis, we applied a stringent filtering criterion to identify biologically relevant lncRNAs. Only those lncRNAs that demonstrated significant upregulation in both datasets were selected. Specifically, we required upregulation in the TNBC cell line-derived exosomes (relative to healthy primary breast cell line exosomes) and a corresponding upregulation in the patient plasma exosomes (relative to healthy controls). This dual-validation approach ensured that the identified lncRNAs were not only present in our in vitro models but also exhibited a consistent expression pattern in TNBC patients.
However, interrogation of the LncSEAv2 database did not reveal any documented interactions between the identified lncRNAs and DEGs, which were defined by comparing each TNBC exosome sample to the mock condition, independent of clustering.
The identified lncRNAs represent novel transcripts whose interactions with other RNA species warrant further investigation. Among them, ENST00000651540, ENST00000574245, and ENST00000674020 demonstrated the most pronounced upregulation, being overexpressed in eight, six, and five samples, respectively, suggesting their potential biological relevance in the lncRNA dataset (Supplementary Fig. 6b).
Bulk and single-cell cytokine profiling uncovers functional impairment of T cells by TNBC exosomes
To gain deeper insights into functional alterations, we assessed cytokine expression using the bulk and single-cell cytokine analysis platforms. In the bulk Luminex approach, cytokine levels were quantified from supernatants collected 48 h post-exposure to TNBC-derived exosomes. Sixteen cytokines were measured, including IFNγ, IL-10, IL-12p70, IL-13, IL-15, IL-17α, IL-1β, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-8, IL-9, MIF, and TNFα (Fig. 5e). Each cytokine was assessed in duplicate per sample, and statistical analyses were performed using nonparametric methods with Dunnett’s multiple comparisons test.
From this panel of cytokines, twelve exhibited differential expression across cancer samples relative to mock, based on adjusted p value < 0.05. TNFα, IL-13, and IL-1β varied significantly across all samples (Fig. 5f–h), and IL-17α, IL-2R, and IL-6 were significantly altered in all but BT549 (Fig. 5i–k). In contrast, IL-12p70, IL-4, IL-15, and MIF remained unchanged across samples (Supplementary Fig. 6c–f). Other cytokines displayed variable expression patterns, with MDA-MB-231 and MDA-MB-453 showing the most pronounced changes (11 cytokines), whereas BT549 exhibited the least variation, with significant alterations in only five cytokines (Fig. 5e).
To assess the impact of TNBC-derived exosomes on cytokine secretion at the single-cell level, we used the IsoCode single-cell cytokine analysis chips. This enabled high-dimensional cytokine profiling, capturing the functional secretion profiles of individual T cells 48–72 h after exposure to TNBC exosomes (Fig. 5l). This method provided a more granular and sensitive assessment, quantifying 30 cytokines and chemokines, including CCL11, GM-CSF, Granzyme B, IFNγ, IL-10, IL-12, IL-13, IL-15, IL-17A, IL-17F, IL-1β, IL-2, IL-21, IL-22, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IP-10, MCP-1, MCP-4, MIP-1α, MIP-1β, Perforin, RANTES, sCD137, sCD40L, TGF-β1, TNFα, and TNFβ. Consistent with the Luminex findings, cytokine expression was analyzed across cancer and control samples.
For both CD4⁺ and CD8⁺ T cells, the polyfunctional strength index (PSI) was decomposed into effector, stimulatory, chemotactic, regulatory, and inflammatory modules. In the CD8⁺ compartment, basal-like exosomes segregated into two patterns—some lines reduced PSI, whereas others increased PSI relative to the matched control. By contrast, exosomes from mesenchymal, LAR, and unclassified lines generally lowered CD8⁺ PSI, with BT549 as the notable exception (Fig. 6a). In the CD4⁺ compartment, PSI was predominantly suppressed across subtypes, with HCC70, HCC38, and MB231 showing the only clear increases (Supplementary Fig. 6g). For CD4⁺ T cells, most basal-like TNBC exosomes suppressed cytokine secretion relative to control (Fig. 6b). Decreases were prominent across type-1 effector mediators (IFN-γ, TNF-α/β), myeloid-activating (GM-CSF), chemotactic (IL-8, MIP-1α/β), and cytotoxic enzyme (Granzyme B) outputs. Two lines—MB468 and HCC1806—were clear exceptions, showing broad upregulation spanning growth/survival (IL-2, IL-15), helper-skewing (IL-4, IL-9), type-1 effector (IFN-γ, TNF-α/β), myeloid-activating (GM-CSF), chemotactic (IL-8, MIP-1α/β), the costimulatory ligand (sCD40L), and cytotoxic enzyme (Granzyme B), indicating a potent CD4⁺ activation signature despite basal-like classification.
Fig. 6
Single-cell cytokine profiling and Polyfunctional Strength Index analysis of T cells following exosomal treatment. a Stacked bar plot representing Polyfunctional Strength Index (PSI) for CD8⁺ T cells across five cytokine classes in four representative sample groups; b Heatmap displaying the log₂ fold changes of dysregulated cytokines in each experimental condition compared with Mock sample and TNBC subtypes detected by single cell cytokine profiling within CD4⁺ and CD8⁺ T cells
Mesenchymal exosomes enhanced CD4⁺ responses with coordinated increases across myeloid-activating (GM-CSF), cytotoxic enzyme (Granzyme B), type-1 effector (IFN-γ, TNF-α/β), growth/survival (IL-2), helper-skewing (IL-4, IL-9), Th17-associated (IL-17A), regulatory (IL-10), and chemotactic (IL-8, MIP-1α/β) categories. Unclassified exosomes (BT20, HCC1395) similarly induced broad upregulation—most strongly in BT20—across myeloid-activating, cytotoxic enzyme, type-1 effector, growth/survival, Th17-associated, and chemotactic mediators. The LAR line MB453 displayed a dominant effector profile with marked increases in Granzyme B, IFN-γ, IL-2, IL-9, MIP-1α/β, TGF-β1 (regulatory), and TNF-α, consistent with a highly activated helper-like CD4⁺ response.
