Imaging flow cytometry for phytoplankton analysis

Stress responses in an Arctic microalga (Pelagophyceae) following sudden salinity change revealed by gene expression analysis

Marine microbes that have for eons been adapted to stable salinity regimes are confronted with sudden decreases in salinity in the Arctic Ocean. The episodic freshening is increasing due to climate change with melting multi-year sea-ice and glaciers, greater inflows from rivers, and increased precipitation. To investigate algal responses to lowered salinity, we analyzed the responses and acclimatation over 24 h in a non-model Arctic marine alga (pelagophyte CCMP2097) following transfer to realistic lower salinities. Using RNA-seq transcriptomics, here we show rapid differentially expressed genes related to stress oxidative responses, proteins involved in the photosystem and circadian clock, and those affecting lipids and inorganic ions. After 24 h the pelagophyte adjusted to the lower salinity seen in the overexpression of genes associated with freezing resistance, cold adaptation, and salt tolerance. Overall, a suite of ancient widespread pathways is recruited enabling the species to adjust to the stress of rapid salinity change.

Rapid detection of microalgae cells based on upconversion nanoprobes

The quantification of microalgae cells is crucial for the treatment of ships' ballast water. However, achieving rapid detection of microalgae cells remains a substantial challenge. Here, we develop a new method for rapid and effective detection of microalgae concentration by utilizing upconversion nanoprobes (UCNPs) of NaYF4:Er3+,Tm3+. Three ligands, carboxylated methoxypolyethylene glycols with 5000 and 2000 molecular weights (mPEG-COOH-5, mPEG-COOH-2) and D-gluconic acid sodium salt (DGAS), were used to convert hydrophobic UCNPs into a hydrophilic state through modification. The results show that the mPEG-COOH-5 modified UCNPs present the highest stability in an aqueous solution. Fourier Transform Infrared Spectroscopy (FTIR) measurements reveal the presence of a significant number of -COOH functional groups on UCNPs after the mPEG-COOH-5 modification. These -COOH groups enhance the hydrophilicity and biocompatibility of UCNPs. The soluble UCNPs were directly mixed with microalgae, and the upconversion luminescence (UCL) spectra of the UCNPs were recorded immediately after thorough shaking. This greatly reduces the measurement time and could realize rapid onboard detection. In this sensing procedure, the UCNPs with red UCL functioned as energy donors, while microalgae with red absorption served as an energy acceptor. The UCL gradually diminishes with an increase in microalgae concentration based on the inner filter effect, thus establishing a relationship between UCL and microalgae concentration. The accuracy of the detection is further validated through the traditional microscope counting method. These findings pave the way for a novel rapid strategy to assess microalgae concentration using UCNPs.

A microfluidic microalgae detection system for cellular physiological response based on an object detection algorithm

The composition of species and the physiological status of microalgal cells serve as significant indicators for monitoring marine environments. Symbiotic with corals, Symbiodiniaceae are more sensitive to the environmental response. However, current methods for evaluating microalgae tend to be population-based indicators that cannot be focused on single-cell level, ignoring potentially heterogeneous cells as well as cell state transitions. In this study, we proposed a microalgal cell detection method based on computer vision and microfluidics, which combined microscopic image processing, microfluidic chip and convolutional neural network to achieve label-free, sheathless, automated and high-throughput microalgae identification and cell state assessment. By optimizing the data import, training process and model architecture, we solved the problem of identifying tiny objects at the micron scale, and the optimized model was able to perform the tasks of cell multi-classification and physiological state assessment with more than 95% mean average precision. We discovered a novel transition state and explored the thermal sensitivity of three clades of Symbiodiniaceae, and discovered the phenomenon of cellular heat shock at high temperatures. The evolution of the physiological state of Symbiodiniaceae cells is very important for directional cell evolution and early warning of coral ecosystem health.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

AI facilitated fluoro-electrochemical phytoplankton classification

Marine phytoplankton is extremely diverse. Counting and characterising phytoplankton is essential for understanding climate change and ocean health not least since phytoplankton extensively biomineralize carbon dioxide whilst generating 50% of the planet's oxygen. We report the use of fluoro-electrochemical microscopy to distinguish different taxonomies of phytoplankton by the quenching of their chlorophyll-a fluorescence using chemical species oxidatively electrogenerated in situ in seawater. The rate of chlorophyll-a quenching of each cell is characteristic of the species-specific structural composition and cellular content. But with increasing diversity and extent of phytoplankton species under study, human interpretation and distinction of the resulting fluorescence transients becomes increasingly and prohibitively difficult. Thus, we further report a neural network to analyse these fluorescence transients, with an accuracy >95% classifying 29 phytoplankton strains to their taxonomic orders. This method transcends the state-of-the-art. The success of the fluoro-electrochemical microscopy combined with AI provides a novel, flexible and highly granular solution to phytoplankton classification and is adaptable for autonomous ocean monitoring.

Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States

Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted R2 = 0.836, p-value < 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (p-value = 0.013) but was comparable to that at the sampling sites in the western rivers (p-value = 0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.

A detection method for Prorocentrum minimum by an aptamer-gold nanoparticles based colorimetric assay

Here, to give early waring for harmful algal blooms caused by Prorocentrum minimum, we reported a simple and rapid colorimetric assay that is named aptamer-gold nanoparticles (GNPs) based colorimetric assay (AGBCA). The GNPs maintain a dispersed state and have a strong characteristic absorption peak at 520 nm. With the addition of NaCl, the stability of the solution will be destroyed and the dispersed GNPs will aggregate. Therefore, the characteristic absorption peak of the GNPs solution will change from 520 nm to 670 nm. Aptamers can be adsorbed on the surface of GNPs, effectively preventing the aggregation of GNPs. In the presence of P. minimum, aptamers will specifically bind to P. minimum, causing the dissociation of the aptamers from GNPs. Consequently, the GNPs will aggregate in the NaCl solution, corresponding to a new absorption peak at 670 nm. A linear relationship between the absorbance ratio variation (ΔA₆₇₀/A₅₂₀) and the P. minimum concentration was observed in the concentration range of 1 × 10² - 1 × 10⁷ cells mL^-1, with a low detection limit of 8 cells mL^-1. The developed AGBCA is characterized by simplicity, strong specificity, and high sensitivity and is thus promising for the quantitative detection of P. minimum in natural samples.

Methodological advances in the detection of biotoxins and pathogens affecting production and consumption of bivalve molluscs in a changing environment

The production, harvesting and safe consumption of bivalve molluscs can be disrupted by biological hazards that can be divided into three categories: (1) biotoxins produced by naturally occurring phytoplankton that are bioaccumulated by bivalves during filter-feeding, (2) human pathogens also bioaccumulated by bivalves and (3) bivalve pathogens responsible for disease outbreaks. Environmental changes caused by human activities, such as climate change, can further aggravate these challenges. Early detection and accurate quantification of these hazards are key to implementing measures to mitigate their impact on production and safeguard consumers. This review summarises the methods currently used and the technological advances in the detection of biological hazards affecting bivalves, for the screening of known hazards and discovery of new ones.

Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

Optimizing FlowCam Imaging Flow Cytometry Operation for Classification and Quantification of Microcystis Morphospecies

Microcystis is a globally known cyanobacterium causing potentially toxic blooms worldwide. Different morphospecies with specific morphological and physiological characters usually co-occur during blooming, and their quantification employing light microscopy can be time-consuming and problematic. A benchtop imaging flow cytometer (IFC) FlowCam (Yokogawa Fluid Imaging Technologies, USA) was used to identify and quantitate different Microcystis morphospecies from environmental samples. We describe here the FlowCam methodology for sample processing and analysis of five European morphospecies of Microcystis common to the temperate zone. The FlowCam technique allows detection of different Microcystis morphospecies providing objective qualitative and quantitative data for statistical analysis.

FlowCam 8400 and FlowCam Cyano Phytoplankton Classification and Viability Staining by Imaging Flow Cytometry

This chapter provides a protocol for a detailed evaluation of phytoplankton and nuisance cyanobacteria with the FlowCam 8400 and the FlowCam Cyano. The chapter includes (i) detailed description of the quality control of fluorescent mode of the FlowCam, (ii) detailing methods for discriminating nuisance cyanobacteria using the FlowCam Cyano, how to set up libraries and classification routines for commonly used classification reports, and (iii) detailing methods for viability staining to quantify LIVE versus DEAD phytoplankton using the FlowCam 8400.

