Stress type–specific small extracellular vesicle signatures reflect divergent biological responses to acute psychosocial and physical challenges

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Moser, Tobias Tertel, Fabian Berg, Elisabeth M. Hummel, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7035709/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Maladaptive stress responses are associated with a variety of psychological and physical disorders, often characterized by molecular indicators of dysregulated stress pathways. Small extracellular vesicles (sEVs), which play a key role in intercellular communication, may be critically involved in these processes. In this study, we quantified sEV concentrations (specifically CD9 + , CD63 + , and CD81 + markers) in the plasma of twenty young, healthy men before and after exposure to both acute psychosocial and physical stress, using imaging flow cytometry (IFCM). All participants showed significant increases in cortisol, catecholamines, and circulating cell-free DNA (cfDNA) following both stressors. In contrast, sEVs were significantly elevated only in response to physical stress. Physical stress induced a rapid increase in sEV release, particularly in CD9- and CD63-positive vesicles, followed by a return to baseline within 40 minutes. Psychosocial stress, however, triggered more variable sEV responses across individuals. Importantly, our classification approach using recursive partitioning revealed distinct sEV patterns associated with psychosocial and physical stress, with highest discriminatory value for CD44⁺ sEVs. These findings indicate that psychosocial and physical stress elicit distinct sEV signatures, which may reflect differential stress communication pathways and highlight their potential as biomarkers for stress-related processes and as possible targets for the effects of psychosocial exposures, including early adversity and trauma. Health sciences/Biomarkers Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Stress is a global burden with significant psychological, physiological, and economic consequences. In Western industrialized nations alone, stress-related costs exceed 200 billion USD annually, reflecting its association with numerous physical and mental disorders 1 . Despite its profound impact, the molecular pathways linking stress to disease remain incompletely understood, underscoring the need for deeper insights into stress-induced physiological processes to inform effective interventions. Since the foundational work of Cannon and Selye 2 – 4 , the field of molecular stress physiology has evolved substantially. A particularly promising area of emerging research focuses on extracellular vesicles (EVs) - including small extracellular vesicles (sEVs) in the exosome size range (70–150 nm 5 , 6 ), microvesicles (100–1000 nm 7 , 8 ) and apoptotic vesicles ( 5 µm 9 – 11 ). These vesicles are critical mediators of intercellular communication, enabling targeted signaling across short and long distances. Exosomes originate as intraluminal vesicles (ILVs) within multivesicular endosomes (MVEs) and are released upon fusion with the plasma membrane 12 . In contrast, microvesicles and apoptotic vesicles are formed by direct budding from the plasma membrane or fragmentation of dying cells, respectively. It is now evident that virtually all cells release EVs via distinct mechanisms, and EVs have emerged as central players in indirect intercellular communication. However, distinguishing between EV subtypes of similar size remains a major challenge. Although many EV preparations exhibit biological activity, some vesicles may primarily function in cellular waste disposal, transporting non-processable byproducts to clearance organs such as the liver or kidneys. While we are not yet able to reliably distinguish EVs based on function, their cell type–specific molecular signatures make them promising biomarkers and tools for probing complex physiological responses. Due to methodological limitations, current isolation techniques cannot reliably distinguish exosomes from other sEVs, necessitating the analysis of heterogeneous populations. These vesicles carry lipids, proteins, and nucleic acids such as mRNA and miRNA - initially considered exclusive to exosomes 13 – 15 . More recent findings show that sEV subtypes differ in composition: some carry exosomal proteins but lack RNA, while others transport RNA with few exosomal surface markers 16 . These observations underscore the complexity of sEV biology and suggest that surface proteins, particularly integrins, may regulate interactions with target cells 17 , 18 . Because stress triggers complex systemic responses that involve both heightened metabolic demands and precise intercellular communication, sEVs plausibly play a central role orchestrating these adaptive processes. The brain, though constituting only about 2% of total body weight, accounts for roughly 20% of the body's total energy consumption 19 , 20 . This immense energy demand not only reflects the brain’s continuous processing and integration of internal and external stimuli but may also support its role in coordinating systemic responses to stress via sEV-mediated signaling. Similarly, skeletal muscle, comprising approximately 40% of body mass, undergoes substantial metabolic reprogramming during physical exertion and stress 21 . The high energy turnover in both tissues suggests that part of their metabolic budget may be allocated to the production and release of sEVs, which serve as metabolically costly but highly efficient vehicles for systemic communication. This mechanism could allow the brain and muscles to modulate peripheral physiological systems in a stressor-specific and temporally precise manner, thus enhancing the organism's capacity to adapt to diverse psychosocial and physical challenges. Crucially, sEVs can traverse the blood-brain barrier bidirectionally 22 , facilitating a dynamic exchange of molecular information between the central nervous system and peripheral tissues. Given the distinct emotional and physiological signatures of psychosocial versus physical stress, sEVs may carry stressor-specific molecular cargo that fine-tunes metabolic and behavioral responses. This positions them not merely as by-products of cellular activity, but as active mediators in psychophysiological regulation and stress adaptation. Although EV dynamics have been studied in the context of physical exertion and stress-related diseases 23 – 34 , direct comparisons of sEV responses to acute psychosocial versus physical stress in healthy individuals are lacking. Addressing this gap, the present study aimed to identify distinct sEV populations in plasma using imaging flow cytometry (IFCM; 35 – 40 ). For this purpose, we re-analyzed plasma samples from a previous study 41 , collected before and after psychosocial (Trier Social Stress Test; 42 ) and physical stress (treadmill ergometry; 41 ). In this study, we previously demonstrated that plasma levels of cortisol, adrenaline, noradrenaline, and cfDNA significantly increased under both stress conditions before gradually returning toward baseline. However, the Social Emotional Response Scale (SERS) revealed distinctly different emotional responses: "Tense Arousal" dominated after physical stress, while "Self-Directed Emotions" and "Anxiety" were predominant following psychosocial stress. Since conventional biomarker analyses failed to capture these differences in individual stress perception, analyzing sEVs as potential novel biomarkers of stress processing appeared promising. To this end, we analyzed plasma samples from 20 healthy young men collected at five time points before and after stress exposure (–2, + 2, +15, + 30, +40 min), using a validated panel of 23 antibodies (Table 1 ), primarily targeting sEVs of hematopoietic origin. Following Brahmer et al., who coined the term “ExerVs” for sEVs released during physical exertion 24 , we hypothesized that psychosocial stress would elicit a unique sEV subpopulation, which we termed “PsychEVs.” Additionally, we proposed the existence of a shared population, “StressEVs,” whose levels increase following both stress modalities. In the present study, i) we assessed the day-to-day stability of plasma sEV expression under baseline conditions; ii) we analyzed changes in sEV levels following acute psychosocial and physical stress exposure; and iii) we applied a mathematical classification model, including machine learning approaches, to evaluate whether our panel could reliably distinguish between the two stress types based on sEVs. By characterizing differential sEV responses to psychosocial and physical stress, this study aimed to elucidate their role in systemic stress adaptation and to identify potential biomarkers for stress-related disorders. MATERIAL AND METHODS Experimental model and subject details Participants (n = 20) were healthy male sport science students, aged between 18 and 36 years (mean = 23.3 ± 3.8 (SD)), with a normal body mass index (mean = 23.4 ± 1.5). They had no history of or current mental health disorders, no chronic or acute physical illnesses, and were not taking any medications or drugs at the time of the study. Participants refrained from exercise for 24 hours before testing and consumed a standardized breakfast on the morning of the tests. As a pilot study, only male participants were recruited to minimize confounding variables and eliminate potential effects related to the female menstrual cycle. Furthermore, all stress tests were conducted at either 9 a.m. or 11 a.m. to minimize cortisol diurnal cycle variations and potential influence of lunchtime food intake. All participants provided written informed consent prior to participation. The study was approved by the local ethics committee of the Faculty of Psychology at Ruhr University Bochum (reference number 153/2014) and conducted in accordance with the Declaration of Helsinki. Method details Participants were exposed to acute psychosocial and physical stressors in a randomized order on separate days. Stress inductions and testing were spaced at least 2 days apart. Half of the participants completed the TSST first, while the other half began with the exercise protocol. Testing order was assigned pseudo-randomly. Upon arrival, participants completed a Physical Activity Readiness Questionnaire (PAR-Q; 43 ), reviewed by a sports physician, and had a venous catheter inserted 45 minutes before stress induction. After completing questionnaires for 25 minutes, they rested until the stress protocol began. Blood and saliva samples were