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Journal Article Review Epublish 9 citations

PubMed: 39496456 DOI: 10.1101/gad.351956.124

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges.

Genes Dev 39(1-2):64-85

Authors: Hirak Sarkar, Eunmi Lee, Sereno L Lopez-Darwin, Yibin Kang

Affiliations

  • Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA.
  • Ludwig Institute for Cancer Research Princeton Branch, Princeton, New Jersey 08544, USA.
  • Department of Computer Science, Princeton, New Jersey 08544, USA.
  • Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA;.
  • Cancer Metabolism and Growth Program, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey 08903, USA.

Abstract

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

MeSH

Cells, Microscopy, Electron, Scanning Transmission, Neoplasms, Stem Cells, Stem Cell Niche, Research, Cytogenetics, Tissues, Gene Expression Regulation, Spatial Transcriptomics

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