Identifying Neutrophil Extracellular Traps (NETs) in Blood Samples Using Peripheral Smear Autoanalyzers

Abstract

Neutrophil Extracellular Traps (NETs) are large neutrophil-derived structures composed of decondensed chromatin, cytosolic, and granule proteins. NETs play an important role in fighting infection, inflammation, thrombosis, and tumor progression processes, yet their fast and reliable identification has been challenging. Smudge cells (SCs) are a subcategory of white cells identified by CellaVision®, a hematology autoanalyzer routinely used in clinical practice that uses digital imaging to generate “manual” differentials of peripheral blood smears. We hypothesize that a proportion of cells identified in the SC category by CellaVision® Hematology Autoanalyzers are actually NETs. We demonstrate that NET-like SCs are not present in normal blood samples, nor are they an artifact of smear preparation. NET-like SCs stain positive for neutrophil markers such as myeloperoxidase, leukocyte alkaline phosphatase, and neutrophil elastase. On flow cytometry, cells from samples with high percent NET-like SCs that are positive for surface DNA are also positive for CD45, myeloperoxidase and markers of neutrophil activation and CD66b. Samples with NET-like SCs have a strong side fluorescent (SFL) signal on the white count and nucleated red cells (WNR) scattergram, representing cells with high nucleic acid content. When compared to patients with low percent SCs, those with a high percentage of SCs have a significantly higher incidence of documented bacterial and viral infections. The current methodology of NET identification is time-consuming, complicated, and cumbersome. In this study, we present data supporting identification of NETs by CellaVision®, allowing for easy, fast, cost-effective, and high throughput identification of NETs that is available in real time and may serve as a positive marker for a bacterial or viral infections.

Neutrophil and eosinophil extracellular traps in intensive care unit Covid patients' peripheral smears

“…As previously described by Fedorov et al, 3 this peripheral blood smear autoanalyzer, is able to identify NETs as a subset of smudge cells (SC) with unique features (Figure 1) different from typical degenerated lymphocytes (DLs).…”

“…After immunohistochemistry and flow cytometry informed the morphologic classification of NETs and DLs, they relied solely on morphology. 3 Matta et al 4 developed new methodologies for the quantification of NETs in patient plasma: a multiplex ELISA to measure NET degradation products (NDPs), including cell-free (cf) DNA, citrullinated histone 3 (citH3), MPO, a plasma NET smear assay by staining plasma with Sytox Green, DAPI, and then utilized anti-MPO and -CitH3 primary antibodies, followed by secondary fluorescent staining. With the latter assay, they were able to visually assess the typical NET architecture characterized by polarized chromatin projections that resemble spider webs.…”

“…In this context, automated digital image analyzers are technological facilitation tools to identify and quantify smudge cells, among which NETs and EETs can be identified. 3 If the role of NETs has been extensively studied through the use of different methods 11 respect to the morphology, little is known about the role of EETs in COVID-19 patients. 10 Considering the increasingly widespread diffusion of this technology in laboratories, in the future it would be desirable to develop an artificial intelligence system to subclassify NETs and EETs, distinguish them from other forms of smudge cells such as DLs and correlate their presence to multiple clinical pictures and their severity, not just in COVID-19 patients.…”

“…NETs appear morphologically as cell remnants with decondensed nuclei, no intact cytoplasm, dispersed neutrophil-like granules, and polarized chromatin projections that resemble spider webs. 3 These extracellular structures are decondensed chromatin fibers loaded with antimicrobial granular proteins, peptides, and enzymes.…”

Neutrophil extracellular traps - a potential trigger for the development of thrombocytopenia during extracorporeal membrane oxygenation

“…All DNA-containing objects were assigned to their respective categories based on their shape and size and colocalization of CD66b and MPO. Objects with an area of 39µm² to 134µm² were identified as granulocyte nuclei by a positive CD66b signal ( 42 – 44 ) and were further differentiated into neutrophils (MPO positive) and eosinophils (MPO negative) based on MPO staining ( 45 – 47 ). Basophils were neglected.…”