Single-Cell and Bulk Fluorescence Excitation Signatures of Seven Phytoplankton Species During Nitrogen Depletion and Resupply.
Stefan T FaulknerCameron M RekullyEric M LachenmyerErgun KaraTammi L RichardsonTimothy J ShawMichael L MyrickPublished in: Applied spectroscopy (2018)
Phytoplankton play a vital role as primary producers in aquatic ecosystems. One common approach to classifying phytoplankton is fluorescence excitation spectroscopy, which leverages the variation in types and concentrations of pigments among different phytoplankton taxonomic groups. Here, we used a fluorescence imaging photometer to measure excitation ratios ("signatures") of single cells and bulk cultures of seven differently pigmented phytoplankton species as they progressed from nitrogen N-replete to N-depleted conditions. Our objective was to determine whether N depletion alters the fluorescence excitation signature of each species and, if so, how quickly they recover when N (as nitrate) was resupplied, because these factors affect our ability to classify the species correctly. Of the seven species studied, only Proteomonas sulcata, a marine cryptophyte, showed measurable changes in single-cell fluorescence excitation ratios and bulk fluorescence excitation spectra. These changes were likely due to decreases in the cellular concentration of phycoerythrin, a N-rich pigment, as N became scarce. Within 3 h of resupply of N, fluorescence signatures began returning to pre-depletion values and were indistinguishable from N-replete cells by 80 h after resupply. These data suggest that our classification approach is robust for non-PE containing phytoplankton. PE-containing phytoplankton might exhibit systematic changes in their signatures depending on their level of N depletion, but this could be detected and the phytoplankton re-classified following a few hours of incubation in N replete conditions.
Keyphrases
- energy transfer
- water quality
- single molecule
- single cell
- quantum dots
- induced apoptosis
- fluorescence imaging
- genome wide
- cell cycle arrest
- nitric oxide
- climate change
- photodynamic therapy
- deep learning
- high throughput
- endoplasmic reticulum stress
- machine learning
- cell death
- dna methylation
- gene expression
- cell proliferation
- big data
- signaling pathway