Monitoring Phenotype Heterogeneity at the Single-Cell Level within Bacillus Populations Producing Poly-3-hydroxybutyrate by Label-Free Super-resolution Infrared Imaging

Phenotypic heterogeneity is commonly found among bacterial cells within microbial populations due to intrinsic factors as well as equipping the organisms to respond to external perturbations. The emergence of phenotypic heterogeneity in bacterial populations, particularly in the context of using these bacteria as microbial cell factories, is a major concern for industrial bioprocessing applications. This is due to the potential impact on overall productivity by allowing the growth of subpopulations consisting of inefficient producer cells. Monitoring the spread of phenotypes across bacterial cells within the same population at the single-cell level is key to the development of robust, high-yield bioprocesses. Here, we discuss the novel development of optical photothermal infrared (O-PTIR) spectroscopy to probe phenotypic heterogeneity within Bacillus strains by monitoring the production of the bioplastic poly-3-hydroxybutyrate (PHB) at the single-cell level. Measurements obtained on single-point and in imaging mode show significant variability in the PHB content within bacterial cells, ranging from whether or not a cell produces PHB to variations in the intragranular biochemistry of PHB within bacterial cells. Our results show the ability of O-PTIR spectroscopy to probe PHB production at the single-cell level in a rapid, label-free, and semiquantitative manner. These findings highlight the potential of O-PTIR spectroscopy in single-cell microbial metabolomics as a whole-organism fingerprinting tool that can be used to monitor the dynamic of bacterial populations as well as for understanding their mechanisms for dealing with environmental stress, which is crucial for metabolic engineering research

Detection and Quantification of Bacterial Spoilage in Milk and Pork Meat Using MALDI-TOF-MS and Multivariate Analysis

Microbiological safety is one of the cornerstones of quality control in the food industry. Identification and quantification of spoilage bacteria in pasteurized milk and meat in the food industry currently relies on accurate and sensitive yet time-consuming techniques which give retrospective values for microbial contamination. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), a proven technique in the field of protein and peptide identification and quantification, may be a valuable alternative approach for the rapid assessment of microbial spoilage. In this work we therefore developed MALDI-TOF-MS as a novel analytical approach for the assessment of food that when combined with chemometrics allows for the detection and quantification of milk and pork meat spoilage bacteria. To develop this approach, natural spoilage of pasteurized milk and raw pork meat samples incubated at 15 °C and at room temperature, respectively, was conducted. Samples were collected for MALDI-TOF-MS analysis (which took 4 min per sample) at regular time intervals throughout the spoilage process, with concurrent calculation and documentation of reference total viable counts using traditional microbiological methods (these took 2 days). Multivariate statistical techniques such as principal component discriminant function analysis, canonical correlation analysis, partial least-squares (PLS) regression, and kernel PLS (KPLS) were used to analyze the data. The results from MALDI-TOF-MS combined with PLS or KPLS gave excellent bacterial quantification results for both milk and meat spoilage, and typical root mean squared errors for prediction in test spectra were between 0.53 and 0.79 log unit. Overall these novel findings strongly indicate that MALDI-TOF-MS when combined with chemometric approaches would be a useful adjunct for routine use in the milk and meat industry as a fast and accurate viable bacterial detection and quantification method

Compositional Equivalence of Grain from Multi-trait Drought-Tolerant Maize Hybrids to a Conventional Comparator: Univariate and Multivariate Assessments

MON 87460 (D1) maize contains a gene that expresses the cold shock protein B (CSPB) from Bacillus subtilis to confer a yield advantage when yield is limited by water availability. This study evaluated the composition of grain from the D1-containing combined-trait maize hybrids D1 × NK603, D1 × MON 89034 × NK603, and D1 × MON 89034 × MON 88017. These stacks offer a combination of insect protection and herbicide tolerance traits. These hybrids were grown under well-watered and water-limited conditions at three replicated field sites across Chile during the 2006–2007 growing season. Compositional analyses included measurement of proximates, fibers, total amino acids, fatty acids, minerals, vitamins, raffinose, phytic acid, <i>p</i>-coumaric acid, and ferulic acid. The statistical analyses included an evaluation of the applicability of multiblock principal component analysis (MB-PCA) and ANOVA–simultaneous component analysis (ASCA) to studies when more than one experimental factor will contribute to compositional variability. Results from these multivariate procedures highlighted that water treatment was the greatest contributor to compositional variability and, as expected, confirmed that the grain of combined-trait drought-tolerant hybrids was compositionally equivalent to that of conventional comparators as established by traditional statistical significance testing

