Resolution Limit in Infrared Chemical Imaging

Label-Free Monitoring of Coculture System Dynamics: Probing Probiotic and Cancer Cell Interactions via Infrared Spectroscopic Imaging

Evaluating the dynamic interaction of microorganisms and mammalian cells is challenging due to the lack of suitable platforms for examining interspecies interactions in biologically relevant coculture conditions. In this work, we demonstrate the interaction between probiotic bacteria (Lactococcus lactis and Escherichia coli) and A498 human cancer cells in vitro, utilizing a hydrogel-based platform in a label-free manner by infrared spectroscopy. The L. lactis strain recapitulated in the compartment system secretes polypeptide molecules such as nisin, which has been reported to trigger cell apoptosis. We propose a mid-infrared (IR) spectroscopic imaging approach to monitor the variation of biological components utilizing kidney cells (A498) as a model system cocultured with bacteria. We characterized the biochemical composition (i.e., nucleic acids, protein secondary structures, and lipid conformations) label-free using an unbiased measurement. Several IR spectral features, including unsaturated fatty acids, β-turns in protein, and nucleic acids, were utilized to predict cellular response. These features were then applied to establish a quantitative relationship through a multivariate regression model to predict cellular dynamics in the coculture system to assess the effect of nisin on A498 kidney cancer cells cocultured with bacteria. Overall, our study sheds light on the potential of using IR spectroscopic imaging as a label-free tool to monitor complex microbe-host cell interactions in biological systems. This integration will enable mechanistic studies of interspecies interactions with insights into their underlying physiological processes.

Microscopic vibrational circular dichroism on the forewings of a European hornet: heterogenous sequences of protein domains with different secondary structures

We developed a microscopic scanning for vibrational circular dichroism (VCD) spectroscopy in which a quantum cascade laser is equipped with a highly focused infrared light source to attain a spatial resolution of 100 μm. This system was applied to the forewing of a European hornet to reveal how the protein domains are organised. Two-dimensional patterns were obtained from the VCD signals with steps of 100 μm. We scanned the 1500-1740 cm-1 wavenumber range, which covers amide I and II absorptions. Zone sequenced α-helical and β-sheet domains within an area of 200 μm2 in membranes close to where two veins cross. The sign of the VCD signal at 1650 cm-1 changed from positive to negative when probed along the zone axis, intermediated by the absence of VCD activity. The significance of this zone is discussed from the viewpoint of the mechanical properties required for flying motion. These features are unattainable using conventional FTIR (Fourier transform infrared) or FT-VCD methods with a spatial resolution of ∼10 mm2.

Modeling the Thermoelastic Sample Response for Subdiffraction Infrared Spectroscopic Imaging

There is significant and increasing interest in using the photothermal effect to record infrared (IR) absorption spectra localized to volumes that are considerably smaller than the wavelength of excitation, i.e., subdiffraction imaging. As opposed to conventional IR microscopy, in which absorption and scattering of the illuminating light is measured, subdiffraction imaging can be achieved through detection of the sample's thermal response to IR absorption-induced heating. While this relationship has been examined by a variety of coarse-grained models, a generalized analysis of the dependence of temperature and surface deformation arising from an absorber below the surface has not been reported. Here, we present an analytical model to understand a sample's thermoelastic response in photothermal measurements. The model shows important dependence of the ability to record subdiffraction data on modulation frequency of exciting light, limitations imposed by optical sensing, and the potential to discern location of objects ultimately limited by noise and sharpness of the detecting mechanism. This foundational analysis should allow for better modeling, understanding, and harnessing of the relationship between absorption and sample response that underlies IR photothermal measurements.

