The influence of intracellular storage material on bacterial identification by means of Raman spectroscopy

Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges

SUMMARYThe development of multicellularity represents a key evolutionary transition that is crucial for the emergence of complex life forms. Although multicellularity has traditionally been studied in eukaryotes, it originates in prokaryotes. Coordinated aggregation of individual cells within the confines of a colony results in emerging, higher-level functions that benefit the population as a whole. During colony differentiation, an almost infinite number of ecological and physiological population-forming forces are at work, creating complex, intricate colony structures with divergent functions. Understanding the assembly and dynamics of such populations requires resolving individual cells or cell groups within such macroscopic structures. Addressing how each cell contributes to the collective action requires pushing the resolution boundaries of key technologies that will be presented in this review. In particular, single-cell techniques provide powerful tools for studying bacterial multicellularity with unprecedented spatial and temporal resolution. These advancements include novel microscopic techniques, mass spectrometry imaging, flow cytometry, spatial transcriptomics, single-bacteria RNA sequencing, and the integration of spatiotemporal transcriptomics with microscopy, alongside advanced microfluidic cultivation systems. This review encourages exploring the synergistic potential of the new technologies in the study of bacterial multicellularity, with a particular focus on individuals in differentiated bacterial biofilms (colonies). It highlights how resolving population structures at the single-cell level and understanding their respective functions can elucidate the overarching functions of bacterial multicellular populations.

Ultrastructural insights into cellular organization, energy storage and ribosomal dynamics of an ammonia-oxidizing archaeon from oligotrophic oceans

Introduction

Nitrososphaeria, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their importance in Earth's ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant unanswered question of how the machinery of these organisms underpins metabolic functions.

Methods

In this study, we combined spherical-chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaeria.

Results and Discussion

Our tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules: one type is possibly composed of polyphosphate and the other polyhydroxyalkanoate. With precise measurements using cryo-ET, we observed low quantity and density of ribosomes in SCM1 cells, which are in alignment with the documented slow growth of AOA in laboratory cultures. Collectively, these findings provide visual evidence supporting the resilience of AOA in the vast oligotrophic marine environment.

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.

The Role of Bacterial Polyhydroalkanoate (PHA) in a Sustainable Future: A Review on the Biological Diversity

Environmental challenges related to the mismanagement of plastic waste became even more evident during the COVID-19 pandemic. The need for new solutions regarding the use of plastics came to the forefront again. Polyhydroxyalkanoates (PHA) have demonstrated their ability to replace conventional plastics, especially in packaging. Its biodegradability and biocompatibility makes this material a sustainable solution. The cost of PHA production and some weak physical properties compared to synthetic polymers remain as the main barriers to its implementation in the industry. The scientific community has been trying to solve these disadvantages associated with PHA. This review seeks to frame the role of PHA and bioplastics as substitutes for conventional plastics for a more sustainable future. It is focused on the bacterial production of PHA, highlighting the current limitations of the production process and, consequently, its implementation in the industry, as well as reviewing the alternatives to turn the production of bioplastics into a sustainable and circular economy.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Phenotypic Differentiation of Autotrophic and Heterotrophic Bacterial Cells Using Raman-D2O Labeling

Carbon cycling is one of the major biogeochemical processes driven by bacteria. Autotrophic bacteria convert carbon dioxide (CO₂) into organic compounds that are used by heterotrophs. Mixotrophic bacteria can employ both autotrophy and heterotrophy for growth. The characterization of the lifestyle of individual cells is essential to understand the microbial activity and thus reveal the implication of bacteria in the carbon flux. In this study, we used groundwater bacteria to investigate the potential of Raman-D₂O labeling in combination with chemometrics to identify the carbon assimilation strategies of bacteria. Classification models were built using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Autotrophs assimilated a significantly higher amount (mean C-D ratio between 16.63 and 21.69%) of deuterium than heterotrophs. The C-D signal only provides information about the activity since it appears in the Raman-silent region, where no interference with the taxonomic information is expected. The classification between autotrophs and heterotrophs achieved an overall accuracy of 96.3%. In the validation step with an independent dataset containing species not included in the model, the PCA-LDA model achieved 100% accuracy. This demonstrated that the C-D signal contributed to the identification of autotrophic and heterotrophic bacterial cells. This work reports a robust, rapid, and nondestructive approach for the identification of single cells based on their carbon acquisition strategies. The present study foresees the potential of Raman-D₂O labeling as a promising method for automated discrimination of in situ functional activities of bacteria in environmental systems.

Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes

Accessing enormous uncultivated microorganisms (microbial dark matter) in various Earth environments requires accurate, nondestructive classification, and molecular understanding of the microorganisms in in situ and at the single-cell level. Here we demonstrate a combined approach of random forest (RF) machine learning and single-cell Raman microspectroscopy for accurate classification of phylogenetically diverse prokaryotes (three bacterial and three archaeal species from different phyla). Our RF classifier achieved a 98.8 ± 1.9% classification accuracy among the six species in pure populations and 98.4% for three species in an artificially mixed population. Feature importance scores against each wavenumber reveal that the presence of carotenoids and structure of membrane lipids play key roles in distinguishing the prokaryotic species. We also find unique Raman markers for an ammonia-oxidizing archaeon. Our approach with moderate data pretreatment and intuitive visualization of feature importance is easy to use for non-spectroscopists, and thus offers microbiologists a new single-cell tool for shedding light on microbial dark matter.

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Random forest models classify prokaryotic species with high accuracy of >98%

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Both bacteria and archaea are classified using minimally preprocessed Raman data

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Feature importance reveals what biomolecules contribute to species classification

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Raman marker bands for some archaeal species are discovered

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.

Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features

This study aimed to evaluate the differences in the Raman spectra of nine clinical species of bacteria isolated from infections (three Gram-positive and six Gram-negative species), correlating the spectra with the chemical composition of each species and to develop a classification model through discriminant analysis to categorize each bacterial strain using the peaks with the most significant differences. Bacteria were cultured in Mueller Hinton agar and a sample of biomass was harvested and placed in an aluminum sample holder. A total of 475 spectra from 115 different strains were obtained through a dispersive Raman spectrometer (830 nm) with exposure time of 50 s. The intensities of the peaks were evaluated by one-way analysis of variance (ANOVA) and the peaks with significant differences were related to the differences in the biochemical composition of the strains. Discriminant analysis based on quadratic distance applied to the peaks with the most significant differences and partial least squares applied to the whole spectrum showed 89.5% and 90.1% of global accuracy, respectively, for classification of the spectra in all the groups. Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.

Molecular Mechanisms Underpinning Aggregation in Acidiphilium sp. C61 Isolated from Iron-Rich Pelagic Aggregates

Iron-rich pelagic aggregates (iron snow) are hot spots for microbial interactions. Using iron snow isolates, we previously demonstrated that the iron-oxidizer Acidithrix sp. C25 triggers Acidiphilium sp. C61 aggregation by producing the infochemical 2-phenethylamine (PEA). Here, we showed slightly enhanced aggregate formation in the presence of PEA on different Acidiphilium spp. but not other iron-snow microorganisms, including Acidocella sp. C78 and Ferrovum sp. PN-J47. Next, we sequenced the Acidiphilium sp. C61 genome to reconstruct its metabolic potential. Pangenome analyses of Acidiphilium spp. genomes revealed the core genome contained 65 gene clusters associated with aggregation, including autoaggregation, motility, and biofilm formation. Screening the Acidiphilium sp. C61 genome revealed the presence of autotransporter, flagellar, and extracellular polymeric substances (EPS) production genes. RNA-seq analyses of Acidiphilium sp. C61 incubations (+/− 10 µM PEA) indicated genes involved in energy production, respiration, and genetic processing were the most upregulated differentially expressed genes in the presence of PEA. Additionally, genes involved in flagellar basal body synthesis were highly upregulated, whereas the expression pattern of biofilm formation-related genes was inconclusive. Our data shows aggregation is a common trait among Acidiphilium spp. and PEA stimulates the central cellular metabolism, potentially advantageous in aggregates rapidly falling through the water column.

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay.

Toward Better Understanding of EBPR Systems via Linking Raman-Based Phenotypic Profiling with Phylogenetic Diversity

This study reports a proof-of concept study to demonstrate the novel approach of phenotyping microbial communities in enhanced biological phosphorus removal (EBPR) systems using single cell Raman microspectroscopy and link it with phylogentic structures. We use hierarchical clustering analysis (HCA) of single-cell Raman spectral fingerprints and intracellular polymer signatures to separate and classify the functionally relevant populations in EBPR systems, namely polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), as well as other microbial populations. We then investigated the link between Raman-based community phenotyping and 16S rRNA gene-based phylogenetic characterization of four lab-scale EBPR systems with varying solid retention time (SRT) to gain insights into possible genotype-function relationships. Combined and simultaneous phylogenetic and phenotypic evaluation of EBPR ecosystems revealed SRT-dependent phylogenetic and phenotypic characteristics of the PAOs and GAOs, and their association with EBPR performance. The phenotypic diversity and plasticity of PAO populations, which otherwise could not be obtained with phylogenetic analysis alone, showed complex but potentially crucial association with EBPR process stability.

Bacterial Community and PHB-Accumulating Bacteria Associated with the Wall and Specialized Niches of the Hindgut of the Forest Cockchafer (Melolontha hippocastani)

A characterization of the bacterial community of the hindgut wall of two larval and the adult stages of the forest cockchafer (Melolontha hippocastani) was carried out using amplicon sequencing of the 16S rRNA gene fragment. We found that, in second-instar larvae, Caulobacteraceae and Pseudomonadaceae showed the highest relative abundances, while in third-instar larvae, the dominant families were Porphyromonadaceae and Bacteroidales-related. In adults, an increase of the relative abundance of Bacteroidetes, Proteobacteria (γ- and δ- classes) and the family Enterococcaceae (Firmicutes) was observed. This suggests that the composition of the hindgut wall community may depend on the insect’s life stage. Additionally, specialized bacterial niches hitherto very poorly described in the literature were spotted at both sides of the distal part of the hindgut chamber. We named these structures “pockets.” Amplicon sequencing of the 16S rRNA gene fragment revealed that the pockets contained a different bacterial community than the surrounding hindgut wall, dominated by Alcaligenaceae and Micrococcaceae-related families. Poly-β-hydroxybutyrate (PHB) accumulation in the pocket was suggested in isolated Achromobacter sp. by Nile Blue staining, and confirmed by gas chromatography–mass spectrometry analysis (GC-MS) on cultured bacterial mass and whole pocket tissue. Raman micro-spectroscopy allowed to visualize the spatial distribution of PHB accumulating bacteria within the pocket tissue. The presence of this polymer might play a role in the colonization of these specialized niches.

Quantitative Raman Spectroscopy Analysis of Polyhydroxyalkanoates Produced by Cupriavidus necator H16

We report herein on the application of Raman spectroscopy to the rapid quantitative analysis of polyhydroxyalkanoates (PHAs), biodegradable polyesters accumulated by various bacteria. This theme was exemplified for quantitative detection of the most common member of PHAs, poly(3-hydroxybutyrate) (PHB) in Cupriavidus necator H16. We have identified the relevant spectral region (800–1800 cm⁻¹) incorporating the Raman emission lines exploited for the calibration of PHB (PHB line at 1736 cm⁻¹) and for the selection of the two internal standards (DNA at 786 cm⁻¹ and Amide I at 1662 cm⁻¹). In order to obtain quantitative data for calibration of intracellular content of PHB in bacterial cells reference samples containing PHB amounts—determined by gas chromatography—from 12% to 90% (w/w) were used. Consequently, analytical results based on this calibration can be used for fast and reliable determination of intracellular PHB content during biotechnological production of PHB since the whole procedure—from bacteria sampling, centrifugation, and sample preparation to Raman analysis—can take about 12 min. In contrast, gas chromatography analysis takes approximately 8 h.

