Stubbs Group

Department                               Pathology and Clinical Bioinformatics

Principal investigator             Andrew Stubbs

E-mail address                        a.stubbs@erasmusmc.nl

Website                                       https://www.erasmusmc.nl/en/research/groups/pathology-stubbs

Supervisor: Andrew Stubbs, a.stubbs@erasmusmc.nl

The aims of the study are to:
1. Develop state-of-the-art python/R based workflows for spatial transcriptomics
2. Validate workflows using public 10X and GeoMx data
3. Demonstrate the utility of these workflows for pan-cancer analysis by determining the
TME boundary and associated DGE biomarkers

Techniques

Implement end to end workflows in python and or R that could include the following:

1. Data processing and normalization using tools like Seurat (R) and Squidpy (Python).
2. Spot deconvolution using tools like SPACEL (SPACEL: deep learning-based characterization of
   spatial transcriptome architectures. Nat Commun. 2023; 14: 7603) or Cell2location (Cell2location
   maps fine-grained cell types in spatial transcriptomics. Nat Biotech. 2022; 40).
3. Resolution resolving (alternative to spot deconvolution) using tools like XFuse (Super-resolved
   spatial transcriptomics by deep data fusion. Nat Brief Commun. 2021; 40).
4. Cell-type identification using tools like Spatial-ID (Spatial-ID: a cell typing method for spatially
   resolved transcriptomics via transfer learning and spatial embedding. Nature Communications
   volume 13, Article number: 7640 (2022).
5. Tumor boundary identification using tools like Cottrazm (Reconstruction of the tumor spatial
   microenvironment along the malignant-boundary-nonmalignant axis. Nat Commun 14, 933
   (2023). https://doi.org/10.1038/s41467-023-36560-7).
6. Differential gene expression analysis between tumor, tumor-micro-environment and nontumor tissue, as well as between different niches within the tumor.
7. Integration of spatial transcriptomic data with histology slides using tools like
   Starfysh (Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous
   tumor–immune hubs. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02173-8).

Further reading

Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy. Bärthel S, Falcomatà C, Rad R, Theis FJ, Saur D. Nat Cancer. 2023 Apr;4(4):454-467. doi: 10.1038/s43018-023-00526-x. Epub 2023 Mar 23.