We area actively updating these resources, so please check back periodically.
Introductory material (Illinois login may be required)
- At our opening workshop, the Han Lab at the University of Illinois presented slides introducing the existing major classes of spatial transcriptomics technology.
- The Han lab has also prepared additional slides introducing one particular type of imaging-based technology.
- See here for a very nice non-technical article about the state of the field.
The following papers provide a good introduction to the field, targeted to a biological audience.
- Bressan, D., Battistoni, G., Hannon, G.J. "The dawn of spatial omics". Science 381, 2023, eabq4964. https://doi.org/10.1126/science.abq4964
- Moffitt, Jeffrey R., Emma Lundberg, and Holger Heyn. “The Emerging Landscape of Spatial Profiling Technologies.” Nature Reviews Genetics, July 20, 2022, 1–19. https://doi.org/10.1038/s41576-022-00515-3.
- Moses, Lambda, and Lior Pachter. “Museum of Spatial Transcriptomics.” Nature Methods 19, no. 5 (May 2022): 534–46. https://doi.org/10.1038/s41592-022-01409-2.
- Palla, Giovanni, David S. Fischer, Aviv Regev, and Fabian J. Theis. “Spatial Components of Molecular Tissue Biology.” Nature Biotechnology 40, no. 3 (March 2022): 308–18. https://doi.org/10.1038/s41587-021-01182-1.
- Rao, Anjali, Dalia Barkley, Gustavo S. França, and Itai Yanai. “Exploring Tissue Architecture Using Spatial Transcriptomics.” Nature 596, no. 7871 (August 2021): 211–20. https://doi.org/10.1038/s41586-021-03634-9.
- Tian, Luyi, Fei Chen, and Evan Z. Macosko. “The Expanding Vistas of Spatial Transcriptomics.” Nature Biotechnology, October 3, 2022, 1–10. https://doi.org/10.1038/s41587-022-01448-2.
These introductory tutorials for analyzing spatial transcriptomics data use popular analysis packages and give a good idea of the possible data structures.
- Analysis of grid-based data using the R package Seurat.
- Analysis of grid- and imaging-based data using the Python package squidpy.
- Analysis of imaging-based data using the Python package starfish.
These datasets may be useful for testing out new analytical methods.
|Spatial Omics Database||Data||Yuan et al. (2023)|
|Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex||Data||Maynard et al. (2021)|
|MERFISH measurements from the mouse hypothalamic preoptic region||Data||Moffitt et al. (2018)|
|MERFISH measurements in the mouse ileum||Data||Petukhov et al. (2021)|
|Slide-seq data from mouse hippocampus||Data||Rodruiques, Stickels et al. (2019)|
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