Single-cell level methods for studying the effect of antibiotics on bacteria during infection

Considerable evidence about phenotypic heterogeneity among bacteria during infection has accumulated during recent years. This heterogeneity has to be considered if the mechanisms of infection and antibiotic action are to be understood, so we need to implement existing and find novel methods to monitor the effects of antibiotics on bacteria at the single-cell level. This review provides an overview of methods by which this aim can be achieved. Fluorescence label-based methods and Raman scattering as a label-free approach are discussed in particular detail. Other label-free methods that can provide single-cell level information, such as impedance spectroscopy and surface plasmon resonance, are briefly summarized. The advantages and disadvantages of these different methods are discussed in light of a challenging in vivo environment.

Isolation and identification of bacteria by means of Raman spectroscopy

Bacterial detection is a highly topical research area, because various fields of application will benefit from the progress being made. Consequently, new and innovative strategies which enable the investigation of complex samples, like body fluids or food stuff, and improvements regarding the limit of detection are of general interest. Within this review the prospects of Raman spectroscopy as a reliable tool for identifying bacteria in complex samples are discussed. The main emphasis of this work is on important aspects of applying Raman spectroscopy for the detection of bacteria like sample preparation and the identification process. Several approaches for a Raman compatible isolation of bacterial cells have been developed and applied to different matrices. Here, an overview of the limitations and possibilities of these methods is provided. Furthermore, the utilization of Raman spectroscopy for diagnostic purposes, food safety and environmental issues is discussed under a critical view.

Raman spectroscopic differentiation of planktonic bacteria and biofilms

Both biofilm formations as well as planktonic cells of water bacteria such as diverse species of the Legionella genus as well as Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli were examined in detail by Raman microspectroscopy. Production of various molecules involved in biofilm formation of tested species in nutrient-deficient media such as tap water was observed and was particularly evident in the biofilms formed by six Legionella species. Biofilms of selected species of the Legionella genus differ significantly from the planktonic cells of the same organisms in their lipid amount. Also, all Legionella species have formed biofilms that differ significantly from the biofilms of the other tested genera in the amount of lipids they produced. We believe that the significant increase in the synthesis of this molecular species may be associated with the ability of Legionella species to form biofilms. In addition, a combination of Raman microspectroscopy with chemometric approaches can distinguish between both planktonic form and biofilms of diverse bacteria and could be used to identify samples which were unknown to the identification model. Our results provide valuable data for the development of fast and reliable analytic methods based on Raman microspectroscopy, which can be applied to the analysis of tap water-adapted microorganisms without any cultivation step.

Direct identification of clinically relevant bacterial and yeast microcolonies and macrocolonies on solid culture media by Raman spectroscopy

Decreasing turnaround time is a paramount objective in clinical diagnosis. We evaluated the discrimination power of Raman spectroscopy when analyzing colonies from 80 strains belonging to nine bacterial and one yeast species directly on solid culture medium after 24-h (macrocolonies) and 6-h (microcolonies) incubation. This approach, that minimizes sample preparation and culture time, would allow resuming culture after identification to perform downstream antibiotic susceptibility testing. Correct identification rates measured for macrocolonies and microcolonies reached 94.1% and 91.5%, respectively, in a leave-one-strain-out cross-validation mode without any correction for possible medium interference. Large spectral differences were observed between macrocolonies and microcolonies, that were attributed to true biological differences. Our results, conducted on a very diversified panel of species and strains, were obtained by using simple and robust sample preparation and preprocessing procedures, while still confirming published results obtained by using more complex elaborated protocols. Instrumentation is simplified by the use of 532-nm laser excitation yielding a Raman signal in the visible range. It is, to our knowledge, the first side-by-side full classification study of microorganisms in the exponential and stationary phases confirming the excellent performance of Raman spectroscopy for early species-level identification of microorganisms directly from an agar culture.

