{"paper_id":"4e12da6e-569b-4b43-b1f3-d1beea79b45b","body_text":"The Genomic Evaluation of Space Travel and Research (GENESTAR) Manual for Biospecimen Collection, Biobanking and Omics Data Generation from Commercial Space Missions | 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 The Genomic Evaluation of Space Travel and Research (GENESTAR) Manual for Biospecimen Collection, Biobanking and Omics Data Generation from Commercial Space Missions Aparna Krishnavajhala, Marie-Claude Gingras, Emmanuel Urquieta, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4993509/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 May, 2025 Read the published version in npj Microgravity → Version 1 posted You are reading this latest preprint version Abstract The surge in commercial and civilian spaceflight enables for the first time, systematic and longitudinal, large-scale biospecimen collection to understand prospective effects of space travel on human health. The Genomics and Space Medicine (Space Omics) project at BCM-HGSC involves a comprehensive biospecimen collection plan from commercial/private space flight participants (SFP). Biospecimens from multiple pre-launch (leading up to quarantine period) and post-return (the day of return, R + 0 onwards) time points are collected. The diverse array of biospecimen collections include venous blood, body swabs, saliva, stool, and urine samples and their derivatives. The manuscript addresses the critical gaps thus far in the biospecimen collection process such as informed consent process and a provision for subjects to obtain custom CLIA-WGS reports. We discuss here, the biospecimens collection, processing methodologies and nucleic acids’ suitability for Omics data generation, including successful generation of 16S rRNA data that have been presented as a ‘Genomic Evaluation of Space Travel and Research (GENESTAR)’ manual. Results from Axiom-2 mission where, a total of 339 biospecimens were collected using this manual, at two different sites, showed that 98% of the accessed blood samples and 91.6% of the non-blood samples passed the QC requirements for Omics assays, underscoring the reliability and effectiveness of the GENESTAR manual. Also for the for the first time, to support Space Omics studies, details of a data dictionary and a LIMS enabled biobank, are provided. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction As humanity looks towards long-duration missions and habitation in space, understanding the biological implications of space travel is crucial. Unlike ground studies, where rigorous scientific studies can be conducted by involving statistically significant sample cohorts, biospecimens available to study the effects of space are highly underpowered. As of July 2024, only 710 people have traveled into space (defined higher than 50 miles (80 km) above mean sea level, according to the U.S. Armed Forces, and NASA) [ 1 ]. Earlier biospecimen collections were driven in partnership with the National Aeronautics and Space Administration’s (NASA) Human Research Program (HRP) studies [ 2 ]. More recently, through the eXploration Platforms and Analog Definition (EXPAND) program, the Translational Research Institute for Space Health (TRISH) implemented proactive strategies in program management, processes, and infrastructure [ 3 ]. These efforts will help enhance engagement with space flight providers, commercial launch providers, space biomedical researchers, and government agencies with the goal of making data-informed decisions. Biological and physiological data from subjects have been collected and analyzed to assess the impact of space flights [ 4 – 7 ]. However, the details of the underlying molecular consequences of space flight are poorly understood, and having the biological data will be informative in analyzing rapid and subtle changes in the human body. A comparative assessment of various bio-fluids, including whole blood, plasma, urine, body swabs, stool and saliva, obtained from the subjects can reveal specific biomarkers, was previously reviewed [ 8 ]. Advances in Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics have provided significant impact on human genetic research, cancer detection and treatment, and personalized medicine. Therefore, there is growing interest in the application of Omics technologies to study the effects of microgravity, radiation exposure, microbiome, and other environmental factors on individuals experiencing either short- or long-duration space flight. The resulting data can be valuable for advancing terrestrial health care as well [ 9 ]. Omics technologies were applied to analyze the data from the NASA Twin study and, more recently, Inspiration4, the first all-civilian orbital spaceflight mission [ 10 ]. Sequencing was also used to analyze Clonal Hematopoiesis of Indeterminate Potential mutations in a retrospective specimen cohort [ 11 ]. A proof-of-concept study showed that exosome sequencing could be applied to study the pathophysiology of Space Associated Neuro-Ocular Syndrome in subjects [ 12 ]. However, space health studies remain vastly underpowered. The recent surge in commercial space missions provide a unique opportunity to systematically collect biospecimens from crew using a standardized methodology for both immediate analysis and biobanking. Also, there is a critical gap in knowledge and know-how of how national and international participants can be enrolled in such Omics studies and how clinical grade reports detailing pathogenic variants and pharmacogenomics markers can be provided to them. The Genomics and Space Medicine project ( ‘Space Omics’) at the Baylor College of Medicine Human Genome Sequencing Center (BCM-HGSC) was initiated in August 2022, and is designed to study pre-, in-, and post-flight biological specimens using an array of Omics assays, including clinical Whole Genome Sequencing (WGS), research assays (RNA-Seq, microbiome, proteomics among others), and biobanking for future use to gain insights into the impact of space travel. Biospecimens from up to 40 individuals will be collected over a five-year period making this the largest Omics dataset and biobank in this field. The knowledge gained from the collected biospecimens and the Omics data from diverse subjects on these commercial missions will be crucial for future mission planning and to enhance human health in space and on Earth. Multiple sites across the country will be collecting biospecimens using the standardized set of protocols presented here as a ‘Genomic Evaluation of Space Travel and Research (GENESTAR) manual for biobanking and omics data generation. Biospecimen collections for the Axiom 2 private mission, which was a 10-day mission (May 21 – May 31) involving a four-person multinational crew to the International Space Station, is described here to demonstrate the methodology for specimen collection, processing, and biospecimen requirements. Additionally, 154 biospecimens from Axiom mission 3 (January 18, 2024 – February 9, 2024) have also been successfully collected using these protocols, and collections for a 3rd private space mission are underway. Results Standard operating procedures were developed for the collection of a wide range of biospecimens from each crew member (Fig. 1). These samples were aliquoted or harvested to obtain either nucleic acids or derivative samples to carry out specified OMICS studies, such as genomic, transcriptomic, proteomic, microbiome, and single-cell sequencing assays (Table 1 ). The remaining aliquots deposited in a biobank for future use along with the generated Omics data and through proper permissions, are accessible to other researchers. Table 1 Biospecimens collected, targeted omics assays and the minimum biospecimen quality and quantity requirements. Biospecimen Assay Minimum requirement Quality Blood Whole blood DNA sequencing WGS†-750 ng Ratio 260/280 ≥ 1.8, MW > 40kb WES‡-250 ng PAXgene RNA Total RNA sequencing (RNA-Seq) 1µg RIN > 6, DV 200 > 70% Plasma Proteomics 50 µl Metabolomics 100 µl PBMC Single-cell RNA sequencing (scRNA-seq) 1,000 cells Cell viability > 80% cfDNA § Cell-free DNA (cfDNA) sequencing Body swabs 10 Sites Microbiome Sequencing (16S rRNA, WGS, RNA-Seq) 5 µl 16S rRNA data; number of reads > rarefaction cut-off. Urine 5 µl Saliva 2 µl Stool 2 µl †WGS; Whole genome sequencing; ‡WES: Whole exome sequencing; §cfDNA: Cell-free DNA Figure 1. GENESTAR Biospecimens Collections Schema. Collection details shown here are for each of the pre-and post-flight timepoints including one-time collection of blood for CLIA-WGS. For a typical mission, there will be 2–3 pre-flight and 2–3 post-flight collections. Data dictionary based on Athena-Ohdsi A data dictionary was created using the Athena-Ohdsi database to describe primary biospecimens and the Omics assays used in the GENESTAR manual [ 13 ]. This dictionary described the 13 primary biospecimens obtained from collections such as, blood, body swabs, saliva, stool and urine, and their derivatives (Table 2 ); and the six planned Omics assays (Tables 2 and Table 3 ). For each of these 19 entries, the field name, concept ID, concept code, concept name, Athena Domain name have been provided (Table 2 and Table 3 ). The data dictionary along with the sample metadata from the biobank will be submitted to the TrialX EXPAND database that will host the Space Omics project data. Table 2 Data dictionary of the biospecimens collected. Field Name Concept ID Concept code Concept Name Athena Domain Blood EDTA 40482922 445295009 Blood specimen with EDTA Specimen EDTA plasma 4000626 119361006 Plasma specimen Specimen CPT PBMC 4229415 CC-MIABIS-032 Peripheral blood mononuclear cell Body Structure Blood BCT 37111674 726740008 Cell free DNA Observation BCT plasma 4000626 119361006 Plasma specimen Specimen Blood PAXgene RNA 40480050 441673008 RNA Specimen Body swab 4122261 258503004 Skin swab Specimen Urine specimen 4046280 122575003 Urine specimen Specimen Urine cell pellet 4046280 122575003 Urine specimen Specimen Urine supernatant 4046280 122575003 Urine specimen Specimen Saliva 4001062 119342007 Saliva specimen Specimen Saliva aliquots 4001062 119342007 Saliva specimen Specimen Stool 4002879 119339001 Saliva specimen Specimen Table 3 Data dictionary of the sequencing assays planned Field Name Concept ID Concept code Concept Name Athena Domain RNA sequencing 36659712 94820-8 Whole mRNA transcriptome sequence analysis in Blood or Tissue by Sequencing Measurement Single cell sequencing 19387953 D000092386 Single-Cell Gene Expression Analysis Observation Proteomics 19399449 D040901 Proteomics Observation Microbiome sequencing 36659879 94603-8 Bacterial 16S rRNA gene Measurement Cell Free DNA 1012141 LP158077-0 Cell free DNA Observation Metabolomics 19401895 D055432 Metabolomics Observation Informed consent process and personal data privacy Participants are enrolled into the Commercial Astronaut Data Repository (CADRE) IRB protocol of which, the Space Omics project is an integral part. This enrollment is carried out at an informed consent briefing meeting where the EXPAND PI, study coordinators, clinical geneticist and subject experts participate. At this briefing, participants are taken through the study's purpose, risks, and potential benefits, as well as how their genetic data will be used, stored, and shared. Navigating international laws, such as the EU’s GDPR or the US’s HIPAA Privacy Rule, and binding to the laws of the host country and space agency that a subject belongs to can pose unique challenges to this enrollment process. The subjects can opt out of the Space Omics enrollment at any point and to address this, biospecimen destruction procedures are in place in the GENESTAR protocol. Each subject receives a coded ID which is also grouped with a mission ID to accommodate tracking of information for the repeat flyers. These IDs are shared for use in the GENESTAR protocol to collect biospecimens. Upon receiving the biospecimens at BCM-HGSC, these IDs go through a second round of coding (de-identification) to enter into the biobank and those are the IDs that are used to generate Omics data through all stages of the project and also data submission to the TrialX EXPAND database. These are the IDs and the associated data that will be released to the public (including this report) which helps to further blind the data from subjects as well as the private agency responsible for that mission (Axiom Space in this report). Biobanking A functional LIMS-enabled biobank to receive, store, and track Space Omics samples has been created. The Exemplar Laboratory Information Management System (LIMS) is built upon a Java 2 Platform, Enterprise Edition (J2EE) architecture and utilizes a Tomcat server for its operation. This LIMS has exceptional adaptability, offering extensive configuration options to align seamlessly with any laboratory workflow. It is designed to efficiently manage biospecimen tracking from initial intake, through library preparation and sequencing, to the final quality control stages. Moreover, this system is engineered for high throughput and minimal latency, featuring a user-friendly, code-free search functionality. Security is a paramount concern, with the system employing a comprehensive security framework and offering role-based access control to accommodate the varied roles within laboratory staff. Additionally, the Exemplar LIMS supports seamless integration capabilities with laboratory automation systems and the analysis pipeline, enhancing operational efficiency and data coherence across laboratory processes (Fig. 2 ). Upon arrival, barcodes on collected biospecimens are scanned into LIMS and marked as “received”. The biospecimen number and barcodes relative to the information in the biospecimen intake forms along with the other metadata are cross checked, and any discrepancies are noted. At this step a second round of de-identification of the subject IDs is done and then uploaded into LIMS. Any problems with the sample condition are also recorded. Samples are either directly stored at -80 o C or aliquoted into smaller vials for storage. In some instances, such as urine collected in a specimen container and whole blood collected in the CPT tubes, the primary biospecimens are processed to separate different components (PBMCs or urine cell pellet, etc). In such a scenario, new barcodes are created to label the tubes containing the aliquots and the derivative products, and that information is also entered into the LIMS (Fig. 2 ). Biospecimens are then advanced to different Omics pipelines and all major lab/informatic processes that the biospecimens undergo. Any aliquots made from that biospecimen are additionally barcoded and linked to the original biospecimen barcode. The chain of custody and biospecimen tracking in the LIMS are achieved by scanning the barcode on the biospecimen tube into LIMS before each processing step to generate Omics data. BCM-HGSC is the genomics data provider. Both the Omics data and Biospecimen metadata will be submitted to the EXPAND database created and managed by TrialX, a clinical research and space health informatics company also supported by TRISH. Requests for data and sample accesses will be managed by EXPAND Data Privacy and Release Board (DPRB). Overview of biospecimens and collection time points The type of samples selected for collection were chosen based on their minimally invasive, cost- and time-effective properties, easily accessible with the requirement of non-specialized equipment or expertise, and high return in data relevance to health assessment by variable Omics (Fig. 1). Biospecimens presented here were collected from four Axiom-2 mission subjects across six collection time points over four months as detailed in Table 4 . The biospecimens were collected at 90, 30, and 3 days pre-launch (L-90, L-30, and L-3), the day of return (R + 0) and at two other time-points (R + 5 or R + 6, R + 13). Blood was collected for the CLIA-WGS from three subjects on R + 13 and from the fourth subject on R + 28 (Table 4 ). In total, for the Axiom-2 mission, 339 primary samples were collected from the four subjects, (Table 4 ). No inflight collections were performed. All but L-3 collections happened at Axiom Space Inc. headquarters in Houston, Texas. The biospecimens were shipped within ~ 2h after collection for initial processing at the Human Genome Sequencing Center (HGSC) at Baylor College of Medicine (BCM), Houston, Texas. While for the L-3 timepoint, biospecimens were collected at Kennedy Space Center (KCS), Florida using the same SOP. The initial processing of those L-3 biospecimens was performed at Burnett School of Biomedical Sciences (BSBS), the University of Central Florida, Florida, and shipped back to BCM-HGSC at recommended shipping conditions as in the GENESTAR manual for further processing and biobanking. Table 