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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Bacteria Counter

Science Applications, Inc.'s ATP Photometer makes a rapid and accurate count of the bacteria in a body fluid sample. Instrument provides information on the presence and quantity of bacteria by measuring the amount of light emitted by the reaction between two substances. Substances are ATP adenosine triphosphate and luciferase. The reactants are applied to a human body sample and the ATP Photometer observes the intensity of the light emitted displaying its findings in a numerical output. Total time lapse is usually less than 10 minutes, which represents a significant time savings in comparison of other techniques. Other applications are measuring organisms in fresh and ocean waters, determining bacterial contamination of foodstuffs, biological process control in the beverage industry, and in assay of activated sewage sludge.

Source record↗
NASA NTRS1973

Automatic instrument for chemical processing to detect microorganism in biological samples by measuring light reactions

An automated apparatus is reported for sequentially assaying urine samples for the presence of bacterial adenosine triphosphate (ATP) that comprises a rotary table which carries a plurality of sample containing vials and automatically dispenses fluid reagents into the vials preparatory to injecting a light producing luciferase-luciferin mixture into the samples. The device automatically measures the light produced in each urine sample by a bioluminescence reaction of the free bacterial adenosine triphosphate with the luciferase-luciferin mixture. The light measured is proportional to the concentration of bacterial adenosine triphosphate which, in turn, is proportional to the number of bacteria present in the respective urine sample.

Kelbaugh, B. N.↗
NASA NTRS1984

Development of a new class of chemical and biological ultrasensors: Ribonuclease contamination and control

In order to define ribonuclease contamination, an assay for ribonuclease having picogram level sensitivity was established. In this assay, polycytidylic acid is digested by ribonuclease leading to smaller fragments of poly C that remain soluble after treatment of the sample with perchloric acid and lanthanum acetate. An absorbance measurement at 260 nm of the supernatant from the centrifuged sample measures the ribonuclease. A standard curve is shown. Using this assay procedure, ribonuclease contamination was found to be significant in routine laboratory proteins, in particular, bovine serum albumin, lysozyme, catalase, and cytochrome C. This was confirmed by demonstrating a considerable reduction in this activity in the presence of phosphate buffer since phosphate inhibits ribonuclease. Ribonuclease contamination was not significantly encountered in routine laboratory glassware, plasticware, column surfaces, chromatographic particles, and buffer reagents, including airborne contamination. Some contamination could be introduced by fingerprints, however.

Source record↗
NASA NTRSDate not supplied

Open Science for Plants in Space: Data Sharing, Standards, and Informatics for Reuse and Knowledge Discovery

Upcoming deep space missions rely on plants and crops for crew and ecosystem health. Access to space plant data enables scientists to gain a deeper understanding of biological responses to ionizing radiation, altered gravity, low atmospheric pressure, elevated CO2, and altered photoperiods. Open Science is the practice of making research available to all, while respecting diverse cultures, fostering collaborations with equity. 2023 is the ‘Year of Open Science’, and NASA has a 5-year Transform to Open Science (TOPS) mission designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) developed by NASA’s Biological and Physical Sciences Division provides access to data from space-relevant biological experiments. OSDR combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. OSDR started in 2014 with the creation of the first space-relevant FAIR (Findable, Accessible, Interoperable, Reusable) biological ‘omics repository (GeneLab), providing detailed metadata on investigation, sample, and assay levels. Today, GeneLab hosts 62 plant datasets which have led to 5 published peer-reviewed meta-analysis publications. Most of these publications were collaboration efforts under the OSDR Analysis Working Groups (AWGs). AWGs provide great opportunities for investigators to collaborate and set new standards for space-relevant data and metadata. The AWGs are welcoming any ASPB members interested in providing plant expertise for space biology. The addition of ALSDA to OSDR is also expanding analysis capability beyond ‘omics. Now is the time to get involved as a Subject Matter Expert as we establish the framework for modern plant data archiving through the AWGs. Investigators are invited to submit their space-relevant plant datasets to OSDR and visit the site to learn about the tools OSDR has to offer (osdr.nasa.gov/bio).

