Photonic-based Prediction Method for the Metabolic Activity of Stem Cells Exposed to Different Cold Plasma Jets

Photonic-based Prediction Method for the Metabolic Activity of Stem Cells Exposed to Different Cold Plasma Jets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Photonic-based Prediction Method for the Metabolic Activity of Stem Cells Exposed to Different Cold Plasma Jets Erfan Tamandeh, Kamal Hajisharifi, Esmaeil Heydari, Sara Emadi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9527256/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Despite promising in vitro outcomes, the clinical translation of plasma jet technologies remains constrained by the absence of reproducible and standardized evaluation parameters. A major challenge lies in quantifying plasma-induced biochemical changes in a manner that is both biologically relevant and independent of specific devices. In this study, we present a dissolved oxygen (DO)-based photonic parameter obtained through time-resolved phosphorescence spectroscopy as a real-time, reagent-free method for characterizing plasma-activated media. By correlating the maximum change in phosphorescence lifetime—an indirect indicator of minimum DO concentration of the liquid—with metabolic activity across various stem cell types treated by the different plasma jet configurations, we introduce a novel prediction metric for the metabolic activity of stem cells that is both cell-specific and jet-independent. This parameter shows promise as a non-invasive, cell-specific redox response signature, potentially aiding future stem cell classification efforts. Moreover, jet-independent characteristic of the parameter supports more consistent cross-study comparisons, simplifies jet calibration, and advances efforts to standardize plasma jet applications in biomedical research. Cold plasma plasma jet dissolved oxygen redox metabolism phosphorescence lifetime spectroscopy metabolic activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Cold atmospheric plasma (CAP) jets are emerging as powerful tools in biomedical research due to their capacity to generate reactive species such as dissolved oxygen (DO)( 1 , 2 ), which play a crucial role in regulating cellular redox balance and metabolism ( 3 , 4 ). These plasmas are produced at room temperature and atmospheric pressure using carrier gases like argon or helium and are delivered to biological or liquid targets via a variety of jet designs ( 5 – 7 ). Although numerous studies have demonstrated the promising in vitro effects of CAP on cells and tissues, a significant challenge remains in translating these findings into reproducible, standardized protocols ( 8 , 9 ). A major barrier to broader clinical translation of the plasma medicine devices is the lack of a quantifiable and biologically relevant parameter that enables comparisons across different plasma jet configurations. Variability in jet design, electrical parameters, and treatment conditions frequently leads to inconsistent biological outcomes, making it difficult to replicate results across laboratories or optimize protocols( 10 , 11 ). While optical emission spectroscopy (OES) is commonly employed to characterize plasma jets, it offers limited information about reactive species in the liquid phase—particularly DO, which is central to redox signaling and cellular responses ( 12 – 14 ). To address this gap, the present study introduces a photonic approach based on time-resolved phosphorescence spectroscopy to indirectly quantify DO in plasma-activated media as a reliable characteristic arameter for evaluating the plasma activity of the media. This method employs an oxygen-sensitive phosphorescent dye to monitor real-time changes in phosphorescence lifetime, reflecting fluctuations in local oxygen quenching and DO activity by time( 15 ). This approach overcomes common limitations of intensity-based methods—such as photobleaching, background interference, and quenching—by providing robust monitoring of ROS dynamics. Using an integrated oxygen sensor, the method continuously tracks DO as a precursor to ROS production, offering insight into the rates of ROS generation and consumption in plasma-treated media( 16 ). In the present study, comparing the obtained linear correlation of the maximum change in phosphorescence lifetime (ΔAₘₐₓ) measured immediately after plasma exposure with metabolic activity (MTT post 48h) across three stem cell types treated by two different plasma jet configurations leads to achieving a jet-independent yet cell-specific parameter called MPACR, the slope of the linear relationship between ΔAₘₐₓ and the measured MTT values. While not aimed at immediate clinical application, this work proposes MPACR parameter as a foundational metric for enhancing reproducibility and comparability in in-vitro CAP studies. It represents a significant step toward standardizing plasma jet performance through a biologically meaningful, optically derived parameter. Figure 1 illustrates the experimental strategy used to assess the relationship between dissolved oxygen (DO) dynamics and cellular metabolic responses, with a focus on comparisons across different stem cell types and plasma jet configurations to validate MPACR as a reproducible, jet-independent parameter. As shown in this figure, we applied the proposed method to evaluate the relationship between phosphorescence decay signals and metabolic activity, measured via the MTT assay at 48 hours, in three stem cell lines: HUC-MSC, BM-MSC, and SSC. Through this strategy, we identified the “maximum activation” parameter (ΔAₘₐₓ) as a key indicator of plasma treatment efficacy. As outlined in the flowchart in Fig. 1 , our approach involves: • First, demonstrating a linear relationship between cellular metabolic activity and ΔAₘₐₓ for each stem cell type; • Second, confirming that the slope of this relationship—MPACR (Maximum Plasma Activation–Cell Response)—is independent of the plasma jet used; • Finally, showing that MPACR is cell-type dependent but jet-independent, reflecting intrinsic metabolic characteristics of each stem cell population. Together, these findings contribute to solving the reproducibility problem by introducing a standardized, biologically relevant metric for CAP research. Moreover, this approach offers a robust framework for cross-platform comparisons and enhances the predictive capability of CAP treatment outcomes. 2. MATERIALS AND METHODS 2.1 Cell Culture Human umbilical cord mesenchymal stem cells (HUC-MSCs), bone marrow mesenchymal stem cells (BM-MSCs), and spermatogonial stem cells (SSCs) were obtained from the Royan Institute (Tehran, Iran). These stem cell types were selected to represent a spectrum of metabolic phenotypes with varying sensitivity to oxidative stress and redox signaling. Cells were cultured in DMEM/F-12 supplemented with 10% fetal bovine serum (FBS) and 0.5% penicillin–streptomycin (Sigma-Aldrich, USA), and maintained at 37°C in a humidified atmosphere containing 5% CO₂. During subculture, HUC-MSCs were centrifuged at 1200 rpm, while BM-MSCs and SSCs were centrifuged at 1500 rpm for 5 minutes. To minimize passage-related changes in stemness, redox behavior, and metabolic activity, all experiments were conducted using cells at passage ≤ 6. This threshold was selected based on literature showing that early-passage mesenchymal stem cells—both from bone marrow (BM-MSCs) and umbilical cord (HUC-MSCs)—retain higher proliferative capacity, trilineage differentiation potential, and a stable redox profile. In contrast, later passages tend to show signs of senescence, oxidative imbalance, and reduced responsiveness ( 17 , 18 ). Similarly, rat spermatogonial stem cells (SSCs) cultured in vitro are known to lose germline marker expression and undergo transcriptional drift when passaged extensively. Limiting their expansion to ≤ 6 passages help preserve their undifferentiated state and ensures consistency in redox-sensitive assays ( 19 ). For 24-well plate experiments, cells were seeded at approximately 20,000 cells per well in complete medium and allowed to adhere and recover for 24 hours before plasma treatment. To control for minor variations in seeding, all results were normalized to the untreated control wells on the same plate. Pilot cell counts were performed weekly to ensure that seeding densities (~ 2.0–2.5 × 10⁴ cells/well) consistently resulted in ~ 70% confluency after 24 hours. 2.2 CAP Jet Design and Treatment Conditions Two custom-built cold atmospheric plasma (CAP) jets were used to evaluate the optical response of plasma-activated media across structurally distinct discharge geometries. Both systems employed dielectric barrier discharge (DBD) in a ring-pin electrode configuration and were powered by a variable high-voltage AC source. CAP Jet 1 consisted of a 15 cm stainless steel pin electrode positioned axially within a 13 cm quartz tube (inner diameter: 3 mm; outer diameter: 5 mm; wall thickness: 1 mm). A 0.5 cm-wide copper ring electrode was affixed 0.7 cm upstream from the tube outlet, while the pin extended 1 mm beyond the ring. CAP Jet 2 featured a similar axial configuration but was built with a borosilicate glass tube (inner diameter: 3 mm; outer diameter: 7 mm; wall thickness: 2 mm). A 1 cm-wide copper ring was positioned 0.5 cm from the outlet, and the pin electrode extended 2 mm beyond the ring. Both jets operated with high-purity argon (99.999%) at a controlled flow rate of 1.5 L/min. Treatment durations were 60 and 90 seconds. Voltage-frequency combinations (T0–T15), ranging from 12.4–16.6 kV and 12.2–16.6 kHz, were selected to generate varying plasma activation conditions (see Table S2). CAP treatment was applied to cell culture medium containing the optical sensor (or cells) placed in standard 24-well plates. For metabolic activity tests, cells were returned to the incubator for 48 h post-treatment prior to analysis. 2.3 Optical DO Sensor Preparation To measure dissolved oxygen (DO) in liquid media, a phosphorescent oxygen-sensitive sensor was fabricated using platinum (II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorophenyl) porphyrin (PtTFPP; Scientific Frontiers), a dye whose phosphorescence is quenched in the presence of DO. The sensor film was prepared by dissolving 2 mg of PtTFPP and 1 g of polystyrene (PS) in 2 mL of toluene (Sigma-Aldrich, USA). After stirring the solution for 1 hour, 50 µL was drop-cast onto a clean glass slide and dried at 80°C to form a uniform film. Once solidified, 8 mm discs were punched from the film and used as optical sensors. During CAP exposure, the reactive species generated in the plasma consume DO in the medium, leading to an increase in the phosphorescence lifetime (τ) of the PtTFPP sensor. This change enables real-time tracking of redox dynamics. The phosphorescence lifetime of the PS-embedded PtTFPP probe is primarily governed by collisional quenching with dissolved oxygen, as confirmed in our real-time reactive oxygen species (ROS) study ( 15 ). Because the optical geometry and temperature were identical to our earlier work, the previously published Stern–Volmer calibration ( 20 ) was applied without modification. To ensure microbial sterility, the PtTFPP/PS probe discs were first attached to glass substrates and then first sterilized by rinsing with 70% ethanol, followed by 30 min UV exposure (254 nm) under a laminar flow hood. The assembled sensors were subsequently rinsed twice with sterile PBS and allowed to air-dry under sterile conditions before being placed in cell culture wells. 