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Future multi-year crewed planetary missions will motivate advances in aerospace nutrition and telehealth. On Earth, the Human Cell Atlas project aims to spatially map all cell types in the human body. Here, we propose that a parallel Human Cell Space Atlas could serve as an openly available, global resource for space life science research. As humanity becomes increasingly spacefaring, high-resolution omics on orbit could permit an advent of precision spaceflight healthcare. Alongside the scientific potential, we consider the complex ethical, cultural, and legal challenges intrinsic to the human space omics discipline, and how philosophical frameworks may benefit from international perspectives. High-resolution omics data have facilitated the ongoing Human Cell Atlas project. In this Perspective, Rutter and colleagues propose that a parallel Human Cell Space Atlas initiative would provide a platform for spaceflight-associated research and healthcare.

Impairment of the central nervous system (CNS) poses a significant health risk for astronauts during long-duration space missions. In this study, we employed an innovative approach by integrating single-cell multiomics (transcriptomics and chromatin accessibility) with spatial transcriptomics to elucidate the impact of spaceflight on the mouse brain in female mice. Our comparative analysis between ground control and spaceflight-exposed animals revealed significant alterations in essential brain processes including neurogenesis, synaptogenesis and synaptic transmission, particularly affecting the cortex, hippocampus, striatum and neuroendocrine structures. Additionally, we observed astrocyte activation and signs of immune dysfunction. At the pathway level, some spaceflight-induced changes in the brain exhibit similarities with neurodegenerative disorders, marked by oxidative stress and protein misfolding. Our integrated spatial multiomics approach serves as a stepping stone towards understanding spaceflight-induced CNS impairments at the level of individual brain regions and cell types, and provides a basis for comparison in future spaceflight studies. For broader scientific impact, all datasets from this study are available through an interactive data portal, as well as the National Aeronautics and Space Administration (NASA) Open Science Data Repository (OSDR). A spatial transcriptomics and single-cell multiomics study performed on mouse brain tissue. Here, authors show region-specific spaceflight-induced alterations in processes of neurogenesis, synaptogenesis and synaptic transmission.

Epigenetic regulatory mechanisms are underappreciated, yet are critical for enteric nervous system (ENS) development and maintenance. We discovered that fetal loss of the epigenetic regulator Bap1 in the ENS lineage caused severe postnatal bowel dysfunction and early death in Tyrosinase-Cre Bap1fl/fl mice. Bap1-depleted ENS appeared normal in neonates; however, by P15, Bap1-deficient enteric neurons were largely absent from the small and large intestine of Tyrosinase-Cre Bap1fl/fl mice. Bowel motility became markedly abnormal with disproportionate loss of cholinergic neurons. Single-cell RNA sequencing at P5 showed that fetal Bap1 loss in Tyrosinase-Cre Bap1fl/fl mice markedly altered the composition and relative proportions of enteric neuron subtypes. In contrast, postnatal deletion of Bap1 did not cause enteric neuron loss or impaired bowel motility. These findings suggest that BAP1 is critical for postnatal enteric neuron differentiation and for early enteric neuron survival, a finding that may be relevant to the recently described human BAP1-associated neurodevelopmental disorder.

In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

The Playbook Workflow Builder (PWB) is a web-based platform to dynamically construct and execute bioinformatics workflows by utilizing a growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem of collaborators. Via a user-friendly user interface, workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflow is added into reports containing textual descriptions, figures, tables, and references. To construct workflows, users can click on cards that represent each step in a workflow, or construct workflows via a chat interface that is assisted by a large language model (LLM). Completed workflows are compatible with Common Workflow Language (CWL) and can be published as research publications, slideshows, and posters. To demonstrate how the PWB generates meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases prioritizes drug targets for individual cancer patients using data from the NIH Common Fund programs GTEx, LINCS, Metabolomics, GlyGen, and ExRNA. The workflows created with PWB can be repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/ .