4 Axiom space mission 2 Biospecimens collection details. Number of primary samples collected Collection 1 2 3 4 5 6 7 Time of collection* L-90 L-30 L-3 R + 0 R + 5/6 R + 13 R + 28 Blood EDTA 12 12 8 12 12 6 Blood CPT 4 4 4 4 4 2 Blood BCT 4 4 4 4 4 2 Blood PAXgene DNA CLIA 3 1 Blood PAXgene RNA 4 4 4 4 4 2 Body swab 40 40 40 40 Saliva 4 4 4 3 Stool 4 4 4 4 Urine 4 4 4 4 TOTAL 76 76 72 24 78 12 1 339 ‡L-: day before launch; *R+: day post-flight Subject de-identification process Space flight providers assign random initial de-identified subject IDs to the participants and HGSC assigns the second de-identified IDs. The second de-identification of the subject IDs involved generating a 9-character alphanumeric code using a random code generation script. These codes will also have a prefix SO (for Space Omics). The IDs will not be used more than once even if the subject is a repeat flyer. These IDs will be assigned upon sample receipt at the time of biobank intake. Biospecimens collections and their derivatives I. Blood collections For research purposes, blood samples were collected via venipuncture from each crew member at six time points. One blood biospecimen was collected post-return to perform genome sequencing and generate clinical data. The collection tubes were labeled with the subject ID, the collection number, analyte, and the aliquot number (Example: Subject ID-1-Saliva-01). Additionally, each tube was labeled with a unique barcode to ensure accurate and efficient identification of the samples. Whole blood was collected at L-90, L-30, L-3, R + 0, R + 6 and R + 13. The R + 13 collection was performed on only two subjects for a total of 22 biospecimen. a.K2 EDTA tubes Whole blood was collected at six time points, L-90, L-30, L-3, R + 0, R + 6 and R + 13. Each collection involved three K2 EDTA tubes of 3 mL each. The R + 13 collection was for only two subjects, for a total of 62 EDTA blood biospecimen. One tube of K2 EDTA from each subject and collection was used to isolate plasma that was then aliquoted into four 300 µL aliquots. For the Axiom-2 mission this added up to 88 plasma aliquots. The second K2 EDTA tube was used for DNA extraction. To avoid batch effect, the DNA from the blood collected at different time points was obtained through a single isolation. The total DNA yield varied between 26.38 µg and 92.0 µg and the DNA yield per mL of blood was between 13.1 µg/mL and 46 µg/mL, respectively (Fig. 3 ). The 260/280 ratio of ≥ 1.8 indicated no major protein contamination. The DNA blood isolation from astronaut SO5QL3KG6H resulted in higher yield per mL of blood, followed by SO6NJ3UF7T over the different collection times. The average yield per mL of blood over collection time was 32 µg: SO6NJ3UF7T, 40 µg: SO5QL3KG6H, 23 µg: SO8KF3EZ1C, 21 µg: SO9WB8CN8A. The third K2 EDTA tube and the plasma aliquots were stored as-is at -80 o C for future use. b. PAXgene blood DNA tubes for CLIA-WGS sequencing Whole blood for the purpose of generating CLIA-WGS data was collected from all four subjects, with each subject providing blood in 2.5 mL in PAXgene DNA tubes at R + 5 or R + 28 (Table 4 ). Total DNA yields varied between 45.08 µg and 89.76 µg, while the DNA yields per milliliter of blood were between 22.54 µg/mL and 44.88 µg/mL. The yield per mL of blood collected the same day showed minimal variation between the PAXgene and the EDTA collection tubes. This DNA was utilized to generate CLIA-WGS data and was analyzed for reportable genetic variants in a specific set of genes. c. Cell preparation tubes (CPT) for PBMC collections For each collection, 4 mL of blood was collected in CPT tubes from which Peripheral Blood Mononuclear Cells (PBMCs) were isolated. The final volume of PBMCs harvested was between 1.5–3 mL among the subjects. PBMC counts and viability for subject SO6NJ3UF7T from L-30 and L-3 was negligible and failed at this QC step. For the remaining 10 biospecimens, cell viability was > 80% and the cell count was between 800 and 1600 cells/µL. The total number of PBMCs harvested among these 10 samples was between 1.3 and 3.2 million, while the minimum required number of cells for single-cell sequencing is only 1,000 cells (Fig. 4 ). d. Cell-free DNA (cfDNA) BCT tubes Between 4–6 mL of plasma was isolated from the 22 blood samples collected in 10 mL BCT tubes and the plasma was stored at -80 o C until cfDNA extractions. Cell-free DNA was extracted using the entire volume of plasma obtained. The DNA yields and fragment sizes when evaluated using the High Sensitivity DNA Bioanalyzer kit showed the yields between 0.65 and 12.8 ng/mL of plasma (Fig. 5 ). Typical cfDNA size profiles of mononucleosome cfDNA (range: 75 and 250 bp, with a166bp peak); a portion of di- (range: 300–400 bp), and tri-nucleosomes (range: 450–650 bp) representation was also seen. e. PAXgene blood RNA tubes RNA was isolated from the 22 blood samples collected in 2.5 mL RNA PAXgene tubes (Table 4 ). RNA yield was between 0.58 µg and 2.55 µg per mL of blood (Fig. 4 ). The RIN scores of these samples were between 7.1 and 9.4 and DV200% values between 84% and 94% (Fig. 6 ). As noticed earlier, the astronaut SO5QL3KG6H had a higher average RNA yield (1.90 µg per mL of blood) than the others. In summary, a total of 102 blood samples which includes 98 samples for research purposes: K2 EDTA DNA (22), RNA (22) EDTA plasma (22) and BCT Plasma (22) and 10/12 PMBC biospecimens and four samples of DNA in PAXgene tubes for CLIA-WGS, with passing QC metrics for Omics data generation, indicating a 98% success rate. II. Non-blood collections Body swab, saliva, stool and urine samples were collected from each crew member across four time points: L-90, L-30, L-3 and R + 6. 16S rRNA sequencing data was generated for samples collected at L-30, L-3, and R + 6 time points. The 16S sequence reads were mapped against the SILVA Database version 138 which contains sequences from the v4 region of the 16S rRNA gene. Nucleic acids were not extracted from L-90 biospecimens. a. Body swabs A total of 160 body swab samples from 10 different body sites were collected (Supplementary Fig. 1). DNA was extracted using 400 µl from 120 out of 160 body swab samples, excluding the L-90 biospecimens. Successful sequencing libraries were obtained from 119 samples with library yields between 98.3–1000 ng. UMB sample from subject SO5QL3KG6H failed at library preparation. The 16S gene sequencing yielded mapped reads between 18 and 145,071 reads. The rarefaction cut off was set at 562 mapped reads and 107 samples passed this metric. Out of the 12 samples that did not meet this cut-off, one was saliva and 11 were swabs from different body sites and from L-30 and R + 6 timepoints. Five of these 11 samples were from subject SO8KF3EZ1C, four from SO9WB8CN8A and one each from the other two subjects. b. Urine Sixteen urine biospecimens between 90–105 mL were collected in a specimen container from which, supernatant and cell pellets were harvested and stored as aliquots at -80 o C. Supernatant aliquot volumes are between 30–35 mL while the cell pellets aliquots are 250 µl each. Successful 16S rRNA libraries were generated from all 12 samples from whom DNA was extracted, with library yields ranging between 126.0–826.0 ng. All the urine samples have generated mapped reads ranging from 5,930–145,071 mapped reads. c. Saliva A total of 15 saliva samples were collected. The biospecimen volumes varied from 2 mL to 6.8 mL and were divided into 250 µL aliquots. One saliva aliquot from each biospecimen was used for DNA extraction. Successful 16S rRNA libraries were generated for all the samples with library yields ranging between 111.1 -492.9 ng. The 16S gene sequencing yielded mapped reads from each saliva sample between 305 and 31,847 reads. The biospecimen collected at L-3 from subject SO8KF3EZ1C failed the rarefaction cut-off as it generated only 305 mapped reads. c. Stool A total of 16 stool samples were collected and the DNA extractions were performed on 200 µl of the sample. Bacterial 16S rRNA sequencing was performed using 2 µl of the extracted DNA from the samples, without quantification. Libraries were successfully generated from all the 12 samples, excluding the L-90 samples. A minimal variation in the library yields (range of 3.01–3.5 µg) was observed among these samples. The 16S gene sequencing yielded mapped reads between 61,820 and 87,796 mapped reads. 16S rRNA data as a QC Bacterial 16S rRNA gene sequencing data from the non-blood samples is presented here to document biospecimens quality and the efficiency of sequencing technology employed. Out of 155 biospecimens across four analytes selected for 16S rRNA sequencing, one failed at the initial quality control step and did not proceed to library preparation. Additionally, 12 samples did not produce sufficient mapped reads and were considered failures. The sequencing data was successfully generated for the remaining 142 biospecimens, representing a 91.61% success rate. Overall, the microbiome data was similar across the four analytes, the subjects and time points studied for observed operational taxonomical units (OUTs) and for Shannon alpha diversity (Fig. 7 ) confirming the usability of the collected biospecimen. The mean number of observed OTUs showed slight variation with a median of 35 and the Shannon diversity index indicated a more or less even spread of microbial abundance among all the subjects and time points. Microbiota did not change significantly among the subjects, across the time points, with adjusted p-values in the range of 0.5–0.9 for richness and 0.8–0.9 for Shannon diversity. Discussion There has been a growing interest in recent years both from government and private industry to explore space including space tourism, returning humans to the moon, and planning missions to Mars [ 14 ]. It is therefore important to understand the effects of space travel on human health. Collection of biospecimens and generation of Omics data are essential to understand the genetic, physiological, cellular, and microbiome changes related to space travel. Rigorous Standard Operating Procedures (SOPs) for specimen collection have been developed along with a LIMS-enabled biobank at HGSC that tracks specimen details and storage information. This document, called Genomic Evaluation of Space Travel and Research (GENESTAR) incorporated biospecimen collection procedures tailored to support all current and anticipated biological and genomic measurements. In GENESTAR, we have standardized the complete set of protocols, based on our knowledge of biospecimens requirements for genomics and other molecular assays, as well as those adopted from earlier studies, NASA-Human Research Program core measures [ 15 ] and Space Omics and Medical Atlas (SOMA) [ 10 ] as described recently for the Inspiration4 mission. For the first time in the GENESTAR manual, a data dictionary, a LIMS enabled biobank, and a customized provision for subjects to obtain CLIA-WGS are provided. Coupled with the informed consent process, GENESTAR fills these critical gaps that were not addressed by those previous studies. Here, we present our collection efforts based on biospecimens gathered from the Axiom 2 mission, representing a typical commercial crew mission from a subject cohort that spent 10 days in microgravity. Several of the presented biospecimen collection methods provide a validation of the recently reported methods, which were intentionally synchronized to allow for joint biospecimens collections by us and the SOMA procedures [ 10 ] for a private mission. Further, our group has exclusively collected samples from Axiom-2 and Axiom-3 missions, in total from six individuals, which will allow for easy comparison of results across different missions. As the physiological changes are very dynamic during a standard spaceflight mission, obtaining Omics measures across multiple time points before and after the travel to space is important for scientific significance. Pre-flight biospecimen collections help assess and establish a baseline and biospecimen collections shortly after the return, help assess the recovery process and any long-term health implications. Collection of biospecimens from at least 2 to 3 pre-flight and 2 to 3 post-flight time points is recommended so that any outliers in data can be identified, then either ignored or accounted for. The primary biospecimens collected include blood, body swabs, saliva, stool and urine samples, that have provided secondary products such as plasma, urine cell pellet and supernatant. This multi-tiered approach in biospecimens collection allows for a comprehensive multi-omics analysis. Blood, urine, stool, swabs, and saliva provide cost-effective, non-invasive and easy sampling options and therefore inherently qualify as appropriate biospecimen types to obtain Omics data from subjects [ 16 ]. We are offering additionally the option of a CLIA-WGS for the first time to the participants [ 17 ]. The HGSC launched a CAP/CLIA certified clinical laboratory in 2016 (CLIA# 45D2027450) and therefore can deliver clinical genetic testing data and interpretation, enabling return of clinical results reports to individuals, via their clinical caregivers. Through CLIA-WGS, the subjects in the Space-Omics program may elect to receive results for reportable genetic variants in 205 genes, including American College of Medical Genetics and Genomics (ACMG) 59 gene set recommended for secondary findings reporting [ 18 ]. A ‘Space Omics’ clinical-grade report will be returned to the participants unless the participants specifically opt-out of the results return. Both our research and NASA HRP aim to advance our understanding of human space exploration. However, our research specializes in genomics and multi-omics studies, which offer a molecular perspective and integrate diverse Omics data types to elucidate the effects of spaceflight on human biology. A data dictionary using the Athena-Ohdsi database components to describe biospecimen and Omics assay types is described. This will facilitate efficient information dissemination to other users globally, for data analysis and interpretation across various missions [ 13 ]. A versatile LIMS-enabled biobank to store and track Space Omics samples has been developed. All biospecimens and their derivatives when stored in the biobank are barcoded and tracked properly. LIMS at BCM-HGSC also provides support for tracking of samples as they progress through the different Omics pipelines as well as to track and retrieve the generated data. Overall, the established biobank enhances the efficiency, accuracy, and security of biospecimen management which are essential for a robust biobank to function [ 19 ]. In the presented study, the extracted DNA and RNA underwent quality assessment and were determined to be of sufficient quality and quantity to conduct multiple genomics assays listed in Table 1 . A proteomics assay run on plasma samples is particularly valued for its ability to simultaneously provide highly sensitive and specific measurements of multiple proteins from small biospecimen volumes [ 20 ]. As plasma contains various molecules such as proteins, metabolites, nucleic acids, and lipids, its analysis may reveal exceptionally specific biomarkers associated with space travel [ 21 , 22 ]. Plasma will be used to generate high-throughput proteomics datasets, such as Olink Explore HT. Cell-free DNA is a potential biomarker for diagnosis and prognosis in conditions such as cancer, cardiovascular and neurological diseases [ 23 ]. Further, cfDNA can be used to measure physiological stresses during space missions [ 24 ]. The PBMCs derived from blood samples were of acceptable quality and quantity, allowing for the generation of single-cell RNA sequence data (Manuscript under preparation). DNA extracted from both low biomass samples including body swabs, saliva, and urine, and high biomass biospecimen including stool was utilized for 16S rRNA gene sequencing. The sequencing methods were adapted from those developed for the NIH-Human Microbiome Project and the Earth Microbiome Project [ 25 ]. 