2.4 MTT Assay for Metabolic Activity The MTT assay was used to quantify (i) metabolic activity (24 h post CAP exposure) and (ii) metabolic activity (48 h post-treatment) ( 21 ). A 0.5 mg mL⁻¹ working solution was prepared by dissolving 5 mg MTT (Sigma-Aldrich, USA) in 1 mL PBS and diluting the stock with 10 mL complete culture medium. For each 24-well plate (typical working volume ≈ 1 mL per well), 250 µL of the working solution was added to the existing ~ 750 µL of medium—yielding a final MTT concentration of 0.125 mg mL⁻¹ (10% v/v). Plates were incubated for 4 h at 37°C to allow formazan formation. The medium was then carefully aspirated, and formazan crystals were solubilised in 300 µL DMSO per well with gentle shaking (10 min). Absorbance was measured at 570 nm using a BioTek Synergy H4 Hybrid Reader. Results are expressed as percentage of the untreated control (set to 100%), enabling direct comparison between 24 h and 48 h metabolic-activity endpoints ( 22 ). 2.5 Sensor Biocompatibility and Cytotoxicity Testing To evaluate the biocompatibility and potential cytotoxicity of the optical oxygen sensor, cells were exposed to four experimental conditions: an untreated Control group, a Sensor-only group where cells were cultured with the sensor to test for material biocompatibility, a Plasma-only group receiving CAP treatment in the absence of the sensor, and a Sensor + Plasma group that combined plasma treatment with sensor exposure. Metabolic activity (24 h post-treatment) was assessed using the MTT assay described in Section 2.4 . As shown in Fig. 2 , the Sensor-only group displayed Metabolic activity levels comparable to the Control group, indicating that the sensor material does not exert cytotoxic effects. Likewise, the Sensor + Plasma group showed no significant difference compared to the Plasma-only group, confirming that the presence of the sensor does not interfere with or alter the biological response to plasma treatment. Quantitative values and statistical comparisons are provided in Table S1 . For metabolic response analysis 48 hours post-treatment, the same experimental protocol was followed. Figure 3 illustrates the effect of various CAP treatment conditions, differing in voltage and frequency, on the metabolic activity of HUC-MSCs, as measured by the MTT assay. These data reveal condition-dependent variations in cellular activity, highlighting the sensitivity of HUC-MSCs to plasma parameters. Comprehensive statistical comparisons, including ANOVA and post-hoc analyses, are detailed in Supplementary Table S3. 2.6 CAP Treatment and Real-Time Phosphorescence Lifetime Spectroscopy A custom optical setup was used to monitor changes in phosphorescence lifetime (τ) of the PtTFPP sensor in real time during CAP treatment. The setup included a blue LED (460 nm) excitation source and a time-resolved photodetector, with lifetime values recorded at 2.9-second intervals. This allowed continuous measurement of dissolved-oxygen (DO) dynamics in the culture medium immediately before, during, and after CAP exposure. The CAP treatment system is illustrated in Fig. 4 . It includes an argon-based cold atmospheric plasma (CAP) jet directed at a 24-well plate containing cell culture medium (with or without cells), while an optical oxygen sensor positioned in the medium detects changes in PL lifetime. The nozzle-to-liquid distance was fixed at 15 mm to ensure reproducible plasma–liquid interaction, and medium temperature was confirmed to remain ≤ 37°C throughout exposure. These changes reflect the reduction in DO concentration due to reactive species generated during plasma discharge, predominantly driven by DO depletion. During plasma treatment, a decrease in DO concentration was detected in the DMEM/F-12 medium, indicated by an increase in PL lifetime. A representative phosphorescence-lifetime curve is shown in Fig. 5 , where the maximum observed change following treatment is denoted as ΔAₘₐₓ. Baseline lifetime (τ₀) was defined as the mean of the data points immediately preceding plasma ignition, and the peak lifetime (τ_peak) was identified within the first data points after plasma shut-off. The maximum activity (ΔAₘₐₓ), calculated as the peak shift in lifetime relative to baseline, serves as an optical indicator of plasma-induced oxidative activity in the medium. ΔAₘₐₓ was determined for each treatment condition according to this equation: \(\:{\Delta\:}{A}_{max}={{\tau\:}}_{\text{peak}}-{{\tau\:}}_{0}\) and later correlated with cellular metabolic response to assess its potential as a predictive, jet-independent parameter 2.7 Statistical Analysis All statistical analyses were conducted using Python (version 3.11) with the pandas (v1.5.3), NumPy (v1.23.5), SciPy (v1.10.1), stats models (v0.14.0), and seaborn (v0.12.2) libraries. Data were tested for normality using the Shapiro–Wilk test and for homogeneity of variances using Levene’s test. For comparisons involving multiple CAP treatment groups (T0–T15), one-way analysis of variance (ANOVA) was performed. When ANOVA indicated significant group differences (p < 0.05), Tukey’s Honest Significant Difference (HSD) test was used for post hoc pairwise comparisons. This approach was selected to appropriately manage multiple comparisons and reduce type I error. Data are reported as mean ± standard deviation (SD) unless otherwise indicated. Full statistical results are provided in Table S3. In the following, unless otherwise stated, all references to metabolic activity hereafter correspond to the MTT assay measured 48 h post-treatment. 3. RESULTS 3.1 ΔAₘₐₓ–Metabolic Activity Correlation as a Robust Predictive Parameter for CAP Performance Assessment We define ΔAₘₐₓ as the largest post-CAP rise in the sensor’s phosphorescence lifetime (τ) above its baseline. This parameter was used to compare peak activation of the medium under different CAP treatments for three stem cell types: HUC-MSCs, BM-MSCs, and SSCs. In this study, the term “cell response” refers specifically to metabolic activity, as measured by the MTT assay 48h post treatment. The MPACR (Maximum Plasma Activation–Cell Response) parameter is defined as the slope of the linear relationship between ΔAₘₐₓ and this measured metabolic activity. To validate the reliability of ΔAₘₐₓ as a predictive parameter, a series of experiments was conducted on HUC-MSCs using the first CAP jet. As shown in Fig. 6 a, a strong linear correlation was observed between ΔAₘₐₓ and MTT-derived metabolic activity. The metabolic activity of HUC-MSCs increased from approximately 100% to 159% in the initial experiments, and up to 196% in follow-up tests, while ΔAₘₐₓ ranged from 1.5 to 3.5 µs under different CAP exposure settings (voltage and frequency). To confirm the stability and reproducibility of this correlation, three additional validation tests were conducted several weeks after the initial data collection. These results (represented by green dots in Fig. 6 a) were plotted on the same graph and closely followed the original trend line. The difference between the predicted values (based on the initial fit) and the actual metabolic activity from the validation experiments was minimal—averaging only 2.17%. This consistency highlights the robustness of ΔAₘₐₓ as a predictive tool. As summarized in Table 1 , the predicted and measured values showed remarkable agreement. Table 1 Predicted vs. Measured Metabolic Activity in Validation tests Validation Test Predicted Metabolic Activity (%) Measured Metabolic Activity (%) Relative absolute error (%) T1 188.12 196.31 4.35 T2 159.73 161.50 0.98 T3 141.91 147.00 3.58 To evaluate the generalizability of the ΔAₘₐₓ–metabolic activity correlation, the same protocol was repeated for BM-MSCs and SSCs. The results, shown in Figs. 6 b and 6 c, respectively, confirm that a strong linear trend also exists for these additional cell types. This relationship is quantified by a slope we define as the MPACR, which varies by cell type and reflects the plasma-activated biological response in terms of metabolic activity. The calculated MPACR values for HUC-MSCs, BM-MSCs, and SSCs are presented in Table 2 , with R² values exceeding 94% for all. These results demonstrate that ΔAₘₐₓ serves as a consistent, predictive, and reproducible parameter for estimating metabolic activity in response to CAP exposure. The low variability and high correlation across independent tests suggest that MPACR could be employed as a standardized quantitative tool for characterizing CAP–cell interactions in a cell-specific manner. These results validate ΔAₘₐₓ as a robust, quantifiable, and biologically meaningful indicator of plasma jet-induced oxidative activity. Its consistent performance across stem cell types and treatment conditions positions it as a promising tool for improving comparability between CAP experiments, enabling future standardization of cold plasma jet applications in biomedical research. The statistical significance of each MPACR slope was assessed using a two-tailed t-test on the regression coefficient. Details of the methodology are provided in Supplementary Section S.4. Table 2 MPACR Parameters for Three Stem Cell Types. Statistical significance of the MPACR slope was confirmed using a two-tailed t-test (p < 0.001 for all cell types); see Supplementary Section S.4. Stem Cell Slope (MPACR) (µs⁻¹) ± SE R² (%) P-Value HUC-MSC 53.27 ± 3.20 96.51 1.28 × 10⁻⁸ BM-MSC 67.41 ± 4.51 95.89 4.12 × 10⁻⁹ SSC 25.68 ± 1.51 95.68 1.34 × 10⁻¹⁰ 3.2 Comparative Analysis of Specific Stem Cells Treated with Different Jets: Toward Demonstrating a Device-Independent Parameter To assess whether the predictive parameter ΔAₘₐₓ is specific to a given plasma jet or generalizable across different jets, metabolic activity was analyzed in HUC-MSCs treated with two distinct argon plasma jets. As shown in Fig. 7 a, both jets produced a strong linear correlation between ΔAₘₐₓ and metabolic activity, although the exact ΔAₘₐₓ values varied depending on jet characteristics. Interestingly, while