collected before, and at 2-, 15-, 30-, and 40-minutes post-stress. Participants completed the Social Emotional Response Scale (SERS) at four time points (-2, + 2, +15, and + 30 minutes), rating their arousal, self-directed emotions, and anxiety on a scale from 1 (not at all) to 4 (a lot). Induction of psychosocial stress Psychosocial stress was induced using the Trier Social Stress Test (TSST), which includes preparation, free speech, and an unanticipated math task in front of judges and a camera. The TSST reliably activates the hypothalamic-pituitary-adrenal (HPA) axis, causing significant cortisol elevation due to the uncontrollability and social evaluative threat elements 42 , 44 . Induction of physical exercise stress To induce physical stress, participants underwent an exhaustive treadmill exercise protocol designed to match the duration of the Trier Social Stress Test (TSST) (10–15 minutes). The protocol began with a 5-minute warm-up walk at 1.0 m/s on a treadmill with a 15% incline. Thereafter, the speed increased by 0.2 m/s every 30 seconds until the participant reached subjective exhaustion, at which point the treadmill was stopped. Plasma preparation and cfDNA quantification Five milliliters of whole blood were collected at each time point in EDTA collection tubes (EDTA Monovettes, Sarstedt, Germany) and immediately centrifuged at 1600 × g for 10 minutes at 4°C. The plasma was transferred to a fresh tube and subjected to a second centrifugation for 10 minutes at 16,000 × g at 4°C. Subsequently, the plasma was filtered through a 0.8 µm filter, and aliquots were stored at − 80°C until further analysis. cfDNA was extracted from 0.9 ml of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany), widely regarded as the gold standard for cfDNA extraction, following the manufacturer's protocol. The elution was performed in a final volume of 100 µl of H 2 O. Quantitative PCR (qPCR) and Hormonal Analysis Quantification of cell-free DNA (cfDNA) was performed using qPCR as described previously 41 . Briefly, primers targeting 70–110 bp amplicons were employed, and a high-affinity, highly sensitive in-house BDNF assay was adapted for cfDNA quantification. qPCR reactions were carried out using the CFX384 Touch™ Real-Time PCR Detection System (Biorad, Hercules; USA), with triplicate assays and standard curves derived from artificial gene fragments. Plasma cortisol levels were measured by commercial ELISA (Demeditec, Kiel; Germany), and plasma catecholamines (adrenaline and noradrenaline) were quantified by high-performance liquid chromatography (HPLC) at the Laboratory for Stress Monitoring (LSM, Göttingen, Germany), following a solvent extraction protocol adapted from Smedes et al. (ref 45). For complete methodological details, see 41 . Imaging flow cytometry (IFCM) To analyze PsychEVs, ExerVs, and StressEVs, an imaging flow cytometry (IFCM) approach was employed using the Amnis ImageStreamX Mk II (Luminex, USA). IFCM combines flow cytometry's throughput with microscopy's precision, allowing high-resolution single-vesicle analysis of sEVs. Plasma samples from the cfDNA study frozen at -80°C were again centrifuged after thawing at 10.000 × g for 10 minutes at 4°C to remove cryoprecipitates. The supernatant was transferred to a fresh tube, and 10 µL aliquots of plasma were incubated with 10 µL of fluorochrome-conjugated antibodies targeting EV markers diluted in PBS, as indicated in Table 1 . Samples were incubated at room temperature for 1 hour in the dark to ensure specific labeling, followed by a dilution in PBS to 100 µL final volume. Controls—antibody-only, buffer-only, isotype, and detergent-treated—ensured background assessment per MIFlowCyt-EV standards 45 . The ImageStreamX Mk II operated at 60x magnification with a low flow rate (0.3795 ± 0.0003 µL/min), acquiring data over 5 minutes per well to optimize single-particle detection. Fluorescence and scatter plots were used to gate EV populations, quantify concentrations, and validate labeling efficiency. 38 , 39 . Data were processed using IDEAS 6.2 software (Luminex), employing customized masks and spot-counting features to identify and quantify individual sEVs. Fluorescence and scatter plots were used to gate populations based on labeling intensity and side scatter, and EV concentrations were calculated. This high-resolution IFCM approach allowed precise EV subpopulation analysis, revealing profiles linked to psychosocial and physical stress. In addition, performing IFCM adheres to the Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines 45 – 47 , which recommend strict quantification, imaging, and molecular characterization. Contrary to widespread assumptions, transmission electron microscopy (TEM), Western blotting (WB), and nanoparticle tracking analysis (NTA) are not mandatory, as they often lack the resolution needed for precise characterization. Table 1 Information on the antibodies Antibody Conjugate Clone Order Nr. Company Isotyp Volume CTLA-4 PE BNI3 555853 BD Bioscience mouse IgG2a, κ 0.10 CD9 PE MEM-61 1P-208-T100 EXBIO mouse IgG1, κ 0.25 CD13 PE QA19A12 111003 BioLegend rat IgG2a, κ 0.25 CD14 APC REA599 130-110-520 Miltenyi human IgG1, rec 0.10 CD16 PE 3G8 555407 BD Bioscience mouse IgG1, κ 0.25 CD24 PE M1/69 130-102-732 Miltenyi rat IgG2bκ 0.25 CD41 AF488 MEM-06 A4-309-T100 EXBIO mouse IgG1, κ 0.25 CD44 APC MEM-85 1A-221-T100 EXBIO mouse IgG2a, κ 0.25 CD61 FITC SZ21 IM1758 Beckman Coulter mouse IgG1, κ 0.25 CD63 APC MEM-259 1A-343-T100 EXBIO mouse IgG1, κ 0.25 CD66b APC 6/40c -* LeukoCom mouse IgG1, κ 0.10 CD81 FITC JS64 B25329 Beckman Coulter mouse IgG2a, κ 0.25 CD82 PE ASL-24 342104 BioLegend mouse IgG1, κ 0.25 CD90 APC 5,00E + 10 1A-652-T100 EXBIO mouse IgG1, κ 0.10 CD100 PE 133-1C6 1P-772-T100 EXBIO mouse IgM, κ 0.25 CD171 AF647 L1-OV198.5 371607 BioLegend mouse IgG2a, κ 0.10 CD206 BV421 15.2 321126 BioLegend mouse IgG1, κ 0.10 CD227 FITC HMPV 559774 BD Bioscience mouse IgG1, κ 0.25 CD235a APC GA-R2 551336 BD Bioscience mouse IgG2a, κ 0.25 HLA-ABC FITC B9.12.1 IM1838U Beckman Coulter mouse IgG2a, κ 0.25 HLA-DR ECD Immu-357 B92438 Beckman Coulter mouse IgG1, κ 0.25 PD-L1 PE 29E.2A3 329705 BioLegend mouse IgG2a, κ 0.25 PS AF488 1H6 16–256 Sigma-Aldrich mouse IgG1, κ 0.10 Listed above are all antibodies used in this study, including conjugate, clone, catalog number, manufacturer, isotype, and dilution. Statistical analysis All statistical analyses were performed using R Studio (version 2024.12.1 + 563). To minimize the risk of false-positive findings, the number of statistical tests was reduced to those necessary to address the study's primary aims. To ensure comparability of baseline sEV levels between psychosocial and physical stress conditions, Bonferroni-corrected paired t-tests were conducted for all 23 sEV subpopulations. Subsequently, repeated-measures analyses of variance (rmANOVA) were applied to test for time × stress-type interactions for each sEV marker. If a significant interaction was observed, follow-up paired t-tests (baseline vs. +2, + 15, +30, and + 45 minutes) were performed. Bonferroni correction was used to adjust for multiple comparisons. To assess the potential of sEV profiles discriminating between stress types, a classification model based on recursive partitioning was employed (rpart package, complexity parameter = 0.05, 10-fold cross-validation) 48 . Half of the data from both stress conditions was randomly selected for model build and training ( n = 20), while the remaining half served as the testing set ( n = 20). Stress conditions were alternated across folds to avoid bias. The full sEV panel across all time points was used as input for the model. Outlier removal was conducted prior to all parametric tests. For each combination of stress type and time point (2 × 5 = 10 per marker), the median and Median Absolute Deviation (MAD) were calculated. Values outside the range of median ± 3×MAD were considered outliers and removed 49 . To preserve the integrity of within-subject comparisons, participants with missing values were excluded from the respective test. However, all available data were retained for classification analysis. Normality assumptions were assessed using Shapiro–Wilk tests for each marker across the 10 condition × time point combinations. Due to violations, CD61, CD81, and CD100 underwent square root transformation to better meet test assumptions. CD9, CD44, and CD171 did not require transformation. However, all other markers were ln-transformed. Transformation was applied selectively to preserve interpretability where possible. All data generated during this study are provided in Supplemental Data File S3. The R script used for the analysis is available in Supplemental Data File S4. RESULTS Psychosocial as well as physical stress leads to significant increases in cortisol, catecholamines, and cell-free DNA . This study aimed to investigate the effects of acute psychosocial and physical stress on sEV release and correlate these effects with hormonal and inflammatory markers. We explored the differential response of sEV populations, measured by 23 different sEV markers, to better understand the distinct physiological mechanisms activated by each stress type. Each participant provided blood samples at multiple time points: before stress exposure (baseline), and at 2-, 15-, 30-, and 40-minutes post-stress. As previously reported in detail 50 , both stress tests led to significant increases in plasma concentrations of cortisol, adrenaline/noradrenaline, and circulating cell-free nucleic acids in all participants (Figure S1 ). Baseline levels of sEV markers are highly reproducible To evaluate the stability and suitability of the selected sEV markers as stress-related biomarkers, we first assessed inter- and intra-individual variation in baseline plasma levels across both study sessions. Establishing baseline stability is essential to ensure that observed changes reflect stress-induced dynamics rather than day-to-day variability. Among the 23 surface markers analyzed, 21 showed no significant differences between the two baseline conditions (psychosocial vs. physical stress), indicating high reproducibility of marker detection across experimental days (Fig. 1 ). CD44⁺ (t(14) = -2.42, p = .03, d = -0.62, 95% CI [-1.41, -0.16]) and CD235a⁺ sEVs (t(14) = -2.36, p = .03, d = -0.61, 95% CI [-1.20, -0.13]) were significantly different, although their distribution curves showed substantial overlap, suggesting that these minor deviations may reflect normal inter-session variation rather than biologically meaningful shifts. Overall, plasma sEV profiles at