Quantitative Online Liquid Chromatography-Surface-Enhanced Raman Scattering of Purine Bases

Raman spectroscopy has been of interest as a detection method for liquid chromatographic separations for a significant period of time, due to the structural information it can provide, allowing the identification and distinction of coeluting analytes. Combined with the rapidly advancing field of enhanced Raman techniques, such as surface-enhanced Raman scattering (SERS), the previous low sensitivity of Raman measurements has also been alleviated. At-line LC-SERS analyses, where SERS measurements are taken of fractions collected during or after HPLC separation have been shown to be sensitive and applicable to a wide variety of analytes; however, quantitative, real-time, online LC-SERS analysis at comparable sensitivity to existing methods, applicable to high-throughput experiments, has not been previously demonstrated. Here we show that by introducing silver colloid, followed by an aggregating agent into the postcolumn flow of an HPLC system, we can quantitatively and reproducibly analyze mixtures of purine bases, with limits of detection in the region of 100–500 pmol. The analysis is performed without the use of a flow cell, thereby eliminating previously detrimental memory effects

Portable, Quantitative Detection of <i>Bacillus</i> Bacterial Spores Using Surface-Enhanced Raman Scattering

Portable rapid detection of pathogenic bacteria such as <i>Bacillus</i> is highly desirable for safety in food manufacture and under the current heightened risk of biological terrorism. Surface-enhanced Raman scattering (SERS) is becoming the preferred analytical technique for bacterial detection, due to its speed of analysis and high sensitivity. However in seeking methods offering the lowest limits of detection, the current research has tended toward highly confocal, microscopy-based analysis, which requires somewhat bulky instrumentation and precisely synthesized SERS substrates. By contrast, in this study we have improved SERS for bacterial analyses using silver colloidal substrates, which are easily and cheaply synthesized in bulk, and which we shall demonstrate permit analysis using portable instrumentation. All analyses were conducted in triplicate to assess the reproducibility of this approach, which was excellent. We demonstrate that SERS is able to detect and quantify rapidly the dipicolinate (DPA) biomarker for <i>Bacillus</i> spores at 5 ppb (29.9 nM) levels which are significantly lower than those previously reported for SERS and well below the infective dose of 10⁴ <i>B. anthracis</i> cells for inhalation anthrax. Finally we show the potential of multivariate data analysis to improve detection levels in complex DPA extracts from viable spores

Monitoring Guanidinium-Induced Structural Changes in Ribonuclease Proteins Using Raman Spectroscopy and 2D Correlation Analysis

Assessing the stability of proteins by comparing their unfolding profiles is a very important characterization and quality control step for any biopharmaceutical, and this is usually measured by fluorescence spectroscopy. In this paper we propose Raman spectroscopy as a rapid, noninvasive alternative analytical method and we shall show this has enhanced sensitivity and can therefore reveal very subtle protein conformational changes that are not observed with fluorescence measurements. Raman spectroscopy is a powerful nondestructive method that has a strong history of applications in protein characterization. In this work we describe how Raman microscopy can be used as a fast and reliable method of tracking protein unfolding in the presence of a chemical denaturant. We have compared Raman spectroscopic data to the equivalent samples analyzed using fluorescence spectroscopy in order to validate the Raman approach. Calculations from both Raman and fluorescence unfolding curves of [D]₅₀ values and Gibbs free energy correlate well with each other and more importantly agree with the values found in the literature for these proteins. In addition, 2D correlation analysis has been performed on both Raman and fluorescence data sets in order to allow further comparisons of the unfolding behavior indicated by each method. As many biopharmaceuticals are glycosylated in order to be functional, we compare the unfolding profiles of a protein (RNase A) and a glycoprotein (RNase B) as measured by Raman spectroscopy and discuss the implications that glycosylation has on the stability of the protein

