Raman spectroscopy is a well-defined spectroscopic technique sensitive to the molecular vibrations of materials, since it provides fingerprint-like information regarding the molecular structure of the analyzed samples. It has been extensively used for non-destructive and label-free cell characterization, particularly in the qualitative and quantitative estimation of amino acids, lipids, nucleic acids, and carbohydrates. Lymphoma cell classification is a crucial task for accurate and prompt lymphoma diagnosis, prognosis, and treatment. Currently, it is mostly based on limited information and requires costly and time-consuming approaches. In this work, we are proposing a fast characterization and differentiation methodology of lymphoma cell subtypes based on Raman spectroscopy. The study was performed in the temperature range of 15–37 °C to identify the best cell measurement conditions. The proposed methodology is fast, accurate, and requires minimal sample preparation, resulting in a potentially promising, non-invasive strategy for early and accurate cell lymphoma characterization.
3D side-scattering imaging flow cytometer and convolutional neural network for label-free cell analysis