16S rRNA gene sequencing was favored over microbiome WGS for cost reasons and the extracted nucleic acids are stored for future use. A total of 155 samples consisting of 12 urine, 12 stool, 120 body swabs, and 11 saliva samples were submitted for bacterial microbiome sequencing. Successful sequence data was obtained for 142 out of 155 samples (91.6%). Twelve samples were body swab samples out of 13 failed samples. Out of the four subjects, SO8KF3EZ1C had the most samples that failed followed by SO9WB8CN8A. This outcome can be attributed to various factors, including but not limited to disparities in the biospecimen collection times among different individuals, contact location, and in the case of body swabs, the pressure applied during the collection process [ 10 , 26 ]. Nevertheless, microbiome sequencing generated 91.6% of usable data underscoring the validation of the collection methodology, efficiency and quality of both the sampling and sequencing processes. As a further confirmation of this data's usability, Alpha diversity and richness measurements were performed to QC the microbial diversity (Fig. 7 ). These biospecimens are being collected at different locations across the country using the GENESTAR manual and shipped to HGSC after initial processing. The unpredictability of shuttle schedules can cause sample collection delays, making it difficult to collect biospecimens as planned, especially if collections happen during late hours and weekends. Shipping samples from collection sites to the local processing labs and back to BCM-HGSC in Houston for long term storage can be challenged by transport and weather-related interruptions and must be documented thoroughly for any deviations. GPS trackers are included in the sample shipments and extra dry ice is packed to avoid running into problems due to such interruptions. Further enhancements to the GENESTAR manual are also planned. For instance, the in-flight whole blood collections are still not part of the manual. Traditionally, blood in-flight is collected using venipuncture. However, there are several microdevices that allow easy self-collection of blood for diagnostic and research purposes. These microdevices can collect several hundred microliters of whole blood, enough to isolate plasma/serum to perform certain Omics assays like proteomics and metabolomics. Our group is evaluating them for use in the GENESTAR protocol and, if found effective in microgravity, will be used for in-flight sample collections. The GENESTAR manual will also be used to collect biospecimens from age and gender matched controls to isolate noise in the data and conduct ground-based analogue experiments such as centrifugation training. In summary, the methodology presented lays a strong foundation for future studies and allows for the comparison of existing Omics datasets. Methods Data dictionary based on Athena-Ohdsi A detailed data dictionary for each of the primary biospecimens collected has been developed using the Athena-Ohdsi database to standardize biospecimen types and medical terminology, aligning codes with the Observational Medical Outcomes Partnership (OMOP) standards, which are a set of guidelines and tools designed to standardize the collection, formatting, and analysis of observational healthcare data. OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies [ 13 ]. Athena is a comprehensive vocabulary repository and searchable database within OHDSI. I. Blood samples Blood draws Venipuncture was performed in the antecubital fossa after sanitizing that area and blood was drawn using a butterfly needle (BD Biosciences, Cat#367281) and a lure lock vacutainer adapter (McKesson, Cat#364815/458363). The blood collection volumes involved, 3 mL of blood in each of the three K2 EDTA blood collection tubes (VWR, Cat#367856), 4 mL of blood in one CPT cell preparation tube (CPT, BD Biosciences, Cat#362760), 10 mL of blood in one Streck cell-free DNA BCT tube (cfDNA BCT, Streck, Cat#230470) and 2.5 mL of blood in one PAXgene blood RNA tube (RNA PAXgene, Fisher, Cat#762165), for a total of 25.5 mL collected from each subject at each time point. An additional 2.5 mL of blood was collected from each subject in CAP CLIA validated PAXgene DNA tubes (VWR, cat# 10589-986) during the second collection after the return, to be used exclusively for CLIA-WGS data generation. Samples collected locally in Houston were immediately transported to the Human Genome Sequencing Center at Baylor College of Medicine for processing. Blood samples collected in Florida at the Kennedy Space Center (KCS), were transported to the University of Central Florida, within 2 hours for initial processing, which included preparation of PBMCs and plasma isolation, as discussed below. Processing of blood samples Whole blood was collected at six time points from the four subjects at L-90, L-30, L-3, R + 0, R + 6 and from 2 subjects at the R + 13 timepoint. K2 EDTA tubes. Whole blood in BD Hemogard K2 EDTA tubes was collected from each subject in three tubes of 3 mL each per time-point. DNA was isolated from one tube (see section Nucleic Acid Extractions from blood samples ), plasma from the second tube and the third tube was stored at − 80 o C as received. Plasma isolation: Plasma was isolated from one of the K2 EDTA blood samples collected from each subject at each time point. The blood was centrifuged at 1,000g for 10 minutes at room temperature. The plasma was collected and aliquots of 300 µL were distributed into four matrix tubes (ThermoFisher, Cat#3741-WP1D-BR) of 300 µL to avoid additional freeze-thaw cycles. The aliquots were stored at -80 o C. PAXgene blood DNA tubes. Blood (2.5 mL) was collected in PAXgene tubes for CLIA-WGS at R + 5 from 3 subjects and at R + 28 from the fourth subject and tubes were stored at -20c. DNA was isolated using a CAP-CLIA validated protocol (see below section Nucleic Acid Extractions from blood samples ). Cell preparation tubes (CPT) for PBMC collections. Blood samples collected from each subject and from each time point were mixed immediately by gently inverting the CPT tube eight to 10 times before centrifuging at 1,800g for 20 minutes at room temperature. Half of the plasma was aspirated without disturbing the cell layer and the cells were transferred to a 15 mL conical tube. 2% FBS (Thermo Fisher, cat# 26140079) in PBS (Gibco, cat#10010-023) was added to resuspend the cells and the volume was brought to 15 mL. The cells were mixed gently by inverting five times and then centrifuged at 120g with break-off for 10 minutes. The supernatant was aspirated and discarded without disturbing the cell pellet. The pellet was dislodged by tapping the tube with index finger, resuspended in 2% FBS in PBS and the volume was brought to 10 mL. The pellet in FBS-PBS was mixed gently by inverting five times followed by a spin at 120g for 10 minutes. The supernatant was discarded, and the pellet was resuspended in 1 mL of RPMI (Cytiva, cat#SH30027.01) medium. An equal volume of freezing medium [30% DMSO (MP Biomedicals, cat#194818) 40% FBS (Thermo Fisher, cat# 26140079), and 30% RPMI (Cytiva, cat#SH30027.01,)] was added to the cell suspension. The cell suspension was then transferred into two cryovials, 1 mL each. The cells were cryopreserved using an isopropanol freezing container and then placed in -80 o C freezer overnight. These cryovials were stored in liquid nitrogen until use. Cell counting : The cell suspension (50 µL) was mixed with trypan blue (50 µL) (Sigma-Aldrich Cat. No. 72-57-1) at a 1:1 ratio and 10 µL of the mixture was loaded onto the hemocytometer (Bulldog Bio, DHC-N420). Then, the hemocytometer was placed on the microscope stage (Zeiss Axio Vert. A1), optimally focused, and manually counted to determine the total, live, and dead cell density and viability. Blood BCT. Blood samples were collected in cell-free DNA (cfDNA) BCT tube (Streck, Cat#230470) at a volume of 10 mL. Each biospecimen was centrifuged at 1,600g for 10 minutes at room temperature. Plasma was separated from each sample, transferred into 15 mL conical tube and stored at -80 o C until cfDNA extractions. The cfDNA extractions were performed as described below. PAXgene blood RNA tubes. Blood samples were collected in PAXgene blood RNA tube (Fisher, Cat#23-021-01) at a volume of 2.5 mL from each subject. The RNA was isolated as described below. Nucleic Acid Extractions from blood samples Genomic DNA was extracted from K2 EDTA tubes and PAXgene blood collection DNA tubes on the Chemagic Prime 8 using the Chemagic Prime DNA blood kit (Revvity, cat# CMG-1497) according to the manufacturer’s instructions in a CAP-CLIA certified laboratory. DNA quality and quantity was assessed by electrophoresis and fluorescent nucleic acid stain (PicoGreen, Thermo Fisher). Cell-free DNA was extracted from the BCT tube using the Apostle MiniMax High-efficiency cfDNA Isolation kit (VWR, cat# 76409-757), as per manufacturer’s instructions. The quantification and fragment size profiling of cfDNA was determined using a 2100 bioanalyzer (Agilent Inc., Santa Clara, CA, USA) with the Agilent High-Sensitivity DNA kit. (Part #5067 − 4626, Agilent) and the cfDNA was stored at -20°C until further use. RNA was extracted from the PAXgene blood collection RNA tubes on the Chemagic Prime 8 using the Chemagic Prime total RNA blood 4k kit (Revvity, cat# CMG-1484) according to the manufacturer’s protocol. The RNA was utilized for total RNA sequencing and the remaining RNA was stored at -80 o C for future use. RNA quality (RIN and DV200) and quantity were assessed by Agilent Bioanalyzer. II. Non-blood samples Collection of non-blood samples. Body swab, saliva, stool and urine samples were collected from each crew member across four time points: L-90, L-30, L-3 and R + 6 and were proceeded as below until nucleic acids were extracted. Body swab sampling. The ten body swab collections involved eight wet and two dry swabs. The wet body swab locations included post-auricular (EAR), axillary vault (PIT), volar forearm (ARM), occiput (NAP), umbilicus (UMB), gluteal crease (GLU), glabella (TZO), and toe web space (WEB) while the two dry body swab collections included oral (ORC) and nasal (NAC) (Supplementary Fig. 1 and Supplementary Table 1). For wet swab collections, the swab (Isohelix, Cat#MS-02) was moistened in nuclease free water and rubbed back and forth on the skin for about 25–30 times along the surface applying firm pressure while rotating the swab head continuously. The swab was then placed into the matrix tube provided (ThermoFisher, Cat#3741-WP1D-BR) with 400 µL of DNA/RNA shield (Zymo Research, Cat#R1100-50). The biospecimen collection process was repeated for all seven other wet swab locations. Buccal swab collection was performed by inserting the dry swab into the mouth and rubbing firmly against the inside of the cheek or underneath the lower or upper lip for about a minute. The nasal swabs were collected by inserting the entire soft tip of the dry swab into one nostril until resistance was experienced. The swab was rubbed in a circle around the nostril four times for at least 15 seconds. The process was repeated in the other nostril. Swabs were returned to the RNA/DNA shield (Zymo Research, Cat#R1100-50) as mentioned above. All the swabs were stored at -80 o C until nucleic acid extractions were performed. Saliva sampling. Crude saliva was collected from each subject into an OMNIgene ORAL (DNAgenotek, Cat# OME-505) saliva collection tube that contains a solution for collection, stabilization, storage, and transportation of the sample. Each subject was allowed to spit repeatedly into the saliva collection tube until the “fill to” line, which is equivalent to 3 mL. The saliva was mixed by inverting the tube. 250 µL aliquots were distributed into cryovials under sterile conditions and the aliquots were stored at -80 o C. Stool sampling. Stool biospecimen collections were carried out in DNA Genotek OMNIgene Gut (OMR 205) tubes using the accessory and spatula provided in the kit. Typically, 545 mg of stool is collected and mixed with the stabilizing buffer supplied in the tube for a total of 4 mL. The samples were stored at -80 o C until nucleic acid extractions were performed. Urine sampling. Approximately, 90 mL of the urine sample from each subject were collected in a sterile Samco Bio-Tite Specimen Container (SAMCO, Cat#13-711-65) to which 6.3 mL of urine conditioning buffer (Zymo research, Cat# D3061-1-140) is added immediately and mixed thoroughly. From each container, three aliquots of 30 mL each were prepared in 50 mL conical tubes. The samples were centrifuged at 3,000g for 15 minutes at room temperature to obtain a cell pellet and supernatant. The supernatant was transferred into clean 50 mL conical tubes and stored at − 80 o C while the urine cell pellet was resuspended in 250 µL of DNA/RNA shield (Zymo Research, Cat#R1100-50) and stored at -80 o C until nucleic acid extraction. Nucleic Acid Extractions from non-blood samples Nucleic acid extractions were performed on low biomass (body swabs, saliva, and urine), and high biomass (stool) samples collected from each subject across three time points (L-30, L-3 and R + 6), excluding the L-90 biospecimens. DNA was extracted from an aliquot of urine cell pellet (250 µL) and skin swab biospecimen (400 µL) types using the Zymobiocs (96) Kit (Cat# 27500-4-EP) following the manufacturer’s protocol. Each extraction was eluted into 50 µL of nuclease-free water (Invitrogen, Ref# AM9906, Lot# 2211042) and the samples were ready for immediate use or frozen until needed. DNA was extracted from 200 µl of primary stool and a 250 µl- saliva aliquot using the Qiagen PowerMicrobiome kit (Cat# D4303) following the manufacturer’s protocol. The VIAFLO96 pipetting station (Integra, Part# 6001) was used for these extractions, and samples were eluted in 100 µl of nuclease free water. The samples were ready for immediate use or frozen until needed. 16S rRNA gene sequencing as a QC metric for non-blood samples 16S rRNA gene sequencing was chosen for microbiome analysis rather than whole genome sequencing (WGS) due to budget constraints. 16S rRNA gene sequencing provides a cost-effective and sufficient method for profiling microbial diversity and community structure, the project objectives. The 16S rRNA gene sequencing methods were adapted from those developed for the NIH-Human Microbiome Project and the Earth Microbiome Project [ 14 ]. Briefly, the 16S rRNA v4 region was amplified by PCR using primers that contain adapters for sequencing on the Illumina platform. Additionally, single-index barcodes were incorporated into the reverse primer allowing PCR products to be pooled and sequenced directly. The PCR template volume was 5 µL for urine and skin swab biospecimen types and 2 µL for saliva and stool biospecimen types. Platinum Taq (Invitrogen – Cat# 15966005) was used for amplification. Library QC was performed using a combination of gel electrophoresis (1% agarose) and an automated PicoGreen assay (Invitrogen Quant-iT P7589) using the Hamilton STARlet Liquid Handler. Finalized libraries were normalized and pooled followed by loading on the Illumina MiSeq platform using the 2x250bp (16S V4) paired-end protocol. The 16S sequence reads were mapped against the SILVA Database version 138 which contains sequences from the v4 region of the 16S rRNA gene. Data is accessible through TrialX EPAND database. Human subjects research All subjects consented and biospecimens were collected and processed under the approval of the Institutional Review Board at Baylor College of Medicine, under MESH protocol H-52035. Manuscript preparation Figure 1 was created with BioRender.com under the Baylor College of Medicine Institutional license. Data availability The data is accessible through TrialX EXPAND database. Declarations Acknowledgements This study was funded by the Translational Research Institute for Space Health through NASA Cooperative Agreement NNX16AO69A. We thank Dr. Jeffrey Rogers for the critical review of this manuscript and Ms. Christie Kovar for advice on sample deidentification process. Author Contributions HD, EUO, and RAG conceptualized the study. SVB, SMG, DPB, HC, and MM contributed to biospecimen collections. MCG, YC, MM and SVB processed the samples and performed QC. AK, MCG, HD prepared the original draft of the manuscript and addressed the edits. VK, HM, JEP, JW and DM were involved in data generation. MM and MJ contributed to biobank and LIMS development. All authors reviewed and approved the manuscript. Competing Interests All authors declare no financial or non-financial competing interests. Correspondence and requests for materials shouls be addressed to Harsha Doddapaneni References Astronaut/ Cosmonaut Statistics . [cited 2024 06/19/2014]; Available from: https://www.worldspaceflight.com/bios/stats.php. Alwood, J.S., et al., From the bench to exploration medicine: NASA life sciences translational research for human exploration and habitation missions. NPJ Microgravity, 2017. 3 : p. 5. Urquieta, E., et al., Establishment of an open biomedical database for commercial spaceflight. Nat Med, 2022. 28 (4): p. 611-612. Convertino, V.A., Status of cardiovascular issues related to space flight: Implications for future research directions. Respir Physiol Neurobiol, 2009. 169 Suppl 1 : p. S34-7. Hughson, R.L., et al., Cardiovascular regulation during long-duration spaceflights to the International Space Station. J Appl Physiol (1985), 2012. 112 (5): p. 719-27. Lathers, C.M., et al., Acute hemodynamic responses to weightlessness in humans. J Clin Pharmacol, 1989. 