the position of the lines differed due to variations in activation magnitude, the slopes of the correlations, quantified by the MPACR parameter, were nearly identical between the two jets. The MPACR value for HUC-MSCs was 53.27 using the first jet and 57.18 with the second, with R² values of 96.51% and 97.26%, respectively (see Table 3 ). This consistency indicates that MPACR is device-independent, meaning it reflects a fundamental cell-specific biological response to plasma-activated media, not a device artifact. To confirm this finding, a similar analysis was performed on BM-MSCs using both plasma jets. As shown in Fig. 7 b, the resulting MPACR slopes were also very close, with minimal variance between jets. The agreement in slope values further supports the conclusion that MPACR is conserved across plasma jet architectures, provided the CAP chemistry and treatment conditions are comparable. These findings lead to two significant conclusions. First, once a linear trend line between ΔAₘₐₓ and metabolic activity is established for a given stem cell type, the biological response to CAP exposure can be predicted using ΔAₘₐₓ, independent of the specific plasma jet used. Second, a single data point from a new jet can be sufficient to compare its biological efficacy against a reference system, making MPACR a valuable parameter for standardizing CAP treatments across devices in biomedical applications. Jet-to-jet MPACR comparisons were conducted using a slope-difference t-test. See Supplementary Section S.4 for the full statistical approach and p-values. Table 3 Comparison of MPACR values for HUC-MSCs and BM-MSCs across two CAP jets. Differences in MPACR slopes between Jet1 and Jet 2 were statistically non-significant (p > 0.05); see Supplementary Section S.4. Stem Cell MPACR – Jet 1 (µs⁻¹) ±SE MPACR – Jet 2 (µs⁻¹) ±SE Jet 1 R² (%) Jet2 R² (%) P-Value HUC-MSC 53.27 ± 3.20 57.18 ± 3.03 96.51 97.26 0.39 BM-MSC 67.41 ± 4.51 69.30 ± 3.43 95.89 97.13 0.74 3.3 Cell Dependence of the Standardized Parameter MPACR: Toward Developing a Biological Diagnostic Tool The results so far have demonstrated that the MPACR parameter is reproducible, predictive, and device-independent. In this section, we explore its cell-type dependence, which opens new possibilities for biological classification or diagnostics based on plasma response. Figure 8 presents the relationship between ΔAₘₐₓ and metabolic activity for all three studied stem cell types HUC-MSCs, BM-MSCs, and SSCs treated with the same plasma jet. Each cell type exhibited a unique linear trend, characterized by a distinct MPACR slope. The MPACR values, previously reported in Table 2 , were 53.27 for HUC-MSCs, 67.41 for BM-MSCs, and 25.68 for SSCs, with R² values exceeding 95% in all cases. This confirms that MPACR is specific to the cellular identity and not interchangeable across cell types. The cell-type dependence of MPACR can be attributed to differences in redox-related metabolic pathways. Each stem cell type exhibits unique metabolic profiles, antioxidant capacities, and signaling responses to oxidative species, which collectively influence how ΔAₘₐₓ translates into biological effects. Thus, MPACR can be viewed as a functional biomarker that reflects both the reactive environment induced by plasma and the cellular ability to metabolically respond to it. This finding suggests that MPACR could serve as a new diagnostic or classification tool to distinguish cell types based on their plasma-response profile. Since it relies on non-invasive measurements (optical sensing and MTT assay), MPACR-based profiling could offer a practical approach to characterizing stem cell populations or verifying cell identities during expansion, therapeutic preparation, or quality control. 4. Discussion The observed hierarchy of MPACR values (BM-MSC > HUC-MSC > SSC) can be interpreted based on the distinct redox-metabolic strategies of these stem-cell types under dissolved-oxygen (DO)-related oxidative stress ( 23 ). BM-MSCs tend to shift toward glycolysis when exposed to oxidative stimuli, providing rapid but less efficient ATP generation reminiscent of the Warburg effect ( 24 , 25 ). HUC-MSCs maintain a flexible metabolic program, balancing glycolysis and oxidative phosphorylation (OXPHOS) to preserve homeostasis ( 26 , 27 ). In contrast, SSCs rely predominantly on OXPHOS, prioritizing genomic stability and long-term stemness over rapid metabolic reprogramming ( 28 ). This spectrum of redox behaviors provides a rigorous test of MPACR cell specificity (see Supplementary Table S4). Cold atmospheric plasma (CAP) jets generate a rich mixture of reactive oxygen and nitrogen species (RONS); among these, DO-derived species are key mediators of cellular responses and initiate most downstream biology ( 3 , 29 ). Our study quantifies DO dynamics via PtTFPP phosphorescence-lifetime sensing ( 16 ), then links those dynamics to metabolic outcome. Moreover, CAP-induced ROS production can reshape cellular metabolism by modulating both glycolysis and OXPHOS pathways ( 30 ). DO-derived ROS may accumulate extracellularly or intracellularly, and the balance between these compartments critically shapes cell fate ( 31 ). Excess intracellular ROS can trigger oxidative damage and apoptosis, whereas moderate levels promote survival, lineage commitment, or enhanced metabolic activity ( 32 ). Upon CAP exposure, extracellular ROS first contact the plasma membrane, activating redox-sensitive signaling cascades that subsequently adjust intracellular ROS handling ( 33 ). As shown in Fig. 8 , these DO-driven dynamics correlate with cell-type-specific shifts in metabolic activity. CAP exposure modulates glycolysis/OXPHOS ratios (Table S4), and each stem-cell type’s ability to manage DO stress defines its metabolic trajectory: BM-MSCs exhibit the steepest rise in metabolic activity with increasing ΔAₘₐₓ, consistent with a glycolytic shift under oxidative stress ( 25 ). HUC-MSCs display a balanced response, leveraging both glycolysis and OXPHOS to maintain energy output while safeguarding genomic integrity ( 25 ). SSCs show a modest increase in metabolic activity and remain OXPHOS-dominated, a conservative strategy that sustains long-term stemness ( 28 ). A notable feature is the intersection of HUC-MSC and SSC curves at a specific ΔAₘₐₓ value (Fig. 8 ), suggesting a DO threshold where both cell types exhibit comparable metabolic output via distinct mechanisms. Beyond this point, HUC-MSCs maintain activity through metabolic flexibility, whereas SSCs plateau or decline, likely to protect genomic integrity. This hierarchy is captured in the experimental MPACR values (BM-MSC > HUC-MSC > SSC). Thus, MPACR serves not only as a predictive metric for CAP-induced metabolic activity but also as a quantitative fingerprint of each cell type’s redox-metabolic strategy. In conclusion, our results confirm MPACR as a robust, biologically meaningful indicator of stem-cell response to DO-derived stimuli following CAP exposure. Although plasma medicine continues to evolve rapidly, only a few applications, such as wound healing, have achieved clinical implementation. Progress is hindered by unresolved issues including the lack of standardization among plasma jet devices, challenges in predicting biological responses, and difficulty in comparing results across studies. To address these barriers, we introduce a new analytical metric, the MPACR parameter, which quantifies the linear correlation between ΔAₘₐₓ (the maximum change in DO activity post-CAP exposure) and cellular metabolic activity. Derived from phosphorescence lifetime-based oxygen sensing, MPACR is device-independent but cell-type–specific, providing a predictive and reproducible descriptor of CAP effects. Our experiments showed that BM-MSCs, HUC-MSCs, and SSCs each exhibit distinct MPACR slopes, corresponding to their redox metabolism and their ability to handle DO stress. BM-MSCs favored glycolysis and showed the strongest metabolic response; HUC-MSCs maintained metabolic flexibility; and SSCs prioritized oxidative phosphorylation to maintain stemness. These distinct patterns make MPACR a strong candidate for standardizing plasma-based studies and designing cell-specific treatment strategies. 4.1 Suggested Analytical Workflows Based on the MPACR Parameter These workflows are proposed for research use and provide methodological guidance for leveraging MPACR in predictive modeling, device evaluation, and cell-specific CAP studies: Workflow 1: Determining MPACR for Specific Cell Types MPACR is identified by measuring ΔAₘₐₓ via phosphorescence lifetime spectroscopy and correlating it with metabolic activity (e.g., via MTT assay) across varying plasma conditions. This establishes a unique slope for each cell type, reflecting its redox behavior. Workflow 2: Predicting Biological Response from a Single Dataset Once MPACR is known for a specific cell type, only one ΔAₘₐₓ–metabolic activity data point is needed to reconstruct the full response trendline for any CAP jet. This enables rapid prediction with minimal experimental effort. Workflow 3: Evaluating Device Consistency and Standardization Two CAP jets can be considered functionally equivalent if their MPACR-based trendlines overlap for the same cell type. This provides a straightforward strategy for comparing devices across labs. Workflow 4: Comparing Relative Biological Performance of Plasma Jets By plotting MPACR curves for the same cell type across different devices, one can quantitatively assess which plasma jet is more effective at inducing metabolic responses, as shown in Fig. 7 . Workflow 5: Exploring Reagent-Free Cell Identification Because MPACR is cell-specific and device-independent, it could serve as a reagent-free biomarker to distinguish cell types based on their redox response to CAP, offering new opportunities in label-free diagnostics. 5. Future Outlook One of the most promising areas for CAP application is oncology. The selective ability of CAP to induce apoptosis in cancer cells while sparing healthy tissue hinges on maintaining DO levels within a therapeutic window. Our future work aims to define this threshold and identify the electrical conditions that enable targeted, non-necrotic CAP treatment of tumor cells. In sum, the MPACR parameter introduced in this work offers a valuable framework for CAP jet standardization, biological outcome prediction, and the development of novel, non-invasive cell identification methods. By bridging biophotonics with redox biology, it opens the door to more predictable, reproducible, and translationally relevant plasma medicine research. Declarations CONFLICT OF INTEREST Authors declare no conflict of interest. ACKNOWLEDGMENT This work was supported by the Iran National Science Foundation (INSF), Grant No. 4020795. Contribution of authors E.T. performed the experimental work, including sensor fabrication, optical setup, and plasma treatments. 