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- ItemEosinophils and Neutrophils-Molecular Differences Revealed by Spontaneous Raman, CARS and Fluorescence Microscopy(Basel : MDPI, 2020) Dorosz, Aleksandra; Grosicki, Marek; Dybas, Jakub; Matuszyk, Ewelina; Rodewald, Marko; Meyer, Tobias; Popp, Jürgen; Malek, Kamilla; Baranska, MalgorzataLeukocytes are a part of the immune system that plays an important role in the host's defense against viral, bacterial, and fungal infections. Among the human leukocytes, two granulocytes, neutrophils (Ne) and eosinophils (EOS) play an important role in the innate immune system. For that purpose, eosinophils and neutrophils contain specific granules containing protoporphyrin-type proteins such as eosinophil peroxidase (EPO) and myeloperoxidase (MPO), respectively, which contribute directly to their anti-infection activity. Since both proteins are structurally and functionally different, they could potentially be a marker of both cells' types. To prove this hypothesis, UV-Vis absorption spectroscopy and Raman imaging were applied to analyze EPO and MPO and their content in leukocytes isolated from the whole blood. Moreover, leukocytes can contain lipidic structures, called lipid bodies (LBs), which are linked to the regulation of immune responses and are considered to be a marker of cell inflammation. In this work, we showed how to determine the number of LBs in two types of granulocytes, EOS and Ne, using fluorescence and coherent anti-Stokes Raman scattering (CARS) microscopy. Spectroscopic differences of EPO and MPO can be used to identify these cells in blood samples, while the detection of LBs can indicate the cell inflammation process.
- ItemFLIM data analysis based on Laguerre polynomial decomposition and machine-learning(Bellingham, Wash. : SPIE, 2021) Guo, Shuxia; Silge, Anja; Bae, Hyeonsoo; Tolstik, Tatiana; Meyer, Tobias; Matziolis, Georg; Schmitt, Michael; Popp, Jürgen; Bocklitz, ThomasSignificance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.