Understanding the effects of culture conditions in bacterial growth: A biochemical perspective using Raman microscopy

Open-set deep learning-enabled single-cell Raman spectroscopy for rapid identification of airborne pathogens in real-world environments

Pathogenic bioaerosols are critical for outbreaks of airborne disease; however, rapidly and accurately identifying pathogens directly from complex air environments remains highly challenging. We present an advanced method that combines open-set deep learning (OSDL) with single-cell Raman spectroscopy to identify pathogens in real-world air containing diverse unknown indigenous bacteria that cannot be fully included in training sets. To test and further enhance identification, we constructed the Raman datasets of aerosolized bacteria. Through optimizing OSDL algorithms and training strategies, Raman-OSDL achieves 93% accuracy for five target airborne pathogens, 84% accuracy for untrained air bacteria, and 36% reduction in false positive rates compared to conventional close-set algorithms. It offers a high detection sensitivity down to 1:1000. When applied to real air containing >4600 bacterial species, our method accurately identifies single or multiple pathogens simultaneously within an hour. This single-cell tool advances rapidly surveilling pathogens in complex environments to prevent infection transmission.

Gaining insights into the responses of individual yeast cells to ethanol fermentation using Raman tweezers and chemometrics

Saccharomyces cerevisiae is the most common microbe used for the industrial production of bioethanol, and it encounters various stresses that inhibit cell growth and metabolism during fermentation. However, little is currently known about the physiological changes that occur in individual yeast cells during ethanol fermentation. Therefore, in this work, Raman spectroscopy and chemometric techniques were employed to monitor the metabolic changes of individual yeast cells at distinct stages during high gravity ethanol fermentation. Raman tweezers was used to acquire the Raman spectra of individual yeast cells. Multivariate curve resolution-alternating least squares (MCR-ALS) and principal component analysis were employed to analyze the Raman spectra dataset. MCR-ALS extracted the spectra of proteins, phospholipids, and triacylglycerols and their relative contents in individual cells. Changes in intracellular biomolecules showed that yeast cells undergo three distinct physiological stages during fermentation. In addition, heterogeneity among yeast cells significantly increased in the late fermentation period, and different yeast cells may respond to ethanol stress via different mechanisms. Our findings suggest that the combination of Raman tweezers and chemometrics approaches allows for characterizing the dynamics of molecular components within individual cells. This approach can serve as a valuable tool in investigating the resistance mechanism and metabolic heterogeneity of yeast cells during ethanol fermentation.

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.

Antimicrobial Resistance Studies Using Raman Spectroscopy on Clinically Relevant Bacterial Strains

There has been a steep rise in the emergence of antibiotic-resistant bacteria in the past few years. A timely diagnosis can help in initiating appropriate antibiotic therapy. However, conventional techniques for diagnosing antibiotic resistance are time-consuming and labor-intensive. Therefore, we investigated the potential of Raman spectroscopy as a rapid surveillance technology for tracking the emergence of antibiotic resistance. In this study, we used Raman spectroscopy to differentiate clinical isolates of antibiotic-resistant and -sensitive bacteria of Escherichia coli, Acinetobacter baumannii, and Enterobacter species. The spectra were collected with or without exposure to various antibiotics (ciprofloxacin, gentamicin, meropenem, and nitrofurantoin), each having a distinct mechanism of action. Ciprofloxacin- and meropenem-treated sensitive strains showed a decrease in the intensity of Raman bands associated with DNA (667, 724, 785, 1378, 1480, and 1575 cm^-1) and proteins (640 and 1662 cm^-1), coupled with an increase in the intensity of lipid bands (891, 960, and 1445 cm^-1). Gentamicin- and nitrofurantoin-treated sensitive strains showed an increase in the intensity of nucleic acid bands (668, 724, 780, 810, 1378, 1480, and 1575 cm^-1) while a decrease in the intensity of protein bands (640, 1003, 1606, and 1662 cm^-1) and the lipid band (1445 cm^-1). The Raman spectral changes observed in the antibiotic-resistant strains were opposite to that of antibiotic-sensitive strains. The Raman spectral data correlated well with the antimicrobial susceptibility test results. The Raman spectral dataset was used for partial least-squares (PLS) analysis to validate the biomarkers obtained from the univariate analysis. Overall, this study showcases the potential of Raman spectroscopy for detecting antibiotic-resistant and -sensitive bacteria.

Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives

BACKGROUND

The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical.

SCOPE OF REVIEW

This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered.

MAJOR CONCLUSIONS

Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level.

