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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2019

GeneLab: A Systems Biology Platform for Omics Analysis

NASA GeneLab is an open-access repository for omics datasets generated by biological experiments conducted in space or experiments relevant to spaceflight (e.g. simulated cosmic radiation, simulated microgravity, bed rest studies). The GeneLab Data Systems (GLDS) version 4.0 will be available on October 1st 2019, and will provide the latest in terms of professional state-of-the-art bioinformatics platform for the space biology and radiation community to upload their data into an omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. Started in 2015 as a repository designed to archive omics data from space experiments, GeneLab has expanded its scope to all ionizing radiation omics experiments conducted on the ground and has put considerable effort in providing carefully characterized radiation metadata on all dataset. GeneLab is also providing processed data derived from the raw data covering a large spectrum of omics (genome, epigenome, transcriptome, epitranscriptome, proteome, metabolome) to help users explore important questions: 1) Which genes or proteins are expressed differently in space for various living organisms? 2) What specific DNA mutations or epigenetic changes happen in space or after exposure to ionizing radiation? and 3) How does genetics affect these responses? Processed data available on GeneLab are derived by standard data analysis workflows vetted by hundreds of scientists who volunteered to join one of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). In this presentation, we will discuss how to bridge the gap between irradiation studies performed on earth and biological experiments conducted in space since the early 1990's. We will discuss how radiation dosimetry was estimated for datasets derived from samples collected during the Space Shuttle era or on the International Space Station. Finally, we will address future strategies regarding dose monitoring in future missions into space, inter-agency efforts to unify data under one umbrella, and knowledge dissemination across the radiation research community and the space biology community.

open-science↗
NASA NTRS2019

NASA GeneLab Space Omics Database: Expanding from Space to Ionizing Radiation Data on the Ground

NASA GeneLab is an open-access repository for omics datasets generated by biological experiments conducted in space or ground experiments relevant to spaceflight (e.g. simulated cosmic radiation, simulated microgravity, bed rest studies). The GeneLab Data Systems (GLDS) version 4.0 will be available on October 1st 2019, and will provide a state-of-the-art bioinformatics platform for the space biology and radiation communities to upload their data into an omics data commons, to process their data with vetted standard workflows and to compare with existing analyses. Started in 2015 as a repository designed to archive omics data from space experiments, GeneLab has expanded its scope to all ionizing radiation omics experiments conducted on the ground and has put considerable effort in providing carefully characterized radiation metadata on all datasets. GeneLab is also providing processed data derived from the raw data covering a large spectrum of omics (genome, epigenome, transcriptome, epitranscriptome, proteome, metabolome) to help users explore important questions: 1) Which genes or proteins are expressed differently in space for various living organisms? 2) What specific DNA mutations or epigenetic changes happen in space or after exposure to ionizing radiation? and 3) How does genetics affect these responses? Processed data available on GeneLab are derived by standard data analysis workflows vetted by hundreds of scientists who volunteered to join one of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). In this presentation, we will discuss how to bridge the gap between irradiation studies performed on earth and biological experiments conducted in space since the early 1990's. We will discuss how radiation dosimetry was estimated for datasets derived from samples collected during the Space Shuttle era on the International Space Station and on other orbiting platforms. Finally, we will address future strategies regarding dose monitoring in future missions into space, inter-agency efforts to unify data under one umbrella, and knowledge dissemination across the radiation research community and the space biology community.

open-science↗
NASA NTRS2019

A Multi-Omics Approach Demonstrates that Spaceflight Leads to Lipid Accumulation in Mouse Livers

