Hyperspectral volumetric coherent anti-Stokes Raman scattering microscopy: quantitative volume determination and NaCl as non-resonant standard

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.

Effect of non-resonant background on the extraction of Raman signals from CARS spectra using deep neural networks

We report the retrieval of the Raman signal from coherent anti-Stokes Raman scattering (CARS) spectra using a convolutional neural network (CNN) model. Three different types of non-resonant backgrounds (NRBs) were explored to simulate the CARS spectra viz (1) product of two sigmoids following the original SpecNet model, (2) Single Sigmoid, and (3) fourth-order polynomial function. Later, 50 000 CARS spectra were separately synthesized using each NRB type to train the CNN model and, after training, we tested its performance on 300 simulated test spectra. The results have shown that imaginary part extraction capability is superior for the model trained with Polynomial NRB, and the extracted line shapes are in good agreement with the ground truth. Moreover, correlation analysis was carried out to compare the retrieved Raman signals to real ones, and a higher correlation coefficient was obtained for the model trained with the Polynomial NRB (on average, ∼0.95 for 300 test spectra), whereas it was ∼0.89 for the other NRBs. Finally, the predictive capability is evaluated on three complex experimental CARS spectra (DMPC, ADP, and yeast), where the Polynomial NRB model performance is found to stand out from the rest. This approach has a strong potential to simplify the analysis of complex CARS spectroscopy and can be helpful in real-time microscopy imaging applications.

Raman signal extraction from CARS spectra using a learned-matrix representation of the discrete Hilbert transform

Removing distortions in coherent anti-Stokes Raman scattering (CARS) spectra due to interference with the nonresonant background (NRB) is vital for quantitative analysis. Popular computational approaches, the Kramers-Kronig relation and the maximum entropy method, have demonstrated success but may generate significant errors due to peaks that extend in any part beyond the recording window. In this work, we present a learned matrix approach to the discrete Hilbert transform that is easy to implement, fast, and dramatically improves accuracy of Raman retrieval using the Kramers-Kronig approach.

Hyperspectral CARS microscopy and quantitative unsupervised analysis of deuterated and non-deuterated fatty acid storage in human cells

Coherent anti-Stokes Raman scattering (CARS) implemented as a vibrational micro-spectroscopy modality eradicates the need for potentially perturbative fluorescent labeling while still providing high-resolution, chemically specific images of biological samples. Isotopic substitution of hydrogen atoms with deuterium introduces minimal change to molecular structures and can be coupled with CARS microscopy to increase chemical contrast. Here, we investigate HeLa cells incubated with non-deuterated or deuterium-labeled fatty acids, using an in-house-developed hyperspectral CARS microscope coupled with an unsupervised quantitative data analysis algorithm, to retrieve Raman susceptibility spectra and concentration maps of chemical components in physically meaningful units. We demonstrate that our unsupervised analysis retrieves the susceptibility spectra of the specific fatty acids, both deuterated and non-deuterated, in good agreement with reference Raman spectra measured in pure lipids. Our analysis, using the cell-silent spectral region, achieved excellent chemical specificity despite having no prior knowledge and considering the complex intracellular environment inside cells. The quantitative capabilities of the analysis allowed us to measure the concentration of deuterated and non-deuterated fatty acids stored within cytosolic lipid droplets over a 24 h period. Finally, we explored the potential use of deuterium-labeled lipid droplets for non-invasive cell tracking, demonstrating an effective application of the technique for distinguishing between cells in a mixed population over a 16 h period. These results further demonstrate the chemically specific capabilities of hyperspectral CARS microscopy to characterize and distinguish specific lipid types inside cells using an unbiased quantitative data analysis methodology.