baseline were largely consistent across both conditions. CD9 + , CD41 + , and CD81 + sEVs dominate the plasma sEV profile Next, we quantified the absolute plasma concentrations of each sEV subset based on marker expression, reported in objects per milliliter of plasma. CD13⁺, CD14⁺, CD24⁺, CD44⁺, CD63⁺, and CD90⁺ sEVs were typically found at concentrations around 10⁵ objects/mL. Intermediate levels (~ 10⁶ objects/mL) were observed for CD16⁺, CD61⁺, CD66b⁺, CD82⁺, CD100⁺, CD206⁺, CD227⁺, CD235a⁺, HLA-DR⁺, and PD-L1⁺ sEVs. The most abundant marker-positive sEV subsets included CD9⁺, CD41⁺, CD81⁺, CD171⁺, CTLA-4⁺, HLA-ABC⁺, and PS⁺ sEVs, often exceeding 10⁷ objects/mL. These findings highlight the pronounced heterogeneity in sEV abundance, with tetraspanin-positive (CD9⁺, CD81⁺) and platelet-derived (CD41⁺) vesicles consistently constituting the most abundant subsets. CD13 + , CD14 + , and CD41 + sEVs increase transiently after physical stress We next investigated whether acute stress exposure induced dynamic changes in sEV subpopulations over time and whether such responses differed between psychosocial and physical stress. Repeated-measures ANOVA identified significant interaction effects between time and stress type for eight markers: CD9, CD13, CD14, CD16, CD41, CD44, CD63, and HLA-DR. Follow-up Bonferroni-corrected paired t-tests indicated that CD13⁺ (aminopeptidase N, associated with myeloid lineage), CD14⁺ (monocytic), CD16⁺ (commonly associated with NK cells and non-classical monocytes), CD41⁺ (platelet-derived), CD44⁺ (expressed on mesenchymal stem cells, activated T cells, and monocytes), and HLA-DR⁺ (antigen-presenting/activated cells) sEVs significantly increased at + 2 minutes after physical stress (p < .05; Fig. 2 ). Several markers remained elevated beyond this point: CD9⁺, CD13⁺, CD41⁺, CD44⁺, and HLA-DR⁺ sEVs at + 15 minutes, and CD16⁺ at + 30 minutes. CD63⁺ sEVs showed a numerical increase at + 2 minutes (p = .058), though this did not reach statistical significance. Likewise, none of these markers did reach statistical significance in response to psychosocial stress. Full statistical details are presented in Table S1 . The remaining 15 markers, which did not yield significant interaction effects, are shown descriptively in Figure S2 . These findings suggest that physical stress elicits a reproducible, short-term release of specific sEV subpopulations - predominantly derived from platelets and myeloid-lineage immune cells - whereas psychosocial stress results in more heterogeneous and less consistent sEV dynamics. CD44 + sEV concentrations enable classification of stress types Lastly, we assessed the suitability of the sEV panel as a whole discriminating between the two stress types. Therefore, we applied a classification analysis using recursive partitioning that explored whether plasma sEV profiles could distinguish between psychosocial and physical stress responses. The model was build using 50% ( n = 20) of all 23 marker-positive sEV subpopulations across five time points and both conditions. Within the training set, the model classified 56% of observations as psychosocial and 44% as physical stress (Fig. 3 A). The initial split was based on CD44⁺ sEV concentration, with a threshold of 9.2 × 10⁴ objects/mL identified as most discriminative. Probabilities for correct assignment reached 75% for psychosocial and 82% for physical stress (Fig. 3 B). CD44⁺ sEVs showed the highest variable importance, followed by CD16⁺, CTLA-4⁺, HLA-DR⁺, CD81⁺, and CD41⁺ subsets (Fig. 3 C). ROC curve analysis revealed an area under the curve (AUC) of 0.78 in the training and 0.76 in the testing dataset (Fig. 3 D). Sensitivity and specificity were 0.84 and 0.72 in the training set, and 0.74 and 0.78 in the test set, respectively. Overall classification accuracy exceeded 76% across both datasets (Fig. 3 E–F), indicating preliminary but promising classification outcome. Hence, these findings show that the physiological response to psychosocial and physical stress is related to distinguishable profiles of hematopoietic sEV markers. DISCUSSION The relationship between stress and health has long been a focus of scientific inquiry, particularly regarding the distinct physiological responses triggered by different stressors. Small extracellular vesicles have recently gained recognition as both mediators and potential biomarkers of stress-related processes, reflecting the underlying molecular mechanisms of adaptation and systemic communication. In this study, we analyzed plasma sEV profiles in healthy young men following acute psychosocial and physical stress, using a within-subject design and high-resolution single-vesicle analysis via imaging flow cytometry (IFCM). We first examined the baseline stability of plasma sEVs across different days. Most sEV markers showed stable baseline concentrations across test days, underscoring their potential as reliable stress-related biomarkers. This temporal consistency aligns with preclinical data on circadian regulation of EV release in animal models 51 . Although corresponding human data remains limited, our findings suggest that at least a subset of sEV markers maintain sufficient stability to support longitudinal monitoring in clinical contexts. The stress-related analyses revealed that physical stress triggered rapid and reproducible increases in several sEV subtypes, including CD13⁺, CD14⁺, CD41⁺, CD44⁺, and HLA-DR⁺ vesicles - predominantly associated with cells of myeloid (e.g., monocytes) and platelet origin. The analyzed hormones and cell-free DNA exhibited significant elevations following both stressors, followed by a rapid homeostatic return to baseline 41 (Figure S1 ). These changes were most pronounced within the first 15 to 30 minutes post-stress. In contrast, sEVs displayed this consistent response pattern only after physical stress. Following psychosocial stress, however, a pronounced dysregulation of sEVs was observed, characterized by marked interindividual variability and a lack of uniform return to baseline at the group level. This variability likely reflects complex individual stress-processing mechanisms rather than an absence of biological response and aligns with prior studies linking extracellular vesicles to mental health and psychological stress. 27 , 28 , 30 , 52 – 54 . A classification model based on CD44⁺ and other responsive sEV subtypes achieved moderate separation between stressor types (AUC 0.76–0.78). CD44⁺ vesicles, expressed by various immune and stromal cells and known to regulate cell adhesion and migration during inflammation, emerged as the most informative feature, alongside CD16⁺, CTLA-4⁺, and HLA-DR⁺ vesicles. While these results are promising, they should be interpreted with caution due to the limited training set, the risk of overfitting and the need for supporting data on the diurnal stability of each marker. Notably, the model's performance appeared to be primarily driven by the more consistent sEV responses observed after physical stress. Our antibody panel focused on sEVs of hematopoietic origin, which represent a major component of the circulating EV pool and are responsive to immune and vascular activation. The predominance of CD9⁺, CD81⁺, and CD41⁺ vesicles align with earlier reports from healthy plasma donors 55 , suggesting that tetraspanin- and platelet-derived sEVs dominate the basal plasma profile. In contrast, CD63⁺ vesicles were less abundant, possibly reflecting differences in biogenesis or clearance. The observed increases in CD13⁺, CD14⁺, and HLA-DR⁺ vesicles are consistent with vesicle release from myeloid immune cells such as monocytes or dendritic cells, although definitive assignment to cell types requires co-staining or cargo-based analyses. Methodologically, IFCM enabled direct quantification of surface marker-positive sEVs in minimally processed plasma, preserving physiological composition and allowing robust time-resolved analyses. This is a key advantage for in vivo studies of dynamic EV release. However, the lack of co-expression data limits resolution of EV subtypes and restricts inferences about cellular origin. Future studies should incorporate multiplexed antibody panels (e.g., multi-channel IFCM or barcoding approaches) and platform-independent workflows to support diagnostic applicability. However, several limitations must be acknowledged. The male-only cohort limits generalizability, and the 40-minute observation window may have missed delayed sEV responses. The absence of molecular cargo profiling restricts insight into functional content. While the sample size aligns with prior EV stress studies 24 , 26 , 31 , 32 , 34 , 56 , 57 , it remains modest and increases the risk of overfitting. These findings should therefore be interpreted with caution. External validation in larger, more diverse cohorts will be essential to confirm the observed patterns and assess their translational relevance. Future studies should also refine classification models using independent datasets and optimized feature selection strategies. In summary, our data demonstrate that acute physical stress reliably triggers the release of distinct sEV subtypes, particularly platelet- and immune-derived vesicles. In contrast, psychosocial stress elicits more heterogeneous responses. Plasma sEV profiling may thus offer novel insights into stress physiology and, if validated, could support individualized diagnostics in stress-related disorders. In particular, the concepts of psychosocial stress-associated vesicles (PsychEVs) and generalized stress-responsive vesicles (StressEVs) warrant further investigation and validation in future studies. Declarations CONFLICT OF INTERESTS The authors declare no conflict of interests. FUNDING This research received no external funding. Author Contribution AUTHOR CONTRIBUTIONSDAM designed the study. DAM, FB, TT, and EMH performed the experiments. DAM, FB, TT, PP, BG, and RK analyzed the data and interpreted the results. DAM drafted the initial manuscript, and all authors reviewed and edited the final version. Acknowledgement The authors extend their gratitude to Angelika Eibl and Maresa Fisch for their invaluable medical assistance in conducting the experiments and collecting blood samples. We acknowledge support by the Open Access Publication Funds of the Ruhr-Universität Bochum. Data Availability Data are provided in the manuscript's supplementary information files. 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Effects of Acute Aerobic Exercise on Rats Serum Extracellular Vesicles Diameter, Concentration and Small RNAs Content. Front. Physiol. 