Analysis of Spatial Molecular Data

Methods Mol Biol. 2023:2614:349-356. doi: 10.1007/978-1-0716-2914-7_20.

Abstract

Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis, recently detecting molecular traits never before recognized in pathology H&E whole-slide images (WSIs). Alongside these advancements in the digital analysis of WSIs, it is becoming increasingly evident that both spatial and overall tumor heterogeneity may be significant determinants of cancer prognosis and treatment outcome. In this chapter, we describe methods that aim to support these two elements. We describe both an end-to-end deep learning pipeline for producing limited spatial transcriptomics from WSIs with associated bulk gene expression data, as well as an algorithm for quantifying spatial tumor heterogeneity based on the results of this pipeline.

Keywords: Cancer; Gene expression; Heterogeneity; Pathology; Spatial transcriptomics; Whole-slide images.

MeSH terms

  • Algorithms
  • Humans
  • Microscopy / methods
  • Neoplasms* / diagnosis
  • Neoplasms* / genetics
  • Phenotype