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Plasma Sources Sci Technol 26(12):123002 Taheri D, Hajisharifi K, Heydari E, MirzaHosseini FK, Mehdian H, Robert E (2024) Realtime RONS monitoring of cold plasma-activated aqueous media based on time-resolved phosphorescence spectroscopy. Sci Rep 14(1):22403 Heydari E, Bagheri P, Zare-Behtash H (2022) Photonic-based time-resolved multipulse oxygen sensor. IEEE Sens J 22(13):12746–12753 Wagner W, Horn P, Castoldi M, Diehlmann A, Bork S, Saffrich R et al (2008) Replicative senescence of mesenchymal stem cells: a continuous and organized process. PLoS ONE 3(5):e2213 Zhao Q, Zhang L, Wei Y, Yu H, Zou L, Huo J et al (2019) Systematic comparison of hUC-MSCs at various passages reveals the variations of signatures and therapeutic effect on acute graft-versus-host disease. Stem Cell Res Ther 10:1–14 Kubota H, Brinster RL (2008) Culture of rodent spermatogonial stem cells, male germline stem cells of the postnatal animal. Methods Cell Biol 86:59–84 Far HR, Amiri M, Elyasigorji Z, Heydari E, Saviz M, Faraji-Dana R et al (2024) Photothermal Oxygen-Releasing IR780/CaO 2 Nanoplatform for Enhanced and On-Demand Photodynamic Therapy. ACS Appl NANO Mater 7(20):23627–23640 Ghasemi M, Turnbull T, Sebastian S, Kempson I (2021) The MTT assay: utility, limitations, pitfalls, and interpretation in bulk and single-cell analysis. Int J Mol Sci 22(23):12827 Vistica DT, Skehan P, Scudiero D, Monks A, Pittman A, Boyd MR (1991) Tetrazolium-based assays for cellular viability: a critical examination of selected parameters affecting formazan production. Cancer Res 51(10):2515–2520 Pietilä M, Palomäki S, Lehtonen S, Ritamo I, Valmu L, Nystedt J et al (2012) Mitochondrial function and energy metabolism in umbilical cord blood-and bone marrow-derived mesenchymal stem cells. Stem Cells Dev 21(4):575–588 Komemi O, Orbuch E, Jarchowsky-Dolberg O, Brin YS, Tartakover-Matalon S, Pasmanik-Chor M et al (2025) Myeloma mesenchymal stem cells’ bioenergetics afford a novel selective therapeutic target. Oncogenesis 14(1):9 Estrada J, Albo C, Benguria A, Dopazo A, Lopez-Romero P, Carrera-Quintanar L et al (2012) Culture of human mesenchymal stem cells at low oxygen tension improves growth and genetic stability by activating glycolysis. Cell Death Differ 19(5):743–755 Ravera S, Podestà M, Sabatini F, Fresia C, Columbaro M, Bruno S et al (2018) Mesenchymal stem cells from preterm to term newborns undergo a significant switch from anaerobic glycolysis to the oxidative phosphorylation. Cell Mol Life Sci 75:889–903 Moniz I, Ramalho-Santos J, Branco AF (2022) Differential oxygen exposure modulates mesenchymal stem cell metabolism and proliferation through mTOR signaling. Int J Mol Sci 23(7):3749 Voigt A, Dardari R, Su L, Lara N, Sinha S, Jaffer A et al (2022) Metabolic transitions define spermatogonial stem cell maturation. Hum Reprod 37(9):2095–2112 Motaln H, Recek N, Rogelj B (2021) Intracellular responses triggered by cold atmospheric plasma and plasma-activated media in cancer cells. Molecules 26(5):1336 Nasri Z, Memari S, Wenske S, Clemen R, Martens U, Delcea M et al (2021) Singlet-Oxygen‐Induced Phospholipase A2 Inhibition: A Major Role for Interfacial Tryptophan Dioxidation. Chemistry–A Eur J 27(59):14702–14710 Riegger J, Schoppa A, Ruths L, Haffner-Luntzer M, Ignatius A (2023) Oxidative stress as a key modulator of cell fate decision in osteoarthritis and osteoporosis: a narrative review. Cell Mol Biol Lett 28(1):76 Matés JM, Segura JA, Alonso FJ, Márquez J (2008) Intracellular redox status and oxidative stress: implications for cell proliferation, apoptosis, and carcinogenesis. Arch Toxicol 82:273–299 Banerjee R (2012) Redox outside the box: linking extracellular redox remodeling with intracellular redox metabolism. J Biol Chem 287(7):4397–4402 Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 May, 2026 Reviewers invited by journal 01 May, 2026 Editor assigned by journal 30 Apr, 2026 Submission checks completed at journal 30 Apr, 2026 First submitted to journal 25 Apr, 2026 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. 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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-9527256","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636083952,"identity":"8316b34f-0320-4750-964d-dd58c620f7b7","order_by":0,"name":"Erfan Tamandeh","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Erfan","middleName":"","lastName":"Tamandeh","suffix":""},{"id":636083954,"identity":"23392586-21d7-43f3-99de-1118966aa776","order_by":1,"name":"Kamal Hajisharifi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3RMW7CMBTG8c+K5CxQVljIFcyEUKWe5SEGFiTEBurQRJFel/YAHION0ZYHFkussJEjZMxGAu3QxWGshP+bLf/kZxkIhf5hCiAtUkBGJiuy9L6rHyNymo8eJUBDIEkOfom3cawLU+3fli8y4s12b9H71MKuPWTyRWS7bjZhKfi8cxZ9RzDON5iuieBIySTjc8EWOAHGN6A6XshU/KFkfcuqIUkrORHpLtsbEbuaqHZyqd/Ch4bkgy3POyM3TVsGW8zKit9VwnFRfvPrcHiwtvQRoEN/lz/f5CvWLQdCoVDo6bsCb05XaUNRcS8AAAAASUVORK5CYII=","orcid":"","institution":"Kharazmi University","correspondingAuthor":true,"prefix":"","firstName":"Kamal","middleName":"","lastName":"Hajisharifi","suffix":""},{"id":636083955,"identity":"d10aef2d-60df-4888-9008-6bfe99d4cbf4","order_by":2,"name":"Esmaeil Heydari","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Esmaeil","middleName":"","lastName":"Heydari","suffix":""},{"id":636083956,"identity":"77ca2693-9f8f-4562-a0ad-4ed15e8963e4","order_by":3,"name":"Sara Emadi","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Emadi","suffix":""},{"id":636083957,"identity":"2a97b75e-ae4a-44ce-9f4f-52551d1530bd","order_by":4,"name":"Hassan Mehdian","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Mehdian","suffix":""},{"id":636083958,"identity":"f7c57030-ee0d-4048-8638-37fb740d6cc6","order_by":5,"name":"Elaheh Amini","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Elaheh","middleName":"","lastName":"Amini","suffix":""},{"id":636083959,"identity":"5c14f7aa-c0e9-4e7d-af9b-85072d3a6c2f","order_by":6,"name":"Ali Hasanbeigi","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Hasanbeigi","suffix":""},{"id":636083960,"identity":"6b86af3a-4386-46e2-b207-a83edfbbd6de","order_by":7,"name":"Eric Robert","email":"","orcid":"","institution":"UMR 7344 GREMI, CNRS/Université d’Orléans","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Robert","suffix":""}],"badges":[],"createdAt":"2026-04-25 16:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9527256/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9527256/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108973853,"identity":"4f51b5aa-edae-42f3-b9e2-0ebd5428722b","added_by":"auto","created_at":"2026-05-11 10:44:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157954,"visible":true,"origin":"","legend":"\u003cp\u003eFramework for Developing a Standardized Plasma Jet Evaluation Method Using DO-Based Optical Parameters\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/2a1a1b41250ed7ba6584681f.jpg"},{"id":108973872,"identity":"2c92d08c-3c24-44ed-a048-1c216673f1e2","added_by":"auto","created_at":"2026-05-11 10:44:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48765,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic activity (24 h post-treatment) under different experimental conditions, assessed using the MTT assay. The control group was normalized to 100%, and other groups (Sensor only, Plasma only, and Sensor + Plasma) are expressed as percentages relative to the control. Error bars represent the standard error of the mean (SEM) from three independent experiments.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/c500dd9bcbfad2dac1e0482f.jpg"},{"id":108973850,"identity":"ca3915e6-b990-4301-8ad5-89e374d0ad04","added_by":"auto","created_at":"2026-05-11 10:44:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMetabolic activity of HUC-MSCs (MTT test) 48 hours after CAP treatment under varying voltage-frequency conditions (T0–T15). Bars represent the mean ± standard error of the mean (SEM) based on MTT assay results. Statistical significance was assessed using one-way ANOVA followed by Tukey’s HSD post hoc test. Corresponding p-values for pairwise comparisons are provided in Tables S3(a–c). A p-value \u0026lt; 0.05 was considered statistically significant.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/c212805e891c6c3df220c2f1.jpg"},{"id":108973851,"identity":"16997b38-cb5a-4eaf-8b24-e6b99dd6aabf","added_by":"auto","created_at":"2026-05-11 10:44:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic representation of the CAP treatment and sensing setup. The system includes an argon-based plasma jet directed at a well plate containing cells or medium, an embedded PtTFPP-based optical sensor, and a probe for detecting phosphorescence lifetime changes in real time. This configuration enables dynamic monitoring of dissolved oxygen (DO) changes during plasma exposure.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/1d91b2e67a8b61bf16894c40.jpg"},{"id":108973869,"identity":"54d28f4e-e87f-42ed-8904-eb61c324ca19","added_by":"auto","created_at":"2026-05-11 10:44:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72699,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative phosphorescence lifetime (τ) curve showing real-time changes in DO (dissolved oxygen) levels during CAP jet exposure. The value ΔAₘₐₓ (Max activity) represents the maximum increase in τ after plasma treatment, relative to the baseline.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/bb2292299b3b8c8d2ccb76c1.jpg"},{"id":108973864,"identity":"e6ed11f1-a979-479d-bdc3-cbeeb4fde2b4","added_by":"auto","created_at":"2026-05-11 10:44:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":75830,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between ΔAₘₐₓ and metabolic activity for the three stem-cell types following CAP treatment. (a) HUC-MSCs: initial data (black symbols) together with three independent validation experiments (green symbols) (b) BM-MSCs and (c) SSCs.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/758a0b8693100f765d2635bb.jpg"},{"id":108973874,"identity":"1b4050e9-baba-47fd-8570-f9aaf28f61eb","added_by":"auto","created_at":"2026-05-11 10:44:42","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72488,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ΔAₘₐₓ–metabolic activity correlations for different plasma jets. (a) HUC-MSCs treated with two distinct CAP jets demonstrate similar MPACR values despite different ΔAₘₐₓ magnitudes. (b) BM-MSCs also show consistent MPACR slopes across jets, confirming the device-independent nature of the parameter.