Masthead

nCounter® Immunology Panel

Masthead

Helping Your Research

Perform multiplex gene expression analysis in human or mouse with over 500 general immunology genes. The nCounter Immunology Panel includes major classes of cytokines and their receptors, enzymes with specific gene families such as the major chemokine ligands and receptors, interferons and their receptors, the TNF-receptor superfamily, and the KIR family genes. 84 genes involved with the anti-fungal immune response are also included.

  • Ideal for the study of allergy, autoimmune diseases, and the immune response to infectious disease
  • Unique mouse genes address known differences such as unique receptor ligands, Ig receptors and chemokines
  • Customizable with up to 55 additional user-defined genes with the Panel Plus option

Panel Selection Tool

Find the gene expression panel for your research with easy to use panel pro

Find Your Panel

Product Information

Specifications
Catalog Information
Specifications
Catalog Information

Publications

View All Publications

Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer.

Purpose: Cancer-associated fibroblasts (CAF) have been implicated as potential mediators of checkpoint immunotherapy response. However, the extensive heterogeneity of these cells has precluded rigorous understanding of their immunoregulatory role in the tumor microenvironment.

Spatial profiling reveals association between WNT pathway activation and T-cell exclusion in acquired resistance of synovial sarcoma to NY-ESO-1 transgenic T-cell therapy.

Background: Genetically engineered T-cell immunotherapies for adoptive cell transfer (ACT) have emerged as a promising form of cancer treatment, but many of these patients develop recurrent disease. Furthermore, delineating mechanisms of resistance may be challenging since the analysis of bulk tumor profiling can be complicated by spatial heterogeneity.

A Novel Artificial Intelligence–Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers.

To develop a novel artificial intelligence (AI)–powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS: Deep convolutional neural networks were used to develop the AI model.

Skysphere

Contact Us

Have questions or simply want to learn more?

Contact our helpful experts and we’ll be in touch soon.

Contact Us