Impact of Delayed Biospecimen Processing on Serum Proteomics in Samples from a Parabolic Flight

Impact of Delayed Biospecimen Processing on Serum Proteomics in Samples from a Parabolic Flight | 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 Article Impact of Delayed Biospecimen Processing on Serum Proteomics in Samples from a Parabolic Flight Zeineen Momin, Qiaoyan Wang, Aparna Krishnavajhala, Marie-Claude Gingras, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7530087/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract In-flight blood collection during spaceflight is limited by both the constraints of available collection methods and the inability to process samples immediately. To bridge this gap, ezdraw, a blood self-collection device was used aboard a parabolic flight. Furthermore, to mimic operational constraints, blood samples were stored at room temperature with delayed processing, and serum was extracted at 24 and 48h. Protein stability was analyzed using the Olink Explore HT platform, profiling up to 5,400 proteins. Of the 3,201 detectable proteins, 2,789 (87%) proteins remained stable at 24h and 2,731 (85%) at 48h, showing resilience to delayed processing. Key proteins involved in signaling immune related and metabolic pathway remained stable. Unstable proteins did not significantly impact representation of the major biochemical pathways. These findings suggest most serum proteins remain robust to delayed processing, supporting the feasibility of in-flight blood collections and their use for molecular profiling as demonstrated here. Biological sciences/Biochemistry Biological sciences/Biological techniques Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Figures Figure 1 Figure 2 Figure 3 Introduction Blood is one of the most frequently collected and analyzed biological materials in both clinical and research contexts. Blood sample processing delays are common challenges under these settings, particularly when immediate serum separation is not feasible. This issue becomes even more complex in unique environments such as space travel, where logistical constraints limit timely blood sample handling. The rapid growth of commercial and civilian spaceflight has opened new opportunities for systematic, longitudinal, and large-scale biospecimen collection to study the long-term effects of space travel on human health. The GENESTAR methodology was successfully established, offering standardized protocols for biospecimen collection, biobanking, and multi-omics data generation from commercial spaceflight missions [ 1 ]. Blood collections are fundamental in space research, providing material for clinical evaluation and the generation of multi-omics data to study the effects of space on human health. A major challenge for commercial missions, both expeditions to the International Space Station (ISS) and free-flying missions orbiting the Earth, is the lack of access to freezers and appropriate processing equipment, making in-flight blood sample collection, preservation and handling especially difficult. As a result of this, the current studies primarily analyze blood samples collected before and after spaceflight, leaving an important gap in its molecular profiling, while in-flight [ 1 , 2 ]. Traditionally, blood collection during spaceflight is performed using venipuncture, which is cumbersome and complex to execute in space. At the same time, the use of dried blood spots can be inconvenient for astronauts due to issues such as adhesive bandage discomfort and skin irritation after biospecimen collection along with very minimal collection volume [ 3 , 4 ]. However, recently developed microneedle capillary blood collection devices are better-suited for spaceflight blood collections aboard the ISS or within the capsule just before returning to Earth [ 5 ]. Even in such a scenario, blood samples will likely be left at ambient temperatures for up to 24h before processing and therefore must be evaluated for protein changes to ensure data integrity. Parabolic flight, which provides brief periods of microgravity zero-gravity interleaved with short hyper-g-phases, serves as a well-established analogue for spaceflight and enables rapid, iterative testing of flight hardware and procedures on Earth [ 6 ]. During each parabola a pull‑up (1.8 g), followed by 20–25 s of zero-gravity, and a pull‑out (1.8 g) before returning to 1 g flight; campaigns usually consist of 15–20 parabolas per flight [ 6 – 8 ]. Because transport‑category aircraft are generally regulated to maintain a maximum cabin pressure altitude of 8,000 ft, the in‑cabin absolute pressure is commonly ~ 75–80 kPa during flight. [ 9 ] This reduced ambient pressure decreases the pressure differential available to evacuated collection tubes, which can lower the achievable fill volumes. Here, we used a minimally invasive self‑collection device (ezdraw) during parabolic flight to assess feasibility of blood collection, operator workload, and the downstream impact of delayed processing on the serum proteome as and example of use of such a collection [ 4 , 7 ]. Serum is widely used in proteomic research, and is obtained after blood coagulation, which removes fibrinogen and other clotting factors but may release cellular proteins due to platelet activation [ 10 – 12 ]. Delayed blood processing at room temperature alters serum protein levels, especially inflammatory and platelet-derived markers, [ 13 ]. While most proteins remain stable, some degrade or vary after 24–48h [ 14 ]. The previous studies confirm that even cold storage cannot fully prevent these changes [ 15 – 17 ]. A previous study used 184 Olink target assays and identified proteins, such as cytokines and growth factors, as being particularly susceptible to ex vivo degradation [ 18 ]. However, this is the first comprehensive comparison of proteome changes in serum with delayed processing using Olink Explore HT of ~ 5,400 proteins. To systematically evaluate the impact of delayed serum processing, the Olink Explore HT platform, a high-throughput proteomics technology, was utilized to characterize the protein profiles in serum under delayed processing [ 19 ]. Serum was extracted from blood samples collected both in-flight and on the ground and was analyzed for protein stability following 24 and 48h of delayed processing. The results of this study would help assess the feasibility of incorporating the use of an ezdraw device for in-flight blood collections both on the ISS or in the space shuttle just prior to mission completion, followed by ambient storage until sample recovery and processing. These findings will help guide protocols for studying such biospecimens in spaceflight related biomedical research as well as other diverse research scenarios and extreme environment studies, where immediate biospecimen processing may not be possible, thereby improving the accuracy and reproducibility of biomarkers found in those settings. Results Parabolic flight experience/results The ezdraw device, configured with serum tubes under medium-vacuum (MVAC) and high-vacuum (HVAC) settings was deployed during 0 g phases. Devices adhered and functioned reliably during transitions through ~ 1.8 g pull‑up and pull‑out. However, both MVAC and HVAC tubes underfilled relative to their nominal targets of 3 mL and 4 mL, respectively, for collecting blood under medium vacuum, yielding ~ 1 mL and 1.5 mL, respectively (Fig. 1A). All collected devices were double‑contained and transported at ambient temperature; serum was separated at 24 and 48 h post‑collection for proteomic analysis. One subject developed significant motion sickness during early phases of flight and had to be strapped down for the rest of the flight, unable to provide blood samples. Biospecimens (blood) collections and their derivatives Whole blood from the one subject who participated in the parabolic flight was also collected on the ground into serum tubes (Fig. 1B). Optimal volume of 10 mL blood was collected in serum tubes and left at room temperature. Serum was extracted at 0, 24, and 48h post-collection. From these tubes the following volumes of serum were isolated across different time points, measuring 3.2 mL at 0h, 2.8 mL at 24h and 2.75 mL at 48h. These blood samples served as baseline control for comparison with blood collected under MVAC and HVAC conditions. Serum Protein Stability over time from 0-24h and 24-48h From each serum sample, 2 µL was used for proteomic analysis, and proteomic profiles were generated for ~ 5,400 proteins using the Olink Explore HT panel. Total proteins after Limit of Detection (LOD) filtering are 3,201. Protein stability results across 24 and 48h are grouped under five different categories (Table 1 ). In the first group, 110 proteins were found to be unstable at both 24 and 48h, where either a protein showed continuous decrease or continuous increase in its levels (Supplementary Table 1). In the second group, instability was observed in 214 proteins between 0 and 24h; however, these proteins did not show further changes after 24h, indicating that their instability is limited to the early phase (Supplementary Table 2). In the third group 88 proteins exhibited inconsistent changes across the two-time intervals, showing variable behavior between 0–24h and 24–48h (Supplementary Table 3). In the fourth group are 58 proteins that were initially stable at 24h became unstable by 48h (Supplementary Table 4). In the fifth group are 2,731 proteins which remained stable at both 24 and 48h (Supplementary Table 5). Table 1 Overview of protein stability after 24, and 48h of post-collection. Total proteins after LOD filtering are 3,201 and are grouped into five different categories based on their stability. Group 1 Group 2 Group 3 Group 4 Group 5 After LOD Filtering Not stable at 24 and 48h Not Stable at 24h, no change at 48h Inconsistent changes at 24 and 48h Stable at 24h, not Stable at 48h Stable at 24 and 48h 3,201 110 214 88 58 2,731 Group 1: Not stable at 24 and 48h There are 110 proteins in the Group 1 category, and of these, 94 (85%) showed a continuous increase in protein levels, while 16 (15%) showed a continuous decrease (Supplementary Table 1). Proteins with the highest fold change (NPX difference) included