RNAseq Analysis of Rodent Spaceflight Experiments Is Confounded by Sample Collection Techniques
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San-Huei Lai Polo↗
Engineering topics
Valery Boyko
Publications and source records attributed to Valery Boyko.
Large Scale Transcriptional Analysis of Legacy Spaceflight Tissues from the NASA Institutional Scientific Collection
The NASA Institutional Scientific Collection (ISC) has amassed a collection of valuable space biology samples spanning from early Space Shuttle missions to recent missions on the International Space Station (ISS). However, the full potential of this archive has not been realized, with many samples having been stored for decades without being re-accessed. Given the pace of analytical advancement since the ISC began accumulating samples, we undertook a systematic transcriptional analysis of these samples to reveal additional patterns that may have been missed during the original investigations. We selected 93 mouse and rat samples from 5 separate Space Shuttle, ISS and ground-analogue studies with a focus on muscle, spleen and thymus tissues to allow identification of important changes related to musculoskeletal unloading and immune function. RNA extracted from these tissues was consistently of a quality and we are now generating transcriptional profiling data from sample set. This resulting data will be immediately released through the GeneLab data systems for open analysis by the space biology community. While this study will stand on its own, it will also serve as a model for future, comprehensive, analyses of the ISC.
Rodent↗
Deep Space Radiation Affects Neurovascular Functions in Human Organ-on-a-Chip Models
A major health risk for human deep space exploration is central nervous system (CNS) damage by galactic cosmic ray radiation. Simulated galactic cosmic rays or their components, especially the high- linear energy transfer (LET) particles such as 56 Fe ions, cause CNS damage, neuroinflammation and cognitive dysfunction in rodent models, but their effects on human CNS remain to be investigated. CNS damage from any insult, including ionizing radiation, is partially mediated by the blood-brain barrier (BBB), which regulates the interactions between CNS and the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal health and neuroinflammation. However, there have been few studies on BBB and astrocyte functions in regulating CNS responses, especially in human tissue/organ analogs. Therefore, we utilized a high-throughput human 3D organ-on-a-chip system, seeded with induced pluripotent stem cell-derived endothelial cells, astrocytes and neurons, to study human neurovascular responses to simulated deep space radiation. We investigated BBB permeability, oxidative stress, cellular and tissue damage, and secreted factors over the time period of 24 hours-1 week after irradiation with 0.25-0.5 Gy 5-ion simplified simulated galactic cosmic rays and 0.3-0.8 Gy high-LET 600MeV/n 56 Fe particles, and compared the outcomes to low-LET irradiation with 0.1-1 Gy doses of X-rays and gamma rays. Both high and low-LET radiation increased neurovascular permeability, caused oxidative stress, damaged endothelial cells and tight junctions, and altered expression of inflammatory cytokines. Ionizing radiation- induced neurovascular permeability and oxidative stress peaked at 3 days after irradiation and were further exacerbated by the presence of astrocytes. Furthermore, in response to particle irradiation, astrocytes stimulated interleukin-1 signaling by inhibiting the expression of interleukin-1 receptor antagonist. Thus, we also evaluated interleukin-1 receptor antagonist as a potential countermeasure against particle radiation. Ultimately, our results may help develop countermeasures to mitigate human CNS damage in deep space exploration.
Sonali D Verma↗
A Pipeline for Assessing the Quality of Rna-Seq Datasets in GeneLab
Transcriptome profiling by RNA sequencing (RNA-seq) is a powerful approach to identify gene expression changes in organisms exposed to unique environments such as spaceflight. One of the challenges of evaluating RNA-seq data both within and across different space-relevant studies is the ability to control for technical differences, including the use of different library preparation kits, sequencing platforms, RNA yield, and person-to-person variation. To help address this issue, the National Institute of Standards and Technology (NIST, nist.gov) initiated a consortium, at the request of industry and academia, to develop a set of controls for gene expression measurements. The result was a set of 92 unlabeled, polyadenylated transcripts that range from 250 – 2,000 nucleotides in length to mimic natural eukaryotic mRNAs. These External RNA Controls Consortium (ERCC) genes can be used in any RNA-seq experiment, by adding known concentrations of the ERCC genes to samples after RNA extraction, to offer a standard measurement for data comparison. At NASA GeneLab, we employ these controls as part of our standard operating procedures for every in-house RNA-seq study to assess the limit of detection, dynamic range, and power of differential expression analysis both within and across experiments. Here we will discuss the use, benefits, and limitations of ERCC genes and other types of controls, such as universal RNA references, to generate quality control information for RNA-seq studies conducted at GeneLab.
