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Related Experiment Video

Updated: Mar 12, 2026

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
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Integrative spatial profiling pipeline for determining TME architectures in archival clinical specimens using CmTSA

Chaoxin Xiao1, Ruihan Zhou1, Qin Chen1

  • 1State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, China.

Cell Discovery
|March 11, 2026
PubMed
Summary

We developed a new imaging technology (HOC-FD) for analyzing the tumor microenvironment (TME) in archival samples. This method accurately maps cellular interactions and functional niches (FNs) for improved cancer research and immunotherapy.

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Area of Science:

  • Oncology
  • Immunology
  • Biotechnology

Background:

  • The tumor microenvironment (TME) is crucial for tumor progression and therapeutic response.
  • Current spatial transcriptomics and proteomics methods face limitations with archival samples (RNA instability, low resolution).
  • Accurate profiling of TME spatial features is essential for understanding cancer biology.

Purpose of the Study:

  • To develop a novel technology for high-resolution spatial profiling of archival FFPE tissues.
  • To overcome limitations of existing spatial analysis methods in clinical specimens.
  • To enable accurate identification and analysis of functional niches within the TME.

Main Methods:

  • Developed hybrid optochemical fluorescence depletion (HOC-FD) technology.
  • Integrated autofluorescence quenching with cyclic multiplex tyramide signal amplification (CmTSA).
  • Implemented a computer vision pipeline with deep learning for cellular segmentation and RNN analysis for functional niche definition.

Main Results:

  • HOC-FD enables concurrent labeling of 30-60 biomarkers with high signal-to-noise ratios in FFPE tissues.
  • The computer vision pipeline accurately performs cellular segmentation and phenotype classification.
  • Radius-constrained neighborhood network (RNN) analysis reliably defines spatially coherent functional niches with biological relevance.

Conclusions:

  • The CmTSA platform combined with RNN analysis provides an integrated framework for TME spatial profiling.
  • This approach enhances visualization and quantification of multicellular states within tumor architectures.
  • The technology holds potential for advancing tumor immunology research and precision immunotherapies.