Intercomparison of Two Fluorescent Dyes to Visualize Parasitic Fungi (Chytridiomycota) on Phytoplankton.

Fungal microparasites (here chytrids) are widely distributed and yet, they are often overlooked in aquatic environments. To facilitate the detection of microparasites, we revisited the applicability of two fungal cell wall markers, Calcofluor White (CFW) and wheat germ agglutinin (WGA), for the direct visualization of chytrid infections on phytoplankton in laboratory-maintained isolates and field-sampled communities. Using a comprehensive set of chytrid-phytoplankton model pathosystems, we verified the staining pattern on diverse morphological structures of chytrids via fluorescence microscopy. Empty sporangia were stained most effectively, followed by encysted zoospores and im-/mature sporangia, while the staining success was more variable for rhizoids, stalks, and resting spores. In a few instances, the staining was unsuccessful (mostly with WGA), presumably due to insufficient cell fixation, gelatinous cell coatings, and multilayered cell walls. CFW and WGA staining could be done in Utermöhl chambers or on polycarbonate filters, but CFW staining on filters seemed less advisable due to high background fluorescence. To visualize chytrids, 1 µg dye mL-1 was sufficient (but 5 µg mL-1 are recommended). Using a dual CFW-WGA staining protocol, we detected multiple, mostly undescribed chytrids in two natural systems (freshwater and coastal), while falsely positive or negative stained cells were well detectable. As a proof-of-concept, we moreover conducted imaging flow cytometry, as a potential high-throughput technology for quantifying chytrid infections. Our guidelines and recommendations are expected to facilitate the detection of chytrid epidemics and to unveil their ecological and economical imprint in natural and engineered aquatic systems.

The potential of multispectral imaging flow cytometry for environmental monitoring.

Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time-intensive and not well-suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant-pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep-learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA- or enzyme-activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.

Beyond nitrogen: phosphorus - estimating the minimum niche dimensionality for resource competition between phytoplankton.

The niche dimensionality required for coexistence is often discussed in terms of the number of limiting resources. N and P limitation are benchmarks for studying phytoplankton interactions. However, it is generally agreed that limitation by small numbers of resources cannot explain the high phytoplankton diversity observed in nature. Here, we parameterised resource competition models using experimental data for six phytoplankton species grown in monoculture with nine potential limiting resources. We tested predicted species biomass from these models against observations in two-species experimental mixtures. Uptake rates were similar across species, following the classic Redfield ratio. Model accuracy levelled out at around three to five resources suggesting the minimum dimensionality of this system. The models included the resources Fe, Mg, Na and S. Models including only N and P always performed poorly. These results suggest that high-dimensional information about resource limitation despite stoichiometric constraints may be needed to accurately predict community assembly.

Pollen analysis using multispectral imaging flow cytometry and deep learning.

Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426 876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen.

Imaging Flow Cytometry for Phylogenetic and MorphologicallyBased Functional Group Clustering of a Natural Phytoplankton Community over 1 Year in an Urban Pond.

Ponds are an insufficiently studied research object but represent a biodiversity hotspot and have a high value for ecosystem services like recreation, water retention, or angling. Especially urban ponds create a direct contact for citizens experiencing nature. But on the other side, these systems also suffer from several pressures caused by humans, for example, high nutrient and salt influxes or high temperatures. Phytoplankton organisms are a crucial part of ponds ecosystem and an understanding of community composition is crucial especially when eutrophication and high temperatures lead to dominance of unpleasant toxic cyanobacteria. With traditional microscopic methods for phytoplankton analysis, monitoring is not feasible with high spatial resolution and frequency. Therefore, a new approach of imaging flow cytometry to classify phytoplankton species in either taxonomic or morphologically based functional groups (MBFGs) is suggested. In this study, both classifications could be successfully applied to a natural phytoplankton community in an urban pond in Leipzig with minor modifications. Both classifications in combination provide a good mechanistic understanding of phytoplankton community dynamics. In addition, a great advantage of the measurements is the archivability of microscopic images allowing a comprehensive respective data analysis. Two examples of detailed trait and image analysis are demonstrated to investigate single-cell traits for cyanobacteria and chlorophytes/euglenophytes and to follow the fate of a cyanobacterial bloom affected by a fungal infection. © 2020 The Author. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Hidden Secrets Behind Dots: Improved Phytoplankton Taxonomic Resolution Using High-Throughput Imaging Flow Cytometry.

