3  Spatial

Gene expression with spatial information

3.1 Overview

There are three major resolution scales in ST data:

  • multi-cell
  • single-cell
  • sub-cellular

Walker et al. (2022), Yue et al. (2023), Moses & Pachter (2022)

3.2 Techniques

3.2.1 10X Visium

As of May 2024, there are three assays: Spatial GEX, Spatial GEX + Protein and Spatial GEX HD.

Assay Spatial GEX Spatial GEX+Protein Spatial GEX HD
Scope Whole transcriptome Whole transcriptome+31 Proteins Whole transcriptome
Spatial resolution multi-cell multi-cell single-cell
Tissue FFPE,Fresh frozen,Fixed frozen FFPE FFPE
Species Various Human,Mouse Human,Mouse
Capture area 6.5x6.5mm 6.5x6.5mm 6.5x6.5mm
Total spots 5K/14K 5K/14K ~11.2M
Spot size 55um circle 55um circle 2um square

In addition, the Visium slides come in different types depending on the assay and version.

Visium slide types. From left. Visium GEX V1 slide with 4 capture areas for direct use. Visium GEX V4 with 2 capture areas to be used with CytAssist. Visium GEX V5 with 2 larger capture areas to be used with CytAssist. Visium HD GEX H1 slide with 2 capture areas.

Visium slide types. From left. Visium GEX V1 slide with 4 capture areas for direct use. Visium GEX V4 with 2 capture areas to be used with CytAssist. Visium GEX V5 with 2 larger capture areas to be used with CytAssist. Visium HD GEX H1 slide with 2 capture areas.

Visium HD GEX slide.

Visium HD GEX slide.

3.3 Methods/Tools

3.3.1 Frameworks

3.3.2 SVG

Identification of spatially variable genes.

  • SpatialDE
  • SPARK
  • SOMDE
  • Sepal
  • scGCO
  • SpaGCN
  • SpatialLIBD
  • stLearn

3.3.3 Spatial deconvolution

3.3.4 Cell interaction

Given the relative stability of cellular locations, spatial transcriptomics allows us to reveal cell–cell interactions (CCI), also referred to as cell-cell communications (CCC), with fewer false positives than similar analysis with scRNA-seq data.

  • SpaOTsc

3.4 Datasets

3.5 Resources

References

Moses, L., & Pachter, L. (2022). Museum of spatial transcriptomics. Nature Methods, 19(5), 534–546. https://www.nature.com/articles/s41592-022-01409-2
Walker, B. L., Cang, Z., Ren, H., Bourgain-Chang, E., & Nie, Q. (2022). Deciphering tissue structure and function using spatial transcriptomics. Communications Biology, 5(1), 220.
Yue, L., Liu, F., Hu, J., Yang, P., Wang, Y., Dong, J., Shu, W., Huang, X., & Wang, S. (2023). A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Computational and Structural Biotechnology Journal. https://www.sciencedirect.com/science/article/pii/S2001037023000156