Exosome based analysis for Space Associated Neuro-Ocular Syndrome and health risks in space exploration

Abstract

Molecular profiling to characterize the effects of environmental exposures is important from the human health and performance as well as the occupational medicine perspective in space exploration. We have developed a novel exosome-based platform that allows profiling of biological processes in the body from a variety of body fluids. The technology is suitable for diagnostic applications as well as studying the pathophysiology of the Space Associated Neuro-Ocular Syndrome in astronauts and monitoring patients with chronically impaired cerebrospinal fluid drainage or elevated intracranial pressure. In this proof-of-concept, we demonstrate that: (a) exosomes from different biofluids contain a specific population of RNA transcripts; (b) urine collection hardware aboard the ISS is compatible with exosome gene expression technology; (c) cDNA libraries from exosomal RNA can be stored in dry form and at room temperature, representing an interesting option for the creation of longitudinal molecular catalogs that can be stored as a repository for retrospective analysis.

Fig. 1. High quality total RNA-Seq data on long RNAs extracted from extracellular vesicles from four different biofluids.

A A schematic summarizing experimental procedure. Whole blood, plasma, and CSF were collected from patients exhibiting high intercranial pressure (ICP) at their initial visit (“Pre-Treatment”) and whole blood, plasma, and urine at a follow-up visit (“Post-Treatment”) were subject to exoRNA isolation, RNA-Seq, and data analysis. B Stacked bar graphs showing the percentage of RNA-Seq reads mapping to transcriptome, intronic, intergenic, and other genomic regions by sample and biofluid type. C Stacked bar graphs showing the number of reads per million (RPM) mapping to various biotypes by sample and biofluid type. D Boxplots of the number of total genes detected and the number of genes covered at 80% or higher by biofluid. The center line represents the median, boxes represent ±1 interquartile range, and whiskers ±1.5 interquartile range. E Principal component analysis (PCA) of all samples showing clear separation of samples by biofluid type. F Exome capture performed on both plasma and urine RNA-Seq libraries resulted in increased proportion of reads mapped to the transcriptome when compared to total-RNA seq. G Exome capture performed on both plasma and urine RNA-Seq libraries resulted in increased proportion of reads mapping to protein coding genes and depletion of reads mapping to rRNA when compared with total RNA-Seq.

Fig. 2. Differential gene expression analysis in plasma is more informative than in whole blood samples from ICP patients.

A Stacked bar graphs showing the expression (in TPM) of the ten most abundant detected genes in individual plasma and whole blood samples. Pink bars on the bottom represent abundance of all other genes detected. B Scatterplot of all genes detected in pre- and post- treatment whole blood samples. Genes that are significantly up-regulated (p-adj. (Benjamini-Hochberg) < 0.05) in post-treatment samples are shown in red, genes that are significantly down-regulated (p-adj. (Benjamini-Hochberg < 0.05) in post-treatment samples are shown in blue. C Scatterplot of all genes detected in pre- and post- treatment plasma samples. Genes that are significantly up-regulated (p-adj. (Benjamini-Hochberg) < 0.05) in post-treatment samples are shown in red, genes that are significantly down-regulated (p-adj. (Benjamini-Hochberg < 0.05) in post-treatment samples are shown in blue. D Heatmap showing differential expression of significantly differentially expressed genes (p-adj. (Benjamini-Hochberg) < 0.05) between pre-treatment (teal) and post-treatment (salmon) plasma samples of three high-ICP patients. E Gene set enrichment analysis of gene expression in pre- vs. post-treatment high-ICP plasma patient samples (p-adj. (Benjamini-Hochberg) < 0.05).

Fig. 3. Patients with high ICP have a different gene expression profile in the CSF extracellular vesicles.

A Scatterplot of all genes detected in high-ICP CSF from patients and normal-ICP CSF samples from a biobank. Genes that are significantly higher (p-adj. (Benjamini-Hochberg) < 0.05) in high-ICP patients are shown in red and genes that are significantly higher in normal-ICP samples (p-adj. (Benjamini-Hochberg) < 0.05) are shown in blue. B PCA of high-ICP (represented in teal) and normal-ICP CSF samples (represented in salmon). C Gene ontology (GO) analysis for genes that are enriched in normal-ICP CSF samples relative to high-ICP patient CSF showing the top 15 enriched GO terms and their relative significance by depth of color. Enrichment is shown on the x axis, and false discovery rate (FDR) on the y axis.

Fig. 4. Urine exosome RNA profiles from post-treatment urine samples and feasibility of using the NASA Sarstedt tube for urine collection and exosome RNA readout.

A Scatterplot of all genes detected in post-treatment patient and control urine samples. Genes that are up-regulated in post-treatment patients are in red and genes that are down-regulated in post-treatment patients are in blue. B Heatmap showing differential expression of significantly differentially expressed genes between post-treatment patient (teal) and healthy control (salmon) urine samples. C Stacked bar graphs showing the percentage of RNA-Seq reads mapping to transcriptome, intronic, intergenic, and other genomic regions of exoRNA derived from urine collected and stored using Exosome Diagnostics control urine cups and NASA Sarstedt collection tubes. D Stacked bar graphs showing the number of reads per million (RPM) mapping to various biotypes of exoRNA derived from urine stored using Exosome Diagnostics control urine cups and NASA Sarstedt collection tubes. E Scatterplot of all genes expressed in urine samples stored using Exosome Diagnostics control urine cups and NASA Sarstedt collection tubes.