Disentangling dynamic changes of multiple cellular components during the yeast cell cycle by in vivo multivariate Raman imaging

Abstract

Cellular processes are intrinsically complex and dynamic, in which a myriad of cellular components including nucleic acids, proteins, membranes, and organelles are involved and undergo spatiotemporal changes. Label-free Raman imaging has proven powerful for studying such dynamic behaviors in vivo and at the molecular level. To construct Raman images, univariate data analysis has been commonly employed, but it cannot be free from uncertainties due to severely overlapped spectral information. Here, we demonstrate multivariate curve resolution analysis for time-lapse Raman imaging of a single dividing yeast cell. A four-dimensional (spectral variable, spatial positions in the two-dimensional image plane, and time sequence) Raman data "hypercube" is unfolded to a two-way array and then analyzed globally using multivariate curve resolution. The multivariate Raman imaging thus accomplished successfully disentangles dynamic changes of both concentrations and distributions of major cellular components (lipids, proteins, and polysaccharides) during the cell cycle of the yeast cell. The results show a drastic decrease in the amount of lipids by ~50% after cell division and uncover a protein-associated component that has not been detected with previous univariate approaches.