29 (7): p. 615-27. Norsk, P., et al., Fluid shifts, vasodilatation and ambulatory blood pressure reduction during long duration spaceflight. J Physiol, 2015. 593 (3): p. 573-84. Kumari, S., et al., A Review on Saliva-Based Health Diagnostics: Biomarker Selection and Future Directions. Biomed Mater Devices, 2023: p. 1-18. Ruyters, G. and K. Stang, Space medicine 2025 – A vision: Space medicine driving terrestrial medicine for the benefit of people on Earth. REACH, 2016. 1 : p. 55-62. Overbey, E.G., et al., Collection of biospecimens from the inspiration4 mission establishes the standards for the space omics and medical atlas (SOMA). Nat Commun, 2024. 15 (1): p. 4964. Brojakowska, A., et al., Retrospective analysis of somatic mutations and clonal hematopoiesis in astronauts. Commun Biol, 2022. 5 (1): p. 828. Chakrabortty, S.K., et al., Exosome based analysis for Space Associated Neuro-Ocular Syndrome and health risks in space exploration. NPJ Microgravity, 2022. 8 (1): p. 40. Reich, C., et al., OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization. J Am Med Inform Assoc, 2024. 31 (3): p. 583-590. Witze, A., 2022 was a record year for space launches. Nature, 2023. 613 (7944): p. 426-undefined. Mason, C.E., et al., A second space age spanning omics, platforms and medicine across orbits. Nature, 2024. Cope, H., et al., Routine omics collection is a golden opportunity for European human research in space and analog environments. Patterns, 2022. 3 (10): p. 100550. Zneimer, S.M. and D. Hongo, Preparing for Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologists (CAP) Inspections. Curr Protoc, 2021. 1 (12): p. e324. Kalia, S.S., et al., Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med, 2017. 19 (2): p. 249-255. Annaratone, L., et al., Basic principles of biobanking: from biological samples to precision medicine for patients. Virchows Arch, 2021. 479 (2): p. 233-246. Williams, S.A., et al., Plasma protein patterns as comprehensive indicators of health. Nat Med, 2019. 25 (12): p. 1851-1857. Ahmad, A., M. Imran, and H. Ahsan, Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases. Pharmaceutics, 2023. 15 (6): p. 1630. Qiu, S., et al., Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther, 2023. 8 (1): p. 132. Szilágyi, M., et al., Circulating Cell-Free Nucleic Acids: Main Characteristics and Clinical Application. Int J Mol Sci, 2020. 21 (18). Bezdan, D., et al., Cell-free DNA (cfDNA) and Exosome Profiling from a Year-Long Human Spaceflight Reveals Circulating Biomarkers. iScience, 2020. 23 (12): p. 101844. Ames, N.J., et al., The Human Microbiome and Understanding the 16S rRNA Gene in Translational Nursing Science. Nurs Res, 2017. 66 (2): p. 184-197. René Rohrmanstorfer, S.Z., Daniel Wallerstorfer, Nicole Bauer, Sheri Hunt, Chung Hyun, Valentina Kazlova, DNA by Mail: Ensure DNA Integrity by use of Self-Drying Buccal Swabs. Letters In Health and Biological Sciences, 2017. 2 (2): p. 1-7. Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFigure1.tif SupplementaryTable1.docx Cite Share Download PDF Status: Published Journal Publication published 14 May, 2025 Read the published version in npj Microgravity → 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-4993509\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":353662372,\"identity\":\"81039e88-a5c1-457c-bbb4-59407a38d3f3\",\"order_by\":0,\"name\":\"Aparna Krishnavajhala\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Aparna\",\"middleName\":\"\",\"lastName\":\"Krishnavajhala\",\"suffix\":\"\"},{\"id\":353662373,\"identity\":\"ce037766-3f04-4439-9c8c-8e5ac9b74481\",\"order_by\":1,\"name\":\"Marie-Claude Gingras\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0003-2570-6360\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Marie-Claude\",\"middleName\":\"\",\"lastName\":\"Gingras\",\"suffix\":\"\"},{\"id\":353662374,\"identity\":\"39fb6578-9b3d-4863-87fa-6959e2d9741e\",\"order_by\":2,\"name\":\"Emmanuel Urquieta\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Emmanuel\",\"middleName\":\"\",\"lastName\":\"Urquieta\",\"suffix\":\"\"},{\"id\":353662375,\"identity\":\"68ac5209-af0a-4f62-9342-3b3f48ac6749\",\"order_by\":3,\"name\":\"Hsu Chao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hsu\",\"middleName\":\"\",\"lastName\":\"Chao\",\"suffix\":\"\"},{\"id\":353662376,\"identity\":\"6a6538ca-7c95-4278-9d09-51526ef5b8bd\",\"order_by\":4,\"name\":\"Dilrukshi Bandaranaike\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dilrukshi\",\"middleName\":\"\",\"lastName\":\"Bandaranaike\",\"suffix\":\"\"},{\"id\":353662377,\"identity\":\"7a45f442-9331-42da-bcac-cbe12ad6a205\",\"order_by\":5,\"name\":\"Yi Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yi\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":353662378,\"identity\":\"c552608d-2f90-4ab8-ae38-1b25847806f0\",\"order_by\":6,\"name\":\"Sravya Bhamidipati\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sravya\",\"middleName\":\"\",\"lastName\":\"Bhamidipati\",\"suffix\":\"\"},{\"id\":353662379,\"identity\":\"7eb3e293-100c-4d84-832a-ac44592d74ea\",\"order_by\":7,\"name\":\"Viktoriya Korchina\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Baylor College of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Viktoriya\",\"middleName\":\"\",\"lastName\":\"Korchina\",\"suffix\":\"\"},{\"id\":353662387,\"identity\":\"5b824286-602e-408e-ba53-471c8b574f9b\",\"order_by\":8,\"name\":\"S. 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For a typical mission, there will be 2-3 pre-flight and 2-3 post-flight collections.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/1eea00ae408461d810288fe9.png\"},{\"id\":69477239,\"identity\":\"2ae5db17-1b0e-4c78-9b10-d59a30cea3ec\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 19:56:52\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2601667,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eBiobank schema.\\u003c/strong\\u003e LIMS-enabled biobank was set up at HGSC-BCM to receive, process, and to track samples for future use\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/cb1059855051fdeb27c27e40.png\"},{\"id\":69476480,\"identity\":\"ab31dd41-fb9c-4390-a1e3-37e4970572fa\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 19:40:52\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":185037,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDNA yields from each subject across time points.\\u003c/strong\\u003e DNA was isolated from one of the blood samples collected in K2 EDTA tubes (n=22) and PAXgene DNA tubes (n=4) to perform whole genome sequencing and CLIA-WGS respectively. The yield per ml blood and ratio 260/280 are depicted on Y-axes with n=26.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/bde392fc40f5ebf02d52c079.png\"},{\"id\":69476202,\"identity\":\"dee8d9dd-8028-439a-81e7-a85e104508a2\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 19:32:52\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91332,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePBMC (counts/µl) and the total number of PBMCs used for sequencingfrom all subjects across three-time points.\\u003c/strong\\u003e PBMCs were isolated from two pre-launch and one post return blood collections to generate single cell RNA-seq data. Cells were counted manually using a hemocytometer. PBMC counts/µl and the total number of the cells used for sequencing were depicted on the y-axis, a line is drawn at 1000 cells across all samples to indicate a minimum requirement for sequencing.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/ef1a760fde8aaaec8663ed3f.png\"},{\"id\":69476842,\"identity\":\"7eda5976-467d-41fd-b58b-20edff2609a6\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 19:48:52\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":64696,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCell-free DNA (cfDNA) yields from each subject across time points. \\u003c/strong\\u003eCfDNA was isolated from the plasma samples obtained from the blood samples collected in blood BCT tubes (n=22) to perform cfDNA sequencing. The yields per ml of plasma are depicted on Y-axis.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/fd239392dea8ed22d52c47b0.png\"},{\"id\":69477238,\"identity\":\"ebc69648-a8f8-4ff9-8357-f85c2e8a4713\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 19:56:52\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":98626,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRNA yields and their quality assessment from all subjects across all six collection time points\\u003c/strong\\u003e. RNA integrity (RIN and DV200%) was assessed using Agilent Bioanalyzer on nanochips. The total RNA yields, RIN and the DV200% values are depicted on the y-axes, with n=4 for each time point except for R+13 collection, where biospecimens were available for only two subjects.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/047d28268a3fe4bd5e13b967.png\"},{\"id\":69476207,\"identity\":\"c143c561-dae3-4933-8c60-6ba18b781efe\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 19:32:52\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":309347,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eObserved alpha diversity and richness within the subjects’ microbiome.\\u003c/strong\\u003e Box plots depict high-level alpha diversity metrics across four biospecimen types including urine, stool, body swabs, and saliva. The observed Operational Taxonomical Units (OTUs) and Shannon alpha diversity provide a visual representation of the microbial diversity, highlighting the consistency in the biospecimens collected, processed and 16S microbiome data generated from them.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/87c982d51f1ebfc30416a4c0.png\"},{\"id\":82775606,\"identity\":\"efbdd01c-2cc2-4353-be5e-2b57e3031a81\",\"added_by\":\"auto\",\"created_at\":\"2025-05-15 07:14:37\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":9352899,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/3c3adf3c-d053-4a2b-9613-5cf34a1f0494.pdf\"},{\"id\":69477376,\"identity\":\"3565e6ee-ecc2-4676-9912-5eb45b796e85\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 20:04:52\",\"extension\":\"tif\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":75968,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFigure1.tif\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/852b851642f5b3bb72e04490.tif\"},{\"id\":69476844,\"identity\":\"e97a13e4-eb70-4bc3-a67d-bc4492703e61\",\"added_by\":\"auto\",\"created_at\":\"2024-11-20 19:48:52\",\"extension\":\"docx\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16282,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4993509/v1/73b40b76e47074d03e8bcc80.docx\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"The Genomic Evaluation of Space Travel and Research (GENESTAR) Manual for Biospecimen Collection, Biobanking and Omics Data Generation from Commercial Space Missions\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAs humanity looks towards long-duration missions and habitation in space, understanding the biological implications of space travel is crucial. Unlike ground studies, where rigorous scientific studies can be conducted by involving statistically significant sample cohorts, biospecimens available to study the effects of space are highly underpowered. As of July 2024, only 710 people have traveled into space (defined higher than 50 miles (80 km) above mean sea level, according to the U.S. Armed Forces, and NASA) [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Earlier biospecimen collections were driven in partnership with the National Aeronautics and Space Administration\\u0026rsquo;s (NASA) Human Research Program (HRP) studies [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. More recently, through the eXploration Platforms and Analog Definition (EXPAND) program, the Translational Research Institute for Space Health (TRISH) implemented proactive strategies in program management, processes, and infrastructure [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. These efforts will help enhance engagement with space flight providers, commercial launch providers, space biomedical researchers, and government agencies with the goal of making data-informed decisions.\\u003c/p\\u003e \\u003cp\\u003eBiological and physiological data from subjects have been collected and analyzed to assess the impact of space flights [\\u003cspan additionalcitationids=\\\"CR5 CR6\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. However, the details of the underlying molecular consequences of space flight are poorly understood, and having the biological data will be informative in analyzing rapid and subtle changes in the human body. A comparative assessment of various bio-fluids, including whole blood, plasma, urine, body swabs, stool and saliva, obtained from the subjects can reveal specific biomarkers, was previously reviewed [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e Advances in Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics have provided significant impact on human genetic research, cancer detection and treatment, and personalized medicine. Therefore, there is growing interest in the application of Omics technologies to study the effects of microgravity, radiation exposure, microbiome, and other environmental factors on individuals experiencing either short- or long-duration space flight. The resulting data can be valuable for advancing terrestrial health care as well [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Omics technologies were applied to analyze the data from the NASA Twin study and, more recently, Inspiration4, the first all-civilian orbital spaceflight mission [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Sequencing was also used to analyze Clonal Hematopoiesis of Indeterminate Potential mutations in a retrospective specimen cohort [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. A proof-of-concept study showed that exosome sequencing could be applied to study the pathophysiology of Space Associated Neuro-Ocular Syndrome in subjects [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. However, space health studies remain vastly underpowered. The recent surge in commercial space missions provide a unique opportunity to systematically collect biospecimens from crew using a standardized methodology for both immediate analysis and biobanking. Also, there is a critical gap in knowledge and know-how of how national and international participants can be enrolled in such Omics studies and how clinical grade reports detailing pathogenic variants and pharmacogenomics markers can be provided to them.\\u003c/p\\u003e \\u003cp\\u003eThe Genomics and Space Medicine project ( \\u0026lsquo;Space Omics\\u0026rsquo;) at the Baylor College of Medicine Human Genome Sequencing Center (BCM-HGSC) was initiated in August 2022, and is designed to study pre-, in-, and post-flight biological specimens using an array of Omics assays, including clinical Whole Genome Sequencing (WGS), research assays (RNA-Seq, microbiome, proteomics among others), and biobanking for future use to gain insights into the impact of space travel. Biospecimens from up to 40 individuals will be collected over a five-year period making this the largest Omics dataset and biobank in this field. The knowledge gained from the collected biospecimens and the Omics data from diverse subjects on these commercial missions will be crucial for future mission planning and to enhance human health in space and on Earth.\\u003c/p\\u003e \\u003cp\\u003eMultiple sites across the country will be collecting biospecimens using the standardized set of protocols presented here as a \\u0026lsquo;Genomic Evaluation of Space Travel and Research (GENESTAR) manual for biobanking and omics data generation. Biospecimen collections for the Axiom 2 private mission, which was a 10-day mission (May 21 \\u0026ndash; May 31) involving a four-person multinational crew to the International Space Station, is described here to demonstrate the methodology for specimen collection, processing, and biospecimen requirements. Additionally, 154 biospecimens from Axiom mission 3 (January 18, 2024 \\u0026ndash; February 9, 2024) have also been successfully collected using these protocols, and collections for a 3rd private space mission are underway.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eStandard operating procedures were developed for the collection of a wide range of biospecimens from each crew member (Fig.\\u0026nbsp;1). These samples were aliquoted or harvested to obtain either nucleic acids or derivative samples to carry out specified OMICS studies, such as genomic, transcriptomic, proteomic, microbiome, and single-cell sequencing assays (Table\\u0026nbsp;\\u003cspan\\u003e1\\u003c/span\\u003e). The remaining aliquots deposited in a biobank for future use along with the generated Omics data and through proper permissions, are accessible to other researchers.