Studying Gene Regulation in a Spatial Contex​

General data

Time schedule of the internship

Requirements and prior knowledge

Bachelor's degree in Bioinformatics, in particular successful completion of the GoBi module. Good Python programming skills. Interest in data visualization and network medicine. Previous experience in software development is an advantage, but not a must.

Description

Background​

Understanding gene regulation in its spatial context is essential for deciphering tissue-specific functions and disease mechanisms [1]. Gene regulatory networks (GRNs), comprising transcription factors (TFs) and their target genes define regulatory relationships [2]. Integrating GRNs with spatial transcriptomics data provides a unique opportunity to localize regulatory activity within tissues. ceRNA networks, derived using methods like SPONGE [3], expand this framework by highlighting miRNA-mediated regulation. Tools like Tangram [4] map single-cell RNA-seq (scRNA-seq) data onto spatial transcriptomics, enabling multi-modal data integration.​

Motivation​

Despite advances in spatial transcriptomics, platforms that integrate GRNs, ceRNA modules, and multi-modal datasets to visualize spatial gene regulation remain underdeveloped. A unified tool for localizing regulatory modules, visualizing enrichment scores (ES) for specific regulators in a spatial context, and overlaying additional data layers (e.g., chromatin accessibility) would address this gap. Such a platform could provide insights into spatial heterogeneity of regulatory mechanisms and their role in diseases like cancer.​

Objectives​

This project aims to develop a platform for visualizing spatially resolved gene regulation. By integrating GRNs (or SPONGE ceRNA networks, e.g. TCGA breast cancer) with spatial transcriptomics data, the platform enables the mapping of regulatory modules and their activities to tissue regions. Key features include the spatial visualization of ES for specific regulators (e.g., TP53) and visualizing spatial data layers, such as scRNA-seq, scATAC-seq, and spatial statistics (e.g., Moran’s I, Getis Ord Gi*, Geary's C).​

Tasks​

• Select and display GRNs or SPONGE-derived ceRNA networks for analysis (e.g., TCGA BRCA dataset).​
• Identify key regulators using SPONGeffects [5] (e.g., TP53).​
• Map single-cell (and chromatin accessibility) data to spatial tissue using e.g. Tangram.​
• Visualize ES scores in a spatial context.​
• Integrate spatial statistics for regional variation analysis.​
• Develop a platform for interactive visualization of gene regulation.​

References​

[1] Williams, C.G., Lee, H.J., Asatsuma, T. et al. An introduction to spatial transcriptomics for biomedical research. Genome Med 14, 68 (2022). https://doi.org/10.1186/s13073-022-01075-1​

[2] Badia-i-Mompel, P., Wessels, L., Müller-Dott, S. et al. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 24, 739–754 (2023). https://doi.org/10.1038/s41576-023-00618-5​

[3] Markus List, Azim Dehghani Amirabad, Dennis Kostka, Marcel H Schulz, Large-scale inference of competing endogenous RNA networks with sparse partial correlation, Bioinformatics, Volume 35, Issue 14, July 2019, Pages i596–i604, https://doi.org/10.1093/bioinformatics/btz314​

[4] Biancalani, T., Scalia, G., Buffoni, L. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods 18, 1352–1362 (2021). https://doi.org/10.1038/s41592-021-01264-7​

[5] Fabio Boniolo, Markus Hoffmann, Norman Roggendorf, Bahar Tercan, Jan Baumbach, Mauro A A Castro, A Gordon Robertson, Dieter Saur, Markus List, spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape, Bioinformatics, Volume 39, Issue 5, May 2023, btad276, https://doi.org/10.1093/bioinformatics/btad276​

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