Demonstration of Carbon Catabolite Repression in Naphthalene Degrading Soil Bacteria via Raman Spectroscopy Based Stable Isotope Probing

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Benzethonium chloride affects short chain fatty acids produced from anaerobic fermentation of waste activated sludge: Performance, biodegradation and mechanisms

Benzethonium chloride (BZC) is viewed as a promising disinfectant and widely applied in daily life. While studies related to its effect on waste activated sludge (WAS) anaerobic fermentation (AF) were seldom mentioned before. To understand how BZC affects AF of WAS, production of short chain fatty acids (SCFAs), characteristics of WAS as well as microbial community were evaluated during AF. Results manifested a dose-specific relationship of dosages between BZC and SCFAs and the optimum yield arrived at 2441.01 mg COD/L with the addition of 0.030 g/g TSS BZC. Spectral results and protein secondary structure variation indicated that BZC denatured proteins in the solid phase into smaller proteins or amino acids with unstable structures. It was also found that BZC could stimulate the extracellular polymeric substances secretion and reduce the surface tension of WAS, leading to the enhancement of solubilization. Beside, BZC promoted the hydrolysis stage (increased by 7.09 % to 0.030 g/g TSS BZC), but inhibited acetogenesis and methanogenesis stages (decreased by 6.85 % and 14.75 % to 0.030 g/g TSS BZC). The microbial community was also regulated by BZC to facilitate the enrichment of hydrolytic and acidizing microorganisms (i.e. Firmicutes). All these variations caused by BZC were conducive to the accumulation of SCFAs. The findings contributed to investigating the effect of BZC on AF of WAS and provided a new idea for the future study of AF mechanism.

Dual-Stable-Isotope-Probed Raman microspectroscopy reveals the metabolic dynamic of Streptococcus mutans

Streptococcus mutans (S. mutans) is regarded as a cariogenic pathogen with the ability to metabolize sugars and form organic acids. However, its actual timely level of glucose consumption and cellular vitality in a polymicrobial culture system remains largely unknown. To tackle this challenge, we employed the S. mutans UA159 as a model and developed a dual-stable-isotope-probed Raman microspectroscopy method (Dual SIP-Raman) to simultaneously profile the general metabolic activity and glucose assimilative activity in situ. (i) Mono-SIP substrate feeding revealed that 0.5% 13C-glucose and 30% D2O were proper doses in the medium to obtain prominent and quantitative band shifts along with the 13C or D2O incorporation. In addition, the intensity of the 13C peak of phenylalanine (Phe) is proposed as a Raman-based biomarker for glucose utilization in a cell. (ii) The state of dual SIP substrate incorporation of 13C-glucose and D2O could be visualized by the corresponding spectral "red shifts" of Raman-scattered emissions; moreover, we also demonstrated that 13C/12C analysis was closely correlated with the C-D ratio. (iii) The application of the dual 13C-glucose and D2O feeding approach on a mock microbiota of S. mutans UA159 and C. albicans ATCC14053 revealed a stimulatory effect of fungus on both the glucose intake rate and general metabolic vitality of S. mutans UA159 (p < 0.05). Therefore, the 13C-glucose and D2O dual-feeding Raman Microspectroscopy approach is a valuable new tool for evaluating the glucose intake rate and general metabolic levels in situ, tracing the changing trend of the above metabolic activities, which is helpful to clarify the changes in the cariogenicity of oral microorganisms caused by the external environment at the single-cell level.

Surface-enhanced Raman spectroscopy combined with stable isotope probing to assess the metabolic activity of Escherichia coli cells in chicken carcass wash water

Rapid evaluation of the metabolic activity of microorganisms is crucial in the assessment of the disinfection ability of various antimicrobial agents in the food industry. In this study, surface-enhanced Raman spectroscopy combined with isotope probing was employed for the analysis of the disinfection of single bacterial cells in the chicken carcass wash water. The Raman signals from single Escherichia coli O157:H7 cells were enhanced by in situ synthesis of silver nanoparticles. The ΔCD of the cells grown in presence of 0.5% hydrogen peroxide and 50 ppm chlorine was 5.86 ± 1.86% and 5.1 ± 2.3%, respectively, which showed significant reduction compared with cells grown in the absence of disinfecting agents (19.86 ± 2.51%) after 2 h of incubation. The study proved that the proposed method had the potential to assess the metabolic activity of microorganisms in other food products and optimize the disinfection process.