9 , 532 (2018). Additional Declarations No competing interests reported. Supplementary Files sEVTableStresstypespecificsEVdataS1.xlsx sEVStresstypespecificsmallextracellularvesiclesignaturesdataS3.xlsx sEVStresstypespecificsmallextracellularvesiclesignaturesRS4.rmd SupplementalFIGURES.dotx Cite Share Download PDF Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviews received at journal 28 Jul, 2025 Reviews received at journal 28 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor assigned by journal 16 Jul, 2025 Editor invited by journal 16 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7035709","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486896004,"identity":"70f31d72-a94e-4d08-b5ea-6146ae5e50cc","order_by":0,"name":"Dirk A. Moser","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDCCwwzMUBbzAQbGBhAiXgtbApFaDsC18BgQp4XvOO9jA8YcG3t+6TPfPvPuYJDtJ6RF8jC7cQLjtrTEmX25m2fznmEwnknIGoPDbMwHGLcdTjA4w7uZmbeNIXHDASK12Nuf4XkM1rKfGC1Ahx1m3MDDwwyxhbBf2JgNEoF+mXGGzZhx7hkJ4xmEbOE7f4xZ4uM2YIj1MD9meLvDRra/gZA1IJCAYEoQo34UjIJRMApGASEAAOR5O02oHUROAAAAAElFTkSuQmCC","orcid":"","institution":"Ruhr-University Bochum","correspondingAuthor":true,"prefix":"","firstName":"Dirk","middleName":"A.","lastName":"Moser","suffix":""},{"id":486896005,"identity":"395baa1e-0ae8-4a54-8309-38b96612413d","order_by":1,"name":"Tobias Tertel","email":"","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Tertel","suffix":""},{"id":486896007,"identity":"b3e9c96a-f71c-4743-8593-9a006a78d345","order_by":2,"name":"Fabian Berg","email":"","orcid":"","institution":"Ruhr-University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Fabian","middleName":"","lastName":"Berg","suffix":""},{"id":486896008,"identity":"e0a748cf-084e-4052-90e5-6a376bd6e379","order_by":3,"name":"Elisabeth M. Hummel","email":"","orcid":"","institution":"Ruhr-University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Elisabeth","middleName":"M.","lastName":"Hummel","suffix":""},{"id":486896009,"identity":"dc13be9c-aeb6-41e7-985d-d02fbb949828","order_by":4,"name":"Petra Platen","email":"","orcid":"","institution":"Ruhr-University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Petra","middleName":"","lastName":"Platen","suffix":""},{"id":486896010,"identity":"4c442532-e00e-4a29-9057-7f6646850ccf","order_by":5,"name":"Bernd Giebel","email":"","orcid":"","institution":"University of Duisburg-Essen","correspondingAuthor":false,"prefix":"","firstName":"Bernd","middleName":"","lastName":"Giebel","suffix":""},{"id":486896011,"identity":"a1ac00b9-798f-40a7-96e0-d0872c43d7a7","order_by":6,"name":"Robert Kumsta","email":"","orcid":"","institution":"Ruhr-University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Kumsta","suffix":""}],"badges":[],"createdAt":"2025-07-03 08:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7035709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7035709/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21575-5","type":"published","date":"2025-10-09T15:57:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87361095,"identity":"a87ee9fd-516d-4acd-8f6b-5bdf0d431ee3","added_by":"auto","created_at":"2025-07-23 05:50:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272464,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of sEV levels at baseline\u003c/p\u003e\n\u003cp\u003eIndividual sEV plasma concentrations, expressed as [objects/mL], and distribution curves are shown for the sEV panel at baseline (–2 minutes). Sample sizes vary across sEVs due to the exclusion of outliers and unpaired observations, enabling paired t-test analysis. Across the study, the 23 sEV populations exhibit largely stable and comparable baseline abundances between psychosocial and physical stress conditions. Even for CD44⁺ and CD235a⁺ sEVs, which show statistically significant differences, the distribution curves substantially overlap. Notably, differences in plasma concentrations across individual sEV types are reflected in distinct y-axis scales. \u003cem\u003ep\u003c/em\u003e \u0026lt; .05.\u003c/p\u003e","description":"","filename":"sEVFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/d80502261a4707c92b2d8e14.jpg"},{"id":87467154,"identity":"05dde53b-1458-46d4-a40f-5b1239a8e1c8","added_by":"auto","created_at":"2025-07-24 08:01:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":480132,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise comparison of sEVs with a significant time ´ stress type interaction\u003c/p\u003e\n\u003cp\u003ePanels A-H show plasma sEV levels measured as [objects/mL] over the course of the experiment. Colored lines represent the intra-individual trajectories of plasma sEV levels, while black lines indicate mean values. Error bars represent the standard error of the mean (SEM). Data are shown for all subjects with available plasma measurements at all time points for at least one stress condition, after removal of outliers. Only sEVs showing a significant time × stress type interaction are presented and tested for significant changes from baseline, to reduce the number of statistical comparisons. Note that the overall abundance of different sEVs in plasma is heterogeneous, as reflected in the varying y-axes across the panels. \u003cem\u003eSignificance: p \u0026lt; .05 (\u003c/em\u003e), p \u0026lt; .01 (\u003cstrong\u003e), p \u0026lt; .001 (\u003c/strong\u003e\u003cem\u003e), p \u0026lt; .0001 (\u003c/em\u003e**\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"sEVFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/0634c64bee57eb21ff1f8f88.jpg"},{"id":87364938,"identity":"134584c0-8ce0-4907-a61b-ac9077e5f123","added_by":"auto","created_at":"2025-07-23 06:22:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":405700,"visible":true,"origin":"","legend":"\u003cp\u003eRecursive partitioning classification of psychosocial and physical stress\u003c/p\u003e\n\u003cp\u003eDecision tree of the classification analysis using plasma levels of all 23 sEV markers across 5 time points and both stress types from the training dataset as input. The training dataset consisted of precisely half the data available. \u003cstrong\u003eB\u003c/strong\u003e) Probabilities of the class prediction show that psychosocial and physical stress were correctly assigned in 75% and 82% of the cases (green) and wrongly assigned in 25% and 18% of the cases (red). \u003cstrong\u003eC\u003c/strong\u003e) Importance of all variables influencing the classification analysis and building a sum of 100% suggest likewise to the decision tree that CD44 was most important in distinguishing stress types. \u003cstrong\u003eD\u003c/strong\u003e) ROC curve presenting the sensitivity, specificity and AUC of the classification for both halves of the data. \u003cstrong\u003eE-F\u003c/strong\u003e) confusion matrix of the training and testing dataset presenting the accuracy of predicting stress types based on the sEV recursive partitioning classification model.\u003c/p\u003e","description":"","filename":"sEVFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/7d94b2dd508917f18f03df17.jpg"},{"id":93419844,"identity":"da635c71-bf92-4e64-a8a0-351cf88f42f8","added_by":"auto","created_at":"2025-10-13 16:08:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2186977,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/65972fe1-d050-4a82-adaf-055e401ed5f5.pdf"},{"id":87361089,"identity":"0aa6d637-c521-4eb6-a079-ce798274fb0f","added_by":"auto","created_at":"2025-07-23 05:50:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15735,"visible":true,"origin":"","legend":"","description":"","filename":"sEVTableStresstypespecificsEVdataS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/427154d7f4260276883e162a.xlsx"},{"id":87363516,"identity":"811bc282-d0bc-423f-b421-7efb2661e669","added_by":"auto","created_at":"2025-07-23 06:06:55","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46750,"visible":true,"origin":"","legend":"","description":"","filename":"sEVStresstypespecificsmallextracellularvesiclesignaturesdataS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/503c0c0c88d52db404d11555.xlsx"},{"id":87361090,"identity":"94db7be6-f79a-4685-bfa9-0f930575a30e","added_by":"auto","created_at":"2025-07-23 05:50:55","extension":"rmd","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":55830,"visible":true,"origin":"","legend":"","description":"","filename":"sEVStresstypespecificsmallextracellularvesiclesignaturesRS4.rmd","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/5785ef35007c6ee8b91fbbe7.rmd"},{"id":87363519,"identity":"7887bff8-10a7-4b1e-80fe-08f718404bce","added_by":"auto","created_at":"2025-07-23 06:06:55","extension":"dotx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":851691,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFIGURES.dotx","url":"https://assets-eu.researchsquare.com/files/rs-7035709/v1/7a00e0098ce8605210f59db1.dotx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stress type–specific small extracellular vesicle signatures reflect divergent biological responses to acute psychosocial and physical challenges","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eStress is a global burden with significant psychological, physiological, and economic consequences. In Western industrialized nations alone, stress-related costs exceed 200\u0026nbsp;billion USD annually, reflecting its association with numerous physical and mental disorders \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite its profound impact, the molecular pathways linking stress to disease remain incompletely understood, underscoring the need for deeper insights into stress-induced physiological processes to inform effective interventions.\u003c/p\u003e\u003cp\u003eSince the foundational work of Cannon and Selye \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, the field of molecular stress physiology has evolved substantially. A particularly promising area of emerging research focuses on extracellular vesicles (EVs) - including small extracellular vesicles (sEVs) in the exosome size range (70\u0026ndash;150 nm \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e), microvesicles (100\u0026ndash;1000 nm \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e) and apoptotic vesicles (\u0026lt;\u0026thinsp;1 \u0026micro;m to \u0026gt;\u0026thinsp;5 \u0026micro;m \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e). These vesicles are critical mediators of intercellular communication, enabling targeted signaling across short and long distances. Exosomes originate as intraluminal vesicles (ILVs) within multivesicular endosomes (MVEs) and are released upon fusion with the plasma membrane \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In contrast, microvesicles and apoptotic vesicles are formed by direct budding from the plasma membrane or fragmentation of dying cells, respectively.