Imaging flow cytometry for phytoplankton analysis

Accepted Manuscript Imaging flow cytometry for phytoplankton analysis Veronika Dashkova, Dmitry Malashenkov, Nicole Poulton, Ivan Vorobjev, Natasha S Barteneva PII: DOI: Reference:

S1046-2023(16)30130-X http://dx.doi.org/10.1016/j.ymeth.2016.05.007 YMETH 3985

To appear in:

Methods

Received Date: Accepted Date:

20 April 2016 13 May 2016

Please cite this article as: V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, N.S. Barteneva, Imaging flow cytometry for phytoplankton analysis, Methods (2016), doi: http://dx.doi.org/10.1016/j.ymeth.2016.05.007

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Imaging flow cytometry for phytoplankton analysis Veronika Dashkova1*, Dmitry Malashenkov1,2, Nicole Poulton3, Ivan Vorobjev1,4, Natasha S Barteneva1,5 1

National

Laboratory

Astana,

Nazarbayev

University,

2

Department

of

Hydrobiology,

M.V.Lomonosov Moscow State University, 3Bigelow Laboratory for Ocean Sciences, 4Department of Cell Biology and Histology and A.N. Belozersky Institute of Physico-Chemical Biology, M.V. Lomonosov Moscow State University, 5Program in Molecular and Cellular Medicine, BCHHarvard Medical School

§

Corresponding author:

Veronika Dashkova National Laboratory Astana Nazarbayev University, Block 9, room 408 53 Kabanbay batyr Ave, Astana, 010000, Kazakhstan e-mail: [email protected] phone: 7-7172-70-9258

Abstract: 151 MS words: 4270

Abbreviations: IFC – imaging flow cytometry; FACS – fluorescence activated cell sorting; ESD – equivalent spherical diameter; ABD – area-based diameter; IFCB – Imaging FlowCytoBot; HAB – harmful algal bloom; TAG – triacylglycerol; SB – SYTOX Blue; PCD – programmed cell death; PS – phosphatidylserine; ROS – reactive oxygen species.

Abstract This review highlights the concepts and instrumentation of imaging flow cytometry technology and in particular its use for phytoplankton analysis. Imaging flow cytometry, a hybrid technology combining speed and statistical capabilities of flow cytometry with imaging features of microscopy, is rapidly advancing as a cell imaging platform that overcomes many of the limitations of current techniques and contributed significantly to the advancement of phytoplankton analysis in recent years. This review presents the various instrumentation relevant to the field and currently used for assessment of complex phytoplankton communities` composition and abundance, size structure determination, biovolume estimation, detection of harmful algal bloom species, evaluation of viability and metabolic activity and other applications. Also we present our data on viability and metabolic assessment of Aphanizomenon sp. cyanobacteria using Imagestream X Mark II imaging cytometer.

Herein, we highlight the

immense potential of imaging flow cytometry for microalgal research, but also discuss limitations and future developments. Keywords: microalgae; phytoplankton; imaging flow cytometry; viability; metabolic activity

1. Introduction Flow cytometry, was first introduced in algal research in the 1980s [1, 2, 3], and is now a well-established tool for phytoplankton analysis. Combined with cell sorting capabilities, it is a critical instrument for aquatic ecology studies, particularly for discrimination of various phytoplankton groups [4, 5], detection of rare and pico-size phytoplankton [6, 7], quantification of microalgal populations/cells [8], monitoring of seasonal dynamics [9, 10, 11], assessment of viability and functional cellular features [12, 13]. Undoubtedly, the most groundbreaking flow cytometric option is the capacity to sort distinct microalgal populations from a sample for various applications including cultivation [14, 15, 16], molecular analysis [17, 18, 19], groupspecific biomass and elemental composition evaluation [20, 21, 22]. In spite of advantageous capability of flow cytometry to measure multiple parameters for each individual cell in highthroughput mode, conventional approaches of characterization and quantification using microscopy are still prevalent in microalgae analysis. This is mainly due to the lack of imaging capacity of conventional flow cytometers that would allow adequate visualization of cellular morphological features. In this light, the emerging of IFC – a hybrid technology combining speed and large sample size capabilities of flow cytometry and imaging capabilities of microscopy (up to 60x for Imagestream X imaging cytometer) contributed significantly to the advancement of microalgae analysis. More insight into microalgae morphology, cellular processes, cell-to-cell interactions, population dynamics and ecology is gained using IFC [23]. 2. Major imaging flow cytometers in phytoplankton research 2.1. FlowCam imaging cytometer There are several commercially available imaging flow cytometers that can be applied to study aquatic photosynthetic microorganisms (Fig. 1). A dynamic imaging particle analyzer FlowCam (Fluid Imaging Technologies, Yarmouth, ME, USA) [24] was originally invented/developed for oceanic plankton studies and later diversified its applications for use in aquatic research, algae technology, water management, biopharmaceuticals, oil and gas industry. The FlowCAM platform [24] is based on the synchronous action of flash illumination of the sample and camera trigger at very short time intervals to obtain an image of a moving particle in real time. The instrument can be operated in either fluorescence-triggered mode or auto trigger (auto image) mode. In the auto image mode, grayscale or color images can be obtained

