Fluorescence Excitation Spectroscopy for Phytoplankton Species Classification Using an All-Pairs Method: Characterization of a System with Unexpectedly Low Rank
Abstract:An all-pairs method is used to analyze phytoplankton fluorescence excitation spectra. An initial set of nine phytoplankton species is analyzed in pairwise fashion to select two optical filter sets, and then the two filter sets are used to explore variations among a total of 31 species in a single-cell fluorescence imaging photometer. Results are presented in terms of pair analyses; we report that 411 of the 465 possible pairings of the larger group of 31 species can be distinguished using the initial nine-spec… Show more
“…Our recent work shows that the ratio of fluorescence excitation intensities in different wavelength bands in cells from a single culture form a much better signature for species than a single fluorescence intensity, with relative cell-to-cell variability (standard deviation relative to average ratio) of $6%. 11 This is consistent with other reports that pigment ratios are more reproducible than absolute pigment content. 12,13 Most of our work has been conducted with phytoplankton cultures grown under non-varying incident irradiance, fixed light-dark cycles, and under nutrient-replete conditions.…”
Section: Introductionsupporting
confidence: 93%
“…For a detailed description of the FIP’s design and operation, see Rekully et al. 11 Generally, as phytoplankton pass through the field of view of a fluorescence microscope objective they are excited by an excitation beam that is modulated by a filter wheel containing different bandpass filters. As the phytoplankton fluoresce, their emission is collected by the same objective and imaged onto a charge-coupled device array.…”
Section: Methodsmentioning
confidence: 99%
“…Samples were measured in a 180 orientation in a custom-made back scattering sample cell apparatus which mimics conditions inside the FIP. For a detailed description of the apparatus and excitation spectrum measurement protocol, see Rekully et al 11 After measurement, samples were filtered through a Whatman GF/F (0.7 mm) filter and the excitation spectrum of the filtrate was measured for background subtraction. Each culture excitation spectrum measurement was repeated five times.…”
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 reclassified following a few hours of incubation in N replete conditions.
“…Our recent work shows that the ratio of fluorescence excitation intensities in different wavelength bands in cells from a single culture form a much better signature for species than a single fluorescence intensity, with relative cell-to-cell variability (standard deviation relative to average ratio) of $6%. 11 This is consistent with other reports that pigment ratios are more reproducible than absolute pigment content. 12,13 Most of our work has been conducted with phytoplankton cultures grown under non-varying incident irradiance, fixed light-dark cycles, and under nutrient-replete conditions.…”
Section: Introductionsupporting
confidence: 93%
“…For a detailed description of the FIP’s design and operation, see Rekully et al. 11 Generally, as phytoplankton pass through the field of view of a fluorescence microscope objective they are excited by an excitation beam that is modulated by a filter wheel containing different bandpass filters. As the phytoplankton fluoresce, their emission is collected by the same objective and imaged onto a charge-coupled device array.…”
Section: Methodsmentioning
confidence: 99%
“…Samples were measured in a 180 orientation in a custom-made back scattering sample cell apparatus which mimics conditions inside the FIP. For a detailed description of the apparatus and excitation spectrum measurement protocol, see Rekully et al 11 After measurement, samples were filtered through a Whatman GF/F (0.7 mm) filter and the excitation spectrum of the filtrate was measured for background subtraction. Each culture excitation spectrum measurement was repeated five times.…”
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 reclassified following a few hours of incubation in N replete conditions.
“…A detailed description of the FIP setup is presented in Rekully et al. ; 5 a simplified schematic of the FIP appears in Fig. 2.…”
Section: Methodsmentioning
confidence: 99%
“…2. Performance comparisons between the two wheel designs were conducted using the same setup described in Rekully et al., 5 with the traditional filter wheel and asymmetric filter wheel being substituted for one another depending on the condition being tested. Figure 2.A schematic of the fluorescence imaging photometer (originally shown in Rekully et al.).…”
The use of rotating filter wheels is common in photometric applications. Traditional filter wheel designs typically exhibit a number of filter openings spaced evenly about the circumference of the wheel. In this work we examine a number of shortcomings of this traditional filter design in measurements of phytoplankton fluorescence made with our fluorescence imaging photometer (FIP). We present an alternative asymmetric wheel design that offers a number of advantages over the traditional design as well as a new processing algorithm designed to accommodate convolution of signals from adjacent channels inherent in measurements collected with the asymmetric design. This approach eliminates the need for a separate signal to establish timing and wheel position, unambiguously establishes filter order even when the direction of rotation is unknown, allows for better estimates of signal baseline, and is more resilient to effects of vibration and other dynamic processes that could occur on the time scale of wheel rotation. We demonstrate performance improvements for phytoplankton fluorescence measurements associated with the new wheel design and algorithm compared with previously published methods using the FIP. Both the improved image processing algorithm and filter wheel design were found to reduce noise in our measurements significantly.
We describe a waterproof, lightweight (1.3 kg) low power (∼1.1 W average power) fluorometer operating on 5 VDC deployed on a small Uncrewed Aircraft System (sUAS) to measure chlorophyll and used for triggering environmental water sampling by the sUAS. The fluorometer uses a 450 nm laser modulated at 10 Hz for excitation and a standard photodiode and transimpedance amplifier for the detection of fluorescence. Additional detectors are available for measuring laser intensity and light scattering. Control of the fluorometer and communication between the fluorometer and the Raspberry Pi 4B computer controlling the sampler were provided by an Arduino microcontroller using the Robot Operating System (ROS). Calibrations were based on standards of dissolved chlorophyll extracted from Chlorella powder (a widely available dietary supplement). The detection limit for chlorophyll from these calibrations was found to be 0.2 micrograms per liter of water for a single 0.1 sec differential measurement. The detection limit decreases with the square root of the integration time as expected. Detection limits increase by a factor of 2 to 3 when mounted in the sUAS due to electrical noise; sUAS acoustic noise and vibration do not appear to contribute significantly.
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