\u003c/p\u003e\u003cp\u003eIt is now evident that virtually all cells release EVs via distinct mechanisms, and EVs have emerged as central players in indirect intercellular communication. However, distinguishing between EV subtypes of similar size remains a major challenge. Although many EV preparations exhibit biological activity, some vesicles may primarily function in cellular waste disposal, transporting non-processable byproducts to clearance organs such as the liver or kidneys. While we are not yet able to reliably distinguish EVs based on function, their cell type\u0026ndash;specific molecular signatures make them promising biomarkers and tools for probing complex physiological responses.\u003c/p\u003e\u003cp\u003eDue to methodological limitations, current isolation techniques cannot reliably distinguish exosomes from other sEVs, necessitating the analysis of heterogeneous populations. These vesicles carry lipids, proteins, and nucleic acids such as mRNA and miRNA - initially considered exclusive to exosomes \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. More recent findings show that sEV subtypes differ in composition: some carry exosomal proteins but lack RNA, while others transport RNA with few exosomal surface markers \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These observations underscore the complexity of sEV biology and suggest that surface proteins, particularly integrins, may regulate interactions with target cells \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBecause stress triggers complex systemic responses that involve both heightened metabolic demands and precise intercellular communication, sEVs plausibly play a central role orchestrating these adaptive processes. The brain, though constituting only about 2% of total body weight, accounts for roughly 20% of the body's total energy consumption \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This immense energy demand not only reflects the brain\u0026rsquo;s continuous processing and integration of internal and external stimuli but may also support its role in coordinating systemic responses to stress via sEV-mediated signaling.\u003c/p\u003e\u003cp\u003eSimilarly, skeletal muscle, comprising approximately 40% of body mass, undergoes substantial metabolic reprogramming during physical exertion and stress \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe high energy turnover in both tissues suggests that part of their metabolic budget may be allocated to the production and release of sEVs, which serve as metabolically costly but highly efficient vehicles for systemic communication. This mechanism could allow the brain and muscles to modulate peripheral physiological systems in a stressor-specific and temporally precise manner, thus enhancing the organism's capacity to adapt to diverse psychosocial and physical challenges.\u003c/p\u003e\u003cp\u003eCrucially, sEVs can traverse the blood-brain barrier bidirectionally \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, facilitating a dynamic exchange of molecular information between the central nervous system and peripheral tissues. Given the distinct emotional and physiological signatures of psychosocial versus physical stress, sEVs may carry stressor-specific molecular cargo that fine-tunes metabolic and behavioral responses. This positions them not merely as by-products of cellular activity, but as active mediators in psychophysiological regulation and stress adaptation.\u003c/p\u003e\u003cp\u003eAlthough EV dynamics have been studied in the context of physical exertion and stress-related diseases \u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, direct comparisons of sEV responses to acute psychosocial versus physical stress in healthy individuals are lacking. Addressing this gap, the present study aimed to identify distinct sEV populations in plasma using imaging flow cytometry (IFCM; \u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e). For this purpose, we re-analyzed plasma samples from a previous study \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, collected before and after psychosocial (Trier Social Stress Test; \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e) and physical stress (treadmill ergometry; \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e). In this study, we previously demonstrated that plasma levels of cortisol, adrenaline, noradrenaline, and cfDNA significantly increased under both stress conditions before gradually returning toward baseline. However, the Social Emotional Response Scale (SERS) revealed distinctly different emotional responses: \"Tense Arousal\" dominated after physical stress, while \"Self-Directed Emotions\" and \"Anxiety\" were predominant following psychosocial stress. Since conventional biomarker analyses failed to capture these differences in individual stress perception, analyzing sEVs as potential novel biomarkers of stress processing appeared promising.\u003c/p\u003e\u003cp\u003eTo this end, we analyzed plasma samples from 20 healthy young men collected at five time points before and after stress exposure (\u0026ndash;2, +\u0026thinsp;2, +15, +\u0026thinsp;30, +40 min), using a validated panel of 23 antibodies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), primarily targeting sEVs of hematopoietic origin. Following Brahmer et al., who coined the term \u0026ldquo;ExerVs\u0026rdquo; for sEVs released during physical exertion \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, we hypothesized that psychosocial stress would elicit a unique sEV subpopulation, which we termed \u0026ldquo;PsychEVs.\u0026rdquo; Additionally, we proposed the existence of a shared population, \u0026ldquo;StressEVs,\u0026rdquo; whose levels increase following both stress modalities.\u003c/p\u003e\u003cp\u003eIn the present study, i) we assessed the day-to-day stability of plasma sEV expression under baseline conditions; ii) we analyzed changes in sEV levels following acute psychosocial and physical stress exposure; and iii) we applied a mathematical classification model, including machine learning approaches, to evaluate whether our panel could reliably distinguish between the two stress types based on sEVs.\u003c/p\u003e\u003cp\u003eBy characterizing differential sEV responses to psychosocial and physical stress, this study aimed to elucidate their role in systemic stress adaptation and to identify potential biomarkers for stress-related disorders.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e\u003cb\u003eExperimental model and subject details\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants (n\u0026thinsp;=\u0026thinsp;20) were healthy male sport science students, aged between 18 and 36 years (mean\u0026thinsp;=\u0026thinsp;23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 (SD)), with a normal body mass index (mean\u0026thinsp;=\u0026thinsp;23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5). They had no history of or current mental health disorders, no chronic or acute physical illnesses, and were not taking any medications or drugs at the time of the study. Participants refrained from exercise for 24 hours before testing and consumed a standardized breakfast on the morning of the tests. As a pilot study, only male participants were recruited to minimize confounding variables and eliminate potential effects related to the female menstrual cycle. Furthermore, all stress tests were conducted at either 9 a.m. or 11 a.m. to minimize cortisol diurnal cycle variations and potential influence of lunchtime food intake. All participants provided written informed consent prior to participation. The study was approved by the local ethics committee of the Faculty of Psychology at Ruhr University Bochum (reference number 153/2014) and conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethod details\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants were exposed to acute psychosocial and physical stressors in a randomized order on separate days. Stress inductions and testing were spaced at least 2 days apart. Half of the participants completed the TSST first, while the other half began with the exercise protocol. Testing order was assigned pseudo-randomly. Upon arrival, participants completed a Physical Activity Readiness Questionnaire (PAR-Q; \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e), reviewed by a sports physician, and had a venous catheter inserted 45 minutes before stress induction. After completing questionnaires for 25 minutes, they rested until the stress protocol began. Blood and saliva samples were collected before, and at 2-, 15-, 30-, and 40-minutes post-stress. Participants completed the Social Emotional Response Scale (SERS) at four time points (-2, +\u0026thinsp;2, +15, and +\u0026thinsp;30 minutes), rating their arousal, self-directed emotions, and anxiety on a scale from 1 (not at all) to 4 (a lot).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInduction of psychosocial stress\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePsychosocial stress was induced using the Trier Social Stress Test (TSST), which includes preparation, free speech, and an unanticipated math task in front of judges and a camera. The TSST reliably activates the hypothalamic-pituitary-adrenal (HPA) axis, causing significant cortisol elevation due to the uncontrollability and social evaluative threat elements \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInduction of physical exercise stress\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo induce physical stress, participants underwent an exhaustive treadmill exercise protocol designed to match the duration of the Trier Social Stress Test (TSST) (10\u0026ndash;15 minutes). The protocol began with a 5-minute warm-up walk at 1.0 m/s on a treadmill with a 15% incline. Thereafter, the speed increased by 0.2 m/s every 30 seconds until the participant reached subjective exhaustion, at which point the treadmill was stopped.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePlasma preparation and cfDNA quantification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFive milliliters of whole blood were collected at each time point in EDTA collection tubes (EDTA Monovettes, Sarstedt, Germany) and immediately centrifuged at 1600 \u0026times; g for 10 minutes at 4\u0026deg;C. The plasma was transferred to a fresh tube and subjected to a second centrifugation for 10 minutes at 16,000 \u0026times; g at 4\u0026deg;C. Subsequently, the plasma was filtered through a 0.8 \u0026micro;m filter, and aliquots were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further analysis.