Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning

Abstract

In this work, biomolecules, such as membrane proteins, lipids, and DNA, were identified and their spatial distribution was mapped within a single Escherichia coli cell by Raman hyperspectral imaging. Raman spectroscopy allows direct, nondestructive, rapid, and cost-effective analysis of biological samples, minimizing the sample preparation and without the need of chemical label or immunological staining. Firstly, a comparison between an air-dried and a freeze-dried cell was made, and the principal vibrational modes associated to the membrane and nucleic acids were identified by the bacterium’s Raman chemical fingerprint. Then, analyzing the Raman hyperspectral images by multivariate statistical analysis, the bacterium biological status was investigated at a subcellular level. Principal components analysis (PCA) was applied for dimensionality reduction of the spectral data, then spectral unmixing was performed by multivariate curve resolution–alternating least squares (MCR-ALS). Thanks to multivariate data analysis, the DNA segregation and the Z-ring formation of a replicating bacterial cell were detected at a sub-micrometer level, opening the way to real-time molecular analysis that could be easily applied on in vivo or ex vivo biological samples, avoiding long preparation and analysis process.

In situ monitoring of the Shikimate pathway: A combinatorial approach of Raman reverse stable isotope probing and hyperspectral imaging.

“…The endogenous labels like fluorescent protein and exogenous labels like dyes (Nile red, BIODPY) or quantum dots can interfere with the metabolic activity or can cause photobleaching or phototoxicity. 7,[23][24][25][26][27][28] Raman hyperspectral imaging combined with multivariate curve resolution validates the sensitivity and specificity of our approach. Visualizing the spatial distribution and extracting the spectral pattern of phenylalanine from intricate spectral data not only contributes to the validation of our study but also enhances our ability to explore shikimate pathway activity at various incubation times in situ.…”

Multi-Excitation Raman Spectroscopy for Label-Free, Strain-Level Characterization of Bacterial Pathogens in Artificial Sputum Media

“…It is also possible that the Raman spectra of the bacteria could be recovered from the mixture using a spectral unmixing technique, such as Multivariate Curve Resolution-Alternating Least Squares, which have been applied to complex biological samples with overlapping peaks. 47,48 As before, SVM was performed on both the single-and combined-excitation data to determine performance of the technique on the more realistic samples, and the findings are presented in Figure 7. The method provided very high classification accuracies, even in the presence of the interfering media.…”

“…The results are presented in Figure and show that the peaks are present. It is also possible that the Raman spectra of the bacteria could be recovered from the mixture using a spectral unmixing technique, such as Multivariate Curve Resolution-Alternating Least Squares, which have been applied to complex biological samples with overlapping peaks. , …”

In situ monitoring of the shikimate pathway: a combinatorial approach of Raman reverse stable isotope probing and hyperspectral imaging

“…The endogenous labels like fluorescent proteins and exogenous labels like dyes (Nile red and BIODPY) or quantum dots can interfere with the metabolic activity or can cause photobleaching or phototoxicity. 7,[24][25][26][27][28][29] Raman hyperspectral imaging combined with multivariate curve resolution validates the sensitivity and specificity of our approach. Visualizing the spatial distribution and extracting the spectral pattern of phenylalanine from intricate spectral data not only contributes to the validation of our study but also enhances our ability to explore shikimate pathway activity at various incubation times in situ.…”