Sparse sampling for fast hyperspectral coherent anti-Stokes Raman scattering imaging

Programmable hyperspectral coherent anti-Stokes Raman scattering microscopy

Hyperspectral coherent Raman scattering microscopy provides a significant improvement in acquisition time compared to spontaneous Raman scattering yet still suffers from the time required to sweep through individual wavenumbers. To address this, we present the use of a pulse shaper with a 2D spatial light modulator for phase- and amplitude-based shaping of the Stokes beam to create programmable spectrally tailored excitation envelopes. This enables collection of useful spectral information in a more rapid and efficient manner.

Rapid identification of micro and nanoplastics by line scan Raman micro-spectroscopy

Microplastic pollution has become an environmental problem that cannot be ignored in our society. Raman spectroscopy technology has been widely used in the field of microplastics detection due to its non-contact, non-destructive chemical specificity. Traditional point confocal Raman micro-spectroscopy technology uses single-point detection, resulting in long measurement times to scan the large areas of interest of typical samples. In this paper, we present a line scan confocal Raman micro-spectroscopy tool for fast detection and identification of microplastic particles. We show size and composition identification of particles and imaging over large areas. Compared with point confocal Raman imaging, the line scan confocal Raman technology increases the imaging speed by 1-2 orders of magnitude.

Low-energy electron microscopy intensity-voltage data - Factorization, sparse sampling and classification

Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves provides hyperspectral images of surfaces, which can be used to identify the surface type, but are difficult to analyse. Here, we demonstrate the use of an algorithm for factorizing the data into spectra and concentrations of characteristic components (FSC³ ) for identifying distinct physical surface phases. Importantly, FSC³ is an unsupervised and fast algorithm. As example data we use experiments on the growth of praseodymium oxide or ruthenium oxide on ruthenium single crystal substrates, both featuring a complex distribution of coexisting surface components, varying in both chemical composition and crystallographic structure. With the factorization result a sparse sampling method is demonstrated, reducing the measurement time by 1-2 orders of magnitude, relevant for dynamic surface studies. The FSC³ concentrations are providing the features for a support vector machine-based supervised classification of the surface types. Here, specific surface regions which have been identified structurally, via their diffraction pattern, as well as chemically by complementary spectro-microscopic techniques, are used as training sets. A reliable classification is demonstrated on both example LEEM I-V data sets.

Hyperspectral analysis applied to micro-Brillouin maps of amyloid-beta plaques in Alzheimer's disease brains

A recent investigation on the architecture and chemical composition of amyloid-β (Aβ) plaques in ex vivo histological sections of an Aβ-overexpressing transgenic mouse hippocampus has shed light on the infrared light signature of cell-activation related biomarkers of Alzheimer's disease. A correlation was highlighted between the biomechanical properties detected by Brillouin microscopy and the molecular make-up of Aβ plaques provided by FTIR spectroscopic imaging and Raman microscopy (with correlative immunofluorescence imaging) in this animal model of the disease. In the Brillouin spectra of heterogeneous materials such as biomedical samples, peaks are likely the result of multiple contributions, more or less overlaid on a spatial and spectral scale. The ability to disentangle these contributions is very important as it may give access to discrete components that would otherwise be buried within the Brillouin peak envelope. Here, we applied an unsupervised non-negative matrix factorization method to analyse the spontaneous Brillouin microscopy maps of Aβ plaques in transgenic mouse hippocampal sections. The method has already been proven successful in decomposing chemical images and is applied here for the first time to acoustic maps acquired with a Fabry-Perot Brillouin microscope. We extracted and visualised a decrease in tissue rigidity from the core through to the periphery of the plaque, with spatially distinct components that we assigned to specific entities. This work demonstrates that it is possible to reveal the structure and mechanical properties of Aβ plaques, with details visualized by the projection of the mechanical contrast into a few relevant channels.

Direct mineralogical imaging of economic ore and rock samples with multi-modal nonlinear optical microscopy

Multi-modal nonlinear optical (NLO) microscopy, including stimulated Raman scattering (SRS) and second harmonic generation (SHG), was used to directly image mineralogical features of economic ore and rock samples. In SRS/SHG imaging, ore samples generally require minimal preparation and may be rapidly imaged, even in their wet state. 3D structural details, at submicron resolution, are revealed tens of microns deep within samples. Standard mineral imaging based on scanning electron microscopy (SEM), with elemental analysis via energy dispersive X-Ray spectroscopy, was used to independently validate the mineral composition of the samples. Spatially-resolved SRS from dominant Raman-resonant bands precisely maps the locations of specific minerals contained within the samples. SHG imaging reveals locally non-centrosymmetric structures, such as quartz grains. Competing absorption and nonlinear scattering processes, however, can reduce contrast in SRS imaging. Importantly, the correlation between standard electron microscopy and multi-modal NLO optical microscopy shows that the latter offers rapid image contrast based on the mineral content of the sample.

Bessel-Beam Hyperspectral CARS Microscopy with Sparse Sampling: Enabling High-Content High-Throughput Label-Free Quantitative Chemical Imaging

Microscopy-based high-content and high-throughput analysis of cellular systems plays a central role in drug discovery. However, for contrast and specificity, the majority of assays require a fluorescent readout which always comes with the risk of alteration of the true biological conditions. In this work, we demonstrate a label-free imaging platform which combines chemically specific hyperspectral coherent anti-Stokes Raman scattering microscopy with sparse sampling and Bessel beam illumination. This enabled us to screen multiwell plates at high speed, while retaining the high-content chemical analysis of hyperspectral imaging. To demonstrate the practical applicability of the method we addressed a critical side effect in drug screens, namely, drug-induced lipid storage within hepatic tissue. We screened 15 combinations of drugs and neutral lipids added to human HepG2 liver cells and developed a high-content quantitative data analysis pipeline which extracted the spectra and spatial distributions of lipid and protein components. We then used their combination to train a support vector machine discriminative algorithm. Classification of the drug responses in terms of phospholipidosis versus steatosis was achieved in a completely label-free assay.

Hyperspectral image analysis for CARS, SRS, and Raman data

In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia et al. 1 The HIA introduced a method to factorize the hyperspectral data into the product of component concentrations and spectra for quantitative analysis of the chemical composition of the sample. The enhancements shown here comprise (1) a spatial weighting to reduce the spatial variation of the spectral error, which improves the retrieval of the chemical components with significant local but small global concentrations; (2) a new selection criterion for the spectra used when applying sparse sampling2 to speed up sequential hyperspectral imaging; and (3) a filter for outliers in the data using singular value decomposition, suited e.g. to suppress motion artifacts. We demonstrate the enhancements on coherent anti-Stokes Raman scattering, stimulated Raman scattering, and spontaneous Raman data. We provide the HIA software as executable for public use. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.