Investigation of the Influence of Stress on Label-Free Bacterial Surface-Enhanced Raman Spectra

Abstract

Label-free surface-enhanced Raman spectroscopy (SERS) has been proposed as a promising bacterial detection technique. However, the quality of the collected bacterial spectra can be affected by the time between sample acquisition and the SERS measurement. This study evaluated how storage stress stimuli influence the label-free SERS spectra of Pseudomonas syringae samples stored in phosphate buffered saline. The results indicate that when faced with nutrient limitations and changes in osmatic pressure, samples at room temperature (25 °C) exhibit more significant spectral changes than those stored at cold temperature (4 °C). At higher temperatures, bacterial communities secrete extracellular biomolecules that induce programmed cell death and result in increases in the supernatant SERS signals. Surviving cells consume cellular components to support their metabolism, thus leading to measurable declines in cell SERS intensity. Two-dimensional correlation spectroscopy analysis suggests that cellular component signatures decline sequentially in the following order: proteins, nucleic acids, and lipids. Extracellular nucleic acids, proteins, and carbohydrates are secreted in turn. After subtracting the SERS changes resulting from storage, we evaluated bacterial response to viral infection. P. syringae SERS profile changes enable accurate bacteriophage Phi6 quantification over the range of 104–1010 PFU/mL. The results indicate that storage conditions impact bacterial label-free SERS signals and that such influences need to be accounted for and if possible avoided when detecting bacteria or evaluating bacterial response to stress stimuli.

Label-Free Surface-Enhanced Raman Scattering Detection of Fire Blight Pathogen Using a Pathogen-Specific Bacteriophage

“…amylovora. This observation is consistent with previously reported studies examining the changes in the SERS spectra for bacteria after bacteriophage incubation. , Similar to E. amylovora, phiEaSP1 was composed of proteins and DNA, showing its own SERS peaks (Figure S8).…”

“…This observation is consistent with previously reported studies examining the changes in the SERS spectra for bacteria after bacteriophage incubation. 47,48 Similar to E. amylovora, phiEaSP1 was composed of proteins and DNA, showing its own SERS peaks (Figure S8). However, there were no peaks at 657 and 1544 cm −1 where significant changes were observed by bacteriophage infection.…”

Metabolite-Mediated Bacterial Antibiotic Resistance Revealed by Surface-Enhanced Raman Spectroscopy

“…Our previous study indicated that these peaks had a homogeneous distribution within both regions dominated by cells and the background culture media, thus reflecting that they primarily originate from bacterial metabolites rather than bacteria themselves . We note that the SERS intensity of metabolites is typically stronger than that of the bacterial cells. ,, Peak changes were observed for both P. aeruginosa 2111 and P.…”

“…Surface-enhanced Raman spectroscopy (SERS) has emerged as a promising technique for metabolite detection due to its high sensitivity, real-time response, fingerprint information, and minimal sample manipulation. − SERS spectra exhibit fingerprint peak features that enable metabolite identification and offer the potential for multiplex metabolomic analyses. , SERS peak intensities can be used for metabolite quantification and monitoring of bacterial metabolic activity. The monitoring of changes in metabolic SERS profiles allows the evaluation of metabolite transformation and the study of bacterial responses to environmental changes such as nutrient deprivation, oxidative pressure, heavy metal exposure, and viral infection. ,− Further, SERS substrates can be designed for on-site, field deployment. − SERS maps generated through area or depth scans enable the evaluation of metabolite spatial distributions and the study of interspecies interactions. − …”

Amoxicillin-Induced Purine Molecules Were Used as Bacterial Markers for SERS Detection and Recognition

“…26 The RF model has good interpretability, which can judge the degree of contribution of SERS features in the classification model and provide the basis for the classification results. 27 CNN-LSTM-Attention (CLA) is a deep learning model that combines convolutional neural networks (CNN), long short-term memory networks (LSTM), and attention mechanisms. The model first performs a convolution operation on the input sequence using a CNN to extract local features.…”