Phytoplankton are aquatic, microscopically small primary producers, accounting for almost half of the worldwide carbon fixation. As early indicators of environmental change, they play a crucial role in water quality management. Human activities like climate change, eutrophication, or international shipping traffic strongly impact diversity of these organisms. Phytoplankton monitoring is a crucial step in the recognition of changes in community composition. The common standard for monitoring programs is manual microscopic counting, which strongly limits sample number and sampling frequency. In contrast, high-throughput technologies like standard flow cytometry (FCM) are restricted to a low taxonomic resolution, which makes them unsuitable for the identification of indicator species. Imaging flow cytometers (IFC) could overcome these limitations as they combine microscopy and high-throughput analysis. In comparison to single fluorescence values, image information not only allows for a wide variety of possibilities to characterize different species as well as immediate and fast measurements but also provides an archivable data output. Taxonomic resolution of IFC (ImageStream X Mk II) was proven comparable to standard FCM (FACSAria II) by the help of numerical evaluations. This is demonstrated on different levels of taxonomic differentiation of laboratory grown cultures in this study. Phytoplankton species discrimination by an imaging flow cytometer could be useful as supportive tool to make machine-learning classifications more robust, reliable, and flexible. Furthermore, this study provides examples, demonstrating the possibility of discrimination between species with similar fluorescence properties, strains, and even subpopulations. In contrast to standard FCM, each cell is not only represented as a dot in a cytogram but is also linked to microscopic brightfield and the author presents a new way to visualize this as image-based cytograms. The source code is supplied and could be useful for all kind of IFC data in general. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton.

BACKGROUND: Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple environmental threats like eutrophication, climate warming and introduction of invasive species more intensive monitoring would be helpful to develop adequate measures. However, traditional methods such as microscopic counting by experts or high throughput flow cytometry based on scattering and fluorescence signals are either too time-consuming or inaccurate for species identification tasks. The combination of high qualitative microscopy with high throughput and latest development in machine learning techniques can overcome this hurdle. RESULTS: In this study, image based cytometry was used to collect ~ 47,000 images for brightfield and Chl a fluorescence at 60× magnification for nine common freshwater species of nano- and micro-phytoplankton. A deep neuronal network trained on these images was applied to identify the species and the corresponding life cycle stage during the batch cultivation. The results show the high potential of this approach, where species identity and their respective life cycle stage could be predicted with a high accuracy of 97%. CONCLUSIONS: These findings could pave the way for reliable and fast phytoplankton species determination of indicator species as a crucial step in water quality assessment.

A Fateful Meeting of Two Phytoplankton Species-Chemical vs. Cell-Cell-Interactions in Co-Cultures of the Green Algae Oocystis marsonii and the Cyanobacterium Microcystis aeruginosa.

