Figure 1.
Influence of the external pH on cell growth and silicon metabolism in T. weissflogii.
(A) Variations of the maximal growth rate (μmax) at different environmental pH values. Boxplots correspond to 5 to 11 independent experiments. The highest growth rate at pH = 7.8 was confirmed in individual experiments, but was only significant as compared to pH = 6.4, 6.8 and 8.5 (p≤0.007). (B) Intracellular silicic acid (Sii) content per cell was determined from cells grown in artificial sea medium adjusted to different pH values. (C) Frustule-bound BSi content per cell. Boxpolts in B and C correspond to 4 to 10 independent experiments, with duplicate measurements for each experiment. (D) Boxplot illustrating the silicon incorporation rate, i.e., the rate of frustule formation (pmol cell−1 μ−D) as a function of environmental pH. For explanations about boxplot representation, see METHODS.
Figure 2.
Influence of external pH on intracellular pH.
Thalassiosira weissflogii cells were loaded with BCECF-AM at different external pH values (pHe). Ratiometric emission (with excitation at 485 and 436 nm) was used to calculate intracellular pH values (pHi). The relation between pHe and pHi measurements was fitted to a linear regression (r2 = 0.306; Fisher's, p<0.0001), and corresponds to 20≤n≤30 measurements.
Figure 3.
Measurement of valve formation dynamics at the single-cell level.
(A) The valve formation process can be separated into several phases: an initiation phase, followed by valve formation and morphogenesis, then by valve exocytosis and finally by daughter-cell separation. The two images correspond to the DIC (digital image correlation) and Z-projections of HCK-123 fluorescence (green). In green we can visualize newly-synthesized valves labeled with the silica-associated dye (HCK-123). Scale bar (black): 10 µm. (B) Schematic representation of the microfluidic device used to record valve formation. During valve formation, light intensity, temperature and renewal of the medium were controlled. (C) To quantify HCK-123 fluorescence in individual cells, we developed new software for cell-tracking and local background estimation (Methods S1). Images at 4 different times are presented. Apparent cell motility is caused by the liquid flow. (D) To precisely quantify fluorescence in each cell we developed a new shape extraction method. The original image was denoised with the TV-means algorithm, leading to a much cleaner image. Then, among the level lines (iso-intensity curves) of the image enclosing the known center of the cell, we considered that the cell boundary was the line with the sharpest contrast (L). This enabled us to compute cell area A(L) as the area of the region enclosed by L), and its width W(L), defined as the minimum width of a band containing L.
Figure 4.
Influence of pHe on the kinetics of valve formation.
(A) The initial phase of valve formation is composed of two phases: an exponential evolution of the HCK-123 signal (tExp) that ends at a maximum named F1, followed by a decay phase that ends at the minimum (tDec), named F2. During the first phase we fitted the data to an exponential and determined the slope (k) and the coordinates on the y-axis (F0). (B) Influence of extracellular pH on k. In the 6.4–8.2 pH range, data were fitted to an exponential (y = 1.1e-3.e0.32×; r2 = 0.98). (C) Variation of HCK-123 concentrations as a function of external pH during the exponential (F1-F0) phase. In the 6.4–8.2 pH range, data were fitted to an exponential (y = 1e-4.e1.50×; r2 = 0.98). (D) Variation of HCK-123 concentrations during the decay (F1-F2) phase. In the 6.4–8.2 pH range, data were fitted to an exponential (y = 5e-5.e1.47×; r2 = 0.95). Data are from 7 to 92 single-cell measurements.
Figure 5.
Influence of the environmental pH on valve morphometry.
(A) TEM image of a valve showing the different traits measured: valve width (W), number (N) and distance (cp) between the fultoportulae present in the central region, and distance between rimoportulae (rp). Scale bar: 5 µm. The right panel corresponds to a higher magnification (64,000×) showing an array of pores and the computed Voronoi diagram (dark blue lines) (Methods S1). We measured pore radius (R), the average distance between two adjacent pores (d1), the width of groups of pores, also known as semi-continuous cribrum (D), and the width of radial ribs (d2). (B) Boxplot showing variations in pore radiuses (R) (971≤n≤3551) as a function of external pH (pHe). (C) Boxplot showing the influence of pHe on overall valve porosity (r) (7≤n≤21).
Figure 6.
Model used to estimate the equilibrium inside Silica Deposition Vesicles (SDVs).
We considered that the fluorescent dye Ffree freely diffused inside the diatom cells. Inside SDVs, the dye is protonated and accumulates (HFbound) as a function of SDV pH. Upon SDV expansion, which follows a non-linear kinetics, HFbound concentrations increased exponentially. At the same time an important fraction of the dye got entrapped inside the newly-formed silica material ((Si)n HFfixed). Therefore, [HCK] inside SDVs corresponds to the sum of the free, bound and fixed fractions.