Single-Cell Raman Microscopy and Machine Learning Identify Neutrophil Extracellular Traps and Necrosis Biochemical Features

Researchers have developed a new technique for distinguishing neutrophil extracellular traps (NETs), which are a form of cell death associated with the breakdown of the nucleus and release of DNA, from other forms of cell death. Existing techniques for characterising NETs, which rely on a combination of extracellular DNA measurement, cell morphology, and protein markers, are limited because of their inability to distinguish NETs from other forms of cell death. In contrast, Raman spectroscopy detects laser scattering patterns produced through molecular bond vibrations and allows the interrogation of molecular content at single-cell resolution, in live cell cultures. The technique uses principles of dimensionality reduction through principal component analysis (PCA) or machine learning to identify critical spectral differences between complex samples and enable the classification of cell states, such as activated versus resting macrophages, or cell types, such as resident versus infiltrating macrophages, and cancerous versus healthy cells; without requiring specific protein markers. In this way, the researchers were able to discriminate NETs from necrotic cells, despite their similar morphology, and distinguish between NETs produced by different stimuli by analysing the Raman spectroscopic fingerprint. The results provide a deeper understanding of the differences between necrosis and different types of NET formation, highlighting molecular differences which can be clearly identified through Raman microscopy, thereby providing an important approach for NET analysis. The technique also has potential for wider applicability, including in the phenotyping and characterization of cell states and functions in neutrophils and other cells.