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Related Experiment Video

Updated: Jul 2, 2026

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
10:41

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Published on: April 5, 2018

Cross-scale bioanalytical integration for decoding tumour regulatory plasticity in oncology.

Shiv Kumar1, Ankita Mondal1, Vikash Kumar Dubey2

  • 1School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005, India.

Critical Reviews in Oncology/Hematology
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

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Advanced bioanalytical technologies offer new ways to understand cancer's complexity. Integrating single-cell, spatial omics, and AI aids in precise cancer diagnosis and personalized treatment strategies.

Area of Science:

  • Oncology
  • Bioanalytical Chemistry
  • Computational Biology

Background:

  • Cancer exhibits genetic variability and complex microenvironment interactions, challenging traditional diagnostic and therapeutic approaches.
  • Bulk analytical methods lack the resolution to address cancer's intricate spatial, temporal, and functional dimensions.
  • There is a critical need for advanced bioanalytical methods to unravel tumor complexity.

Purpose of the Study:

  • To review emerging integrated bioanalytical technologies for high-resolution cancer profiling.
  • To highlight applications in maintaining tissue architecture and monitoring disease dynamics.
  • To discuss the role of these technologies in advancing precision oncology.

Main Methods:

  • Single-cell transcriptomics and chromatin accessibility profiling for cellular diversity.
Keywords:
Cancer bioanalysisLiquid biopsyMulti omicsPrecision oncologyScRNA-seqSpatial omicsSpatial transcriptomicsTumor microenvironment

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Last Updated: Jul 2, 2026

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
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Published on: April 5, 2018

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Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

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  • Spatial transcriptomics and multiplexed imaging for tumor-immune ecosystem mapping.
  • Mass spectrometry-based proteomics and metabolomics for functional insights.
  • AI-enabled computational analysis for multi-omics integration and predictive modeling.

    • Main Results:

    • These integrated technologies provide high-resolution molecular profiling and maintain tissue architecture.
    • Single-cell and spatial omics reveal cellular diversity and spatial organization of tumor-immune interactions.
    • Proteomic and metabolomic approaches offer insights into signaling networks and metabolic adaptations.
    • AI frameworks facilitate multi-omics convergence, biomarker discovery, and predictive modeling.

    Conclusions:

    • Next-generation bioanalytical strategies integrating molecular, spatial, and temporal data are crucial for precision oncology.
    • These approaches enable earlier cancer detection, adaptive therapies, and personalized management.
    • Key challenges include cross-scale data integration, dynamic disease tracking, and developing clinically interpretable tools.