ENSA (+ 5.81 at 0–24h; +0.98 at 24–48h), SRGAP2B (+ 5.50; +1.31), and ARHGAP31 (+ 3.84; +1.64), all showing strong time-dependent accumulation. In contrast, proteins that decreased after 24h and 48h included ASB6 (–3.91; − 1.04), GHRL (–1.06; − 0.52), and SELENOP (–0.64; − 0.21). Group 2: Not stable at 24h, no change at 48h There are 214 proteins in the Group 2 category and of these, 150 (70.1%) showed a pattern of instability during the initial 0–24h window, followed by relative stability from 24–48h (Supplementary Table 2). Proteins with the highest fold change included, IL4 (+ 7.18 at 0–24h; +0.21 at 24–48h), PPFIA4 (+ 6.85; +0.28), and SECISBP2L (+ 6.73; − 0.10). These were followed by WHRN (–6.59; +0.23), CXCL8 (+ 6.47; +0.17), and IL13RA2 (+ 6.44; − 0.06). Additional proteins with similar patterns included ZIC3, DNAH7, EP400P1, and CEP112, all of which showed marked elevation or reduction in the first 24h, followed by relative stability afterward. Group 3: Inconsistent changes at 24 and 48h Of the 88 proteins in the Group 3 category, 40 (45.5%) showed a pattern of increase during the initial 0–24h window, followed by a decrease from 24–48h (Supplementary Table 3). In contrast, 48 proteins (54.5%) demonstrated the opposite trend, with a decrease during 0–24h followed by an increase in the 24–48h interval. Proteins with the highest fold change included FAM131B (+ 7.84 at 0–24h; − 3.63 at 24–48h), KRT12 (+ 5.34; − 5.16), and RGS5 (+ 5.32; − 5.14), all showing strong initial upregulation followed by a marked decline. ANKRD52 and DRC1 followed similar patterns with substantial fluctuations. On the other hand, proteins like CD200R1L (–2.70; +1.92) and GLTP (–2.07; +1.40) demonstrated the opposite trend, where they initially decreased, followed by rebound. Group 4: Stable proteome at 24h and not stable at 48h There are 58 proteins in the Group 4 category that were found to be stable at 24h and not at 48h (Table 1 ). Pathway enrichment analysis using ShinyGO was performed on proteins representing a stable proteomic signature at 24h. This analysis includes 2,731 proteins that are stable at 48h and 58 proteins that are stable at 24h and not stable at 48h (Supplementary Table 4). The top 30 enriched KEGG pathways include immune, signaling, and cellular interactions. (Fig. 2 and Supplementary Table 6). This list includes cytokine-cytokine receptor interaction (FDR = 2.36 × 10⁻ 31 ; fold enrichment = 3.31), involving key immune mediators such as CCL26, CXCL13, CSF1, and IL10RA (Supplementary Table 6). Other highly enriched signaling pathways included PI3K-Akt signaling (FDR = 2.75 × 10⁻ 17 ; fold enrichment = 2.49), MAPK signaling, JAK-STAT, and NF-kappa B, all of which contained stable proteins like PIK3AP1, AKT2, MAP2K1, STAT2, STAT5B, CHUK, NFKB1, and TNFRSF1A (Supplementary Table 6). Additional enriched pathways involved structural and adhesion components such as focal adhesion, ECM-receptor interaction, and regulation of the actin cytoskeleton. Notably, disease-associated and stress response pathways, including those related to rheumatoid arthritis, osteoclast differentiation, apoptosis, and HIF-1 signaling, also appeared in the stable serum proteome at 24h. Among the 58 proteins in Group 4, there are seven proteins in the top 30 pathways. These included IL1R1 from the cytokine–cytokine receptor interaction pathway; VEGFA, EFNA4, and IGF1R, which are involved in both the PI3K-Akt and MAPK signaling pathways; MASP1 and F2 from the complement and coagulation cascades; and F11R from the cell adhesion molecules pathway (Supplementary Table 7). Group 5: Stable proteome at 24 and 48h Pathway enrichment analysis using ShinyGO was performed on the stable serum proteome at 24 and 48h (Group 5), represented by 2,731 proteins and was enriched in immune, signaling, and structural pathways (Fig. 3 and Supplementary Table 5). Among the top 30 KEGG pathways were cytokine-cytokine receptor interaction (FDR = 1.62 × 10⁻ 31 ; fold enrichment = 3.35), PI3K-Akt signaling (FDR = 1.96 × 10⁻ 16 ; fold enrichment = 2.47), complement and coagulation cascades (FDR = 2.83 × 10⁻ 13 ; fold enrichment = 3.93), JAK-STAT signaling (FDR = 2.32 × 10⁻ 09 ; fold enrichment = 2.64) and MAPK signaling (FDR = 4.39 × 10⁻ 08 ; fold enrichment = 2.07) (Supplementary Table 8). Discussion With the rise of commercial space missions, standardized biospecimen collection methods like GENESTAR or other methods are essential, yet in-flight blood collections remain difficult due to limited access to freezers and equipment [ 1 , 2 ]. To address this, microneedle-based blood collection devices were tested during parabolic flights to simulate brief periods of microgravity as an analogue for spaceflight use. Blood samples collected were stored at room temperature, and serum was extracted at 24 and 48h post-collection. Using the Olink Explore HT platform, which measured 5,400 proteins, the effects of delayed blood sample processing on serum proteome integrity were systematically evaluated [ 19 ]. The parabolic flight experiment demonstrated the mechanical functionality of the Preci-Health ezdraw device, including its deployment and adherence in a microgravity environment [ 20 ]. However, the blood collection volumes were suboptimal, where both MVAC and HVAC tubes yielded lower blood volumes of 1 mL and 1.5 mL than the target 3 mL and 4 mL respectively. The shortfall is consistent with the calculated reduction in pressure differential at typical cabin pressures, which decreases the driving vacuum by ~ 30–50% compared with sea‑level conditions [ 9 ]. Calculations indicate a ~ 32% reduction in pressure differential for HVAC tubes and a ~ 51% reduction for MVAC tubes compared to ground-level conditions, which are also reflected in the blood volumes collected in these two tubes. This altitude effect on evacuated tubes can produce lower draw volumes and alter additive‑to-blood ratios [ 21 ]. These findings suggest that while self-collection in microgravity using ezdraw is feasible. For this device to be used on a future parabolic flight, it will require further optimization to maintain efficacy under reduced ambient pressure conditions. However, it should not be an issue to collect blood in a space shuttle or in the ISS during spaceflight, where such a pressure differential does not exist. In fact, ezdraw was successfully used for blood collection by another research group during the Polaris Dawn space mission in September 2024 [ 22 ] Serum was extracted from blood samples collected during parabolic flight using the ezdraw device, as well as from the blood samples collected on the ground and left at room temperature. While the serum volumes obtained from the ezdraw device were sub-optimal (300 µL – 450 µL), the resulting volumes were sufficient for proteome profiling and for biobanking. To comprehensively assess the impact of delayed blood sample processing on serum proteome integrity, the Olink Explore HT platform, a high-throughput proteomics technology, was employed to analyze protein expression profiles under varying storage conditions [ 19 ]. The serum proteome exhibited distinct behavioral categories based on temporal dynamics across 0–24h and 24–48h intervals. A total of 110 proteins showed continuous directional changes (Group 1), with fold changes ranging from − 3.91 to + 5.81 between 0–24h and − 1.04 to + 1.64 between 24–48h, suggesting sensitivity to both early leakage and progressive degradation (Supplementary Table 1). A larger subset of 214 proteins (Group 2) was classified as unstable primarily during the first 24h (− 7.46 to + 8.37), likely reflecting active leakage from leukocytes, platelets, or damaged endothelial cells triggered by room temperature exposure, followed by relative stabilization (− 0.63 to + 1.47) thereafter (Supplementary Table 2). Inconsistent behavior was observed for 88 proteins (Group 3), initially increasing or decreasing (− 2.70 to + 7.84 at 0–24h) but reversing or stabilizing (− 5.16 to + 1.92 at 24–48h) (Supplementary Table 3). In contrast, most of the profiled proteins in Groups 4 and 5 were stable at 24h (58 plus 2,731) and 48h (2,731), respectively (Table 1 ). These proteins mapped to 108 (24h) and 110 (48h) biochemical pathways, encompassing components of PI3K-Akt signaling, focal adhesion, and ECM-receptor interactions, and showing minimal variability despite delayed processing (Supplementary Tables 4, 5, 6, and 8). This amounts to 87% (2,789/3201) of the detectable proteins at 24h and 85% (2,731/3201) detectable proteins at 48h, enhancing the usability of such data. These patterns are consistent with prior literature predominantly based on plasma studies [ 14 – 16 , 18 , 23 – 26 ]. The exception to this was a study where serum proteome was analyzed [ 13 ]. Despite matrix differences, common trends emerge, particularly the early sensitivity of inflammatory cytokines (e.g., CXCL8, MIP-1α) and the relative resilience of structural and adhesion-related pathways such as PI3K-Akt signaling and ECM-receptor interaction. To further evaluate the consistency of serum protein behavior under delayed processing conditions, fold change data were compared from this dataset with those reported in the serum study, where they analyzed cytokine dynamics in serum samples stored at room temperature for 24h [ 13 ]. They reported a dramatic increase in six key cytokines: IL-1β (23.3-fold), IL-6 (24.1-fold), IL-8 (17.0-fold), MIP-1α (55.0-fold), MIP-1β (5.0-fold), and CD40L (114.3-fold) [ 13 ]. In this dataset, IL-8 (CXCL8) demonstrated a 6.47-fold increase from 0 to 24h, followed by stabilization between 24 and 48h (0.17-fold), suggesting a biphasic profile consistent with early instability and subsequent plateau. Similarly, MIP-1α (CCL3) showed a 1.17-fold increase during the first 24h and a negligible change thereafter. IL-8 and MIP-1α, therefore, can be considered as consistent markers of early serum degradation. Compared to the previous study that evaluated the stability of 62 cytokines and chemokines in serum under delayed processing conditions, the current study analyzed over 5,400 proteins spanning a broad range of biological pathways. This expanded scope allowed for a more comprehensive characterization of serum proteome