Towards Mapping Mouse Metabolic Tissue Atlas by Mid-Infrared Imaging with Heavy Water Labeling

Understanding metabolism is of great significance to decipher various physiological and pathogenic processes. While great progress has been made to profile gene expression, how to capture organ-, tissue-, and cell-type-specific metabolic profile (i.e., metabolic tissue atlas) in complex mammalian systems is lagging behind, largely owing to the lack of metabolic imaging tools with high resolution and high throughput. Here, the authors applied mid-infrared imaging coupled with heavy water (D₂ O) metabolic labeling to a scope of mouse organs and tissues. The premise is that, as D₂ O participates in the biosynthesis of various macromolecules, the resulting broad C-D vibrational spectrum should interrogate a wide range of metabolic pathways. Applying multivariate analysis to the C-D spectrum, the authors successfully identified both inter-organ and intra-tissue metabolic signatures of mice. A large-scale metabolic atlas map between different organs from the same mice is thus generated. Moreover, leveraging the power of unsupervised clustering methods, spatially-resolved metabolic signatures of brain tissues are discovered, revealing tissue and cell-type specific metabolic profile in situ. As a demonstration of this technique, the authors captured metabolic changes during brain development and characterized intratumoral metabolic heterogeneity of glioblastoma. Altogether, the integrated platform paves a way to map the metabolic tissue atlas for complex mammalian systems.

Methylotrophs and Hydrocarbon-Degrading Bacteria Are Key Players in the Microbial Community of an Abandoned Century-Old Oil Exploration Well

In this work, we studied the microbial community and the physicochemical conditions prevailing in an exploratory oil well, abandoned a century ago, located in the Cahuita National Park (Costa Rica). According to our analysis, Cahuita well is characterized by a continuous efflux of methane and the presence of a mixture of hydrocarbons including phenanthrene/anthracene, fluoranthene, pyrene, dibenzothiophene, tricyclic terpanes, pyrene, sesquiterpenes, sterane, and n-alkanes. Based on the analysis of 16S rRNA gene amplicons, we detected a significant abundance of methylotrophic bacteria such as Methylobacillus (6.3-26.0% of total reads) and Methylococcus (4.1-30.6%) and the presence of common genera associated with hydrocarbon degradation, such as Comamonas (0.8-4.6%), Hydrogenophaga (1.5-3.3%) Rhodobacter (1.0-4.9%), and Flavobacterium (1.1-6.5%). The importance of C1 metabolism in this niche was confirmed by amplifying the methane monooxygenase (MMO)-encoding gene (pmo) from environmental DNA and the isolation of two strains closely related to Methylorubrum rhodesianum and Paracoccus communis with the ability to growth using methanol and formate as sole carbon source respectively. In addition, we were able to isolated 20 bacterial strains from the genera Pseudomonas, Acinetobacter, and Microbacterium which showed the capability to grow using the hydrocarbons detected in the oil well as sole carbon source. This work describes the physicochemical properties and microbiota of an environment exposed to hydrocarbons for 100 years, and it not only represents a contribution to the understanding of microbial communities in environments with permanently high concentrations of these compounds but also has biotechnological implications for bioremediation of petroleum-polluted sites.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Using Stable Isotope Probing and Raman Microspectroscopy To Measure Growth Rates of Heterotrophic Bacteria

