Neutrophils, constituting 50%-70% of circulating leukocytes, play crucial roles in host defense and exhibit anti-tumorigenic properties. An elevated peripheral blood neutrophil-to-lymphocyte ratio is associated with decreased survival rates in cancer patients. In response to exposure to various antigens, neutrophils release neutrophil granular proteins, which combine to form web-like structures known as neutrophil extracellular traps (NETs). Previously, the relative percentage of NETs was found to be increased in resected tumor tissue samples from patients with gastrointestinal malignancies. The presence of NETs in peripheral blood is indicative of underlying pathological conditions. Hence, employing a non-invasive method to detect NETs in peripheral blood, along with other diagnostic tests, shows potential as a valuable tool not just for identifying different inflammatory disorders but also for assessing disease severity and determining patient suitability for surgical resection. While reliable methods exist for identifying NETs in tissue, accurately quantifying them in whole blood remains challenging. Many previous methods are time-consuming and rely on a limited set of markers that are inadequate for fully characterizing NETs. Therefore, we established a unique sensitive smear immunofluorescence assay based on blood smears to identify NETs in only as little as 2 μL of whole blood. To identify the NET complexes that have enhanced specificities, this combines the use of various antibodies against neutrophil-specific CD15, NET-specific myeloperoxidase (MPO), citrullinated histone H3 (Cit H3), and nuclear DNA. This protocol offers an easy, affordable, rapid, and non-invasive method for identifying NETs; thus, it can be utilized as a diagnostic marker and targeted through various therapeutic approaches for treating human malignancies. Key features • Characterization of neutrophil extracellular traps in whole blood smears through immunofluorescence staining. • Affordable and quantitative approach to neutrophil extracellular trap detection.
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