Herbicides 2,4-Dichlorophenoxy Acetic Acid and Glyphosate Induce Distinct Biochemical Changes in E. coli during Phenotypic Antibiotic Resistance: A Raman Spectroscopic Study

Antibiotic resistance is a major global health concern. The increased use of herbicides may lead to multiple antibiotic resistance in bacteria. Conventional techniques for diagnosing antibiotic resistance are laborious, time-intensive, expensive, and lack information about antibiotic susceptibility. On the other hand, Raman spectroscopy is a rapid, label-free, noninvasive alternative to traditional techniques to detect antibiotic resistance. In this study, two popular herbicides 2,4-dichlorophenoxy acetic acid (2,4-D) and N-(phosphonomethyl)glycine (glyphosate) were used to study their effects on the emergence of antibiotic resistance. The Escherichia coli wild-type (WT) MG1655 strain and two isogenic mutants, Δlon and ΔacrB, were used together with Raman spectroscopy. The WT E. coli is sensitive to antibiotics, but exposure to both herbicides induces antibiotic resistance. Using an excitation wavelength of 785 nm, the intensity ratios (e.g., I₇₄₀/I₇₈₅, I₇₄₀/I₁₀₀₃, I₁₄₈₀/I₁₄₄₅, I₂₉₃₄/I₂₈₆₈, and I₂₉₃₄/I₂₈₄₅) were identified as biomarkers to study the induction of antibiotic resistance in bacteria but not NaCl-mediated stress. Using an excitation wavelength of 633 nm, the peak intensity at 740 cm^-1 assigned to cytochrome bd decreases under antibiotic stress but increases upon exposure to both herbicides and antibiotics, indicating the development of resistance. Thus, this study can be applied to monitor antibiotic resistance using Raman spectroscopy.

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

Growth Kinetics Monitoring of Gram-Negative Pathogenic Microbes Using Raman Spectroscopy

Optical density based measurements are routinely performed to monitor the growth of microbes. These measurements solely depend upon the number of cells and do not provide any information about the changes in the biochemical milieu or biological status. An objective information about these parameters is essential for evaluation of novel therapies and for maximizing the metabolite production. In the present study, we have applied Raman spectroscopy to monitor growth kinetics of three different pathogenic Gram-negative microbes Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii. Spectral measurements were performed under 532 nm excitation with 5 seconds of exposure time. Spectral features suggest temporal changes in the "peptide" and "nucleic acid" content of cells under different growth stages. Using principal component analysis (PCA), successful discrimination between growth phases was also achieved. Overall, the findings are supportive of the prospective adoption of Raman based approaches for monitoring microbial growth.

Bacterial Resuscitation from Starvation-Induced Dormancy Results in Phenotypic Diversity Coupled with Translational Activity Depending on Carbon Substrate Availability

Dormancy is a survival strategy of stressed bacteria inhabiting a various environment. Frequent dormant-active transitions owing to environmental changes play an important role in functional redundancy. However, a proper understanding of the phenotypic changes in bacteria during these transitions remains to be clarified. In this study, orthogonal approaches, such as electron microscopy, flow cytometry, and Raman spectroscopy, which can evaluate phenotypic heterogeneity at the single-cell level, were used to observe morphological and molecular phenotypic changes in resuscitated cells, and RNA sequencing (RNASeq) was used to determine the genetic characteristics associated with phenotypes. Within 12 h of the resuscitation process, morphological (cell size and shape) and physiological (growth and viability) characteristics as well as molecular phenotypes (cellular components) were found to be recovered to the extent that they were similar to those in active cells. The recovery rate and detailed phenotypic properties of the resuscitated cells differed significantly depending on the type or concentration of carbon sources. RNASeq analysis revealed that genes related to translation were significantly upregulated under all resuscitation conditions. The simpler the carbon source (e.g., glucose), the higher the expression of genes involved in cellular repair, and the more complex the carbon source (e.g., beef extract), the higher the expression of genes associated with increased energy production associated with cellular aerobic respiration. This study of phenotypic plasticity of resuscitated cells provides fundamental insight into understanding the adaptive fine-tuning of the microbiome in response to environmental changes and the functional redundancy resulting from phenotype heterogeneity.

Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy

As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%.