Photonic Microfluidic Technologies for Phytoplankton Research

Phytoplankton is a crucial component for the correct functioning of different ecosystems, climate regulation and carbon reduction. Being at least a quarter of the biomass of the world's vegetation, they produce approximately 50% of atmospheric O2 and remove nearly a third of the anthropogenic carbon released into the atmosphere through photosynthesis. In addition, they support directly or indirectly all the animals of the ocean and freshwater ecosystems, being the base of the food web. The importance of their measurement and identification has increased in the last years, becoming an essential consideration for marine management. The gold standard process used to identify and quantify phytoplankton is manual sample collection and microscopy-based identification, which is a tedious and time-consuming task and requires highly trained professionals. Microfluidic Lab-on-a-Chip technology represents a potential technical solution for environmental monitoring, for example, in situ quantifying toxic phytoplankton. Its main advantages are miniaturisation, portability, reduced reagent/sample consumption and cost reduction. In particular, photonic microfluidic chips that rely on optical sensing have emerged as powerful tools that can be used to identify and analyse phytoplankton with high specificity, sensitivity and throughput. In this review, we focus on recent advances in photonic microfluidic technologies for phytoplankton research. Different optical properties of phytoplankton, fabrication and sensing technologies will be reviewed. To conclude, current challenges and possible future directions will be discussed.

To Die or Not to Die—Regulated Cell Death and Survival in Cyanobacteria

Regulated cell death (RCD) is central to the development, integrity, and functionality of multicellular organisms. In the last decade, evidence has accumulated that RCD is a universal phenomenon in all life domains. Cyanobacteria are of specific interest due to their importance in aquatic and terrestrial habitats and their role as primary producers in global nutrient cycling. Current knowledge on cyanobacterial RCD is based mainly on biochemical and morphological observations, often by methods directly transferred from vertebrate research and with limited understanding of the molecular genetic basis. However, the metabolism of different cyanobacteria groups relies on photosynthesis and nitrogen fixation, whereas mitochondria are the central executioner of cell death in vertebrates. Moreover, cyanobacteria chosen as biological models in RCD studies are mainly colonial or filamentous multicellular organisms. On the other hand, unicellular cyanobacteria have regulated programs of cellular survival (RCS) such as chlorosis and post-chlorosis resuscitation. The co-existence of different genetically regulated programs in cyanobacterial populations may have been a top engine in life diversification. Development of cyanobacteria-specific methods for identification and characterization of RCD and wider use of single-cell analysis combined with intelligent image-based cell sorting and metagenomics would shed more light on the underlying molecular mechanisms and help us to address the complex colonial interactions during these events. In this review, we focus on the functional implications of RCD in cyanobacterial communities.

Changes in Phytoplankton Community Composition and Phytoplankton Cell Size in Response to Nitrogen Availability Depend on Temperature

The climate-driven changes in temperature, in combination with high inputs of nutrients through anthropogenic activities, significantly affect phytoplankton communities in shallow lakes. This study aimed to assess the effect of nutrients on the community composition, size distribution, and diversity of phytoplankton at three contrasting temperature regimes in phosphorus (P)–enriched mesocosms and with different nitrogen (N) availability imitating eutrophic environments. We applied imaging flow cytometry (IFC) to evaluate complex phytoplankton communities changes, particularly size of planktonic cells, biomass, and phytoplankton composition. We found that N enrichment led to the shift in the dominance from the bloom-forming cyanobacteria to the mixed-type blooming by cyanobacteria and green algae. Moreover, the N enrichment stimulated phytoplankton size increase in the high-temperature regime and led to phytoplankton size decrease in lower temperatures. A combination of high temperature and N enrichment resulted in the lowest phytoplankton diversity. Together these findings demonstrate that the net effect of N and P pollution on phytoplankton communities depends on the temperature conditions. These implications are important for forecasting future climate change impacts on the world’s shallow lake ecosystems.

Salinity tolerance mechanisms of an Arctic Pelagophyte using comparative transcriptomic and gene expression analysis

Little is known at the transcriptional level about microbial eukaryotic adaptations to short-term salinity change. Arctic microalgae are exposed to low salinity due to sea-ice melt and higher salinity with brine channel formation during freeze-up. Here, we investigate the transcriptional response of an ice-associated microalgae over salinities from 45 to 8. Our results show a bracketed response of differential gene expression when the cultures were exposed to progressively decreasing salinity. Key genes associated with salinity changes were involved in specific metabolic pathways, transcription factors and regulators, protein kinases, carbohydrate active enzymes, and inorganic ion transporters. The pelagophyte seemed to use a strategy involving overexpression of Na+-H+ antiporters and Na+ -Pi symporters as salinity decreases, but the K+ channel complex at higher salinities. Specific adaptation to cold saline arctic conditions was seen with differential expression of several antifreeze proteins, an ice-binding protein and an acyl-esterase involved in cold adaptation.

