Targeted proteomic analysis of dry blood spot samples collected during long-term spaceflights

Targeted proteomic analysis of dry blood spot samples collected during long-term spaceflights | 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 Targeted proteomic analysis of dry blood spot samples collected during long-term spaceflights Irina Larina, Alexey Kononikhin, Alexander Brzhozovskiy, Savva Semenov, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4694174/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The molecular mechanisms of organismal adaptation to spaceflight (SF) conditions are not fully investigated and remain relevant for study. Newly emerging multi-omics technologies may provide information at the molecular level regarding physiological changes caused by SF factors. The aim of the study was to apply blood proteome analysis using dried blood spots (DBS) micro-sampling technique to study at the molecular level the physiological response to SF conditions for 7 cosmonauts during 169–199 days long-duration missions (one of the cosmonauts participated in a year-long mission). The level of 119 blood proteins was quantified in DBS samples by targeted mass spectrometry based proteomics with a validated MRM assay using stable isotope-labeled peptide standards (SIS). To identify proteins whose concentrations are sensitive to the high physiological stress of landing and takeoff the Mann-Whitney and Cohen's d-size effect tests were used. As a result, 10 proteins (p-value 0.8) were found to be significant. In order to determine additional stress marker proteins, the dynamic changes in protein concentration during SF were analyzed to find proteins whose concentrations change monotonously after stress exposure (takeoff or landing). A simple exponential decay model for protein concentration relaxation was proposed which allows to reveal additional 14 proteins. Intersections of both protein panels mentioned above resulted in a final list of 19 blood proteins that are related to SF factors such as takeoff and landing. The panel includes 2 plasma proteins (Lumican and Insulin-like protein binding growth factor 3) that were previously mentioned in other studies to be related with SF. As far as we know this is the first DBS proteomic study for monitoring adaptive reactions of the organism to long-term SF. Health sciences/Health care/Disease prevention/Preventive medicine Biological sciences/Biological techniques/Mass spectrometry spaceflight proteomics cosmonauts astronauts mass spectrometry dried blood spots Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Spaceflight (SF) is one of the most extreme conditions and is resembled by a complex of factors, such as microgravity, extreme acceleration and cosmic radiation, which affect many physiological changes in the human organism [ 1 , 2 ]. The molecular mechanisms of adaptation to SF conditions are not fully investigated and are important to study at all stages of long-duration missions on the International Space Station (ISS) [ 3 – 5 ]. Due to some technical, logistical and economical limitations (including those caused by weightlessness), the identification of changes occurring in the organism onboard of the ISS remains a difficult task [ 6 ]. Multi-omics technologies may provide new information at the molecular level regarding organism adaptation to SF conditions including changes in expression profiles of DNA and RNA [ 7 ], concentration profiles of proteins and metabolites [ 8 ]. The NASA multi-omics project collected 317 samples from twin astronauts (one of them spent a year on the ISS, while the other remained on Earth), and identified proteins that could be associated with observed changes in vascular wall dimensions [ 9 ]. This study has paved the way for future multi-omics studies, including those at the single-cell level, and has provided the first comprehensive molecular profile of an astronaut [ 10 ]. The proper collection and storage of biomaterial during SF is an important aspect of aerospace medicine, and has been given significant emphasis in the frame of new Space Omics and Medical Atlas (SOMA) initiative [ 5 ]. One of the relevant methods available is the dried blood spots (DBS) micro-sampling technique which is useful for various research studies and wide-population screenings [ 11 – 13 ]. DBS micro-sampling acquires a very small amount of blood (~ 20µl) and can be self-collected via a non-invasive finger capillary puncture. The tool kit for this procedure is simple, small, and easy to use onboard. Sheets of Whatman paper used as DBS sample holders can be periodically delivered by transport shuttles to Earth for DBS analysis in the lab. DBS samples are stable under standard environmental conditions for many years and contain thousands of analytes that can be re-eluted for subsequent analysis [ 14 ]. Analysis of the human blood proteome is challenging because almost all proteins synthesized in the human body eventually turn up in plasma and the dynamic range of protein concentrations in it is about eleven orders of magnitude. Liquid chromatography coupled to mass spectrometry (LC-MS) is capable for reproducibly quantifying up to 700 proteins in DBS samples, with a concentration range that spans four orders of magnitude [ 12 , 15 , 16 ]. Targeted quantitative proteomic analysis revealed that the concentrations of 190 proteins were stable in DBS samples stored up to 2 months at ambient temperature [ 12 ]. Thus, DBS can be considered as a valuable object for monitoring physiological changes at the molecular level under extreme conditions and is available for collection even during SF as it is suitable for long-term storage at room temperature. A deep blood proteome analysis can facilitate the discovery of new data and mechanisms of human adaptive response to SF conditions. Mass-spectrometry-based proteomic techniques allow to detect alterations caused by SF. In a recent study initiated by NASA the first plasma samples for proteomic analysis were collected during SF from 4 astronauts [ 5 ]. Multidimensional Protein Identification Technology (MudPIT) was successfully applied and revealed 19 proteins whose expression was significantly altered due to SF. Further, GO analysis revealed that these proteins are involved in inflammatory responses, the cytoskeleton system, and metabolism that potentially have functional roles in response to SF or re-adaptation to Earth environment [ 4 ]. Our group has previously demonstrated changes in the proteome composition after long-duration (169–199 days) missions on the ISS, but only samples collected before and after SF were analyzed [ 17 , 18 ]. As spaceflight becomes more common with commercial crews, blood-based measures become actual for health monitoring during short-duration spaceflight and the first study already performed within the SpaceX Inspiration4 (i4) mission [ 19 ] The aim of the current study was to apply the method of blood proteome analysis with DBS micro-sampling technique to study the physiological response to SF conditions at the molecular level. Targeted mass-spectrometry based proteomics with a validated assay of stable isotope-labeled peptide standard (SIS) for 119 blood proteins was selected as a robust and precise tool for quantitative analysis. We considered the proteins and corresponding peptide panel from the BAK270 MRM assay [ 20 ]. The assay was developed for analysis of potential protein biomarkers for cardiovascular diseases, including 61 FDA-approved biomarkers in blood plasma. The robustness of the MRM assay for selected blood proteins was recently demonstrated [ 21 ]. Finally, a targeted proteomic analysis of 58 DBS samples collected from 7 cosmonauts during long-duration SF (169–199 days) was performed. To our knowledge this is the first DBS proteomic study for monitoring the adaptive reactions of cosmonauts during long-term SF. 2. Results 2.1. Study design DBS samples were collected from 7 cosmonauts (male, age: 47 ± 5.3 years) before, during and after long-duration (169–199 days; one of them participated in a year-long mission) missions on the ISS. More details of samples collection time-points (up to 10) from each cosmonaut is specified in Table 1 . All subjects provided written informed consent to participate in the ‘‘OMICs-DBS’’ experiment. The study was approved (Code «OMICs-DBS», dated 06 March 2020) by the Ethics Committee of the Institute of Biomedical Problems, Russian Academy of Sciences/Physiology Section of the Russian Bioethics Committee Russian Federation National Commission for UNESCO and Human Research Multilateral Review Board, NASA, Houston, TX, USA. Table 1 The time points for 58 DBS samples collected from 7 cosmonauts (C1-C7) during long-duration (169–199 days) spaceflight (SF). Time points during SF 7 cosmonauts C1 C2 C3 C4 C5 C6 C7 Before spaceflight (B) (B1) − 45 days before spaceflight + + + (B2) − 15 days before spaceflight + + + + + + + During spaceflight (SF) (SF0) - after 5–7 days onboard the ISS + + + + + + + (SF3) − 3 months of SF + + + + + + + (SF6) − 6 months + + + + + + + (SF9) − 9 months + (SF12)* -12 months + Remission (R) after landing (R + 1) – 1st day + + + + + + + (R + 7) − 7 days + + + + + + + (R + 14) − 14 days + + + + + + + (R + 26) − 26 days + + + + *- The last sample for each cosmonaut was collected 2 days before landing; Capillary blood was obtained by puncturing the phalanx of the fourth finger with an automatic scarifier. To normalize blood volume an automatic pipette was used to collect 40 µl of blood and place it on filter paper for drying. After collection, the blood stains were dried on filters at ambient temperature (19–26°C) for 2–3 h and then placed in a zip-lock bag (Fig. 1 ). Capillary blood samples collection was carried out on an empty stomach in the morning. Filters were returned to Earth onboard the Russian Federation’s Soyuz spacecraft. 