The suitability of stable isotope probing (SIP) and Raman microspectroscopy to measure growth rates of heterotrophic bacteria at the single-cell level was evaluated. Label assimilation into Escherichia coli biomass during growth on a complex ¹³C-labeled carbon source was monitored in time course experiments. ¹³C incorporation into various biomolecules was measured by spectral "red shifts" of Raman-scattered emissions. The ¹³C- and ¹²C-isotopologues of the amino acid phenylalanine (Phe) proved to be quantitatively accurate reporter molecules of cellular isotopic fractional abundances (f_cell). Values of f_cell determined by Raman microspectroscopy and independently by isotope ratio mass spectrometry (IRMS) over a range of isotopic enrichments were statistically indistinguishable. Progressive labeling of Phe in E. coli cells among a range of ¹³C/¹²C organic substrate admixtures occurred predictably through time. The relative isotopologue abundances of Phe determined by Raman spectral analysis enabled the accurate calculation of bacterial growth rates as confirmed independently by optical density (OD) measurements. The results demonstrate that combining SIP and Raman microspectroscopy can be a powerful tool for studying bacterial growth at the single-cell level on defined or complex organic ¹³C carbon sources, even in mixed microbial assemblages. IMPORTANCE Population growth dynamics and individual cell growth rates are the ultimate expressions of a microorganism's fitness under its environmental conditions, whether natural or engineered. Natural habitats and many industrial settings harbor complex microbial assemblages. Their heterogeneity in growth responses to existing and changing conditions is often difficult to grasp by standard methodologies. In this proof-of-concept study, we tested whether Raman microspectroscopy can reliably quantify the assimilation of isotopically labeled nutrients into E. coli cells and enable the determination of individual growth rates among heterotrophic bacteria. Raman-derived growth rate estimates were statistically indistinguishable from those derived by standard optical density measurements of the same cultures. Raman microspectroscopy can also be combined with methods for phylogenetic identification. We report the development of Raman-based techniques that enable researchers to directly link genetic identity to functional traits and rate measurements of single cells within mixed microbial assemblages, currently a major technical challenge in microbiological research.

Raman Stable Isotope Probing of Bacteria in Visible and Deep UV-Ranges

Raman stable isotope probing (Raman-SIP) is an excellent technique that can be used to access the overall metabolism of microorganisms. Recent studies have mainly used an excitation wavelength in the visible range to characterize isotopically labeled bacteria. In this work, we used UV resonance Raman spectroscopy (UVRR) to evaluate the spectral red-shifts caused by the uptake of isotopes (¹³C, ¹⁵N, ²H(D) and ¹⁸O) in E. coli cells. Moreover, we present a new approach based on the extraction of labeled DNA in combination with UVRR to identify metabolically active cells. The proof-of-principle study on E. coli revealed heterogeneities in the Raman features of both the bacterial cells and the extracted DNA after labeling with ¹³C, ¹⁵N, and D. The wavelength of choice for studying ¹⁸O- and deuterium-labeled cells is 532 nm is, while ¹³C-labeled cells can be investigated with visible and deep UV wavelengths. However, ¹⁵N-labeled cells are best studied at the excitation wavelength of 244 nm since nucleic acids are in resonance at this wavelength. These results highlight the potential of the presented approach to identify active bacterial cells. This work can serve as a basis for the development of new techniques for the rapid and efficient detection of active bacteria cells without the need for a cultivation step.

Recent developments in Raman spectral analysis of microbial single cells: Techniques and applications

The conventional microbial cell analyses are mostly population-averaged methods that conceal the characteristics of single-cell in the community. Single-cell analysis can provide information on the functional and structural variation of each cell, resulting in the elimination of long and tedious microbial cultivation techniques. Raman spectroscopy is a label-free, noninvasive, and in-vivo method ideal for single-cell measurement to obtain spatially resolved chemical information. In the current review, recent developments in Raman spectroscopic techniques for microbial characterization at the single-cell level are presented, focusing on Raman imaging of single cells to study the intracellular distribution of different components. The review also discusses the limitation and challenges of each technique and put forward some future outlook for improving Raman spectroscopy-based techniques for single-cell analysis. Raman spectroscopic methods at the single-cell level have potential in precision measurements, metabolic analysis, antibiotic susceptibility testing, resuscitation capability, and correlating phenotypic information to genomics for cells, the integration of Raman spectroscopy with other techniques such as microfluidics, stable isotope probing (SIP), and atomic force microscope can further improve the resolution and provide extensive information. Future focuses should be given to advance algorithms for data analysis, standardized reference libraries, and automated cell isolation techniques in future.