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/c34209f9186210b043ca9e2d.jpg"},{"id":108973865,"identity":"bed9cc99-501f-4cf6-b84f-8b35cddcb726","added_by":"auto","created_at":"2026-05-11 10:44:29","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53259,"visible":true,"origin":"","legend":"\u003cp\u003eLinear correlations between ΔAₘₐₓ and metabolic activity for three different stem cell types treated under identical CAP Jet 1 conditions. Each cell type exhibits a unique slope (MPACR), confirming that the parameter is cell-dependent and potentially useful for biological classification or diagnostics.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/9195ffffbfa0b7aa142c6413.jpg"},{"id":108978212,"identity":"c3c9a9a3-06f6-491b-96af-5c3b115e4a46","added_by":"auto","created_at":"2026-05-11 11:35:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935091,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/1f9ba1a4-a07f-4fdc-981a-6e1e894ccb98.pdf"},{"id":108973868,"identity":"684d33bc-d78c-4536-b7ed-a9e188805064","added_by":"auto","created_at":"2026-05-11 10:44:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":58415,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-9527256/v1/41b7d9198d4da76d9cd8e920.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Photonic-based Prediction Method for the Metabolic Activity of Stem Cells Exposed to Different Cold Plasma Jets","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCold atmospheric plasma (CAP) jets are emerging as powerful tools in biomedical research due to their capacity to generate reactive species such as dissolved oxygen (DO)(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), which play a crucial role in regulating cellular redox balance and metabolism (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These plasmas are produced at room temperature and atmospheric pressure using carrier gases like argon or helium and are delivered to biological or liquid targets via a variety of jet designs (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Although numerous studies have demonstrated the promising in vitro effects of CAP on cells and tissues, a significant challenge remains in translating these findings into reproducible, standardized protocols (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA major barrier to broader clinical translation of the plasma medicine devices is the lack of a quantifiable and biologically relevant parameter that enables comparisons across different plasma jet configurations. Variability in jet design, electrical parameters, and treatment conditions frequently leads to inconsistent biological outcomes, making it difficult to replicate results across laboratories or optimize protocols(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). While optical emission spectroscopy (OES) is commonly employed to characterize plasma jets, it offers limited information about reactive species in the liquid phase\u0026mdash;particularly DO, which is central to redox signaling and cellular responses (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). To address this gap, the present study introduces a photonic approach based on time-resolved phosphorescence spectroscopy to indirectly quantify DO in plasma-activated media as a reliable characteristic arameter for evaluating the plasma activity of the media. This method employs an oxygen-sensitive phosphorescent dye to monitor real-time changes in phosphorescence lifetime, reflecting fluctuations in local oxygen quenching and DO activity by time(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This approach overcomes common limitations of intensity-based methods\u0026mdash;such as photobleaching, background interference, and quenching\u0026mdash;by providing robust monitoring of ROS dynamics. Using an integrated oxygen sensor, the method continuously tracks DO as a precursor to ROS production, offering insight into the rates of ROS generation and consumption in plasma-treated media(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, comparing the obtained linear correlation of the maximum change in phosphorescence lifetime (ΔAₘₐₓ) measured immediately after plasma exposure with metabolic activity (MTT post 48h) across three stem cell types treated by two different plasma jet configurations leads to achieving a jet-independent yet cell-specific parameter called MPACR, the slope of the linear relationship between ΔAₘₐₓ and the measured MTT values. While not aimed at immediate clinical application, this work proposes MPACR parameter as a foundational metric for enhancing reproducibility and comparability in in-vitro CAP studies. It represents a significant step toward standardizing plasma jet performance through a biologically meaningful, optically derived parameter.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the experimental strategy used to assess the relationship between dissolved oxygen (DO) dynamics and cellular metabolic responses, with a focus on comparisons across different stem cell types and plasma jet configurations to validate MPACR as a reproducible, jet-independent parameter. As shown in this figure, we applied the proposed method to evaluate the relationship between phosphorescence decay signals and metabolic activity, measured via the MTT assay at 48 hours, in three stem cell lines: HUC-MSC, BM-MSC, and SSC. Through this strategy, we identified the \u0026ldquo;maximum activation\u0026rdquo; parameter (ΔAₘₐₓ) as a key indicator of plasma treatment efficacy. As outlined in the flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, our approach involves:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e\u0026bull;\u0026emsp;First, demonstrating a linear relationship between cellular metabolic activity and ΔAₘₐₓ for each stem cell type;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull;\u0026emsp;Second, confirming that the slope of this relationship\u0026mdash;MPACR (Maximum Plasma Activation\u0026ndash;Cell Response)\u0026mdash;is independent of the plasma jet used;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull;\u0026emsp;Finally, showing that MPACR is cell-type dependent but jet-independent, reflecting intrinsic metabolic characteristics of each stem cell population.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTogether, these findings contribute to solving the reproducibility problem by introducing a standardized, biologically relevant metric for CAP research. Moreover, this approach offers a robust framework for cross-platform comparisons and enhances the predictive capability of CAP treatment outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell Culture\u003c/h2\u003e \u003cp\u003eHuman umbilical cord mesenchymal stem cells (HUC-MSCs), bone marrow mesenchymal stem cells (BM-MSCs), and spermatogonial stem cells (SSCs) were obtained from the Royan Institute (Tehran, Iran). These stem cell types were selected to represent a spectrum of metabolic phenotypes with varying sensitivity to oxidative stress and redox signaling. Cells were cultured in DMEM/F-12 supplemented with 10% fetal bovine serum (FBS) and 0.5% penicillin\u0026ndash;streptomycin (Sigma-Aldrich, USA), and maintained at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂. During subculture, HUC-MSCs were centrifuged at 1200 rpm, while BM-MSCs and SSCs were centrifuged at 1500 rpm for 5 minutes.\u003c/p\u003e \u003cp\u003eTo minimize passage-related changes in stemness, redox behavior, and metabolic activity, all experiments were conducted using cells at passage\u0026thinsp;\u0026le;\u0026thinsp;6. This threshold was selected based on literature showing that early-passage mesenchymal stem cells\u0026mdash;both from bone marrow (BM-MSCs) and umbilical cord (HUC-MSCs)\u0026mdash;retain higher proliferative capacity, trilineage differentiation potential, and a stable redox profile. In contrast, later passages tend to show signs of senescence, oxidative imbalance, and reduced responsiveness (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Similarly, rat spermatogonial stem cells (SSCs) cultured in vitro are known to lose germline marker expression and undergo transcriptional drift when passaged extensively. Limiting their expansion to \u0026le;\u0026thinsp;6 passages help preserve their undifferentiated state and ensures consistency in redox-sensitive assays (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor 24-well plate experiments, cells were seeded at approximately 20,000 cells per well in complete medium and allowed to adhere and recover for 24 hours before plasma treatment. To control for minor variations in seeding, all results were normalized to the untreated control wells on the same plate. Pilot cell counts were performed weekly to ensure that seeding densities (~\u0026thinsp;2.0\u0026ndash;2.5 \u0026times; 10⁴ cells/well) consistently resulted in ~\u0026thinsp;70% confluency after 24 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 CAP Jet Design and Treatment Conditions\u003c/h2\u003e \u003cp\u003eTwo custom-built cold atmospheric plasma (CAP) jets were used to evaluate the optical response of plasma-activated media across structurally distinct discharge geometries. Both systems employed dielectric barrier discharge (DBD) in a ring-pin electrode configuration and were powered by a variable high-voltage AC source.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCAP Jet 1\u003c/b\u003e consisted of a 15 cm stainless steel pin electrode positioned axially within a 13 cm quartz tube (inner diameter: 3 mm; outer diameter: 5 mm; wall thickness: 1 mm). A 0.5 cm-wide copper ring electrode was affixed 0.7 cm upstream from the tube outlet, while the pin extended 1 mm beyond the ring.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCAP Jet 2\u003c/b\u003e featured a similar axial configuration but was built with a borosilicate glass tube (inner diameter: 3 mm; outer diameter: 7 mm; wall thickness: 2 mm). A 1 cm-wide copper ring was positioned 0.5 cm from the outlet, and the pin electrode extended 2 mm beyond the ring.\u003c/p\u003e \u003cp\u003eBoth jets operated with high-purity argon (99.999%) at a controlled flow rate of 1.5 L/min. Treatment durations were 60 and 90 seconds. Voltage-frequency combinations (T0\u0026ndash;T15), ranging from 12.4\u0026ndash;16.6 kV and 12.2\u0026ndash;16.6 kHz, were selected to generate varying plasma activation conditions (see Table S2).\u003c/p\u003e \u003cp\u003eCAP treatment was applied to cell culture medium containing the optical sensor (or cells) placed in standard 24-well plates. For metabolic activity tests, cells were returned to the incubator for 48 h post-treatment prior to analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Optical DO Sensor Preparation\u003c/h2\u003e \u003cp\u003eTo measure dissolved oxygen (DO) in liquid media, a phosphorescent oxygen-sensitive sensor was fabricated using platinum (II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorophenyl) porphyrin (PtTFPP; Scientific Frontiers), a dye whose phosphorescence is quenched in the presence of DO.