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, ß-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.

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.

Cell Phase Identification in a Three-Dimensional Engineered Tumor Model by Infrared Spectroscopic Imaging.

Cell cycle progression plays a vital role in regulating proliferation, metabolism, and apoptosis. Three-dimensional (3D) cell cultures have emerged as an important class of in vitro disease models, and incorporating the variation occurring from cell cycle progression in these systems is critical. Here, we report the use of Fourier transform infrared (FT-IR) spectroscopic imaging to identify subtle biochemical changes within cells, indicative of the G1/S and G2/M phases of the cell cycle. Following previous studies, we first synchronized samples from two-dimensional (2D) cell cultures, confirmed their states by flow cytometry and DNA quantification, and recorded spectra. We determined two critical wavenumbers (1059 and 1219 cm-1) as spectral indicators of the cell cycle for a set of isogenic breast cancer cell lines (MCF10AT series). These two simple spectral markers were then applied to distinguish cell cycle stages in a 3D cell culture model using four cell lines that represent the main stages of cancer progression from normal cells to metastatic disease. Temporal dependence of spectral biomarkers during acini maturation validated the hypothesis that the cells are more proliferative in the early stages of acini development; later stages of the culture showed stability in the overall composition but unique spatial differences in cells in the two phases. Altogether, this study presents a computational and quantitative approach for cell phase analysis in tissue-like 3D structures without any biomarker staining and provides a means to characterize the impact of the cell cycle on 3D biological systems and disease diagnostic studies using IR imaging.