Massive growth of single species of cyanobacteria is a common phenomenon in many eutrophicated waters worldwide. Allelopathic growth control of phytoplankton species is one suggested mechanism, but still controversially discussed. The fact that the synthesis of biological active compounds requires high energy costs and carbon investment for a single cell in contrast to high dilution rates in natural systems questions the universal validity of allelopathic mechanisms, even more as high concentrations of allelopathic substances are often needed in several experiments to cause biological effects. In this study, it was tested, if growth inhibition is induced by chemical signaling alone or via direct cell-cell interaction. As a test system, we used a co-culture of the green algae Oocystis marsonii (Trebouxiophyceae) with the cyanobacterium Microcystis aeruginosa which is known to strongly reduce the growth of the green algal competitor. In this study, direct co-culturing as well as membrane-separated growth chambers were used to test for chemical and contact-mediated interactions. In the membrane-separated chambers, both species can be co-cultivated and a membrane allows the exchange of metabolites. Growth of O. marsonii was only affected in the direct co-cultivation situation, where direct cell-to-cell contact was possible. During direct co-cultivation, deviating cellular traits, namely cell cycle pattern and large cell-aggregate formation of both species, could be detected. These data strongly support the hypothesis of a direct cell-cell-contact necessary for allelopathic growth control in this model system. Such direct contact would allow targeting allelopathic metabolites directly towards the competitor and thereby minimizing dilution effects.

Eco-evolutionary processes shaping floral nectar sugar composition.

Floral nectar sugar composition is assumed to reflect the nutritional demands and foraging behaviour of pollinators, but the relative contributions of evolutionary and abiotic factors to nectar sugar composition remain largely unknown across the angiosperms. We compiled a comprehensive dataset on nectar sugar composition for 414 insect-pollinated plant species across central Europe, along with phylogeny, paleoclimate, flower morphology, and pollinator dietary demands, to disentangle their relative effects. We found that phylogeny was strongly related with nectar sucrose content, which increased with the phylogenetic age of plant families, but even more strongly with historic global surface temperature. Nectar sugar composition was also defined by floral morphology, though it was not related to our functional measure of pollinator dietary demands. However, specialist pollinators of current plant-pollinator networks predominantly visited plant species with sucrose-rich nectar. Our results suggest that both physiological mechanisms related to plant water balance and evolutionary effects related to paleoclimatic changes have shaped floral nectar sugar composition during the radiation and specialisation of plants and pollinators. As a consequence, the high velocity of current climate change may affect plant-pollinator interaction networks due to a conflicting combination of immediate physiological responses and phylogenetic conservatism.

Invasive Growth of Ailanthus altissima Trees is Associated with a High Rate of Sensitization in Atopic Patients.

Purpose: Ailanthus altissima is one of the world's most invasive species with a globally problematic spread. Pollen is dispersed locally and partially airborne. We aimed at investigating if (i) A. altissima pollen can be detected in relevant quantity in the air and if (ii) sensitization to A. altissima can be detected in patients with seasonal exacerbation of atopic diseases. Patients and Methods: We recorded distribution of A. altissima in Leipzig, Germany. In 2019 and 2020, pollen was collected with a Hirst-type pollen trap placed on the roof of the University Hospital. Specific IgE investigations were performed in children and adults with history of atopic diseases with deterioration between May and July. We analysed specific IgE for A. altissima, Alternaria sp., birch, grasses, profilins, polcalcins and crossreacting carbohydrates. Results: We found abundant growth of A. altissima and pollen was detected from early June to mid-July with a maximum pollen concentration of 31 pollen/m3. Out of 138 patients (63 female, 69 children/adolescents), 95 (69%) had seasonal allergic rhinitis, 84 (61%) asthma, and 43 (31%) atopic dermatitis. Sensitization to A. altissima was shown in 59 (42%). There were no significant differences between age groups. In 59% of patients sensitized (35/59), there was no sensitization to possibly cross-reacting structures. Conclusion: Sensitization to A. altissima pollen could be detected in 42% of our patients with atopic diseases, suggesting allergenic potential of this neophyte. In the context of further spread with climate change, eradication strategies and population-based sensitization studies are needed.

No SARS-CoV-2 detected in air samples (pollen and particulate matter) in Leipzig during the first spread.