Raman 18 O-labeling of bacteria in visible and deep UV-ranges

Raman stable isotope labeling with ² H, ¹³ C or ¹⁵ N has been reported as an elegant approach to investigate cellular metabolic activity, which is of great importance to reveal the functions of microorganisms in native environments. A new strategy termed Raman ¹⁸ O-labeling was developed to probe the metabolic activity of bacteria. Raman ¹⁸ O-labeling refers to the combination of Raman microspectroscopy with ¹⁸ O-labeling using H₂¹⁸ O. At an excitation wavelength of 532 nm, the incorporation of ¹⁸ O into the amide I group of proteins and DNA/RNA bases was observed in Escherichia coli cells, while for an excitation wavelength electronically resonant with DNA or aromatic amino acid absorption at 244 nm ¹⁸ O assimilation was detected using chemometric tools rather than visual inspection. Raman ¹⁸ O-labeling at 532 nm combined with 2D correlation analysis confirmed the assimilation of ¹⁸ O in proteins and nucleic acids and revealed the growth strategy of E. coli cells; they underwent protein synthesis followed by nucleic acid synthesis. Independent cultural replicates at different incubation times corroborated the reproducibility of these results. The variations in spectral features of ¹⁸ O-labeled cells revealed changes in physiological information of cells. Hence, Raman ¹⁸ O-labeling could provide a powerful tool to identify metabolically active bacterial cells.

Monitoring Deuterium Uptake in Single Bacterial Cells via Two-Dimensional Raman Correlation Spectroscopy

Raman-stable isotope labeling using heavy water (Raman-D₂O) is attracting great interest as a fast technique with various applications ranging from the identification of pathogens in medical samples to the determination of microbial activity in the environment. Despite its widespread applications, little is known about the fundamental processes of hydrogen-deuterium (H/D) exchange, which are crucial for understanding molecular interactions in microorganisms. By combining two-dimensional (2D) correlation spectroscopy and Raman deuterium labeling, we have investigated H/D exchange in bacterial cells under time dependence. Most C-H stretching signals decreased in intensity over time, prior to the formation of the C-D stretching vibration signals. The intensity of the C-D signal gradually increased over time, and the shape of the C-D signal was more uniform after longer incubation times. Deuterium uptake showed high variability between the bacterial genera and mainly led to an observable labeling of methylene and methyl groups. Thus, the C-D signal encompassed a combination of symmetric and antisymmetric CD₂ and CD₃ stretching vibrations, depending on the bacterial genera. The present study allowed for the determination of the sequential order of deuterium incorporation into the functional groups of proteins, lipids, and nucleic acids and hence understanding the process of biomolecule synthesis and the growth strategies of different bacterial taxa. We present the combination of Raman-D₂O labeling and 2D correlation spectroscopy as a promising approach to gain a fundamental understanding of molecular interactions in biological systems.

Comparison of functional and discrete data analysis regimes for Raman spectra

Raman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data.

Metabolism in action: stable isotope probing using vibrational spectroscopy and SIMS reveals kinetic and metabolic flux of key substrates

Microbial communities play essential functions which drive various ecosystems supporting animal and aquatic life. However, linking bacteria with specific metabolic functions is difficult, since microbial communities consist of numerous and phylogenetically diverse microbes. Stable isotope probing (SIP) combined with single-cell tools has emerged as a novel culture-independent strategy for unravelling microbial metabolic roles and intertwined interactions in complex communities. In this study, we applied Raman and Fourier-transform infrared (FT-IR) spectroscopies, secondary ion mass spectrometry (SIMS) with SIP to probe the rate of ¹³C incorporation in Escherichia coli at 37 and 25 °C. Our results indicate quantitative enrichment and flow of ¹³C into E. coli at various time points. Multivariate and univariate analyses of Raman and FT-IR data demonstrated distinctive ¹³C concentration-dependent trends that were due to vibrational bands shifting to lower frequencies and these shifts were a result of incubation time and metabolic rate. SIMS results were in complete agreement with the spectroscopy findings, and confirmed the detected levels of ¹³C incorporation into microbial biomass at the investigated conditions. Having established that FT-IR and Raman spectroscopy with SIP can measure metabolism kinetics in this simple system, we have applied the kinetics concept to study the metabolism of phenol by Pseudomonas putida and metabolic interactions within a two-species consortia with E. coli that could not degrade phenol. Raman spectroscopy combined with SIP identified quantitative shifts in P. putida due to temporal assimilation of phenol. Although E. coli was unable to grow on phenol, in co-culture with P. putida, general metabolic probing using deuterated water for SIP revealed that E. coli displayed increasing metabolic activity, presumably due to cross feeding from metabolites generated by P. putida. This study clearly demonstrates that Raman and FT-IR combined with SIP provide rapid and sensitive detection of carbon incorporation rates and microbial interactions. These novel findings may guide the identification of primary substrate consumers in complex microbial communities in situ, which is a key step towards the characterisation of novel genes, enzymes and metabolic flux analysis in microbial consortia.