\u003c/p\u003e \u003cp\u003eThe sensor film was prepared by dissolving 2 mg of PtTFPP and 1 g of polystyrene (PS) in 2 mL of toluene (Sigma-Aldrich, USA). After stirring the solution for 1 hour, 50 \u0026micro;L was drop-cast onto a clean glass slide and dried at 80\u0026deg;C to form a uniform film. Once solidified, 8 mm discs were punched from the film and used as optical sensors.\u003c/p\u003e \u003cp\u003eDuring CAP exposure, the reactive species generated in the plasma consume DO in the medium, leading to an increase in the phosphorescence lifetime (τ) of the PtTFPP sensor. This change enables real-time tracking of redox dynamics.\u003c/p\u003e \u003cp\u003eThe phosphorescence lifetime of the PS-embedded PtTFPP probe is primarily governed by collisional quenching with dissolved oxygen, as confirmed in our real-time reactive oxygen species (ROS) study (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Because the optical geometry and temperature were identical to our earlier work, the previously published Stern\u0026ndash;Volmer calibration (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) was applied without modification.\u003c/p\u003e \u003cp\u003eTo ensure microbial sterility, the PtTFPP/PS probe discs were first attached to glass substrates and then first sterilized by rinsing with 70% ethanol, followed by 30 min UV exposure (254 nm) under a laminar flow hood. The assembled sensors were subsequently rinsed twice with sterile PBS and allowed to air-dry under sterile conditions before being placed in cell culture wells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 MTT Assay for Metabolic Activity\u003c/h2\u003e \u003cp\u003eThe MTT assay was used to quantify (i) metabolic activity (24 h post CAP exposure) and (ii) metabolic activity (48 h post-treatment) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A 0.5 mg mL⁻\u0026sup1; working solution was prepared by dissolving 5 mg MTT (Sigma-Aldrich, USA) in 1 mL PBS and diluting the stock with 10 mL complete culture medium. For each 24-well plate (typical working volume\u0026thinsp;\u0026asymp;\u0026thinsp;1 mL per well), 250 \u0026micro;L of the working solution was added to the existing\u0026thinsp;~\u0026thinsp;750 \u0026micro;L of medium\u0026mdash;yielding a final MTT concentration of 0.125 mg mL⁻\u0026sup1; (10% v/v). Plates were incubated for 4 h at 37\u0026deg;C to allow formazan formation. The medium was then carefully aspirated, and formazan crystals were solubilised in 300 \u0026micro;L DMSO per well with gentle shaking (10 min). Absorbance was measured at 570 nm using a BioTek Synergy H4 Hybrid Reader. Results are expressed as percentage of the untreated control (set to 100%), enabling direct comparison between 24 h and 48 h metabolic-activity endpoints (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sensor Biocompatibility and Cytotoxicity Testing\u003c/h2\u003e \u003cp\u003eTo evaluate the biocompatibility and potential cytotoxicity of the optical oxygen sensor, cells were exposed to four experimental conditions: an untreated Control group, a Sensor-only group where cells were cultured with the sensor to test for material biocompatibility, a Plasma-only group receiving CAP treatment in the absence of the sensor, and a Sensor\u0026thinsp;+\u0026thinsp;Plasma group that combined plasma treatment with sensor exposure.\u003c/p\u003e \u003cp\u003eMetabolic activity (24 h post-treatment) was assessed using the MTT assay described in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the Sensor-only group displayed Metabolic activity levels comparable to the Control group, indicating that the sensor material does not exert cytotoxic effects. Likewise, the Sensor\u0026thinsp;+\u0026thinsp;Plasma group showed no significant difference compared to the Plasma-only group, confirming that the presence of the sensor does not interfere with or alter the biological response to plasma treatment. Quantitative values and statistical comparisons are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor metabolic response analysis 48 hours post-treatment, the same experimental protocol was followed. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the effect of various CAP treatment conditions, differing in voltage and frequency, on the metabolic activity of HUC-MSCs, as measured by the MTT assay. These data reveal condition-dependent variations in cellular activity, highlighting the sensitivity of HUC-MSCs to plasma parameters. Comprehensive statistical comparisons, including ANOVA and post-hoc analyses, are detailed in Supplementary Table S3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 CAP Treatment and Real-Time Phosphorescence Lifetime Spectroscopy\u003c/h2\u003e \u003cp\u003eA custom optical setup was used to monitor changes in phosphorescence lifetime (τ) of the PtTFPP sensor in real time during CAP treatment. The setup included a blue LED (460 nm) excitation source and a time-resolved photodetector, with lifetime values recorded at 2.9-second intervals. This allowed continuous measurement of dissolved-oxygen (DO) dynamics in the culture medium immediately before, during, and after CAP exposure. The CAP treatment system is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It includes an argon-based cold atmospheric plasma (CAP) jet directed at a 24-well plate containing cell culture medium (with or without cells), while an optical oxygen sensor positioned in the medium detects changes in PL lifetime. The nozzle-to-liquid distance was fixed at 15 mm to ensure reproducible plasma\u0026ndash;liquid interaction, and medium temperature was confirmed to remain\u0026thinsp;\u0026le;\u0026thinsp;37\u0026deg;C throughout exposure. These changes reflect the reduction in DO concentration due to reactive species generated during plasma discharge, predominantly driven by DO depletion.\u003c/p\u003e \u003cp\u003eDuring plasma treatment, a decrease in DO concentration was detected in the DMEM/F-12 medium, indicated by an increase in PL lifetime. A representative phosphorescence-lifetime curve is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, where the maximum observed change following treatment is denoted as ΔAₘₐₓ. Baseline lifetime (τ₀) was defined as the mean of the data points immediately preceding plasma ignition, and the peak lifetime (τ_peak) was identified within the first data points after plasma shut-off. The maximum activity (ΔAₘₐₓ), calculated as the peak shift in lifetime relative to baseline, serves as an optical indicator of plasma-induced oxidative activity in the medium. ΔAₘₐₓ was determined for each treatment condition according to this equation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{A}_{max}={{\\tau\\:}}_{\\text{peak}}-{{\\tau\\:}}_{0}\\)\u003c/span\u003e \u003c/span\u003e and later correlated with cellular metabolic response to assess its potential as a predictive, jet-independent parameter\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using Python (version 3.11) with the pandas (v1.5.3), NumPy (v1.23.5), SciPy (v1.10.1), stats models (v0.14.0), and seaborn (v0.12.2) libraries. Data were tested for normality using the Shapiro\u0026ndash;Wilk test and for homogeneity of variances using Levene\u0026rsquo;s test.\u003c/p\u003e \u003cp\u003eFor comparisons involving multiple CAP treatment groups (T0\u0026ndash;T15), one-way analysis of variance (ANOVA) was performed. When ANOVA indicated significant group differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Tukey\u0026rsquo;s Honest Significant Difference (HSD) test was used for post hoc pairwise comparisons. This approach was selected to appropriately manage multiple comparisons and reduce type I error.\u003c/p\u003e \u003cp\u003eData are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) unless otherwise indicated. Full statistical results are provided in Table S3.\u003c/p\u003e \u003cp\u003eIn the following, unless otherwise stated, all references to metabolic activity hereafter correspond to the MTT assay measured 48 h post-treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 ΔAₘₐₓ\u0026ndash;Metabolic Activity Correlation as a Robust Predictive Parameter for CAP Performance Assessment\u003c/h2\u003e \u003cp\u003eWe define \u003cb\u003eΔAₘₐₓ\u003c/b\u003e as the largest post-CAP rise in the sensor\u0026rsquo;s phosphorescence lifetime (τ) above its baseline. This parameter was used to compare peak activation of the medium under different CAP treatments for three stem cell types: HUC-MSCs, BM-MSCs, and SSCs.\u003c/p\u003e \u003cp\u003eIn this study, the term \u0026ldquo;cell response\u0026rdquo; refers specifically to metabolic activity, as measured by the MTT assay 48h post treatment. The MPACR (Maximum Plasma Activation\u0026ndash;Cell Response) parameter is defined as the slope of the linear relationship between ΔAₘₐₓ and this measured metabolic activity.\u003c/p\u003e \u003cp\u003eTo validate the reliability of ΔAₘₐₓ as a predictive parameter, a series of experiments was conducted on HUC-MSCs using the first CAP jet. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, a strong linear correlation was observed between ΔAₘₐₓ and MTT-derived metabolic activity. The metabolic activity of HUC-MSCs increased from approximately 100% to 159% in the initial experiments, and up to 196% in follow-up tests, while ΔAₘₐₓ ranged from 1.5 to 3.5 \u0026micro;s under different CAP exposure settings (voltage and frequency).\u003c/p\u003e \u003cp\u003eTo confirm the stability and reproducibility of this correlation, three additional validation tests were conducted several weeks after the initial data collection. These results (represented by green dots in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) were plotted on the same graph and closely followed the original trend line. The difference between the predicted values (based on the initial fit) and the actual metabolic activity from the validation experiments was minimal\u0026mdash;averaging only 2.17%. This consistency highlights the robustness of ΔAₘₐₓ as a predictive tool. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the predicted and measured values showed remarkable agreement.