Growth Characteristic Analysis of Haematococcus pluvialis in a Microfluidic Chip Using Digital in-Line Holographic Flow Cytometry

In order to obtain high yield of astaxanthin, a high-value compound with ultrastrong antioxidant capacity, it is necessary to identify the growth characteristics (biomass, morphology, and size) of Haematococcus pluvialis. The current detection methods have the disadvantages of labor-consuming operation or complicated measurement system. It is an urgent need to explore a simple and cost-effective method for the detection of H. pluvialis with large size distribution during its growth period. In this work, a digital in-line holographic flow cytometry using a linear array sensor is proposed to measure the growth characteristics of H. pluvialis in a two-dimensional (2-D) hydrodynamic focusing microfluidic chip. Based on the modified angular spectrum method, the distorting holograms caused by the asynchrony of sample flow velocity and acquisition speed of the linear array sensor were rectified and reconstructed. In addition, the depth-of-focus of the imaging system were digitally extended to cover the entire depth of the microfluidic channel for optimized imaging quality. We have utilized the proposed method to statistically investigate the biomass, morphology and size of H. pluvialis under different culture conditions and growth durations.

Multispectral Imaging Flow Cytometry with Spatially and Spectrally Resolving Snapshot-Mosaic Cameras for the Characterization and Classification of Bioparticles

In the development and optimization of biotechnological cultivation processes the continuous monitoring through the acquisition and interpretation of spectral and morphological properties of bioparticles are challenging. There is therefore a need for the parallel acquisition and interpretation of spatially and spectrally resolved measurements with which particles can be characterized and classified in-flow with high throughput. Therefore, in this paper we investigated the scientific and technological connectivity of standard imaging flow cytometry (IFC) with filter-on-chip based spatially and spectrally resolving snapshot-mosaic cameras for photonic sensing and control in a smart and innovative microfluidic device. For the investigations presented here we used the microalgae Haematococcus pluvialis (HP). These microalgae are used commercially to produce the antioxidant keto-carotenoid astaxanthin. Therefore, HP is relevant to practically demonstrate the usability of the developed system for Multispectral Imaging Flow Cytometry (MIFC) platform. The extension of standard IFC with snapshot-mosaic cameras and multivariate data processing is an innovative approach for the in-flow characterization and derived classification of bioparticles. Finally, the multispectral data acquisition and the therefore developed methodology is generalizable and enables further applications far beyond the here characterized population of HP cells.

Comparative analysis of freshwater phytoplankton communities in two lakes of Burabay National Park using morphological and molecular approaches

We analyzed phytoplankton assemblages' variations in oligo-mesotrophic Shchuchie and Burabay lakes using traditional morphological and next-generation sequencing (NGS) approaches. The total phytoplankton biodiversity and abundance estimated by both microscopy and NGS were significantly higher in Lake Burabay than in Lake Shchuchie. NGS of 16S and 18S rRNA amplicons adequately identify phytoplankton taxa only on the genera level, while species composition obtained by microscopic examination was significantly larger. The limitations of NGS analysis could be related to insufficient coverage of freshwater lakes phytoplankton by existing databases, short algal sequences available from current instrumentation, and high homology of chloroplast genes in eukaryotic cells. However, utilization of NGS, together with microscopy allowed us to perform a complete taxonomic characterization of phytoplankton lake communities including picocyanobacteria, often overlooked by traditional microscopy. We demonstrate the high potential of an integrated morphological and molecular approach in understanding the processes of organization in aquatic ecosystem assemblages.

Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

A machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray-Curtis dissimilarity, and Kullback-Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96-100%) but relatively low intrageneric accuracy (67-78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

The ecology of wine fermentation: a model for the study of complex microbial ecosystems

The general interest in microbial ecology has skyrocketed over the past decade, driven by technical advances and by the rapidly increasing appreciation of the fundamental services that these ecosystems provide. In biotechnology, ecosystems have many more functionalities than single species, and, if properly understood and harnessed, will be able to deliver better outcomes for almost all imaginable applications. However, the complexity of microbial ecosystems and of the interactions between species has limited their applicability. In research, next generation sequencing allows accurate mapping of the microbiomes that characterise ecosystems of biotechnological and/or medical relevance. But the gap between mapping and understanding, to be filled by "functional microbiomics", requires the collection and integration of many different layers of complex data sets, from molecular multi-omics to spatial imaging technologies to online ecosystem monitoring tools. Holistically, studying the complexity of most microbial ecosystems, consisting of hundreds of species in specific spatial arrangements, is beyond our current technical capabilities, and simpler model systems with fewer species and reduced spatial complexity are required to establish the fundamental rules of ecosystem functioning. One such ecosystem, the ecosystem responsible for natural alcoholic fermentation, can provide an excellent tool to study evolutionarily relevant interactions between multiple species within a relatively easily controlled environment. This review will critically evaluate the approaches that are currently implemented to dissect the cellular and molecular networks that govern this ecosystem. KEY POINTS: • Evolutionarily isolated fermentation ecosystem can be used as an ecological model. • Experimental toolbox is gearing towards mechanistic understanding of this ecosystem. • Integration of multidisciplinary datasets is key to predictive understanding.

Cell Death in Cyanobacteria: Current Understanding and Recommendations for a Consensus on Its Nomenclature

Cyanobacteria are globally widespread photosynthetic prokaryotes and are major contributors to global biogeochemical cycles. One of the most critical processes determining cyanobacterial eco-physiology is cellular death. Evidence supports the existence of controlled cellular demise in cyanobacteria, and various forms of cell death have been described as a response to biotic and abiotic stresses. However, cell death research in this phylogenetic group is a relatively young field and understanding of the underlying mechanisms and molecular machinery underpinning this fundamental process remains largely elusive. Furthermore, no systematic classification of modes of cell death has yet been established for cyanobacteria. In this work, we analyzed the state of knowledge in the field of cyanobacterial cell death. Based on that, we propose unified criterion for the definition of accidental, regulated, and programmed forms of cell death in cyanobacteria based on molecular, biochemical, and morphologic aspects following the directions of the Nomenclature Committee on Cell Death (NCCD). With this, we aim to provide a guide to standardize the nomenclature related to this topic in a precise and consistent manner, which will facilitate further ecological, evolutionary, and applied research in the field of cyanobacterial cell death.

Lab-on-a-chip technology for in situ combined observations in oceanography

The oceans sustain the global environment and diverse ecosystems through a variety of biogeochemical processes and their complex interactions. In order to understand the dynamism of the local or global marine environments, multimodal combined observations must be carried out in situ. On the other hand, instrumentation of in situ measurement techniques enabling biological and/or biochemical combined observations is challenging in aquatic environments, including the ocean, because biochemical flow analyses require a more complex configuration than physicochemical electrode sensors. Despite this technical hurdle, in situ analyzers have been developed to measure the concentrations of seawater contents such as nutrients, trace metals, and biological components. These technologies have been used for cutting-edge ocean observations to elucidate the biogeochemical properties of water mass with a high spatiotemporal resolution. In this context, the contribution of lab-on-a-chip (LoC) technology toward the miniaturization and functional integration of in situ analyzers has been gaining momentum. Due to their mountability, in situ LoC technologies provide ideal instrumentation for underwater analyzers, especially for miniaturized underwater observation platforms. Consequently, the appropriate combination of reliable LoC and underwater technologies is essential to realize practical in situ LoC analyzers suitable for underwater environments, including the deep sea. Moreover, the development of fundamental LoC technologies for underwater analyzers, which operate stably in extreme environments, should also contribute to in situ measurements for public or industrial purposes in harsh environments as well as the exploration of the extraterrestrial frontier.

Characterization of phytoplankton community in a river ecosystem using pigment composition: a feasibility study

CHEMTAX is a mathematical software for phytoplankton composition evaluation using pigment composition. Although this method has been previously applied in the ocean environment, we firstly utilized the combination of matrix factorization program CHEMTAX and high-performance liquid chromatography (HPLC) to characterize the phytoplankton community from a river system (western part of Weihe River Basin). The obtained results were compared with those from microscopic examination. Based on the comparison, it is suggested that after increasing the ratio of characteristic pigment to chlorophyll a of diatoms and euglena, the diatoms calculated by the CHEMTAX method accounted for 80% of the total biomass, and the results were consistent with microscopic examination, but diatoms obtained from F2, C1 and W5 sample sites were significantly overestimated 33%~60%. The comparison also showed that the model always underestimated cyanobacteria (sample sites F2, C1 were underestimated 25%) and euglena were overestimated (sample sites W3, Q1 were respectively overestimated 33%, 23%), but for chlorophytes, both overestimation and underestimation could occur. When the relevant results from previous applications in the ocean phytoplankton community evaluation were taken into consideration, it can be concluded that CHEMTAX-HPLC method was not accurate enough to characterize the phytoplankton communities in the freshwater (river/lake) ecosystem.

