Extensive primary production promoted the recovery of the Ediacaran Shuram excursion

Member IV of the Ediacaran Doushantuo Formation records the recovery from the most negative carbon isotope excursion in Earth history. However, the main biogeochemical controls that ultimately drove this recovery have yet to be elucidated. Here, we report new carbon and nitrogen isotope and concentration data from the Nanhua Basin (South China), where δ13C values of carbonates (δ13Ccarb) rise from − 7‰ to −1‰ and δ15N values decrease from +5.4‰ to +2.3‰. These trends are proposed to arise from a new equilibrium in the C and N cycles where primary production overcomes secondary production as the main source of organic matter in sediments. The enhanced primary production is supported by the coexisting Raman spectral data, which reveal a systematic difference in kerogen structure between depositional environments. Our new observations point to the variable dominance of distinct microbial communities in the late Ediacaran ecosystems, and suggest that blooms of oxygenic phototrophs modulated the recovery from the most negative δ13Ccarb excursion in Earth history.

Raman Characterization of Fungal DHN and DOPA Melanin Biosynthesis Pathways

Fungal melanins represent a resource for important breakthroughs in industry and medicine, but the characterization of their composition, synthesis, and structure is not well understood. Raman spectroscopy is a powerful tool for the elucidation of molecular composition and structure. In this work, we characterize the Raman spectra of wild-type Aspergillus fumigatus and Cryptococcus neoformans and their melanin biosynthetic mutants and provide a rough “map” of the DHN (A. fumigatus) and DOPA (C. neoformans) melanin biosynthetic pathways. We compare this map to the Raman spectral data of Aspergillus nidulans wild-type and melanin biosynthetic mutants obtained from a previous study. We find that the fully polymerized A. nidulans melanin cannot be classified according to the DOPA pathway; nor can it be solely classified according to the DHN pathway, consistent with mutational analysis and chemical inhibition studies. Our approach points the way forward for an increased understanding of, and methodology for, investigating fungal melanins.

A Novel and Rapid Serum Detection Technology for Non-Invasive Screening of Gastric Cancer Based on Raman Spectroscopy Combined With Different Machine Learning Methods

Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.

Label-free Raman microspectroscopy for identifying virocells

Raman microspectroscopy has been thoroughly used to assess growth dynamics and heterogeneity of prokaryotic cells. Yet, little is known about how the chemistry of individual cells changes during infection with lytic viruses, resulting in so-called virocells. Here, we investigate biochemical changes of bacterial and archaeal cells of three different species in laboratory cultures before and after addition of their respective viruses using single-cell Raman microspectroscopy. By applying multivariate statistics, we identified significant differences in the spectra of single cells and cells after addition of lytic phage (phi6) for Pseudomonas syringae. A general ratio of wavenumbers that contributed the greatest differences in the recorded spectra was defined as an indicator for virocells. Based on reference spectra, this difference is likely attributable to an increase in nucleic acid vs. protein ratio of virocells. This method proved also successful for identification of Bacillus subtilis cells infected with phi29 displaying a decrease in respective ratio but failed for archaeal virocells (Methanosarcina mazei with Methanosarcina Spherical Virus) due to autofluorescence. Multivariate and univariate analyses suggest that Raman spectral data of infected cells can also be used to explore the complex biology behind viral infections of bacteria. Using this method, we confirmed the previously described two-stage infection of P. syringae’s phi6 and that infection of B. subtilis by phi29 results in a stress response within single cells. We conclude that Raman microspectroscopy is a promising tool for chemical identification of Gram-positive and Gram-negative virocells undergoing infection with lytic DNA or RNA viruses.ImportanceViruses are highly diverse biological entities shaping many ecosystems across Earth. Yet, understanding the infection of individual microbial cells and the related biochemical changes remains limited. Using Raman microspectroscopy in conjunction with univariate and unsupervised machine learning approaches, we established a marker for identification of infected Gram-positive and Gram-negative bacteria. This non-destructive, label-free analytical method at single-cell resolution paves the way for future studies geared towards analyzing complex biological changes of virus-infected cells in pure culture and natural ecosystems.

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.