\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\u003ePredicted vs. Measured Metabolic Activity in Validation tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicted Metabolic Activity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasured Metabolic Activity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative absolute error (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.58\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\u003eTo evaluate the generalizability of the ΔAₘₐₓ\u0026ndash;metabolic activity correlation, the same protocol was repeated for BM-MSCs and SSCs. The results, shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, respectively, confirm that a strong linear trend also exists for these additional cell types. This relationship is quantified by a slope we define as the MPACR, which varies by cell type and reflects the plasma-activated biological response in terms of metabolic activity. The calculated MPACR values for HUC-MSCs, BM-MSCs, and SSCs are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with R\u0026sup2; values exceeding 94% for all. These results demonstrate that ΔAₘₐₓ serves as a consistent, predictive, and reproducible parameter for estimating metabolic activity in response to CAP exposure. The low variability and high correlation across independent tests suggest that MPACR could be employed as a standardized quantitative tool for characterizing CAP\u0026ndash;cell interactions in a cell-specific manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese results validate ΔAₘₐₓ as a robust, quantifiable, and biologically meaningful indicator of plasma jet-induced oxidative activity. Its consistent performance across stem cell types and treatment conditions positions it as a promising tool for improving comparability between CAP experiments, enabling future standardization of cold plasma jet applications in biomedical research. The statistical significance of each MPACR slope was assessed using a two-tailed t-test on the regression coefficient. Details of the methodology are provided in Supplementary Section S.4.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMPACR Parameters for Three Stem Cell Types. Statistical significance of the MPACR slope was confirmed using a two-tailed t-test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all cell types); see Supplementary Section S.4.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem Cell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope (MPACR)\u003c/p\u003e \u003cp\u003e(\u0026micro;s⁻\u0026sup1;)\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026sup2; (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHUC-MSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.28 \u0026times; 10⁻⁸\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM-MSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e4.12 \u0026times; 10⁻⁹\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e1.34 \u0026times; 10⁻\u0026sup1;⁰\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparative Analysis of Specific Stem Cells Treated with Different Jets: Toward Demonstrating a Device-Independent Parameter\u003c/h2\u003e \u003cp\u003eTo assess whether the predictive parameter ΔAₘₐₓ is specific to a given plasma jet or generalizable across different jets, metabolic activity was analyzed in HUC-MSCs treated with two distinct argon plasma jets. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, both jets produced a strong linear correlation between ΔAₘₐₓ and metabolic activity, although the exact ΔAₘₐₓ values varied depending on jet characteristics.\u003c/p\u003e \u003cp\u003eInterestingly, while the position of the lines differed due to variations in activation magnitude, the slopes of the correlations, quantified by the MPACR parameter, were nearly identical between the two jets. The MPACR value for HUC-MSCs was 53.27 using the first jet and 57.18 with the second, with R\u0026sup2; values of 96.51% and 97.26%, respectively (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This consistency indicates that MPACR is device-independent, meaning it reflects a fundamental cell-specific biological response to plasma-activated media, not a device artifact.\u003c/p\u003e \u003cp\u003eTo confirm this finding, a similar analysis was performed on BM-MSCs using both plasma jets. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, the resulting MPACR slopes were also very close, with minimal variance between jets. The agreement in slope values further supports the conclusion that MPACR is conserved across plasma jet architectures, provided the CAP chemistry and treatment conditions are comparable.\u003c/p\u003e \u003cp\u003eThese findings lead to two significant conclusions. First, once a linear trend line between ΔAₘₐₓ and metabolic activity is established for a given stem cell type, the biological response to CAP exposure can be predicted using ΔAₘₐₓ, independent of the specific plasma jet used. Second, a single data point from a new jet can be sufficient to compare its biological efficacy against a reference system, making MPACR a valuable parameter for standardizing CAP treatments across devices in biomedical applications. Jet-to-jet MPACR comparisons were conducted using a slope-difference t-test. See Supplementary Section S.4 for the full statistical approach and p-values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of MPACR values for HUC-MSCs and BM-MSCs across two CAP jets. Differences in MPACR slopes between Jet1 and Jet 2 were statistically non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05); see Supplementary Section S.4.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem Cell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPACR \u0026ndash; Jet 1 (\u0026micro;s⁻\u0026sup1;)\u003c/p\u003e \u003cp\u003e\u0026plusmn;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMPACR \u0026ndash; Jet 2 (\u0026micro;s⁻\u0026sup1;)\u003c/p\u003e \u003cp\u003e\u0026plusmn;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJet 1\u003c/p\u003e \u003cp\u003eR\u0026sup2; (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJet2\u003c/p\u003e \u003cp\u003eR\u0026sup2; (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHUC-MSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e53.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e57.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM-MSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e67.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e69.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Cell Dependence of the Standardized Parameter MPACR: Toward Developing a Biological Diagnostic Tool\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results so far have demonstrated that the MPACR parameter is reproducible, predictive, and device-independent. In this section, we explore its cell-type dependence, which opens new possibilities for biological classification or diagnostics based on plasma response.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the relationship between ΔAₘₐₓ and metabolic activity for all three studied stem cell types HUC-MSCs, BM-MSCs, and SSCs treated with the same plasma jet. Each cell type exhibited a unique linear trend, characterized by a distinct MPACR slope. The MPACR values, previously reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, were 53.27 for HUC-MSCs, 67.41 for BM-MSCs, and 25.68 for SSCs, with R\u0026sup2; values exceeding 95% in all cases. This confirms that MPACR is specific to the cellular identity and not interchangeable across cell types.\u003c/p\u003e \u003cp\u003eThe cell-type dependence of MPACR can be attributed to differences in redox-related metabolic pathways. Each stem cell type exhibits unique metabolic profiles, antioxidant capacities, and signaling responses to oxidative species, which collectively influence how ΔAₘₐₓ translates into biological effects. Thus, MPACR can be viewed as a functional biomarker that reflects both the reactive environment induced by plasma and the cellular ability to metabolically respond to it.\u003c/p\u003e \u003cp\u003eThis finding suggests that MPACR could serve as a new diagnostic or classification tool to distinguish cell types based on their plasma-response profile. Since it relies on non-invasive measurements (optical sensing and MTT assay), MPACR-based profiling could offer a practical approach to characterizing stem cell populations or verifying cell identities during expansion, therapeutic preparation, or quality control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe observed hierarchy of MPACR values (BM-MSC\u0026thinsp;\u0026gt;\u0026thinsp;HUC-MSC\u0026thinsp;\u0026gt;\u0026thinsp;SSC) can be interpreted based on the distinct redox-metabolic strategies of these stem-cell types under dissolved-oxygen (DO)-related oxidative stress (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). BM-MSCs tend to shift toward glycolysis when exposed to oxidative stimuli, providing rapid but less efficient ATP generation reminiscent of the Warburg effect (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). HUC-MSCs maintain a flexible metabolic program, balancing glycolysis and oxidative phosphorylation (OXPHOS) to preserve homeostasis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In contrast, SSCs rely predominantly on OXPHOS, prioritizing genomic stability and long-term stemness over rapid metabolic reprogramming (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This spectrum of redox behaviors provides a rigorous test of MPACR cell specificity (see Supplementary Table S4).\u003c/p\u003e \u003cp\u003eCold atmospheric plasma (CAP) jets generate a rich mixture of reactive oxygen and nitrogen species (RONS); among these, DO-derived species are key mediators of cellular responses and initiate most downstream biology (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our study quantifies DO dynamics via PtTFPP phosphorescence-lifetime sensing (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), then links those dynamics to metabolic outcome. Moreover, CAP-induced ROS production can reshape cellular metabolism by modulating both glycolysis and OXPHOS pathways (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDO-derived ROS may accumulate extracellularly or intracellularly, and the balance between these compartments critically shapes cell fate (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Excess intracellular ROS can trigger oxidative damage and apoptosis, whereas moderate levels promote survival, lineage commitment, or enhanced metabolic activity (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Upon CAP exposure, extracellular ROS first contact the plasma membrane, activating redox-sensitive signaling cascades that subsequently adjust intracellular ROS handling (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, these DO-driven dynamics correlate with cell-type-specific shifts in metabolic activity. CAP exposure modulates glycolysis/OXPHOS ratios (Table S4), and each stem-cell type\u0026rsquo;s ability to manage DO stress defines its metabolic trajectory:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBM-MSCs exhibit the steepest rise in metabolic activity with increasing ΔAₘₐₓ, consistent with a glycolytic shift under oxidative stress (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHUC-MSCs display a balanced response, leveraging both glycolysis and OXPHOS to maintain energy output while safeguarding genomic integrity (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSSCs show a modest increase in metabolic activity and remain OXPHOS-dominated, a conservative strategy that sustains long-term stemness (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA notable feature is the intersection of HUC-MSC and SSC curves at a specific ΔAₘₐₓ value (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), suggesting a DO threshold where both cell types exhibit comparable metabolic output via distinct mechanisms. Beyond this point, HUC-MSCs maintain activity through metabolic flexibility, whereas SSCs plateau or decline, likely to protect genomic integrity.