Space agencies have announced plans for human missions to the Moon to prepare for Mars. However, the space environment presents stressors that include radiation, microgravity, and isolation. Understanding how these factors affect biology is crucial for safe and effective crewed space exploration. There is a need to develop countermeasures, to adapt plants and microbes for nutrient sources and bioregenerative life support, and to limit pathogen infection. Scientists across the world are conducting space omics experiments on model organisms and, more recently, on humans. Optimal extraction of actionable scientific discoveries from these precious datasets will only occur at the collective level with improved standardization. To address this shortcoming, we established ISSOP (International Standards for Space Omics Processing), an international consortium of scientists who aim to enhance standard guidelines between space biologists at a global level. Here we introduce our consortium and share past lessons learned and future challenges related to spaceflight omics.
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http://dx.doi.org/10.1016/j.patter.2020.100148 | DOI Listing |
BMC Plant Biol
January 2025
Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, 650201, China.
Background: Space-induced plant mutagenesis, driven by cosmic radiation, offers a promising approach for the selective breeding of new plant varieties. By leveraging the unique environment of outer space, we successfully induced mutagenesis in 'Deqin' alfalfa and obtained a fast-growing mutant. However, the molecular mechanisms underlying its rapid growth remain poorly unexplored.
View Article and Find Full Text PDFPlant Physiol Biochem
December 2024
Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, 116026, Liaoning, China. Electronic address:
To explore the bio-effects during Moon exploration missions, we utilized the Chang'E 5 probe to carry the seeds of Oryza. Sativa L., which were later returned to Earth after 23 days in lunar orbit and planted in an artificial climate chamber.
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
A key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.
View Article and Find Full Text PDFExtracell Vesicles Circ Nucl Acids
June 2024
Division Cell Biology, Metabolism & Cancer, Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht 3584 CM, the Netherlands.
Arthritis, a diverse group of inflammatory joint disorders, poses great challenges in early diagnosis and targeted treatment. Timely intervention is imperative, yet conventional diagnostic methods are not able to detect subtle early symptoms. Hence, there is an urgent need for specific biomarkers that discriminate between different arthritis forms and for early diagnosis.
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
RNA velocities and generalizations emerge as powerful approaches for exacting dynamical information from high-throughput snapshot single-cell data. 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, and data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses RNA velocities inferred from existing methods as input and infer velocity vectors lie in the tangent space of the low-dimensional manifold formed by the single cell data.
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