Raman spectral analysis in the CHx-stretching region as a guiding beacon for non-targeted, disruption-free monitoring of germination and biofilm formation in the green seaweed Ulva.

Raman spectroscopy was used to study the complex interactions and morphogenesis of the green seaweed Ulva (Chlorophyta) and its associated bacteria under controlled conditions in a reductionist model system. Integrating multiple imaging techniques contributes to a more comprehensive understanding of these biological processes. Therefore, Raman spectroscopy was introduced as a non-invasive, label-free tool for examining chemical information of the tripartite community Ulva mutabilis-Roseovarius sp.-Maribacter sp. The study explored cell differentiation, cell wall protrusion, and bacterial-macroalgae interactions of intact algal thalli. Using Raman spectroscopy, the analysis of the CHx-stretching wavenumber region distinguished spatial regions in Ulva germination and cellular malformations under axenic conditions and upon inoculation with a specific bacterium in bipartite communities. The spectral information was used to guide in-depth analyses within the fingerprint region and to identify substance classes such as proteins, lipids, and polysaccharides, including evidence for ulvan found in cell wall protrusions.

A polyyne toxin produced by an antagonistic bacterium blinds and lyses a Chlamydomonad alga.

Algae are key contributors to global carbon fixation and form the basis of many food webs. In nature, their growth is often supported or suppressed by microorganisms. The bacterium Pseudomonas protegens Pf-5 arrests the growth of the green unicellular alga Chlamydomonas reinhardtii, deflagellates the alga by the cyclic lipopeptide orfamide A, and alters its morphology [P. Aiyar et al., Nat. Commun. 8, 1756 (2017)]. Using a combination of Raman microspectroscopy, genome mining, and mutational analysis, we discovered a polyyne toxin, protegencin, which is secreted by P. protegens, penetrates the algal cells, and causes destruction of the carotenoids of their primitive visual system, the eyespot. Together with secreted orfamide A, protegencin thus prevents the phototactic behavior of C. reinhardtii A mutant of P. protegens deficient in protegencin production does not affect growth or eyespot carotenoids of C. reinhardtii Protegencin acts in a direct and destructive way by lysing and killing the algal cells. The toxic effect of protegencin is also observed in an eyeless mutant and with the colony-forming Chlorophyte alga Gonium pectorale These data reveal a two-pronged molecular strategy involving a cyclic lipopeptide and a conjugated tetrayne used by bacteria to attack select Chlamydomonad algae. In conjunction with the bloom-forming activity of several chlorophytes and the presence of the protegencin gene cluster in over 50 different Pseudomonas genomes [A. J. Mullins et al., bioRxiv [Preprint] (2021). https://www.biorxiv.org/content/10.1101/2021.03.05.433886v1 (Accessed 17 April 2021)], these data are highly relevant to ecological interactions between Chlorophyte algae and Pseudomonadales bacteria.

Multimodal Molecular Imaging and Identification of Bacterial Toxins Causing Mushroom Soft Rot and Cavity Disease.

Soft rot disease of edible mushrooms leads to rapid degeneration of fungal tissue and thus severely affects farming productivity worldwide. The bacterial mushroom pathogen Burkholderia gladioli pv. agaricicola has been identified as the cause. Yet, little is known about the molecular basis of the infection, the spatial distribution and the biological role of antifungal agents and toxins involved in this infectious disease. We combine genome mining, metabolic profiling, MALDI-Imaging and UV Raman spectroscopy, to detect, identify and visualize a complex of chemical mediators and toxins produced by the pathogen during the infection process, including toxoflavin, caryoynencin, and sinapigladioside. Furthermore, targeted gene knockouts and in vitro assays link antifungal agents to prevalent symptoms of soft rot, mushroom browning, and impaired mycelium growth. Comparisons of related pathogenic, mutualistic and environmental Burkholderia spp. indicate that the arsenal of antifungal agents may have paved the way for ancestral bacteria to colonize niches where frequent, antagonistic interactions with fungi occur. Our findings not only demonstrate the power of label-free, in vivo detection of polyyne virulence factors by Raman imaging, but may also inspire new approaches to disease control.

FLIM data analysis based on Laguerre polynomial decomposition and machine-learning.

SIGNIFICANCE: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. AIM: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). APPROACH: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. RESULTS: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components. CONCLUSIONS: The ML-based approach shows great performance in FLIM data analysis.

COVID-19 Diagnostics: Past, Present, and Future.