Quantification of the nonlinear susceptibility of the hydrogen and deuterium stretch vibration for biomolecules in coherent Raman micro-spectroscopy

Deuterium labelling is increasingly used in coherent Raman imaging of complex systems, such as biological cells and tissues, to improve chemical specificity. Nevertheless, quantitative coherent Raman susceptibility spectra for deuterated compounds have not been previously reported. Interestingly, it is expected theoretically that -D stretch vibrations have a Raman susceptibility lower than -H stretch vibrations, with the area of their imaginary part scaling with their wavenumber, which is shifted from around 2900 cm-1 for C-H into the silent region around 2100 cm-1 for C-D. Here, we report quantitative measurements of the nonlinear susceptibility of water, succinic acid, oleic acid, linoleic acid and deuterated isoforms. We show that the -D stretch vibration has indeed a lower area, consistent with the frequency reduction due to the doubling of atomic mass from hydrogen to deuterium. This finding elucidates an important trade-off between chemical specificity and signal strength in the adoption of deuterium labelling as an imaging strategy for coherent Raman microscopy.

Identifying subpopulations in multicellular systems by quantitative chemical imaging using label-free hyperspectral CARS microscopy

Quantitative hyperspectral coherent Raman scattering microscopy merges imaging with spectroscopy and utilises quantitative data analysis algorithms to extract physically meaningful chemical components, spectrally and spatially-resolved, with sub-cellular resolution. This label-free non-invasive method has the potential to significantly advance our understanding of the complexity of living multicellular systems. Here, we have applied an in-house developed hyperspectral coherent anti-Stokes Raman scattering (CARS) microscope, combined with a quantitative data analysis pipeline, to imaging living mouse liver organoids as well as fixed mouse brain tissue sections xenografted with glioblastoma cells. We show that the method is capable of discriminating different cellular sub-populations, on the basis of their chemical content which is obtained from an unsupervised analysis, i.e. without prior knowledge. Specifically, in the organoids, we identify sub-populations of cells at different phases in the cell cycle, while in the brain tissue, we distinguish normal tissue from cancer cells, and, notably, tumours derived from transplanted cancer stem cells versus non-stem glioblastoma cells. The ability of the method to identify different sub-populations was validated by correlative fluorescence microscopy using fluorescent protein markers. These examples expand the application portfolio of quantitative chemical imaging by hyperspectral CARS microscopy to multicellular systems of significant biomedical relevance, pointing the way to new opportunities in non-invasive disease diagnostics.

Quantitative Label-Free Imaging of Lipid Domains in Single Bilayers by Hyperspectral Coherent Raman Scattering

Lipid phase separation in cellular membranes is thought to play an important role in many biological functions. This has prompted the development of synthetic membranes to study lipid-lipid interactions in vitro, alongside optical microscopy techniques aimed at directly visualizing phase partitioning. In this context, there is a need to overcome the limitations of fluorescence microscopy, where added fluorophores can significantly perturb lipid packing. Raman-based optical imaging is a promising analytical tool for label-free chemically specific microscopy of lipid bilayers. In this work, we demonstrate the application of hyperspectral coherent Raman scattering microscopy combined with a quantitative unsupervised data analysis methodology developed in-house to visualize lipid partitioning in single planar membrane bilayers exhibiting liquid-ordered and liquid-disordered domains. Two home-built instruments were utilized, featuring coherent anti-Stokes Raman scattering and stimulated Raman scattering modalities. Ternary mixtures of dioleoylphosphatidylcholine, sphingomyelin, and cholesterol were used to form phase-separated domains. We show that domains are consistently resolved, both chemically and spatially, in a completely label-free manner. Quantitative Raman susceptibility spectra of the domains are provided alongside their spatially resolved concentration maps.

Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging

We present a new collection of processing techniques, collectively "factorized Kramers-Kronig and error correction" (fKK-EC), for (a) Raman signal extraction, (b) denoising, and (c) phase- and scale-error correction in coherent anti-Stokes Raman scattering (CARS) hyperspectral imaging and spectroscopy. These new methods are orders-of-magnitude faster than conventional methods and are capable of real-time performance, owing to the unique core concept: performing all processing on a small basis vector set and using matrix/vector multiplication afterwards for direct and fast transformation of the entire dataset. Experimentally, we demonstrate that a 703026 spectra image of chicken cartilage can be processed in 70 s (≈ 0.1 ms / spectrum), which is ≈ 70 times faster than with the conventional workflow (≈7.0 ms / spectrum). Additionally, we discuss how this method may be used for machine learning (ML) by re-using the transformed basis vector sets with new data. Using this ML paradigm, the same tissue image was processed (post-training) in ≈ 33 s, which is a speed-up of ≈ 150 times when compared with the conventional workflow.