dynamics, including the inflammatory markers. At 24h, the serum proteome showed significant (> 2-fold) dysregulation involving 84 proteins, reflecting early stress responses, immune activation, and cytoskeletal remodeling. Out of 84 proteins with major fold changes, 23 proteins came from the ‘Continuous change’ Group 1 category, 52 proteins were classified under the ‘not Stable at 24h’ Group 2 category, and 9 proteins were derived from the ‘Inconsistent change’ Group 3 category (Supplementary Table 9). Major increases were observed in CDCA2 (+ 8.37-fold), HBG2 (+ 7.37-fold), CXCL8 (IL-8) (+ 6.47-fold), and SRGAP2B (+ 5.50-fold). These proteins have a role in these biological processes, including immune cell activation (CXCL8, CCL7), regulation of cell survival (BIRC3, TSC22D3), and structural cell remodeling (SRGAP2B, ITGAX) [ 27 – 32 ]. Conversely, transcriptional regulators such as TBX22 (− 7.46-fold) and cytoskeletal proteins like SPATS1 (− 6.56-fold) were markedly downregulated, suggesting selective degradation or inhibition early during storage [ 33 , 34 ]. By 48h, fewer proteins showed major changes (only 4 proteins had significant fold changes > 2 and all from the ‘Inconsistent change’ Group 3 category). Notably, KRT12 (− 5.16-fold) and RGS5 (− 5.14-fold) showed strong decreases, indicating cellular degradation and vascular stress associated with prolonged room temperature exposure (Supplementary Table 9). These results show that while most serum proteins remained stable over the first 24h, a subset, particularly cytokines and extracellular matrix modulators demonstrated time-sensitive variability by 48h. For instance, IL-8 (CXCL8) exhibited a 6.47-fold increase between 0–24h in this dataset, stabilizing thereafter. This pattern aligns with previously reported 17.0-fold increase in serum after 24h [ 13 ]. In plasma study 2-10-fold increase were observed ([ 24 ]. TNFSF14 (LIGHT) rose by 0.82-fold from 0–24h and 0.07-fold from 24–48h, and Oncostatin M (OSM) exhibited moderate time-dependent increases, with fold changes of 0.69 at 0-24h and 0.20-fold at 24-48h in this dataset. In a previous study a > 6-fold and > 5.21-fold increase in TNFSF14 (LIGHT) and Oncostatin M (OSM) were observed in plasma with delayed processing [ 18 ]. These results suggest that inflammatory and immune-modulating proteins are sensitive to room temperature storage. In the current serum proteomics dataset, several core biological pathways demonstrated consistent stability across both 24h and 48h timepoints (Figs. 2 and 3 ). When comparing venipuncture-derived plasma to capillary blood collected via the Tasso + microneedle device, was found to produce only 11.3% of proteins that achieved acceptable reproducibility (r ≥ 0.5, CV ≤ 0.20) [ 5 ],[ 16 ]. Most proteins, especially those associated with inflammation, immune signaling, and cytokine activity, showed high variability and poor correlation with venous samples. The authors attributed this discrepancy to microvolume sampling variability, differences in cellular content, and potential stress-induced biomarker release due to the self-collection process. As a result, they concluded that current microdevices like Tasso + are not yet reliable for large-scale proteomic discovery, particularly when using untargeted, high-throughput platforms. In contrast, this study used ezdraw device that can collect larger blood volumes, and there is agreement in 4/6 cytokines between this dataset and those reported previously [ 13 ]. Collectively, these results highlight proteins such as CXCL8, CCL7, and KRT12 can serve as sensitive indicators of serum instability, while pathway-associated proteins like AKT1, TLN1, and COL1A1 offer robust internal controls, even under delayed processing conditions. Use of dried blood spots (DBS) could be another option [ 3 , 4 ]. However, it also comes with the challenges of collection in microgravity, such as small blood sample volume (up to 40 µL per punch), which limits the number of proteomic and metabolomic assays that can be conducted and reduces the amount of residual material available for future biobanking. As shown in this study, using ezdraw, 300–450 µL of serum was obtained from the collected blood, which suffices for proteomics assay and biobanking for future use. Results from this study showed that 87% of the detectable serum proteins remained stable at 24h, and 85% at 48h post-collection under delayed processing conditions, enabling the study of over 100 biochemical pathways. These results demonstrate that delayed processing of self-collected blood samples under simulated microgravity conditions has minimal impact on overall serum proteome integrity. The stability of key proteins and preservation of major biochemical pathways support the use of the ezdraw self-collection device as a viable strategy for in-flight blood collection during space missions. This approach, therefore, offers a practical solution to overcome logistical constraints in spaceflight and can be implemented for future collections of blood samples. Methods Parabolic flight experiment To assess the feasibility of serum collection in zero-gravity using a minimally invasive self-collection device, a parabolic flight was conducted on April 15, 2024, aboard the Zero-G® Boeing 727-227F Advanced aircraft departing from Ft. Lauderdale, FL. The flight completed 27 zero‑gravity parabolas and 2 lunar‑gravity parabolas. Each 0 g parabola provided 20–25 s of near‑weightlessness bracketed by 1.8 g pull‑up and pull‑out segments. Cabin pressure was maintained near transport‑category conditions (maximum cabin altitude ≤ 8,000 ft), corresponding to approximately 75–80 kPa (10.9–11.6 psi) (Supplementary Fig. 1A). The primary goal was to evaluate the performance of the Preci-Health ezdraw device using MVAC and HVAC tubes under zero-gravity conditions. Two participants alternated roles as subject and operator during the flight. Each wore two ezdraw devices, one with an MVAC tube and one with an HVAC tube, applied sequentially to each shoulder. The operator documented the procedure using a wrist-mounted camera and a smartphone with a gravity meter app to confirm microgravity phases. The ezdraw device is designed for capillary blood collection without conventional venipuncture. It attaches to the upper arm near the deltoid, performs two small incisions and applies vacuum to the built-in chamber using the vacuum of the collection tubes. Upon activation, blood is passively collected into sterile tubes 4 mL (BD Biosciences, SKU#367812) for HVAC 387 and 3 mL (BD Biosciences, SKU#366668) for MVAC. The device automatically seals to prevent contamination and is preset to collect a fixed volume (typically 500 µL to 1.5 mL). At sea level conditions, the version of ezdraw device used in this study holds two tubes that could draw per tube as much as 2 mL using a 3 mL MVAC tube and 2.5 mL using a 4 mL HVAC tube. The ezdraw devices were manufactured and provided as early access devices to the team for the parabolic flight. Preci Health is expecting to get European Union approval for commercial use in quarter two of 2026. Before the flight, sterile bandages were applied to the collection sites. During 0g phases, the bandages were removed and the ezdraw devices were activated via a release button, creating two small incisions to initiate blood collection. Devices were secured with elastic wraps to maintain adhesion. After each collection, devices were removed, double-contained in biohazard bags, and stored for post-flight analysis (Supplementary Fig. 1B). Biospecimens (blood) collections and their derivatives For the ground collection, which served as controls, blood was collected in 10 mL serum tubes (BD Biosciences, SKU# 367820). Venipuncture was performed in the antecubital fossa after sanitizing that area, and blood was drawn using a butterfly needle (BD Biosciences, Cat#367281) and a lure lock vacutainer adapter (McKesson, Cat#364815/458363). Serum was isolated from the blood samples collected in serum tubes at 0, 24, and 48h by centrifugation at 1600g for 10 minutes at room temperature. For the Olink Explore HT assay, 2 µL was used and the remaining volume was biobanked at − 80 o C for future use (Fig. 1). Olink Explore HT assay and Sequencing The Olink Explore HT platform is a high-throughput, multiplex proteomics technology that utilizes proximity extension assay (PEA) technology [ 19 ]. It measures over 5,400 biomarkers in a single run from minimal sample volumes with high specificity and sensitivity. This method utilizes pairs of DNA-labeled antibodies that bind specifically to target proteins, enabling quantification through DNA hybridization while minimizing cross-reactivity. It can detect proteins at sub-picogram per milliliter concentrations. Serum samples were analyzed to assess protein stability under different storage conditions using OLINK Explore HT protocol. The Olink Explore HT assay (Cat # 98100) is processed using automated liquid handlers (mosquito LV genomics, Dragonfly Discovery, and Biomek FxP). Approximately 10 µL of serum were used for the assay and ~ 5,400 proteins were assayed across 8 blocks of antibody pools. Serum samples were then serially diluted (1:10 to 1:100,000) and loaded into two 384-well plates. Probes with unique DNA tags are divided into eight assay blocks. Undiluted serum samples are used for blocks 1–4, and diluted serum samples for blocks 5–8, following the standard Olink Explore HT setup followed by overnight incubation at + 4°C. Only when both the antibody probe pairs bind to the target protein, the complementary oligonucleotides in proximity hybridize and are extended using a DNA polymerase. Annealed sequences are then extended, amplified, barcoded and sequenced on the Illumina platform using NovaSeq 6000 S4 Reagent Kit v1.5 (35 cycles) to generate 24bp reads. Data analysis Sequence data (bcl files) were converted to counts using Olink’s pre-processing software, ngs2counts v4.7.1. Olink’s Explore CLI v.2.3.1 was next used to export a parquet file containing intensity-normalized NPX values. NPX (Normalized Protein Expression) is an arbitrary, relative quantification unit used by Olink, which is log2 scale to protein concentration. Quality control and Limit