Label‐free quantitative chemical imaging and classification analysis of adipogenesis using mouse embryonic stem cells. Issue 7 (19th April 2018)

• Record Type: Journal Article
• Authors: Masia, Francesco; Glen, Adam; Stephens, Phil; Langbein, Wolfgang; Borri, Paola
• Is Part Of: Journal of biophotonics. Volume 11:Issue 7(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-04-19
• Subjects: adipogenesis -- classification analysis -- coherent anti‐Stokes Raman Scattering -- hyperspectral imaging -- stem cells; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201700219
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract : Stem cells have received much attention recently for their potential utility in regenerative medicine. The identification of their differentiated progeny often requires complex staining procedures, and is challenging for intermediary stages which are a priori unknown. In this work, the ability of label‐free quantitative coherent anti‐Stokes Raman scattering (CARS) micro‐spectroscopy to identify populations of intermediate cell states during the differentiation of murine embryonic stem cells into adipocytes is assessed. Cells were imaged at different days of differentiation by hyperspectral CARS, and images were analysed with an unsupervised factorization algorithm providing Raman‐like spectra and spatially resolved maps of chemical components. Chemical decomposition combined with a statistical analysis of their spatial distributions provided a set of parameters that were used for classification analysis. The first 2 principal components of these parameters indicated 3 main groups, attributed to undifferentiated cells, cells differentiated into committed white pre‐adipocytes, and differentiating cells exhibiting a distinct protein globular structure with adjacent lipid droplets. An unsupervised classification methodology was developed, separating undifferentiated cell from cells in other stages, using a novel method to estimate the optimal number of clusters. The proposed unsupervised classification pipeline of hyperspectral CARS data offers a promising new tool for automated cell sorting in lineage analysis. Abstract : We investigated the differentiation of murine embryonic stem cells into adipocytes using label‐free hyperspectral coherent anti‐Stokes Raman scattering microspectroscopy. We retrieved quantitative concentrations and spectra of proteins, lipids and aqueous chemical components. By using an unsupervised classification method, we identified undifferentiated cells, committed white pre‐adipocytes and intermediate differentiating cells showing peculiar protein globular structures. Correlation analysis suggests that the protein globular structures are formed during adipogenesis, and initiate lipid droplet formation.