Interpreting Several Types of Measurements in Bioscience

Köhler, Achim; Hanafi, Mohamed; Bertrand, Dominique; Qannari, El Mostafa; Janbu, Astrid Oust; Møretrø, Trond; Naterstad, Kristine; Martens, Harald

doi:10.1002/9780470283172.ch15

Cited by 14 publications

(16 citation statements)

References 15 publications

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“…The high fraction of explained variance for a single block indicates that the global pattern of the CPCA is providing good explanation for the variation within that block, and vice versa. In correlation loading plots the correlations between global scores (principal components) and vibrational bands are plotted [ 19 , 23 ] ( Fig 6 ). This allows the visualization of correlations between vibrational bands and principal components for all methods in one picture (blocks).…”

Section: Resultsmentioning

confidence: 99%

“…For MGR and SGR measurements, the number of replicate measurements was between 5 and 10 spectra, and it was not uniform for all species. CPCA was performed as described in literature [ 19 , 23 ]. In order to illustrate the relations and variation patterns in sample and variable spaces, score and correlation loading plots were used [ 19 , 24 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

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A Multiscale Vibrational Spectroscopic Approach for Identification and Biochemical Characterization of Pollen

2015

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BackgroundAnalysis of pollen grains reveals valuable information on biology, ecology, forensics, climate change, insect migration, food sources and aeroallergens. Vibrational (infrared and Raman) spectroscopies offer chemical characterization of pollen via identifiable spectral features without any sample pretreatment. We have compared the level of chemical information that can be obtained by different multiscale vibrational spectroscopic techniques.MethodologyPollen from 15 different species of Pinales (conifers) were measured by seven infrared and Raman methodologies. In order to obtain infrared spectra, both reflectance and transmission measurements were performed on ground and intact pollen grains (bulk measurements), in addition, infrared spectra were obtained by microspectroscopy of multigrain and single pollen grain measurements. For Raman microspectroscopy measurements, spectra were obtained from the same pollen grains by focusing two different substructures of pollen grain. The spectral data from the seven methodologies were integrated into one data model by the Consensus Principal Component Analysis, in order to obtain the relations between the molecular signatures traced by different techniques.ResultsThe vibrational spectroscopy enabled biochemical characterization of pollen and detection of phylogenetic variation. The spectral differences were clearly connected to specific chemical constituents, such as lipids, carbohydrates, carotenoids and sporopollenins. The extensive differences between pollen of Cedrus and the rest of Pinaceae family were unambiguously connected with molecular composition of sporopollenins in pollen grain wall, while pollen of Picea has apparently higher concentration of carotenoids than the rest of the family. It is shown that vibrational methodologies have great potential for systematic collection of data on ecosystems and that the obtained phylogenetic variation can be well explained by the biochemical composition of pollen. Out of the seven tested methodologies, the best taxonomical differentiation of pollen was obtained by infrared measurements on bulk samples, as well as by Raman microspectroscopy measurements of the corpus region of the pollen grain. Raman microspectroscopy measurements indicate that measurement area, as well as the depth of focus, can have crucial influence on the obtained data.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Multiscale Vibrational Spectroscopic Approach for Identification and Biochemical Characterization of Pollen

2015

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“…In the correlation loading plots, correlation between variables and scores are displayed. When scores and correlation loading plots are studied together for the same pair of components, variable variation patterns can be directly related to sample variation patterns for the respective components [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

High-Throughput Biochemical Fingerprinting of Saccharomyces cerevisiae by Fourier Transform Infrared Spectroscopy

et al. 2015

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Single-channel optical density measurements of population growth are the dominant large scale phenotyping methodology for bridging the gene-function gap in yeast. However, a substantial amount of the genetic variation induced by single allele, single gene or double gene knock-out technologies fail to manifest in detectable growth phenotypes under conditions readily testable in the laboratory. Thus, new high-throughput phenotyping technologies capable of providing information about molecular level consequences of genetic variation are sorely needed. Here we report a protocol for high-throughput Fourier transform infrared spectroscopy (FTIR) measuring biochemical fingerprints of yeast strains. It includes high-throughput cultivation for FTIR spectroscopy, FTIR measurements and spectral pre-treatment to increase measurement accuracy. We demonstrate its capacity to distinguish not only yeast genera, species and populations, but also strains that differ only by a single gene, its excellent signal-to-noise ratio and its relative robustness to measurement bias. Finally, we illustrated its applicability by determining the FTIR signatures of all viable Saccharomyces cerevisiae single gene knock-outs corresponding to lipid biosynthesis genes. Many of the examined knock-out strains showed distinct, highly reproducible FTIR phenotypes despite having no detectable growth phenotype. These phenotypes were confirmed by conventional lipid analysis and could be linked to specific changes in lipid composition. We conclude that the introduced protocol is robust to noise and bias, possible to apply on a very large scale, and capable of generating biologically meaningful biochemical fingerprints that are strain specific, even when strains lack detectable growth phenotypes. Thus, it has a substantial potential for application in the molecular functionalization of the yeast genome.

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“…As above, regular PCA can be used to find the solution. It should be mentioned that several variants of the Tucker modelling have been developed for improved interpretation (Kohler et al, 2008).…”

Section: Tucker-1 and Tucker-2 Modelsmentioning

confidence: 99%

Identifying outlying assessors in sensory profiling using fuzzy clustering and multi-block methodology

Dahl

Næs

2009

Food Quality and Preference

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Interpreting Several Types of Measurements in Bioscience

Cited by 14 publications

References 15 publications

A Multiscale Vibrational Spectroscopic Approach for Identification and Biochemical Characterization of Pollen

A Multiscale Vibrational Spectroscopic Approach for Identification and Biochemical Characterization of Pollen

High-Throughput Biochemical Fingerprinting of Saccharomyces cerevisiae by Fourier Transform Infrared Spectroscopy

Identifying outlying assessors in sensory profiling using fuzzy clustering and multi-block methodology

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