Spaceflight has several detrimental effects on the physiology of astronauts, many of which are recapitulated in rodent models. We analyzed liver transcriptomic and proteomic data from three mouse spaceflight experiments flown aboard the International Space Station (Rodent Research-1 NASA (RR-1 NASA), Rodent Research-1 CASIS (RR-1 CASIS), Rodent Research-3 (RR-3)), and one mouse experiment flown on the Space Shuttle (Commercial Biomedical Testing Module-3 (CBTM-3) aboard STS-135). Despite the differences in genetic background and time of exposure to microgravity it was shown through Oil Red staining and histology that increased lipid accumulation was occurring in the liver of all mice flown in space compared to the ground controls. This led to further pursue the existing GeneLab datasets related to liver omics data from these mice. We were able to discover key conserved pathways across all the mice independent of the flight conditions that were related to increased lipid metabolism, fatty acid metabolism, both lipid and fatty acid processing, lipid catabolic processing, and lipid localization. In addition, key upstream regulators were predicted to be commonly regulated across all conditions which include ESR1, GCG, and NR1I2 being inhibited and INS being activated. Interestingly, estrogen receptor alpha (ESR1) expression has been known to be heavily involved with lipoprotein metabolism. In addition, insulin (INS) is the primary driver for fat metabolism and increased INS has been associated with increased fatty acids in the liver. Through additional proteomic analysis we were able to identify the majority of the key proteins related to lipids for both the RR-1 and RR-3 rodents were being up-regulated in the livers when comparing flight to ground controls. This additional confirmation of the lipid associated activity also showed that the lipid related proteins are heavily involved with lipid metabolism, cholesterol binding, and cholesterol metabolism. Lastly, the analysis also revealed that the circadian clock related pathways in the liver are commonly being increased across all space flight conditions which has also been reported in the literature to potentially cause increased liver damage. The combination of the very strong lipid uptake in the liver and the transcriptomic/proteomic signatures (including the circadian clock pathways) following spaceflight are consistent with early onset of liver disease. Taken together, these data indicate that, activation of lipotoxic pathways could persist during longer duration spaceflight which might result in the development of liver disease

Lipids↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRS2015

NASA's Rodent Research Project on ISS: Validation of a New Platform for Conducting Biomedical and Basic Research into the Consequences of Long Duration Habitation in Space

Rodent research has played a key role in advancing biomedical discoveries both on Earth and in space. The National Research Counsel’s Decadal survey(1) emphasized the importance of expanding NASAs life sciences research to perform long duration, rodent experiments on the International Space Station (ISS). To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA ARC to support both commercial and government-sponsored rodent research.Rodent Research-1 (RR-1) was the first mission in which animals were delivered and maintained in the ISS for a long duration mission in modified Animal Enclosure Module hardware. Both RR validation and commercial science objectives were pursued on the RR-1 mission. Adult female mice (20 total Flight, FLT) were launched Sept 21, 2014 in RR hardware within a Dragon Capsule (SpaceX4), then after 4 days in transit, were transferred for habitation on the ISS for 17 days (commercial) or 33 days (validation), when animals were euthanized and select tissues recovered on orbit. Various controls groups consisted of: 1) Basal mice from the same cohorts as FLT mice, but tissues were recovered at time of launch, 2) Vivarium (VIV) were housed in standard cages 3) Ground Controls (GC) were housed in flight hardware within an environmental chamber at Kennedy Space Center. The health and behavior of all mice on the ISS were monitored by video feed on a daily basis. Mice were euthanized by injection of Euthasol, then either fast frozen intact or dissected to preserve livers (fast frozen) and spleens (RNAlater). Samples were stored at ≤ -80˚C until their return to Earth for later analyses.Hardware performed nominally throughout the mission and the planned in-flight science operations were completed successfully. FLT mice appeared generally more physically active on orbit than respective GC groups. After 33 days on the ISS, mean body weights of FLT mice did not differ from GC, with both groups showing a 6% rise compared to time of launch, while VIV mice showed an 8% rise over the same period. Importantly, there were no significant differences in body weights between groups at the end of 33 days on the ISS, providing an indication that the RR hardware supported the health of the mice both on Earth and in space. Based on the preliminary data obtained from the livers and spleens of mice after 17 days on the ISS, purified RNA was of high quality (RIN values of spleen: FLT=9.48 +0.40, GC=9.28 +0.44, n=5/group); therefore, RNA quality from samples retrieved on orbit was acceptable for even the most demanding transcriptomic analyses. In addition, liver enzyme activity levels (units/mg protein) of FLT mice (after 17d on ISS) and all control mice were similar in magnitude to samples that were optimally prepared by freezing in liquid nitrogen in the laboratory (enzymes analyzed included catalase, glutathione reductase and glyceraldehyde-3-phosphate dehydrogenase). Validation analyses still in progress include behavior and tissue biochemistries, as well as optimization of science return by post-flight recovery of tissues for biospecimen sharing and global expression analyses.Together, these preliminary findings demonstrate new capability for supporting long duration rodent research on the ISS to achieve both basic science and biomedical objectives.

