High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes:A Global Clustering Approach

Leukemia comprises a diverse group of bone marrow tumors marked by cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, by conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated preprocessing pipeline followed by a single-step global clustering approach performed over the entire data set identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudostained images at the single-cell level. Furthermore, this approach provided a semiquantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.</p

Evaluation of the changes in human milk lipid composition and conformational state with Raman spectroscopy during a breastfeed

Human milk fat forms the main energy source for breastfed infants, and is highly variable in terms of concentration and composition. Understanding the changes in human milk lipid composition and conformational state during a breastfeed can provide insight into lipid synthesis and secretion in the mammary gland. Therefore, the aim of this study was to evaluate human milk fatty acid length, degree of unsaturation (lipid composition) and lipid phase (lipid conformational state) at different stages during a single breastfeed (fore-, bulk- and hindmilk). A total of 48 samples from 16 lactating subjects were investigated with confocal Raman spectroscopy. We did not observe any significant changes in lipid composition between fore-, bulk and hindmilk. A new finding from this study is that lipid conformational state at room temperature changed significantly during a breastfeed, from almost crystalline to almost liquid. This observation suggests that lipid synthesis in the mammary gland changes during a single breastfeed

Classifying Raman Spectra of Extracellular Vesicles based on Convolutional Neural Networks for Prostate Cancer Detection

Since early 2000s, machine learning algorithms have been widely used in many research and industrial fields, most prominently in computer vison. Lately, many fields of study have tried to use these automated methods, and there are several reports from the field of spectroscopy. In this study, we demonstrate a classification model based on machine learning to classify Raman spectra. We obtained Raman spectra from extracellular vesicles (EVs) to find tumor derived EVs. The convolutional neural network (CNN) was trained on preprocessed Raman data and raw Raman data. We compare the result from CNN with results from principal component analysis that is widely used among in spectroscopy. The new model classifies EVs with an accuracy of >90%. Moreover, the new model based on CNN is also suitable for classifying the raw Raman data directly without preprocessing with a minimum accuracy of 93%

Prism-based excitation wavelength selection for multicolor fluorescence coincidence measurements

We have designed and constructed a prism setup for multiple-color confocal fluorescence experiments. The prism setup permits easy selection of any color or any combination of colors from a multicolor light source, such as a mixed-gas argon-krypton-ion laser. The selected colors emerging from the prism setup are, by design, optimally overlapped in the focus of a high-numerical-aperture objective, such as that commonly used in single-molecule fluorescence experiments. The various excitation powers can be easily adjusted in this setup. We will exemplify the potential of this setup in single-molecule fluorescence emission spectroscop

Raman micro-spectroscopy for quantitative thickness measurement of nanometer thin polymer films

The sensitivity of far-field Raman micro-spectroscopy was investigated to determine quantitatively the actual thickness of organic thin films. It is shown that the thickness of organic films can be quantitatively determined down to 3 nm with an error margin of 20% and down to 1.5 nm with an error margin of 100%. Raman imaging of thin-film surfaces with a far-field optical microscope establishes the distribution of a polymer with a lateral resolution of ~400 nm and the homogeneity of the film. Raman images are presented for spin-coated thin films of polysulfone (PSU) with average thicknesses between 3 and 50 nm. In films with an average thickness of 43 nm, the variation in thickness was around 5% for PSU. In films with an average thickness of 3 nm for PSU, the detected thickness variation was 100%. Raman imaging was performed in minutes for a surface area of 900 µm2. The results illustrate the ability of far-field Raman microscopy as a sensitive method to quantitatively determine the thickness of thin films down to the nanometer range

Single molecule fluorescence microscopy on Nucleotide Excision Repair complexes using GFP fusion proteins

Scanning Confocal Fluorescence Microscopy is used for single molecule studies on DNA-protein complexes that occur in Nucleotide Excision Repair (NER). During DNA-damage elimination by the NER-pathway, complex protein structures assemble over DNA. It is our aim to resolve the architecture of these DNA-protein complexes and to study dynamic changes that occur in these structures. For this purpose NER-complexes are partly reconstituted onto DNA-substrates using NER-proteins fused to different Green Fluorescent Protein (GFP) mutants. Here we describe the recombinant production of NER-GFP fusion proteins. Characterization of GFP fluorescence is shown together with results of GFP single molecule imaging. First results with NER-GFP fusion proteins are presented as well.</p