Metabolomics is an emerging field of cell biology that aims at the comprehensive identification of metabolite levels in biological fluids or cells in a specific functional state. Currently, the major tools for determining metabolite concentrations are mass spectrometry coupled with chromatographic techniques and nuclear magnetic resonance, which are expensive, time consuming and destructive for the samples. Here, we report a time resolved approach to monitor metabolite dynamics in cell cultures, based on Surface Enhanced Raman Scattering (SERS). This method is label-free, easy to use and provides the opportunity to simultaneously study a broad range of molecules, without the need to process the biological samples. As proof of concept, NIH/3T3 cells were cultured in vitro, and the extracellular medium was collected at different time points to be analyzed with our engineered SERS substrates. By identifying individual peaks of the Raman spectra, we showed the simultaneous detection of several components of the conditioned medium, such as L-tyrosine, L-tryptophan, glycine, L-phenylalanine, L-histidine and fetal bovine serum proteins, as well as their intensity changes during time. Furthermore, analyzing the whole Raman data set with the Principal Component Analysis (PCA), we demonstrated that the Raman spectra collected at different days of culture and clustered by similarity, described a well-defined trajectory in the principal component plot. This approach was then utilized to determine indirectly the functional state of the macrophage cell line Raw 264.7, stimulated with the lipopolysaccharide (LPS) for 24 hours. The collected spectra at different time points, clustered by the PCA analysis, followed a well-defined trajectory, corresponding to the functional change of cells toward the activated pro-inflammatory state induced by the LPS. This study suggests that our engineered SERS surfaces can be used as a versatile tool both for the characterization of cell culture conditions and the functional state of cells over time.
Optical stimulation technologies are gaining great consideration in cardiology, neuroscience studies, and drug discovery pathways by providing control over cell activity with high spatio-temporal resolution. However, this high precision requires manipulation of biological processes at genetic level concealing its development from broad scale application. Therefore, translating these technologies into tools for medical or pharmacological applications remains a challenge. Here, an all-optical nongenetic method for the modulation of electrogenic cells is introduced. It is demonstrated that plasmonic metamaterials can be used to elicit action potentials by converting near infrared laser pulses into stimulatory currents. The suggested approach allows for the stimulation of cardiomyocytes and neurons directly on commercial complementary metal-oxide semiconductor microelectrode arrays coupled with ultrafast pulsed laser, providing both stimulation and network-level recordings on the same device.
Prompt estimation of phytoplankton biomass is critical in determining the ecological quality of freshwaters. Remote Sensing (RS) may provide new opportunities to integrate with situ traditional monitoring techniques. Nonetheless, wide regional and temporal variability in freshwater optical constituents makes it difficult to design universally applicable RS protocols. Here, we assessed the potential of two neural networks-based models, namely the Case 2 Regional CoastColour (C2RCC) processor and the Mixture Density Network (MDN), applied to MSI Sentinel-2 data for monitoring Chlorophyll (Chl) content in three monomictic volcanic lakes while accounting for the effect of their specific water circulation pattern on the remotely-sensed and in situ data relation. Linear mixed models were used to test the relationship between the remote sensing indices calculated through C2RCC (INN) and MDN (IMDN), and in situ Chl concentration. Both indices proved to explain a large portion of the variability in the field data and exhibited a positive and significant relationship between Chl concentration and satellite data, but only during the mixing phase. The significant effect of the water circulation period can be explained by the low responsiveness of the RS approaches applied here to the low phytoplankton biomass, typical of the stratification phase. Sentinel-2 data proved their valuable potential for the remote sensing of phytoplankton in small inland water bodies, otherwise challenging with previous sensors. However, caution should be taken, since the applicability of such an approach on certain water bodies may depend on hydrological and ecological parameters (e.g., thermal stratification and seasonal nutrient availability) potentially altering RS chlorophyll detection by neural networks-based models, despite their alleged global validity.
Inland freshwaters are of great importance for human health and activities, but major stressors such as nutrient pollution, deforestation, and urbanization are compromising their status. Water quality degradation and freshwater ecosystem preservation are current issues worldwide requiring frequent and efficient monitoring protocols. The increasing need for large amounts of data to comply with national and international regulations on water quality monitoring highlights traditional procedures limits. Therefore, the purpose of the present study is to investigate the potential of alternative and rapid methods for chlorophyll concentration surveys in freshwaters. The Phyto-PAM (pulse amplitude-modulated) instrument and the Case-2 Regional Coast Colour (C2RCC) satellite image processor were selected to estimate chlorophyll concentration in the surface waters of Lake Albano (Central Italy), selected as a pilot area for the project BLOOWATER (Water JPI 2018 Joint Call Closing the Water Cycle Gap). The correlation tests’ results indicate significant relations with chlorophyll data measured spectrophotometrically, confirming the suitability of both methods for chlorophyll retrieval. However, the relatively low strength of the correlation between remotely sensed and spectrophotometric data (r = 0.57, p < 2.2 × 10−16) was not as satisfactory as with Phyto-PAM values (r = 0.97, p = 1.2 × 10−4). Even though the techniques in this study proved to be promising in the water body under investigation, their current limitations suggest the need for further calibration and integration with other systems (e.g., unmanned aerial vehicles).
Biodiversity monitoring is crucial for ecosystem conservation, yet field data collection is limited by costs, time, and extent. Remote sensing represents a convenient approach providing frequent, near-real-time information over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (e.g., metrics applied, ecosystem type) and the conditions under which such a relationship holds true. This study investigates the applicability of the SVH for plant diversity monitoring at the landscape scale by comparing the performance of three different types of SD metrics. Species richness and functional diversity were calculated for more than 2000 cells forming a grid covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 'greenest pixel' composite by applying: i) the standard deviation of NDVI, ii) Rao's Q entropy index, and iii) richness of 'spectral communities'. Habitat type (i.e., land cover) was included in the models describing the relationship between SD and ground biodiversity. Both species richness and functional diversity show positive and significant relationships with each SD metric tested. However, SD alone accounts for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depends significantly on habitat type and is highest in natural transitional areas. Our results underline that, despite the stability in the significance of the link between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context-dependent and the factors mediating such a relationship must be carefully considered to avoid drawing misleading conclusions.
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