Inspiration4 data access through the NASA Open Science Data Repository.pdf
npj | microgravity Brief communication Published in cooperation with the Biodesign Institute at Arizona State University, with the support of NASA https://doi.org/10.1038/s41526-024-00393-5 Inspiration4 data access through the NASA Open Science Data Repository Check for updates Lauren M. Sanders 1,2, Kirill A. Grigorev1,2, Ryan T. Scott 1,3, Amanda M. Saravia-Butler1,3, San-huei Lai Polo 1,3, Rachel Gilbert 1,3, Eliah G. Overbey 4,5,6, JangKeun Kim 4,5, Christopher E. Mason 4,5,7 & Sylvain V. Costes 1 The increasing accessibility of commercial and private space travel necessitates a profound understanding of its impact on human health. The NASA Open Science Data Repository (OSDR) provides transparent and FAIR access to biological studies, notably the SpaceX Inspiration4 (I4) mission, which amassed extensive data from civilian astronauts. This dataset encompasses omics and clinical assays, facilitating comprehensive research on space-induced biological responses. These data allow for multi-modal, longitudinal assessments, bridging the gap between human and model organism studies. Crucially, community-driven data standards established by NASA’s OSDR Analysis Working Groups empower artiﬁcial intelligence and machine learning to glean invaluable insights, guiding future mission planning and health risk mitigation. This article presents a concise guide to access and analyze I4 data in OSDR, including programmatic access through GLOpenAPI. This pioneering effort establishes a precedent for post-mission health monitoring programs within space agencies, propelling research in the burgeoning ﬁeld of commercial space travel’s impact on human physiology. The increased accessibility of commercial and private spaceﬂight travel has made it imperative to understand the short- and long-term effects of space stressors on human health. The NASA Open Science Data Repository (OSDR)1–3providesopenandFAIR4accesstospaceﬂightandspace-relevant biological studies from the past decades. Research enabled by OSDR data has revealed a complex network of molecular and physiological effects of spaceﬂight across living systems, from microbes to plants to mammals (https://osdr.nasa.gov/bio/data/publications.html). Most prior research using OSDR data has focused on model organisms such as rodents, worms, and fruit ﬂies. These valuable measurements provide insight into the response of neuromuscular, immune, and developmental biosystems to the stressors and hazards of spaceﬂight. Integrating human in vivo data takes these studies one step further and map previously characterized model organisms’ biological responses to the limited knowledge on human phy- siology in space. The 2021 SpaceX Inspiration4 (I4) mission collected a com- prehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceﬂight on the human body. In the current special Nature package titled “The Second Space Age: Omics, Platforms, and Medicine Across Orbits,” a wide battery of analyses of these data have already provided valuable insights (Mason et al., Nature. 2023). The I4 mission stands as a signiﬁcant milestone,amassing an extensive collection of biological measurements from four civilian astronauts, thereby adding an invaluable series of datasets for deciphering the repercussions of spaceﬂight on human health. The open access of these datasets in the NASA OSDR provides a unique opportunity for the scientiﬁc community, citizen scientists, researchers, and students to continue using OSDR resources to further unlock profound insights into the consequences of space travel on the human body. In this short article, we guide the readers on how to best conduct future analysis by presenting a series of vignettes illustrating pathways for accessing metadata and data from this rich resource through OSDR. We hope these guidelines will help the community contribute fur- therto the vast compendium of ﬁndings already published from OSDR data and enable new hypotheses to be tested with both the human and model organism data. 1Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, CA, USA. 2Blue Marble Space, Seattle, WA, USA. 3KBR, Houston, TX, USA. 4Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA. 5The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA. 6Center for STEM, University of Austin, Austin, TX, USA. 7The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA. e-mail: sylvain.v.costes@nasa.gov npj Microgravity |   (2024) 10:56  1 1234567890():,; 1234567890():,; Compared to traditional model organisms used for research, one unique aspect of these data is the breadth of longitudinal measurements available from several human astronauts. Such temporal information for several individuals is unprecedented and pushes further the well-known “NASA’s Twin Study”5 where a single astronaut was monitored over 340 days on the international space station, referred to as a “N of 1” experiment. In contrast, even though the duration of the I4 mission was much shorter (3 days), it was at a higher altitude (590 km vs. 420 km), and since it featured four individuals monitored with such biological depth, the data opens the door to