In winter of 2020, SARS-CoV-2 emerged as a global threat, impacting not only health but also financial and political stability. To address the societal need for monitoring the spread of SARS-CoV-2, many existing diagnostic technologies were quickly adapted to detect SARS-CoV-2 RNA and antigens as well as the immune response, and new testing strategies were developed to accelerate time-to-decision. In parallel, the infusion of research support accelerated the development of new spectroscopic methods. While these methods have significantly reduced the impact of SARS-CoV-2 on society when coupled with behavioral changes, they also lay the groundwork for a new generation of platform technologies. With several epidemics on the horizon, such as the rise of antibiotic-resistant bacteria, the ability to quickly pivot the target pathogen of this diagnostic toolset will continue to have an impact.

Non-invasive Imaging Techniques: From Histology to In Vivo Imaging : Chapter of Imaging in Oncology.

In this chapter, we will introduce and review molecular-sensitive imaging techniques, which close the gap between ex vivo and in vivo analysis. In detail, we will introduce spontaneous Raman spectral imaging, coherent anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS), second-harmonic generation (SHG) and third-harmonic generation (THG), two-photon excited fluorescence (TPEF), and fluorescence lifetime imaging (FLIM). After reviewing these imaging techniques, we shortly introduce chemometric methods and machine learning techniques, which are needed to use these imaging techniques in diagnostic applications.

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.

Raman spectroscopy-based identification of toxoid vaccine products.

Vaccines are complex biomedicines. Manufacturing is time consuming and requires a high level of quality control (QC) to guarantee consistent safety and potency. An increasing global demand has led to the need to reduce time and cost of manufacturing. The evolving concepts for QC and the upcoming threat of falsification of biomedicines define a new need for methods that allow the fast and reliable identification of vaccines. Raman spectroscopy is a non-destructive technology already established in QC of classical medicines. We hypothesized that Raman spectroscopy could be used for identification and differentiation of vaccine products. Raman maps obtained from air-dried samples of combination vaccines containing antigens from tetanus, diphtheria and pertussis (DTaP vaccines) were summarized to compile product-specific Raman signatures. Sources of technical variance were emphasized to evaluate the robustness and sensitivity in downstream data analysis. The data management approach corrects for spatial inhomogeneities in the dried sample while offering a proper representation of the original samples inherent chemical signature. Reproducibility of the identification was validated by a leave-one-replicate-out cross-validation. The results highlighted the high specificity and sensitivity of Raman measurements in identifying DTaP vaccine products. The results pave the way for further exploitation of the Raman technology for identification of vaccines in batch release and cases of suspected falsification.

The application of UV resonance Raman spectroscopy for the differentiation of clinically relevant Candida species.

Candida-related infections have become a major problem in hospitals. The species identification of yeast is the prerequisite for the initiation of adequate antifungal therapy. In the present study, the connection between inherent UV resonance Raman (RR) spectral profiles of Candida species and taxonomic differences was investigated for the first time. UV RR in combination with statistical modeling was applied to extract taxonomic information from the spectral fingerprints for subsequent differentiation. The identification accuracies of independent batch cultures were determined by applying a leave-one-batch-out cross validation. The quality of differentiation can be divided into three levels. Within a defined taxonomic group comprising the species C. glabrata, C. guilliermondii, and C. haemulonii, the identification accuracy was low. On the next level, the identification results of C. albicans and C. tropicalis were characterized by high sensitivities of 98 and 95% but simultaneously challenged by false-positive predictions due to the misallocation of C. spherica (as C. albicans) and C. viswanathii (as C. tropicalis). The highest level of identification accuracies was reached for the species C. dubliniensis, C. krusei, C. africana, C. novergica, and C. parapsilosis. Reliable identification results were observed with accuracies ranging from 93 up to 100%. The species allocation based on the UV RR spectral profiles could be reproduced by the identification of independent batch cultures. We conclude that the introduced spectroscopic approach is capable of transforming the high-dimensional UV RR data of Candida species into clinically useful decision parameters. Graphical abstract.

The interaction of an amino-modified ZrO2 nanomaterial with macrophages-an in situ investigation by Raman microspectroscopy.