2.2. Proteomic analysis Targeted proteomic analysis was performed for 58 DBS samples collected from 7 Russian cosmonauts during long-duration SF (see details in Materials and Methods—Section 4.1 ). In our study, we used the approach for targeted analysis of more than 200 proteins in DBS by liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) [ 12 ]. We used the MRM assays that includes stable-isotope-labeled internal standards (SIS) and natural (NAT) synthetic proteotypic peptides for measuring the concentrations of corresponding 200 proteins in the blood. These assays have been developed for revealing non-communicable diseases and include medium-to-low abundance plasma proteins associated with this type of diseases. All MRM assays were previously characterized according to the guidelines of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [ 22 ]. LC-MRM MS analysis revealed 119 proteins that were quantified in ≥ 2/3 of the analyzed samples in total (Table S1 ). Statistical analysis revealed that the proteomic data was heterogeneous, as we could not find any proteins that were present in all cosmonauts and had significant concentration variations during SF stages. Instead, stronger clustering of experimental data was observed for individual cosmonauts, rather than for SF (Fig. 2 ). In order to identify protein biomarkers of physiological stress related to landing and takeoff, all samples were divided into two groups: group1 – represented the “stress” state (samples taken immediately after takeoff and landing); group 2 – represented the “calm” state (samples taken at maximum relaxation both in space and on the ground: before the flight, 6 months into the flight and the last sample in remission after landing). We used the Mann-Whitney test to compare protein concentrations between these groups and Cohen's d-size effect (Table S2). We considered changes in protein concentration to be statistically significant if the p-value for them was less than 0.05, and Cohen's d size > 0.8 (Table 2 ). Table 2 The list of the proteins significantly changed (p-value 0.8) due to stress conditions (takeoff and landing). Protein Names p-value Cohen's d-size Regulation Apolipoprotein F 0.0019 -1.357 DOWN Lumican 0.0026 1.041 UP Glutathione peroxidase 3 0.0037 -1.423 DOWN Fibulin-1 0.0059 1.190 UP Zinc-alpha-2-glycoprotein 0.0109 -0.846 DOWN Insulin-like growth factor-binding protein 3 0.0253 -1.109 DOWN Apolipoprotein C-III 0.0288 -0.774 DOWN Peroxiredoxin-1 0.0328 -1.281 DOWN Protein_deglycase_DJ-1 0.0477 -0.696 DOWN Apolipoprotein A-IV 0.0477 0.812 UP A panel of 10 proteins found to be associated with takeoff and landing demonstrated a clearer separation of proteomic data for 58 DBS samples from 7 cosmonauts (Fig. 3 A). The most significantly changed proteins are presented on the Volcano plot (Fig. 3 B). 2.3. Dynamic changes in protein concentrations during SF In order to identify the stress marker proteins, we analyzed the dynamic changes in protein concentrations during SF. The aim was to find proteins whose concentration changes were monotonous after exposure (takeoff or landing). Then such proteins were considered as stress markers. We proposed the exponential decay model for protein concentration relaxation after a stress factor (takeoff and landing), which can be expressed using a formula: $$\:C\left(t\right)\:\:=\:Bexp(-At),\:$$ 1 where \(\:C\left(t\right)\) is observable protein concentration and t is the time after the introduction of the stress factor, B is a stress response constant, A is the decay constant. In order to calculate the constants A and B for each protein, we used a linear regression of the linearized data: $$\:ln\left[C\right(t\left)\right]\:=\:lnB\:-At$$ 2 . In order to determine the monotonicity of the obtained dependence, we used the Kendall 's tau coefficient (Table S3-S4). As a result 15 proteins (Kendall p-value < 0.05) showed monotonous concentration relaxation after such stress factors as takeoff and landing (Table S3-S4). As an example, a characteristic picture of the dynamic for two up-regulated proteins (Fibulin-1 and Lumican) is presented in Fig. 4 . 3. Discussion Quantitative analysis of blood proteins is not an easy task, and in-depth analysis of a wide panel of proteins using mass- spectrometric methods requires additional efforts and should rely on well-validated reproducible protocols [ 11 , 14 , 18 , 19 ]. There are several strategies that are recommended for blood proteomics and clinical studies. We followed protocols and recommendations for absolute concentration measurements of more than 200 proteins in plasma/serum and DBS which was proposed by the Prof. Borcher’s team [ 11 , 18 , 19 ]. Finally, 119 proteins were quantified by LC-MRM MS in ≥ 2/3 of 58 DBS samples collected from 7 Russian cosmonauts during long-duration SF. Further analysis of the data revealed its heterogeneity and different proteins had different dynamics of concentration changes caused by SF. To identify proteins whose concentrations are sensitive to the high physiological stress of landing and takeoff the Mann-Whitney and Cohen's d-size effect tests were used. As a result, 10 significant proteins (p-value 0.8) were found. In order to determine additional stress marker proteins, the dynamic changes in protein concentration during SF were analyzed to find proteins whose concentrations change monotonously after stress exposure (takeoff or landing). A simple exponential decay model for protein concentration relaxation was proposed which allowed to reveal additional 14 proteins. Intersection between both protein panels mentioned above resulted in a final list of 19 blood proteins affected by SF factors such as takeoff and landing. GO-database annotation and database search showed that these proteins are involved in the inflammatory response, metabolism and oxidative stress [ 23 – 26 ]. Two proteins important for the organization of the extracellular matrix, Lumican and Fibulin-1, were positively regulated by stress. Lumican expression is known to be increased in clinical and experimental heart failure, during which a pressure overload occurs [ 23 ]. It is possible that the overloads during takeoff and landing contribute to the increase in the blood level of these proteins. Negative regulation by stress has been observed for proteins associated with lipid and carbohydrate metabolism: Apolipoprotein F, Zinc-alpha-2-glycoprotein. Apolipoprotein F is one of the most important regulators of cholesterol transport, inhibiting cholesteryl ester transfer protein (CETP) activity. Zinc-alpha-2-glycoprotein stimulates lipid degradation in adipocytes. During SF, especially prolonged, exposure to microgravity leads to a number of pathophysiological changes, among which are metabolic changes [ 24 ]. Apparently, stress during takeoff and landing makes its own adjustments to metabolic processes, and it possibly even changes insulin sensitivity [ 25 ], but more thorough research is needed. As a result of stress, Insulin-like growth factor-binding protein 3 (IGFBP-3) also decreased, which, through interaction with IGF, regulates cellular processes such as cell proliferation, apoptosis, and differentiation [ 26 ]. Also negative regulation by stress was observed for proteins that protect cells from oxidative stress by detoxifying peroxides. Two proteins such as Glutathione peroxidase 3 and Peroxiredoxin-1 were down regulated, what can reduce cell resistance to oxidative stress and have negative consequences for the body, especially in SF conditions. Despite the limitations of our study such as a relatively small number of samples and a restricted panel of proteins for analysis, however, we were able to identify proteins whose concentrations significantly change under the influence of SF factors. Quantification of more proteins and more sample collections is an actual task for future research and validation of current results. Recently it was demonstrated using a 60-Biomarker Health Surveillance Panel (HSP) that the majority of proteins have a relatively similar intensity in capillary blood, venous blood, and matched plasma [ 15 ]. In this view, one may use complementary collection devices available for blood tests. The last thing that is important to note is that two plasma proteins (lumican and insulin-like protein binding growth factor 3) which were identified as related to SF in our study with DBS samples were already confirmed in previous studies with plasma samples [ 4 , 18 ]. 4. Materials and Methods 4.1. DBS sample preparation DBS sample was excised and placed in a 1.5 ml polypropylene Eppendorf tube. Proteins were extracted with 1 ml of solution of 25 mM ammonium bicarbonate, 1% sodium deoxycholate, and 5 mM TCEP (tris (2-carboxyethyl) phosphine hydrochloride) (Thermo Scientific) at 60°C by vortexing at 1,000 rpm (Thermomixer, Eppendorf) for 1 h. For additional normalization the protein concentration was measured with the BCA assay (Thermo Scientific, Waltham, MA, USA). Sample preparation included reduction with 0.1 M dithiothreitol in 0.1 M Tris buffer (pH 8.5) containing 8 M urea for 30 min at 47°C, followed by alkylation with 0.05 M iodoacetate and incubation for 30 min in the dark at room temperature. Then precipitation was carried out with five volumes of acetone in the presence of 0.1% TFA at − 20°C overnight. The preparation procedure was completed by trypsinolysis in 0.05 M ammonium bicarbonate buffer: 100 µl of the buffer and 2 µl of trypsin solution at a concentration of 1 µg/µl in 50 mM acetic acid were added to the precipitated mixture of proteins. Incubation was carried out overnight in a thermomixer at 37°C with shaking at 750 rpm. Then, 1 µl of a 10% aqueous formic acid solution was added to inactivate trypsin and precipitate DOC. The sample was centrifuged at 21,000 g for 10 min, and 20 µl of the supernatant was transferred to a new tube. At this stage, the sample was ready for mass-spectrometric analysis. 4.2. Targeted proteomic analysis Targeted quantitative liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) analysis was carried out using synthetic stable-isotope-labeled internal standards (SIS) and natural synthetic proteotypic peptides (NAT) for measuring the corresponding proteins in DBS. The selected 119 SIS and NAT synthetic peptides had been previously validated for use in LC-MRM MS experiments for blood plasma [ 20 , 21 ]. The SIS peptide mixture was spiked in each sample at a balanced concentration, which was optimized in experiments with a dilution of a series of samples. Standard curves were generated using NAT and SIS peptide standards with BSA as a surrogate matrix, as previously described in detail for blood plasma analysis [ 20 ]. All samples were analyzed in duplicate on a LC-MS system consisting of an ExionLC™ UHPLC system (Thermo Fisher Scientific, USA) coupled online with a SCIEX QTRAP 6500 + triple-quadrupole mass spectrometer (SCIEX, Toronto, ON, Canada). LC-MS parameters, such as the LC gradient and MRM parameters (Q1/Q3 masses), were adapted and optimized based on previous studies [ 20 ]. The loaded sample volume was 10 µL per injection. HPLC separation was carried out using an Acquity UPLC Peptide BEH column (С18, 300 Å, 1.7 µm, 2.1 mm × 150 mm, 1/pkg) (Waters, USA) with gradient elution. Mobile phase A was 0.1% FA in water; mobile phase B was 0.1% FA in acetonitrile. LC separation was performed at a flow rate of 0.4 mL/min using a 53 min gradient from 2 to 45% of mobile phase B. Mass spectrometric measurements were carried out using the MRM acquisition method. The electrospray ionization (ESI) source settings were as follows: ion spray voltage 4000 V, temperature 450 °С, ion source gas 40 L/min. For quantitative analysis of the raw LC-MS/MS data Skyline Quantitative Analysis software (version 20.2.0.343, University of Washington) was used. To calculate the peptide concentrations in the measured samples, calibration curves were generated using the 1/(x × x)-weighted linear regression method. 