Globus, R. K.↗
NASA NTRS2014

NASA GeneLab Concept of Operations

NASA's GeneLab aims to greatly increase the number of scientists that are using data from space biology investigations on board ISS, emphasizing a systems biology approach to the science. When completed, GeneLab will provide the integrated software and hardware infrastructure, analytical tools and reference datasets for an assortment of model organisms. GeneLab will also provide an environment for scientists to collaborate thereby increasing the possibility for data to be reused for future experimentation. To maximize the value of data from life science experiments performed in space and to make the most advantageous use of the remaining ISS research window, GeneLab will apply an open access approach to conducting spaceflight experiments by generating, and sharing the datasets derived from these biological studies in space.Onboard the ISS, a wide variety of model organisms will be studied and returned to Earth for analysis. Laboratories on the ground will analyze these samples and provide genomic, transcriptomic, metabolomic and proteomic data. Upon receipt, NASA will conduct data quality control tasks and format raw data returned from the omics centers into standardized, annotated information sets that can be readily searched and linked to spaceflight metadata. Once prepared, the biological datasets, as well as any analysis completed, will be made public through the GeneLab Space Bioinformatics System webb as edportal. These efforts will support a collaborative research environment for spaceflight studies that will closely resemble environments created by the Department of Energy (DOE), National Center for Biotechnology Information (NCBI), and other institutions in additional areas of study, such as cancer and environmental biology. The results will allow for comparative analyses that will help scientists around the world take a major leap forward in understanding the effect of microgravity, radiation, and other aspects of the space environment on model organisms. These efforts will speed the process of scientific sharing, iteration, and discovery.

Space Life Science↗
NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Data, Analysis Tools, and Informatic Collaborations

Increased biomedical risks and challenges associated with deep space missions require knowledge discovery, health countermeasures, and biomedical support capabilities. Maximally open-access and reusable data is needed by developers, scientists, and engineers to develop these systems. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database (ie., findable, accessible, interoperable, and reusable), and meets various scientific, technical, and operational needs. It offers users and submitters the ability to upload, download, search, share, analyze, cite, and visualize data across ‘omics, physiological, phenotypic, payload, hardware, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR is an expanded database, based upon the successes of NASA GeneLab. OSDR has >460 studies with datasets covering model organisms to non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets with raw files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) developed from industry norms. OSDR is collecting and curating biomedical human data from a new sub-orbital research flight and is open to more space life science/biomedical submissions from the international and commercial sectors. OSDR also recently began a collaboration with the European Space Agency (ESA) to collect and curate >200 terabytes of human and model organism data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics and ~50 physiological-phenotypic-imaging assay data types. Tools available for OSDR users include: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, and 3) a Multi-study visualization tool which enables users to look across and combine ‘omics datasets. There are ~600 volunteer OSDR Analysis Working Group (AWG) members providing feedback on scientific data/metadata standards and collaborating to mine-reuse OSDR in research. OSDR/GeneLab has enabled ~60 publications reusing data as of October 2023.

