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Routine omics collection is a golden opportunity for European human research in space and analog environments

Cope, Henry; Willis, Craig R.G.; MacKay, Matthew J.; Rutter, Lindsay A.; Toh, Li Shean; Williams, Philip M.; Herranz, Raúl; Borg, Joseph; Bezdan, Daniela; Giacomello, Stefania; Muratani, Masafumi; Mason, Christopher E.; Etheridge, Timothy; Szewczyk, Nathaniel J.

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Henry Cope

Craig R.G. Willis

Matthew J. MacKay

Lindsay A. Rutter

Professor of Biophysics

Raúl Herranz

Joseph Borg

Daniela Bezdan

Stefania Giacomello

Masafumi Muratani

Christopher E. Mason

Timothy Etheridge

Nathaniel J. Szewczyk


Widespread generation and analysis of omics data have revolutionized molecular medicine on Earth, yet its power to yield new mechanistic insights and improve occupational health during spaceflight is still to be fully realized in humans. Nevertheless, rapid technological advancements and ever-regular spaceflight programs mean that longitudinal, standardized, and cost-effective collection of human space omics data are firmly within reach. Here, we consider the practicality and scientific return of different sampling methods and omic types in the context of human spaceflight. We also appraise ethical and legal considerations pertinent to omics data derived from European astronauts and spaceflight participants (SFPs). Ultimately, we propose that a routine omics collection program in spaceflight and analog environments presents a golden opportunity. Unlocking this bright future of artificial intelligence (AI)-driven analyses and personalized medicine approaches will require further investigation into best practices, including policy design and standardization of omics data, metadata, and sampling methods.

Journal Article Type Article
Acceptance Date Jun 17, 2022
Online Publication Date Jul 29, 2022
Publication Date Oct 14, 2022
Deposit Date Aug 8, 2022
Publicly Available Date Aug 10, 2022
Journal Patterns
Electronic ISSN 2666-3899
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 3
Issue 10
Article Number 100550
Keywords General Decision Sciences
Public URL
Publisher URL


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