4.3. Data Analysis Statistical analysis and data visualization were performed on Python (3.7.3) with the following packages: SciPy [ 27 ], Seaborn [ 28 ], Matplotlib [ 29 ], and Pandas [ 30 ]. Significant differences in protein concentrations between sample groups were estimated using the Mann–Whitney U-test and Cohen's d-size effect. Only proteins identified in ≥ 70% of samples of any group were considered for statistical analysis (Supplementary Table S1 ). Since missing values often represent low abundant measurements, the “Nan” values were filled with a Gaussian distribution, with parameters of shift down = 0.4 and width = 0.2 of the mean value for each group. In order to identify protein biomarkers of physiological stress related to landing and takeoff, all samples were divided into two groups: group1 – represented the “stress” state (samples taken immediately after takeoff and landing); group 2 – represented the “calm” state (samples taken at maximum relaxation both in space and on the ground: before the flight, after 6 months of SF and the last sample in remission after the landing). Kendall's tau correlation coefficient was used to evaluate the correlation between features. A Uniform Manifold Approximation and Projection (UMAP) was used as a dimension reduction technique to visualize patterns of clustering in DBS proteomic data [ 31 ]. Protein-protein interactions were analyzed using the STRING database [ 32 ]. The interactions included physical and functional associations derived from computational prediction, automated text mining, co-expression databases and genomic context prediction aggregated from other databases. As a result, STRING‐generated network images was constructed using the spring model. Each association had a score that was derived from the P value indicating the enrichment of similar processes, functions, etc. Only associations with P < .05 were included in the final networks. Protein categorical annotations were derived from GeneOntology via the SwissProt database. Declarations Competing interests: All authors declare no financial or non-financial competing interests. Author Contributions: Conceptualization, E.N.N., A.S.K. and I.M.L.; methodology, A.G.B., S.S., D.N.K., A.E.B., M.I.I., L.K.P. and A.S.K.; software, A.G.B., M.I.I. and S.S.; formal analysis, A.G.B., M.I.I., S.S. and A.S.K.; investigation, A.G.B., A.S.K., S.S., M.I.I., D.N.K. and L.K.P.; resources, E.N.N, O.I.O., L.K.P. and I.M.L.; funding acquisition, E.N.N., O.I.O. and I.M.L.; project administration, E.N.N., A.S.K., L.K.P., O.I.O. and I.M.L.; writing—original draft preparation, A.G.B., S.S., D.N.K. and A.S.K.; writing—review and editing, E.N.N., M.I.I. and I.M.L. All authors have read and agreed to the published version of the manuscript. Acknowledgements: The work was carried out within the framework of the basic theme of RAS 64.1, 65.3 for 2013–2024. In part of sample preparation and data analysis A.E.B, M.I.I. work in the frame of the basic theme of RAS 44.2 and 44.4. Data Availability: All data generated or analyzed during this study are included in this article and Supplementary Materials. All relevant data are available from the authors. References Yatagai, F.; Honma, M.; Dohmae, N.; Ishioka, N. Biological Effects of Space Environmental Factors: A Possible Interaction between Space Radiation and Microgravity. Life Sci. Sp. Res. 2019, 20 , 113–123, doi: 10.1016/j.lssr.2018.10.004 . Williams, D.; Kuipers, A.; Mukai, C.; Thirsk, R. Acclimation during Space Flight: Effects on Human Physiology. CMAJ 2009, 180 , 1317–1323, doi: 10.1503/cmaj.090628 . Blottner, D.; Moriggi, M.; Trautmann, G.; Hastermann, M.; Capitanio, D.; Torretta, E.; Block, K.; Rittweger, J.; Limper, U.; Gelfi, C.; et al. Space Omics and Tissue Response in Astronaut Skeletal Muscle after Short and Long Duration Missions. Int. J. Mol. Sci. 2023, 24 , 1–25, doi: 10.3390/ijms24044095 . Martin, D.; Makedonas, G.; Crucian, B.; Peanlikhit, T.; Rithidech, K. The Use of the Multidimensional Protein Identification Technology (MudPIT) to Analyze Plasma Proteome of Astronauts Collected before, during, and after Spaceflights. Acta Astronaut. 2022, 193 , 9–19, doi: 10.1016/j.actaastro.2021.12.054 . Overbey, E.G.; Ryon, K.; Kim, J.K.; Tierney, B.T.; Klotz, R.; Ortiz, V.; Mullane, S.; Schmidt, J.C.; MacKay, M.; Damle, N.; et al. Collection of Biospecimens from the Inspiration4 Mission Establishes the Standards for the Space Omics and Medical Atlas (SOMA). Nat. Commun. 2024, 15 , 1–14, doi: 10.1038/s41467-024-48806-z . Overbey, E.G.; Ryon, K.; Kim, J.; Tierney, B.; Klotz, R.; Ortiz, V.; Mullane, S.; Schmidt, J.C.; MacKay, M.; Damle, N.; et al. Collection of Biospecimens from the Inspiration4 Mission Establishes the Standards for the Space Omics and Medical Atlas (SOMA). bioRxiv 2023, 2023.05.02.539108. Luxton, J.J.; McKenna, M.J.; Lewis, A.; Taylor, L.E.; George, K.A.; Dixit, S.M.; Moniz, M.; Benegas, W.; Mackay, M.J.; Mozsary, C.; et al. Telomere Length Dynamics and DNA Damage Responses Associated with Long-Duration Spaceflight. Cell Rep. 2020, 33 , 108457, doi: 10.1016/j.celrep.2020.108457 . Stroud, J.E.; Gale, M.S.; Zwart, S.R.; Heer, M.; Smith, S.M.; Montina, T.; Metz, G.A.S. Longitudinal Metabolomic Profiles Reveal Sex-Specific Adjustments to Long-Duration Spaceflight and Return to Earth. Cell. Mol. Life Sci. 2022, 79 , 1–18, doi: 10.1007/s00018-022-04566-x . Garrett-Bakelman, F.E.; Darshi, M.; Green, S.J.; Gur, R.C.; Lin, L.; Macias, B.R.; McKenna, M.J.; Meydan, C.; Mishra, T.; Nasrini, J.; et al. The NASA Twins Study: A Multidimensional Analysis of a Year-Long Human Spaceflight. Science (80-.). 2019, 364 , 1–52, doi: 10.1126/science.aau8650 . Gertz, M.L.; Chin, C.R.; Tomoiaga, D.; MacKay, M.; Chang, C.; Butler, D.; Afshinnekoo, E.; Bezdan, D.; Schmidt, M.A.; Mozsary, C.; et al. Multi-Omic, Single-Cell, and Biochemical Profiles of Astronauts Guide Pharmacological Strategies for Returning to Gravity. Cell Rep. 2020, 33 , doi: 10.1016/j.celrep.2020.108429 . Lehmann, S.; Delaby, C.; Vialaret, J.; Ducos, J.; Hirtz, C. Current and Future Use of “Dried Blood Spot” Analyses in Clinical Chemistry. Clin. Chem. Lab. Med. 2013, 51 , 1897–1909, doi: 10.1515/cclm-2013-0228 . Eshghi, A.; Pistawka, A.J.; Liu, J.; Chen, M.; Sinclair, N.J.T.; Hardie, D.B.; Elliott, M.; Chen, L.; Newman, R.; Mohammed, Y.; et al. Concentration Determination of > 200 Proteins in Dried Blood Spots for Biomarker Discovery and Validation. Mol. Cell. Proteomics 2020, 19 , 540–553, doi: 10.1074/mcp.TIR119.001820 . Lim, M.D. Review Article: Dried Blood Spots for Global Health Diagnostics and Surveillance: Opportunities and Challenges. Am. J. Trop. Med. Hyg. 2018, 99 , 256–265, doi: 10.4269/ajtmh.17-0889 . Freeman, J.D.; Rosman, L.M.; Ratcliff, J.D.; Strickland, P.T.; Graham, D.R.; Silbergeld, E.K. State of the Science in Dried Blood Spots. Clin. Chem. 2018, 64 , 656–679, doi: 10.1373/clinchem.2017.275966 . Whelan, S.A.; Hendricks, N.; Dwight, Z.L.; Fu, Q.; Moradian, A.; Van Eyk, J.E.; Mockus, S.M. Assessment of a 60-Biomarker Health Surveillance Panel (HSP) on Whole Blood from Remote Sampling Devices by Targeted LC/MRM-MS and Discovery DIA-MS Analysis. Anal. Chem. 2023, doi: 10.1021/acs.analchem.3c01189 . Kashirina, D.N.; Brzhozovskiy, A.G.; Sun, W.; Pastushkova, L.K.; Popova, O. V.; Rusanov, V.B.; Nikolaev, E.N.; Larina, I.M.; Kononikhin, A.S. Proteomic Characterization of Dry Blood Spots of Healthy Women During Simulation the Microgravity Effects Using Dry Immersion. Front. Physiol. 2022, 12 , 1–7, doi: 10.3389/fphys.2021.753291 . Brzhozovskiy, A.G.; Kononikhin, A.S.; Pastushkova, L.C.; Kashirina, D.N.; Indeykina, M.I.; Popov, I.A.; Custaud, M.A.; Larina, I.M.; Nikolaev, E.N. The Effects of Spaceflight Factors on the Human Plasma Proteome, Including Both Real Space Missions and Ground-Based Experiments. Int. J. Mol. Sci. 2019, 20 , doi: 10.3390/ijms20133194 . Larina, I.M.; Percy, A.J.; Yang, J.; Borchers, C.H.; Nosovsky, A.M.; Grigoriev, A.I.; Nikolaev, E.N. Protein Expression Changes Caused by Spaceflight as Measured for 18 Russian Cosmonauts. Sci. Rep. 2017, 7 , 1–7, doi: 10.1038/s41598-017-08432-w . Houerbi, N.; Kim, J.K.; Overbey, E.G.; Batra, R.; Schweickart, A.; Patras, L.; Lucotti, S.; Ryon, K.A.; Najjar, D.; Meydan, C.; et al. Secretome Profiling Reveals Acute Changes in Oxidative Stress, Brain Homeostasis, and Coagulation Following Short-Duration Spaceflight. Nat. Commun. 2024, 15 , 1–17, doi: 10.1038/s41467-024-48841-w . Gaither, C.; Popp, R.; Mohammed, Y.; Borchers, C.H. Determination of the Concentration Range for 267 Proteins from 21 Lots of Commercial Human Plasma Using Highly Multiplexed Multiple Reaction Monitoring Mass Spectrometry. Analyst 2020, 145 , 3634–3644, doi: 10.1039/c9an01893j . Gaither, C.; Popp, R.; Zahedi, R.P.; Borchers, C.H. Multiple Reaction Monitoring-Mass Spectrometry Enables Robust Quantitation of Plasma Proteins Regardless of Whole Blood Processing Delays That May Occur in the Clinic. Mol. Cell. Proteomics 2022, 21 , 0–11, doi: 10.1016/j.mcpro.2022.100212 . Whiteaker, J.R.; Halusa, G.N.; Hoofnagle, A.N.; Sharma, V.; MacLean, B.; Yan, P.; Wrobel, J.A.; Kennedy, J.; Mani, D.R.; Zimmerman, L.J.; et al. CPTAC Assay Portal: A Repository of Targeted Proteomic Assays. Nat. Methods 2014, 11 , 703–704, doi: 10.1038/nmeth.3002 . Mohammadzadeha, N.; Arne, M.O.; Palmeroa, S.; Sjaastada, I.; Chakravartic, S.; Engebretsene, K.V.T.; Christensena, G.; Lundea, I.G.; Tønnessena, T. Moderate Loss of the Extracellular Matrix Proteoglycan Lumican Attenuates Cardiac Fibrosis in Mice Subjected to Pressure Overload. Cardiology 2020, 145 , 187–198, doi: 10.1159/000505318.Moderate . Strollo, F.; Gentile, S.; Pipicelli, A.M. V.; Mambro, A.; Monici, M.; Magni, P. Space Flight-Promoted Insulin Resistance as a Possible Disruptor of Wound Healing. Front. Bioeng. Biotechnol. 