depending on the camera type. Once the sample is loaded using a syringe or peristaltic pump into a flow cell, the contents of the sample can be instantly visualized on the monitor and the images of magnified particles are automatically captured to generate an image library for that sample. Fluorescence measurements can be additionally obtained by employing an optional 488 nm and/or 532 nm laser source triggering on chlorophyll fluorescence of the individual algal cells. Analysis software allows for characterization of each particle image using 40 measurements including length, width, aspect ratio, equivalent spherical diameter (ESD), area-based diameter (ABD) and others [25]. Finally, a broad range of particle sizes (from 2 µm to 2 mm) that can be analyzed by FlowCam makes it suitable for laboratory and field microalgae analysis, especially for heterogeneous environmental samples. 2.2. Submersible Imaging FlowCytobot An alternative imaging flow cytometer that is specialized for aquatic applications is the Imaging FlowCytobot (IFCB) (McLane Research Laboratories, East Falmouth, USA) [26] with a submersible option developed from its prototype FlowCytoBot [27]. IFCB can be submersed and operated at maximum depth of 40 m for up to 6 months with the transmission of acquired data to a remote facility in real time. The functionality of the IFCB is similar to that of the standard flow cytometer and relies on hydrodynamic focusing of a sample stream passing a laser interrogation point (red diode laser beam 635 nm for chlorophyll). Laser excitation of the sample results in light scattering and fluorescence emission from chlorophyll in cells, which in turn triggers a xenon flash lamp to illuminate the flow cell by filter-selected green light [26]. Automated taxonomic classification algorithm based on comparative evaluation a number of classifiers (22 categories) can be applied to microalgae images acquired by a monochrome CCD camera. The majority of the categories comprise diatom genera, specific to the described study site (instrument allows the analysis of large cells up to 150 µm, Fig. 2), and therefore the classifier requires adaptation and/or update according to the local phytoplankton composition [28]. 2.3. Imagestream family of imaging flow cytometers In recent years the number of imaging cytometers has increased to include the Imagestream X Mark II (equipped with 20x, 40x and 60x objectives) and its less complex version the FlowSight (equipped with only 20x objective) (Amnis-Merck Inc, Seattle, USA). Unlike the other imaging flow cytometers for aquatic studies, the Imagestream instrument family

has been primarily used and developed within the biomedical industry for cellular and subcellular analysis applications including cell signaling, cell cycle and mitosis, cell-to-cell interactions, internalization and co-localization, cell death and many others. This is a powerful instrumentation platform that combines cell imaging in bright field and dark field (side scatter), and a series of fluorescent images (fluorescence panel with up to 10 fluorescent channels) that can be generated using a maximum number of 7 lasers with varying, software-controlled power from tenths of 1 mW up to hundreds of mW. The operation principle of Imagestream instruments is based on simultaneous (co-linear) illumination of the flow cell by laser light sources. Transmitted and scattered light, as well as fluorescence emissions are directed through a microscope objective and further separated through a dichroic filter stack onto the surface of a CCD camera with six channels serving as detectors for the system [29]. Up to 2 CCD cameras can be employed by the instrument. The unique time delay integration feature allows integration of several images of a single moving-in-flow particle captured over the entire field of view of CCD camera. 3. Major applications of IFC for phytoplankton analysis The aim of this review is to summarize main applications of IFC technology for the analysis of microalgae cultures and natural phytoplankton communities that have been published to date (Table 1) and also to present new data on viability and metabolic activities of cyanobacteria (Aphanizomenon sp.) obtained using Imagestream X. The criteria for selecting different applications as listed in the table are based on the primary measurements/features derived from the IFC software and exclude secondary parameters obtained through additional manipulations.

