Computational analysis of super-resolved in situ sequencing data reveals genes modified by immune-tumor contact events

Michal Danino-Levi, Tal Goldberg, Maya Keter, Nikol Akselrod, Noa Shprach-Buaron, Modi Safra, Gonen Singer, Shahar Alon

Research output: Contribution to journalArticlepeer-review

Abstract

Cancer cells can manipulate immune cells and escape from the immune system response. Quantifying the molecular changes that occur when an immune cell touches a tumor cell can increase our understanding of the underlying mechanisms. Recently, it became possible to perform such measurements in situ-for example, using expansion sequencing, which enabled in situ sequencing of genes with super-resolution. We systematically examined whether individual immune cells from specific cell types express genes differently when in physical proximity to individual tumor cells. First, we demonstrated that a dense mapping of genes in situ can be used for the segmentation of cell bodies in 3D, thus improving our ability to detect likely touching cells. Next, we used three different computational approaches to detect the molecular changes that are triggered by proximity: differential expression analysis, tree-based machine learning classifiers, and matrix factorization analysis. This systematic analysis revealed tens of genes, in specific cell types, whose expression separates immune cells that are proximal to tumor cells from those that are not proximal, with a significant overlap between the different detection methods. Remarkably, an order of magnitude more genes are triggered by proximity to tumor cells in CD8 T cells compared to CD4 T cells, in line with the ability of CD8 T cells to directly bind major histocompatibility complex (MHC) class I on tumor cells. Thus, in situ sequencing of an individual biopsy can be used to detect genes likely involved in immune-tumor cell-cell interactions. The data used in this manuscript and the code of the InSituSeg, machine learning, cNMF, and Moran's I methods are publicly available at doi:10.5281/zenodo.7497981.

Original languageEnglish
Pages (from-to)749-759
Number of pages11
JournalRNA
Volume30
Issue number7
StatePublished - 17 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 Danino-Levi et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Keywords

  • cancer biology
  • cell–cell interactions
  • genomics
  • RNA imaging
  • spatial genomics

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