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Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes. However, documenting microbial shifts during spaceflight has been difficult due to mission constraints that lead to limited sampling and profiling. Here we executed a six-month longitudinal study to quantify the high-resolution human microbiome response to three days in orbit for four individuals. Using paired metagenomics and metatranscriptomics alongside single-nuclei immune cell profiling, we characterized time-dependent, multikingdom microbiome changes across 750 samples and 10 body sites before, during and after spaceflight at eight timepoints. We found that most alterations were transient across body sites; for example, viruses increased in skin sites mostly during flight. However, longer-term shifts were observed in the oral microbiome, including increased plaque-associated bacteria (for example, Fusobacteriota ), which correlated with immune cell gene expression. Further, microbial genes associated with phage activity, toxin–antitoxin systems and stress response were enriched across multiple body sites. In total, this study reveals in-depth characterization of microbiome and immune response shifts experienced by astronauts during short-term spaceflight and the associated changes to the living environment, which can help guide future missions, spacecraft design and space habitat planning. Longitudinal multi-omics reveals shifts to the human microbiome across multiple body sites and the associated immune responses during short-term spaceflight.

Abstract Background The Inspiration4 (I4) mission, the first all-civilian orbital flight mission, investigated the physiological effects of short-duration spaceflight through a multi-omic approach. Despite advances, there remains much to learn about human adaptation to spaceflight's unique challenges, including microgravity, immune system perturbations, and radiation exposure. Methods To provide a detailed genetics analysis of the mission, we collected dried blood spots pre-, during, and post-flight for DNA extraction. Telomere length was measured by quantitative PCR, while whole genome and cfDNA sequencing provided insight into genomic stability and immune adaptations. A robust bioinformatic pipeline was used for data analysis, including variant calling to assess mutational burden. Result Telomere elongation occurred during spaceflight and shortened after return to Earth. Cell-free DNA analysis revealed increased immune cell signatures post-flight. No significant clonal hematopoiesis of indeterminate potential (CHIP) or whole-genome instability was observed. The long-term gene expression changes across immune cells suggested cellular adaptations to the space environment persisting months post-flight. Conclusion Our findings provide valuable insights into the physiological consequences of short-duration spaceflight, with telomere dynamics and immune cell gene expression adapting to spaceflight and persisting after return to Earth. CHIP sequencing data will serve as a reference point for studying the early development of CHIP in astronauts, an understudied phenomenon as previous studies have focused on career astronauts. This study will serve as a reference point for future commercial and non-commercial spaceflight, low Earth orbit (LEO) missions, and deep-space exploration.

The accuracy of a sequencing platform has traditionally been measured by the %Q30, or percentage of data exceeding a basecall accuracy of 99.9%. Improvements to accuracy beyond Q30 may be beneficial for certain applications such as the identification of low frequency alleles or the improvement of reference genomes. Here we demonstrate how we achieved over 70% Q50 (99.999% accuracy) data on the AVITI sequencer. This level of accuracy required us to not only improve sequencing quality but also to mitigate library preparation errors and analysis artifacts.Competing Interest StatementAll authors are employees of Element Biosciences and may hold stock options.

We present a platform that directly sequences single guide RNAs and endogenous 3′UTRs in fixed cells while simultaneously measuring protein abundance and cellular morphology. We demonstrate platform capability by performing optical pooled screening of CRISPR-perturbed lung cancer cells. This approach unites direct in-sample RNA sequencing with complementary phenotypic readouts, enabling comprehensive, scalable, and functional genomics analyses within a single experiment.

We present a multiomics platform comprising Teton, a detection assay system, and AVITI24, a dual-flowcell instrument that performs both cellular imaging and sequencing readout. Teton integrates a compartmentalized flowcell for cell culture with methods to measure morphology, RNA, and protein at subcellular resolution. The platform quantifies morphological features through cell painting of 6 cellular components, RNA expression of up to 350 transcripts via sequencing of oligonucleotides hybridized to mRNA, and protein expression of up to 200 targets using antibody-linked oligonucleotide sequencing. The flow cell accommodates >1 million cells in a 10 cm sqaured open-well format or can be subdivided into 12 or 48 wells to support experiments with multiple conditions or time points. We describe and validate the detection methods of the platform and showcase its capabilities by co-culturing three cancer cell lines and elucidating the cellular pathways triggered by various drug treatments as a function of time. Using multiple time points enables us to capture the dynamics of cellular processes including receptor activation and signaling cascades. The results demonstrate how different cancer cells evade TNFalpha-induced apoptosis by activating compensatory signaling programs that maintain survival despite pro-apoptotic cues. Our model system replicates previously published results and highlights the versatility of the platform in enabling rapid, high-throughput analysis of complex cellular responses in varied biological contexts.

We present avidity sequencing - a novel sequencing chemistry that separately optimizes the process of stepping along a DNA template and the process of identifying each nucleotide within the template. Nucleotide identification uses multivalent nucleotide ligands on dye-labeled cores to form polymerase-polymer nucleotide complexes bound to clonal copies of DNA targets. These polymer-nucleotide substrates, termed avidites, decrease the required concentration of reporting nucleotides from micromolar to nanomolar, and yield negligible dissociation rates. We demonstrate the use of avidites as a key component of a sequencing technology that surpasses Q40 accuracy and enables a diversity of applications that include single cell RNA-seq and whole human genome sequencing. We also show the advantages of this technology in sequencing through long homopolymers.