For CD8⁺ T cells, most basal-like TNBC derived exosomes suppressed CD8⁺ cytokine/cytotoxic outputs relative to control (Fig. 6b). Decreases were evident across type-1 effector and chemotactic/cytotoxic mediators, including TNF-α, MIP-1α/β, and Granzyme B. Notably, MB468 and HCC1806 were clear exceptions, showing strong upregulation across multiple effector and stimulatory mediators, including GM-CSF, Granzyme B, IFN-γ, IL-2, IL-22, IL-5, IL-8, IL-9, MIP-1α/β, TNF-α/β indicating a potent CD8⁺ activation signature despite basal-like classification.
Mesenchymal exosomes largely enhanced CD8⁺ programs, characterized by coordinated increases in GM-CSF, Granzyme B, IFN-γ, IL-2, IL-22, IL-4, IL-8, IL-9, MIP-1α, MIP-1β, Perforin, TNF-α, and TNF-β.
Unclassified exosomes (BT20, HCC1395) induced a broad, consistent upregulation of CD8⁺ mediators, most prominently GM-CSF, Granzyme B, IFN-γ, IL-17A, IL-8, MIP-1α, and TNF-α, reflecting a pro-effector, recruitment-competent profile.
The LAR line MB453 exhibited a mixed effector signature, with increased Granzyme B, IL-22, and IL-9 alongside reduced IL-2 and Perforin.
Single-cell TCR sequencing identifies tumor-specific clonotypes following exosome-mediated T cell stimulation
All clonotype data, including VDJ chain compositions, were analyzed across both Gamma-Delta (γδ) and Alpha-Beta (αβ) T cell populations. The distribution of γδ TCR subtypes, classified based on Delta (δ) and Gamma (γ) chain associations, is depicted in Fig. 7a. This dataset encompasses V (variable), J (joining), and C (constant) gene segments, with the left panel representing all samples, including mock (control) samples, and the right panel displaying only tumor-derived samples for comparison. Each dot is color-coded according to its clonotype ID, illustrating the genetic diversity within distinct TCR repertoires. While we acknowledge that differences in cell counts between samples could visually influence the dot distribution, our focus remains on the identification of specific γδ T-cell receptor subtypes and their recurrence across conditions.
Fig. 7
Clonotypic landscape of γδ T cell repertoires reveals TNBC exosome-induced skewing and tumor-associated TCR signatures. a Distribution of γδ T cell receptor (TCR) subtypes based on the combinatorial pairing of delta (δ) and gamma (γ) chain gene segments, highlighting subtype diversity across samples; b Dot plot showing complementarity-determining region 3 (CDR3) sequences uniquely detected in γδ T cells from tumor-exosome-treated samples but absent in Mock controls, indicating tumor-associated clonotype emergence; c Frequency distribution of V-J-C gene combinations corresponding to the three most dominant γδ TCR clonotype identifiers, illustrating usage patterns in response to exosomal stimulation
The complementarity-determining region 3 (CDR3), critical for antigen recognition, is the most informative segment for assessing T-cell receptor (TCR) functionality. In our γδ TCR clonotype dataset, we identified six CDR3 sequences present across all tumor-derived samples but absent in mock samples (Fig. 7b). All of these unique CDR3 sequences were presented by “γδ T cells.CD8+” cells. Notably, DU4475 was the only sample harboring all six identified sequences. These sequences were associated with distinct V-J-C gene combinations, suggesting their selective expansion within the tumor microenvironment. Furthermore, the distribution of V-J-C gene combinations was displayed through the three most dominant γδ clonotype IDs, which were detected across all 10 tumor samples (Fig. 7c).
A comprehensive analysis of clonotype IDs uniquely detected in TNBC exosome-treated samples revealed γδ T cells (green dots) expansion in 10 cancer samples, whereas αβ T cells (red dots) were expanded in nine. Among αβ T cells, 743 unique clonotype IDs were identified as specific to TNBC exosome samples, of which the 200 most frequently occurring clonotypes are reported (Supplementary Fig. 7).
We further analyzed clonotype distributions within αβ TCR repertoires, where V, J, and C gene subtypes were detected at a much higher frequency (Supplementary Fig. 8).
Additionally, within the αβ TCR dataset, we identified 32 unique CDR3 sequences that were undetectable in Mock samples but observed in TNBC exosome-treated samples (Table 1 and Supplementary Fig. 9). These sequences were distributed across distinct V-J-C gene combinations, further supporting the notion that TNBC exosome-specific TCR clonotypes undergo selective expansion.
Table 1 List of 32 unique CDR3 sequences related to αβ TCR
Through the integration of scRNA-seq, non-coding RNA regulation data, cytokine expression patterns, and VDJ-seq, we provide a comprehensive synthesis of our findings. This analysis unifies shared and distinct mechanisms underlying exosome-mediated immunomodulation, specifically in T cells, offering deeper insights into their regulatory networks and functional implications in immune responses.