Improved Descriptors for the Quantitative Structure–Activity Relationship Modeling of Peptides and Proteins

The ability to model the activity of a protein using quantitative structure–activity relationships (QSAR) requires descriptors for the 20 naturally coded amino acids. In this work we show that by modifying some established descriptors we were able to model the activity data of 140 mutants of the enzyme epoxide hydrolase with improved accuracy. These new descriptors (referred to as physical descriptors) also gave very good results when tested against a series of four dipeptide data sets. The physical descriptors encode the amino acids using only two orthogonal scales: the first is strongly linked to hydrophilicity/hydrophobicity, and the second, to the volume of the amino acid residue. The use of these new amino acid descriptors should result in simpler and more readily interpretable models for the enzyme activity (and potentially other functions of interest, e.g., secondary and tertiary structure) of peptides and proteins

Combining Raman and FT-IR Spectroscopy with Quantitative Isotopic Labeling for Differentiation of E. coli Cells at Community and Single Cell Levels

There is no doubt that the contribution of microbially mediated bioprocesses toward maintenance of life on earth is vital. However, understanding these microbes <i>in situ</i> is currently a bottleneck, as most methods require culturing these microorganisms to suitable biomass levels so that their phenotype can be measured. The development of new culture-independent strategies such as stable isotope probing (SIP) coupled with molecular biology has been a breakthrough toward linking gene to function, while circumventing <i>in vitro</i> culturing. In this study, for the first time we have combined Raman spectroscopy and Fourier transform infrared (FT-IR) spectroscopy, as metabolic fingerprinting approaches, with SIP to demonstrate the quantitative labeling and differentiation of Escherichia coli cells. E. coli cells were grown in minimal medium with fixed final concentrations of carbon and nitrogen supply, but with different ratios and combinations of ¹³C/¹²C glucose and ¹⁵N/¹⁴N ammonium chloride, as the sole carbon and nitrogen sources, respectively. The cells were collected at stationary phase and examined by Raman and FT-IR spectroscopies. The multivariate analysis investigation of FT-IR and Raman data illustrated unique clustering patterns resulting from specific spectral shifts upon the incorporation of different isotopes, which were directly correlated with the ratio of the isotopically labeled content of the medium. Multivariate analysis results of single-cell Raman spectra followed the same trend, exhibiting a separation between E. coli cells labeled with different isotopes and multiple isotope levels of C and N

Enhancing Surface Enhanced Raman Scattering (SERS) Detection of Propranolol with Multiobjective Evolutionary Optimization

Colloidal-based surface-enhanced Raman scattering (SERS) is a complex technique, where interaction between multiple parameters, such as colloid type, its concentration, and aggregating agent, is poorly understood. As a result SERS has so far achieved limited reproducibility. Therefore the aim of this study was to improve enhancement and reproducibility in SERS, and to achieve this, we have developed a multiobjective evolutionary algorithm (MOEA) based on Pareto optimality. In this MOEA approach, we tested a combination of five different colloids with six different aggregating agents, and a wide range of concentrations for both were explored; in addition we included in the optimization process three laser excitation wavelengths. For this optimization of experimental conditions for SERS, we chose the β-adrenergic blocker drug propranolol as the target analyte. The objective functions chosen suitable for this multiobjective problem were the ratio between the full width at half-maximum and the half-maximum intensity for enhancement and correlation coefficient for reproducibility. To analyze a full search of all the experimental conditions, 7785 experiments would have to be performed empirically; however, we demonstrated the search for acceptable experimental conditions of SERS can be achieved using only 4% of these possible experiments. The MOEA identified several experimental conditions for each objective which allowed a limit of detection of 2.36 ng/mL (7.97 nM) propranolol, and this is significantly lower (>25 times) than previous SERS studies aimed at detecting this β-blocker

PC-DFA score plots of GC-MS profiles.

<p>25 PCs were extracted from PCA and used as inputs to DFA, explaining 99% of the TEV. The legend in the figure shows the 95% CI for the correct classification of the 8 conditions. Significantly altered metabolites were mined through a combination of PC-DFA loadings and univariate significance testing (Student <i>t</i>-test). C, control.</p