A new method for isolating and analysing coccospheres within sediment

Size is a fundamental cellular trait that is important in determining phytoplankton physiological and ecological processes. Fossil coccospheres, the external calcite structure produced by the excretion of interlocking plates by the phytoplankton coccolithophores, can provide a rare window into cell size in the past. Coccospheres are delicate however and are therefore poorly preserved in sediment. We demonstrate a novel technique combining imaging flow cytometry and cross-polarised light (ISX^+PL) to rapidly and reliably visually isolate and quantify the morphological characteristics of coccospheres from marine sediment by exploiting their unique optical and morphological properties. Imaging flow cytometry combines the morphological information provided by microscopy with high sample numbers associated with flow cytometry. High throughput imaging overcomes the constraints of labour-intensive manual microscopy and allows statistically robust analysis of morphological features and coccosphere concentration despite low coccosphere concentrations in sediments. Applying this technique to the fine-fraction of sediments, hundreds of coccospheres can be visually isolated quickly with minimal sample preparation. This approach has the potential to enable rapid processing of down-core sediment records and/or high spatial coverage from surface sediments and may prove valuable in investigating the interplay between climate change and coccolithophore physiological/ecological response.

Cell-by-cell estimation of PAH sorption and subsequent toxicity in marine phytoplankton

Polycyclic Aromatic Hydrocarbons (PAHs) have elicited increasing concern due to their ubiquitous occurrence in coastal marine environments and resultant toxicity in organisms. Due to their lipophilic nature, PAHs tend to accumulate in phytoplankton cells and thus subsequently transfer to other compartments of the marine ecosystem. The intrinsic fluorescence properties of PAHs in the ultraviolet (UV)/blue spectral range have recently been exploited to investigate their uptake modes, localization, and aggregation in various biological tissues. Here, we quantitatively evaluate the sorption of two model PAHs (phenanthrene and pyrene) in three marine phytoplankton species (Chaetoceros tenuissimus, Thalassiosira sp. and Proteomonas sp.) using a combined approach of UV excitation flow cytometry and fluorescence microscopy. Over a 48-h exposure to a gradient of PAHs, Thalassiosira sp. showed the highest proportion of PAH-sorbed cells (29% and 97% of total abundance for phenanthrene and pyrene, respectively), which may be attributed to its relatively high total lipid content (33.87 percent dry weight). Moreover, cell-specific pulse amplitude modulation (PAM) microscope fluorometry revealed that PAH sorption significantly reduced the photosynthetic quantum efficiency (F_v/F_m) of individual phytoplankton cells. We describe a rapid and precise hybrid method for the detection of sorption of PAHs on phytoplankton cells. Our results emphasize the ecologically relevant sub-lethal effects of PAHs in phytoplankton at the cellular level, even at concentrations where no growth inhibition was apparent. This work is the first study to address the cell-specific impacts of fluorescent toxicants in a more relevant toxicant-sorbed subpopulation; these cell-specific impacts have to date been unidentified in traditional population-based phytoplankton toxicity assays.

The Application of Nanopore Sequencing Technology to the Study of Dinoflagellates: A Proof of Concept Study for Rapid Sequence-Based Discrimination of Potentially Harmful Algae

