nCounter® Metabolic Pathways Panel
Helping Your Research
Quantify the expression of hundreds of genes involved in core metabolic processes and immunometabolism for human or mouse samples in a reliable, simple, and robust way using the nCounter Metabolic Pathways Panel. Understand the complex mechanisms behind metabolic adaptation, metabolic switching, metabolic alterations, and study changes in mitochondrial respiration and glycolysis.
The Metabolic Pathways Panel helps advance efforts towards novel therapeutic targets that take advantage of altered metabolism in cancer and other diseases. Get results in less than 24 hours from multiple sample types with a workflow that maximizes insight and minimizes turnaround time:
- Quantify the presence and relative abundance of 14 different immune cell types for immunometabolism studies
- Bypass RT and amplification by direct detection, yielding highly reproducible data
- Process your samples with < 30 minutes hands-on time and get results in < 24 hours
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Product Information
Related Resources
Publications
The spatial landscape of lung pathology during COVID-19 progression.
Recent studies have provided insights into the pathology and immune response to coronavirus disease 2019 (COVID-19)1–8. However, thorough interrogation of the interplay between infected cells and the immune system at sites of infection is lacking.
Spatial mapping of SARS-CoV-2 and H1N1 Lung Injury Identifies Differential Transcriptional Signatures.
Severe SARS-CoV-2 infection often leads to development of acute respiratory distress syndrome (ARDS), with profound pulmonary patho-histological changes post-mortem. It is not clear if ARDS from SARS-CoV-2 is similar to that observed in Influenza H1N1, another common viral cause of lung injury.
In-silico performance, validation, and modeling of the Nanostring Banff Human Organ transplant gene panel using archival data from human kidney transplants
RNA gene expression of renal transplantation biopsies is commonly used to identify the immunological patterns of graft rejection. Mostly done with microarrays, seminal findings defined the patterns of gene sets associated with rejection and non-rejection kidney allograft diagnoses.