Table 1. Applications of imaging flow cytometry for phytoplankton analysis. ANALYSIS TYPE

INSTRUMENT USED FlowCam

Composition and/or abundance

+ + + + + +

IFCB

Imagestream/Flow Sight

PHYTOPLANKTON

REFERENCE

SPECIES

Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities

[30] [31] [32] [33] [34] [35]

+

+ + + + + + + + + + + + + + + + + +

+ + + + + + + + + + + Size structure

+ + + + +

Alexandrium tamarense Biddulphia sp. Cyclotella meneghiniana Gambierdiscus toxicus Nitzschia ovalis Navicula sp. Natural communities Natural communities Natural communities Heterosigma akashiwo Natural communities Natural communities Natural communities Natural communities Rhodomonas salina Natural communities Cyanobacteria Karenia brevis Natural communities Natural communities Natural communities Phaeocystis spp. Natural communities Natural communities Phaeodactylum tricornutum, Chlorella pyrenoidosa, Scenedesmus obliquus, Isochrysis sp. Natural communities Skeletonema marinoi Phaeocystis pouchetii Cultures** Skeletonema marinoi Natural communities Nannochloropsis salina Nannochloropsis salina Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Prorocentrum

[24]

[36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53]

[54] [55] [56] [57] [58] [59] [60] [61] [62] [26] [63] [64] [65] [37] [32] [66]

+ +

+

+ + + + + + + + + Biovolume

+ + +

+ + + + + + + + + + + + + +

minimum Thalassiosira weissflogii Rhodomonas salina Skeletonema marinoi Natural communities Natural communities Alexandrium tamarense Biddulphia sp. Cyclotella meneghiniana Gambierdiscus toxicus Nitzschia ovalis Navicula sp. Natural communities Achnanthidium minutissimum, Didymosphenia geminata, Hannaea baicalensis Cultures* Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Natural communities Prorocentrum minimum Thalassiosira weissflogii Rhodomonas salina Skeletonema marinoi Natural communities Rhodomonas salina Natural communities; diatoms Natural communities Natural communities Natural communities Natural communities Natural communities Cyanobacteria Natural communities Natural communities Natural communities Natural communities Ditylum brightwellii Nannochloropsis

[67] [24]

[68]

[69] [39] [70] [71] [49] [31] [72] [73] [62] [74] [30] [66]

[43] [34] [75] [76] [65] [36] [37] [45] [47] [70] [71] [31] [77] [60]

salina Natural communities Natural communities Natural communities Alexandrium fundyense Natural communities Karenia brevis Karenia brevis Microcystis spp. Microcystis spp. Karenia mikimotoi Dinophysis spp. Karenia brevis Brachidinium capitatum Dinophysis sp. Mesodinium sp. Alexandrium fundyense Microcystis aeruginosa Oscillatoria sp. Lyngbia sp. Alexandrium pseudogonyaulax Asterionella formosa Synedra cf. acus Aulacoseira cf. granulate Fragilaria crotonensis Tabellaria cf. fenestrata

+ + + + + + HAB detection

+ + + + + + + + + +

Morphology (including life cycle and cell cycle)

+ +

+ + + + + + + + + + + + Viability and

Botryococcus braunii Botryococcus braunii Nannochloropsis sp. Brachidinium capitatum Alexandrium fundyense Alexandrium fundyense Dinophysis spp. Dinophysis sp. Mesodinium sp. Guinardia delicatula Alexandrium minutum Craspedostauros australis Chlorococcum littorale

[61] [73] [78] [79] [62] [80] [81] [82] [83] [84] [85] [86] [87] [88] [79] [89]

[90] [91]

[92] [93] [94] [87] [95] [79] [85] [88] [96] [97] [23] [98]

metabolic activity

+ + +

Cyclotella cryptica Alexandrium minutum Chlorella protothecoides

[99] [97] [23]

*Fragillaria pinnata, Thalassiosira oceanica, Minutocellus polymorphus, Thalassiosira pseudonanna, Thalassiosira weissflogii, Chaetoceros calcitrans, Thalassiosira rotula, Guillardia theta, Hemiselmis virescens, Rhodomonas lens, Amphidinium carterae, Heterocapsa triquetra, Alexandrium tamarense, Katodinium rotundatum, Gymnodinium simplex, Heterosigma akashiwo, Dunaliella tertiolecta, Nannochloropsis sp., Isochrysis galbana, Chrysochromulina polylepis, Pavlova sp., Prasinococcus capsulatus, Micromonas pusilla, Pycnococcus provasolii, Synechococcus elongtatus, Pelagococcus subviridis, Pelagomonas calceolate. ** Ankistrodesmus falcatus, Chlorella sorokiniana, Coelastrum microporum, Cosmarium turpinii, Elakatothrix obtusata, Scenedesmus acuminatus, Selenastrum capricornotum, Staurastrum punctulatum, Tetraedron minimum