\u003c/p\u003e\u003cp\u003ecfDNA was extracted from 0.9 ml of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany), widely regarded as the gold standard for cfDNA extraction, following the manufacturer's protocol. The elution was performed in a final volume of 100 \u0026micro;l of H\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantitative PCR (qPCR) and Hormonal Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQuantification of cell-free DNA (cfDNA) was performed using qPCR as described previously \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Briefly, primers targeting 70\u0026ndash;110 bp amplicons were employed, and a high-affinity, highly sensitive in-house BDNF assay was adapted for cfDNA quantification. qPCR reactions were carried out using the CFX384 Touch\u0026trade; Real-Time PCR Detection System (Biorad, Hercules; USA), with triplicate assays and standard curves derived from artificial gene fragments.\u003c/p\u003e\u003cp\u003ePlasma cortisol levels were measured by commercial ELISA (Demeditec, Kiel; Germany), and plasma catecholamines (adrenaline and noradrenaline) were quantified by high-performance liquid chromatography (HPLC) at the Laboratory for Stress Monitoring (LSM, G\u0026ouml;ttingen, Germany), following a solvent extraction protocol adapted from Smedes et al. (ref 45). For complete methodological details, see \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImaging flow cytometry (IFCM)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo analyze PsychEVs, ExerVs, and StressEVs, an imaging flow cytometry (IFCM) approach was employed using the Amnis ImageStreamX Mk II (Luminex, USA). IFCM combines flow cytometry's throughput with microscopy's precision, allowing high-resolution single-vesicle analysis of sEVs. Plasma samples from the cfDNA study frozen at -80\u0026deg;C were again centrifuged after thawing at 10.000 \u0026times; g for 10 minutes at 4\u0026deg;C to remove cryoprecipitates. The supernatant was transferred to a fresh tube, and 10 \u0026micro;L aliquots of plasma were incubated with 10 \u0026micro;L of fluorochrome-conjugated antibodies targeting EV markers diluted in PBS, as indicated in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Samples were incubated at room temperature for 1 hour in the dark to ensure specific labeling, followed by a dilution in PBS to 100 \u0026micro;L final volume. Controls\u0026mdash;antibody-only, buffer-only, isotype, and detergent-treated\u0026mdash;ensured background assessment per MIFlowCyt-EV standards \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The ImageStreamX Mk II operated at 60x magnification with a low flow rate (0.3795\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003 \u0026micro;L/min), acquiring data over 5 minutes per well to optimize single-particle detection. Fluorescence and scatter plots were used to gate EV populations, quantify concentrations, and validate labeling efficiency. \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Data were processed using IDEAS 6.2 software (Luminex), employing customized masks and spot-counting features to identify and quantify individual sEVs. Fluorescence and scatter plots were used to gate populations based on labeling intensity and side scatter, and EV concentrations were calculated. This high-resolution IFCM approach allowed precise EV subpopulation analysis, revealing profiles linked to psychosocial and physical stress. In addition, performing IFCM adheres to the Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines \u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, which recommend strict quantification, imaging, and molecular characterization. Contrary to widespread assumptions, transmission electron microscopy (TEM), Western blotting (WB), and nanoparticle tracking analysis (NTA) are not mandatory, as they often lack the resolution needed for precise characterization.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInformation on the antibodies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibody\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConjugate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrder Nr.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCompany\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIsotyp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVolume\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCTLA-4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBNI3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e555853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBD Bioscience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMEM-61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1P-208-T100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEXBIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQA19A12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBioLegend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003erat IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREA599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130-110-520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMiltenyi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ehuman IgG1, rec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3G8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e555407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBD Bioscience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM1/69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130-102-732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMiltenyi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003erat IgG2bκ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAF488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMEM-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA4-309-T100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEXBIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMEM-85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1A-221-T100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEXBIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFITC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSZ21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIM1758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeckman Coulter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMEM-259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1A-343-T100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEXBIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD66b\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6/40c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeukoCom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFITC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJS64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB25329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeckman Coulter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASL-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e342104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBioLegend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD90\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,00E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1A-652-T100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEXBIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133-1C6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1P-772-T100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEXBIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgM, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD171\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAF647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL1-OV198.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e371607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBioLegend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD206\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBV421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e321126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBioLegend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD227\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFITC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHMPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e559774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBD Bioscience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCD235a\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGA-R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e551336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBD Bioscience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHLA-ABC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFITC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB9.12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIM1838U\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeckman Coulter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHLA-DR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImmu-357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB92438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeckman Coulter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePD-L1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29E.2A3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e329705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBioLegend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG2a, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAF488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1H6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u0026ndash;256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSigma-Aldrich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emouse IgG1, κ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eListed above are all antibodies used in this study, including conjugate, clone, catalog number, manufacturer, isotype, and dilution.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R Studio (version 2024.12.1\u0026thinsp;+\u0026thinsp;563). To minimize the risk of false-positive findings, the number of statistical tests was reduced to those necessary to address the study's primary aims.