2022, 10 , 1–8, doi: 10.3389/fbioe.2022.868999 . Smith, S.R.; Bai, F.; Charbonneau, C.; Janderová, L.; Argyropoulos, G. A Promoter Genotype and Oxidative Stress Potentially Link Resistin to Human Insulin Resistance. Diabetes 2003, 52 , 1611–1618, doi: 10.2337/diabetes.52.7.1611 . Ranke, M.B. Insulin-like Growth Factor Binding-Protein-3 (IGFBP-3). Best Pract. Res. Clin. Endocrinol. Metab. 2015, 29 , 701–711, doi: 10.1016/j.beem.2015.06.003 . Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods 2020, 17 , 261–272, doi: 10.1038/s41592-019-0686-2 . Waskom, M. Seaborn: Statistical Data Visualization. J. Open Source Softw. 2021, 6 , 3021, doi: 10.21105/joss.03021 . Hunter, J.D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 2007, 9 , 90–95, doi: 10.1109/MCSE.2007.55 . McKinney, W. Data Structures for Statistical Computing in Python. Proc. 9th Python Sci. Conf. 2010, 1 , 56–61, doi: 10.25080/majora-92bf1922-00a . McInnes, L.; Healy, J.; Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. 2018. Mering, C. von; Huynen, M.; Jaeggi, D.; Schmidt, S.; Bork, P.; Snel, B. STRING: A Database of Predicted Functional Associations between Proteins. Nucleic Acids Res. 2003, 31 , 258–261, doi: 10.1093/nar/gkg034 . Additional Declarations (Not answered) Supplementary Files XnewsupplTablesS1S5.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4694174","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":324773596,"identity":"e4eaffcd-4c83-437b-b414-2e07635f244e","order_by":0,"name":"Irina Larina","email":"","orcid":"","institution":"Institute of Biomedical Problems, Russian Federation State Scientific Research Center, Russian Academy of Sciences, 119991 Moscow, Russia","correspondingAuthor":false,"prefix":"","firstName":"Irina","middleName":"","lastName":"Larina","suffix":""},{"id":324773595,"identity":"691bd60e-2df3-42e7-9c6a-b11d5ff00649","order_by":1,"name":"Alexey Kononikhin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3RsWrDMBCA4QuBy3KtVxm5zis4CEqHQF7FwuAuCQSyeDQErlN3Q1/GQdAseYAMIRAMnguG0sFD7WwdZDx20D8IBPqQDgG4XP8yvK8kxCSHL4jhsdtNR5NJ0RHEkQSE6E7SGOLlqKttdgn8jz1XyzYNcb4/NFu46txCRIlGFaeaZHB4UxteK0RMZAE7K4FyxvKBDYVCs9zkmWak5/6FVjL/Q17annjfgyQq8fNOZE8A1/0tOEgWBhNFJ0N+odl/57SbJVWSoljZSHjkRUWZWYnzay1+2iT0pubWUBY/Wce3fEFkO+9yuVyuMf0CoVtJIUD/KRYAAAAASUVORK5CYII=","orcid":"","institution":"Skolkovo Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Alexey","middleName":"","lastName":"Kononikhin","suffix":""},{"id":324773597,"identity":"116eb61f-0cd1-4638-86c1-cc80cfcdf85f","order_by":2,"name":"Alexander Brzhozovskiy","email":"","orcid":"","institution":"Skolkovo Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Brzhozovskiy","suffix":""},{"id":324773598,"identity":"ea2e32c8-c617-49be-9c8a-261cebb9a31c","order_by":3,"name":"Savva Semenov","email":"","orcid":"","institution":"Moscow Institute of Physics and Technology","correspondingAuthor":false,"prefix":"","firstName":"Savva","middleName":"","lastName":"Semenov","suffix":""},{"id":324773599,"identity":"58090ae7-4c44-449a-a5fb-9792f5402b84","order_by":4,"name":"Daria Kashirina","email":"","orcid":"","institution":"Institute of Biomedical Problems, Russian Federation State Scientific Research Center, Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Daria","middleName":"","lastName":"Kashirina","suffix":""},{"id":324773600,"identity":"d9280f80-1f21-4f6b-8815-c530583c9343","order_by":5,"name":"Maria Indeykina","email":"","orcid":"","institution":"Emanuel Institute of Biochemical Physics, Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Indeykina","suffix":""},{"id":324773601,"identity":"b47d5b02-0ccc-44a9-9212-c21e47539cbf","order_by":6,"name":"Anna Bugrova","email":"","orcid":"","institution":"Emanuel Institute of Biochemical Physics, Russian Academy of Sciencesr","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Bugrova","suffix":""},{"id":324773602,"identity":"cd3049d4-8ff7-484c-bb14-c2c6475a41a7","order_by":7,"name":"Lyudmila Pastushkova","email":"","orcid":"","institution":"Institute of Biomedical Problems, Russian Federation State Scientific Research Center, Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lyudmila","middleName":"","lastName":"Pastushkova","suffix":""},{"id":324773603,"identity":"e4af8591-adc4-484c-959a-bec5d68ba847","order_by":8,"name":"Oleg Orlov","email":"","orcid":"","institution":"Institute of Biomedical Problems, Russian Federation State Scientific Research Center, Russian Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Oleg","middleName":"","lastName":"Orlov","suffix":""},{"id":324773604,"identity":"e1b94566-4329-4e45-8cca-6f658c9f799f","order_by":9,"name":"Evgeny Nikolaev","email":"","orcid":"","institution":"Skolkovo Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Evgeny","middleName":"","lastName":"Nikolaev","suffix":""}],"badges":[],"createdAt":"2024-07-05 20:55:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4694174/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4694174/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60833796,"identity":"99954b27-3336-467f-8bb6-9ec160ed6d79","added_by":"auto","created_at":"2024-07-22 15:28:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":553948,"visible":true,"origin":"","legend":"\u003cp\u003e(I) Blood collection procedure: a) puncture of the fourth finger with a scarifier; b) sufficient amount of blood for collection of 40 µl of capillary blood; c) placement of 40 µl of capillary blood on Whatman paper; (II) Space package «OMICs-DBS»: a) a «common part» of the package contains pipet, filter holders and tweezers; b) «personal part» contains individual filters, pipet tips, sterile alcohol-free tissues, scarifiers, adhesive plasters and waste bags; с) «descent part» contains dry blood samples.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4694174/v1/1e0a581578f2ee6d319e5290.png"},{"id":60833797,"identity":"6e97f3a2-d17f-4ec9-9339-d7feb257e953","added_by":"auto","created_at":"2024-07-22 15:28:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140214,"visible":true,"origin":"","legend":"\u003cp\u003eClusterization by Uniform Manifold Approximation and Projection (UMAP) of proteomic data for all 58 DBS samples collected during the spaceflight (SF) from 7 cosmonauts (C1-C7). Colored dots depict the DBS samples for a particular cosmonaut: C1 - blue ; C2- yellow; C3- green; C4- red; C5- brown; C6 – violet; C7 – grey.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4694174/v1/e2b93c362f5503d513f9372d.png"},{"id":60833800,"identity":"6a5b1b3e-7b12-4ad7-89f6-f7bfd1b24112","added_by":"auto","created_at":"2024-07-22 15:28:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":329096,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Separation of proteomic data for 58 DBS samples from 7 cosmonauts into groups using the UMAP method: group1 – represents the“stress” state (DBS samples taken immediately after takeoff and landing); group 2 – represents the “calm” state (DBS samples taken at maximum relaxation both in space and on the ground: before the flight, 6 months into the flight and the last sample in remission after landing)); (B) Volcano plot representing the results of the Mann-Whitney tests groups and Cohen's d size effect. The difference for each protein was plotted against the −log10 of the P value and Cohen's d size.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4694174/v1/367cc047b450828a0e5e3df1.png"},{"id":60833798,"identity":"8f2ddd1f-5583-4840-a310-8855f3c6ff2f","added_by":"auto","created_at":"2024-07-22 15:28:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":379143,"visible":true,"origin":"","legend":"\u003cp\u003eDynamics of the concentration (C(t)) changes during relaxation after stress factors (takeoff – Flight and Landing) is presented for all cosmonauts (C1-C7) during space flight for two up-regulated proteins: (A),(B) – Fibulin-1; (C),(D) - Lumican. Grey background color represents the 95% confidence interval (CI).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4694174/v1/41764361d52b1cf9ad428fb3.png"},{"id":64276656,"identity":"a013c535-1682-452e-a308-41c2d8639d46","added_by":"auto","created_at":"2024-09-11 06:45:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2275963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4694174/v1/2a14daf0-826a-4718-a03b-04803d56f1d7.pdf"},{"id":60833801,"identity":"caa3ab21-8f65-4186-b6d1-6a5eb5822bc0","added_by":"auto","created_at":"2024-07-22 15:28:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":465348,"visible":true,"origin":"","legend":"","description":"","filename":"XnewsupplTablesS1S5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4694174/v1/01bccf9c8c8ba53e8131ffcc.xlsx"}],"financialInterests":"(Not answered)","formattedTitle":"Targeted proteomic analysis of dry blood spot samples collected during long-term spaceflights","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSpaceflight (SF) is one of the most extreme conditions and is resembled by a complex of factors, such as microgravity, extreme acceleration and cosmic radiation, which affect many physiological changes in the human organism [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The molecular mechanisms of adaptation to SF conditions are not fully investigated and are important to study at all stages of long-duration missions on the International Space Station (ISS) [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Due to some technical, logistical and economical limitations (including those caused by weightlessness), the identification of changes occurring in the organism onboard of the ISS remains a difficult task [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMulti-omics technologies may provide new information at the molecular level regarding organism adaptation to SF conditions including changes in expression profiles of DNA and RNA [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], concentration profiles of proteins and metabolites [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The NASA multi-omics project collected 317 samples from twin astronauts (one of them spent a year on the ISS, while the other remained on Earth), and identified proteins that could be associated with observed changes in vascular wall dimensions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This study has paved the way for future multi-omics studies, including those at the single-cell level, and has provided the first comprehensive molecular profile of an astronaut [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The proper collection and storage of biomaterial during SF is an important aspect of aerospace medicine, and has been given significant emphasis in the frame of new