Metal oxide nanoparticles (NP) are applied in the fields of biomedicine, pharmaceutics, and in consumer products as textiles, cosmetics, paints, or fuels. In this context, the functionalization of the NP surface is a common method to modify and modulate the product performance. A chemical surface modification of NP such as an amino-functionalization can be used to achieve a positively charged and hydrophobic surface. Surface functionalization is known to affect the interaction of nanomaterials (NM) with cellular macromolecules and the responses of tissues or cells, like the uptake of particles by phagocytic cells. Therefore, it is important to assess the possible risk of those modified NP for human health and environment. By applying Raman microspectroscopy, we verified in situ the interaction of amino-modified ZrO2 NP with cultivated macrophages. The results demonstrated strong adhesion properties of the NP to the cell membrane and internalization into the cells. The intracellular localization of the NP was visualized via Raman depth scans of single cells. After the cells were treated with sodium azide (NaN3) and 2-deoxy-glucose to inhibit the phagocytic activity, NP were still detected inside cells to comparable percentages. The observed tendency of amino-modified ZrO2 NP to interact with the cultivated macrophages may influence membrane integrity and cellular functions of alveolar macrophages in the respiratory system. Graphical abstract Detection of ZrO2 NM at subcellular level.

ZrO2 nanoparticles labeled via a native protein corona: detection by fluorescence microscopy and Raman microspectroscopy in rat lungs.

ZrO2 nanoparticles are frequently used in composite materials such as dental fillers from where they may be released and inhaled upon polishing and grinding. Since the overall distribution of ZrO2 NP inside the lung parenchyma can hardly be observed by routine histology, here a labeling with a fluorphore was used secondary to the adsorption of serum proteins. Particles were then intratracheally instilled into rat lungs. After 3 h fluorescent structures consisted of agglomerates scattered throughout the lung parenchyma, which were mainly concentrated in alveolar macrophages after 3 d. A detection method based on Raman microspectroscopy was established to investigate the chemical composition of those fluorescent structures in detail. Raman measurements were arranged such that no spectral interference with the protein-bound fluorescence label was evident. Applying chemometrical methods, Raman signals of the ZrO2 nanomaterial were co-localized with the fluorescence label, indicating the stability of the nanomaterial-protein-dye complex inside the rat lung. The combination of Raman microspectroscopy and adsorptive fluorescence labeling may, therefore, become a useful tool for studying the localization of protein-coated nanomaterials in cells and tissues.

Shedding light on host niches: label-free in situ detection of Mycobacterium gordonae via carotenoids in macrophages by Raman microspectroscopy.

Macrophages are the primary habitat of pathogenic mycobacteria during infections. Current research about the host-pathogen interaction on the cellular level is still going on. The present study proves the potential of Raman microspectroscopy as a label-free and non-invasive method to investigate intracellular mycobacteria in situ. Therefore, macrophages were infected with Mycobacterium gordonae, a mycobacterium known to cause inflammation linked to intracellular survival in macrophages. Here, we show that Raman maps provided spatial and spectral information about the position of bacteria within determined cell margins of macrophages in two-dimensional scans and in three-dimensional image stacks. Simultaneously, the relative intracellular concentration and distributions of cellular constituents such as DNA, proteins and lipids provided phenotypic information about the infected macrophages. Locations of bacteria outside or close to the outer membrane of the macrophages were notably different in their spectral pattern compared with intracellular once. Furthermore, accumulations of bacteria inside of macrophages exhibit distinct spectral/molecular information because of the chemical composition of the intracellular microenvironment. The data show that the connection of microscopically and chemically gained information provided by Raman microspectroscopy offers a new analytical way to detect and to characterize the mycobacterial infection of macrophages.

Identification of water-conditioned Pseudomonas aeruginosa by Raman microspectroscopy on a single cell level.

The identification of Pseudomonas aeruginosa from samples of bottled natural mineral water by the analysis of subcultures is time consuming and other species of the authentic Pseudomonas group can be a problem. Therefore, this study aimed to investigate the influence of different aquatic environmental conditions (pH, mineral content) and growth phases on the cultivation-free differentiation between water-conditioned Pseudomonas spp. by applying Raman microspectroscopy. The final dataset was comprised of over 7500 single-cell Raman spectra, including the species Pseudomonas aeruginosa, P. fluorescens and P. putida, in order to prove the feasibility of the introduced approach. The collection of spectra was standardized by automated measurements of viable stained bacterial cells. The discrimination was influenced by the growth phase at the beginning of the water adaptation period and by the type of mineral water. Different combinations of the parameters were tested and they resulted in accuracies of up to 85% for the identification of P. aeruginosa from independent samples by applying chemometric analysis.