3.1. Phytoplankton abundance and composition Assessment of the abundance and composition of phytoplankton communities is one of the most common and critical components in aquatic ecology studies. Traditionally, phytoplankton cells have been identified and counted manually by a taxonomist under the microscope [100, 101]. In this sense, imaging cytometry can be complimentary to conventional microscopy allowing for the examination of larger volume samples processed at high speed with reduced reliance on the subjective visual inspection skills [24, 30, 35]. Furthermore, automatic recording of every particle passing through the flow cell using the image capture criteria of the imaging flow cytometer prevents a biased analysis of selected cells/particles by the operator using microscopy [102]. IFC is particularly suitable for the characterization of natural phytoplankton assemblages. Though IFC is not providing high taxonomic resolution [24], it is enabling fast sample processing without the need of preservation [36]. Application of imaging cytometric measurements to marine studies on phytoplankton spatial and temporal distribution has shown that phytoplankton community structure agrees well with environmental gradients [33, 37, 48, 62, 63, 103]. Relative abundances of phytoplankton species estimated through IFC can be used to describe biodiversity patterns of natural communities [30, 48]. Although imaging cytometers, e.g. FlowCam tend to overestimate cell abundances when analyzed in autoimage mode, possibly due to lower image resolution and insufficient sampling of some nanoplankton and microplankton size classes, the estimation of cell abundances in fluorescence mode provides improved accuracy [24, 30, 32]. While IFC has not completely solved the problem of the manual

taxonomic identification, attempts have been made to develop analysis algorithms for automated classification to the genus level and above (28, 37, 74, 79, 86, 104, 105]. 3.2. Size structure Size structure of phytoplankton communities is another important parameter for characterization of aquatic ecosystems. Phytoplankton size spectrum and relative abundance of each size class may provide insights into trophic interactions [39, 106], energy transfer fluxes, productivity [67, 107, 108], and bloom dynamics [37, 62, 109]. Comprehensive size structure assessment requires adequate sampling of each size class, which in real conditions is difficult to achieve due to lower abundance of large-sized phytoplankton compared to the small-sized groups [64]. Depending on the sample volume and processing time, the degree of sampling of certain size classes may vary based on characteristics of flow-based imaging systems [64]. In their experiments, [64] tested the capability of the FlowCam to efficiently sample phytoplankton size spectra covering 0.2-2000 µm using the size-abundance relationship [110] to determine optimal time required for sufficient sampling of each size class with a certain cell concentration in different analysis modes. In case of FlowCam, to cover full size spectra it is necessary to handle different combinations of magnification - using flow chambers of different size as well as concentrate samples prior to analysis and record sample characteristics in different working modes [64, 66]. Also, it is necessary to select the most suitable size parameter [66]. Numerous studies have shown good agreement of size structure measurements of mono-, mixed cultures and natural communities using imaging cytometers with those obtained with a help of conventional microscopy and Coulter counter [24, 66, 68] with some variability that appears when dealing with chain-forming and asymmetric cells [66, 68]. Generally, IFC is capable of capturing size-specific phytoplankton growth rates and grazing mortality [70], metabolic rates [31], and size structure variations driven by environmental factors [49]. Additional techniques may be used in combination with IFC to construct a whole plankton size spectra ranging from picoplankton to mesoplankton [31]. Imagestream platform has a limited size flow chamber allowing for analysis of pico- and nanoplankton (up to 120 µm). 3.3. Biovolume estimation Biovolume is a useful proxy for studying the ecology of natural phytoplankton communities as well as monitoring the properties of laboratory maintained cultures. Cell