of Detection (LOD) filtering was completed using Olink’s R package, OlinkAnalyze v4.2.0 [ 35 ]. Assays below LOD were excluded where LOD was calculated, via package function olink_lod using Olink’s predetermined fixed LOD values. Refer to the Olink Analyze’s Vignette for details [ 36 ]. Linear Mixed-effects Regression (LMER) was performed on the remaining protein assays (n = 3,201 for parabolic serum samples) of different timepoints: NPX~(1|SubjectID). Output p-values were adjusted by the Benjamini-Hochberg method, with adjusted p value < 0.05 defined as the significant threshold. This resulted in 470 assays being found to be significantly changed across timepoints 0-24h and 24-48h. Post-hoc analysis performed on the significantly changed assays determined detailed differences between timepoints. analysis performed on the significantly changed assays determined detailed differences between timepoints. Pathway Methods To identify biological pathways potentially unaffected by experimental conditions, a subset of proteins from the Olink® proteomics dataset was analyzed. These proteins that were not statistically significantly differentially expressed, defined by an adjusted p-value > 0.05 and an absolute log2 fold change < 0.2 and were considered to represent a stable proteomic profile. The corresponding not significant proteins were uploaded into ShinyGO v0.82 for gene set enrichment analysis [ 37 ]. Enriched biochemical pathways (FDR-adjusted p < 0.05) were interpreted as biological functions populated by proteins that remained stable across conditions. Human subjects research All subjects consented and biospecimens were collected and processed under the approval of the Institutional Review Board at Baylor College of Medicine, under MESH protocol H-536332. Manuscript preparation Figure 1A and 1B were created with BioRender.com under the Baylor College of Medicine Institutional license. Declarations Competing Interests Philippe Margairaz and Laurence Blazianu are employees of Preci-Health™. Rest of the authors declare no financial or non-financial competing interests. Supplementary information Author Contribution HD and EU conceptualized the study. ZM, FM, PM, LB, AK, MCG, JW, HD, EU contributed to biospecimen collection and data generation. QW, ZM, KW, AK, AS, QX, RAG, EU, HD were involved in data analysis, prepared the original draft of the manuscript and addressed the edits. All the authors reviewed, edited and approved the manuscript. Acknowledgements: This study was funded (Grant# INN0010) by the Translational Research Institute for Space Health through NASA Cooperative Agreement NNX16AO69A. Data Availability All subjects consented and biospecimens were collected and processed under the approval of the Institutional Review Board at Baylor College of Medicine, under MESH protocol H-536332. References Krishnavajhala, A., et al., The GENESTAR manual for biospecimen collection biobanking and omics data generation from commercial space missions . NPJ Microgravity, 2025. 11(1): p. 16. Overbey, E.G., et al., Collection of biospecimens from the inspiration4 mission establishes the standards for the space omics and medical atlas (SOMA) . Nat Commun, 2024. 15(1): p. 4964. Fredolini, C., et al., Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections . Commun Med (Lond), 2024. 4(1): p. 55. 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Supplementary Files SupplementaryTableTitlesandFigure.pdf SupplementalTable1.xlsx SupplementalTable2.xlsx SupplementalTable3.xlsx SupplementalTable4.xlsx SupplementalTable5.xlsx SupplementalTable6.xlsx SupplementalTable7.xlsx SupplementalTable8.xlsx SupplementalTable9.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 Jan, 2026 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviewers invited by journal 02 Oct, 2025 Editor assigned by journal 25 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 03 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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18:50:29","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97190,"visible":true,"origin":"","legend":"","description":"","filename":"e3bad6ede19e4a4ea8aa1258f7b703641structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/13f2e0ce93ccd73425cc75f0.xml"},{"id":92112785,"identity":"9cfcaaeb-2f0f-466c-b6d9-7cf343a2cb15","added_by":"auto","created_at":"2025-09-24 18:50:29","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109685,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/38e04496c0fb9935746c5395.html"},{"id":92112746,"identity":"878b5330-d466-4ef0-8dc1-33c869b045cd","added_by":"auto","created_at":"2025-09-24 18:50:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":661443,"visible":true,"origin":"","legend":"\u003cp\u003eParabolic flight blood sample Collections, Processing, and Biobanking. \u003cstrong\u003eA.\u003c/strong\u003e Blood samples were collected using the ezdraw\u003cstrong\u003e \u003c/strong\u003edevice on the parabolic flight under HVAC and MVAC conditions and were processed for serum at 24 and 48h post-collection. \u003cstrong\u003eB.\u003c/strong\u003e Blood samples collected using venipuncture on the ground, a day after parabolic flight run were processed for serum at 0, 24, and 48h post-collection. Serum aliquots of 300 µL were prepared, one aliquot from each collection was used for proteomics study, and the remaining aliquots were biobanked at -80\u003csup\u003eo\u003c/sup\u003eC.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/a3393f85d5c45bf87474e776.jpeg"},{"id":92112747,"identity":"b5172f69-8761-4bcb-9644-265943944aad","added_by":"auto","created_at":"2025-09-24 18:50:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108726,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot illustrating pathway enrichment analysis of the stable proteome at 24h which includes 2,789 proteins which are Group 4 and 5. \u0026nbsp;The x-axis represents the number of genes, while the y-axis lists the top 30 biochemical pathways. Each dot represents a pathway, with its size corresponding to the number of genes involved in that pathway and its color indicating the -log10(FDR) value. Larger dots indicate a higher number of genes, and more intense colors (from purple to red) represent lower FDR values\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/4bc1556bd4663708a1306a16.png"},{"id":92113708,"identity":"d0ef84b3-6648-454f-8b82-7f3f682f6d71","added_by":"auto","created_at":"2025-09-24 18:58:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108574,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot illustrating pathway enrichment analysis of the stable proteome at 48h which are in Group 5. The x-axis represents the number of genes, while the y-axis lists the top 30 biochemical pathways. Each dot represents a pathway, with its size corresponding to the number of genes involved in that pathway and its color indicating the -log10(FDR) value. Larger dots indicate a higher number of genes, and more intense colors (from purple to red) represent lower FDR values\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/0112450be8057a39201a83e9.png"},{"id":92115090,"identity":"2dde6a86-b79e-4f50-bfae-01e3157fcbd8","added_by":"auto","created_at":"2025-09-24 19:22:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1729181,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/9a2603c9-30d7-41bc-ab79-8aa72bddd64f.pdf"},{"id":92114785,"identity":"e20921b9-53fd-4798-a623-61f303848ca0","added_by":"auto","created_at":"2025-09-24 19:14:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":234763,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableTitlesandFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/55f836197eddbd6bedfe47b3.pdf"},{"id":92112757,"identity":"a28a366e-631d-4ed3-b0a1-bdd1258d996b","added_by":"auto","created_at":"2025-09-24 18:50:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":85407,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/aca548d72f7c9f791be043e0.xlsx"},{"id":92112750,"identity":"e0a2f332-413e-4312-bba4-e8018a332ef0","added_by":"auto","created_at":"2025-09-24 18:50:28","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":41758,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/30ab884c9173b0f4fc943e5e.xlsx"},{"id":92112751,"identity":"b7b71ed4-bb6a-4884-800e-c7b87171357d","added_by":"auto","created_at":"2025-09-24 18:50:28","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":24784,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/cffd6431facc287e5504410b.xlsx"},{"id":92113719,"identity":"f06e2f45-b1fc-4489-ab80-9abea7d35587","added_by":"auto","created_at":"2025-09-24 18:58:28","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":270837,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/34e170d6b2e97565931f7535.xlsx"},{"id":92112760,"identity":"38492336-0d2e-4a13-9568-607dcbd095b6","added_by":"auto","created_at":"2025-09-24 18:50:28","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":240887,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/2cd728578ba478ba98564cfd.xlsx"},{"id":92114045,"identity":"984da4d8-e843-4544-8b00-7d02ccdf8585","added_by":"auto","created_at":"2025-09-24 19:06:29","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":18379,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/4ac2089157ddc3db63494369.xlsx"},{"id":92113720,"identity":"2680e24a-0ef6-4fb7-93d7-df20e4444215","added_by":"auto","created_at":"2025-09-24 18:58:29","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":12805,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/3381ac2c35dd870cd55a73a4.xlsx"},{"id":92114046,"identity":"ec62442d-f158-422d-a997-433ae3334b32","added_by":"auto","created_at":"2025-09-24 19:06:29","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":18826,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/db1a14658692d3565108899a.xlsx"},{"id":92112775,"identity":"8af0a90f-1e32-4244-a0cd-53378873837e","added_by":"auto","created_at":"2025-09-24 18:50:29","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":19337,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7530087/v1/7f6abf3ad93f6369b5faff70.