Experiments on disordered alloys 1 – 3 suggest that spin glasses can be brought into low-energy states faster by annealing quantum fluctuations than by conventional thermal annealing. Owing to the importance of spin glasses as a paradigmatic computational testbed, reproducing this phenomenon in a programmable system has remained a central challenge in quantum optimization 4 – 13 . Here we achieve this goal by realizing quantum-critical spin-glass dynamics on thousands of qubits with a superconducting quantum annealer. We first demonstrate quantitative agreement between quantum annealing and time evolution of the Schrödinger equation in small spin glasses. We then measure dynamics in three-dimensional spin glasses on thousands of qubits, for which classical simulation of many-body quantum dynamics is intractable. We extract critical exponents that clearly distinguish quantum annealing from the slower stochastic dynamics of analogous Monte Carlo algorithms, providing both theoretical and experimental support for large-scale quantum simulation and a scaling advantage in energy optimization. Using a quantum annealing processor to study three-dimensional spin glasses demonstrates an accurate large-scale quantum simulation of critical dynamics and a scaling advantage over analogous classical methods for energy optimization.

The work of Berezinskii, Kosterlitz and Thouless in the 1970s 1 , 2 revealed exotic phases of matter governed by the topological properties of low-dimensional materials such as thin films of superfluids and superconductors. A hallmark of this phenomenon is the appearance and interaction of vortices and antivortices in an angular degree of freedom—typified by the classical XY model—owing to thermal fluctuations. In the two-dimensional Ising model this angular degree of freedom is absent in the classical case, but with the addition of a transverse field it can emerge from the interplay between frustration and quantum fluctuations. Consequently, a Kosterlitz–Thouless phase transition has been predicted in the quantum system—the two-dimensional transverse-field Ising model—by theory and simulation 3 – 5 . Here we demonstrate a large-scale quantum simulation of this phenomenon in a network of 1,800 in situ programmable superconducting niobium flux qubits whose pairwise couplings are arranged in a fully frustrated square-octagonal lattice. Essential to the critical behaviour, we observe the emergence of a complex order parameter with continuous rotational symmetry, and the onset of quasi-long-range order as the system approaches a critical temperature. We describe and use a simple approach to statistical estimation with an annealing-based quantum processor that performs Monte Carlo sampling in a chain of reverse quantum annealing protocols. Observations are consistent with classical simulations across a range of Hamiltonian parameters. We anticipate that our approach of using a quantum processor as a programmable magnetic lattice will find widespread use in the simulation and development of exotic materials. A large-scale programmable quantum simulation is described, using a D-Wave quantum processor to simulate a two-dimensional magnetic lattice in the vicinity of a topological phase transition.

The promise of quantum computing lies in harnessing programmable quantum devices for practical applications such as efficient simulation of quantum materials and condensed matter systems. One important task is the simulation of geometrically frustrated magnets in which topological phenomena can emerge from competition between quantum and thermal fluctuations. Here we report on experimental observations of equilibration in such simulations, measured on up to 1440 qubits with microsecond resolution. By initializing the system in a state with topological obstruction, we observe quantum annealing (QA) equilibration timescales in excess of one microsecond. Measurements indicate a dynamical advantage in the quantum simulation compared with spatially local update dynamics of path-integral Monte Carlo (PIMC). The advantage increases with both system size and inverse temperature, exceeding a million-fold speedup over an efficient CPU implementation. PIMC is a leading classical method for such simulations, and a scaling advantage of this type was recently shown to be impossible in certain restricted settings. This is therefore an important piece of experimental evidence that PIMC does not simulate QA dynamics even for sign-problem-free Hamiltonians, and that near-term quantum devices can be used to accelerate computational tasks of practical relevance. Experimental demonstration of quantum speedup that scales with the system size is the goal of near-term quantum computing. Here, the authors demonstrate such scaling advantage for a D-Wave quantum annealer over analogous classical algorithms in simulations of frustrated quantum magnets.

Quantum simulation has emerged as a valuable arena for demonstrating and understanding the capabilities of near-term quantum computers 1 – 3 . Quantum annealing 4 , 5 has been successfully used in simulating a range of open quantum systems, both at equilibrium 6 – 8 and out of equilibrium 9 – 11 . However, in all previous experiments, annealing has been too slow to coherently simulate a closed quantum system, due to the onset of thermal effects from the environment. Here we demonstrate coherent evolution through a quantum phase transition in the paradigmatic setting of a one-dimensional transverse-field Ising chain, using up to 2,000 superconducting flux qubits in a programmable quantum annealer. In large systems, we observe the quantum Kibble–Zurek mechanism with theoretically predicted kink statistics, as well as characteristic positive kink–kink correlations, independent of temperature. In small chains, excitation statistics validate the picture of a Landau–Zener transition at a minimum gap. In both cases, the results are in quantitative agreement with analytical solutions to the closed-system quantum model. For slower anneals, we observe anti-Kibble–Zurek scaling in a crossover to the open quantum regime. The coherent dynamics of large-scale quantum annealers demonstrated here can be exploited to perform approximate quantum optimization, machine learning and simulation tasks. The coherent dynamics of the transverse-field Ising model driven through a quantum phase transition can be accurately simulated using a large-scale quantum annealer.