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INDIANA: An in-cell diffusion method to characterize the size, abundance and permeability of cells.

ABSTRACT: NMR and MRI diffusion experiments contain information describing the shape, size, abundance, and membrane permeability of cells although extracting this information can be challenging. Here we present the INDIANA (IN-cell DIffusion ANAlysis) method to simultaneously and non-invasively measure cell abundance, effective radius, permeability and intrinsic relaxation rates and diffusion coefficients within the inter- and intra-cellular populations. The method couples an experimental dataset comprising stimulated-echo diffusion measurements, varying both the gradient strength and the diffusion delay, together with software to fit a model based on the Kärger equations to robustly extract the relevant parameters. A detailed error analysis is presented by comparing the results from fitting simulated data from Monte Carlo simulations, establishing its effectiveness. We note that for parameters typical of mammalian cells the approach is particularly effective, and the shape of the underlying cells does not unduly affect the results. Finally, we demonstrate the performance of the experiment on systems of suspended yeast and mammalian cells. The extracted parameters describing cell abundance, size, permeability and relaxation are independently validated.

SUBMITTER: Karunanithy G

PROVIDER: S-EPMC7611012 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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INDIANA: An in-cell diffusion method to characterize the size, abundance and permeability of cells.

Karunanithy Gogulan G Wheeler Richard J RJ Tear Louise R LR Farrer Nicola J NJ Faulkner Stephen S Baldwin Andrew J AJ

Journal of magnetic resonance (San Diego, Calif. : 1997) 20190106

NMR and MRI diffusion experiments contain information describing the shape, size, abundance, and membrane permeability of cells although extracting this information can be challenging. Here we present the INDIANA (IN-cell DIffusion ANAlysis) method to simultaneously and non-invasively measure cell abundance, effective radius, permeability and intrinsic relaxation rates and diffusion coefficients within the inter- and intra-cellular populations. The method couples an experimental dataset comprisi ...[more]

PMID: 30904779

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Project description:BackgroundIt is important to understand the composition of cell type and its proportion in intact tissues, as changes in certain cell types are the underlying cause of disease in humans. Although compositions of cell type and ratios can be obtained by single-cell sequencing, single-cell sequencing is currently expensive and cannot be applied in clinical studies involving a large number of subjects. Therefore, it is useful to apply the bulk RNA-Seq dataset and the single-cell RNA dataset to deconvolute and obtain the cell type composition in the tissue.ResultsBy analyzing the existing cell population prediction methods, we found that most of the existing methods need the cell-type-specific gene expression profile as the input of the signature matrix. However, in real applications, it is not always possible to find an available signature matrix. To solve this problem, we proposed a novel method, named DCap, to predict cell abundance. DCap is a deconvolution method based on non-negative least squares. DCap considers the weight resulting from measurement noise of bulk RNA-seq and calculation error of single-cell RNA-seq data, during the calculation process of non-negative least squares and performs the weighted iterative calculation based on least squares. By weighting the bulk tissue gene expression matrix and single-cell gene expression matrix, DCap minimizes the measurement error of bulk RNA-Seq and also reduces errors resulting from differences in the number of expressed genes in the same type of cells in different samples. Evaluation test shows that DCap performs better in cell type abundance prediction than existing methods.ConclusionDCap solves the deconvolution problem using weighted non-negative least squares to predict cell type abundance in tissues. DCap has better prediction results and does not need to prepare a signature matrix that gives the cell-type-specific gene expression profile in advance. By using DCap, we can better study the changes in cell proportion in diseased tissues and provide more information on the follow-up treatment of diseases.

Project description:Introduction Cannabinoids including cannabidiol (CBD) have attracted enormous interest as bioactive ingredients for various dermatological and/or cosmeceutical uses. However, topical applications of cannabinoids might be limited without a fundamental understanding of their skin permeability. Herein, we aimed to evaluate the skin permeability of CBD and its topical formulations using artificial skin membrane assays. The solubility and stability of CBD in various surfactants that are commonly used in topical applications were also evaluated. Methods CBD and two CBD-incorporated topical formulations (cream and gel) were prepared for this study. Computational predictions (SwissADME and DERMWIN™) and the parallel artificial membrane permeability assay (PAMPA) were used to evaluate the skin permeability of CBD isolate. The Franz cell diffusion (in vitro release testing) assay was used to evaluate the skin permeability of CBD formulations. The solubility and stability of CBD in surfactants were assessed by high-performance liquid chromatography and mass spectrometry analysis. Results CBD isolate showed favorable skin permeability in the SwissADME and DERMWIN™ predictions (−Log Kp of 3.6 and 5.7 cm/s, respectively) and PAMPA (−LogPe value of 5.0 at pH of 6.5 and 7.4). In addition, CBD had higher solubility (378.4 μg/mL) in surfactant Tween 20 as compared to its solubility in polyisobutene. In an acidic environment (pH 5 and 6), Tween 20 maintained the CBD content at 81% and 70% over 30 days, respectively. CBD in the formulations of cream and gel also had moderate skin permeability in the Franz cell diffusion assay. Conclusion Data from artificial membrane-based assays support that CBD is a skin permeable cannabinoid and the permeability and stability of its formulations may be influenced by several factors such as surfactant and pH environment. Findings from our study suggest that CBD may have suitable skin permeability for the development of dermatological and/or cosmeceutical applications but further studies using in vivo models are warranted to confirm this.

Dataset Information

INDIANA: An in-cell diffusion method to characterize the size, abundance and permeability of cells.

Publications

INDIANA: An in-cell diffusion method to characterize the size, abundance and permeability of cells.

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