Dataset Information

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis.

ABSTRACT: Fungal cell walls are medically important since they represent a drug target site for antifungal medication. So far there is no method to directly visualize structurally similar cell wall components such as ?-glucan, ?-glucan and mannan with high specificity, especially in a label-free manner. In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide components simultaneously at the single cell level. Our results show that vegetative cell and ascus walls are made up of both ?- and ?-glucans while spore wall is exclusively made of ?-glucan. Co-localization studies reveal the absence of mannans in ascus wall but are distributed primarily in spores. Such detailed picture is believed to further enhance our understanding of the dynamic spore wall architecture, eventually leading to advancements in drug discovery and development in the near future.

SUBMITTER: Noothalapati H

PROVIDER: S-EPMC4899791 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis.

Noothalapati Hemanth H Sasaki Takahiro T Kaino Tomohiro T Kawamukai Makoto M Ando Masahiro M Hamaguchi Hiro-O HO Yamamoto Tatsuyuki T

Scientific reports 20160609

Fungal cell walls are medically important since they represent a drug target site for antifungal medication. So far there is no method to directly visualize structurally similar cell wall components such as α-glucan, β-glucan and mannan with high specificity, especially in a label-free manner. In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide com ...[more]

PMID: 27278218

Similar Datasets

Project description:BackgroundPlant cell walls are nanocomposites based on cellulose microfibrils embedded in a matrix of polysaccharides and aromatic polymers. They are optimized for different functions (e.g. mechanical stability) by changing cell form, cell wall thickness and composition. To reveal the composition of plant tissues in a non-destructive way on the microscale, Raman imaging has become an important tool. Thousands of Raman spectra are acquired, each one being a spatially resolved molecular fingerprint of the plant cell wall. Nevertheless, due to the multicomponent nature of plant cell walls, many bands are overlapping and classical band integration approaches often not suitable for imaging. Multivariate data analysing approaches have a high potential as the whole wavenumber region of all thousands of spectra is analysed at once.ResultsThree multivariate unmixing algorithms, vertex component analysis, non-negative matrix factorization and multivariate curve resolution-alternating least squares were applied to find the purest components within datasets acquired from micro-sections of spruce wood and Arabidopsis. With all three approaches different cell wall layers (including tiny S1 and S3 with 0.09-0.14 µm thickness) and cell contents were distinguished and endmember spectra with a good signal to noise ratio extracted. Baseline correction influences the results obtained in all methods as well as the way in which algorithm extracts components, i.e. prioritizing the extraction of positive endmembers by sequential orthogonal projections in VCA or performing a simultaneous extraction of non-negative components aiming at explaining the maximum variance in NMF and MCR-ALS. Other constraints applied (e.g. closure in VCA) or a previous principal component analysis filtering step in MCR-ALS also contribute to the differences obtained.ConclusionsVCA is recommended as a good preliminary approach, since it is fast, does not require setting many input parameters and the endmember spectra result in good approximations of the raw data. Yet the endmember spectra are more correlated and mixed than those retrieved by NMF and MCR-ALS methods. The latter two give the best model statistics (with lower lack of fit in the models), but care has to be taken about overestimating the rank as it can lead to artificial shapes due to peak splitting or inverted bands.

Dataset Information

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis.

Publications

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets