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Spaceflight induces molecular, cellular and physiological shifts in astronauts and poses myriad biomedical challenges to the human body, which are becoming increasingly relevant as more humans venture into space. Yet current frameworks for aerospace medicine are nascent and lag far behind advancements in precision medicine on Earth, underscoring the need for rapid development of space medicine databases, tools and protocols. Here we present the Space Omics and Medical Atlas (SOMA), an integrated data and sample repository for clinical, cellular and multi-omic research profiles from a diverse range of missions, including the NASA Twins Study, JAXA CFE study, SpaceX Inspiration4 crew, Axiom and Polaris. The SOMA resource represents a more than tenfold increase in publicly available human space omics data, with matched samples available from the Cornell Aerospace Medicine Biobank. The Atlas includes extensive molecular and physiological profiles encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome datasets, which reveal some consistent features across missions, including cytokine shifts, telomere elongation and gene expression changes, as well as mission-specific molecular responses and links to orthologous, tissue-specific mouse datasets. Leveraging the datasets, tools and resources in SOMA can help to accelerate precision aerospace medicine, bringing needed health monitoring, risk mitigation and countermeasure data for upcoming lunar, Mars and exploration-class missions.
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http://dx.doi.org/10.1038/s41586-024-07639-y | DOI Listing |
Cell Stem Cell
September 2025
Sanford Stem Cell Institute Integrated Space Stem Cell Orbital Research (ISSCOR) Center, Division of Regenerative Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA. Electronic address:
Human hematopoietic stem and progenitor cell (HSPC) fitness declines following exposure to stressors that reduce survival, dormancy, telomere maintenance, and self-renewal, thereby accelerating aging. While previous National Aeronautics and Space Administration (NASA) research revealed immune dysfunction in low-earth orbit (LEO), the impact of spaceflight on human HSPC aging had not been studied. To study HSPC aging, our NASA-supported Integrated Space Stem Cell Orbital Research (ISSCOR) team developed bone marrow niche nanobioreactors with lentiviral bicistronic fluorescent, ubiquitination-based cell-cycle indicator (FUCCI2BL) reporter for real-time HSPC tracking in artificial intelligence (AI)-driven CubeLabs.
View Article and Find Full Text PDFBioinformatics
September 2025
Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, United States.
Motivation: Identifying regulatory elements in various chromosomal regions that influence gene expression is a fundamental challenge in epigenomics, with profound implications for understanding gene regulation and disease mechanisms. The advent of paired single-cell RNA sequencing and single-cell ATAC sequencing has created unprecedented opportunities to address this challenge by enabling simultaneous profiling of gene expression and chromatin accessibility at single-cell resolution. However, the inherent signals between them are weak due to the highly sparse and noisy nature of data.
View Article and Find Full Text PDFBrief Bioinform
July 2025
Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States.
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder, posing a growing public health challenge. Traditional machine learning models for AD prediction have relied on single omics data or phenotypic assessments, limiting their ability to capture the disease's molecular complexity and resulting in poor performance. Recent advances in high-throughput multi-omics have provided deeper biological insights.
View Article and Find Full Text PDFBrief Bioinform
July 2025
Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Ghods 37, Tehran, 1417763135, Iran.
The rapid advancement of single-cell omics technologies such as single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin with high throughput sequencing has transformed our understanding of cellular heterogeneity and regulatory mechanisms. However, integrating these data types remains challenging due to distributional discrepancies and distinct feature spaces. To address this, we present a novel single-cell Contrastive INtegration framework (sCIN) that integrates different omics modalities into a shared low-dimensional latent space.
View Article and Find Full Text PDFNat Commun
August 2025
Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data.
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