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Related Experiment Video

Updated: Sep 13, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

Decrypting cancer's spatial code: from single cells to tissue niches.

Cenk Celik¹, Shi Pan¹, Eloise Withnell¹

¹Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, UK.

Molecular Oncology

|July 25, 2025

Summary

Spatial transcriptomics (ST) advances cancer research by mapping gene expression to tissue structure. New analytical frameworks are crucial for understanding cellular heterogeneity and paving the way for clinical translation.

Keywords:

AI cancer cell state cellular niche digital pathology geospatial statistics spatial transcriptomics

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Spatial transcriptomics (ST) offers novel insights into cancer by linking gene expression to tissue architecture.
Understanding cellular heterogeneity and tumor microenvironments is critical for cancer research.

Purpose of the Study:

To highlight key challenges and emerging analytical frameworks in cancer spatial transcriptomics.
To discuss approaches for defining cell states, delineating cellular niches, and integrating multimodal data for clinical translation.

Main Methods:

Review and discussion of analytical approaches for spatial transcriptomics data.
Exploration of biostatistics, geospatial analytics, and artificial intelligence methods.
Focus on challenges in cell state definition, niche delineation, and data integration.

Main Results:

Identification of critical challenges in analyzing spatial transcriptomics data in cancer.
Discussion of diverse analytical methodologies applicable to ST data.
Emphasis on the need for spatially informed frameworks.

Conclusions:

Developing advanced analytical frameworks is essential for leveraging spatial transcriptomics in cancer research.
These methodologies are foundational for understanding cancer as an evolving system of interconnected niches.
Integration of ST with other data modalities can accelerate clinical translation.