\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eBiospecimens collected, targeted omics assays and the minimum biospecimen quality and quantity requirements.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eBiospecimen\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAssay\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMinimum requirement\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eQuality\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWhole blood\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eDNA sequencing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWGS\\u0026dagger;-750 ng\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eRatio 260/280\\u0026thinsp;\\u0026ge;\\u0026thinsp;1.8, MW\\u0026thinsp;\\u0026gt;\\u0026thinsp;40kb\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWES\\u0026Dagger;-250 ng\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePAXgene RNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal RNA sequencing (RNA-Seq)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003e1\\u0026micro;g\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRIN\\u0026thinsp;\\u0026gt;\\u0026thinsp;6, DV 200\\u0026thinsp;\\u0026gt;\\u0026thinsp;70%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePlasma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eProteomics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e50 \\u0026micro;l\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eMetabolomics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e100 \\u0026micro;l\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePBMC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSingle-cell RNA sequencing (scRNA-seq)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,000 cells\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCell viability\\u0026thinsp;\\u0026gt;\\u0026thinsp;80%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ecfDNA\\u003csup\\u003e\\u0026sect;\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eCell-free DNA (cfDNA) sequencing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBody swabs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10 Sites\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" rowspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eMicrobiome Sequencing (16S rRNA, WGS, RNA-Seq)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 \\u0026micro;l\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003e16S rRNA data; number of reads\\u0026thinsp;\\u0026gt;\\u0026thinsp;rarefaction cut-off.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5 \\u0026micro;l\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSaliva\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 \\u0026micro;l\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStool\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 \\u0026micro;l\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"6\\\"\\u003e\\u0026dagger;WGS; Whole genome sequencing; \\u0026Dagger;WES: Whole exome sequencing; \\u0026sect;cfDNA: Cell-free DNA\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFigure\\u0026nbsp;1. GENESTAR Biospecimens Collections Schema.\\u003c/strong\\u003e Collection details shown here are for each of the pre-and post-flight timepoints including one-time collection of blood for CLIA-WGS. For a typical mission, there will be 2\\u0026ndash;3 pre-flight and 2\\u0026ndash;3 post-flight collections.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData dictionary based on Athena-Ohdsi\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA data dictionary was created using the Athena-Ohdsi database to describe primary biospecimens and the Omics assays used in the GENESTAR manual [\\u003cspan\\u003e13\\u003c/span\\u003e]. This dictionary described the 13 primary biospecimens obtained from collections such as, blood, body swabs, saliva, stool and urine, and their derivatives (Table\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e); and the six planned Omics assays (Tables\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). For each of these 19 entries, the field name, concept ID, concept code, concept name, Athena Domain name have been provided (Table\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e and Table\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). The data dictionary along with the sample metadata from the biobank will be submitted to the TrialX EXPAND database that will host the Space Omics project data.\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eData dictionary of the biospecimens collected.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eField Name\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConcept ID\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConcept code\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConcept Name\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAthena Domain\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood EDTA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40482922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e445295009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood specimen with EDTA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEDTA plasma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4000626\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e119361006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePlasma specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCPT PBMC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4229415\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCC-MIABIS-032\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePeripheral blood mononuclear cell\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBody Structure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood BCT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e37111674\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e726740008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCell free DNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObservation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBCT plasma\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4000626\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e119361006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePlasma specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood PAXgene RNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40480050\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e441673008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBody swab\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4122261\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e258503004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSkin swab\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4046280\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e122575003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine cell pellet\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4046280\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e122575003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine supernatant\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4046280\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e122575003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSaliva\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4001062\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e119342007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSaliva specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSaliva aliquots\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4001062\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e119342007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSaliva specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStool\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4002879\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e119339001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSaliva specimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpecimen\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eData dictionary of the sequencing assays planned\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eField Name\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConcept ID\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConcept code\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConcept Name\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAthena Domain\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRNA sequencing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e36659712\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e94820-8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWhole mRNA transcriptome sequence analysis in Blood or Tissue by Sequencing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMeasurement\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSingle cell sequencing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19387953\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD000092386\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSingle-Cell Gene Expression Analysis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObservation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProteomics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19399449\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD040901\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProteomics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObservation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMicrobiome sequencing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e36659879\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e94603-8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBacterial 16S rRNA gene\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMeasurement\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCell Free DNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1012141\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLP158077-0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCell free DNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObservation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMetabolomics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19401895\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD055432\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMetabolomics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObservation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed consent\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eprocess and personal data privacy\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants are enrolled into the Commercial Astronaut Data Repository (CADRE) IRB protocol of which, the Space Omics project is an integral part. This enrollment is carried out at an informed consent briefing meeting where the EXPAND PI, study coordinators, clinical geneticist and subject experts participate. At this briefing, participants are taken through the study\\u0026apos;s purpose, risks, and potential benefits, as well as how their genetic data will be used, stored, and shared. Navigating international laws, such as the EU\\u0026rsquo;s GDPR or the US\\u0026rsquo;s HIPAA Privacy Rule, and binding to the laws of the host country and space agency that a subject belongs to can pose unique challenges to this enrollment process. The subjects can opt out of the Space Omics enrollment at any point and to address this, biospecimen destruction procedures are in place in the GENESTAR protocol. Each subject receives a coded ID which is also grouped with a mission ID to accommodate tracking of information for the repeat flyers. These IDs are shared for use in the GENESTAR protocol to collect biospecimens. Upon receiving the biospecimens at BCM-HGSC, these IDs go through a second round of coding (de-identification) to enter into the biobank and those are the IDs that are used to generate Omics data through all stages of the project and also data submission to the TrialX EXPAND database. These are the IDs and the associated data that will be released to the public (including this report) which helps to further blind the data from subjects as well as the private agency responsible for that mission (Axiom Space in this report).\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec3\\\"\\u003e\\n \\u003ch2\\u003eBiobanking\\u003c/h2\\u003e\\n \\u003cp\\u003eA functional LIMS-enabled biobank to receive, store, and track Space Omics samples has been created. The Exemplar Laboratory Information Management System (LIMS) is built upon a Java 2 Platform, Enterprise Edition (J2EE) architecture and utilizes a Tomcat server for its operation. This LIMS has exceptional adaptability, offering extensive configuration options to align seamlessly with any laboratory workflow. It is designed to efficiently manage biospecimen tracking from initial intake, through library preparation and sequencing, to the final quality control stages. Moreover, this system is engineered for high throughput and minimal latency, featuring a user-friendly, code-free search functionality. Security is a paramount concern, with the system employing a comprehensive security framework and offering role-based access control to accommodate the varied roles within laboratory staff. Additionally, the Exemplar LIMS supports seamless integration capabilities with laboratory automation systems and the analysis pipeline, enhancing operational efficiency and data coherence across laboratory processes (Fig.\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eUpon arrival, barcodes on collected biospecimens are scanned into LIMS and marked as \\u0026ldquo;received\\u0026rdquo;. The biospecimen number and barcodes relative to the information in the biospecimen intake forms along with the other metadata are cross checked, and any discrepancies are noted. At this step a second round of de-identification of the subject IDs is done and then uploaded into LIMS. Any problems with the sample condition are also recorded. Samples are either directly stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC or aliquoted into smaller vials for storage. In some instances, such as urine collected in a specimen container and whole blood collected in the CPT tubes, the primary biospecimens are processed to separate different components (PBMCs or urine cell pellet, etc). In such a scenario, new barcodes are created to label the tubes containing the aliquots and the derivative products, and that information is also entered into the LIMS (Fig.\\u0026nbsp;\\u003cspan\\u003e2\\u003c/span\\u003e). Biospecimens are then advanced to different Omics pipelines and all major lab/informatic processes that the biospecimens undergo. Any aliquots made from that biospecimen are additionally barcoded and linked to the original biospecimen barcode. The chain of custody and biospecimen tracking in the LIMS are achieved by scanning the barcode on the biospecimen tube into LIMS before each processing step to generate Omics data. BCM-HGSC is the genomics data provider. Both the Omics data and Biospecimen metadata will be submitted to the EXPAND database created and managed by TrialX, a clinical research and space health informatics company also supported by TRISH. Requests for data and sample accesses will be managed by EXPAND Data Privacy and Release Board (DPRB).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003ch3\\u003eOverview of biospecimens and collection time points\\u003c/h3\\u003e\\n\\u003cp\\u003eThe type of samples selected for collection were chosen based on their minimally invasive, cost- and time-effective properties, easily accessible with the requirement of non-specialized equipment or expertise, and high return in data relevance to health assessment by variable Omics (Fig.\\u0026nbsp;1). Biospecimens presented here were collected from four Axiom-2 mission subjects across six collection time points over four months as detailed in Table\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e. The biospecimens were collected at 90, 30, and 3 days pre-launch (L-90, L-30, and L-3), the day of return (R\\u0026thinsp;+\\u0026thinsp;0) and at two other time-points (R\\u0026thinsp;+\\u0026thinsp;5 or R\\u0026thinsp;+\\u0026thinsp;6, R\\u0026thinsp;+\\u0026thinsp;13). Blood was collected for the CLIA-WGS from three subjects on R\\u0026thinsp;+\\u0026thinsp;13 and from the fourth subject on R\\u0026thinsp;+\\u0026thinsp;28 (Table\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e). In total, for the Axiom-2 mission, 339 primary samples were collected from the four subjects, (Table\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e). No inflight collections were performed. All but L-3 collections happened at Axiom Space Inc. headquarters in Houston, Texas. The biospecimens were shipped within ~\\u0026thinsp;2h after collection for initial processing at the Human Genome Sequencing Center (HGSC) at Baylor College of Medicine (BCM), Houston, Texas. While for the L-3 timepoint, biospecimens were collected at Kennedy Space Center (KCS), Florida using the same SOP. The initial processing of those L-3 biospecimens was performed at Burnett School of Biomedical Sciences (BSBS), the University of Central Florida, Florida, and shipped back to BCM-HGSC at recommended shipping conditions as in the GENESTAR manual for further processing and biobanking.