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Related Concept Videos

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors08:32

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Here we describe a method to separate and enrich components of the tumor immune and non-immune microenvironment in established subcutaneous tumors. This technique allows for the separate analysis of tumor immune infiltrate and non-immune tumor fractions which can permit comprehensive characterization of the tumor immune...
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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
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This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA...
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We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop...
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Related Experiment Video

Updated: Jan 19, 2026

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors
08:32

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

Published on: June 7, 2018

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Using RNA Sequencing to Characterize the Tumor Microenvironment.

C C Smith1,2, L M Bixby2, K L Miller2

  • 1Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA.

Methods in Molecular Biology (Clifton, N.J.)
|September 11, 2019
PubMed
Summary
This summary is machine-generated.

RNA sequencing (RNA-seq) provides crucial immunogenomic insights by analyzing tumor transcriptomes. This workflow details RNA extraction, library preparation, and bioinformatics for identifying immune cell gene expression patterns.

Keywords:
AlignmentImmunogenomicsLibrary preparationNext-generation sequencingQuantificationRNA extractionRNA-seq

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Last Updated: Jan 19, 2026

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors
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Area of Science:

  • Immunogenomics
  • Transcriptomics
  • Bioinformatics

Background:

  • RNA sequencing (RNA-seq) is vital for studying tumor and microenvironment transcriptomes.
  • Analytical methods can infer gene expression linked to immune cell presence.
  • RNA-seq is feasible across various tissue types and preservation methods.

Purpose of the Study:

  • To present a comprehensive workflow for RNA sequencing (RNA-seq) in immunogenomics.
  • To detail the process from bulk tissue to actionable immunogenomic data.
  • To outline key steps including RNA extraction, library preparation, sequencing, and initial analysis.

Main Methods:

  • RNA extraction from diverse tissue types (FFPE, fresh-frozen, fresh).
  • Selection of appropriate RNA sequencing library preparation methods.
  • Sequencing, data alignment, and quality assurance.
  • Downstream bioinformatics analyses for immunogenomic feature extraction.

Main Results:

  • A standardized workflow for processing RNA samples for immunogenomic analysis.
  • Identification of methods for optimizing RNA-seq based on tissue and RNA characteristics.
  • Successful extraction of immunogenomic features through a defined bioinformatics pipeline.

Conclusions:

  • The described RNA-seq workflow enables robust immunogenomic data generation.
  • This approach facilitates the inference of gene expression patterns related to immune cell populations.
  • The methodology provides a foundation for further systems biology investigations in immunogenomics.