\u003c/p\u003e\u003cp\u003eTo ensure comparability of baseline sEV levels between psychosocial and physical stress conditions, Bonferroni-corrected paired t-tests were conducted for all 23 sEV subpopulations. Subsequently, repeated-measures analyses of variance (rmANOVA) were applied to test for time \u0026times; stress-type interactions for each sEV marker. If a significant interaction was observed, follow-up paired t-tests (baseline vs. +2, +\u0026thinsp;15, +30, and +\u0026thinsp;45 minutes) were performed. Bonferroni correction was used to adjust for multiple comparisons.\u003c/p\u003e\u003cp\u003eTo assess the potential of sEV profiles discriminating between stress types, a classification model based on recursive partitioning was employed (rpart package, complexity parameter\u0026thinsp;=\u0026thinsp;0.05, 10-fold cross-validation) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Half of the data from both stress conditions was randomly selected for model build and training (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20), while the remaining half served as the testing set (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20). Stress conditions were alternated across folds to avoid bias. The full sEV panel across all time points was used as input for the model.\u003c/p\u003e\u003cp\u003eOutlier removal was conducted prior to all parametric tests. For each combination of stress type and time point (2 \u0026times; 5\u0026thinsp;=\u0026thinsp;10 per marker), the median and Median Absolute Deviation (MAD) were calculated. Values outside the range of median\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u0026times;MAD were considered outliers and removed \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. To preserve the integrity of within-subject comparisons, participants with missing values were excluded from the respective test. However, all available data were retained for classification analysis.\u003c/p\u003e\u003cp\u003eNormality assumptions were assessed using Shapiro\u0026ndash;Wilk tests for each marker across the 10 condition \u0026times; time point combinations. Due to violations, CD61, CD81, and CD100 underwent square root transformation to better meet test assumptions. CD9, CD44, and CD171 did not require transformation. However, all other markers were ln-transformed. Transformation was applied selectively to preserve interpretability where possible.\u003c/p\u003e\u003cp\u003eAll data generated during this study are provided in Supplemental Data File S3. The R script used for the analysis is available in Supplemental Data File S4.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePsychosocial as well as physical stress leads to significant increases in cortisol, catecholamines, and cell-free DNA\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThis study aimed to investigate the effects of acute psychosocial and physical stress on sEV release and correlate these effects with hormonal and inflammatory markers. We explored the differential response of sEV populations, measured by 23 different sEV markers, to better understand the distinct physiological mechanisms activated by each stress type. Each participant provided blood samples at multiple time points: before stress exposure (baseline), and at 2-, 15-, 30-, and 40-minutes post-stress. As previously reported in detail \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, both stress tests led to significant increases in plasma concentrations of cortisol, adrenaline/noradrenaline, and circulating cell-free nucleic acids in all participants (Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline levels of sEV markers are highly reproducible\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the stability and suitability of the selected sEV markers as stress-related biomarkers, we first assessed inter- and intra-individual variation in baseline plasma levels across both study sessions. Establishing baseline stability is essential to ensure that observed changes reflect stress-induced dynamics rather than day-to-day variability. Among the 23 surface markers analyzed, 21 showed no significant differences between the two baseline conditions (psychosocial vs. physical stress), indicating high reproducibility of marker detection across experimental days (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). CD44⁺ (t(14) = -2.42, p\u0026thinsp;=\u0026thinsp;.03, d = -0.62, 95% CI [-1.41, -0.16]) and CD235a⁺ sEVs (t(14) = -2.36, p\u0026thinsp;=\u0026thinsp;.03, d = -0.61, 95% CI [-1.20, -0.13]) were significantly different, although their distribution curves showed substantial overlap, suggesting that these minor deviations may reflect normal inter-session variation rather than biologically meaningful shifts. Overall, plasma sEV profiles at baseline were largely consistent across both conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCD9\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eCD41\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eand CD81\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003esEVs dominate the plasma sEV profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we quantified the absolute plasma concentrations of each sEV subset based on marker expression, reported in objects per milliliter of plasma. CD13⁺, CD14⁺, CD24⁺, CD44⁺, CD63⁺, and CD90⁺ sEVs were typically found at concentrations around 10⁵ objects/mL. Intermediate levels (~\u0026thinsp;10⁶ objects/mL) were observed for CD16⁺, CD61⁺, CD66b⁺, CD82⁺, CD100⁺, CD206⁺, CD227⁺, CD235a⁺, HLA-DR⁺, and PD-L1⁺ sEVs. The most abundant marker-positive sEV subsets included CD9⁺, CD41⁺, CD81⁺, CD171⁺, CTLA-4⁺, HLA-ABC⁺, and PS⁺ sEVs, often exceeding 10⁷ objects/mL. These findings highlight the pronounced heterogeneity in sEV abundance, with tetraspanin-positive (CD9⁺, CD81⁺) and platelet-derived (CD41⁺) vesicles consistently constituting the most abundant subsets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCD13\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eCD14\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003eand CD41\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003esEVs increase transiently after physical stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next investigated whether acute stress exposure induced dynamic changes in sEV subpopulations over time and whether such responses differed between psychosocial and physical stress. Repeated-measures ANOVA identified significant interaction effects between time and stress type for eight markers: CD9, CD13, CD14, CD16, CD41, CD44, CD63, and HLA-DR. Follow-up Bonferroni-corrected paired t-tests indicated that CD13⁺ (aminopeptidase N, associated with myeloid lineage), CD14⁺ (monocytic), CD16⁺ (commonly associated with NK cells and non-classical monocytes), CD41⁺ (platelet-derived), CD44⁺ (expressed on mesenchymal stem cells, activated T cells, and monocytes), and HLA-DR⁺ (antigen-presenting/activated cells) sEVs significantly increased at +\u0026thinsp;2 minutes after physical stress (p\u0026thinsp;\u0026lt;\u0026thinsp;.05; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Several markers remained elevated beyond this point: CD9⁺, CD13⁺, CD41⁺, CD44⁺, and HLA-DR⁺ sEVs at +\u0026thinsp;15 minutes, and CD16⁺ at +\u0026thinsp;30 minutes. CD63⁺ sEVs showed a numerical increase at +\u0026thinsp;2 minutes (p\u0026thinsp;=\u0026thinsp;.058), though this did not reach statistical significance. Likewise, none of these markers did reach statistical significance in response to psychosocial stress. Full statistical details are presented in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. The remaining 15 markers, which did not yield significant interaction effects, are shown descriptively in Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that physical stress elicits a reproducible, short-term release of specific sEV subpopulations - predominantly derived from platelets and myeloid-lineage immune cells - whereas psychosocial stress results in more heterogeneous and less consistent sEV dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCD44\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003esEV concentrations enable classification of stress types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLastly, we assessed the suitability of the sEV panel as a whole discriminating between the two stress types. Therefore, we applied a classification analysis using recursive partitioning that explored whether plasma sEV profiles could distinguish between psychosocial and physical stress responses. The model was build using 50% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20) of all 23 marker-positive sEV subpopulations across five time points and both conditions. Within the training set, the model classified 56% of observations as psychosocial and 44% as physical stress (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). The initial split was based on CD44⁺ sEV concentration, with a threshold of 9.2 \u0026times; 10⁴ objects/mL identified as most discriminative.\u003c/p\u003e\n\u003cp\u003eProbabilities for correct assignment reached 75% for psychosocial and 82% for physical stress (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). CD44⁺ sEVs showed the highest variable importance, followed by CD16⁺, CTLA-4⁺, HLA-DR⁺, CD81⁺, and CD41⁺ subsets (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). ROC curve analysis revealed an area under the curve (AUC) of 0.78 in the training and 0.76 in the testing dataset (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). Sensitivity and specificity were 0.84 and 0.72 in the training set, and 0.74 and 0.78 in the test set, respectively. Overall classification accuracy exceeded 76% across both datasets (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE\u0026ndash;F), indicating preliminary but promising classification outcome. Hence, these findings show that the physiological response to psychosocial and physical stress is related to distinguishable profiles of hematopoietic sEV markers.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe relationship between stress and health has long been a focus of scientific inquiry, particularly regarding the distinct physiological responses triggered by different stressors. Small extracellular vesicles have recently gained recognition as both mediators and potential biomarkers of stress-related processes, reflecting the underlying molecular mechanisms of adaptation and systemic communication. In this study, we analyzed plasma sEV profiles in healthy young men following acute psychosocial and physical stress, using a within-subject design and high-resolution single-vesicle analysis via imaging flow cytometry (IFCM).