GeneLab↗
Optimizing Single Nuclei Sequencing of Brain Samples From Space Flown Mice Across Age and Strain
The NASA GeneLab Sample Processing Laboratory offers high-throughput sequencing services to NASA-funded space biology researchers. Space biology studies have specific challenges such as low sample numbers, introducing susceptibility to batch effects from sample handling. These issues are compounded by complex protocols such as single-nuclei isolation and sequencing, which has recently become an attractive methodology for assessing the cellular diversity within spaceflight samples. High quality single-nuclei sequencing requires reproducible protocols to dissociate tissue and generate clean suspension of intact single nuclei. Producing single-nuclei suspension from brain tissue is particularly challenging due to cell type heterogeneity and the myelin sheath that carries over into the nuclei suspension as debris. Current procedures tend to be time consuming and sometimes include steps that can alter gene expression and create cell-type bias. Commercially available nuclei isolation kits, such as the 10X Genomics nuclei isolation kit, offers a streamlined way to process samples for nuclei isolation, thereby minimizing batch effects and enabling reproducibility. In this study, we report on the performance of the 10X Genomics nuclei isolation kit and Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit to generate sequencing libraries from space-flown mouse brain samples. Single nuclei sequencing was performed on frozen mouse brain tissue from two spaceflight missions, Rodent Research-10 (RR-10) and RR Reference Mission-2 (RRRM-2). RR-10 mice were female B6129SF2/J, euthanized at 18-19 weeks whereas RRRM-2 mice were female C57BL/6NTac, euthanized at 20 or 37 weeks. Sequencing data was processed using standard GeneLab data processing pipelines. We report evaluation of the performance of the 10X Genomics nuclei isolation kit for spaceflight samples from mouse brain, and evaluation of reproducibility across different mouse strains and age groups. We also report preliminary scientific results including cell type inference, cell clustering, and differentially expressed genes and pathways between spaceflight and ground control samples.
RR-10↗
Batch Effect Correction Methods for NASA GeneLab Transcriptomic Datasets
RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or "batch effects", often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals. In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). We quantitatively evaluated the ability of these methods to correct for technical batch variables in space biology RNA-seq data using the following criteria: BatchQC, principal component analysis, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable / correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure. Finally, we describe the way in which the GeneLab multi-study analysis and visualization portal will allow users to examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.
Lauren M. Sanders↗
Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory
NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.
GeneLab↗
Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory
NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.
GeneLab↗
A Novel Bone Marrow Single Cell Atlas of Mechanotransduction in Microgravity, Normal Gravity, Exercise, and Hindlimb Unloading for WT and CDKN1A-Null Regenerative Mice
Mechanical loading of adult stem cell progenitors is a key factor in modulating their proliferation, differentiation, and tissue regenerative potential, with loading generally promoting tissue formation and unloading mediating tissue loss. To address the role of mechanotransduction in maintaining stem cell-based tissue regenerative health, we generated a single cell transcriptomic atlas mapping responses of femur bone marrow mesenchymal and hematopoietic lineages to a range of altered mechanical loading conditions, both in WT B1629SF2/J mice as well as the p21/CDKN1A-null regenerative mice. We selected the femur marrow compartment as a model because of the diversity and high numbers of stem cell progenitor stages present, and because it undergoes static and cyclic hydrostatic pressure loading associated with weight-bearing and ambulation. Our study included normally loaded mice at 1g, unloading in microgravity during spaceflight as well as tail suspension hindlimb unloading, and voluntary running wheel exercise. Basal, one-year natural aging, and habitat controls were also conducted. Overall, the atlas encompasses 18 different experimental conditions with N=3 mice per condition and includes more than 500,000 single cell expressomes. Key specific findings include: increased mature reticulocyte populations in aging and unloading mice compared to active mice; greater hematopoietic and mesenchymal differentiating progenitors cluster identification in CDKN1A-null samples, especially the exercise model; distinct pseudotime cell trajectory shifts in the hematopoietic lineage for spaceflight and unloaded mice; and shifts in monocytic cell populations toward osteoclastic bone degenerative lineages in unloading and spaceflight samples. Overall mechanical loading shows increased marrow progenitor population differentiation while unloading is associated with increased CDKN1A expression and maintenance of marrow population stemness. Deletion of CDKN1A appears to remove a negative check on progenitor lineages commitment to differentiation. Finally, the Bone Marrow Mechanotransduction Single Cell Atlas will serve as a reference tool for studying marrow regenerative responses across a range of mechanical environments as they relate to CDKN1A status.
single-cell↗