Enhancement of polyhydroxyalkanoate production by co-feeding lignin derivatives with glycerol in Pseudomonas putida KT2440

BACKGROUND

Efficient utilization of all available carbons from lignocellulosic biomass is critical for economic efficiency of a bioconversion process to produce renewable bioproducts. However, the metabolic responses that enable Pseudomonas putida to utilize mixed carbon sources to generate reducing power and polyhydroxyalkanoate (PHA) remain unclear. Previous research has mainly focused on different fermentation strategies, including the sequential feeding of xylose as the growth stage substrate and octanoic acid as the PHA-producing substrate, feeding glycerol as the sole carbon substrate, and co-feeding of lignin and glucose. This study developed a new strategy-co-feeding glycerol and lignin derivatives such as benzoate, vanillin, and vanillic acid in Pseudomonas putida KT2440-for the first time, which simultaneously improved both cell biomass and PHA production.

RESULTS

Co-feeding lignin derivatives (i.e. benzoate, vanillin, and vanillic acid) and glycerol to P. putida KT2440 was shown for the first time to simultaneously increase cell dry weight (CDW) by 9.4-16.1% and PHA content by 29.0-63.2%, respectively, compared with feeding glycerol alone. GC-MS results revealed that the addition of lignin derivatives to glycerol decreased the distribution of long-chain monomers (C10 and C12) by 0.4-4.4% and increased the distribution of short-chain monomers (C6 and C8) by 0.8-3.5%. The ¹H-¹³C HMBC, ¹H-¹³C HSQC, and ¹H-¹H COSY NMR analysis confirmed that the PHA monomers (C6-C14) were produced when glycerol was fed to the bacteria alone or together with lignin derivatives. Moreover, investigation of the glycerol/benzoate/nitrogen ratios showed that benzoate acted as an independent factor in PHA synthesis. Furthermore, ¹H, ¹³C and ³¹P NMR metabolite analysis and mass spectrometry-based quantitative proteomics measurements suggested that the addition of benzoate stimulated oxidative-stress responses, enhanced glycerol consumption, and altered the intracellular NAD⁺/NADH and NADPH/NADP⁺ ratios by up-regulating the proteins involved in energy generation and storage processes, including the Entner-Doudoroff (ED) pathway, the reductive TCA route, trehalose degradation, fatty acid β-oxidation, and PHA biosynthesis.

CONCLUSIONS

This work demonstrated an effective co-carbon feeding strategy to improve PHA content/yield and convert lignin derivatives into value-added products in P. putida KT2440. Co-feeding lignin break-down products with other carbon sources, such as glycerol, has been demonstrated as an efficient way to utilize biomass to increase PHA production in P. putida KT2440. Moreover, the involvement of aromatic degradation favours further lignin utilization, and the combination of proteomics and metabolomics with NMR sheds light on the metabolic and regulatory mechanisms for cellular redox balance and potential genetic targets for a higher biomass carbon conversion efficiency.

Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms

Microscale processes are critically important to soil ecology and biogeochemistry yet are difficult to study due to soil's opacity and complexity. To advance the study of soil processes, we constructed transparent soil microcosms that enable the visualization of microbes via fluorescence microscopy and the non-destructive measurement of microbial activity and carbon uptake in situ via Raman microspectroscopy. We assessed the polymer Nafion and the crystal cryolite as optically transparent soil substrates. We demonstrated that both substrates enable the growth, maintenance, and visualization of microbial cells in three dimensions over time, and are compatible with stable isotope probing using Raman. We applied this system to ascertain that after a dry-down/rewetting cycle, bacteria on and near dead fungal hyphae were more metabolically active than those far from hyphae. These data underscore the impact fungi have facilitating bacterial survival in fluctuating conditions and how these microcosms can yield insights into microscale microbial activities.

Imaging the invisible-Bioorthogonal Raman probes for imaging of cells and tissues

A revolutionary avenue for vibrational imaging with super-multiplexing capability can be seen in the recent development of Raman-active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug-cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence-based imaging without the need of bulky fluorescent tags.