Space Omics and Medical Atlas (SOMA) initiative [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the relevant methods available is the dried blood spots (DBS) micro-sampling technique which is useful for various research studies and wide-population screenings [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. DBS micro-sampling acquires a very small amount of blood (~\u0026thinsp;20\u0026micro;l) and can be self-collected via a non-invasive finger capillary puncture. The tool kit for this procedure is simple, small, and easy to use onboard. Sheets of Whatman paper used as DBS sample holders can be periodically delivered by transport shuttles to Earth for DBS analysis in the lab. DBS samples are stable under standard environmental conditions for many years and contain thousands of analytes that can be re-eluted for subsequent analysis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnalysis of the human blood proteome is challenging because almost all proteins synthesized in the human body eventually turn up in plasma and the dynamic range of protein concentrations in it is about eleven orders of magnitude. Liquid chromatography coupled to mass spectrometry (LC-MS) is capable for reproducibly quantifying up to 700 proteins in DBS samples, with a concentration range that spans four orders of magnitude [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Targeted quantitative proteomic analysis revealed that the concentrations of 190 proteins were stable in DBS samples stored up to 2 months at ambient temperature [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, DBS can be considered as a valuable object for monitoring physiological changes at the molecular level under extreme conditions and is available for collection even during SF as it is suitable for long-term storage at room temperature.\u003c/p\u003e \u003cp\u003eA deep blood proteome analysis can facilitate the discovery of new data and mechanisms of human adaptive response to SF conditions. Mass-spectrometry-based proteomic techniques allow to detect alterations caused by SF. In a recent study initiated by NASA the first plasma samples for proteomic analysis were collected during SF from 4 astronauts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Multidimensional Protein Identification Technology (MudPIT) was successfully applied and revealed 19 proteins whose expression was significantly altered due to SF. Further, GO analysis revealed that these proteins are involved in inflammatory responses, the cytoskeleton system, and metabolism that potentially have functional roles in response to SF or re-adaptation to Earth environment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Our group has previously demonstrated changes in the proteome composition after long-duration (169\u0026ndash;199 days) missions on the ISS, but only samples collected before and after SF were analyzed [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As spaceflight becomes more common with commercial crews, blood-based measures become actual for health monitoring during short-duration spaceflight and the first study already performed within the SpaceX Inspiration4 (i4) mission [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe aim of the current study was to apply the method of blood proteome analysis with DBS micro-sampling technique to study the physiological response to SF conditions at the molecular level. Targeted mass-spectrometry based proteomics with a validated assay of stable isotope-labeled peptide standard (SIS) for 119 blood proteins was selected as a robust and precise tool for quantitative analysis. We considered the proteins and corresponding peptide panel from the BAK270 MRM assay [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The assay was developed for analysis of potential protein biomarkers for cardiovascular diseases, including 61 FDA-approved biomarkers in blood plasma. The robustness of the MRM assay for selected blood proteins was recently demonstrated [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Finally, a targeted proteomic analysis of 58 DBS samples collected from 7 cosmonauts during long-duration SF (169\u0026ndash;199 days) was performed. To our knowledge this is the first DBS proteomic study for monitoring the adaptive reactions of cosmonauts during long-term SF.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDBS samples were collected from 7 cosmonauts (male, age: 47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 years) before, during and after long-duration (169\u0026ndash;199 days; one of them participated in a year-long mission) missions on the ISS. More details of samples collection time-points (up to 10) from each cosmonaut is specified in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All subjects provided written informed consent to participate in the \u0026lsquo;\u0026lsquo;OMICs-DBS\u0026rsquo;\u0026rsquo; experiment. The study was approved (Code \u0026laquo;OMICs-DBS\u0026raquo;, dated 06 March 2020) by the Ethics Committee of the Institute of Biomedical Problems, Russian Academy of Sciences/Physiology Section of the Russian Bioethics Committee Russian Federation National Commission for UNESCO and Human Research Multilateral Review Board, NASA, Houston, TX, USA.\u003c/p\u003e\u003c/div\u003e\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\u003eThe time points for 58 DBS samples collected from 7 cosmonauts (C1-C7) during long-duration (169\u0026ndash;199 days) spaceflight (SF).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTime points during SF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e7 cosmonauts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eBefore spaceflight (B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(B1) \u0026minus;\u0026thinsp;45 days before spaceflight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(B2) \u0026minus;\u0026thinsp;15 days before spaceflight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDuring spaceflight (SF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(SF0) - after 5\u0026ndash;7 days\u003c/p\u003e \u003cp\u003eonboard the ISS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(SF3) \u0026minus;\u0026thinsp;3 months of SF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(SF6) \u0026minus;\u0026thinsp;6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(SF9) \u0026minus;\u0026thinsp;9 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(SF12)* -12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eRemission (R) after landing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(R\u0026thinsp;+\u0026thinsp;1) \u0026ndash; 1st day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(R\u0026thinsp;+\u0026thinsp;7) \u0026minus;\u0026thinsp;7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(R\u0026thinsp;+\u0026thinsp;14) \u0026minus;\u0026thinsp;14 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(R\u0026thinsp;+\u0026thinsp;26) \u0026minus;\u0026thinsp;26 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*- The last sample for each cosmonaut was collected 2 days before landing;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCapillary blood was obtained by puncturing the phalanx of the fourth finger with an automatic scarifier. To normalize blood volume an automatic pipette was used to collect 40 \u0026micro;l of blood and place it on filter paper for drying. After collection, the blood stains were dried on filters at ambient temperature (19\u0026ndash;26\u0026deg;C) for 2\u0026ndash;3 h and then placed in a zip-lock bag (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Capillary blood samples collection was carried out on an empty stomach in the morning. Filters were returned to Earth onboard the Russian Federation\u0026rsquo;s Soyuz spacecraft.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Proteomic analysis\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTargeted proteomic analysis was performed for 58 DBS samples collected from 7 Russian cosmonauts during long-duration SF (see details in Materials and Methods\u0026mdash;Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e). In our study, we used the approach for targeted analysis of more than 200 proteins in DBS by liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We used the MRM assays that includes stable-isotope-labeled internal standards (SIS) and natural (NAT) synthetic proteotypic peptides for measuring the concentrations of corresponding 200 proteins in the blood. These assays have been developed for revealing non-communicable diseases and include medium-to-low abundance plasma proteins associated with this type of diseases. All MRM assays were previously characterized according to the guidelines of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLC-MRM MS analysis revealed 119 proteins that were quantified in \u0026ge;\u0026thinsp;2/3 of the analyzed samples in total (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Statistical analysis revealed that the proteomic data was heterogeneous, as we could not find any proteins that were present in all cosmonauts and had significant concentration variations during SF stages. Instead, stronger clustering of experimental data was observed for individual cosmonauts, rather than for SF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn order to identify protein biomarkers of physiological stress related to landing and takeoff, all samples were divided into two groups: group1 \u0026ndash; represented the \u0026ldquo;stress\u0026rdquo; state (samples taken immediately after takeoff and landing); group 2 \u0026ndash; represented the \u0026ldquo;calm\u0026rdquo; state (samples taken at maximum relaxation both in space and on the ground: before the flight, 6 months into the flight and the last sample in remission after landing). We used the Mann-Whitney test to compare protein concentrations between these groups and Cohen's d-size effect (Table S2). We considered changes in protein concentration to be statistically significant if the p-value for them was less than 0.05, and Cohen's d size\u0026thinsp;\u0026gt;\u0026thinsp;0.8 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe list of the proteins significantly changed (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Cohen's d size\u0026thinsp;\u0026gt;\u0026thinsp;0.8) due to stress conditions (takeoff and landing).