new and innovative follow-up studies (e.g. Polaris Dawn missions). Incorporating environmental data with microbiome data and a wide variety of phenotypic and high-throughput sequencing assays provides a rare opportunity for multi-modal, longitudinal assessment of short-term and long-term spaceﬂight effects on humans. These datasets also provide a much-needed abilityto compare human spaceﬂight effects to the previously characterized spaceﬂight consequences in model organisms. The analyses conducted thus far on the I4 mission’s data have already yielded substantial revelations, shedding light on the intricate interplay between space stressors and the immune system, cell-speciﬁc responses to spaceﬂight, rapid microbial interchange of the crew, and detailed maps of the changes in the skin from astronauts (Overbey et al., Nature 2023; Tierney et al., Nature Microbiology 2023; Kim et al., Nature 2023). These analyses demonstrate the great potential of these data for future knowledge. The inclusion of multi-modal data, suchas the blood datasets, which cover a diverse array of measurements (OSD-569, −570, −571, −575), also pre- sents an exciting avenue for future exploration. This broader scope for analysis could unveil deeper connections between different biological responses and offer a more comprehensive understanding of the body’s adaptations to the space environment. Combining omics and medical assay dataistheﬁrststeptounderstandingthebiologicalsigniﬁcanceofintegrated ﬁndings. Translating them into actionable insights for space health will be our next step, which remains challenging but, for the ﬁrst time, attainable. Looking ahead, integrating artiﬁcial intelligence and machine learning (AI/ML) in the analysis of biomedical data from the I4 mission also holds great promise. With all OSDR data accessible in a cloud-based public repository, standardized metadata, and a robust API query system, this effort demonstrates AI readiness and empowers our user community to explore beyond current ﬁndings. The sheer volume and complexity of the data call for advanced computational approaches that can uncoverpatterns, correlations, and anomalies that might otherwise remain hidden. Powerful techniques, such as machine learning and deep learning, hold the potential to extractmeaningful but complex insights from the vast dataset, facilitating the identiﬁcation of subtle trends and potential health risks associated with space travel. NASA OSDR’s commitment to FAIR practices ensures that researchers across the globe can harness the power of AI to conduct cutting- edgeanalyses, fostering collaborationand accelerating the pace of discovery. Finally, this work sets a precedent for space agencies to establish compre- hensive, post-mission health monitoring programs, incorporating omics and medical assay data, while informing future mission planning and health risk mitigation strategies. Methods In the realm of scientiﬁc inquiry involving I4 data, it is imperative to note that publicly accessible information pertains solely to processed data, given that raw data pertaining to individual human subjects, encompassing genetic sequence data among other facets, necessitate an application pro- cedure that mandates approval from an ethical oversight board for access. The protocol for controlled data access has been meticulously devised, drawing inspiration from established guidelines within the Database of Genotypes and Phenotypes (dbGAP) and incorporating best practices derived from the United Kingdom Biobank. This comprehensive frame- work encompasses an expansive array of data and sample categories, as illustrated in Fig. 1. These data are captured across eight studies, delineated by tissue and sample type, and includes omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics/meta- transcriptomics, spatial transcriptomics, and proteomics to comprehensive metabolic and immune panels. Moreover, clinical assays for blood proﬁling, suchascompletebloodcount(CBC)andmetabolicpanels,arealsoincluded in the data resource. All subjects were consented at an informed consent brieﬁng (ICB) at SpaceX (Hawthorne, CA), and samples were collected and processed under the approval of the Institutional Review Board (IRB) at Weill Cornell Medicine, under Protocol 21-05023569. All crew members have consented for data and sample sharing. Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data records For illustration purposes, a single dataset will be used as an example for researcher access of I4 data from OSDR: OSD-572, which holds Fig. 1 | Overview of available I4 data in OSDR across 10 studies separated by tissue. I4 mission data are separated into 10 OSD studies based on sample/tissue type as shown above. For each OSD, the associated omics and non-omics data are listed under the GLDS-ID (light gray) and LSDS-ID (dark gray), respectively. The time points samples were collected for each OSD are color-coded in the I4 mission timeline at the top. L Launch, F Flight, R Return, d Days. https://doi.org/10.1038/s41526-024-00393-5 Brief communication npj Microgravity |   (2024) 10:56  2 As it is common for differential expression analyses, sample names are column names