Milestones:
1) Analysis of image data from the FLUMIAS-ISS project 2023/2024
2) Cross-project analysis of our µg and s-µg obtained RNAseq data
3) Development of a high-resolution histologic-, physiologic-, transcriptional and epigenetic model for metastasis formation.

PI: Prof. Dr. med. Daniela Grimm

Co-I: Project Spokesperson: Dr. rer. medic. Herbert Schulz

Co-I: Dr. rer. nat. Markus Wehland-von Trebra

The strength of AIMS is the interdisciplinary linkage of scientists with complementary expertise. The role of AMCRIS in this network is linked to the biomedical and bioinformatics expertise of the AMCRIS executives. Our expertise and the available and new collected data will be further used in close interaction with the AIMS AI experts. This interaction has the purpose of proving causal relationships of biological and medical nature by development and application of AI image analysis techniques for the evaluation of microgravity-based image data on the one hand and the AI-supported integration of transcriptional, proteomic and epigenetic data on the other.

In the course of space exploration (Moon, Mars), astronauts are increasingly exposed to both microgravity (µg) and cosmic radiation. It is known that µg leads to changes in the differentiation and growth of both benign and malignant cells. Previous missions (SimBox, CellBox-1 and -2) indicated antiproliferative effects and a redifferentiation of tumor cells in space. Furthermore, three-dimensional (3D) growth (multicellular spheroids) was found with an as yet unclear mechanism.

Goals

Image data: Establishment and application of AI image-analytical methods for the evaluation of FLUMIAS image data. After the formation of spheroids from MCF-7 breast cancer cells grown under simulated microgravity (s-µg) in the ‘Random Positioning Machine‘ (RPM), these are systematically fed to the image data acquisition. The image data are analyzed by the AI partners AIDIA and DAIMLER to optimize the workflow of the FLUMIAS-ISS project CANCEROIDS (Grimm et al., Int J Mol Sci. 2022, doi: 10.3390/ijms23063073). 

Big data analysis: Cross-project analysis of our RNAseq data obtained under r-µg and s-µg, including deep learning algorithms and their analytical linkage with general (ENCODE, RoadMap, GEO etc.) and our own epigenetic data obtained under long-term s-µg. The r-µg RNAseq data already obtained in our previous parabolic flight missions for the prostate cancer cell lines DU145, LNCaP (moderate metastatic potential) and PC-3 (high metastatic potential) and the follicular thyroid carcinoma cell lines FTC-133 and WRO. The data will be analyzed integratively, prioritizing the epigenetic DNA-methylation information, if available. The subsequent data evaluation is carried out by the AI partners XTRAS and IAI-XPRESS and our own bioinformatics team.