Photoinduced force microscopy as a novel method for the study of microbial nanostructures

A detailed comparison of the capabilities of electron microscopy and nano-infrared (IR) microscopy for imaging microbial nanostructures has been carried out for the first time. The surface sensitivity, chemical specificity, and non-destructive nature of spectroscopic mapping is shown to offer significant advantages over transmission electron microscopy (TEM) for the study of biological samples. As well as yielding important topographical information, the distribution of amides, lipids, and carbohydrates across cross-sections of bacterial (Escherichia coli, Staphylococcus aureus) and fungal (Candida albicans) cells was demonstrated using PiFM. The unique information derived from this new mode of spectroscopic mapping of the surface chemistry and biology of microbial cell walls and membranes, may provide new insights into fungal/bacterial cell function as well as having potential use in determining mechanisms of antimicrobial resistance, especially those targeting the cell wall.

Enhancing spatial resolution in Fourier transform infrared spectral image via machine learning algorithms

Owing to the intrinsic signal noise in the characterization of chemical structures through Fourier transform infrared (FT-IR) spectroscopy, the determination of the signal-to-noise ratio (SNR) depends on the level of the concentration of the chemical structures. In situations characterized by limited concentrations of chemical structures, the traditional approach involves mitigating the resulting low SNR by superimposing repetitive measurements. In this study, we achieved comparable high-quality results to data scanned 64 times and superimposed by employing machine learning algorithms such as the principal component analysis and non-negative matrix factorization, which perform the dimensionality reduction, on FT-IR spectral image data that was only scanned once. Furthermore, the spatial resolution of the mapping images correlated to each chemical structure was enhanced by applying both the machine learning algorithms and the Gaussian fitting simultaneously. Significantly, our investigation demonstrated that the spatial resolution of the mapping images acquired through relative intensity is further improved by employing dimensionality reduction techniques. Collectively, our findings imply that by optimizing research data through noise reduction enhancing spatial resolution using the machine learning algorithms, research processes can be more efficient, for instance by reducing redundant physical measurements.

Infrared spectroscopic laser scanning confocal microscopy for whole-slide chemical imaging

Chemical imaging, especially mid-infrared spectroscopic microscopy, enables label-free biomedical analyses while achieving expansive molecular sensitivity. However, its slow speed and poor image quality impede widespread adoption. We present a microscope that provides high-throughput recording, low noise, and high spatial resolution where the bottom-up design of its optical train facilitates dual-axis galvo laser scanning of a diffraction-limited focal point over large areas using custom, compound, infinity-corrected refractive objectives. We demonstrate whole-slide, speckle-free imaging in ~3 min per discrete wavelength at 10× magnification (2 μm/pixel) and high-resolution capability with its 20× counterpart (1 μm/pixel), both offering spatial quality at theoretical limits while maintaining high signal-to-noise ratios (>100:1). The data quality enables applications of modern machine learning and capabilities not previously feasible - 3D reconstructions using serial sections, comprehensive assessments of whole model organisms, and histological assessments of disease in time comparable to clinical workflows. Distinct from conventional approaches that focus on morphological investigations or immunostaining techniques, this development makes label-free imaging of minimally processed tissue practical.

Video-rate mid-infrared photothermal imaging by single-pulse photothermal detection per pixel

By optically sensing absorption-induced photothermal effect, mid-infrared (IR) photothermal (MIP) microscope enables super-resolution IR imaging of biological systems in water. However, the speed of current sample-scanning MIP system is limited to milliseconds per pixel, which is insufficient for capturing living dynamics. By detecting the transient photothermal signal induced by a single IR pulse through fast digitization, we report a laser-scanning MIP microscope that increases the imaging speed by three orders of magnitude. To realize single-pulse photothermal detection, we use synchronized galvo scanning of both mid-IR and probe beams to achieve an imaging line rate of more than 2 kilohertz. With video-rate speed, we observed the dynamics of various biomolecules in living organisms at multiple scales. Furthermore, by using hyperspectral imaging, we chemically dissected the layered ultrastructure of fungal cell wall. Last, with a uniform field of view more than 200 by 200 square micrometer, we mapped fat storage in free-moving Caenorhabditis elegans and live embryos.

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.