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein Names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCohen's d-size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutathione peroxidase 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibulin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc-alpha-2-glycoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin-like growth factor-binding protein 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein C-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeroxiredoxin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein_deglycase_DJ-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein A-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA panel of 10 proteins found to be associated with takeoff and landing demonstrated a clearer separation of proteomic data for 58 DBS samples from 7 cosmonauts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The most significantly changed proteins are presented on the Volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Dynamic changes in protein concentrations during SF\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn order to identify the stress marker proteins, we analyzed the dynamic changes in protein concentrations during SF. The aim was to find proteins whose concentration changes were monotonous after exposure (takeoff or landing). Then such proteins were considered as stress markers.\u003c/p\u003e \u003cp\u003eWe proposed the exponential decay model for protein concentration relaxation after a stress factor (takeoff and landing), which can be expressed using a formula:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:C\\left(t\\right)\\:\\:=\\:Bexp(-At),\\:$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is observable protein concentration and t is the time after the introduction of the stress factor, B is a stress response constant, A is the decay constant.\u003c/p\u003e \u003cp\u003eIn order to calculate the constants A and B for each protein, we used a linear regression of the linearized data:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:ln\\left[C\\right(t\\left)\\right]\\:=\\:lnB\\:-At$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn order to determine the monotonicity of the obtained dependence, we used the Kendall 's tau coefficient (Table S3-S4).\u003c/p\u003e \u003cp\u003eAs a result 15 proteins (Kendall p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) showed monotonous concentration relaxation after such stress factors as takeoff and landing (Table S3-S4).\u003c/p\u003e \u003cp\u003eAs an example, a characteristic picture of the dynamic for two up-regulated proteins (Fibulin-1 and Lumican) is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eQuantitative analysis of blood proteins is not an easy task, and in-depth analysis of a wide panel of proteins using mass- spectrometric methods requires additional efforts and should rely on well-validated reproducible protocols [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. There are several strategies that are recommended for blood proteomics and clinical studies. We followed protocols and recommendations for absolute concentration measurements of more than 200 proteins in plasma/serum and DBS which was proposed by the Prof. Borcher\u0026rsquo;s team [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Finally, 119 proteins were quantified by LC-MRM MS in \u0026ge;\u0026thinsp;2/3 of 58 DBS samples collected from 7 Russian cosmonauts during long-duration SF. Further analysis of the data revealed its heterogeneity and different proteins had different dynamics of concentration changes caused by SF. To identify proteins whose concentrations are sensitive to the high physiological stress of landing and takeoff the Mann-Whitney and Cohen's d-size effect tests were used. As a result, 10 significant proteins (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Cohen's d size\u0026thinsp;\u0026gt;\u0026thinsp;0.8) were found. In order to determine additional stress marker proteins, the dynamic changes in protein concentration during SF were analyzed to find proteins whose concentrations change monotonously after stress exposure (takeoff or landing). A simple exponential decay model for protein concentration relaxation was proposed which allowed to reveal additional 14 proteins. Intersection between both protein panels mentioned above resulted in a final list of 19 blood proteins affected by SF factors such as takeoff and landing. GO-database annotation and database search showed that these proteins are involved in the inflammatory response, metabolism and oxidative stress [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Two proteins important for the organization of the extracellular matrix, Lumican and Fibulin-1, were positively regulated by stress. Lumican expression is known to be increased in clinical and experimental heart failure, during which a pressure overload occurs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It is possible that the overloads during takeoff and landing contribute to the increase in the blood level of these proteins. Negative regulation by stress has been observed for proteins associated with lipid and carbohydrate metabolism: Apolipoprotein F, Zinc-alpha-2-glycoprotein. Apolipoprotein F is one of the most important regulators of cholesterol transport, inhibiting cholesteryl ester transfer protein (CETP) activity. Zinc-alpha-2-glycoprotein stimulates lipid degradation in adipocytes. During SF, especially prolonged, exposure to microgravity leads to a number of pathophysiological changes, among which are metabolic changes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Apparently, stress during takeoff and landing makes its own adjustments to metabolic processes, and it possibly even changes insulin sensitivity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], but more thorough research is needed. As a result of stress, Insulin-like growth factor-binding protein 3 (IGFBP-3) also decreased, which, through interaction with IGF, regulates cellular processes such as cell proliferation, apoptosis, and differentiation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Also negative regulation by stress was observed for proteins that protect cells from oxidative stress by detoxifying peroxides. Two proteins such as Glutathione peroxidase 3 and Peroxiredoxin-1 were down regulated, what can reduce cell resistance to oxidative stress and have negative consequences for the body, especially in SF conditions.\u003c/p\u003e \u003cp\u003eDespite the limitations of our study such as a relatively small number of samples and a restricted panel of proteins for analysis, however, we were able to identify proteins whose concentrations significantly change under the influence of SF factors. Quantification of more proteins and more sample collections is an actual task for future research and validation of current results. Recently it was demonstrated using a 60-Biomarker Health Surveillance Panel (HSP) that the majority of proteins have a relatively similar intensity in capillary blood, venous blood, and matched plasma [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In this view, one may use complementary collection devices available for blood tests. The last thing that is important to note is that two plasma proteins (lumican and insulin-like protein binding growth factor 3) which were identified as related to SF in our study with DBS samples were already confirmed in previous studies with plasma samples [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1. DBS sample preparation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDBS sample was excised and placed in a 1.5 ml polypropylene Eppendorf tube. Proteins were extracted with 1 ml of solution of 25 mM ammonium bicarbonate, 1% sodium deoxycholate, and 5 mM TCEP (tris (2-carboxyethyl) phosphine hydrochloride) (Thermo Scientific) at 60\u0026deg;C by vortexing at 1,000 rpm (Thermomixer, Eppendorf) for 1 h. For additional normalization the protein concentration was measured with the BCA assay (Thermo Scientific, Waltham, MA, USA).\u003c/p\u003e \u003cp\u003eSample preparation included reduction with 0.1 M dithiothreitol in 0.1 M Tris buffer (pH 8.5) containing 8 M urea for 30 min at 47\u0026deg;C, followed by alkylation with 0.05 M iodoacetate and incubation for 30 min in the dark at room temperature. Then precipitation was carried out with five volumes of acetone in the presence of 0.1% TFA at \u0026minus;\u0026thinsp;20\u0026deg;C overnight.\u003c/p\u003e \u003cp\u003eThe preparation procedure was completed by trypsinolysis in 0.05 M ammonium bicarbonate buffer: 100 \u0026micro;l of the buffer and 2 \u0026micro;l of trypsin solution at a concentration of 1 \u0026micro;g/\u0026micro;l in 50 mM acetic acid were added to the precipitated mixture of proteins. Incubation was carried out overnight in a thermomixer at 37\u0026deg;C with shaking at 750 rpm. Then, 1 \u0026micro;l of a 10% aqueous formic acid solution was added to inactivate trypsin and precipitate DOC. The sample was centrifuged at 21,000 g for 10 min, and 20 \u0026micro;l of the supernatant was transferred to a new tube. At this stage, the sample was ready for mass-spectrometric analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Targeted proteomic analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTargeted quantitative liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) analysis was carried out using synthetic stable-isotope-labeled internal standards (SIS) and natural synthetic proteotypic peptides (NAT) for measuring the corresponding proteins in DBS. The selected 119 SIS and NAT synthetic peptides had been previously validated for use in LC-MRM MS experiments for blood plasma [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The SIS peptide mixture was spiked in each sample at a balanced concentration, which was optimized in experiments with a dilution of a series of samples. Standard curves were generated using NAT and SIS peptide standards with BSA as a surrogate matrix, as previously described in detail for blood plasma analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll samples were analyzed in duplicate on a LC-MS system consisting of an ExionLC\u0026trade; UHPLC system (Thermo Fisher Scientific, USA) coupled online with a SCIEX QTRAP 6500\u0026thinsp;+\u0026thinsp;triple-quadrupole mass spectrometer (SCIEX, Toronto, ON, Canada). LC-MS parameters, such as the LC gradient and MRM parameters (Q1/Q3 masses), were adapted and optimized based on previous studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe loaded sample volume was 10 \u0026micro;L per injection. HPLC separation was carried out using an Acquity UPLC Peptide BEH column (С18, 300 \u0026Aring;, 1.7 \u0026micro;m, 2.1 mm \u0026times; 150 mm, 1/pkg) (Waters, USA) with gradient elution. Mobile phase A was 0.1% FA in water; mobile phase B was 0.1% FA in acetonitrile. LC separation was performed at a flow rate of 0.4 mL/min using a 53 min gradient from 2 to 45% of mobile phase B. Mass spectrometric measurements were carried out using the MRM acquisition method. The electrospray ionization (ESI) source settings were as follows: ion spray voltage 4000 V, temperature 450 \u0026deg;С, ion source gas 40 L/min.