in the data table, and gene names are row names. Gene names always come ﬁrst, so we pass “row.names=1” to the get.csv() function. The header of the data table consists of three lines (accession, assay name, sample name), but as we are working with a single assay, we can skip the ﬁrst two lines: q = "id=OSD-569&ﬁle.datatype=unnormalized counts" url = paste0(GLOPENAPI, "data/?", URLencode(q)) data = get.csv( url, check.names=F, skip=2, row.names=1 ) Differential expression and pathway enrichment analysis At this point, the data is ready to be ingested into standard analysis tools, for example, DESeq27and fgsea8 with the use of MSigDb9 pathways. For DESeq2, sample names in the metadata should be represented as row names; and if, for example, we want to classify all timestamps into “preﬂight” (anything before launch, i.e. timestamps starting with “L-“), “postﬂight” (immediately after landing, “R+1”) and “recovery” (all other timestamps starting with “R+“), we also add a “status” column: rownames(metadata) = metadata$sample.name metadata$status = sapply(metadata$timestamp, function (t) { ifelse(t=="R+1", "postﬂight", ifelse(startsWith(t, "R+"), "recovery", "preﬂight")) }) Finally, we run the analyses and visualize enrichment of a particular MSigDb pathway (DIAZ_CHRONIC_MYELOGENOUS_LEUKE- MIA_UP) with result displayed in Fig. 4: library(DESeq2) dds = DESeqDataSetFromMatrix( round(na.omit(data)), # DESeq2 requires integer values and absence of NaNs metadata, ~status+subject, ) lrt = DESeq(dds, test="LRT", reduced=~subject) deg = results(lrt, contrast=c("status", "postﬂight", "preﬂight")) library(msigdbr) C2 = msigdbr(species="human", category="C2") pathways = split(x=C2$ensembl_gene, f=C2$gs_name) library(fgsea) deg$rank = deg$padj * sign(deg$log2FoldChange) ranks = with(na.omit(deg), setNames(rank, rownames(na.omit(deg)))) plotEnrichment(pathways$DIAZ_CHRONIC_MYELOGENOUS_ LEUKEMIA_UP, ranks) Technical validation The establishment of standardized data formats, uniform units of mea- surement, ontological frameworks, and comprehensive metadata ﬁelds is imperative to ensure the seamless integration of new data types into a repository and to enhance the capacity for cross-study biological analyses. Further, the integration of medical assays with multi-omics data, including Fig. 4 | Enrichment analysis. Enrichment of the DIAZ_CHRONIC_MYELOGENOUS_LEUKEMIA_UP MSigDb pathway in the RNA-Seq data associated with dataset OSD-569 at the postﬂight timestamp compared to preﬂight conditions, analyzed and visualized from data retrieved via GLOpenAPI. https://doi.org/10.1038/s41526-024-00393-5 Brief communication npj Microgravity |   (2024) 10:56  5 genomics, proteomics, metabolomics, and other data modalities, facilitates meta-analyses geared towards better understanding of the human response to the space environment. The correlation between omicsdata and clinically signiﬁcant endpoints derived from medical data holds paramount sig- niﬁcance in elucidating health implications. Nonetheless, the integration of disparate data types remains a persistent challenge in the biomedical research domain, exacerbated by inconsistencies across laboratories and studies, as well as inherent variability in individual responses. Moreover, space missions like I4, which encompass multiple data collection points (pre-ﬂight, in-ﬂight, post-ﬂight, etc.), provide longitudinal datasets that enable the intricate yet potent exploration of space-induced responses over time. Over the ﬁve years, the OSDR Analysis Working Groups (AWGs) were convened in order to ensure the reusability and standardization of data available within OSDR. These AWGs consist of diverse OSDR data stakeholders, including investigators, commercial entities, academic researchers, and students. Each AWG is dedicated to a distinct data category or domain of expertise, including animals, artiﬁcial intelligence, microbes, multi-omics, phenotypic data, and plants. Professionals within these AWGs contributed their expertise towards the formulation of standardized data processing pipelines and the development of assay metadata conﬁgurations tailored to each new data type. Consequently, OSDR was well-equipped to receive, curate, standardize, and process the wide spectrum of medical and omics data collected during the I4 mission. Medical data serves as a crucial resource for gaining insights into biological responses. However, balancing the imperative to protect sensitive health information of astronauts while enabling data sharing for research necessitates the robust implementation of privacy and security protocols. OSDR has introduced an innovative capability, allowing users to access metadata associated with private medical datasets, albeit without direct access to the data itself. OSDR’s dedication to comprehensive and unam- biguous metadata, coupled with the utilization of the GLOpenAPI, under- scores its readiness for analysis and artiﬁcial intelligence applications. The data query method illustrated here marks an initial step toward realizing the goal of Precision Space Health10,11. In cases where users seek to employ private medical datasets such as those collected on I4 for research purposes, an application process is in place that mandates approval from an institu- tional review board (IRB). Data availability All data are available on the NASA Open Science Data Repository. OSD- 569, OSD-570, OSD-571, OSD-572, OSD-573, OSD-574, OSD-575, OSD- 630, OSD-656, OSD-687: Code availability GeneLab processed data is generated using the pipelines that are publicly availableontheGeneLabDataProcessingGitHubpage:https://github.com/ nasa/GeneLab_Data_Processing. Received: 14 December 2023; Accepted: 3 April 2024; References 1. Berrios, D. C., Galazka, J., Grigorev, K., Gebre, S. & Costes, S. V. NASA GeneLab: interfaces for the exploration of space omics data. Nucleic Acids Res. 49, D1515–D1522 (2021). 