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Delayed Biospecimen Processing on Serum Proteomics in Samples from a Parabolic Flight","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBlood is one of the most frequently collected and analyzed biological materials in both clinical and research contexts. Blood sample processing delays are common challenges under these settings, particularly when immediate serum separation is not feasible. This issue becomes even more complex in unique environments such as space travel, where logistical constraints limit timely blood sample handling. The rapid growth of commercial and civilian spaceflight has opened new opportunities for systematic, longitudinal, and large-scale biospecimen collection to study the long-term effects of space travel on human health. The GENESTAR methodology was successfully established, offering standardized protocols for biospecimen collection, biobanking, and multi-omics data generation from commercial spaceflight missions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Blood collections are fundamental in space research, providing material for clinical evaluation and the generation of multi-omics data to study the effects of space on human health. A major challenge for commercial missions, both expeditions to the International Space Station (ISS) and free-flying missions orbiting the Earth, is the lack of access to freezers and appropriate processing equipment, making in-flight blood sample collection, preservation and handling especially difficult.\u003c/p\u003e\u003cp\u003eAs a result of this, the current studies primarily analyze blood samples collected before and after spaceflight, leaving an important gap in its molecular profiling, while in-flight [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Traditionally, blood collection during spaceflight is performed using venipuncture, which is cumbersome and complex to execute in space. At the same time, the use of dried blood spots can be inconvenient for astronauts due to issues such as adhesive bandage discomfort and skin irritation after biospecimen collection along with very minimal collection volume [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, recently developed microneedle capillary blood collection devices are better-suited for spaceflight blood collections aboard the ISS or within the capsule just before returning to Earth [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Even in such a scenario, blood samples will likely be left at ambient temperatures for up to 24h before processing and therefore must be evaluated for protein changes to ensure data integrity.\u003c/p\u003e\u003cp\u003eParabolic flight, which provides brief periods of microgravity zero-gravity interleaved with short hyper-g-phases, serves as a well-established analogue for spaceflight and enables rapid, iterative testing of flight hardware and procedures on Earth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. During each parabola a pull‑up (1.8 g), followed by 20\u0026ndash;25 s of zero-gravity, and a pull‑out (1.8 g) before returning to 1 g flight; campaigns usually consist of 15\u0026ndash;20 parabolas per flight [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Because transport‑category aircraft are generally regulated to maintain a maximum cabin pressure altitude of 8,000 ft, the in‑cabin absolute pressure is commonly\u0026thinsp;~\u0026thinsp;75\u0026ndash;80 kPa during flight. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] This reduced ambient pressure decreases the pressure differential available to evacuated collection tubes, which can lower the achievable fill volumes. Here, we used a minimally invasive self‑collection device (ezdraw) during parabolic flight to assess feasibility of blood collection, operator workload, and the downstream impact of delayed processing on the serum proteome as and example of use of such a collection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSerum is widely used in proteomic research, and is obtained after blood coagulation, which removes fibrinogen and other clotting factors but may release cellular proteins due to platelet activation [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Delayed blood processing at room temperature alters serum protein levels, especially inflammatory and platelet-derived markers, [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While most proteins remain stable, some degrade or vary after 24\u0026ndash;48h [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The previous studies confirm that even cold storage cannot fully prevent these changes [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A previous study used 184 Olink target assays and identified proteins, such as cytokines and growth factors, as being particularly susceptible to ex vivo degradation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, this is the first comprehensive comparison of proteome changes in serum with delayed processing using Olink Explore HT of ~\u0026thinsp;5,400 proteins.\u003c/p\u003e\u003cp\u003eTo systematically evaluate the impact of delayed serum processing, the Olink Explore HT platform, a high-throughput proteomics technology, was utilized to characterize the protein profiles in serum under delayed processing [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Serum was extracted from blood samples collected both in-flight and on the ground and was analyzed for protein stability following 24 and 48h of delayed processing. The results of this study would help assess the feasibility of incorporating the use of an ezdraw device for in-flight blood collections both on the ISS or in the space shuttle just prior to mission completion, followed by ambient storage until sample recovery and processing. These findings will help guide protocols for studying such biospecimens in spaceflight related biomedical research as well as other diverse research scenarios and extreme environment studies, where immediate biospecimen processing may not be possible, thereby improving the accuracy and reproducibility of biomarkers found in those settings.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParabolic flight experience/results\u003c/h2\u003e\u003cp\u003eThe ezdraw device, configured with serum tubes under medium-vacuum (MVAC) and high-vacuum (HVAC) settings was deployed during 0 g phases. Devices adhered and functioned reliably during transitions through ~\u0026thinsp;1.8 g pull‑up and pull‑out. However, both MVAC and HVAC tubes underfilled relative to their nominal targets of 3 mL and 4 mL, respectively, for collecting blood under medium vacuum, yielding\u0026thinsp;~\u0026thinsp;1 mL and 1.5 mL, respectively (Fig.\u0026nbsp;1A). All collected devices were double‑contained and transported at ambient temperature; serum was separated at 24 and 48 h post‑collection for proteomic analysis. One subject developed significant motion sickness during early phases of flight and had to be strapped down for the rest of the flight, unable to provide blood samples.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBiospecimens (blood) collections and their derivatives\u003c/h3\u003e\n\u003cp\u003eWhole blood from the one subject who participated in the parabolic flight was also collected on the ground into serum tubes (Fig.\u0026nbsp;1B). Optimal volume of 10 mL blood was collected in serum tubes and left at room temperature. Serum was extracted at 0, 24, and 48h post-collection. From these tubes the following volumes of serum were isolated across different time points, measuring 3.2 mL at 0h, 2.8 mL at 24h and 2.75 mL at 48h. These blood samples served as baseline control for comparison with blood collected under MVAC and HVAC conditions.\u003c/p\u003e\n\u003ch3\u003eSerum Protein Stability over time from 0-24h and 24-48h\u003c/h3\u003e\n\u003cp\u003eFrom each serum sample, 2 \u0026micro;L was used for proteomic analysis, and proteomic profiles were generated for ~\u0026thinsp;5,400 proteins using the Olink Explore HT panel. Total proteins after Limit of Detection (LOD) filtering are 3,201. Protein stability results across 24 and 48h are grouped under five different categories (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the first group, 110 proteins were found to be unstable at both 24 and 48h, where either a protein showed continuous decrease or continuous increase in its levels (Supplementary Table\u0026nbsp;1). In the second group, instability was observed in 214 proteins between 0 and 24h; however, these proteins did not show further changes after 24h, indicating that their instability is limited to the early phase (Supplementary Table\u0026nbsp;2). In the third group 88 proteins exhibited inconsistent changes across the two-time intervals, showing variable behavior between 0\u0026ndash;24h and 24\u0026ndash;48h (Supplementary Table\u0026nbsp;3). In the fourth group are 58 proteins that were initially stable at 24h became unstable by 48h (Supplementary Table\u0026nbsp;4). In the fifth group are 2,731 proteins which remained stable at both 24 and 48h (Supplementary Table\u0026nbsp;5).\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\u003eOverview of protein stability after 24, and 48h of post-collection. Total proteins after LOD filtering are 3,201 and are grouped into five different categories based on their stability.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGroup 5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAfter LOD Filtering\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNot stable at 24 and 48h\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNot Stable at 24h, no change at 48h\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eInconsistent changes at 24 and 48h\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eStable at 24h, not Stable at 48h\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eStable at 24 and 48h\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3,201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eGroup 1: Not stable at 24 and 48h\u003c/h3\u003e\n\u003cp\u003eThere are 110 proteins in the Group 1 category, and of these, 94 (85%) showed a continuous increase in protein levels, while 16 (15%) showed a continuous decrease (Supplementary Table\u0026nbsp;1). Proteins with the highest fold change (NPX difference) included ENSA (+\u0026thinsp;5.81 at 0\u0026ndash;24h; +0.98 at 24\u0026ndash;48h), SRGAP2B (+\u0026thinsp;5.50; +1.31), and ARHGAP31 (+\u0026thinsp;3.84; +1.64), all showing strong time-dependent accumulation. In contrast, proteins that decreased after 24h and 48h included ASB6 (\u0026ndash;3.91; \u0026minus;\u0026thinsp;1.04), GHRL (\u0026ndash;1.06; \u0026minus;\u0026thinsp;0.52), and SELENOP (\u0026ndash;0.64; \u0026minus;\u0026thinsp;0.21).