\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eAxiom space mission 2 Biospecimens collection details.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"7\\\"\\u003e\\n \\u003cp\\u003eNumber of primary samples collected\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCollection\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTime of collection*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eL-90\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eL-30\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eL-3\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR\\u0026thinsp;+\\u0026thinsp;0\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR\\u0026thinsp;+\\u0026thinsp;5/6\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR\\u0026thinsp;+\\u0026thinsp;13\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eR\\u0026thinsp;+\\u0026thinsp;28\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood EDTA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood CPT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood BCT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood PAXgene DNA CLIA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBlood PAXgene RNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBody swab\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSaliva\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStool\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUrine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTOTAL\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e76\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e76\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e72\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e24\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e78\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e12\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e339\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\"\\u003e\\u0026Dagger;L-: day before launch; *R+: day post-flight\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003ch3\\u003eSubject de-identification process\\u003c/h3\\u003e\\n\\u003cp\\u003eSpace flight providers assign random initial de-identified subject IDs to the participants and HGSC assigns the second de-identified IDs. The second de-identification of the subject IDs involved generating a 9-character alphanumeric code using a random code generation script. These codes will also have a prefix SO (for Space Omics). The IDs will not be used more than once even if the subject is a repeat flyer. These IDs will be assigned upon sample receipt at the time of biobank intake.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBiospecimens collections and their derivatives\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eI. Blood collections\\u003c/h3\\u003e\\n\\u003cp\\u003eFor research purposes, blood samples were collected via venipuncture from each crew member at six time points. One blood biospecimen was collected post-return to perform genome sequencing and generate clinical data.\\u003c/p\\u003e\\n\\u003cp\\u003eThe collection tubes were labeled with the subject ID, the collection number, analyte, and the aliquot number (Example: Subject ID-1-Saliva-01). Additionally, each tube was labeled with a unique barcode to ensure accurate and efficient identification of the samples.\\u003c/p\\u003e\\n\\u003cp\\u003eWhole blood was collected at L-90, L-30, L-3, R\\u0026thinsp;+\\u0026thinsp;0, R\\u0026thinsp;+\\u0026thinsp;6 and R\\u0026thinsp;+\\u0026thinsp;13. The R\\u0026thinsp;+\\u0026thinsp;13 collection was performed on only two subjects for a total of 22 biospecimen.\\u003c/p\\u003e\\n\\u003ch3\\u003ea.K2 EDTA tubes\\u003c/h3\\u003e\\n\\u003cp\\u003eWhole blood was collected at six time points, L-90, L-30, L-3, R\\u0026thinsp;+\\u0026thinsp;0, R\\u0026thinsp;+\\u0026thinsp;6 and R\\u0026thinsp;+\\u0026thinsp;13. Each collection involved three K2 EDTA tubes of 3 mL each. The R\\u0026thinsp;+\\u0026thinsp;13 collection was for only two subjects, for a total of 62 EDTA blood biospecimen.\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eOne tube of K2 EDTA from each subject and collection was used to isolate plasma that was then aliquoted into four 300 \\u0026micro;L aliquots. For the Axiom-2 mission this added up to 88 plasma aliquots.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eThe second K2 EDTA tube was used for DNA extraction. To avoid batch effect, the DNA from the blood collected at different time points was obtained through a single isolation. The total DNA yield varied between 26.38 \\u0026micro;g and 92.0 \\u0026micro;g and the DNA yield per mL of blood was between 13.1 \\u0026micro;g/mL and 46 \\u0026micro;g/mL, respectively (Fig.\\u0026nbsp;\\u003cspan\\u003e3\\u003c/span\\u003e). The 260/280 ratio of \\u0026ge;\\u0026thinsp;1.8 indicated no major protein contamination. The DNA blood isolation from astronaut SO5QL3KG6H resulted in higher yield per mL of blood, followed by SO6NJ3UF7T over the different collection times. The average yield per mL of blood over collection time was 32 \\u0026micro;g: SO6NJ3UF7T, 40 \\u0026micro;g: SO5QL3KG6H, 23 \\u0026micro;g: SO8KF3EZ1C, 21 \\u0026micro;g: SO9WB8CN8A. The third K2 EDTA tube and the plasma aliquots were stored as-is at -80\\u003csup\\u003eo\\u003c/sup\\u003eC for future use.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cdiv id=\\\"Sec8\\\"\\u003e\\n \\u003ch2\\u003eb. PAXgene blood DNA tubes for CLIA-WGS sequencing\\u003c/h2\\u003e\\n \\u003cp\\u003eWhole blood for the purpose of generating CLIA-WGS data was collected from all four subjects, with each subject providing blood in 2.5 mL in PAXgene DNA tubes at R\\u0026thinsp;+\\u0026thinsp;5 or R\\u0026thinsp;+\\u0026thinsp;28 (Table\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e). Total DNA yields varied between 45.08 \\u0026micro;g and 89.76 \\u0026micro;g, while the DNA yields per milliliter of blood were between 22.54 \\u0026micro;g/mL and 44.88 \\u0026micro;g/mL. The yield per mL of blood collected the same day showed minimal variation between the PAXgene and the EDTA collection tubes. This DNA was utilized to generate CLIA-WGS data and was analyzed for reportable genetic variants in a specific set of genes.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003ch3\\u003ec. Cell preparation tubes (CPT) for PBMC collections\\u003c/h3\\u003e\\n\\u003cp\\u003eFor each collection, 4 mL of blood was collected in CPT tubes from which Peripheral Blood Mononuclear Cells (PBMCs) were isolated. The final volume of PBMCs harvested was between 1.5\\u0026ndash;3 mL among the subjects. PBMC counts and viability for subject SO6NJ3UF7T from L-30 and L-3 was negligible and failed at this QC step. For the remaining 10 biospecimens, cell viability was \\u0026gt;\\u0026thinsp;80% and the cell count was between 800 and 1600 cells/\\u0026micro;L. The total number of PBMCs harvested among these 10 samples was between 1.3 and 3.2\\u0026nbsp;million, while the minimum required number of cells for single-cell sequencing is only 1,000 cells (Fig.\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003ed. Cell-free DNA (cfDNA) BCT tubes\\u003c/h3\\u003e\\n\\u003cp\\u003eBetween 4\\u0026ndash;6 mL of plasma was isolated from the 22 blood samples collected in 10 mL BCT tubes and the plasma was stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC until cfDNA extractions. Cell-free DNA was extracted using the entire volume of plasma obtained. The DNA yields and fragment sizes when evaluated using the High Sensitivity DNA Bioanalyzer kit showed the yields between 0.65 and 12.8 ng/mL of plasma (Fig.\\u0026nbsp;\\u003cspan\\u003e5\\u003c/span\\u003e). Typical cfDNA size profiles of mononucleosome cfDNA (range: 75 and 250 bp, with a166bp peak); a portion of di- (range: 300\\u0026ndash;400 bp), and tri-nucleosomes (range: 450\\u0026ndash;650 bp) representation was also seen.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec11\\\"\\u003e\\n \\u003ch2\\u003ee. PAXgene blood RNA tubes\\u003c/h2\\u003e\\n \\u003cp\\u003eRNA was isolated from the 22 blood samples collected in 2.5 mL RNA PAXgene tubes (Table\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e). RNA yield was between 0.58 \\u0026micro;g and 2.55 \\u0026micro;g per mL of blood (Fig.\\u0026nbsp;\\u003cspan\\u003e4\\u003c/span\\u003e). The RIN scores of these samples were between 7.1 and 9.4 and DV200% values between 84% and 94% (Fig.\\u0026nbsp;\\u003cspan\\u003e6\\u003c/span\\u003e). As noticed earlier, the astronaut SO5QL3KG6H had a higher average RNA yield (1.90 \\u0026micro;g per mL of blood) than the others.\\u003c/p\\u003e\\n \\u003cp\\u003eIn summary, a total of 102 blood samples which includes 98 samples for research purposes: K2 EDTA DNA (22), RNA (22) EDTA plasma (22) and BCT Plasma (22) and 10/12 PMBC biospecimens and four samples of DNA in PAXgene tubes for CLIA-WGS, with passing QC metrics for Omics data generation, indicating a 98% success rate.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\"\\u003e\\n \\u003ch2\\u003eII. Non-blood collections\\u003c/h2\\u003e\\n \\u003cp\\u003eBody swab, saliva, stool and urine samples were collected from each crew member across four time points: L-90, L-30, L-3 and R\\u0026thinsp;+\\u0026thinsp;6. 16S rRNA sequencing data was generated for samples collected at L-30, L-3, and R\\u0026thinsp;+\\u0026thinsp;6 time points. The 16S sequence reads were mapped against the SILVA Database version 138 which contains sequences from the v4 region of the 16S rRNA gene. Nucleic acids were not extracted from L-90 biospecimens.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\"\\u003e\\n \\u003ch2\\u003ea. Body swabs\\u003c/h2\\u003e\\n \\u003cp\\u003eA total of 160 body swab samples from 10 different body sites were collected (Supplementary Fig.\\u0026nbsp;1). DNA was extracted using 400 \\u0026micro;l from 120 out of 160 body swab samples, excluding the L-90 biospecimens. Successful sequencing libraries were obtained from 119 samples with library yields between 98.3\\u0026ndash;1000 ng. UMB sample from subject SO5QL3KG6H failed at library preparation. The 16S gene sequencing yielded mapped reads between 18 and 145,071 reads. The rarefaction cut off was set at 562 mapped reads and 107 samples passed this metric. Out of the 12 samples that did not meet this cut-off, one was saliva and 11 were swabs from different body sites and from L-30 and R\\u0026thinsp;+\\u0026thinsp;6 timepoints. Five of these 11 samples were from subject SO8KF3EZ1C, four from SO9WB8CN8A and one each from the other two subjects.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\"\\u003e\\n \\u003ch2\\u003eb. Urine\\u003c/h2\\u003e\\n \\u003cp\\u003eSixteen urine biospecimens between 90\\u0026ndash;105 mL were collected in a specimen container from which, supernatant and cell pellets were harvested and stored as aliquots at -80 \\u003csup\\u003eo\\u003c/sup\\u003eC. Supernatant aliquot volumes are between 30\\u0026ndash;35 mL while the cell pellets aliquots are 250 \\u0026micro;l each.\\u003c/p\\u003e\\n \\u003cp\\u003eSuccessful 16S rRNA libraries were generated from all 12 samples from whom DNA was extracted, with library yields ranging between 126.0\\u0026ndash;826.0 ng. All the urine samples have generated mapped reads ranging from 5,930\\u0026ndash;145,071 mapped reads.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\"\\u003e\\n \\u003ch2\\u003ec. Saliva\\u003c/h2\\u003e\\n \\u003cp\\u003eA total of 15 saliva samples were collected. The biospecimen volumes varied from 2 mL to 6.8 mL and were divided into 250 \\u0026micro;L aliquots. One saliva aliquot from each biospecimen was used for DNA extraction. Successful 16S rRNA libraries were generated for all the samples with library yields ranging between 111.1 -492.9 ng. The 16S gene sequencing yielded mapped reads from each saliva sample between 305 and 31,847 reads. The biospecimen collected at L-3 from subject SO8KF3EZ1C failed the rarefaction cut-off as it generated only 305 mapped reads.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\"\\u003e\\n \\u003ch2\\u003ec. Stool\\u003c/h2\\u003e\\n \\u003cp\\u003eA total of 16 stool samples were collected and the DNA extractions were performed on 200 \\u0026micro;l of the sample. Bacterial 16S rRNA sequencing was performed using 2 \\u0026micro;l of the extracted DNA from the samples, without quantification. Libraries were successfully generated from all the 12 samples, excluding the L-90 samples. A minimal variation in the library yields (range of 3.01\\u0026ndash;3.5 \\u0026micro;g) was observed among these samples. The 16S gene sequencing yielded mapped reads between 61,820 and 87,796 mapped reads.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\"\\u003e\\n \\u003ch2\\u003e16S rRNA data as a QC\\u003c/h2\\u003e\\n \\u003cp\\u003eBacterial 16S rRNA gene sequencing data from the non-blood samples is presented here to document biospecimens quality and the efficiency of sequencing technology employed. Out of 155 biospecimens across four analytes selected for 16S rRNA sequencing, one failed at the initial quality control step and did not proceed to library preparation. Additionally, 12 samples did not produce sufficient mapped reads and were considered failures. The sequencing data was successfully generated for the remaining 142 biospecimens, representing a 91.61% success rate. Overall, the microbiome data was similar across the four analytes, the subjects and time points studied for observed operational taxonomical units (OUTs) and for Shannon alpha diversity (Fig.\\u0026nbsp;\\u003cspan\\u003e7\\u003c/span\\u003e) confirming the usability of the collected biospecimen. The mean number of observed OTUs showed slight variation with a median of 35 and the Shannon diversity index indicated a more or less even spread of microbial abundance among all the subjects and time points. Microbiota did not change significantly among the subjects, across the time points, with adjusted p-values in the range of 0.5\\u0026ndash;0.9 for richness and 0.8\\u0026ndash;0.9 for Shannon diversity.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThere has been a growing interest in recent years both from government and private industry to explore space including space tourism, returning humans to the moon, and planning missions to Mars [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. It is therefore important to understand the effects of space travel on human health. Collection of biospecimens and generation of Omics data are essential to understand the genetic, physiological, cellular, and microbiome changes related to space travel. Rigorous Standard Operating Procedures (SOPs) for specimen collection have been developed along with a LIMS-enabled biobank at HGSC that tracks specimen details and storage information. This document, called Genomic Evaluation of Space Travel and Research (GENESTAR) incorporated biospecimen collection procedures tailored to support all current and anticipated biological and genomic measurements.\\u003c/p\\u003e \\u003cp\\u003eIn GENESTAR, we have standardized the complete set of protocols, based on our knowledge of biospecimens requirements for genomics and other molecular assays, as well as those adopted from earlier studies, NASA-Human Research Program core measures [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] and Space Omics and Medical Atlas (SOMA) [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e] as described recently for the Inspiration4 mission. For the first time in the GENESTAR manual, a data dictionary, a LIMS enabled biobank, and a customized provision for subjects to obtain CLIA-WGS are provided. Coupled with the informed consent process, GENESTAR fills these critical gaps that were not addressed by those previous studies.\\u003c/p\\u003e \\u003cp\\u003eHere, we present our collection efforts based on biospecimens gathered from the Axiom 2 mission, representing a typical commercial crew mission from a subject cohort that spent 10 days in microgravity. Several of the presented biospecimen collection methods provide a validation of the recently reported methods, which were intentionally synchronized to allow for joint biospecimens collections by us and the SOMA procedures [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e] for a private mission. Further, our group has exclusively collected samples from Axiom-2 and Axiom-3 missions, in total from six individuals, which will allow for easy comparison of results across different missions.