FAIR↗
NASA NTRS2000

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Dhaeseleer, Patrik↗
NASA NTRS2001

Twisting integrin receptors increases endothelin-1 gene expression in endothelial cells

A magnetic twisting stimulator was developed based on the previously published technique of magnetic twisting cytometry. Using ligand-coated ferromagnetic microbeads, this device can apply mechanical stresses with varying amplitudes, duration, frequencies, and waveforms to specific cell surface receptors. Biochemical and biological responses of the cells to the mechanical stimulation can be assayed. Twisting integrin receptors with RGD (Arg-Gly-Asp)-containing peptide-coated beads increased endothelin-1 (ET-1) gene expression by >100%. In contrast, twisting scavenger receptors with acetylated low-density lipoprotein-coated beads or twisting HLA antigen with anti-HLA antibody-coated beads did not lead to alterations in ET-1 gene expression. In situ hybridization showed that the increase in ET-1 mRNA was localized in the cells that were stressed with the RGD-coated beads. Blocking stretch-activated ion channels with gadolinium, chelating Ca2+ with EGTA, or inhibiting tyrosine phosphorylation with genistein abolished twist-induced ET-1 mRNA elevation. Abolishing cytoskeletal tension with an inhibitor of the myosin ATPase, with an inhibitor of myosin light chain kinase, or with an actin microfilament disrupter blocked twisted-induced increases in ET-1 expression. Our results are consistent with the hypothesis that the molecular structural linkage of integrin-cytoskeleton is an important pathway for stress-induced ET-1 gene expression.

Non-NASA Center↗
NASA NTRS2005

Microfabricated Genomic Analysis System

Genetic sequencing and many genetic tests and assays require electrophoretic separation of DNA. In this technique, DNA fragments are separated by size as they migrate through a sieving gel under the influence of an applied electric field. In order to conduct these analyses on-orbit, it is essential to acquire the capability to efficiently perform electrophoresis in a microgravity environment. Conventional bench top electrophoresis equipment is large and cumbersome and does not lead itself to on-orbit utilization. Much of the previous research regarding on-orbit electrophoresis involved altering conventional electrophoresis equipment for bioprocessing, purification, and/or separation technology applications. A new and more efficient approach to on-orbit electrophoresis is the use of a microfabricated electrophoresis platform. These platforms are much smaller, less expensive to produce and operate, use less power, require smaller sample sizes (nanoliters), and achieve separation in a much shorter distance (a few centimeters instead of 10 s or 100 s of centimeters.) In contrast to previous applications, this platform would be utilized as an analytical tool for life science/medical research, environmental monitoring, and medical diagnoses. Identification of infectious agents as well as radiation related damage are significant to NASA s efforts to maintain, study, and monitor crew health during and in support of near-Earth and interplanetary missions. The capability to perform genetic assays on-orbit is imperative to conduct relevant and insightful biological and medical research, as well as continuing NASA s search for life elsewhere. This technology would provide an essential analytical tool for research conducted in a microgravity environment (Shuttle, ISS, long duration/interplanetary missions.) In addition, this technology could serve as a critical and invaluable component of a biosentinel system to monitor space environment genotoxic insults to include radiation.

Gonda, Steve↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRS2002

The Virtual Glovebox (VGX): An Immersive Simulation System for Training Astronauts to Perform Glovebox Experiments in Space

The era of the International Space Station (ISS) has finally arrived, providing researchers on Earth a unique opportunity to study long-term effects of weightlessness and the space environment on structures, materials and living systems. Many of the physical, biological and material science experiments planned for ISS will require significant input and expertise from astronauts who must conduct the research, follow complicated assay procedures and collect data and samples in space. Containment is essential for Much of this work, both to protect astronauts from potentially harmful biological, chemical or material elements in the experiments as well as to protect the experiments from contamination by air-born particles In the Space Station environment. When astronauts must open the hardware containing such experiments, glovebox facilities provide the necessary barrier between astronaut and experiment. On Earth, astronauts are laced with the demanding task of preparing for the many glovebox experiments they will perform in space. Only a short time can be devoted to training for each experimental task and gl ovebox research only accounts for a small portion of overall training and mission objectives on any particular ISS mission. The quality of the research also must remain very high, requiring very detailed experience and knowledge of instrumentation, anatomy and specific scientific objectives for those who will conduct the research. This unique set of needs faced by NASA has stemmed the development of a new computer simulation tool, the Virtual Glovebox (VGB), which is designed to provide astronaut crews and support personnel with a means to quickly and accurately prepare and train for glovebox experiments in space.