space biology↗
NASA NTRS2016

Advances in Rodent Research Missions on the International Space Station

A Research platform for rodent experiment on the ISS is an essential tool for advancing biomedical research in space. The Rodent Research allows for experiments of much longer duration that experiments on the Shuttle. NASA’s Rodent Research (RR)-1 mission was successfully completed, including post-flight analysis and achieved a number of objectives including validation of flight hardware, on-orbit operations, and science capabilities that were developed at the NASA Ames Research Center. Briefly, twenty C57BL/6J adult female mice were launched on the SpX4 Dragon vehicle, which thrived for up to 37 days in microgravity. Daily health checks of the mice were performed during the mission via downlinked video; all animals were healthy and displayed normal behavior without any significant signs of stress. Behavioral analysis demonstrated that Flight and Ground Control mice exhibited the same range of behaviors, including eating, drinking, exploratory behavior, self- and allo-grooming, and social interactions indicative of healthy animals. The animals were euthanized and select tissues were collected from some of the mice on orbit to assess the long-term sample storage capabilities of the ISS. The data obtained from the flight mice were comparable to those from the 3 groups of control mice (baseline, vivarium and ground controls), suggesting that the ISS has adequate capability to support long-duration rodent experimentations. We recovered over 35 tissues from 40 RR1 frozen carcasses, yielded over 3200 aliquots of tissues, and distributed to the scientific community, including NASA’s GeneLab and scientists in the U.S. through Biospecimen Sharing Program via Ames Life Science Data Archive. Tissues were also distributed to Russian research colleagues at the Institute for Biomedical Problems. The expression levels of select genes including albumin, catalase, GAPDH, HMGCoA Reductase, and IGF1 were determined using RNA isolated from the livers by qPCR and no significant differences by one factor ANOVA were found between flight and ground control groups. In addition, some of the liver samples were subject to transcriptomics, epigenomics and proteomics. The data are now available to the scientific community through GeneLab’s open science data website. Since the RR1 mission, another long duration mission (Rodent Research-2) was completed on the ISS in 2015 in which 20 female C57 BL/6J mice were successfully maintained on the ISS for varying time points, with the last group of 5 animals being on-orbit for 54 days. This second Rodent Research flight expanded the program’s capabilities with the introduction of new technologies including blood collection and separation and bone densitometry scanning. Furthermore, we have continued to expand the ISS’s capabilities by running a series of ground-based verification testing using male mice. Our next step is to fly male mice for Rodent Research-4 on SpaceX-10 to study the effects of microgravity on bone healing and regeneration. It will be the first long-duration mission using male mice using Rodent Hardware. In addition, the number of mice will increase from 20 mice (on RR-1 and RR-2) to 40 for RR-4. When samples return to Earth, a number of tissues will be dissected from the frozen carcasses and select tissue samples will become available to the scientific community via BSP. Altogether, we have continued to expand our capabilities for performing long-duration missions on the ISS as emphasized in the National Research Council’s Decadal Survey released in 2011 and to maximize science return from each mission.

Choi, S.↗
NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗
NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗
NASA NTRS2016

