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Fourier transform infrared with attenuated total reflectance applied to the discrimination of freshwater planktonic coccoid green microalgae.

ABSTRACT: Despite the recent advances on fine taxonomic discrimination in microorganisms, namely using molecular biology tools, some groups remain particularly problematic. Fine taxonomy of green algae, a widely distributed group in freshwater ecosystems, remains a challenge, especially for coccoid forms. In this paper, we propose the use of the Fourier Transform Infrared (FTIR) spectroscopy as part of a polyphasic approach to identify and classify coccoid green microalgae (mainly order Sphaeropleales), using triplicated axenic cultures. The attenuated total reflectance (ATR) technique was tested to reproducibility of IR spectra of the biological material, a primary requirement to achieve good discrimination of microalgal strains. Spectral window selection was also tested, in conjunction with the first derivative treatment of spectra, to determine which regions of the spectrum provided better separation and clustering of strains. The non-metric multidimensional scaling (NMDS), analysis of similarities (ANOSIM) and hierarchical clusters (HCA), demonstrated a correct discrimination and classification of closely related strains of chlorophycean coccoid microalgae, with respect to currently accepted classifications. FTIR-ATR was highly reproducible, and provided an excellent discrimination at the strain level. The best separation was achieved by analyzing the spectral windows of 1500-1200 cm(-1) and 900-675 cm(-1), which differs from those used in previously studies for the discrimination of broad algal groups, and excluding spectral regions related to storage compounds, which were found to give poor discrimination. Furthermore, hierarchical cluster analyses have positioned the strains tested into clades correctly, reproducing their taxonomic orders and families. This study demonstrates that FTIR-ATR has great potential to complement classical approaches for fine taxonomy of coccoid green microalgae, though a careful spectrum region selection is needed.

SUBMITTER: de Moraes GP

PROVIDER: S-EPMC4277285 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Fourier transform infrared with attenuated total reflectance applied to the discrimination of freshwater planktonic coccoid green microalgae.

de Moraes Guilherme Pavan GP Vieira Armando Augusto Henriques AA

PloS one 20141226 12

Despite the recent advances on fine taxonomic discrimination in microorganisms, namely using molecular biology tools, some groups remain particularly problematic. Fine taxonomy of green algae, a widely distributed group in freshwater ecosystems, remains a challenge, especially for coccoid forms. In this paper, we propose the use of the Fourier Transform Infrared (FTIR) spectroscopy as part of a polyphasic approach to identify and classify coccoid green microalgae (mainly order Sphaeropleales), u ...[more]

PMID: 25541701

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Project description:In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700-1500 cm-1 region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700-1500 cm-1 region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800-900 cm-1. Exosome spectra in the 1700-1500 and combined 3000-2800 and 1800-900 cm-1 regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800-900 cm-1 region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

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Dataset Information

Fourier transform infrared with attenuated total reflectance applied to the discrimination of freshwater planktonic coccoid green microalgae.

Publications

Fourier transform infrared with attenuated total reflectance applied to the discrimination of freshwater planktonic coccoid green microalgae.

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