Bacterial metabolic heterogeneity: origins and applications in engineering and infectious disease

Bacteria within an isoclonal population display significant heterogeneity in metabolism, even under tightly controlled environmental conditions. Metabolic heterogeneity enables influential functions not possible or measurable at the ensemble scale. Several molecular and cellular mechanisms are likely to give rise to metabolic heterogeneity including molecular noise in metabolic enzyme expression, positive feedback loops, and asymmetric partitioning of cellular components during cell division. Dissection of the mechanistic origins of metabolic heterogeneity has been enabled by recent developments in single-cell analytical tools. Finally, we provide a discussion of recent studies examining the importance of metabolic heterogeneity in applied settings such as infectious disease and metabolic engineering.

Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications

The chemometric analysis of Raman spectra of biological materials is hampered by spectral variations due to the instrumental setup that overlay the subtle biological changes of interest. Thus, an established statistical model may fail when applied to Raman spectra of samples acquired with a different device. Therefore, model transfer strategies are essential. Herein we report a model transfer approach based on extended multiplicative signal correction (EMSC). As opposed to existing model transfer methods, the EMSC based approach does not require group information on the secondary data sets, thus no extra measurements are required. The proposed model-transfer approach is a preprocessing procedure and can be combined with any method for regression and classification. The performance of EMSC as a model transfer method was demonstrated with a data set of Raman spectra of three Bacillus bacteria spore species ( B. mycoides, B. subtilis, and B. thuringiensis), which were acquired on four Raman spectrometers. A three-group classification by partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV) was performed. The mean sensitivities of the prediction on the independent device were considerably improved by the EMSC method. Besides the mean sensitivity, the model transferability was additionally benchmarked by the newly defined numeric markers: (1) relative Pearson's correlation coefficient and (2) relative Fisher's discriminant ratio. We show that these markers have led to consistent conclusions compared to the mean sensitivity of the classification. The advantage of our defined markers is that the evaluation is more effective and objective, because it is independent of the classification models.

Biological and Medical Applications of Multivariate Curve Resolution Assisted Raman Spectroscopy

Biological specimens such as cells, tissues and biofluids (urine, blood) contain mixtures of many different biomolecules, all of which contribute to a Raman spectrum at any given point. The separation and identification of pure biochemical components remains one of the biggest challenges in Raman spectroscopy. Multivariate curve resolution, a matrix factorization method, is a powerful, yet flexible, method that can be used with constraints, such as non-negativity, to decompose a complex spectroscopic data matrix into a small number of physically meaningful pure spectral components along with their relative abundances. This paper reviews recent applications of multivariate curve resolution by alternating least squares analysis to Raman spectroscopic and imaging data obtained either in vivo or in vitro from biological and medical samples.

Using Raman spectroscopy and chemometrics to identify the growth phase of Lactobacillus casei Zhang during batch culture at the single-cell level

Background

As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment.

Methods

In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS.

Results

Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%.

Conclusions

To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.

Label-free, simultaneous quantification of starch, protein and triacylglycerol in single microalgal cells

BACKGROUND

Current approaches for quantification of major energy-storage forms in microalgae, including starch, protein and lipids, generally require cell cultivation to collect biomass followed by tedious and time-consuming analytical procedures. Thus, label-free, non-destructive and simultaneous quantification of such macromolecules at single-cell resolution is highly desirable in microalgal feedstock development and bioprocess control.

RESULTS

Here, we established a method based on single-cell Raman spectra (SCRS) that simultaneously quantifies the contents of starch, protein, triacylglycerol (TAG) and lipid unsaturation degree in individual Chlamydomonas reinhardtii cells. Measurement accuracy for the contents based on full SCRS spectrum each reached 96.86-99.24%, all significantly higher than single peak-based models. However, accuracy and reliability of measurement are dependent on the number of cells sampled, thus a formal mathematical framework was proposed and validated to rationally define "minimal sampling depth" for a given state of cellular population. Furthermore, a barcode consisting of 13 marker Raman peaks was proposed to characterize the temporal dynamics of these energy-storage products, which revealed that the average contents of starch and TAG increased, while their heterogeneity indices decreased, with those of protein being exactly the opposite. Finally, our method is widely applicable, as measurements among cells from liquid suspension culture, wet paste and frozen dried powder all exhibited excellent consistency.

CONCLUSIONS

When sampled at proper depth, SCRS can serve as a quantitative and generally applicable tool for characterization and screening of strains and bioprocesses based on the profile of energy-storage macromolecules and their among-cell heterogeneity.