Smith, Jeffrey D.↗
NASA NTRS2008

Rapid Biochemical Analysis on the International Space Station (ISS): Preparing for Human Exploration of the Moon and Mars

The Lab-on-a-Chip Application Development - Portable Test System, known as LOCAD-PTS, was launched to the International Space Station (ISS) aboard Space Shuttle Discovery (STS-116) on December 9th,2006. Since that time, it has remained onboard ISS and has been operated by the crew on 10 separate occasions LOCAD-PTS is a handheld device for rapid biochemical analysis; it consists of a spectrophotometer, a series of interchangeable cartridges, a pipette and several clean/sterilized swabbing kits to obtain samples from ISS surfaces. Sampling, quantitative analysis and data retrieval is performed onboard, therefore reducing the need to return samples to Earth. Less than 20 minutes are required from sampling to data, significantly faster than existing culture-based methods on ISS, which require 3-5 days. Different cartridges are available for the detection of different target molecules (simply by changing the formulation within each cartridge), thereby maximizing the benefit and applications addressed by a single instrument. Initial tests on ISS have focused on the detection of the bact.erial macromolecule endotoxin, a component of bacterial cell walls. LOCAD-PTS detects endotoxin with a cartridge that contains a formulation known as Limulus Amebocyte Lysate (LAL) assay. LAL is derived from blood of the horseshoe crab, Limulus polyphemus, and detects enodotoxin with an enzyme cascade that triggers generation Of a yellow colored dye, p-nitroanaline. The more p-nitroanaline product, the more endotoxin is in the original sample. To enable quantitative analysis, the absorbance of this color is measured by LOCAD-PTS through a 395 nm filter and compared with an internal calibration curve, to provide a reading on the LED display that ranges from 0.05 Endotoxin Units (EU)/ml to 5 EU/ml. Several surface sites were analyzed within ISS between March 2007 and February 2008, including multiple locations in the US Laboratory Destiny, Node 1 Unity, AMock, and Service Module Zvezda. The goals of this initial study were to i) test the cleanliness of reagents/supplies on orbit, ii) test the crew's ability to collect and process a sample in microgravity without contamination, iii) demonstrate nominal function of the LOCAD-PTS, and iv) provide a general survey of endotoxin within the ISS. The surface sites varied greatly in terms of their frequency-of-use and material texture/composition; from relatively smooth aluminum, to fabric, to the room temperature vulcanizing (RTV) rubber of a Extravehicular Mobility Unit (EMU) spacesuit. Results showed that: i) the swabbing kits and reagents remained clean on orbit, ii) the crew could collect and process a sample without contamination, and iii) the LOCAD-PTS functioned nominally in > 99% of the 55 tests completed. We will present detailed results of the survey of endotoxin on ISS surfaces. These results and technology are important in the near-term - by providing an extra tool in the toolbox for ISS microbial monitoring. They are also important in the longer term as valuable preparation for human exploration of the Moon and Mars. One of the proposed science goals for the human exploration of Mars will be to detect and characterize any indigenous biological molecules that may exist on the Martian surface. To achieve that goal, the crew must have the technology available onboard to differentiate indigenous biology from any terrestrial biological material brought to Mars by the spacecraft and crew (termed 'forward contamination'). The LAL assay is already one of the official methods used by NASA's planetary protection program to certify cleanliness of interplanetary robotic spacecraft prior to launch; and therefore endotoxin is a good marker of forward contamination (as well as other microbial molecules detectable with LOCAD-PTS e.g. box-1, 3-glucan and lipoteichoic acid). Furthermore, the distribution and abundance of these molecules on the ISS provides a good indicator of what to expect on the Crew Exploratioehicle Orion, the lunar lander Antares, and future crewed spacecraft destined for Mars. In addition, technology such as LOCAD-PTS has been proposed to help evaluate forward contamination during lunar surface operations by the crew, as preparation for the human exploration of Mars.

Maule, J.↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.