Validation of biosignatures confirms the informative nature of fossil organic Raman spectra

Raman spectroscopy has facilitated rapid progress in the understanding of patterns and processes associated with biomolecule fossilization and revealed the preservation of biological and geological signatures in fossil organic matter. Nonetheless six large-scale statistical studies of Raman spectra of carbonaceous fossils, selected from a number of independent assessments producing similar trends, have been disputed. Alleon et al. (21) applied a wavelet transform analysis in an unconventional way to identify frequency components contributing to two baselined spectra selected from these studies and claimed similarities with a downloaded edge filter transmission spectrum. On the basis of indirect comparisons and qualitative observations they argued that all spectral features detected, including significant mineral peaks, can be equated to edge filter ripples and are therefore artefactual. Alleon et al. (21) extrapolated this conclusion to dispute not only the validity of n>200 spectra in the studies in question, but also the utility of Raman spectroscopy, a well established method, for analysing organic materials in general. Here we test the claims by Alleon et al. (21) using direct spectral comparisons and statistical analyses. We present multiple independent lines of evidence that demonstrate the original, biologically and geologically informative nature of the Raman spectra in question. We demonstrate that the methodological approach introduced by Alleon et al. (21) is unsuitable for assessing the quality of spectra and identifying noise within them. Statistical analyses of large Raman spectral data sets provide a powerful tool in the search for compositional patterns in biomaterials and yield invaluable insights into the history of life.

Analyzing Raman spectral data without separabiliy assumption

Raman spectroscopy is a well established tool for the analysis of vibration spectra, which then allow for the determination of individual substances in a chemical sample, or for their phase transitions. In the time-resolved-Raman-sprectroscopy the vibration spectra of a chemical sample are recorded sequentially over a time interval, such that conclusions for intermediate products (transients) can be drawn within a chemical process. The observed data-matrix M from a Raman spectroscopy can be regarded as a matrix product of two unknown matrices W and H, where the first is representing the contribution of the spectra and the latter represents the chemical spectra. One approach for obtaining W and H is the non-negative matrix factorization. We propose a novel approach, which does not need the commonly used separability assumption. The performance of this approach is shown on a real world chemical example.

Confocal Raman microspectral analysis and imaging of the drug response of osteosarcoma to cisplatin

Confocal Raman microspectral analysis and imaging was used to elucidate the drug response of osteosarcoma (OS) to cisplatin. Raman spectral data were obtained from OS cells that were untreated (UT...

WE-ASCA: The Weighted-Effect ASCA for Analyzing Unbalanced Multifactorial Designs—A Raman Spectra-Based Example

Analyses of multifactorial experimental designs are used as an explorative technique describing hypothesized multifactorial effects based on their variation. The procedure of analyzing multifactorial designs is well established for univariate data, and it is known as analysis of variance (ANOVA) tests, whereas only a few methods have been developed for multivariate data. In this work, we present the weighted-effect ASCA, named WE-ASCA, as an enhanced version of ANOVA-simultaneous component analysis (ASCA) to deal with multivariate data in unbalanced multifactorial designs. The core of our work is to use general linear models (GLMs) in decomposing the response matrix into a design matrix and a parameter matrix, while the main improvement in WE-ASCA is to implement the weighted-effect (WE) coding in the design matrix. This WE-coding introduces a unique solution to solve GLMs and satisfies a constrain in which the sum of all level effects of a categorical variable equal to zero. To assess the WE-ASCA performance, two applications were demonstrated using a biomedical Raman spectral data set consisting of mice colorectal tissue. The results revealed that WE-ASCA is ideally suitable for analyzing unbalanced designs. Furthermore, if WE-ASCA is applied as a preprocessing tool, the classification performance and its reproducibility can significantly improve.

Identification of toxic mold species through Raman spectroscopy of fungal conidia

We use a 785 nm shifted excitation Raman difference (SERDS) technique to measure the Raman spectra of the conidia of 10 mold species of especial toxicological, medical, and industrial importance, including Stachybotrys chartarum, Penicillium chrysogenum, Aspergillus fumigatus, Aspergillus flavus, Aspergillus oryzae, Aspergillus niger, and others. We find that both the pure Raman and fluorescence signals support the hypothesis that for an excitation wavelength of 785 nm the Raman signal originates from the melanin pigments bound within the cell wall of the conidium. In addition, the major features of the pure Raman spectra group into profiles that we hypothesize may be due to differences in the complex melanin biosynthesis pathways. We then combine the Raman spectral data with neural network models to predict species classification with an accuracy above 99%. Finally, the Raman spectral data of all species investigated is made freely available for download and use.