Label-Free Volumetric Quantitative Imaging of the Human Somatic Cell Division by Hyperspectral Coherent Anti-Stokes Raman Scattering

Quantifying the chemical composition of unstained intact tissue and cellular samples with high spatio-temporal resolution in three dimensions would provide a step change in cell and tissue analytics critical to progress the field of cell biology. Label-free optical microscopy offers the required resolution and noninvasiveness, yet quantitative imaging with chemical specificity is a challenging endeavor. In this work, we show that hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy can be used to provide quantitative volumetric imaging of human osteosarcoma cells at various stages through cell division, a fundamental component of the cell cycle progress resulting in the segregation of cellular content to produce two progeny. We have developed and applied a quantitative data analysis method to produce volumetric three-dimensional images of the chemical composition of the dividing cell in terms of water, proteins, DNAP (a mixture of proteins and DNA, similar to chromatin), and lipids. We then used these images to determine the dry masses of the corresponding organic components. The attribution of proteins and DNAP components was validated using specific well-characterized fluorescent probes, by comparison with correlative two-photon fluorescence microscopy of DNA and mitochondria. Furthermore, we map the same chemical components under perturbed conditions, employing a drug that interferes directly with cell division (Taxol), showing its influence on cell organization and the masses of proteins, DNAP, and lipids.

Nuclear cytometry and chromatin organization

The nuclear-targeting chemical probe, for the detection and quantification of DNA within cells, has been a mainstay of cytometry-from the colorimetric Feulgen stain to smart fluorescent agents with tuned functionality. The level of nuclear structure and function at which the probe aims to readout, or indeed at which a DNA-targeted drug acts, is shadowed by a wide range of detection modalities and analytical methods. These methods are invariably limited in terms of the resolution attainable versus the volume occupied by targeted chromatin structures. The scalar challenge arises from the need to understand the extent and different levels of compaction of genomic DNA and how such structures can be re-modeled, reported, or even perturbed by both probes and drugs. Nuclear cytometry can report on the complex levels of chromatin order, disorder, disassembly, and even active disruption by probes and drugs. Nuclear probes can report defining features of clinical and therapeutic interest as in NETosis and other cell death processes. New cytometric approaches continue to bridge the scalar challenges of analyzing chromatin organization. Advances in super-resolution microscopy address the resolution and depth of analysis issues in cellular systems. Typical of recent insights into chromatin organization enabled by exploiting a DNA interacting probe is ChromEM tomography (ChromEMT). ChromEMT uses the unique properties of the anthraquinone-based cytometric dye DRAQ5™ to reveal that local and global 3D chromatin structures effect differences in compaction. The focus of this review is nuclear and chromatin cytometry, with linked reference to DNA targeting probes and drugs as exemplified by the anthracenediones.

Label-free quantitative chemical imaging and classification analysis of adipogenesis using mouse embryonic stem cells

Stem cells have received much attention recently for their potential utility in regenerative medicine. The identification of their differentiated progeny often requires complex staining procedures, and is challenging for intermediary stages which are a priori unknown. In this work, the ability of label-free quantitative coherent anti-Stokes Raman scattering (CARS) micro-spectroscopy to identify populations of intermediate cell states during the differentiation of murine embryonic stem cells into adipocytes is assessed. Cells were imaged at different days of differentiation by hyperspectral CARS, and images were analysed with an unsupervised factorization algorithm providing Raman-like spectra and spatially resolved maps of chemical components. Chemical decomposition combined with a statistical analysis of their spatial distributions provided a set of parameters that were used for classification analysis. The first 2 principal components of these parameters indicated 3 main groups, attributed to undifferentiated cells, cells differentiated into committed white pre-adipocytes, and differentiating cells exhibiting a distinct protein globular structure with adjacent lipid droplets. An unsupervised classification methodology was developed, separating undifferentiated cell from cells in other stages, using a novel method to estimate the optimal number of clusters. The proposed unsupervised classification pipeline of hyperspectral CARS data offers a promising new tool for automated cell sorting in lineage analysis.

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.