Disentangling Dynamic Changes of Multiple Cellular Components during the Yeast Cell Cycle by <i>in Vivo</i> Multivariate Raman Imaging

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 <i>in vivo</i> 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

Estimating Percent Crystallinity of Polyethylene as a Function of Temperature by Raman Spectroscopy Multivariate Curve Resolution by Alternating Least Squares

We have recently demonstrated a methodology to estimate the percent crystallinity (PC) of polymers directly with Raman spectroscopy and multivariate curve resolution (MCR) by alternating least-squares (ALS). In the MCR-ALS methodology, the Raman spectrum of a semicrystalline polymer is separated into two constituent components (crystalline and molten/amorphous) and their corresponding concentrations. The methodology necessitates that the Raman spectrum at any temperature be a linear combination of two MCR spectral components (one molten and one crystalline). This is true in the case of simple systems such as crystalline pendant alkyl domains in polymers (Samuel et al. <i>Anal. Chem.</i> <b>2016</b>, <i>88</i>, 4644). However, in the case of main chain polymer crystals (e.g., polyethylene), the situation can be complicated owing to several molecular changes in the lattice in addition to conformational reorganizations during melting. Under this circumstance, a simple two-state model may not be adequate and we describe the modifications required to treat such systems, keeping the basic principles of the proposed methodology unchanged. A comparative study with wide-angle X-ray scattering (WAXS) and Raman spectroscopy is also performed to substantiate our findings. In addition to estimating percent crystallinity (PC), our methodology is capable of revealing additional information, such as interchain interactions in crystal lattice, that in principle will help distinguishing polymorphic transformations, subtle changes in lamellar lattice dimensions, and other phase changes in polymers

The concentration dependence of d₂₅-SDS cell lysis efficiency; ++ corresponds to clear cell lysis, +: moderate cell lysis, −: remaining stable.

<p>The concentration dependence of d₂₅-SDS cell lysis efficiency; ++ corresponds to clear cell lysis, +: moderate cell lysis, −: remaining stable.</p

SDS molecules are condensed in a CHL cell.

<p><b>A</b>. The molecular structure of d₂₅-SDS. <b>B</b>. Im[χ⁽³⁾] spectrum obtained from one point of a CHL cell indicated as the cross in the inset several minutes after the addition of d₂₅-SDS. <b>C</b>. The expanded spectrum of <b>B</b>. <b>D</b>. Im[χ⁽³⁾] spectrum of 1% d₂₅- SDS aqueous solution. The exposure time for <b>B</b>–<b>D</b> is 50 msec and <b>B</b>–<b>D</b> are measured under the same experimental condition.</p

Accumulation of SDS in a CHL cell and subsequent cellular death.

<p><b>A</b>. Time-resolved Im[χ⁽³⁾] spectra obtained with the summation over all the spectra in the cell shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093401#pone-0093401-g003" target="_blank">Fig. 3</a>. Time-profiles of band amplitudes at 2100 cm⁻¹ (<b>B</b>), 2930 cm⁻¹ (<b>C</b>), 2850 cm⁻¹ (<b>D</b>), 1655 cm⁻¹ (<b>E</b>), 1446 cm⁻¹ (<b>F</b>) and 1003 cm⁻¹ (<b>G</b>).</p

Im[χ⁽³⁾] spectra and images from a CHL cell.

<p>Im[χ⁽³⁾] spectra from the two points of the CHL cell. <b>A</b> and <b>B</b> are obtained from the points indicated as × and + in <b>C</b>, respectively. The inset of each spectrum is the expanded spectrum in the fingerprint region. The exposure time is 50 msec. Im[χ⁽³⁾] images at 2930 cm⁻¹ (<b>C</b>), 2850 cm⁻¹ (<b>D</b>), 2655 cm⁻¹ (<b>E</b>), 2446 cm⁻¹ (<b>F</b>) and 1003 cm⁻¹ (<b>G</b>), respectively. The scale bar in the image is 10 µm. The image consists of 91×81 pixels and the exposure time for each pixel is 50 msec. Each image is normalized at the intensity maximal of each band.</p

Time-resolved Im[χ⁽³⁾] images of the CHL cell with the surfactant.

<p>The scale bar in the image is 10 µm. The image consists of 71×51 pixels and the exposure time for each pixel is 50 msec. Each row of the CARS images is measured every 3.5 min. Each column is normalized at the intensity maximal of each band.</p

Nocodazole lowers the surfactant uptake rate of a CHL cell.

<p><b>A</b>. Time-resolved Im[χ⁽³⁾] spectra obtained with the summation over all the spectra in the cell shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093401#pone-0093401-g005" target="_blank">Fig. 5</a>. <b>B</b>. Time-profiles of band amplitudes at 2100 cm⁻¹ (circle, left axis) and 1003 cm⁻¹ (cross, right axis).</p