\u003c/p\u003e\n\u003ch3\u003eGroup 2: Not stable at 24h, no change at 48h\u003c/h3\u003e\n\u003cp\u003eThere are 214 proteins in the Group 2 category and of these, 150 (70.1%) showed a pattern of instability during the initial 0\u0026ndash;24h window, followed by relative stability from 24\u0026ndash;48h (Supplementary Table\u0026nbsp;2). Proteins with the highest fold change included, IL4 (+\u0026thinsp;7.18 at 0\u0026ndash;24h; +0.21 at 24\u0026ndash;48h), PPFIA4 (+\u0026thinsp;6.85; +0.28), and SECISBP2L (+\u0026thinsp;6.73; \u0026minus;\u0026thinsp;0.10). These were followed by WHRN (\u0026ndash;6.59; +0.23), CXCL8 (+\u0026thinsp;6.47; +0.17), and IL13RA2 (+\u0026thinsp;6.44; \u0026minus;\u0026thinsp;0.06). Additional proteins with similar patterns included ZIC3, DNAH7, EP400P1, and CEP112, all of which showed marked elevation or reduction in the first 24h, followed by relative stability afterward.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGroup 3: Inconsistent changes at 24 and 48h\u003c/h2\u003e\u003cp\u003eOf the 88 proteins in the Group 3 category, 40 (45.5%) showed a pattern of increase during the initial 0\u0026ndash;24h window, followed by a decrease from 24\u0026ndash;48h (Supplementary Table\u0026nbsp;3). In contrast, 48 proteins (54.5%) demonstrated the opposite trend, with a decrease during 0\u0026ndash;24h followed by an increase in the 24\u0026ndash;48h interval. Proteins with the highest fold change included FAM131B (+\u0026thinsp;7.84 at 0\u0026ndash;24h; \u0026minus;\u0026thinsp;3.63 at 24\u0026ndash;48h), KRT12 (+\u0026thinsp;5.34; \u0026minus;\u0026thinsp;5.16), and RGS5 (+\u0026thinsp;5.32; \u0026minus;\u0026thinsp;5.14), all showing strong initial upregulation followed by a marked decline. ANKRD52 and DRC1 followed similar patterns with substantial fluctuations. On the other hand, proteins like CD200R1L (\u0026ndash;2.70; +1.92) and GLTP (\u0026ndash;2.07; +1.40) demonstrated the opposite trend, where they initially decreased, followed by rebound.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGroup 4: Stable proteome at 24h and not stable at 48h\u003c/h3\u003e\n\u003cp\u003eThere are 58 proteins in the Group 4 category that were found to be stable at 24h and not at 48h (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Pathway enrichment analysis using ShinyGO was performed on proteins representing a stable proteomic signature at 24h. This analysis includes 2,731 proteins that are stable at 48h and 58 proteins that are stable at 24h and not stable at 48h (Supplementary Table\u0026nbsp;4). The top 30 enriched KEGG pathways include immune, signaling, and cellular interactions. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;6). This list includes cytokine-cytokine receptor interaction (FDR\u0026thinsp;=\u0026thinsp;2.36 \u0026times; 10⁻\u003csup\u003e31\u003c/sup\u003e; fold enrichment\u0026thinsp;=\u0026thinsp;3.31), involving key immune mediators such as CCL26, CXCL13, CSF1, and IL10RA (Supplementary Table\u0026nbsp;6). Other highly enriched signaling pathways included PI3K-Akt signaling (FDR\u0026thinsp;=\u0026thinsp;2.75 \u0026times; 10⁻\u003csup\u003e17\u003c/sup\u003e; fold enrichment\u0026thinsp;=\u0026thinsp;2.49), MAPK signaling, JAK-STAT, and NF-kappa B, all of which contained stable proteins like PIK3AP1, AKT2, MAP2K1, STAT2, STAT5B, CHUK, NFKB1, and TNFRSF1A (Supplementary Table\u0026nbsp;6). Additional enriched pathways involved structural and adhesion components such as focal adhesion, ECM-receptor interaction, and regulation of the actin cytoskeleton. Notably, disease-associated and stress response pathways, including those related to rheumatoid arthritis, osteoclast differentiation, apoptosis, and HIF-1 signaling, also appeared in the stable serum proteome at 24h. Among the 58 proteins in Group 4, there are seven proteins in the top 30 pathways. These included IL1R1 from the cytokine\u0026ndash;cytokine receptor interaction pathway; VEGFA, EFNA4, and IGF1R, which are involved in both the PI3K-Akt and MAPK signaling pathways; MASP1 and F2 from the complement and coagulation cascades; and F11R from the cell adhesion molecules pathway (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGroup 5: Stable proteome at 24 and 48h\u003c/h3\u003e\n\u003cp\u003ePathway enrichment analysis using ShinyGO was performed on the stable serum proteome at 24 and 48h (Group 5), represented by 2,731 proteins and was enriched in immune, signaling, and structural pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;5). Among the top 30 KEGG pathways were cytokine-cytokine receptor interaction (FDR\u0026thinsp;=\u0026thinsp;1.62 \u0026times; 10⁻\u003csup\u003e31\u003c/sup\u003e; fold enrichment\u0026thinsp;=\u0026thinsp;3.35), PI3K-Akt signaling (FDR\u0026thinsp;=\u0026thinsp;1.96 \u0026times; 10⁻\u003csup\u003e16\u003c/sup\u003e; fold enrichment\u0026thinsp;=\u0026thinsp;2.47), complement and coagulation cascades (FDR\u0026thinsp;=\u0026thinsp;2.83 \u0026times; 10⁻\u003csup\u003e13\u003c/sup\u003e; fold enrichment\u0026thinsp;=\u0026thinsp;3.93), JAK-STAT signaling (FDR\u0026thinsp;=\u0026thinsp;2.32 \u0026times; 10⁻\u003csup\u003e09\u003c/sup\u003e; fold enrichment\u0026thinsp;=\u0026thinsp;2.64) and MAPK signaling (FDR\u0026thinsp;=\u0026thinsp;4.39 \u0026times; 10⁻\u003csup\u003e08\u003c/sup\u003e; fold enrichment\u0026thinsp;=\u0026thinsp;2.07) (Supplementary Table\u0026nbsp;8).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith the rise of commercial space missions, standardized biospecimen collection methods like GENESTAR or other methods are essential, yet in-flight blood collections remain difficult due to limited access to freezers and equipment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. To address this, microneedle-based blood collection devices were tested during parabolic flights to simulate brief periods of microgravity as an analogue for spaceflight use.\u003c/p\u003e\u003cp\u003eBlood samples collected were stored at room temperature, and serum was extracted at 24 and 48h post-collection. Using the Olink Explore HT platform, which measured 5,400 proteins, the effects of delayed blood sample processing on serum proteome integrity were systematically evaluated [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe parabolic flight experiment demonstrated the mechanical functionality of the Preci-Health ezdraw device, including its deployment and adherence in a microgravity environment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, the blood collection volumes were suboptimal, where both MVAC and HVAC tubes yielded lower blood volumes of 1 mL and 1.5 mL than the target 3 mL and 4 mL respectively. The shortfall is consistent with the calculated reduction in pressure differential at typical cabin pressures, which decreases the driving vacuum by ~ 30–50% compared with sea‑level conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Calculations indicate a ~ 32% reduction in pressure differential for HVAC tubes and a ~ 51% reduction for MVAC tubes compared to ground-level conditions, which are also reflected in the blood volumes collected in these two tubes. This altitude effect on evacuated tubes can produce lower draw volumes and alter additive‑to-blood ratios [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese findings suggest that while self-collection in microgravity using ezdraw is feasible. For this device to be used on a future parabolic flight, it will require further optimization to maintain efficacy under reduced ambient pressure conditions. However, it should not be an issue to collect blood in a space shuttle or in the ISS during spaceflight, where such a pressure differential does not exist. In fact, ezdraw was successfully used for blood collection by another research group during the Polaris Dawn space mission in September 2024 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eSerum was extracted from blood samples collected during parabolic flight using the ezdraw device, as well as from the blood samples collected on the ground and left at room temperature. While the serum volumes obtained from the ezdraw device were sub-optimal (300 µL – 450 µL), the resulting volumes were sufficient for proteome profiling and for biobanking.\u003c/p\u003e\u003cp\u003eTo comprehensively assess the impact of delayed blood sample processing on serum proteome integrity, the Olink Explore HT platform, a high-throughput proteomics technology, was employed to analyze protein expression profiles under varying storage conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The serum proteome exhibited distinct behavioral categories based on temporal dynamics across 0–24h and 24–48h intervals. A total of 110 proteins showed continuous directional changes (Group 1), with fold changes ranging from − 3.91 to + 5.81 between 0–24h and − 1.04 to + 1.64 between 24–48h, suggesting sensitivity to both early leakage and progressive degradation (Supplementary Table\u0026nbsp;1). A larger subset of 214 proteins (Group 2) was classified as unstable primarily during the first 24h (− 7.46 to + 8.37), likely reflecting active leakage from leukocytes, platelets, or damaged endothelial cells triggered by room temperature exposure, followed by relative stabilization (− 0.63 to + 1.47) thereafter (Supplementary Table\u0026nbsp;2). Inconsistent behavior was observed for 88 proteins (Group 3), initially increasing or decreasing (− 2.70 to + 7.84 at 0–24h) but reversing or stabilizing (− 5.16 to + 1.92 at 24–48h) (Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eIn contrast, most of the profiled proteins in Groups 4 and 5 were stable at 24h (58 plus 2,731) and 48h (2,731), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These proteins mapped to 108 (24h) and 110 (48h) biochemical pathways, encompassing components of PI3K-Akt signaling, focal adhesion, and ECM-receptor interactions, and showing minimal variability despite delayed processing (Supplementary Tables\u0026nbsp;4, 5, 6, and 8). This amounts to 87% (2,789/3201) of the detectable proteins at 24h and 85% (2,731/3201) detectable proteins at 48h, enhancing the usability of such data.