\u003c/p\u003e \u003cp\u003eFor quantitative analysis of the raw LC-MS/MS data Skyline Quantitative Analysis software (version 20.2.0.343, University of Washington) was used. To calculate the peptide concentrations in the measured samples, calibration curves were generated using the 1/(x \u0026times; x)-weighted linear regression method.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Data Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStatistical analysis and data visualization were performed on Python (3.7.3) with the following packages: SciPy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Seaborn [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Matplotlib [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and Pandas [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Significant differences in protein concentrations between sample groups were estimated using the Mann\u0026ndash;Whitney U-test and Cohen's d-size effect.\u003c/p\u003e \u003cp\u003eOnly proteins identified in \u0026ge;\u0026thinsp;70% of samples of any group were considered for statistical analysis (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Since missing values often represent low abundant measurements, the \u0026ldquo;Nan\u0026rdquo; values were filled with a Gaussian distribution, with parameters of shift down =\u0026thinsp;0.4 and width\u0026thinsp;=\u0026thinsp;0.2 of the mean value for each group.\u003c/p\u003e \u003cp\u003eIn order to identify protein biomarkers of physiological stress related to landing and takeoff, all samples were divided into two groups: group1 \u0026ndash; represented the \u0026ldquo;stress\u0026rdquo; state (samples taken immediately after takeoff and landing); group 2 \u0026ndash; represented the \u0026ldquo;calm\u0026rdquo; state (samples taken at maximum relaxation both in space and on the ground: before the flight, after 6 months of SF and the last sample in remission after the landing).\u003c/p\u003e \u003cp\u003eKendall's tau correlation coefficient was used to evaluate the correlation between features. A Uniform Manifold Approximation and Projection (UMAP) was used as a dimension reduction technique to visualize patterns of clustering in DBS proteomic data [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProtein-protein interactions were analyzed using the STRING database [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The interactions included physical and functional associations derived from computational prediction, automated text mining, co-expression databases and genomic context prediction aggregated from other databases. As a result, STRING‐generated network images was constructed using the spring model. Each association had a score that was derived from the P value indicating the enrichment of similar processes, functions, etc. Only associations with P\u0026thinsp;\u0026lt;\u0026thinsp;.05 were included in the final networks. Protein categorical annotations were derived from GeneOntology via the SwissProt database.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eConceptualization, E.N.N., A.S.K. and I.M.L.; methodology, A.G.B., S.S., D.N.K., A.E.B., M.I.I., L.K.P. and A.S.K.; software, A.G.B., M.I.I. and S.S.; formal analysis, A.G.B., M.I.I., S.S. and A.S.K.; investigation, A.G.B., A.S.K., S.S., M.I.I., D.N.K. and L.K.P.; resources, E.N.N, O.I.O., L.K.P. and I.M.L.; funding acquisition, E.N.N., O.I.O. and I.M.L.; project administration, E.N.N., A.S.K., L.K.P., O.I.O. and I.M.L.; writing\u0026mdash;original draft preparation, A.G.B., S.S., D.N.K. and A.S.K.; writing\u0026mdash;review and editing, E.N.N., M.I.I. and I.M.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe work was carried out within the framework of the basic theme of RAS 64.1, 65.3 for 2013\u0026ndash;2024. In part of sample preparation and data analysis A.E.B, M.I.I. work in the frame of the basic theme of RAS 44.2 and 44.4.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are included in this article and Supplementary Materials. All relevant data are available from the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYatagai, F.; Honma, M.; Dohmae, N.; Ishioka, N. Biological Effects of Space Environmental Factors: A Possible Interaction between Space Radiation and Microgravity. Life Sci. Sp. Res. 2019, \u003cem\u003e20\u003c/em\u003e, 113\u0026ndash;123, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lssr.2018.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.lssr.2018.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams, D.; Kuipers, A.; Mukai, C.; Thirsk, R. Acclimation during Space Flight: Effects on Human Physiology. CMAJ 2009, \u003cem\u003e180\u003c/em\u003e, 1317\u0026ndash;1323, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1503/cmaj.090628\u003c/span\u003e\u003cspan address=\"10.1503/cmaj.090628\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlottner, D.; Moriggi, M.; Trautmann, G.; Hastermann, M.; Capitanio, D.; Torretta, E.; Block, K.; Rittweger, J.; Limper, U.; Gelfi, C.; et al. Space Omics and Tissue Response in Astronaut Skeletal Muscle after Short and Long Duration Missions. Int. J. Mol. Sci. 2023, \u003cem\u003e24\u003c/em\u003e, 1\u0026ndash;25, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms24044095\u003c/span\u003e\u003cspan address=\"10.3390/ijms24044095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin, D.; Makedonas, G.; Crucian, B.; Peanlikhit, T.; Rithidech, K. The Use of the Multidimensional Protein Identification Technology (MudPIT) to Analyze Plasma Proteome of Astronauts Collected before, during, and after Spaceflights. Acta Astronaut. 2022, \u003cem\u003e193\u003c/em\u003e, 9\u0026ndash;19, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.actaastro.2021.12.054\u003c/span\u003e\u003cspan address=\"10.1016/j.actaastro.2021.12.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOverbey, E.G.; Ryon, K.; Kim, J.K.; Tierney, B.T.; Klotz, R.; Ortiz, V.; Mullane, S.; Schmidt, J.C.; MacKay, M.; Damle, N.; et al. Collection of Biospecimens from the Inspiration4 Mission Establishes the Standards for the Space Omics and Medical Atlas (SOMA). Nat. Commun. 2024, \u003cem\u003e15\u003c/em\u003e, 1\u0026ndash;14, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-024-48806-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-024-48806-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOverbey, E.G.; Ryon, K.; Kim, J.; Tierney, B.; Klotz, R.; Ortiz, V.; Mullane, S.; Schmidt, J.C.; MacKay, M.; Damle, N.; et al. Collection of Biospecimens from the Inspiration4 Mission Establishes the Standards for the Space Omics and Medical Atlas (SOMA). \u003cem\u003ebioRxiv\u003c/em\u003e 2023, 2023.05.02.539108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuxton, J.J.; McKenna, M.J.; Lewis, A.; Taylor, L.E.; George, K.A.; Dixit, S.M.; Moniz, M.; Benegas, W.; Mackay, M.J.; Mozsary, C.; et al. Telomere Length Dynamics and DNA Damage Responses Associated with Long-Duration Spaceflight. Cell Rep. 2020, \u003cem\u003e33\u003c/em\u003e, 108457, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.celrep.2020.108457\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2020.108457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStroud, J.E.; Gale, M.S.; Zwart, S.R.; Heer, M.; Smith, S.M.; Montina, T.; Metz, G.A.S. Longitudinal Metabolomic Profiles Reveal Sex-Specific Adjustments to Long-Duration Spaceflight and Return to Earth. Cell. Mol. Life Sci. 2022, \u003cem\u003e79\u003c/em\u003e, 1\u0026ndash;18, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00018-022-04566-x\u003c/span\u003e\u003cspan address=\"10.1007/s00018-022-04566-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarrett-Bakelman, F.E.; Darshi, M.; Green, S.J.; Gur, R.C.; Lin, L.; Macias, B.R.; McKenna, M.J.; Meydan, C.; Mishra, T.; Nasrini, J.; et al. The NASA Twins Study: A Multidimensional Analysis of a Year-Long Human Spaceflight. \u003cem\u003eScience (80-.).\u003c/em\u003e 2019, \u003cem\u003e364\u003c/em\u003e, 1\u0026ndash;52, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aau8650\u003c/span\u003e\u003cspan address=\"10.1126/science.aau8650\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGertz, M.L.; Chin, C.R.; Tomoiaga, D.; MacKay, M.; Chang, C.; Butler, D.; Afshinnekoo, E.; Bezdan, D.; Schmidt, M.A.; Mozsary, C.; et al. Multi-Omic, Single-Cell, and Biochemical Profiles of Astronauts Guide Pharmacological Strategies for Returning to Gravity. Cell Rep. 2020, \u003cem\u003e33\u003c/em\u003e, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.celrep.2020.108429\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2020.108429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehmann, S.; Delaby, C.; Vialaret, J.; Ducos, J.; Hirtz, C. Current and Future Use of \u0026ldquo;Dried Blood Spot\u0026rdquo; Analyses in Clinical Chemistry. Clin. Chem. Lab. Med. 2013, \u003cem\u003e51\u003c/em\u003e, 1897\u0026ndash;1909, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/cclm-2013-0228\u003c/span\u003e\u003cspan address=\"10.1515/cclm-2013-0228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEshghi, A.; Pistawka, A.J.; Liu, J.; Chen, M.; Sinclair, N.J.T.; Hardie, D.B.; Elliott, M.; Chen, L.; Newman, R.; Mohammed, Y.; et al. Concentration Determination of \u0026gt;\u0026thinsp;200 Proteins in Dried Blood Spots for Biomarker Discovery and Validation. Mol. Cell. Proteomics 2020, \u003cem\u003e19\u003c/em\u003e, 540\u0026ndash;553, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/mcp.TIR119.001820\u003c/span\u003e\u003cspan address=\"10.1074/mcp.TIR119.001820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim, M.D. Review Article: Dried Blood Spots for Global Health Diagnostics and Surveillance: Opportunities and Challenges. Am. J. Trop. Med. Hyg. 2018, \u003cem\u003e99\u003c/em\u003e, 256\u0026ndash;265, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4269/ajtmh.17-0889\u003c/span\u003e\u003cspan address=\"10.4269/ajtmh.17-0889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreeman, J.D.; Rosman, L.M.; Ratcliff, J.D.; Strickland, P.T.; Graham, D.R.; Silbergeld, E.K. State of the Science in Dried Blood Spots. Clin. Chem. 2018, \u003cem\u003e64\u003c/em\u003e, 656\u0026ndash;679, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1373/clinchem.2017.275966\u003c/span\u003e\u003cspan address=\"10.1373/clinchem.2017.275966\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhelan, S.A.; Hendricks, N.; Dwight, Z.L.; Fu, Q.; Moradian, A.; Van Eyk, J.E.; Mockus, S.M. Assessment of a 60-Biomarker Health Surveillance Panel (HSP) on Whole Blood from Remote Sampling Devices by Targeted LC/MRM-MS and Discovery DIA-MS Analysis. Anal. Chem. 2023, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.analchem.3c01189\u003c/span\u003e\u003cspan address=\"10.1021/acs.analchem.3c01189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashirina, D.N.; Brzhozovskiy, A.G.; Sun, W.; Pastushkova, L.K.; Popova, O. V.; Rusanov, V.B.; Nikolaev, E.N.; Larina, I.M.; Kononikhin, A.S. Proteomic Characterization of Dry Blood Spots of Healthy Women During Simulation the Microgravity Effects Using Dry Immersion. Front. Physiol. 