Harmful algal blooms (HABs) are a naturally occurring global phenomena that have the potential to impact fisheries, leisure and ecosystems, as well as posing a significant hazard to animal and human health. There is significant interest in the development and application of methodologies to study all aspects of the causative organisms and toxins associated with these events. This paper reports the first application of nanopore sequencing technology for the detection of eukaryotic harmful algal bloom organisms. The MinION sequencing platform from Oxford Nanopore technologies provides long read sequencing capabilities in a compact, low cost, and portable format. In this study we used the MinION to sequence long-range PCR amplicons from multiple dinoflagellate species with a focus on the genus Alexandrium. Primers applicable to a wide range of dinoflagellates were selected, meaning that although the study was primarily focused on Alexandrium the applicability to three additional genera of toxic algae, namely; Gonyaulax, Prorocentrum, and Lingulodinium was also demonstrated. The amplicon generated here spanned approximately 3 kb of the rDNA cassette, including most of the 18S, the complete ITS1, 5.8S, ITS2 and regions D1 and D2 of the 28S. The inclusion of barcode genes as well as highly conserved regions resulted in identification of organisms to the species level. The analysis of reference cultures resulted in over 99% of all sequences being attributed to the correct species with an average identity above 95% from a reference list of over 200 species (see Supplementary Material 1). The use of mock community analysis within environmental samples highlighted that complex matrices did not prevent the ability to distinguish between phylogenetically similar species. Successful identification of causative organisms in environmental samples during natural toxic events further highlighted the potential of the assay. This study proves the suitability of nanopore sequencing technology for taxonomic identification of harmful algal bloom organisms and acquisition of data relevant to the World Health Organisations "one health" approach to marine monitoring.

Effects of pre-ozonation on the cell characteristics and N-nitrosodimethylamine formation at three growth phases of Microcystis aeruginosa

Pre-oxidation in water treatment is considered an effective method to enhance the removal of algal cells and their exuded organic matters. However, pre-oxidation also alters the characteristics of algae and consequently influences disinfection processes. The existing studies mainly focused on the stationary growth phase, but little is known for the exponential and declined phases. The objectives of this study were to examine the effects of pre-ozonation on the integrity of algal cells, the release of algal organic matters, and the formation of disinfection by-products like N-nitrosodimethylamine (NDMA) from Microcystis aeruginosa (M. aeruginosa) at three growth phases. The results demonstrated that pre-ozonation was efficient to inactivate M. aeruginosa cells. The severity of M. aeruginosa cell damage increased as the ozone dosage increased from 0.5 to 2.0 mg/L. The damage of cell membranes resulted in the release of intracellular organic matters. Excitation-emission matrix spectra (EEMS) analysis indicated that ozone mainly reacted with soluble microbial products (SMP). With the increase of ozone concentration, although the trend of NDMA formation was similar for all three growth phases, more production of NDMA by algal cells was observed at the declined phase. In the post-disinfection process, chloramine showed the potential as a more suitable disinfectant than chlorination after pre-ozonation to minimize the NDMA formation. Therefore, appropriate pre-ozonation is beneficial to reduce the NDMA formation from exponential algae, while has no significant change during both stationary and declined phases.

Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.

Sizing biological cells using a microfluidic acoustic flow cytometer

We describe a new technique that combines ultrasound and microfluidics to rapidly size and count cells in a high-throughput and label-free fashion. Using 3D hydrodynamic flow focusing, cells are streamed single file through an ultrasound beam where ultrasound scattering events from each individual cell are acquired. The ultrasound operates at a center frequency of 375 MHz with a wavelength of 4 μm; when the ultrasound wavelength is similar to the size of a scatterer, the power spectra of the backscattered ultrasound waves have distinct features at specific frequencies that are directly related to the cell size. Our approach determines cell sizes through a comparison of these distinct spectral features with established theoretical models. We perform an analysis of two types of cells: acute myeloid leukemia cells, where 2,390 measurements resulted in a mean size of 10.0 ± 1.7 μm, and HT29 colorectal cancer cells, where 1,955 measurements resulted in a mean size of 15.0 ± 2.3 μm. These results and histogram distributions agree very well with those measured from a Coulter Counter Multisizer 4. Our technique is the first to combine ultrasound and microfluidics to determine the cell size with the potential for multi-parameter cellular characterization using fluorescence, light scattering and quantitative photoacoustic techniques.

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.

Microfluidic Based Optical Microscopes on Chip

Last decade's advancements in optofluidics allowed obtaining an ever increasing integration of different functionalities in lab on chip devices to culture, analyze, and manipulate single cells and entire biological specimens. Despite the importance of optical imaging for biological sample monitoring in microfluidics, imaging is traditionally achieved by placing microfluidics channels in standard bench-top optical microscopes. Recently, the development of either integrated optical elements or lensless imaging methods allowed optical imaging techniques to be implemented in lab on chip systems, thus increasing their automation, compactness, and portability. In this review, we discuss known solutions to implement microscopes on chip that exploit different optical methods such as bright-field, phase contrast, holographic, and fluorescence microscopy.