\u003c/p\u003e\u003cp\u003eThese patterns are consistent with prior literature predominantly based on plasma studies [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The exception to this was a study where serum proteome was analyzed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite matrix differences, common trends emerge, particularly the early sensitivity of inflammatory cytokines (e.g., CXCL8, MIP-1α) and the relative resilience of structural and adhesion-related pathways such as PI3K-Akt signaling and ECM-receptor interaction. To further evaluate the consistency of serum protein behavior under delayed processing conditions, fold change data were compared from this dataset with those reported in the serum study, where they analyzed cytokine dynamics in serum samples stored at room temperature for 24h [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. They reported a dramatic increase in six key cytokines: IL-1β (23.3-fold), IL-6 (24.1-fold), IL-8 (17.0-fold), MIP-1α (55.0-fold), MIP-1β (5.0-fold), and CD40L (114.3-fold) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this dataset, IL-8 (CXCL8) demonstrated a 6.47-fold increase from 0 to 24h, followed by stabilization between 24 and 48h (0.17-fold), suggesting a biphasic profile consistent with early instability and subsequent plateau. Similarly, MIP-1α (CCL3) showed a 1.17-fold increase during the first 24h and a negligible change thereafter. IL-8 and MIP-1α, therefore, can be considered as consistent markers of early serum degradation. Compared to the previous study that evaluated the stability of 62 cytokines and chemokines in serum under delayed processing conditions, the current study analyzed over 5,400 proteins spanning a broad range of biological pathways. This expanded scope allowed for a more comprehensive characterization of serum proteome dynamics, including the inflammatory markers.\u003c/p\u003e\u003cp\u003eAt 24h, the serum proteome showed significant (\u0026gt; 2-fold) dysregulation involving 84 proteins, reflecting early stress responses, immune activation, and cytoskeletal remodeling. Out of 84 proteins with major fold changes, 23 proteins came from the ‘Continuous change’ Group 1 category, 52 proteins were classified under the ‘not Stable at 24h’ Group 2 category, and 9 proteins were derived from the ‘Inconsistent change’ Group 3 category (Supplementary Table\u0026nbsp;9).\u003c/p\u003e\u003cp\u003eMajor increases were observed in CDCA2 (+ 8.37-fold), HBG2 (+ 7.37-fold), CXCL8 (IL-8) (+ 6.47-fold), and SRGAP2B (+ 5.50-fold). These proteins have a role in these biological processes, including immune cell activation (CXCL8, CCL7), regulation of cell survival (BIRC3, TSC22D3), and structural cell remodeling (SRGAP2B, ITGAX) [\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Conversely, transcriptional regulators such as TBX22 (− 7.46-fold) and cytoskeletal proteins like SPATS1 (− 6.56-fold) were markedly downregulated, suggesting selective degradation or inhibition early during storage [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBy 48h, fewer proteins showed major changes (only 4 proteins had significant fold changes \u0026gt; 2 and all from the ‘Inconsistent change’ Group 3 category). Notably, KRT12 (− 5.16-fold) and RGS5 (− 5.14-fold) showed strong decreases, indicating cellular degradation and vascular stress associated with prolonged room temperature exposure (Supplementary Table\u0026nbsp;9).\u003c/p\u003e\u003cp\u003eThese results show that while most serum proteins remained stable over the first 24h, a subset, particularly cytokines and extracellular matrix modulators demonstrated time-sensitive variability by 48h. For instance, IL-8 (CXCL8) exhibited a 6.47-fold increase between 0–24h in this dataset, stabilizing thereafter. This pattern aligns with previously reported 17.0-fold increase in serum after 24h [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In plasma study 2-10-fold increase were observed ([\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. TNFSF14 (LIGHT) rose by 0.82-fold from 0–24h and 0.07-fold from 24–48h, and Oncostatin M (OSM) exhibited moderate time-dependent increases, with fold changes of 0.69 at 0-24h and 0.20-fold at 24-48h in this dataset. In a previous study a \u0026gt; 6-fold and \u0026gt; 5.21-fold increase in TNFSF14 (LIGHT) and Oncostatin M (OSM) were observed in plasma with delayed processing [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These results suggest that inflammatory and immune-modulating proteins are sensitive to room temperature storage. In the current serum proteomics dataset, several core biological pathways demonstrated consistent stability across both 24h and 48h timepoints (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When comparing venipuncture-derived plasma to capillary blood collected via the Tasso + microneedle device, was found to produce only 11.3% of proteins that achieved acceptable reproducibility (r ≥ 0.5, CV ≤ 0.20) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e],[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Most proteins, especially those associated with inflammation, immune signaling, and cytokine activity, showed high variability and poor correlation with venous samples. The authors attributed this discrepancy to microvolume sampling variability, differences in cellular content, and potential stress-induced biomarker release due to the self-collection process. As a result, they concluded that current microdevices like Tasso + are not yet reliable for large-scale proteomic discovery, particularly when using untargeted, high-throughput platforms. In contrast, this study used ezdraw device that can collect larger blood volumes, and there is agreement in 4/6 cytokines between this dataset and those reported previously [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Collectively, these results highlight proteins such as CXCL8, CCL7, and KRT12 can serve as sensitive indicators of serum instability, while pathway-associated proteins like AKT1, TLN1, and COL1A1 offer robust internal controls, even under delayed processing conditions.\u003c/p\u003e\u003cp\u003eUse of dried blood spots (DBS) could be another option [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, it also comes with the challenges of collection in microgravity, such as small blood sample volume (up to 40 µL per punch), which limits the number of proteomic and metabolomic assays that can be conducted and reduces the amount of residual material available for future biobanking. As shown in this study, using ezdraw, 300–450 µL of serum was obtained from the collected blood, which suffices for proteomics assay and biobanking for future use. Results from this study showed that 87% of the detectable serum proteins remained stable at 24h, and 85% at 48h post-collection under delayed processing conditions, enabling the study of over 100 biochemical pathways.\u003c/p\u003e\u003cp\u003eThese results demonstrate that delayed processing of self-collected blood samples under simulated microgravity conditions has minimal impact on overall serum proteome integrity. The stability of key proteins and preservation of major biochemical pathways support the use of the ezdraw self-collection device as a viable strategy for in-flight blood collection during space missions. This approach, therefore, offers a practical solution to overcome logistical constraints in spaceflight and can be implemented for future collections of blood samples.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eParabolic flight experiment\u003c/h2\u003e\u003cp\u003eTo assess the feasibility of serum collection in zero-gravity using a minimally invasive self-collection device, a parabolic flight was conducted on April 15, 2024, aboard the Zero-G® Boeing 727-227F Advanced aircraft departing from Ft. Lauderdale, FL. The flight completed 27 zero‑gravity parabolas and 2 lunar‑gravity parabolas. Each 0 g parabola provided 20–25 s of near‑weightlessness bracketed by 1.8 g pull‑up and pull‑out segments. Cabin pressure was maintained near transport‑category conditions (maximum cabin altitude ≤ 8,000 ft), corresponding to approximately 75–80 kPa (10.9–11.6 psi) (Supplementary Fig.\u0026nbsp;1A).\u003c/p\u003e\u003cp\u003eThe primary goal was to evaluate the performance of the Preci-Health ezdraw device using MVAC and HVAC tubes under zero-gravity conditions.\u003c/p\u003e\u003cp\u003eTwo participants alternated roles as subject and operator during the flight. Each wore two ezdraw devices, one with an MVAC tube and one with an HVAC tube, applied sequentially to each shoulder. The operator documented the procedure using a wrist-mounted camera and a smartphone with a gravity meter app to confirm microgravity phases.\u003c/p\u003e\u003cp\u003eThe ezdraw device is designed for capillary blood collection without conventional venipuncture. It attaches to the upper arm near the deltoid, performs two small incisions and applies vacuum to the built-in chamber using the vacuum of the collection tubes. Upon activation, blood is passively collected into sterile tubes 4 mL (BD Biosciences, SKU#367812) for HVAC 387 and 3 mL (BD Biosciences, SKU#366668) for MVAC. The device automatically seals to prevent contamination and is preset to collect a fixed volume (typically 500 µL to 1.5 mL). At sea level conditions, the version of ezdraw device used in this study holds two tubes that could draw per tube as much as 2 mL using a 3 mL MVAC tube and 2.5 mL using a 4 mL HVAC tube. The ezdraw devices were manufactured and provided as early access devices to the team for the parabolic flight. Preci Health is expecting to get European Union approval for commercial use in quarter two of 2026.