Raman-Deuterium Isotope Probing and Metagenomics Reveal the Drought Tolerance of the Soil Microbiome and Its Promotion of Plant Growth

Drought has become a major agricultural threat leading crop yield loss. Although a few species of rhizobacteria have the ability to promote plant growth under drought, the drought tolerance of the soil microbiome and its relationship with the promotion of plant growth under drought are scarcely studied. This study aimed to develop a novel approach for assessing drought tolerance in agricultural land by quantitatively measuring microbial phenotypes using stable isotopes and Raman spectroscopy. Raman spectroscopy with deuterium isotope probing was used to identify the Raman signatures of drought effects from drought-tolerant bacteria. Counting drought-tolerant cells by applying these phenotypic properties to agricultural samples revealed that 0% to 52.2% of all measured single cells had drought-tolerant properties, depending on the soil sample. The proportions of drought-tolerant cells in each soil type showed similar tendencies to the numbers of revived pea plants cultivated under drought. The phenotype of the soil microbiome and plant behavior under drought conditions therefore appeared to be highly related. Studying metagenomics suggested that there was a reliable link between the phenotype and genotype of the soil microbiome that could explain mechanisms that promote plant growth in drought. In particular, the proportion of drought-tolerant cells was highly correlated with genes encoding phytohormone production, including tryptophan synthase and isopentenyl-diphosphate delta-isomerase; these enzymes are known to alleviate drought stress. Raman spectroscopy with deuterium isotope probing shows high potential as an alternative technology for quantitatively assessing drought tolerance through phenotypic analysis of the soil microbiome. IMPORTANCE Soil microbiome has played a critical role in the plant survival during drought. However, the drought tolerance of soil microbiome and its ability to promote plant growth under drought is still scarcely studied. In this study, we identified the Raman signature (i.e., phenotype) of drought effects from drought-tolerant bacteria in agricultural soil samples using Raman-deuterium isotope probing (Raman-DIP). Moreover, the number of drought-tolerant cells measured by Raman-DIP was highly related to the survival rate of plant cultivation under drought and the abundance of genes encoding phytohormone production alleviating drought stress in plant. These results suggest Raman-DIP is a promising technology for measuring drought tolerance of soil microbiome. This result give us important insight into further studies of a reliable link between phenotype and genotype of soil microbiome for future plant-bacteria interaction research.

Potential of Raman spectroscopic techniques to study proteins

Proteins are large, complex molecules responsible for various biological processes. However, protein misfolding may lead to various life-threatening diseases. Therefore, it is vital to understand the shape and structure of proteins. Despite numerous techniques, a mechanistic understanding of the protein folding process is still unclear. Therefore, new techniques are continually being explored. In the present article, we have discussed the importance of Raman spectroscopy, Raman Optical Activity (ROA) and various other advancements in Raman spectroscopy to understand protein structure and conformational changes based on the review of our earlier work and recent literature. A Raman spectrum of a protein provides unique signatures for various secondary structures like helices, beta-sheets, turns, random structures, etc., and various amino acid residues such as tyrosine, tryptophan, and phenylalanine. We have shown how Raman spectra can differentiate between bovine serum albumin (BSA) and lysozyme protein based on their difference in sequence and structure (primary, secondary and tertiary). Although it is challenging to elucidate the structure of a protein using a Raman spectrum alone, Raman spectra can be used to differentiate small changes in conformations of proteins such as BSA during melting. Various new advancements in technique and data analyses in Raman spectroscopic studies of proteins have been discussed. The last part of the review focuses on the importance of the ROA spectrum to understand additional features about proteins. The ROA spectrum is rich in information about the protein backbone due to its rigidity compared to its side chains. Furthermore, the ROA spectra of lysozyme and BSA have been presented to show how ROA provides extra information about the solvent properties of proteins.

Bacterial phenotype dependency from CO2 measured by Raman spectroscopy

In recent years, Raman spectroscopy has become an established method to study medical, biological or environmental samples. Since Raman spectroscopy is a phenotypic method, many parameters can influence the spectra. One of these parameters is the concentration of CO₂, as this never remains stable in nature, but always adjusts itself in a dynamic equilibrium. So, it is obvious that the concentration of CO₂ cannot be controlled but it might have a big impact on the bacteria and bacterial composition in medical samples. When using a phenotypic method like Raman spectroscopy it is also important to know the influence of CO₂ to the dataset. To investigate the influence of CO₂ towards Raman spectra we cultivated E. coli at different concentration of CO₂ since this bacterium is able to switch metabolism from aerobic to microaerophilic conditions. After applying statistic methods small changes in the spectra became visible and it was even possible to observe the change of metabolism in this species according to the concentration of CO₂.

