RESUMEN
Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the ß-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.
RESUMEN
BACKGROUND: Valorization of lignin has the potential to significantly improve the economics of lignocellulosic biorefineries. However, its complex structure makes conversion to useful products elusive. One promising approach is depolymerization of lignin and subsequent bioconversion of breakdown products into value-added compounds. Optimizing transport of these depolymerization products into one or more organism(s) for biological conversion is important to maximize carbon utilization and minimize toxicity. Current methods assess internalization of depolymerization products indirectly-for example, growth on, or toxicity of, a substrate. Furthermore, no method has been shown to provide visualization of depolymerization products in individual cells. RESULTS: We applied mass spectrometry to provide direct measurements of relative internalized concentrations of several lignin depolymerization compounds and single-cell microscopy methods to visualize cell-to-cell differences in internalized amounts of two lignin depolymerization compounds. We characterized internalization of 4-hydroxybenzoic acid, vanillic acid, p-coumaric acid, syringic acid, and the model dimer guaiacylglycerol-beta-guaiacyl ether (GGE) in the lignolytic organisms Phanerochaete chrysosporium and Enterobacter lignolyticus and in the non-lignolytic but genetically tractable organisms Saccharomyces cerevisiae and Escherichia coli. The results show varying degrees of internalization in all organisms for all the tested compounds, including the model dimer, GGE. Phanerochaete chrysosporium internalizes all compounds in non-lignolytic and lignolytic conditions at comparable levels, indicating that the transporters for these compounds are not specific to the lignolytic secondary metabolic system. Single-cell microscopy shows that internalization of vanillic acid and 4-hydroxybenzoic acid analogs varies greatly among individual fungal and bacterial cells in a given population. Glucose starvation and chemical inhibition of ATP hydrolysis during internalization significantly reduced the internalized amount of vanillic acid in bacteria. CONCLUSIONS: Mass spectrometry and single-cell microscopy methods were developed to establish a toolset for providing direct measurement and visualization of relative internal concentrations of mono- and di-aryl compounds in microbes. Utilizing these methods, we observed broad variation in intracellular concentration between organisms and within populations and this may have important consequences for the efficiency and productivity of an industrial process for bioconversion. Subsequent application of this toolset will be useful in identifying and characterizing specific transporters for lignin-derived mono- and di-aryl compounds.