The SARS-CoV-2 pandemic co-occurred with pollen season in Europe 2020 and recent studies suggest a potential link between both. Air samples collected at our measuring station in Leipzig and purified pollen were analyzed for SARS-CoV-2 typical signals or for virus-induced cytopathic effects, to test if the virus could bind to bioaerosols and if so, whether these complexes are infectious. The results show that neither our air samples nor purified pollen were infectious or could act as carrier for virus particles.

Temperature and stoichiometric dependence of phytoplankton traits.

Understanding the links between intraspecific trait variability and environmental gradients is an important step toward unravelling the mechanisms that link species performance to environmental variation. Here, we performed a comparative, experimental study to investigate variability of cellular traits in three prokaryotic and three eukaryotic freshwater phytoplankton species along gradients of temperature and nitrogen:phosphorus ratio (N:P) in laboratory microcosms. Temperature and N:P strongly affect phytoplankton growth and are changing due to climate change and eutrophication. Metabolic theory and allometric scaling predict that smaller organisms should be favored at higher temperatures through improved metabolic uptake partly due to greater surface area to volume ratios. In addition, chlorophyll a (chl a) concentration should increase due to higher chlorophyll synthesis in response to light limitation at higher cell densities. We found that cell volume both increased and decreased with temperature, whereas intermediate N:P yielded higher growth rates and more extreme conditions yielded bigger cell volumes. Species growth responses to these gradients were distinct and not related to phylogenetic differences. Meaningfully coupled traits like the chl a fluorescence and cell volume shifted consistently and can improve our understanding of individual cell responses to abiotic drivers. This study showed that intraspecific trait variability of freshwater phytoplankton harbors potential for short term acclimation to environmental gradients. Finally, the high trait variability in some species has strong implications for their ecology and the accuracy of predictions where responses may differ when based on mean or fixed trait values.

Cell Wall Structure of Coccoid Green Algae as an Important Trade-Off Between Biotic Interference Mechanisms and Multidimensional Cell Growth.

Coccoid green algae can be divided in two groups based on their cell wall structure. One group has a highly chemical resistant cell wall (HR-cell wall) containing algaenan. The other group is more susceptible to chemicals (LR-cell wall - Low resistant cell wall). Algaenan is considered as important molecule to explain cell wall resistance. Interestingly, cell wall types (LR- and HR-cell wall) are not in accordance with the taxonomic classes Chlorophyceae and Trebouxiophyceae, which makes it even more interesting to consider the ecological function. It was already shown that algaenan helps to protect against virus, bacterial and fungal attack, but in this study we show for the first time that green algae with different cell wall properties show different sensitivity against interference competition with the cyanobacterium Microcystis aeruginosa. Based on previous work with co-cultures of M. aeruginosa and two green algae (Acutodesmus obliquus and Oocystis marssonii) differing in their cell wall structure, it was shown that M. aeruginosa could impair only the growth of the green algae if they belong to the LR-cell wall type. In this study it was shown that the sensitivity to biotic interference mechanism shows a more general pattern within coccoid green algae species depending on cell wall structure.

Temperature effects on prey and basal resources exceed that of predators in an experimental community.

Climate warming alters the structure of ecological communities by modifying species interactions at different trophic levels. Yet, the consequences of warming-led modifications in biotic interactions at higher trophic levels on lower trophic groups are lesser known. Here, we test the effects of multiple predator species on prey population size and traits and subsequent effects on basal resources along an experimental temperature gradient (12-15°C, 17-20°C, and 22-25°C). We experimentally assembled food web modules with two congeneric predatory mites (Hypoaspis miles and Hypoaspis aculeifer) and two Collembola prey species (Folsomia candida and Proisotoma minuta) on a litter and yeast mixture as the basal resources. We hypothesized that warming would modify interactions within and between predator species, and that these alterations would cascade to basal resources via changes in the density and traits (body size and lipid: protein ratio) of the prey species. The presence of congeners constrained the growth of the predatory species independent of warming despite warming increased predator density in their respective monocultures. We found that warming effects on both prey and basal resources were greater than the effects of predator communities. Our results further showed opposite effects of warming on predator (increase) and prey densities (decrease), indicating a warming-induced trophic mismatch, which are likely to alter food web structures. We highlight that warmer environments can restructure food webs by its direct effects on lower trophic groups even without modifying top-down effects.