Profiling antibiotic resistance in Escherichia coli strains displaying differential antibiotic susceptibilities using Raman spectroscopy

The rapid identification of antibiotic resistant bacteria is important for public health. In the environment, bacteria are exposed to sub-inhibitory antibiotic concentrations which has implications in the generation of multi-drug resistant strains. To better understand these issues, Raman spectroscopy was employed coupled with partial least squares-discriminant analysis to profile Escherichia coli strains treated with sub-inhibitory concentrations of antibiotics. Clear differences were observed between cells treated with bacteriostatic (tetracycline and rifampicin) and bactericidal (ampicillin, ciprofloxacin, and ceftriaxone) antibiotics for 6 hr: First, atomic force microscopy revealed that bactericidal antibiotics cause extensive cell elongation whereas short filaments are observed with bacteriostatic antibiotics. Second, Raman spectral analysis revealed that bactericidal antibiotics lower nucleic acid to protein (I₈₁₂ /I₈₃₀ ) and nucleic acid to lipid ratios (I₁₄₈₃ /I₁₄₅₂ ) whereas the opposite is seen with bacteriostatic antibiotics. Third, the protein to lipid ratio (I₂₉₃₆ /I₂₈₈₅ and I₂₉₃₆ /I₂₈₅₀ ) is a Raman stress signature common to both the classes. These signatures were validated using two mutants, Δlon and ΔacrB, that exhibit relatively high and low resistance towards antibiotics, respectively. In addition, these spectral markers correlated with the emergence of phenotypic antibiotic resistance. Overall, this study demonstrates the efficacy of Raman spectroscopy to identify resistance in bacteria to sub-lethal concentrations of antibiotics.

Identification of a resonance Raman marker for cytochrome to monitor stress responses in Escherichia coli

Raman spectroscopy and resonance Raman spectroscopy are widely used to study bacteria and their responses to different environmental conditions. In the present study, the identification of a novel resonance Raman peak for Escherichia coli, recorded with 633 nm laser excitation is discussed. A peak at 740 cm^-1 is observed exclusively with 633 nm excitation but not with 514 nm or 785 nm excitation. This peak is absent in the lag phase but appears in the log phase of bacterial growth. The intensity of the peak increases at high temperature (45 °C) compared with growth at low temperature (25 °C) or the physiological temperature (37 °C). Although osmotic stress lowered bacterial growth, the intensity of this peak was unaffected. However, treatment with chemical uncouplers of oxidative phosphorylation resulted in significantly lower intensity of this Raman band, indicating its possible involvement in respiration. Cytochromes, a component of bacterial respiration' can show resonance enhancement at 633 nm due to the presence of a shoulder in that region depending on the type and conformation of cytochrome. Therefore, the peak intensity was monitored in different genetic mutants of E. coli lacking cytochromes. This peak is absent in the Escherichia coli mutant lacking cydB, but not ccmE, demonstrating the contribution of cytochrome bd subunit II in the peak's origin. In future, this newly found cytochrome marker can be used for biochemical assessment of bacteria exposed to various conditions. Overall, this finding opens the scope for use of red laser excitation in resonance Raman in monitoring stress and respiration in bacteria. Graphical abstract.

Vibrational Spectroscopy as a Sensitive Probe for the Chemistry of Intra-Phase Bacterial Growth

Bacterial growth in batch cultures occurs in four phases (lag, exponential/log, stationary and death phase) that differ distinctly in number of different bacteria, biochemistry and physiology. Knowledge regarding the growth phase and its kinetics is essential for bacterial research, especially in taxonomic identification and monitoring drug interactions. However, the conventional methods by which to assess microbial growth are based only on cell counting or optical density, without any insight into the biochemistry of cells or processes. Both Raman and Fourier transform infrared (FTIR) spectroscopy have shown potential to determine the chemical changes occurring between different bacterial growth phases. Here, we extend the application of spectroscopy and for the first time combine both Raman and FTIR microscopy in a multimodal approach to detect changes in the chemical compositions of bacteria within the same phase (intra-phase). We found a number of spectral markers associated with nucleic acids (IR: 964, 1082, 1215 cm⁻¹; RS: 785, 1483 cm⁻¹), carbohydrates (IR: 1035 cm⁻¹; RS: 1047 cm⁻¹) and proteins (1394 cm⁻¹, amide II) reflecting not only inter-, but also intra-phase changes in bacterial chemistry. Principal component analysis performed simultaneously on FTIR and Raman spectra enabled a clear-cut, time-dependent discrimination between intra-lag phase bacteria probed every 30 min. This demonstrates the unique capability of multimodal vibrational spectroscopy to probe the chemistry of bacterial growth even at the intra-phase level, which is particularly important for the lag phase, where low bacterial numbers limit conventional analytical approaches.