nCounter® Myeloid Innate Immunity Panel
Helping Your Research
The nCounter® Myeloid Innate Immunity Panel provides comprehensive coverage of myeloid-derived cells in a targeted gene expression assay. These panels can be used with multiple sample types like peripheral blood mononuclear cells (PBMCs) or formalin-fixed paraffin-embedded (FFPE) tissue sections and provide results in less than 24 hours with minimal hands-on time and data analysis.
The Myeloid Innate Immunity Panel is designed to encompass all aspects of the innate immune response of myeloid-derived cells and can be used for basic and translational research in immuno-oncology, autoimmunity, and infectious disease. The panel is curated to include the most current and relevant genes and is available in both human and mouse versions. Use the Myeloid Innate Immunity Panel to study:
- Mechanisms of immune evasion
- Damage response, wound healing & tissue repair
- Immune regulation
- Disease pathogenesis
- Treatment response vs. non-response
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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.
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