Monitoring cellular C:N ratio in phytoplankton by means of FTIR‐spectroscopy

Abstract

Statistical growth rate modelling can be applied in a variety of ecological and biotechnological applications. Such models are frequently based on Monod or Droop equations and, especially for the latter, require reliable determination of model input parameters such as C:N quotas. Besides growth rate modelling, a C:N quota quantification can be useful for monitoring and interpretation of physiological acclimation to abiotic and biotic disturbances (e.g., nutrient limitations). However, as high throughput C:N quota determination is difficult to perform, alternatives need to be established. Fourier‐transformed infrared (FTIR) spectroscopy is used to analyze a variety of biochemical, chemical, and physiological parameters in phytoplankton. Hence, a quantification of the C:N quota should also be feasible. Therefore, using FTIR spectroscopy, six phytoplankton species from among different phylogenetic groups have been analyzed to determine the effect of nutrient limitation on C:N quota patterns. The typical species‐specific response to increasing nitrogen limitation was an increase in the C:N quota. Irrespective of this species specificity, we were able to develop a reliable multi‐species C:N quota prediction model based on FTIR spectroscopy using the partial least square regression (PLSR) algorithm. Our data demonstrate that the PLSR approach is more robust in C:N quota quantification (R2 = 0.93) than linear correlation of C:N quota versus growth rate (R2 ranges from 0.74 to 0.86) or biochemical information based on FTIR spectra (R2 ranges from 0.82 to 0.89). This accurate prediction of C:N values may support high throughput measurements in a broad range of future approaches.

Biomass production and physiology of Chlorella vulgaris during the early stages of immobilized state are affected by light intensity and inoculum cell density

“…5b). These results suggest an uncoupling of carbon assimilation from nitrogen uptake which could be associated with nitrogen limitation, in agreement with data reported in suspended cultures [64][65][66].…”

Concentration and distribution of phytoplankton nitrogen and carbon in the Northwest Atlantic and Indian Ocean: A simple model with applications in satellite remote sensing

“…Considerable uncertainties were observed over the extreme ends of the chlorophyll axis in this fit, challenging the accuracy of estimates and their applicability to real world scenarios (Figure 3). Nonetheless, the mean and range values are in broad agreement with earlier investigations (Körtzinger et al, 2001;Geider and La Roche, 2002;Staehr et al, 2002;Frigstad et al, 2011;Frigstad et al, 2014;Wagner et al, 2019). Stoichiometry estimates yielded are above the canonical 6.625 for most of the chlorophyll range, before approaching Redfield ratio and dropping below it at higher Chl-a.…”

Phytoplankton Temporal Strategies Increase Entropy Production in a Marine Food Web Model

“…The model also predicts phytoplankton C:N ratio, which varies between 140:1 and 270:1 for the nominal simulation, which is considerably higher than the Redfield ratio of 6.6:1. The C:N ratio for phytoplankton is known to vary as a function of growth rate and nutrient limitations, but the maximum observed values are closer to 50:1 [66][67][68]. In the MEP simulations, the high levels of internal carbon storage in both the passive and circadian strategies is used to capture and dissipate light to enhance entropy production, which differs from how storage is typically used, such a survival in fluctuating environments [65].…”