\u003c/p\u003e\u003cp\u003eBefore the flight, sterile bandages were applied to the collection sites. During 0g phases, the bandages were removed and the ezdraw devices were activated via a release button, creating two small incisions to initiate blood collection. Devices were secured with elastic wraps to maintain adhesion. After each collection, devices were removed, double-contained in biohazard bags, and stored for post-flight analysis (Supplementary Fig.\u0026nbsp;1B).\u003c/p\u003e\u003ch2\u003eBiospecimens (blood) collections and their derivatives\u003c/h2\u003e\u003cp\u003eFor the ground collection, which served as controls, blood was collected in 10 mL serum tubes (BD Biosciences, SKU# 367820). Venipuncture was performed in the antecubital fossa after sanitizing that area, and blood was drawn using a butterfly needle (BD Biosciences, Cat#367281) and a lure lock vacutainer adapter (McKesson, Cat#364815/458363). Serum was isolated from the blood samples collected in serum tubes at 0, 24, and 48h by centrifugation at 1600g for 10 minutes at room temperature. For the Olink Explore HT assay, 2 µL was used and the remaining volume was biobanked at − 80\u003csup\u003eo\u003c/sup\u003eC for future use (Fig.\u0026nbsp;1).\u003c/p\u003e\u003ch2\u003eOlink Explore HT assay and Sequencing\u003c/h2\u003e\u003cp\u003eThe Olink Explore HT platform is a high-throughput, multiplex proteomics technology that utilizes proximity extension assay (PEA) technology [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It measures over 5,400 biomarkers in a single run from minimal sample volumes with high specificity and sensitivity. This method utilizes pairs of DNA-labeled antibodies that bind specifically to target proteins, enabling quantification through DNA hybridization while minimizing cross-reactivity. It can detect proteins at sub-picogram per milliliter concentrations. Serum samples were analyzed to assess protein stability under different storage conditions using OLINK Explore HT protocol.\u003c/p\u003e\u003cp\u003eThe Olink Explore HT assay (Cat # 98100) is processed using automated liquid handlers (mosquito LV genomics, Dragonfly Discovery, and Biomek FxP). Approximately 10 µL of serum were used for the assay and ~ 5,400 proteins were assayed across 8 blocks of antibody pools. Serum samples were then serially diluted (1:10 to 1:100,000) and loaded into two 384-well plates. Probes with unique DNA tags are divided into eight assay blocks. Undiluted serum samples are used for blocks 1–4, and diluted serum samples for blocks 5–8, following the standard Olink Explore HT setup followed by overnight incubation at + 4°C. Only when both the antibody probe pairs bind to the target protein, the complementary oligonucleotides in proximity hybridize and are extended using a DNA polymerase. Annealed sequences are then extended, amplified, barcoded and sequenced on the Illumina platform using NovaSeq 6000 S4 Reagent Kit v1.5 (35 cycles) to generate 24bp reads.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eSequence data (bcl files) were converted to counts using Olink’s pre-processing software, ngs2counts v4.7.1. Olink’s Explore CLI v.2.3.1 was next used to export a parquet file containing intensity-normalized NPX values. NPX (Normalized Protein Expression) is an arbitrary, relative quantification unit used by Olink, which is log2 scale to protein concentration. Quality control and Limit of Detection (LOD) filtering was completed using Olink’s R package, OlinkAnalyze v4.2.0 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Assays below LOD were excluded where LOD was calculated, via package function olink_lod using Olink’s predetermined fixed LOD values. Refer to the Olink Analyze’s Vignette for details [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Linear Mixed-effects Regression (LMER) was performed on the remaining protein assays (n = 3,201 for parabolic serum samples) of different timepoints: NPX~(1|SubjectID). Output p-values were adjusted by the Benjamini-Hochberg method, with adjusted p value \u0026lt; 0.05 defined as the significant threshold. This resulted in 470 assays being found to be significantly changed across timepoints 0-24h and 24-48h. Post-hoc analysis performed on the significantly changed assays determined detailed differences between timepoints. analysis performed on the significantly changed assays determined detailed differences between timepoints.\u003c/p\u003e\u003ch2\u003ePathway Methods\u003c/h2\u003e\u003cp\u003eTo identify biological pathways potentially unaffected by experimental conditions, a subset of proteins from the Olink® proteomics dataset was analyzed. These proteins that were not statistically significantly differentially expressed, defined by an adjusted p-value \u0026gt; 0.05 and an absolute log2 fold change \u0026lt; 0.2 and were considered to represent a stable proteomic profile.\u003c/p\u003e\u003cp\u003eThe corresponding not significant proteins were uploaded into ShinyGO v0.82 for gene set enrichment analysis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Enriched biochemical pathways (FDR-adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) were interpreted as biological functions populated by proteins that remained stable across conditions.\u003c/p\u003e\u003ch2\u003eHuman subjects research\u003c/h2\u003e\u003cp\u003eAll subjects consented and biospecimens were collected and processed under the approval of the Institutional Review Board at Baylor College of Medicine, under MESH protocol H-536332.\u003c/p\u003e\u003ch2\u003eManuscript preparation\u003c/h2\u003e\u003cp\u003eFigure 1A and 1B were created with BioRender.com under the Baylor College of Medicine Institutional license.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003ePhilippe Margairaz and Laurence Blazianu are employees of Preci-Health\u0026trade;. Rest of the authors declare no financial or non-financial competing interests.\u003c/p\u003e\u003ch2\u003eSupplementary information\u003c/h2\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHD and EU conceptualized the study. ZM, FM, PM, LB, AK, MCG, JW, HD, EU contributed to biospecimen collection and data generation. QW, ZM, KW, AK, AS, QX, RAG, EU, HD were involved in data analysis, prepared the original draft of the manuscript and addressed the edits. All the authors reviewed, edited and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003e This study was funded (Grant# INN0010) by the Translational Research Institute for Space Health through NASA Cooperative Agreement NNX16AO69A.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll subjects consented and biospecimens were collected and processed under the approval of the Institutional Review Board at Baylor College of Medicine, under MESH protocol H-536332.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKrishnavajhala, A., et al., \u003cem\u003eThe GENESTAR manual for biospecimen collection biobanking and omics data generation from commercial space missions\u003c/em\u003e. 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Bioinformatics, 2020. 36(8): p. 2628\u0026ndash;2629.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"npj-microgravity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjmgrav","sideBox":"Learn more about [npj Microgravity](http://www.nature.com/npjmgrav/)","snPcode":"41526","submissionUrl":"https://submission.springernature.com/new-submission/41526/3","title":"npj Microgravity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7530087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7530087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn-flight blood collection during spaceflight is limited by both the constraints of available collection methods and the inability to process samples immediately. To bridge this gap, ezdraw, a blood self-collection device was used aboard a parabolic flight. Furthermore, to mimic operational constraints, blood samples were stored at room temperature with delayed processing, and serum was extracted at 24 and 48h. Protein stability was analyzed using the Olink Explore HT platform, profiling up to 5,400 proteins. Of the 3,201 detectable proteins, 2,789 (87%) proteins remained stable at 24h and 2,731 (85%) at 48h, showing resilience to delayed processing. Key proteins involved in signaling immune related and metabolic pathway remained stable. Unstable proteins did not significantly impact representation of the major biochemical pathways. These findings suggest most serum proteins remain robust to delayed processing, supporting the feasibility of in-flight blood collections and their use for molecular profiling as demonstrated here.\u003c/p\u003e","manuscriptTitle":"Impact of Delayed Biospecimen Processing on Serum Proteomics in Samples from a Parabolic Flight","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 18:50:23","doi":"10.21203/rs.3.rs-7530087/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-13T15:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25613338578529687251787214510679613437","date":"2025-12-16T10:21:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301552591381881119958697678133858655430","date":"2025-12-06T08:53:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T17:31:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T05:06:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T13:59:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Microgravity","date":"2025-09-03T20:13:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-microgravity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjmgrav","sideBox":"Learn more about [npj Microgravity](http://www.nature.com/npjmgrav/)","snPcode":"41526","submissionUrl":"https://submission.springernature.com/new-submission/41526/3","title":"npj Microgravity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e1c4aa7-496c-4069-9e3f-a0f2182144ae","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55211052,"name":"Biological sciences/Biochemistry"},{"id":55211053,"name":"Biological sciences/Biological techniques"},{"id":55211054,"name":"Health sciences/Biomarkers"},{"id":55211055,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2025-10-02T17:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 18:50:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7530087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7530087","identity":"rs-7530087","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}