nCounter® Fibrosis Panel
Helping Your Research
The cellular and molecular basis for fibrotic disease is still poorly understood, and the lack of biomarkers for progression and therapeutic response have hampered efforts to develop treatments. The nCounter Fibrosis Panel helps uncover the mechanisms of disease pathogenesis, identify biomarkers of progression, and develop signatures for therapeutic response. This gene expression panel combines hundreds of genes involved in the initial tissue damage response, chronic inflammation, proliferation of pro-fibrotic cells, and tissue modification that leads to fibrotic disease of the lungs, heart, liver, kidney, and skin.
How it Works
Profile 770 genes across 51 annotated pathways in human or mouse.
Study pathogenesis and identify biomarkers for fibrotic diseases of the lungs, heart, liver, kidney, and skin
Elucidate the mechanism of action behind the four stages of fibrosis: initiation, inflammation, proliferation, and modification
Understand the signaling cascade from cell stress to inflammation
Quantify the relative abundance of 14 different immune cell types
Panel Selection Tool
Find the gene expression panel for your research with easy to use panel proFind Your Panel
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.