A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples

We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h. The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel. These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling. Motion blur is eliminated by simultaneously illuminating the sample with red, green, and blue light-emitting diodes that are pulsed. Operated by a laptop computer, this portable device measures 15.5 cm × 15 cm × 12.5 cm, weighs 1 kg, and compared to standard imaging flow cytometers, it provides extreme reductions of cost, size and weight while also providing a high volumetric throughput over a large object size range. We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro- and nanoplankton composition. Furthermore, we measured the concentration of a potentially toxic alga (Pseudo-nitzschia) in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health. The cost-effectiveness, compactness, and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for large-scale and continuous monitoring of the ocean microbiome, including its plankton composition.

Flow Cytometry for Rapid Enumeration and Biomass Quantification of Thraustochytrids in Coastal Seawaters

Marine fungus-like eukaryotic unicellular protists (thraustochytrids) are considered to play an important role in the marine microbial food web. However, their abundance, distribution, and relative biomass in coastal waters have not yet been examined in detail. By using a flow cytometry method (FCM) for the rapid enumeration of thraustochytrids in nearshore and offshore stations along the Gulf of Bohai, China, we herein expanded current knowledge on their ecological significance. The FCM method allows for the rapid detection and quantification of prokaryotic and eukaryotic cells, but is rarely applied to the enumeration of small eukaryotic protists. Epifluorescence microscopy (EpiM) has been commonly used for the direct detection and enumeration of thraustochytrids; however, this method is time-consuming and inapplicable to a large-scale analysis of complex seawater samples. There is no available FCM method to track the abundance and biomass of thraustochytrids in marine habitats. The FCM enumeration of thraustochytrids in seawater samples ranged between 400 and 4,080 cells mL-1 with a biomass range of 8.15-83.96 μg C L-1. The thraustochytrid biomass contributed 10.9% to 98.1% of the total biomass of the heterotrophic microbial community comprising bacterioplankton and thraustochytrids. Their overall abundance in nearshore stations was significantly different from that in offshore stations (P<0.5). The present results provide an optimized method for the rapid detection and enumeration of thraustochytrids in seawater and facilitate large-scale studies of the ecological role of thraustochytrids in the microbial food web of coastal waters.

Simultaneous dual-color light sheet fluorescence imaging flow cytometry for high-throughput marine phytoplankton analysis

This paper reports the development of a dual-color light sheet fluorescence imaging flow cytometer exclusively designed for rapid phytoplankton analysis. By simultaneously exciting chlorophyll and phycoerythrin fluorescence, the system is enabled to discriminate phycoerythrin-containing and phycoerythrin-lacking phytoplankton groups through simultaneous two-channel spectral imaging-in-flow. It is demonstrated the system has good sensitivity and resolution to detect picophytoplankton down to the size of ~1μm, high throughput of 1.3 × 10⁵cells/s and 5 × 10³cells/s at 100μL/min and 3mL/min volume flow rates for cultured picophytoplankton and nanophytoplankton detection, respectively, and a broad imaging range from ~1μm up to 300μm covering most marine phytoplankton cell sizes with just one 40 × objective. The simultaneous realization of high resolution, high sensitivity and high throughput with spectral resolving power of the system is expected to promote the technology towards more practical applications that demand automated phytoplankton analysis.

Flow cytometry what you see matters: Enhanced clinical detection using image-based flow cytometry

Image-based flow cytometry combines the throughput of traditional flow cytometry with the ability to visually confirm findings and collect novel data that would not be possible otherwise. Since image-based flow cytometry borrows measurement parameters and analysis techniques from microscopy, it is possible to collect unique measures (i.e. nuclear translocation, co-localization, cellular synapse, cellular endocytosis, etc.) that would not be possible with traditional flow cytometry. The ability to collect unique outcomes has led many researchers to develop novel assays for the monitoring and detection of a variety of clinical conditions and diseases. In many cases, investigators have innovated and expanded classical assays to provide new insight regarding clinical conditions and chronic disease. Beyond human clinical applications, image-based flow cytometry has been used to monitor marine biology changes, nano-particles for solar cell production, and particle quality in pharmaceuticals. This review article summarizes work from the major scientists working in the field of image-based flow cytometry.