\\u003c/p\\u003e \\u003cp\\u003eAs the physiological changes are very dynamic during a standard spaceflight mission, obtaining Omics measures across multiple time points before and after the travel to space is important for scientific significance. Pre-flight biospecimen collections help assess and establish a baseline and biospecimen collections shortly after the return, help assess the recovery process and any long-term health implications. Collection of biospecimens from at least 2 to 3 pre-flight and 2 to 3 post-flight time points is recommended so that any outliers in data can be identified, then either ignored or accounted for.\\u003c/p\\u003e \\u003cp\\u003eThe primary biospecimens collected include blood, body swabs, saliva, stool and urine samples, that have provided secondary products such as plasma, urine cell pellet and supernatant. This multi-tiered approach in biospecimens collection allows for a comprehensive multi-omics analysis. Blood, urine, stool, swabs, and saliva provide cost-effective, non-invasive and easy sampling options and therefore inherently qualify as appropriate biospecimen types to obtain Omics data from subjects [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. We are offering additionally the option of a CLIA-WGS for the first time to the participants [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. The HGSC launched a CAP/CLIA certified clinical laboratory in 2016 (CLIA# 45D2027450) and therefore can deliver clinical genetic testing data and interpretation, enabling return of clinical results reports to individuals, via their clinical caregivers. Through CLIA-WGS, the subjects in the Space-Omics program may elect to receive results for reportable genetic variants in 205 genes, including American College of Medical Genetics and Genomics (ACMG) 59 gene set recommended for secondary findings reporting [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. A ‘Space Omics’ clinical-grade report will be returned to the participants unless the participants specifically opt-out of the results return.\\u003c/p\\u003e \\u003cp\\u003eBoth our research and NASA HRP aim to advance our understanding of human space exploration. However, our research specializes in genomics and multi-omics studies, which offer a molecular perspective and integrate diverse Omics data types to elucidate the effects of spaceflight on human biology.\\u003c/p\\u003e \\u003cp\\u003eA data dictionary using the Athena-Ohdsi database components to describe biospecimen and Omics assay types is described. This will facilitate efficient information dissemination to other users globally, for data analysis and interpretation across various missions [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eA versatile LIMS-enabled biobank to store and track Space Omics samples has been developed. All biospecimens and their derivatives when stored in the biobank are barcoded and tracked properly. LIMS at BCM-HGSC also provides support for tracking of samples as they progress through the different Omics pipelines as well as to track and retrieve the generated data. Overall, the established biobank enhances the efficiency, accuracy, and security of biospecimen management which are essential for a robust biobank to function [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn the presented study, the extracted DNA and RNA underwent quality assessment and were determined to be of sufficient quality and quantity to conduct multiple genomics assays listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. A proteomics assay run on plasma samples is particularly valued for its ability to simultaneously provide highly sensitive and specific measurements of multiple proteins from small biospecimen volumes [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. As plasma contains various molecules such as proteins, metabolites, nucleic acids, and lipids, its analysis may reveal exceptionally specific biomarkers associated with space travel [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Plasma will be used to generate high-throughput proteomics datasets, such as Olink Explore HT. Cell-free DNA is a potential biomarker for diagnosis and prognosis in conditions such as cancer, cardiovascular and neurological diseases [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Further, cfDNA can be used to measure physiological stresses during space missions [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. The PBMCs derived from blood samples were of acceptable quality and quantity, allowing for the generation of single-cell RNA sequence data (Manuscript under preparation). DNA extracted from both low biomass samples including body swabs, saliva, and urine, and high biomass biospecimen including stool was utilized for 16S rRNA gene sequencing. The sequencing methods were adapted from those developed for the NIH-Human Microbiome Project and the Earth Microbiome Project [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. 16S rRNA gene sequencing was favored over microbiome WGS for cost reasons and the extracted nucleic acids are stored for future use. A total of 155 samples consisting of 12 urine, 12 stool, 120 body swabs, and 11 saliva samples were submitted for bacterial microbiome sequencing. Successful sequence data was obtained for 142 out of 155 samples (91.6%). Twelve samples were body swab samples out of 13 failed samples. Out of the four subjects, SO8KF3EZ1C had the most samples that failed followed by SO9WB8CN8A. This outcome can be attributed to various factors, including but not limited to disparities in the biospecimen collection times among different individuals, contact location, and in the case of body swabs, the pressure applied during the collection process [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eNevertheless, microbiome sequencing generated 91.6% of usable data underscoring the validation of the collection methodology, efficiency and quality of both the sampling and sequencing processes. As a further confirmation of this data's usability, Alpha diversity and richness measurements were performed to QC the microbial diversity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThese biospecimens are being collected at different locations across the country using the GENESTAR manual and shipped to HGSC after initial processing. The unpredictability of shuttle schedules can cause sample collection delays, making it difficult to collect biospecimens as planned, especially if collections happen during late hours and weekends. Shipping samples from collection sites to the local processing labs and back to BCM-HGSC in Houston for long term storage can be challenged by transport and weather-related interruptions and must be documented thoroughly for any deviations. GPS trackers are included in the sample shipments and extra dry ice is packed to avoid running into problems due to such interruptions.\\u003c/p\\u003e \\u003cp\\u003eFurther enhancements to the GENESTAR manual are also planned. For instance, the in-flight whole blood collections are still not part of the manual. Traditionally, blood in-flight is collected using venipuncture. However, there are several microdevices that allow easy self-collection of blood for diagnostic and research purposes. These microdevices can collect several hundred microliters of whole blood, enough to isolate plasma/serum to perform certain Omics assays like proteomics and metabolomics. Our group is evaluating them for use in the GENESTAR protocol and, if found effective in microgravity, will be used for in-flight sample collections. The GENESTAR manual will also be used to collect biospecimens from age and gender matched controls to isolate noise in the data and conduct ground-based analogue experiments such as centrifugation training. In summary, the methodology presented lays a strong foundation for future studies and allows for the comparison of existing Omics datasets.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003cp\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section3\\\"\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003c/div\\u003e \\u003cp\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003c/div\\u003e \\u003cp\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section3\\\"\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section3\\\"\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e \\u003cstrong\\u003eData dictionary based on Athena-Ohdsi\\u003c/strong\\u003e \\u003c/p\\u003e\\u003cp\\u003e A detailed data dictionary for each of the primary biospecimens collected has been developed using the Athena-Ohdsi database to standardize biospecimen types and medical terminology, aligning codes with the Observational Medical Outcomes Partnership (OMOP) standards, which are a set of guidelines and tools designed to standardize the collection, formatting, and analysis of observational healthcare data. OHDSI Standardized Vocabularies comprise over 10\\u0026nbsp;million concepts from 136 vocabularies [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Athena is a comprehensive vocabulary repository and searchable database within OHDSI.\\u003c/p\\u003e\\u003ch2\\u003eI. Blood samples\\u003c/h2\\u003e\\u003ch2\\u003eBlood draws\\u003c/h2\\u003e\\u003cp\\u003eVenipuncture was performed in the antecubital fossa after sanitizing that area and blood was drawn using a butterfly needle (BD Biosciences, Cat#367281) and a lure lock vacutainer adapter (McKesson, Cat#364815/458363). The blood collection volumes involved, 3 mL of blood in each of the three K2 EDTA blood collection tubes (VWR, Cat#367856), 4 mL of blood in one CPT cell preparation tube (CPT, BD Biosciences, Cat#362760), 10 mL of blood in one Streck cell-free DNA BCT tube (cfDNA BCT, Streck, Cat#230470) and 2.5 mL of blood in one PAXgene blood RNA tube (RNA PAXgene, Fisher, Cat#762165), for a total of 25.5 mL collected from each subject at each time point. An additional 2.5 mL of blood was collected from each subject in CAP CLIA validated PAXgene DNA tubes (VWR, cat# 10589-986) during the second collection after the return, to be used exclusively for CLIA-WGS data generation. Samples collected locally in Houston were immediately transported to the Human Genome Sequencing Center at Baylor College of Medicine for processing. Blood samples collected in Florida at the Kennedy Space Center (KCS), were transported to the University of Central Florida, within 2 hours for initial processing, which included preparation of PBMCs and plasma isolation, as discussed below.\\u003c/p\\u003e\\u003ch2\\u003eProcessing of blood samples\\u003c/h2\\u003e\\u003cp\\u003eWhole blood was collected at six time points from the four subjects at L-90, L-30, L-3, R + 0, R + 6 and from 2 subjects at the R + 13 timepoint.\\u003c/p\\u003e\\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003eK2 EDTA tubes.\\u003c/b\\u003e Whole blood in BD Hemogard K2 EDTA tubes was collected from each subject in three tubes of 3 mL each per time-point. DNA was isolated from one tube (see section \\u003cspan refid=\\\"Sec23\\\" class=\\\"InternalRef\\\"\\u003e\\u003cb\\u003eNucleic Acid Extractions from blood samples\\u003c/b\\u003e\\u003c/span\\u003e), plasma from the second tube and the third tube was stored at − 80\\u003csup\\u003eo\\u003c/sup\\u003eC as received.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e\\u003cp\\u003ePlasma isolation: Plasma was isolated from one of the K2 EDTA blood samples collected from each subject at each time point. The blood was centrifuged at 1,000g for 10 minutes at room temperature. The plasma was collected and aliquots of 300 µL were distributed into four matrix tubes (ThermoFisher, Cat#3741-WP1D-BR) of 300 µL to avoid additional freeze-thaw cycles. The aliquots were stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC.\\u003c/p\\u003e\\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003ePAXgene blood DNA tubes.\\u003c/b\\u003e Blood (2.5 mL) was collected in PAXgene tubes for CLIA-WGS at R + 5 from 3 subjects and at R + 28 from the fourth subject and tubes were stored at -20c. DNA was isolated using a CAP-CLIA validated protocol (see below section \\u003cspan refid=\\\"Sec23\\\" class=\\\"InternalRef\\\"\\u003eNucleic Acid Extractions from blood samples\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003eCell preparation tubes (CPT) for PBMC collections.\\u003c/b\\u003e Blood samples collected from each subject and from each time point were mixed immediately by gently inverting the CPT tube eight to 10 times before centrifuging at 1,800g for 20 minutes at room temperature. Half of the plasma was aspirated without disturbing the cell layer and the cells were transferred to a 15 mL conical tube. 2% FBS (Thermo Fisher, cat# 26140079) in PBS (Gibco, cat#10010-023) was added to resuspend the cells and the volume was brought to 15 mL. The cells were mixed gently by inverting five times and then centrifuged at 120g with break-off for 10 minutes. The supernatant was aspirated and discarded without disturbing the cell pellet. The pellet was dislodged by tapping the tube with index finger, resuspended in 2% FBS in PBS and the volume was brought to 10 mL. The pellet in FBS-PBS was mixed gently by inverting five times followed by a spin at 120g for 10 minutes. The supernatant was discarded, and the pellet was resuspended in 1 mL of RPMI (Cytiva, cat#SH30027.01) medium. An equal volume of freezing medium [30% DMSO (MP Biomedicals, cat#194818) 40% FBS (Thermo Fisher, cat# 26140079), and 30% RPMI (Cytiva, cat#SH30027.01,)] was added to the cell suspension. The cell suspension was then transferred into two cryovials, 1 mL each. The cells were cryopreserved using an isopropanol freezing container and then placed in -80\\u003csup\\u003eo\\u003c/sup\\u003eC freezer overnight. These cryovials were stored in liquid nitrogen until use.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e\\u003cp\\u003e \\u003cb\\u003eCell counting\\u003c/b\\u003e: The cell suspension (50 µL) was mixed with trypan blue (50 µL) (Sigma-Aldrich Cat. No. 72-57-1) at a 1:1 ratio and 10 µL of the mixture was loaded onto the hemocytometer (Bulldog Bio, DHC-N420). Then, the hemocytometer was placed on the microscope stage (Zeiss Axio Vert. A1), optimally focused, and manually counted to determine the total, live, and dead cell density and viability.\\u003c/p\\u003e\\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003eBlood BCT.\\u003c/b\\u003e Blood samples were collected in cell-free DNA (cfDNA) BCT tube (Streck, Cat#230470) at a volume of 10 mL. Each biospecimen was centrifuged at 1,600g for 10 minutes at room temperature. Plasma was separated from each sample, transferred into 15 mL conical tube and stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC until cfDNA extractions. The cfDNA extractions were performed as described below.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cb\\u003ePAXgene blood RNA tubes.\\u003c/b\\u003e Blood samples were collected in PAXgene blood RNA tube (Fisher, Cat#23-021-01) at a volume of 2.5 mL from each subject. The RNA was isolated as described below.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e\\u003ch2\\u003eNucleic Acid Extractions from blood samples\\u003c/h2\\u003e\\u003cp\\u003eGenomic DNA was extracted from K2 EDTA tubes and PAXgene blood collection DNA tubes on the Chemagic Prime 8 using the Chemagic Prime DNA blood kit (Revvity, cat# CMG-1497) according to the manufacturer’s instructions in a CAP-CLIA certified laboratory. DNA quality and quantity was assessed by electrophoresis and fluorescent nucleic acid stain (PicoGreen, Thermo Fisher).\\u003c/p\\u003e\\u003cp\\u003eCell-free DNA was extracted from the BCT tube using the Apostle MiniMax High-efficiency cfDNA Isolation kit (VWR, cat# 76409-757), as per manufacturer’s instructions. The quantification and fragment size profiling of cfDNA was determined using a 2100 bioanalyzer (Agilent Inc., Santa Clara, CA, USA) with the Agilent High-Sensitivity DNA kit. (Part #5067 − 4626, Agilent) and the cfDNA was stored at -20°C until further use.\\u003c/p\\u003e\\u003cp\\u003eRNA was extracted from the PAXgene blood collection RNA tubes on the Chemagic Prime 8 using the Chemagic Prime total RNA blood 4k kit (Revvity, cat# CMG-1484) according to the manufacturer’s protocol. The RNA was utilized for total RNA sequencing and the remaining RNA was stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC for future use. RNA quality (RIN and DV200) and quantity were assessed by Agilent Bioanalyzer.