Astrosenescence: The role of senescence and cell cycle disruption in Space Travel.

Spaceflight presents unique stressors, including microgravity and cosmic radiation, which may accelerate cellular aging processes such as senescence. Cellular senescence is characterized by permanent cell cycle arrest, secretion of pro-inflammatory factors, and DNA damage. Understanding how spaceflight impacts cellular senescence could provide crucial insights for long-term human space exploration, including strategies to counteract tissue degeneration.

Supervisor - Dr Eoin O’Sullivan (eoin.osullivan@health.qld.gov.au)

To identify and validate the presence of cellular senescence and associated pathways in mice and human tissues exposed to spaceflight, using single-cell and bulk RNA sequencing data.

1. Data Collection: Obtain and process single-cell and bulk RNA-Seq datasets of mice and human tissues subjected to spaceflight from NASA or SpaceX missions.
2. Senescence and cell cycle signature optimisation Identification: Using in house expertise, as well as established signatures.
3. Validation of Senescence Pathways: Investigate senescent pathway activity, such as DNA damage responses, telomeric RNA , and altered metabolic profiles, across different tissues.
4. Heterogeneity in Senescence: Explore the heterogeneity of senescent cells using single-cell RNA sequencing, identifying which cell types are most affected by spaceflight conditions.

technologies:

• Single-Cell and Single Nucleus RNA-Seq
• Bulk RNA-Seq
• Spatial RNA-Seq

Expected Outcomes

• A validated set of senescence markers and pathways activated by spaceflight in both single-cell and bulk datasets.
• Insights into the prevalence and characteristics of senescent cells in different tissue types after spaceflight exposure.
• Foundation for future work investigating interventions to mitigate senescence-related tissue damage during space missions.

Available for semester 1, 2 and summer.

Students placed overseas who want to conduct a project remotely are welcome.

The Ideal candidate should have an interest in single cell and bulk RNA-Seq workflows and will be able to programme in R/Python.