Advances in Rodent Research Missions on the International Space Station

A research platform for rodent experiment on the ISS is a valuable tool for advancing biomedical research in space. Capabilities offered by the Rodent Research project developed at NASA Ames Research Center can support experiments of much longer duration on the ISS than previous experiments performed on the Space Shuttle. NASAs Rodent Research (RR)-1 mission was completed successfully and achieved a number of objectives, including validation of flight hardware, on-orbit operations, and science capabilities as well as support of a CASIS-sponsored experiment (Novartis) on muscle atrophy. Twenty C57BL6J adult female mice were launched on the Space-X (SpX) 4 Dragon vehicle, and thrived for up to 37 days in microgravity. Daily health checks of the mice were performed during the mission via downlinked video; all flight animals were healthy and displayed normal behavior, and higher levels of physical activity compared to ground controls. Behavioral analysis demonstrated that Flight and Ground Control mice exhibited the same range of behaviors, including eating, drinking, exploratory behavior, self- and allo-grooming, and social interactions indicative of healthy animals. The animals were euthanized on-orbit and select tissues were collected from some of the mice on orbit to assess the long-term sample storage capabilities of the ISS. In general, the data obtained from the flight mice were comparable to those from the three groups of control mice (baseline, vivarium and ground controls, which were housed in flight hardware), showing that the ISS has adequate capability to support long-duration rodent experiments. The team recovered 35 tissues from 40 RR-1 frozen carcasses, yielding 3300 aliquots of tissues to distribute to the scientific community in the U.S., including NASAs GeneLab project and scientists via Space Biology's Biospecimen Sharing Program Ames Life Science Data Archive. Tissues also were distributed to Russian research colleagues at the Institute for Biomedical Problems. The expression levels of select genes including albumin, catalase, GAPDH, HMGCoA Reductase, and IGF1 were determined using RNA isolated from the livers by qPCR and no significant differences by one factor ANOVA were found between flight and ground control groups. In addition, some of the liver samples were analyzed for transcriptomic, epigenomic and proteomic profiles; some of the data sets are now available to the scientific community through GeneLabs open science data website. A second long duration mission, Rodent Research-2 (RR-2) was completed on the ISS in 2015; 20 female C57BL6J mice were successfully maintained on the ISS for various durations, with the last group of 5 animals living on-orbit for 54 days. Furthermore, we continue to expand the ISSs capabilities by introducing new on-orbit technologies including blood collection and separation, bone densitometry scanning, muscle grip strength and anesthesia with recovery. In addition, series of ground-based verification testing to fly male mice and increase the total number of mice on-orbit from 20 to 40. Subsequent missions will provide the capability to return live mice from the ISS animals to evaluate recovery on Earth, further expanding operational and science capabilities of the RR project on the ISS.

Choi, S. Y.↗
NASA NTRSDate not supplied

A Novel Bone Marrow Single Cell Atlas of Mechanotransduction in Microgravity, Normal Gravity, Exercise, and Hindlimb Unloading for WT and CDKN1A-Null Regenerative Mice

Mechanical loading of adult stem cell progenitors is a key factor in modulating their proliferation, differentiation, and tissue regenerative potential, with loading generally promoting tissue formation and unloading mediating tissue loss. To address the role of mechanotransduction in maintaining stem cell-based tissue regenerative health, we generated a single cell transcriptomic atlas mapping responses of femur bone marrow mesenchymal and hematopoietic lineages to a range of altered mechanical loading conditions, both in WT B1629SF2/J mice as well as the p21/CDKN1A-null regenerative mice. We selected the femur marrow compartment as a model because of the diversity and high numbers of stem cell progenitor stages present, and because it undergoes static and cyclic hydrostatic pressure loading associated with weight-bearing and ambulation. Our study included normally loaded mice at 1g, unloading in microgravity during spaceflight as well as tail suspension hindlimb unloading, and voluntary running wheel exercise. Basal, one-year natural aging, and habitat controls were also conducted. Overall, the atlas encompasses 18 different experimental conditions with N=3 mice per condition and includes more than 500,000 single cell expressomes. Key specific findings include: increased mature reticulocyte populations in aging and unloading mice compared to active mice; greater hematopoietic and mesenchymal differentiating progenitors cluster identification in CDKN1A-null samples, especially the exercise model; distinct pseudotime cell trajectory shifts in the hematopoietic lineage for spaceflight and unloaded mice; and shifts in monocytic cell populations toward osteoclastic bone degenerative lineages in unloading and spaceflight samples. Overall mechanical loading shows increased marrow progenitor population differentiation while unloading is associated with increased CDKN1A expression and maintenance of marrow population stemness. Deletion of CDKN1A appears to remove a negative check on progenitor lineages commitment to differentiation. Finally, the Bone Marrow Mechanotransduction Single Cell Atlas will serve as a reference tool for studying marrow regenerative responses across a range of mechanical environments as they relate to CDKN1A status.

single-cell↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory

NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.

GeneLab↗
NASA NTRSDate not supplied

Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory

NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.

GeneLab↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the respective standard approach. Of the methods tested, standard ComBat and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

GeneLab↗