2022, \u003cem\u003e12\u003c/em\u003e, 1\u0026ndash;7, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphys.2021.753291\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2021.753291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrzhozovskiy, A.G.; Kononikhin, A.S.; Pastushkova, L.C.; Kashirina, D.N.; Indeykina, M.I.; Popov, I.A.; Custaud, M.A.; Larina, I.M.; Nikolaev, E.N. The Effects of Spaceflight Factors on the Human Plasma Proteome, Including Both Real Space Missions and Ground-Based Experiments. Int. J. Mol. Sci. 2019, \u003cem\u003e20\u003c/em\u003e, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms20133194\u003c/span\u003e\u003cspan address=\"10.3390/ijms20133194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarina, I.M.; Percy, A.J.; Yang, J.; Borchers, C.H.; Nosovsky, A.M.; Grigoriev, A.I.; Nikolaev, E.N. Protein Expression Changes Caused by Spaceflight as Measured for 18 Russian Cosmonauts. Sci. Rep. 2017, \u003cem\u003e7\u003c/em\u003e, 1\u0026ndash;7, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-017-08432-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-08432-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouerbi, N.; Kim, J.K.; Overbey, E.G.; Batra, R.; Schweickart, A.; Patras, L.; Lucotti, S.; Ryon, K.A.; Najjar, D.; Meydan, C.; et al. Secretome Profiling Reveals Acute Changes in Oxidative Stress, Brain Homeostasis, and Coagulation Following Short-Duration Spaceflight. Nat. Commun. 2024, \u003cem\u003e15\u003c/em\u003e, 1\u0026ndash;17, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-024-48841-w\u003c/span\u003e\u003cspan address=\"10.1038/s41467-024-48841-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaither, C.; Popp, R.; Mohammed, Y.; Borchers, C.H. Determination of the Concentration Range for 267 Proteins from 21 Lots of Commercial Human Plasma Using Highly Multiplexed Multiple Reaction Monitoring Mass Spectrometry. Analyst 2020, \u003cem\u003e145\u003c/em\u003e, 3634\u0026ndash;3644, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1039/c9an01893j\u003c/span\u003e\u003cspan address=\"10.1039/c9an01893j\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaither, C.; Popp, R.; Zahedi, R.P.; Borchers, C.H. Multiple Reaction Monitoring-Mass Spectrometry Enables Robust Quantitation of Plasma Proteins Regardless of Whole Blood Processing Delays That May Occur in the Clinic. Mol. Cell. Proteomics 2022, \u003cem\u003e21\u003c/em\u003e, 0\u0026ndash;11, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mcpro.2022.100212\u003c/span\u003e\u003cspan address=\"10.1016/j.mcpro.2022.100212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhiteaker, J.R.; Halusa, G.N.; Hoofnagle, A.N.; Sharma, V.; MacLean, B.; Yan, P.; Wrobel, J.A.; Kennedy, J.; Mani, D.R.; Zimmerman, L.J.; et al. CPTAC Assay Portal: A Repository of Targeted Proteomic Assays. Nat. Methods 2014, \u003cem\u003e11\u003c/em\u003e, 703\u0026ndash;704, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.3002\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.3002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadzadeha, N.; Arne, M.O.; Palmeroa, S.; Sjaastada, I.; Chakravartic, S.; Engebretsene, K.V.T.; Christensena, G.; Lundea, I.G.; T\u0026oslash;nnessena, T. Moderate Loss of the Extracellular Matrix Proteoglycan Lumican Attenuates Cardiac Fibrosis in Mice Subjected to Pressure Overload. Cardiology 2020, \u003cem\u003e145\u003c/em\u003e, 187\u0026ndash;198, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000505318.Moderate\u003c/span\u003e\u003cspan address=\"10.1159/000505318.Moderate\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrollo, F.; Gentile, S.; Pipicelli, A.M. V.; Mambro, A.; Monici, M.; Magni, P. Space Flight-Promoted Insulin Resistance as a Possible Disruptor of Wound Healing. Front. Bioeng. Biotechnol. 2022, \u003cem\u003e10\u003c/em\u003e, 1\u0026ndash;8, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fbioe.2022.868999\u003c/span\u003e\u003cspan address=\"10.3389/fbioe.2022.868999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, S.R.; Bai, F.; Charbonneau, C.; Janderov\u0026aacute;, L.; Argyropoulos, G. A Promoter Genotype and Oxidative Stress Potentially Link Resistin to Human Insulin Resistance. Diabetes 2003, \u003cem\u003e52\u003c/em\u003e, 1611\u0026ndash;1618, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diabetes.52.7.1611\u003c/span\u003e\u003cspan address=\"10.2337/diabetes.52.7.1611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanke, M.B. Insulin-like Growth Factor Binding-Protein-3 (IGFBP-3). Best Pract. Res. Clin. Endocrinol. Metab. 2015, \u003cem\u003e29\u003c/em\u003e, 701\u0026ndash;711, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.beem.2015.06.003\u003c/span\u003e\u003cspan address=\"10.1016/j.beem.2015.06.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods 2020, \u003cem\u003e17\u003c/em\u003e, 261\u0026ndash;272, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41592-019-0686-2\u003c/span\u003e\u003cspan address=\"10.1038/s41592-019-0686-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaskom, M. Seaborn: Statistical Data Visualization. J. Open Source Softw. 2021, \u003cem\u003e6\u003c/em\u003e, 3021, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21105/joss.03021\u003c/span\u003e\u003cspan address=\"10.21105/joss.03021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter, J.D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 2007, \u003cem\u003e9\u003c/em\u003e, 90\u0026ndash;95, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/MCSE.2007.55\u003c/span\u003e\u003cspan address=\"10.1109/MCSE.2007.55\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKinney, W. Data Structures for Statistical Computing in Python. \u003cem\u003eProc. 9th Python Sci. Conf.\u003c/em\u003e 2010, \u003cem\u003e1\u003c/em\u003e, 56\u0026ndash;61, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.25080/majora-92bf1922-00a\u003c/span\u003e\u003cspan address=\"10.25080/majora-92bf1922-00a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcInnes, L.; Healy, J.; Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMering, C. von; Huynen, M.; Jaeggi, D.; Schmidt, S.; Bork, P.; Snel, B. STRING: A Database of Predicted Functional Associations between Proteins. Nucleic Acids Res. 2003, \u003cem\u003e31\u003c/em\u003e, 258\u0026ndash;261, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkg034\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkg034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"spaceflight, proteomics, cosmonauts, astronauts, mass spectrometry, dried blood spots","lastPublishedDoi":"10.21203/rs.3.rs-4694174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4694174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe molecular mechanisms of organismal adaptation to spaceflight (SF) conditions are not fully investigated and remain relevant for study. Newly emerging multi-omics technologies may provide information at the molecular level regarding physiological changes caused by SF factors. The aim of the study was to apply blood proteome analysis using dried blood spots (DBS) micro-sampling technique to study at the molecular level the physiological response to SF conditions for 7 cosmonauts during 169\u0026ndash;199 days long-duration missions (one of the cosmonauts participated in a year-long mission). The level of 119 blood proteins was quantified in DBS samples by targeted mass spectrometry based proteomics with a validated MRM assay using stable isotope-labeled peptide standards (SIS). To identify proteins whose concentrations are sensitive to the high physiological stress of landing and takeoff the Mann-Whitney and Cohen's d-size effect tests were used. As a result, 10 proteins (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and Cohen's d size\u0026thinsp;\u0026gt;\u0026thinsp;0.8) were found to be significant. In order to determine additional stress marker proteins, the dynamic changes in protein concentration during SF were analyzed to find proteins whose concentrations change monotonously after stress exposure (takeoff or landing). A simple exponential decay model for protein concentration relaxation was proposed which allows to reveal additional 14 proteins. Intersections of both protein panels mentioned above resulted in a final list of 19 blood proteins that are related to SF factors such as takeoff and landing. The panel includes 2 plasma proteins (Lumican and Insulin-like protein binding growth factor 3) that were previously mentioned in other studies to be related with SF. As far as we know this is the first DBS proteomic study for monitoring adaptive reactions of the organism to long-term SF.\u003c/p\u003e","manuscriptTitle":"Targeted proteomic analysis of dry blood spot samples collected during long-term spaceflights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 15:28:23","doi":"10.21203/rs.3.rs-4694174/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"021ad7a7-8ee3-448e-922d-9f0c161c3a65","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34352859,"name":"Health sciences/Health care/Disease prevention/Preventive medicine"},{"id":34352860,"name":"Biological sciences/Biological techniques/Mass spectrometry"}],"tags":[],"updatedAt":"2024-09-11T06:37:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 15:28:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4694174","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4694174","identity":"rs-4694174","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}