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The Omics of Stem Cell Mediated Regeneration: A Pilot Single Cell RNA-Seq Study of Mechanotransduction

Mechanical forces are potent modulators of stem cell based tissue regenerative mechanisms, inducing cell fate decisions and tissue specific commitment. A unique platform for investigating mechanotransduction is spaceflight, where microgravity and altered fluid mechanics provide a loading-null experimental condition. Seminal investigations of regenerative capacity in a wholly regenerative species, the newt model, and in a variety of totipotent and adult stem cell populations have demonstrated the detrimental effects of unloading on maintenance of stem cell based regeneration. Of particular interest is the observation that unloading interferes with the transition of stem cell pools from proliferative state to differentiation commitment. In this work we sought to test the hypothesis that gravity mechanotransduction regulates stem cell tissue regenerative processes by modulating stem cell proliferation and differentiation fates at specific cell cycle stages. To do this, clonally-derived ESCs were plated on a collagen matrix and expanded for 36 hours before re-plating on a non-adherent culture dish in the absence of leukemia inhibitory factor (LIF) to form spheroid aggregate EBs. After formation, the EBs were transferred to a collagen matrix coated culture dishes and given 4 days to allow implantation and outgrowth. In parallel, totipotent ESCs were plated 24 hours before mechanical stimulation on collagen matrix culture dishes in the presence of LIF to maintain totipotency and serve as un-differentiation committed controls. The EBs and ESCs were then subjected to either a 60 minute pulse of gravity (static loading) or 60 minutes of cyclic stretch (dynamic loading) mechanotransduction. Six hours post-stimulation, we used a 10X Genomics Single Cell controller to generate bar-coded single cell Illumina libraries and sequenced expressomes for 5,000 static loaded cells, representative of a change in gravity mechanotransduction, 5,000 dynamic loaded cells, representative of tissue loading associate with physiologic function, and 5,000 unstimulated 1g control cells. The comparison of these 3 libraries by cluster assignment based on like gene expression patterns show substantial alteration in cluster geometry due to mechanical loading. Specifically the mechanically loaded EB outgrowth cells to retain potency markers (PAX6, SOX2, CD34) and suppress early commitment markers (Dhh, VCAN, Igf1). Whereas the EBs cultured under the non-stimulated conditions display clear departure from the ESC expressome with lineage commitment markers upregulated and several tissue specific markers being expressed (BMP "early musculoskeletal development, Mesp1" early cardiovascular cell lineage). These markers are not seen in the mechano-stimulated cultures or the totipotent ESC cultures. Comparison of like clusters between our experimental conditions revealed an array of regenerative and stem cell genes are significantly mechano-regulated. Of particular importance CDKN1a/p21, a gene shown by previous investigation of our research team to be significantly upregulated in unloading, was suppressed in the static and dynamic loaded EBS. In addition to CDKN1a/p21 many genes related to cell cycle and transitory differentiation markers had elevated expression in the mechano-stimulated EBs, but surprisingly these trends were not observed in the ESC cultures. This study is the first of its kind investigating for mechano-signaling and mechano-regulated pathways, and has alre

Single cell RNA-sequecing↗

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 350 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab Sequencing Lab. The GLDS contains rich metadata about each experiment and has integrated radiation dosimetry data from experiments flown on the Space Shuttle, International Space Station, and Free Flying spacecrafts. With the increasing amount and complexity of omics data being generated, GeneLab utilizes community-defined, common models for metadata and terminology so that omics data and results are discoverable and reliably reproducible. GeneLab uses the ISA-Tab specification and semantic model for organizing and representing omics metadata. In addition to metadata standards, data files must be open-source file or common exchange formats to ensure accessibility and usability by all users. To ease data ingestion and transfer, the web-based submission tool allows PIs a user-friendly user interface to curate, organize, and publish their space relevant omics data. In the more recent years, data curation and submission portal has incorporated the FAIR principles making data findable, accessible, interoperable, and reusable. To increase reusability of data, GeneLab has implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 200 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. To train the next generation of scientists, NASA offers training programs such as GeneLab 4 High School (GL4HS) and GeneLab 4 Universities. NLM Curation at a Scale Workshop 2022 | NASA GeneLab (GL4U) to teach students bioinformatics and computational biology methods to analyze omics data. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

GeneLab↗