Spatially Resolved Transcriptomics in Life Sciences Research

  • Spatial transcriptomics provides the location of cells relative to neighboring cells and cell structures.
  • A cell’s location is useful data for describing its phenotype, state, and cell and tissue function.
  • Spatial transcriptomics addresses a key obstacle in bulk and single-cell RNA sequencing studies: their loss of spatial information.
  • The main goal of spatial transcriptomics studies is to integrate expression with spatial information.

Data and Study Design

  • Use .md files for episodes when you want static content
  • Use .Rmd files for episodes when you need to generate output
  • Run sandpaper::check_lesson() to identify any issues with your lesson
  • Run sandpaper::build_lesson() to preview your lesson locally

Data Preprocessing

  • The 10x Space Ranger pipeline provides you with an unfiltered and a filtered data file.
  • The HDF5 file ends with an “h5” extension and contains the barcodes, features (genes), and counts matrix.
  • Seurat is one, of several, popular environments for analyzing spatial transcriptomics data.
  • It is important to know something about the structure of the tissue which you are analyzing.
  • Plotting total counts and genes in each spot may help to identify quality control issues.

Remove Low-quality Spots

  • Spot filtering should be light.
  • Inspect the spots that you are filtering to confirm that you are not discarding important tissue structures.

Normalization in Spatial Transcriptomics

  • Normalization is essential but must be selectively applied based on the unique characteristics of each dataset and the specific biological questions at hand.
  • Techniques like SCTransform and log scaling offer ways to balance technical correction with biological integrity.
  • Examining both raw and normalized data can provide comprehensive insights into the absolute and relative characteristics of cellular components in spatial transcriptomics.

Feature Selection, Dimensionality Reduction, and Spot Clustering

  • Feature selection is a crucial step in spatial transcriptomics analysis, particularly for non-variance-stabilizing normalization methods like NormalizeData.
  • Techniques such as VST and mean-variance plotting enable researchers to focus on genes that provide the most biological insight.
  • Different proportions of highly variable genes and feature selection methods can significantly influence the analytical outcomes, emphasizing the need for tailored approaches based on the specific characteristics of each dataset.
  • Linear dimensionality reduction methods like PCA are crucial for initial data simplification and noise reduction.
  • Nonlinear methods like UMAP are valuable for detailed exploration of data structures post-linear preprocessing.
  • The sequential application of PCA and UMAP can provide a comprehensive view of the spatial transcriptomics data, leveraging the strengths of both linear and nonlinear approaches.

Deconvolution in Spatial Transcriptomics

  • Deconvolution enhances spatial transcriptomics by quantifying the different cell types within spatial spots.
  • Integrating scRNA-seq data with spatial transcriptomics data facilitates accurate deconvolution.
  • RCTD is a supervised deconvolution method that quantifies the proportion of different cell types in spatial transcriptomics data.

Differential Expression Testing

  • Differential expression testing pinpoints genes with significant expression variations across regions or clusters.
  • Moran’s I statistic reveals spatial autocorrelation in gene expression, critical for examining spatially dependent biological activities.
  • Moran’s I algorithm effectively identifies genes expressed in anatomically distinct regions, as validated from the correlation analysis with the DE genes from the annotated regions.

Putting it all Together

  • There are many decisions which need to be made in spatial transcriptomics.
  • It is essential to have an understanding of the tissue morphology before proceeding with a spatial transcriptomics analysis.