Skip to main content

Illinois IGB

Services

 

Spatial Omics: Resources

We area actively updating these resources, so please check back periodically.

Introductory material (Illinois login may be required)

  1. At our opening workshop, the Han Lab at the University of Illinois presented slides introducing the existing major classes of spatial transcriptomics technology.
  2. The Han lab has also prepared additional slides introducing one particular type of imaging-based technology.
  3. See here for a very nice non-technical article about the state of the field.

Review papers

The following papers provide a good introduction to the field, targeted to a biological audience.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Analysis tutorials

These introductory tutorials for analyzing spatial transcriptomics data use popular analysis packages and give a good idea of the possible data structures.

  1. Analysis of grid-based data using the R package Seurat.
  2. Analysis of grid- and imaging-based data using the Python package squidpy.
  3. Analysis of imaging-based data using the Python package starfish.

Public datasets

These datasets may be useful for testing out new analytical methods.

Description Link Source
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)

Mailing list and contact information

Join the mailing list if you are interested in taking part and contact us at spatial@igb.illinois.edu with any other questions.