Identification of water pathogens by Raman microspectroscopy

Legionella species can be found living in water mostly in a viable but nonculturable state or associated with protozoa and complex biofilm formations. Isolation and afterwards identification of these pathogens from environmental samples by using common identification procedures based on cultivation are extremely difficult and prolonged. The development of fast and sensitive method based on the cultivation free identification of bacteria is necessary. In this study Raman microspectroscopy combined with multiclass support vector machines have been used to discriminate between Legionella and other common aquatic bacteria, to distinguish among clinically relevant Legionella species and to classify unknown Raman spectra for a fast and reliable identification. Recorded Raman spectra of the twenty-two Legionella species as well as the Raman spectra of Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa were utilized to build the classification model. Afterwards, independent Raman spectra of eleven species were used to identify them on the basis of the classification model that was created. The present study shows that Raman microspectroscopy can be used as a rapid and reliable method to distinguish between Legionella species recognized as human pathogens and to identify samples which are unknown to the model based on multiclass support vector machines (MC-SVM).

STXM and NanoSIMS investigations on EPS fractions before and after adsorption to goethite

Extracellular polymeric substances (EPS) are expected to be an important source for the formation of mineral-organic associations in soil. Because such formations affect the composition of mobile and immobile organic matter as well as the reactivity of minerals, we investigated the composition of EPS before and after adsorption to goethite. Raman measurements on EPS extracted from Bacillus subtilis distinguished four fractions rich in proteins, polysaccharides, lipids, or lipids and proteins. Scanning transmission X-ray microscopy identified three different EPS-fractions that varied in their composition in proteins, nonaromatic proteins, and polysaccharides. Reaction of EPS with goethite led to a preferential adsorption of lipids and proteins. The organic coverage was heterogeneous, consisting of ~100 × 200 nm large patches of either lipid-rich or protein-rich material. Nanoscale secondary ion mass spectrometry showed a strong S enrichment in aggregates of ~400 nm in the goethite adsorbed EPS. From our simplified model system, we learned that only a small portion (<10%) of EPS was immobilized via adsorption to goethite. This fraction formed a coating of subμm spaced protein-rich and lipid-rich domains, i.e., of two materials which will strongly differ in their reactive sites. This will finally affect further adsorption, the particle mobility and eventually also colloidal stability.

Raman spectroscopy as a potential tool for detection of Brucella spp. in milk

Detection of Brucella, causing brucellosis, is very challenging, since the applied techniques are mostly time-demanding and not standardized. While the common detection system relies on the cultivation of the bacteria, further classical typing up to the biotype level is mostly based on phenotypic or genotypic characteristics. The results of genotyping do not always fit the existing taxonomy, and misidentifications between genetically closely related genera cannot be avoided. This situation gets even worse, when detection from complex matrices, such as milk, is necessary. For these reasons, the availability of a method that allows early and reliable identification of possible Brucella isolates for both clinical and epidemiological reasons would be extremely useful. We evaluated micro-Raman spectroscopy in combination with chemometric analysis to identify Brucella from agar plates and directly from milk: prior to these studies, the samples were inactivated via formaldehyde treatment to ensure a higher working safety. The single-cell Raman spectra of different Brucella, Escherichia, Ochrobactrum, Pseudomonas, and Yersinia spp. were measured to create two independent databases for detection in media and milk. Identification accuracies of 92% for Brucella from medium and 94% for Brucella from milk were obtained while analyzing the single-cell Raman spectra via support vector machine. Even the identification of the other genera yielded sufficient results, with accuracies of >90%. In summary, micro-Raman spectroscopy is a promising alternative for detecting Brucella. The measurements we performed at the single-cell level thus allow fast identification within a few hours without a demanding process for sample preparation.

Droplet formation via flow-through microdevices in Raman and surface enhanced Raman spectroscopy--concepts and applications

This review outlines concepts and applications of droplet formation via flow-through microdevices in Raman and surface enhanced Raman spectroscopy (SERS) as well as the advantages of the approach. Even though the droplet-based flow-through technique is utilized in various fields, the review focuses on implementing droplet-based fluidic systems in Raman and SERS as these highly specific detection methods are of major interest in the field of analytics. With the combination of Raman or SERS with droplet-based fluidics, it is expected to achieve novel opportunities for analytics. Besides the approach of using droplet-based microfluidic devices as a detection platform, the unique properties of flow-through systems for the formation of droplets are capitalized to produce SERS active substrates and to accomplish uniform sample preparation. Within this contribution, previous reported applications on droplet-based flow-through Raman and SERS approaches and the additional benefit with regard to the importance in the field of analytics are considered.