2. Scott, R. T. et al. Advancing the integration of biosciences data sharing to further enable space exploration. Cell Rep. 33, 108441 (2020). 3. Ray, S. et al. GeneLab: Omics database for spaceﬂight experiments. Bioinformatics 35, 1753–1759 (2019). 4. Berrios, D. C., Beheshti, A. & Costes, S. V. FAIRness and usability for open-access Omics data systems. AMIA Annu. Symp. Proc. 2018, 232–241 (2018). 5. Garrett-Bakelman, F. E. et al. The NASA Twins Study: A multidimensional analysis of a year-long human spaceﬂight. Science 364, eaau8650 (2019). 6. González-Beltrán, A., Maguire, E., Sansone, S.-A. & Rocca-Serra, P. linkedISA: semantic representation of ISA-Tab experimental metadata. BMC Bioinforma. 15, S4 (2014). 7. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). 8. Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. bioRxiv 060012 (2019) https://doi.org/10.1101/ 060012. 9. Castanza, A. S. et al. Extending support for mouse data in the Molecular Signatures Database (MSigDB). Nat. Methods 20, 1619–1620 (2023). 10. Scott, R. T. et al. Biomonitoring and precision health in deep space supported by artiﬁcial intelligence. Nat. Mach. Intell. 5, 196–207 (2023). 11. Morris, J. H. et al. The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information. Bioinformatics 39, btad080 (2023). Acknowledgements OSDR is funded by the NASA Space Biology Program, part of the NASA Biological and Physical Sciences Division within the NASA Science Mission Directorate; non-omics data are also partially supported by the NASA Human Research Program (HRP). C.E.M. thanks WorldQuant, the GI Research Foundation, NASA (NNX14AH50G, NNX17AB26G, NNH18ZTT001N-FG2, 80NSSC22K0254, 80NSSC23K0832), the National Institutes of Health (R01MH117406), and the LLS (MCL7001- 18, LLS 9238-16, 7029-23). J.K. was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00241586). J.K. acknowledges Boryung for their ﬁnancial support and research enhancement ground, provided through their Global Space Healthcare Initiative, Humans In Space, including mentorship and access to relevant expert networks. Author contributions Study design: S.V.C., L.M.S. and K.A.G. wrote the main manuscript. R.T.S., A.M.S.-B., E.O., J.K., and C.E.M. provided ﬁgures and expert review. All authors reviewed the ﬁnal manuscript. Competing interests CEM is a co-Founder of Onegevity, Twin Orbit, and Cosmica Biosciences. All other authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41526-024-00393-5. Correspondence and requests for materials should be addressed to Sylvain V. Costes. Reprints and permissions information is available at http://www.nature.com/reprints Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. https://doi.org/10.1038/s41526-024-00393-5 Brief communication npj Microgravity |   (2024) 10:56  6 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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Title: Inspiration4 data access through the NASA Open Science Data Repository ​
Authors: Lauren M. Sanders, Kirill A. Grigorev, Ryan T. Scott, Amanda M. Saravia-Butler, San-huei Lai Polo, Rachel Gilbert, Eliah G. Overbey, Jang Keun Kim, Christopher E. Mason, Sylvain V. Costes
Publisher: Published in cooperation with the Biodesign Institute at Arizona State University, with the support of NASA
Date: 2024-05-14 00:00:00
Abstract: The increasing accessibility of commercial and private space travel necessitates a profound understanding of its impact on human health. ​ The NASA Open Science Data Repository (OSDR) provides transparent and FAIR access to biological studies, notably the SpaceX Inspiration4 (I4) mission, which amassed extensive data from civilian astronauts. ​ This dataset encompasses omics and clinical assays, facilitating comprehensive research on space-induced biological responses. ​ These data allow for multi-modal, longitudinal assessments, bridging the gap between human and model organism studies. ​ Crucially, community-driven data standards established by NASA’s OSDR Analysis Working Groups empower artificial intelligence and machine learning to glean invaluable insights, guiding future mission planning and health risk mitigation. ​ This article presents a concise guide to access and analyze I4 data in OSDR, including programmatic access through GLOpenAPI. ​ This pioneering effort establishes a precedent for post-mission health monitoring programs within space agencies, propelling research in the burgeoning field of commercial space travel’s impact on human physiology.