\u003c/p\u003e\u003cp\u003eWe first examined the baseline stability of plasma sEVs across different days. Most sEV markers showed stable baseline concentrations across test days, underscoring their potential as reliable stress-related biomarkers. This temporal consistency aligns with preclinical data on circadian regulation of EV release in animal models \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Although corresponding human data remains limited, our findings suggest that at least a subset of sEV markers maintain sufficient stability to support longitudinal monitoring in clinical contexts.\u003c/p\u003e\u003cp\u003eThe stress-related analyses revealed that physical stress triggered rapid and reproducible increases in several sEV subtypes, including CD13⁺, CD14⁺, CD41⁺, CD44⁺, and HLA-DR⁺ vesicles - predominantly associated with cells of myeloid (e.g., monocytes) and platelet origin. The analyzed hormones and cell-free DNA exhibited significant elevations following both stressors, followed by a rapid homeostatic return to baseline\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These changes were most pronounced within the first 15 to 30 minutes post-stress. In contrast, sEVs displayed this consistent response pattern only after physical stress. Following psychosocial stress, however, a pronounced dysregulation of sEVs was observed, characterized by marked interindividual variability and a lack of uniform return to baseline at the group level. This variability likely reflects complex individual stress-processing mechanisms rather than an absence of biological response and aligns with prior studies linking extracellular vesicles to mental health and psychological stress. \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA classification model based on CD44⁺ and other responsive sEV subtypes achieved moderate separation between stressor types (AUC 0.76\u0026ndash;0.78). CD44⁺ vesicles, expressed by various immune and stromal cells and known to regulate cell adhesion and migration during inflammation, emerged as the most informative feature, alongside CD16⁺, CTLA-4⁺, and HLA-DR⁺ vesicles. While these results are promising, they should be interpreted with caution due to the limited training set, the risk of overfitting and the need for supporting data on the diurnal stability of each marker. Notably, the model's performance appeared to be primarily driven by the more consistent sEV responses observed after physical stress.\u003c/p\u003e\u003cp\u003eOur antibody panel focused on sEVs of hematopoietic origin, which represent a major component of the circulating EV pool and are responsive to immune and vascular activation. The predominance of CD9⁺, CD81⁺, and CD41⁺ vesicles align with earlier reports from healthy plasma donors \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, suggesting that tetraspanin- and platelet-derived sEVs dominate the basal plasma profile. In contrast, CD63⁺ vesicles were less abundant, possibly reflecting differences in biogenesis or clearance. The observed increases in CD13⁺, CD14⁺, and HLA-DR⁺ vesicles are consistent with vesicle release from myeloid immune cells such as monocytes or dendritic cells, although definitive assignment to cell types requires co-staining or cargo-based analyses.\u003c/p\u003e\u003cp\u003eMethodologically, IFCM enabled direct quantification of surface marker-positive sEVs in minimally processed plasma, preserving physiological composition and allowing robust time-resolved analyses. This is a key advantage for in vivo studies of dynamic EV release. However, the lack of co-expression data limits resolution of EV subtypes and restricts inferences about cellular origin. Future studies should incorporate multiplexed antibody panels (e.g., multi-channel IFCM or barcoding approaches) and platform-independent workflows to support diagnostic applicability.\u003c/p\u003e\u003cp\u003eHowever, several limitations must be acknowledged. The male-only cohort limits generalizability, and the 40-minute observation window may have missed delayed sEV responses. The absence of molecular cargo profiling restricts insight into functional content. While the sample size aligns with prior EV stress studies \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, it remains modest and increases the risk of overfitting. These findings should therefore be interpreted with caution. External validation in larger, more diverse cohorts will be essential to confirm the observed patterns and assess their translational relevance. Future studies should also refine classification models using independent datasets and optimized feature selection strategies.\u003c/p\u003e\u003cp\u003eIn summary, our data demonstrate that acute physical stress reliably triggers the release of distinct sEV subtypes, particularly platelet- and immune-derived vesicles. In contrast, psychosocial stress elicits more heterogeneous responses. Plasma sEV profiling may thus offer novel insights into stress physiology and, if validated, could support individualized diagnostics in stress-related disorders. In particular, the concepts of psychosocial stress-associated vesicles (PsychEVs) and generalized stress-responsive vesicles (StressEVs) warrant further investigation and validation in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCONFLICT OF INTERESTS\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e\n\u003ch2\u003eFUNDING\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAUTHOR CONTRIBUTIONSDAM designed the study. DAM, FB, TT, and EMH performed the experiments. DAM, FB, TT, PP, BG, and RK analyzed the data and interpreted the results. DAM drafted the initial manuscript, and all authors reviewed and edited the final version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors extend their gratitude to Angelika Eibl and Maresa Fisch for their invaluable medical assistance in conducting the experiments and collecting blood samples. We acknowledge support by the Open Access Publication Funds of the Ruhr-Universit\u0026auml;t Bochum.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are provided in the manuscript\u0026apos;s supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHassard, J., Teoh, K. R. H., Visockaite, G., Dewe, P. \u0026amp; Cox, T. The Cost of Work-Related Stress to Society: A Systematic Review. \u003cem\u003eJ. Occup. 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Physiol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 532 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7035709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaladaptive stress responses are associated with a variety of psychological and physical disorders, often characterized by molecular indicators of dysregulated stress pathways. Small extracellular vesicles (sEVs), which play a key role in intercellular communication, may be critically involved in these processes. In this study, we quantified sEV concentrations (specifically CD9\u003csup\u003e+\u003c/sup\u003e, CD63\u003csup\u003e+\u003c/sup\u003e, and CD81\u003csup\u003e+\u003c/sup\u003e markers) in the plasma of twenty young, healthy men before and after exposure to both acute psychosocial and physical stress, using imaging flow cytometry (IFCM).\u003c/p\u003e\u003cp\u003eAll participants showed significant increases in cortisol, catecholamines, and circulating cell-free DNA (cfDNA) following both stressors. In contrast, sEVs were significantly elevated only in response to physical stress. Physical stress induced a rapid increase in sEV release, particularly in CD9- and CD63-positive vesicles, followed by a return to baseline within 40 minutes. Psychosocial stress, however, triggered more variable sEV responses across individuals.\u003c/p\u003e\u003cp\u003eImportantly, our classification approach using recursive partitioning revealed distinct sEV patterns associated with psychosocial and physical stress, with highest discriminatory value for CD44⁺ sEVs. These findings indicate that psychosocial and physical stress elicit distinct sEV signatures, which may reflect differential stress communication pathways and highlight their potential as biomarkers for stress-related processes and as possible targets for the effects of psychosocial exposures, including early adversity and trauma.\u003c/p\u003e","manuscriptTitle":"Stress type–specific small extracellular vesicle signatures reflect divergent biological responses to acute psychosocial and physical challenges","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 05:50:50","doi":"10.21203/rs.3.rs-7035709/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-05T12:47:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T09:18:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T02:51:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T09:52:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178954309174554508213947151268727692913","date":"2025-07-18T21:31:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8165154737993845472785933070458924035","date":"2025-07-18T06:06:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211827055249907755261262684548461208910","date":"2025-07-17T12:35:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257096731465749591827147009079305215908","date":"2025-07-17T07:59:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136450136442043450878925059478827068105","date":"2025-07-16T10:39:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T08:27:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T08:25:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-16T06:31:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T08:00:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-09T07:55:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef42830d-e759-41be-a461-a7be9e36f80a","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51693231,"name":"Health sciences/Biomarkers"},{"id":51693232,"name":"Biological sciences/Neuroscience"},{"id":51693233,"name":"Biological sciences/Psychology"},{"id":51693234,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-10-13T16:04:35+00:00","versionOfRecord":{"articleIdentity":"rs-7035709","link":"https://doi.org/10.1038/s41598-025-21575-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-09 15:57:59","publishedOnDateReadable":"October 9th, 2025"},"versionCreatedAt":"2025-07-23 05:50:50","video":"","vorDoi":"10.1038/s41598-025-21575-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-21575-5","workflowStages":[]},"version":"v1","identity":"rs-7035709","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035709","identity":"rs-7035709","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}