\u003c/p\u003e \u003cp\u003eThis hierarchy is captured in the experimental MPACR values (BM-MSC\u0026thinsp;\u0026gt;\u0026thinsp;HUC-MSC\u0026thinsp;\u0026gt;\u0026thinsp;SSC). Thus, MPACR serves not only as a predictive metric for CAP-induced metabolic activity but also as a quantitative fingerprint of each cell type\u0026rsquo;s redox-metabolic strategy.\u003c/p\u003e \u003cp\u003eIn conclusion, our results confirm MPACR as a robust, biologically meaningful indicator of stem-cell response to DO-derived stimuli following CAP exposure.\u003c/p\u003e \u003cp\u003eAlthough plasma medicine continues to evolve rapidly, only a few applications, such as wound healing, have achieved clinical implementation. Progress is hindered by unresolved issues including the lack of standardization among plasma jet devices, challenges in predicting biological responses, and difficulty in comparing results across studies.\u003c/p\u003e \u003cp\u003eTo address these barriers, we introduce a new analytical metric, the MPACR parameter, which quantifies the linear correlation between ΔAₘₐₓ (the maximum change in DO activity post-CAP exposure) and cellular metabolic activity. Derived from phosphorescence lifetime-based oxygen sensing, MPACR is device-independent but cell-type\u0026ndash;specific, providing a predictive and reproducible descriptor of CAP effects.\u003c/p\u003e \u003cp\u003eOur experiments showed that BM-MSCs, HUC-MSCs, and SSCs each exhibit distinct MPACR slopes, corresponding to their redox metabolism and their ability to handle DO stress. BM-MSCs favored glycolysis and showed the strongest metabolic response; HUC-MSCs maintained metabolic flexibility; and SSCs prioritized oxidative phosphorylation to maintain stemness. These distinct patterns make MPACR a strong candidate for standardizing plasma-based studies and designing cell-specific treatment strategies.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Suggested Analytical Workflows Based on the MPACR Parameter\u003c/h2\u003e \u003cp\u003eThese workflows are proposed for research use and provide methodological guidance for leveraging MPACR\u003c/p\u003e \u003cp\u003ein predictive modeling, device evaluation, and cell-specific CAP studies:\u003c/p\u003e \u003cp\u003e \u003cb\u003eWorkflow 1: Determining MPACR for Specific Cell Types\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMPACR is identified by measuring ΔAₘₐₓ via phosphorescence lifetime spectroscopy and correlating it with metabolic activity (e.g., via MTT assay) across varying plasma conditions. This establishes a unique slope for each cell type, reflecting its redox behavior.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWorkflow 2: Predicting Biological Response from a Single Dataset\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOnce MPACR is known for a specific cell type, only one ΔAₘₐₓ\u0026ndash;metabolic activity data point is needed to reconstruct the full response trendline for any CAP jet. This enables rapid prediction with minimal experimental effort.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWorkflow 3: Evaluating Device Consistency and Standardization\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTwo CAP jets can be considered functionally equivalent if their MPACR-based trendlines overlap for the same cell type. This provides a straightforward strategy for comparing devices across labs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWorkflow 4: Comparing Relative Biological Performance of Plasma Jets\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBy plotting MPACR curves for the same cell type across different devices, one can quantitatively assess which plasma jet is more effective at inducing metabolic responses, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWorkflow 5: Exploring Reagent-Free Cell Identification\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBecause MPACR is cell-specific and device-independent, it could serve as a reagent-free biomarker to distinguish cell types based on their redox response to CAP, offering new opportunities in label-free diagnostics.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Future Outlook","content":"\u003cp\u003eOne of the most promising areas for CAP application is oncology. The selective ability of CAP to induce apoptosis in cancer cells while sparing healthy tissue hinges on maintaining DO levels within a therapeutic window. Our future work aims to define this threshold and identify the electrical conditions that enable targeted, non-necrotic CAP treatment of tumor cells.\u003c/p\u003e \u003cp\u003eIn sum, the MPACR parameter introduced in this work offers a valuable framework for CAP jet standardization, biological outcome prediction, and the development of novel, non-invasive cell identification methods. By bridging biophotonics with redox biology, it opens the door to more predictable, reproducible, and translationally relevant plasma medicine research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Iran National Science Foundation (INSF), Grant No. 4020795.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution of authors \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.T. performed the experimental work, including sensor fabrication, optical setup, and plasma treatments. E.T. and S.E. carried out cellular assays, data collection, and visualization. S.E. and K.H. wrote the draft manuscript. E. A., E. H., E. R., K. H., A. H., and H. M. contributed to supervision, methodology, and validation. K.H. performed data analysis. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLaroussi M (2020) Cold plasma in medicine and healthcare: The new frontier in low temperature plasma applications. Front Phys 8:74\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrivat-Maldonado A, Schmidt A, Lin A, Weltmann K-D, Wende K, Bogaerts A et al (2019) ROS from physical plasmas: Redox chemistry for biomedical therapy. 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J Biol Chem 287(7):4397\u0026ndash;4402\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plasma-chemistry-and-plasma-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":" Learn more about [Plasma Chemistry and Plasma Processing](https://www.springer.com/journal/11090 ","snPcode":"11090","submissionUrl":"https://mc.manuscriptcentral.com/pcpp","title":"Plasma Chemistry and Plasma Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cold plasma, plasma jet, dissolved oxygen, redox metabolism, phosphorescence lifetime spectroscopy, metabolic activity","lastPublishedDoi":"10.21203/rs.3.rs-9527256/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9527256/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite promising in vitro outcomes, the clinical translation of plasma jet technologies remains constrained by the absence of reproducible and standardized evaluation parameters. A major challenge lies in quantifying plasma-induced biochemical changes in a manner that is both biologically relevant and independent of specific devices. In this study, we present a dissolved oxygen (DO)-based photonic parameter obtained through time-resolved phosphorescence spectroscopy as a real-time, reagent-free method for characterizing plasma-activated media. By correlating the maximum change in phosphorescence lifetime\u0026mdash;an indirect indicator of minimum DO concentration of the liquid\u0026mdash;with metabolic activity across various stem cell types treated by the different plasma jet configurations, we introduce a novel prediction metric for the metabolic activity of stem cells that is both cell-specific and jet-independent. This parameter shows promise as a non-invasive, cell-specific redox response signature, potentially aiding future stem cell classification efforts. Moreover, jet-independent characteristic of the parameter supports more consistent cross-study comparisons, simplifies jet calibration, and advances efforts to standardize plasma jet applications in biomedical research.\u003c/p\u003e","manuscriptTitle":"Photonic-based Prediction Method for the Metabolic Activity of Stem Cells Exposed to Different Cold Plasma Jets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:44:14","doi":"10.21203/rs.3.rs-9527256/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"86874329509958039139279017537044494028","date":"2026-05-04T00:11:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-01T07:02:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-01T03:48:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T03:47:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plasma Chemistry and Plasma Processing","date":"2026-04-25T16:36:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plasma-chemistry-and-plasma-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":" Learn more about [Plasma Chemistry and Plasma Processing](https://www.springer.com/journal/11090 ","snPcode":"11090","submissionUrl":"https://mc.manuscriptcentral.com/pcpp","title":"Plasma Chemistry and Plasma Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b0698f22-5cbf-4b76-9cc8-a98f3edb7bbe","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"86874329509958039139279017537044494028","date":"2026-05-04T00:11:22+00:00","index":12,"fulltext":""},{"type":"reviewersInvited","content":"5","date":"2026-05-01T07:02:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-01T03:48:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T03:47:59+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:44:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:44:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9527256","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9527256","identity":"rs-9527256","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}