Assessing in situ dominance pattern of phytoplankton classes by dominance analysis as a proxy for realized niches.

This study looks at two facets of dominant phytoplankton classes during phytoplankton succession. A detailed assessment of this issue is of special interest with regard to realized niches from a theoretical point of view but also for lake management as practical application. A realized niche mirrors the functional adaptability of an organism in a lake-specific constellation of environmental parameters. Therefore, the characterization of realized niches could be a key factor for management of problematic waters. Different strategies exist to control eutrophication and the risk of blooms by harmful algae. During the last decades, many restoration measures were initiated to manage eutrophicated inland lakes. In the past, it has become evident several times that restoration strategies do not necessarily lead to a reduction of biomass of undesirable cyanobacteria but can even promote their development. Due to this uncertainty of success and the high costs for remediation strategies, new prediction tools are required - ideally, based on routine monitoring data. Therefore, we developed a new method to extract potential optimal growth conditions (POGC) as indicators of realized niches for different phytoplankton taxa from existing data to improve existing strategies used in lake remediation and restoration. The analysis presented in this work is based on dominance pattern of different phytoplankton groups relative to environmental variables. Interpretation of these dominance patterns as indicators of POGC showed distinct pattern for several phytoplankton classes for all investigated objects. We identified low nitrogen and phosphate concentrations as favorable condition for cyanobacteria in Lake Auensee and Lake Feldberger Haussee. The reservoir Bleilochtalsperre showed a high N/P-concentration and cyanobacteria dominance was generally very low.

Subcommunity FTIR-spectroscopy to determine physiological cell states.

Estimation of growth potential in a complex community is a great challenge in biotechnical processes and environmental water quality control. Recently it has been shown that the macromolecular structure is a good indicator for the growth potential of phytoplankton cells. A functional understanding of natural phytoplankton communities requires a community analysis by means of single particles technologies. However, conventional biochemical methods are not sensitive enough to determine the macromolecular composition of a single cell or cell aggregates. This problem can be resolved by Fourier transform infrared (FTIR) spectroscopy, which delivers results similar to biochemical analysis with a much smaller sample size. The combined approach of flow cytometric analysis with subcommunity sorting and subsequent FTIR-analysis offers new perspectives for the understanding of community functioning and process optimization.

Recovery of soil unicellular eukaryotes: an efficiency and activity analysis on the single cell level.

Eukaryotic unicellular organisms are an important part of the soil microbial community, but they are often neglected in soil functional microbial diversity analysis, principally due to the absence of specific investigation methods in the special soil environment. In this study we used a method based on high-density centrifugation to specifically isolate intact algal and yeast cells, with the aim to analyze them with flow cytometry and sort them for further molecular analysis such as deep sequencing. Recovery efficiency was tested at low abundance levels that fit those in natural environments (10(4) to 10(6) cells per g soil). Five algae and five yeast morphospecies isolated from soil were used for the testing. Recovery efficiency was between 1.5 to 43.16% and 2 to 30.2%, respectively, and was dependent on soil type for three of the algae. Control treatments without soil showed that the majority of cells were lost due to the method itself (58% and 55.8% respectively). However, the cell extraction technique did not much compromise cell vitality because a fluorescein di-acetate assay indicated high viability percentages (73.3% and 97.2% of cells, respectively). The low abundant algae and yeast morphospecies recovered from soil were cytometrically analyzed and sorted. Following, their DNA was isolated and amplified using specific primers. The developed workflow enables isolation and enrichment of intact autotrophic and heterotrophic soil unicellular eukaryotes from natural environments for subsequent application of deep sequencing technologies.