Single-Cell Level Raman Molecular Profiling Reveals the Classification of Growth Phases of Chaetoceros tenuissimus.

Harmful algal blooms (HABs) are a natural phenomenon caused by outbreaks of algae, resulting in serious problems for aquatic ecosystems and the coastal environment. Chaetoceros tenuissimus (C. tenuissimus) is one of the diatoms responsible for HABs. The growth curve of C. tenuissimus can be observed from beginning to end of HABs: therefore, detailed analysis is necessary to characterize each growth phase of C. tenuissimus. It is important to examine the phenotype of each diatom cell individually, as they display heterogeneity even in the same growth phase. Raman spectroscopy is a label-free technique to elucidate biomolecular profiles and spatial information at the cellular level. Multivariate data analysis (MVA) is an efficient method for the analysis of complicated Raman spectra, to identify molecular features. Here, we utilized Raman microspectroscopy to identify the molecular information of each diatom cell, at the single-cell level. The MVA, together with a support vector machine, which is a machine learning technique, allowed the classification of proliferating and nonproliferating cells. The classification includes polyunsaturated fatty acids such as linoleic acid, eicosapentaenoic acid, and docosahexaenoic acid. This study indicated that Raman spectroscopy is an appropriate technique to examine C. tenuissimus at the single-cell level, providing relevant data to assess the correlation between the molecular details obtained from the Raman analysis, at each growth phase.

Mycelial differentiation linked avermectin production in Streptomyces avermitilis studied with Raman imaging.

Streptomyces avermitilis is a gram-positive bacterium that undergoes complex physiological and morphological differentiation during its life cycle, which has implications in secondary metabolite production. Avermectin, produced by S. avermitilis, is widely used as an anthelmintic and insecticidal agent. In this study, we have applied Raman microspectroscopic imaging to elucidate the correlation between production of avermectin and the morphological differentiation in S. avermitilis. We demonstrate distinctive variations in the localization of secondary metabolites at various stages of morphological differentiation. Under solid culture, avermectin was detected in the mycelia formed at the later stages of morphological differentiation (e.g., spore-bearing mycelium and spiral spore chains), but not in the early-stage substrate mycelium. On the contrary, under liquid culture condition, avermectin was found concentrated in the mycelial pellet formed at the early MII stage of differentiation. Furthermore, the chemical profiles of the mycelia were substantially different depending on the culture condition. Raman spectra corresponding to proteins, lipids, and cytochrome were observed in the mycelia irrespective of the stage of morphological differentiation, however, carotenoid was observed under solid culture condition particularly in spore-bearing mycelium and spiral spore chains. KEY POINTS: • Avermectin production is regulated during mycelial differentiation • Liquid and solid culture conditions affects mycelial differentiation • Raman microspectroscopic analysis reveals localization profiles of avermectin.

Raman Microspectroscopy Imaging Analysis of Extracellular Vesicles Biogenesis by Filamentous Fungus Penicilium chrysogenum.

The mechanism of production of extracellular vesicles (EVs) and their molecular contents are of great interest due to their diverse roles in biological systems and are far from being completely understood. Even though cellular cargo releases mediated by EVs have been demonstrated in several cases, their role in secondary metabolite production and release remains elusive. In this study, this aspect is investigated in detail using Raman microspectroscopic imaging. Considerable evidence is provided to suggest that the release of antibiotic penicillin by the filamentous fungus Penicillium chrysogenum involves EVs. Further, the study also reveals morphological modifications of the fungal body during biogenesis, changes in cell composition at the locus of biogenesis, and major molecular contents of the released EVs. The results suggest a possible general role of EVs in the release of antibiotics from the producing organisms.

Deconstruction of Obscure Features in SVD-Decomposed Raman Images from P. chrysogenum Reveals Complex Mixing of Spectra from Five Cellular Constituents.

Raman imaging has transcended in recent times from being an analytical tool to a molecular profiling technique. Biomedical applications of this technique often rely on singular-value decomposition (SVD), principal component analysis (PCA), etc. for data analysis. These methods, however, obliterate the molecular information contained in the original Raman data leading to speculative interpretations based on relative intensities. In the present study, SVD analysis of the Raman images from Penicillium chrysogenum resulted in 11 spectral components and corresponding images with highly distorted spectral features and complex image contrast, respectively. To interpret the SVD results in molecular terms, we have developed a combined multivariate approach. By applying this methodology, we have successfully extracted the contribution of five biomolecular constituents of the P. chrysogenum filamentous cell to the SVD vectors. Molecular interpretability will help SVD/PCA surpass the realm of variance-based classification to a more meaningful molecular domain.

On Selecting a Suitable Spectral Matching Method for Automated Analytical Applications of Raman Spectroscopy.

Raman spectra are molecular structure-specific and hence are employed in applications requiring chemical identification. The advent of efficient handheld and smartphone-based Raman instruments is promoting widespread applications of the technique, which often involve less trained end users. Software modules that enable spectral library searches based on spectral pattern matching is an essential part of such applications. The Raman spectrum recorded by end users will naturally have varying levels of signal to noise (SN), baseline fluctuations, etc., depending on the sample environment. Further, in biological, forensic, food, pharmaceuticals, etc., fields where a vast amount of Raman spectral data is generated, careful removal of background is often impossible. In other words, a 100% match between the library spectrum and user input cannot be often guaranteed or expected. Often, such influences are discounted upon developing mathematical methods for general applications. In this manuscript, we carefully examine how such effects would determine the results of spectral similarity-based library search. We show that several popular mathematical spectral matching approaches give incorrect results under the influence of small changes in the baseline and/or the noise. We also discuss the points to be carefully considered while generating a spectral library. We believe our results will be a guiding note for developing applications of Raman spectroscopy that uses a standard spectral library and mathematical spectral matching.

Detection of Penicillin G Produced by Penicillium chrysogenum with Raman Microspectroscopy and Multivariate Curve Resolution-Alternating Least-Squares Methods.

Raman microspectroscopy is a minimally invasive technique that can identify molecules without labeling. In this study, we demonstrate the detection of penicillin G inside Penicillium chrysogenum KF425 fungal cells. Raman spectra acquired from the fungal cells had highly overlapped spectroscopic signatures and hence were analyzed with multivariate curve resolution by alternating least-squares (MCR-ALS) to extract the spectra of individual molecular constituents. In addition to detecting spatial distribution of multiple constituents such as proteins and lipids inside the fungal body, we could also observe the subcellular localization of penicillin G. This methodology has the potential to be employed in screening the production of bioactive compounds by microorganisms.