Intrauterine fetal demise (IUFD) – fetal loss after 20 weeks – affects 6 pregnancies per 1,000 live births in the United States, and the majority are of unknown etiology. Maternal systemic regulatory T cell (Treg) deficits have been implicated in fetal loss, but whether mucosal immune cells at the maternal-fetal interface contribute to fetal loss is under-explored. We hypothesized that the immune cell composition and function of the uterine mucosa would contribute to the pathogenesis of IUFD. To investigate local immune mechanisms of IUFD, we used the CBA mouse strain, which naturally has mid-late gestation fetal loss. We performed a Treg adoptive transfer and interrogated both pregnancy outcomes and the impact of systemic maternal Tregs on mucosal immune populations at the maternal-fetal interface. Treg transfer prevented fetal loss and increased an MHC-II low population of uterine macrophages. Single-cell RNA-sequencing was utilized to precisely evaluate the impact of systemic Tregs on uterine myeloid populations. A population of C1q+, Trem2+, MHC-II low uterine macrophages were increased in Treg-recipient mice. The transcriptional signature of this novel uterine macrophage subtype is enriched in multiple studies of human healthy decidual macrophages, suggesting a conserved role for these macrophages in preventing fetal loss.

Human space exploration poses inherent risks to astronauts’ health, leading to molecular changes that can significantly impact their well-being. These alterations encompass genomic instability, mitochondrial dysfunction, increased inflammation, homeostatic dysregulation, and various epigenomic changes. Remarkably, these changes bear similarities to those observed during the aging process on Earth. However, our understanding of the connection between these molecular shifts and disease development in space remains limited. Frailty syndrome, a clinical syndrome associated with biological aging, has not been comprehensively investigated during spaceflight. To bridge this knowledge gap, we leveraged murine data obtained from NASA’s GeneLab, along with astronaut data gathered from the JAXA and Inspiration4 missions. Our objective was to assess the presence of biological markers and pathways related to frailty, aging, and sarcopenia within the spaceflight context. Through our analysis, we identified notable changes in gene expression patterns that may be indicative of the development of a frailty-like condition during space missions. These findings suggest that the parallels between spaceflight and the aging process may extend to encompass frailty as well. Consequently, further investigations exploring the utility of a frailty index in monitoring astronaut health appear to be warranted.

Telomere biology disorders (TBDs) are genetic diseases caused by defective telomere maintenance. TBD patients often develop bone marrow failure and have an increased risk of myeloid neoplasms. To better understand the factors underlying hematopoietic outcomes in TBD, we comprehensively evaluated acquired genetic alterations in hematopoietic cells from 166 pediatric and adult TBD patients. Of these patients, 47.6% (28.8% of children, 56.1% of adults) had clonal hematopoiesis. Recurrent somatic alterations involved telomere maintenance genes (7.6%), spliceosome genes (10.4%, mainly U2AF1 p.S34), and chromosomal alterations (20.2%), including 1q gain (5.9%). Somatic variants affecting the DNA damage response (DDR) were identified in 21.5% of patients, including 20 presumed loss-of-function variants in ataxia-telangiectasia mutated (ATM). Using multimodal approaches, including single-cell sequencing, assays of ATM activation, telomere dysfunction-induced foci analysis, and cell-growth assays, we demonstrate telomere dysfunction-induced activation of the ATM-dependent DDR pathway with increased senescence and apoptosis in TBD patient cells. Pharmacologic ATM inhibition, modeling the effects of somatic ATM variants, selectively improved TBD cell fitness by allowing cells to bypass DDR-mediated senescence without detectably inducing chromosomal instability. Our results indicate that ATM-dependent DDR induced by telomere dysfunction is a key contributor to TBD pathogenesis and suggest dampening hyperactive ATM-dependent DDR as a potential therapeutic intervention.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53 -mutant, Sonic Hedgehog TP53 -wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.

Over the past decade, there has been substantial growth in both the quantity and complexity of available biomedical data. In order to more efficiently harness this extensive data and alleviate challenges associated with integration of multi-omics data, we developed Petagraph, a biomedical knowledge graph that encompasses over 32 million nodes and 118 million relationships. Petagraph leverages more than 180 ontologies and standards in the Unified Biomedical Knowledge Graph (UBKG) to embed millions of quantitative genomics data points. Petagraph provides a cohesive data environment that enables users to efficiently analyze, annotate, and discern relationships within and across complex multi-omics datasets supported by UBKG’s annotation scaffold. We demonstrate how queries on Petagraph can generate meaningful results across various research contexts and use cases.