Title: Bessel-beam hyperspectral CARS microscopy with sparse sampling: Enabling high-content high-throughput label-free quantitative chemical imaging

Citation
Masia F, Pope I, Watson PD, et al. (2018). Bessel-beam hyperspectral CARS microscopy with sparse sampling: Enabling high-content high-throughput label-free quantitative chemical imaging. Cardiff University. http://doi.org/10.17035/d.2017.0038287344


Access Rights: Data can be made freely available subject to attribution
Access Method: Click to email a request for this data to opendata@cardiff.ac.uk

Dataset Details
Publisher: Cardiff University
Date (year) of data becoming publicly available: 2018
Coverage start date: 01/01/2012
Coverage end date: 01/01/2018
Data format: .tif, .dat,
Software Required: For images .tif any software that opens/imports TIFF files (e.g. PowerPoint, or the freely available ImageJ).
For numerical .dat any software that opens/imports ascii files (e.g. Excel, Origin, Notepad)

Estimated total storage size of dataset: Less than 1 terabyte
Number of Files In Dataset: 23
DOI: 10.17035/d.2017.0038287344

Description

Microscopy based high-content and high-throughput analysis of cellular systems plays a central role in drug discovery. However, for contrast and specificity, the majority of assays require a fluorescent readout which always comes with the risk of alteration of the true biological conditions.

These data show the development of a novel label-free imaging platform which combines chemically-specific hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy with sparse sampling and Bessel beam illumination.

The data show hyperspectral CARS images in fixed cells, that that were analyzed using a quantitative data analysis pipeline which retrieves Raman-like susceptibility spectra and factorizes them into water, proteins
and lipid chemical components with associated concentration maps in vol:vol units.

To demonstrate the practical applicability of the method, we addressed a critical side effect in drug screens, namely drug-induced lipid storage within hepatic tissue. The data show the effect of 15 combinations of drugs and neutral lipids added to human HepG2 liver cells.

The data sets consists of images and numerical data. 

Images consist of two groups: experimental optical microscopy images and calculated images. Experimental microscopy images show i) a test sample made of a polystyrene bead, ii) HeLa cells and iii) HepG2 liver cells. Images are obtained using CARS microscopy (for i, ii, and iii), Differential Interference Contrast microscopy (DIC) (for ii and iii), and wide-field epifluorescence microscopy (for ii). Experimental CARS images are shown as spatial maps of the concentration of chemical components. 

Calculated images show the field intensity distribution at different positions along the beam path, to exemplify the Bessel beam formation.

Numerical data consist of:

1) Calculated intensity distributions along the axial position across the focus of a microscope objective. The data set is an ascii file with X and Y columns. X is the axial position. Y is the field intensity.

2) Vibrational Raman-like spectra of chemical components obtained from CARS hyperspectral images. The data set is an ascii file with X and Y columns. X is the wavenumber and Y is CARS susceptibility (imaginary part).

3) Spatial profiles of concentrations of chemical components obtained from CARS hyperspectral images. The data set is an ascii file with X and Y columns. X is the spatial coordinate and Y is concentration (vol:vol).

4) Histograms of the concentrations of chemical components. The data set is an ascii file with X and Y columns. X is the concentration value (vol:vol). Y is the occurrence. 

5) Autocorrelation of the concentration maps as a function of the distance from the origin. The data set is an ascii file with X and Y columns. X is the distance. Y is the autocorrelation value.

6) Ratio of integrated susceptibility spectra in specific wavenumber regions. The data set is an ascii table with X, Y, and DY columns. X is the label of the considered ratio. Y is the value of the ratio given as a mean over 10 repetitions in the analysis, and DY is the standard deviation. 

7) Results of drug screening using binary classification with a support vector machine (SVM) discriminative algorithm. The data set is an ascii table with X and Y columns. X is the normalized distance to the hyperplane returned by the classification algorithm, Y is the label of the specific drug.

Research results based upon these data are published at http://doi.org/10.1021/acs.analchem.7b04039



Keywords

Coherent Anti-Stokes Raman Scattering (CARS)

Research Areas

Related Projects

Last updated on 2019-23-07 at 11:26