Ryan T Scott↗
NASA NTRS1999

Modeling the Activity of Single Genes

The central dogma of molecular biology states that information is stored in DNA, transcribed to messenger RNA (mRNA) and then translated into proteins. This picture is significantly augmentated when we consider the action of certain proteins in regulating transcription. These transcription factors provide a feedback pathway by which genes can regulate one another's expression as mRNA and then as protein. To review: DNA, RNA and proteins have different functions. DNA is the molecular storehouse of genetic information. When cells divide, the DNA is replicated, so that each daughter cell maintains the same genetic information as the mother cell. RNA acts as a go-between from DNA to proteins. Only a single copy of DNA is present, but multiple copies of the same piece of RNA may be present, allowing cells to make huge amounts of protein. In eukaryotes (organisms with a nucleus), DNA is found in the nucleus only. RNA is copied in the nucleus then translocates(moves) outside the nucleus, where it is transcribed into proteins. Along the way, the RNA may be spliced, i.e., may have pieces cut out. RNA then attaches to ribosomes and is translated to proteins. Proteins are the machinery of the cell other than DNA and RNA, all the complex molecules of the cell are proteins. Proteins are specialized machines, each of which fulfills its own task, which may be transporting oxygen, catalyzing reactions, or responding to extracellular signals, just to name a few. One of the more interesting functions a protein may have is binding directly or indirectly to DNA to perform transcriptional regulation, thus forming a closed feedback loop of gene regulation. The structure of DNA and the central dogma were understood in the 50s; in the early 80s it became possible to make arbitrary modifications to DNA and use cellular machinery to transcribe and translate the resulting genes; more recently, genomes (i.e., the complete DNA sequence) of many organisms have been sequenced. This large-scale sequencing began with simple organisms, viruses and bacteria, progressed to eukaryotes such as yeast, and more recently (1998) progressed to a multi-cellular animal, the nematode Caenorhabditis elegans. Sequencers have now moved on to the fruit fly Drosophila melanogaster, whose sequence is slated for completion by the end of 1999. The human genome project is expected to determine the complete sequence of all 3 billion bases of human DNA within the next five years. In the wake of genome-scale sequencing, further instrumentation is being developed to assay gene expression and function on a comparably large scale. Much of the work in computational biology focuses on computational tools used in sequencing, finding genes that are related to a particular gene, finding which parts of the DNA code for proteins and which do not, understanding what proteins will be formed from a given length of DNA, predicting how the proteins will fold from a one-dimensional structure into a three dimensional structure, and so on. Much less computational work has been done regarding the function of proteins. One reason for this is that different proteins function very differently, and so work on protein function is very specific to certain classes of proteins. There are, for example, proteins such enzymes that catalyze various intracellular reactions, receptors that respond to extracellular signals and ion channels that regulate the flow of charged particles into and out of the cell. In this chapter, we will consider a particular class of proteins called transcription factors(TFs), which are responsible for regulating when a certain gene is expressed in a certain cell, which cells it is express in, and how much is expressed. Understanding these processes will involve developing a deeper understanding of transcription, translation, and the cellular processes that control those processes. All of these elements fall under the aegis of gene regulation or more narrowly transcriptional regulation. Some of the key questions in gene regulation are: What genes are expressed in a certain cell at a certain time? How does gene expression differ from cell to cell in a multicellular organism? Which proteins act as transcription factors, i.e., are important in regulating gene expression? From questions like these, we hope to understand which genes are important for various macroscopic processes. Nearly all of the cells of a multicellular organism contain the same DNA. Yet this same genetic information yields a large number of different cell types. The fundamental difference between a neuron and a liver cell, for example, is which genes are expressed. Thus understanding gene regulation is an important step in understanding development. Furthermore, understanding the usual genes that are expressed in cells may give important clues about various diseases. Some diseases, such as sickle cell anemia and cystic fibrosis, are caused by defects in single, non-regulatory genes; others, such as certain cancers, are caused when the cellular control circuitry malfunctions - an understanding of these diseases will involve pathways of multiple interacting gene products. There are numerous challenges in the area of understanding and modeling gene regulation. First and foremost, biologists would like to develop a deeper understanding of the processes involved, including which genes and families of genes are important, how they interact, etc. From a computation point of view, there has been embarrassingly little work done. In this chapter there are many areas in which we can phrase meaningful, non-trivial computational questions, but questions that have not been addressed. Some of these are purely computational (what is a good algorithm for dealing with a model of type X) and others are more mathematical (given a system with certain characteristics, what sort of model can one use? How does one find biochemical parameters from system-level behavior using as few experiments as possible?). In addition to biological and algorithmic problems, there is also the ever-present issue of theoretical biology - what general principles can be derived from these systems, what can one do with models other than just simulate time-courses, what can be deduced about a class of systems without knowing all the details? The fundamental challenge to computationalists and theorists is to add value to the biology - to use models, modeling techniques and algorithms to understand the biology in new ways.

Mjolsness, Eric↗
NASA NTRS1978

Bioprocessing feasibility analysis

The biology and pathophysiology of the thymus gland is discussed and a clinical procedure for thymic hormone assay is described. The separation of null lymphocytes from mice spleens and the functional characteristics of the cells after storage and transportation were investigated to develop a clinical procedure for thymic hormone assay, and to determine whether a ground-based approach will provide the desired end-product in sufficient quantities, or whether the microgravity of space should be exploited for more economical preparation of the hormone.

Source record↗
NASA NTRSDate not supplied

Electrochemical Life Detection Methods for Ocean World Exploration

Ubiquitous across terrestrial life is cellular machinery that allows chemical energy flow by facilitating and regulating electron-transfer and chemical modification pathways. Key classes of energy transport molecules enable this movement of electrons for a variety of biological purposes. Additionally, biological enzymes function to add or remove functional groups such asphosphate moieties to redox biomolecules. Presumably, extraterrestrial life is likely to rely on similar energy transport mechanisms. With the search for life in our solar system focused on the icy satellites of Jupiter and Saturn, Europa and Enceladus, developing instrumentation capable of measuring electrochemical redox signatures representative of biomolecules or enzymatic activity in seawater appears a promising and novel means of life detection. Here, we report our adaptation of the Mars Phoenix Wet Chemistry Laboratory (WCL) electroanalytical voltammetry capabilities to assay life-critical redox molecules in synthetic seawater representative of a saline alkaline solution similar to what has been predicted from the Cassini mission data of Enceladus’ sub-surface ocean. In addition, we employ a well-established electrochemical assay that indicates phosphatase activity by comparing substrate and product redox signatures. Our study demonstrated a 10 nM limit of detection for biological redox molecules and a 3 aM limit of detection for alkaline phosphatase in seawater. Incorporation of these methods into next generation WCL payloads aimed at ocean world life detection will enable the search for biological redox-active species and enzymatic activity as indicators of life.

planetary instruments↗
NASA NTRSDate not supplied

Enabling Single Cell Research in Hollow Hydrogel Microparticles

Studying biology at the single cell level enables novel discoveries through ultra-high throughput identification and isolation of rare cells and interesting subpopulations. Encapsulation of single cells in hollow hydrogel microparticles, or PicoShells, enables easy visualization, manipulation, and assaying of single cells. The PicoShell’s porous hydrogel shell allows diffusion of nutrients and wastes into and out of the hollow liquid core, allowing uninhibited cell growth. The porous shell also allows diffusion of assay reagents, enabling solution changes, and multi-step assays. Finally, single cells and their progeny can be tracked through time, for example to measure single cell growth rates. However, PicoShells are fabricated using microfluidic techniques, which require specialized expertise, equipment, and facilities. This limits PicoShell’s widespread utility for single cell biology. For Saccharomyces cerevisiae and other desiccation-tolerant microbes, desiccating PicoShells containing cells of interest offers a solution, enabling those without microfluidic capabilities to obtain dried, shelf-stable PicoShells containing their cells of interest from a specialized microfluidics facility. Researchers could then rehydrate the PicoShells and perform their single cell assays, bypassing the microfluidic steps. Desiccated PicoShells could also be flown in space, where desiccation is already a common practice for microbes (i.e. BioSentinel and BioNutrients), bringing single cell analysis capabilities to space. In this work, we develop a method to desiccate PicoShells containing S. cerevisiae while maintaining characteristics similar to fresh, non-desiccated PicoShells. We show good microparticle morphology and hydrogel pore size as well as high yeast viability in line with previous studies. We also demonstrate sorting of a fast-growing yeast population using size-based filtration, as S. cerevisiae in PicoShells are able to physically stretch the microparticle as they grow, increasing the PicoShell diameter. These efforts contribute to the feasibility of leveraging microparticle PicoShells for single cell space biology research on Earth and in space.

Simon Ng↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.

omics↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

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