\\u003c/p\\u003e\\u003ch2\\u003eII. Non-blood samples\\u003c/h2\\u003e\\u003cp\\u003e \\u003cb\\u003eCollection of non-blood samples.\\u003c/b\\u003e Body swab, saliva, stool and urine samples were collected from each crew member across four time points: L-90, L-30, L-3 and R + 6 and were proceeded as below until nucleic acids were extracted.\\u003c/p\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eBody swab sampling.\\u003c/b\\u003e The ten body swab collections involved eight wet and two dry swabs. The wet body swab locations included post-auricular (EAR), axillary vault (PIT), volar forearm (ARM), occiput (NAP), umbilicus (UMB), gluteal crease (GLU), glabella (TZO), and toe web space (WEB) while the two dry body swab collections included oral (ORC) and nasal (NAC) (Supplementary Fig.\\u0026nbsp;1 and Supplementary Table\\u0026nbsp;1). For wet swab collections, the swab (Isohelix, Cat#MS-02) was moistened in nuclease free water and rubbed back and forth on the skin for about 25–30 times along the surface applying firm pressure while rotating the swab head continuously. The swab was then placed into the matrix tube provided (ThermoFisher, Cat#3741-WP1D-BR) with 400 µL of DNA/RNA shield (Zymo Research, Cat#R1100-50). The biospecimen collection process was repeated for all seven other wet swab locations. Buccal swab collection was performed by inserting the dry swab into the mouth and rubbing firmly against the inside of the cheek or underneath the lower or upper lip for about a minute. The nasal swabs were collected by inserting the entire soft tip of the dry swab into one nostril until resistance was experienced. The swab was rubbed in a circle around the nostril four times for at least 15 seconds. The process was repeated in the other nostril. Swabs were returned to the RNA/DNA shield (Zymo Research, Cat#R1100-50) as mentioned above. All the swabs were stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC until nucleic acid extractions were performed.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSaliva sampling.\\u003c/b\\u003e Crude saliva was collected from each subject into an OMNIgene ORAL (DNAgenotek, Cat# OME-505) saliva collection tube that contains a solution for collection, stabilization, storage, and transportation of the sample. Each subject was allowed to spit repeatedly into the saliva collection tube until the “fill to” line, which is equivalent to 3 mL. The saliva was mixed by inverting the tube. 250 µL aliquots were distributed into cryovials under sterile conditions and the aliquots were stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eStool sampling.\\u003c/b\\u003e Stool biospecimen collections were carried out in DNA Genotek OMNIgene Gut (OMR 205) tubes using the accessory and spatula provided in the kit. Typically, 545 mg of stool is collected and mixed with the stabilizing buffer supplied in the tube for a total of 4 mL. The samples were stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC until nucleic acid extractions were performed.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eUrine sampling.\\u003c/b\\u003e Approximately, 90 mL of the urine sample from each subject were collected in a sterile Samco Bio-Tite Specimen Container (SAMCO, Cat#13-711-65) to which 6.3 mL of urine conditioning buffer (Zymo research, Cat# D3061-1-140) is added immediately and mixed thoroughly. From each container, three aliquots of 30 mL each were prepared in 50 mL conical tubes. The samples were centrifuged at 3,000g for 15 minutes at room temperature to obtain a cell pellet and supernatant. The supernatant was transferred into clean 50 mL conical tubes and stored at − 80\\u003csup\\u003eo\\u003c/sup\\u003eC while the urine cell pellet was resuspended in 250 µL of DNA/RNA shield (Zymo Research, Cat#R1100-50) and stored at -80\\u003csup\\u003eo\\u003c/sup\\u003eC until nucleic acid extraction.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003ch2\\u003eNucleic Acid Extractions from non-blood samples\\u003c/h2\\u003e\\u003cp\\u003eNucleic acid extractions were performed on low biomass (body swabs, saliva, and urine), and high biomass (stool) samples collected from each subject across three time points (L-30, L-3 and R + 6), excluding the L-90 biospecimens.\\u003c/p\\u003e\\u003cp\\u003eDNA was extracted from an aliquot of urine cell pellet (250 µL) and skin swab biospecimen (400 µL) types using the Zymobiocs (96) Kit (Cat# 27500-4-EP) following the manufacturer’s protocol. Each extraction was eluted into 50 µL of nuclease-free water (Invitrogen, Ref# AM9906, Lot# 2211042) and the samples were ready for immediate use or frozen until needed.\\u003c/p\\u003e\\u003cp\\u003eDNA was extracted from 200 µl of primary stool and a 250 µl- saliva aliquot using the Qiagen PowerMicrobiome kit (Cat# D4303) following the manufacturer’s protocol. The VIAFLO96 pipetting station (Integra, Part# 6001) was used for these extractions, and samples were eluted in 100 µl of nuclease free water. The samples were ready for immediate use or frozen until needed.\\u003c/p\\u003e\\u003cp\\u003e \\u003cb\\u003e16S rRNA gene sequencing as a QC metric for non-blood samples\\u003c/b\\u003e \\u003c/p\\u003e\\u003cp\\u003e16S rRNA gene sequencing was chosen for microbiome analysis rather than whole genome sequencing (WGS) due to budget constraints. 16S rRNA gene sequencing provides a cost-effective and sufficient method for profiling microbial diversity and community structure, the project objectives.\\u003c/p\\u003e\\u003cp\\u003eThe 16S rRNA gene sequencing methods were adapted from those developed for the NIH-Human Microbiome Project and the Earth Microbiome Project [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Briefly, the 16S rRNA v4 region was amplified by PCR using primers that contain adapters for sequencing on the Illumina platform. Additionally, single-index barcodes were incorporated into the reverse primer allowing PCR products to be pooled and sequenced directly. The PCR template volume was 5 µL for urine and skin swab biospecimen types and 2 µL for saliva and stool biospecimen types. Platinum Taq (Invitrogen – Cat# 15966005) was used for amplification.\\u003c/p\\u003e\\u003cp\\u003eLibrary QC was performed using a combination of gel electrophoresis (1% agarose) and an automated PicoGreen assay (Invitrogen Quant-iT P7589) using the Hamilton STARlet Liquid Handler. Finalized libraries were normalized and pooled followed by loading on the Illumina MiSeq platform using the 2x250bp (16S V4) paired-end protocol. The 16S sequence reads were mapped against the SILVA Database version 138 which contains sequences from the v4 region of the 16S rRNA gene. Data is accessible through TrialX EPAND database.\\u003c/p\\u003e\\u003ch2\\u003eHuman subjects research\\u003c/h2\\u003e\\u003cp\\u003eAll subjects consented and biospecimens were collected and processed under the approval of the Institutional Review Board at Baylor College of Medicine, under MESH protocol H-52035.\\u003c/p\\u003e\\u003ch2\\u003eManuscript preparation\\u003c/h2\\u003e\\u003cp\\u003eFigure 1 was created with BioRender.com under the Baylor College of Medicine Institutional license.\\u003c/p\\u003e\\u003ch2\\u003eData availability\\u003c/h2\\u003e\\u003cp\\u003eThe data is accessible through TrialX EXPAND database.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was funded by the\\u0026nbsp;Translational Research Institute for Space Health through NASA Cooperative Agreement NNX16AO69A.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eWe thank Dr.\\u0026nbsp;Jeffrey Rogers for the critical review of this manuscript and Ms. Christie Kovar for advice on sample deidentification process.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHD, EUO, and RAG conceptualized the study. \\u0026nbsp;SVB, SMG, DPB, HC, and MM contributed to biospecimen collections. \\u0026nbsp; MCG, YC, MM and SVB processed the samples and performed QC. AK, MCG, HD prepared the original draft of the manuscript and addressed the edits. VK, HM, JEP, JW and DM were involved in data generation. MM and MJ contributed to biobank and LIMS development. All authors reviewed and approved the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors declare no financial or non-financial competing interests.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCorrespondence\\u0026nbsp;\\u003c/strong\\u003eand requests for materials shouls be addressed to Harsha Doddapaneni\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003e\\u003cem\\u003eAstronaut/ Cosmonaut Statistics\\u003c/em\\u003e. 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Stang, \\u003cem\\u003eSpace medicine 2025 \\u0026ndash; A vision: Space medicine driving terrestrial medicine for the benefit of people on Earth.\\u003c/em\\u003e REACH, 2016. \\u003cstrong\\u003e1\\u003c/strong\\u003e: p. 55-62.\\u003c/li\\u003e\\n\\u003cli\\u003eOverbey, E.G., et al., \\u003cem\\u003eCollection of biospecimens from the inspiration4 mission establishes the standards for the space omics and medical atlas (SOMA).\\u003c/em\\u003e Nat Commun, 2024. \\u003cstrong\\u003e15\\u003c/strong\\u003e(1): p. 4964.\\u003c/li\\u003e\\n\\u003cli\\u003eBrojakowska, A., et al., \\u003cem\\u003eRetrospective analysis of somatic mutations and clonal hematopoiesis in astronauts.\\u003c/em\\u003e Commun Biol, 2022. \\u003cstrong\\u003e5\\u003c/strong\\u003e(1): p. 828.\\u003c/li\\u003e\\n\\u003cli\\u003eChakrabortty, S.K., et al., \\u003cem\\u003eExosome based analysis for Space Associated Neuro-Ocular Syndrome and health risks in space exploration.\\u003c/em\\u003e NPJ Microgravity, 2022. \\u003cstrong\\u003e8\\u003c/strong\\u003e(1): p. 40.\\u003c/li\\u003e\\n\\u003cli\\u003eReich, C., et al., \\u003cem\\u003eOHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.\\u003c/em\\u003e J Am Med Inform Assoc, 2024. \\u003cstrong\\u003e31\\u003c/strong\\u003e(3): p. 583-590.\\u003c/li\\u003e\\n\\u003cli\\u003eWitze, A., \\u003cem\\u003e2022 was a record year for space launches.\\u003c/em\\u003e Nature, 2023. \\u003cstrong\\u003e613\\u003c/strong\\u003e(7944): p. 426-undefined.\\u003c/li\\u003e\\n\\u003cli\\u003eMason, C.E., et al., \\u003cem\\u003eA second space age spanning omics, platforms and medicine across orbits.\\u003c/em\\u003e Nature, 2024.\\u003c/li\\u003e\\n\\u003cli\\u003eCope, H., et al., \\u003cem\\u003eRoutine omics collection is a golden opportunity for European human research in space and analog environments.\\u003c/em\\u003e Patterns, 2022. \\u003cstrong\\u003e3\\u003c/strong\\u003e(10): p. 100550.\\u003c/li\\u003e\\n\\u003cli\\u003eZneimer, S.M. and D. Hongo, \\u003cem\\u003ePreparing for Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologists (CAP) Inspections.\\u003c/em\\u003e Curr Protoc, 2021. \\u003cstrong\\u003e1\\u003c/strong\\u003e(12): p. e324.\\u003c/li\\u003e\\n\\u003cli\\u003eKalia, S.S., et al., \\u003cem\\u003eRecommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics.\\u003c/em\\u003e Genet Med, 2017. \\u003cstrong\\u003e19\\u003c/strong\\u003e(2): p. 249-255.\\u003c/li\\u003e\\n\\u003cli\\u003eAnnaratone, L., et al., \\u003cem\\u003eBasic principles of biobanking: from biological samples to precision medicine for patients.\\u003c/em\\u003e Virchows Arch, 2021. \\u003cstrong\\u003e479\\u003c/strong\\u003e(2): p. 233-246.\\u003c/li\\u003e\\n\\u003cli\\u003eWilliams, S.A., et al., \\u003cem\\u003ePlasma protein patterns as comprehensive indicators of health.\\u003c/em\\u003e Nat Med, 2019. \\u003cstrong\\u003e25\\u003c/strong\\u003e(12): p. 1851-1857.\\u003c/li\\u003e\\n\\u003cli\\u003eAhmad, A., M. Imran, and H. Ahsan, \\u003cem\\u003eBiomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases.\\u003c/em\\u003e Pharmaceutics, 2023. \\u003cstrong\\u003e15\\u003c/strong\\u003e(6): p. 1630.\\u003c/li\\u003e\\n\\u003cli\\u003eQiu, S., et al., \\u003cem\\u003eSmall molecule metabolites: discovery of biomarkers and therapeutic targets.\\u003c/em\\u003e Signal Transduct Target Ther, 2023. \\u003cstrong\\u003e8\\u003c/strong\\u003e(1): p. 132.\\u003c/li\\u003e\\n\\u003cli\\u003eSzil\\u0026aacute;gyi, M., et al., \\u003cem\\u003eCirculating Cell-Free Nucleic Acids: Main Characteristics and Clinical Application.\\u003c/em\\u003e Int J Mol Sci, 2020. \\u003cstrong\\u003e21\\u003c/strong\\u003e(18).\\u003c/li\\u003e\\n\\u003cli\\u003eBezdan, D., et al., \\u003cem\\u003eCell-free DNA (cfDNA) and Exosome Profiling from a Year-Long Human Spaceflight Reveals Circulating Biomarkers.\\u003c/em\\u003e iScience, 2020. \\u003cstrong\\u003e23\\u003c/strong\\u003e(12): p. 101844.\\u003c/li\\u003e\\n\\u003cli\\u003eAmes, N.J., et al., \\u003cem\\u003eThe Human Microbiome and Understanding the 16S rRNA Gene in Translational Nursing Science.\\u003c/em\\u003e Nurs Res, 2017. \\u003cstrong\\u003e66\\u003c/strong\\u003e(2): p. 184-197.\\u003c/li\\u003e\\n\\u003cli\\u003eRen\\u0026eacute; Rohrmanstorfer, S.Z., Daniel Wallerstorfer, Nicole Bauer, Sheri Hunt, Chung Hyun, Valentina Kazlova, \\u003cem\\u003eDNA by Mail: Ensure DNA Integrity by use of Self-Drying Buccal Swabs.\\u003c/em\\u003e Letters In Health and Biological Sciences, 2017. \\u003cstrong\\u003e2\\u003c/strong\\u003e(2): p. 1-7.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-microgravity\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"npjmgrav\",\"sideBox\":\"Learn more about [npj Microgravity](http://www.nature.com/npjmgrav/)\",\"snPcode\":\"41526\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/41526/3\",\"title\":\"npj Microgravity\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4993509/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4993509/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe surge in commercial and civilian spaceflight enables for the first time, systematic and longitudinal, large-scale biospecimen collection to understand prospective effects of space travel on human health. The Genomics and Space Medicine (Space Omics) project at BCM-HGSC involves a comprehensive biospecimen collection plan from commercial/private space flight participants (SFP). Biospecimens from multiple pre-launch (leading up to quarantine period) and post-return (the day of return, R\\u0026thinsp;+\\u0026thinsp;0 onwards) time points are collected. The diverse array of biospecimen collections include venous blood, body swabs, saliva, stool, and urine samples and their derivatives. The manuscript addresses the critical gaps thus far in the biospecimen collection process such as informed consent process and a provision for subjects to obtain custom CLIA-WGS reports. We discuss here, the biospecimens collection, processing methodologies and nucleic acids\\u0026rsquo; suitability for Omics data generation, including successful generation of 16S rRNA data that have been presented as a \\u0026lsquo;Genomic Evaluation of Space Travel and Research (GENESTAR)\\u0026rsquo; manual. Results from Axiom-2 mission where, a total of 339 biospecimens were collected using this manual, at two different sites, showed that 98% of the accessed blood samples and 91.6% of the non-blood samples passed the QC requirements for Omics assays, underscoring the reliability and effectiveness of the GENESTAR manual. Also for the for the first time, to support Space Omics studies, details of a data dictionary and a LIMS enabled biobank, are provided.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Genomic Evaluation of Space Travel and Research (GENESTAR) Manual for Biospecimen Collection, Biobanking and Omics Data Generation from Commercial Space Missions\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-11-20 19:24:47\",\"doi\":\"10.21203/rs.3.rs-4993509/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"npj-microgravity\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"npjmgrav\",\"sideBox\":\"Learn more about [npj Microgravity](http://www.nature.com/npjmgrav/)\",\"snPcode\":\"41526\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/41526/3\",\"title\":\"npj Microgravity\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"1e1c4aa7-496c-4069-9e3f-a0f2182144ae\",\"owner\":[],\"postedDate\":\"November 20th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-05-15T07:14:26+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4993509\",\"link\":\"https://doi.org/10.1038/s41526-025-00472-1\",\"journal\":{\"identity\":\"npj-microgravity\",\"isVorOnly\":false,\"title\":\"npj Microgravity\"},\"publishedOn\":\"2025-05-14 04:00:00\",\"publishedOnDateReadable\":\"May 14th, 2025\"},\"versionCreatedAt\":\"2024-11-20 19:24:47\",\"video\":\"\",\"vorDoi\":\"10.1038/s41526-025-00472-1\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41526-025-00472-1\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4993509\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4993509\",\"identity\":\"rs-4993509\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}