biovolume can be derived from size measurements [111, 112]. Therefore, the degree of sizing accuracy appears to be critical for biovolume estimates [66]. However, because only twodimensional images are used to calculate cell volume in most imaging systems, it may lead to the biovolume misestimates [74]. Typically, ESD or ABD derived from two-dimensional images are used to calculate volume of phytoplankton cells [45, 60, 65, 76, 103]. However, using ESD for cells that are not spherical, but cylindrical, may also produce inaccuracies in biovolume estimates [74]. In addition, plankton cells can be oriented in different ways during the image capture in the flow cell [66] further complicating size-based biovolume estimation. Alvarez et al. [30, 74] suggested to combine taxonomical and morphological information of the objects to obtain more accurate biovolume estimates based on two-dimensional images. Similarly, other researchers used shape-based information such as aspect ratio (relation between width and height or length) of а cell to correct spherical volume estimates obtained from the software [34, 75]. An alternative approach for automatic biovolume calculation is the distance map algorithm [78], originally developed for IFCB system. The distance map algorithm is based on the identifying boundaries of a single organism on two-dimensional image, calculating distances within the boundaries in pixels and extrapolating obtained pixel values into the third dimension hidden from the camera view. For example, the latter approach was implemented to estimate cell biovolume of HAB species and enabled to determine the timing of specific life cycle stages [79] and cell parameters in response to osmotic stress [80]. Derived biovolume values can be then converted into carbon biomass using different conversion equations [113, 114, 115, 116]. 3.4. HAB detection and morphology IFC is advantageous for detection and monitoring of HAB species, enabling continuous sampling of water and adequate image quality acquisition [86]. For example, utilization of the IFCB in the Gulf of Mexico, USA enabled to provide early warnings for several blooms by detecting of increasing abundances of the harmful phytoplankton species [85, 86]. Efficient automated recognition of the target species is one of the critical requirements for successful HAB detection [81, 86] and may be supplemented by an alert system activated when the cell numbers reach a threshold [86]. Combining imaging cytometric measurements with specific RNA probes and DNA dyes may also provide insight into transitions of life cycle and cell cycle stages of HAB species associated with bloom dynamics [95, 97]. For example, Dapena et al. [97] was able to visualize with Flowsight (Amnis-Merck, Seattle, USA) cellular and nuclear morphological

changes of the toxic dinoflagellate, Alexandrium minitum, during cell cycle phases. Numerous studies adopted IFC to characterize cell morphology of marine and freshwater HAB species in respect to their population dynamics, cell-interactions and their importance for bloom development [79, 85, 89, 90, 96]. Variations in cell or colony size, surface/volume and length/width ratios of cells, presence or absence of protuberances (e.g. spines, arms, tubercules), mucilage formation, number and arrangement of cells in colony, coiling of filaments and other morphological features may reflect the changes in surrounding environment related to light and nutrient availability, grazing, heavy metal exposition, sinking rates, etc. [117]. This strongly applies to the changes in silica-made cell walls of diatoms that can be detected by fluorescent labeling of the silica structures and visualizing them using IFC [23]. 3.5. Viability and metabolic activity Imaging flow cytometers capable of simultaneously displaying cells in bright field and fluorescence channels open new opportunities for exploring physiological parameters (viability and metabolic activity) of microalgae. Traller and Hildebrand, 2013 [99] demonstrate the use of IFC to assess microalgal metabolic activity using the Imagestream X, by examining differential triacylglycerol (TAG) accumulation in response to nutrient limitation in a diatom culture of Cyclotella cryptica. In this case, IFC provides a link to variation in metabolic activity such as TAG accumulation to phenotypic features of diatom cells. Herein, we present examples of viability and metabolic assessment of cyanobacteria cells using 5-laser Imagestream X Mark II (Amnis-Merck Inc, Seattle, USA). Viability of algal cells is typically assessed via chlorophyll fluorescence [118, 119] or by using fluorescent nucleic acid dyes and metabolic dyes [12, 120]. To evaluate proportion of live and dead cells in culture of filamentous heterocyte-forming cyanobacterium Aphanizomenon sp. (CCMP2764), the sample was stained with Sytox Blue (SB) dye (Life Technologies, Carlsbad, USA). SB is a dead cell stain that penetrates cells with compromised plasma membranes and binds to nucleic acids. Based on IFC analysis, it is possible to discriminate live and dead cell populations within a sample (Fig. 3). The genomic DNA of cyanobacteria as well as in other prokaryotes is highly compacted in bodies known as nucleoids [121, 122], which is consistent with our results. Notably, cells stained with SB (Fig. 3; Suppl. file 1) might have a strong chlorophyll signal,

confirming earlier studies that chlorophyll fluorescence can be detected in non-viable cells for rather long period of time [123, 124]. Programmed cell death (PCD) is characterized by a number of morphological features with vital role in multicellular organisms [125], and has recently been described in multiple taxa of unicellular protists and prokaryotes including cyanobacteria [126, 127, 128, 129]. Early stages of apoptosis-like PCD can be visualized in cyanobacteria using AnnexinV assay [129] due to the externalization of negatively charged phosphatildyserine (PS) on the cell surface [130]. AnnexinV assay is commonly used for detection of eukaryotic apoptotic cells. By combining SB nucleic acid dye and Annexin-V dye (method description in Suppl. File 1), it was possible to discriminate several cell populations: live cells, which have neither AnnexinV nor SB staining; apoptotic or dying cells, which were stained with AnnexinV, but not with SB; dead cells that were positive both for AnnexinV and SB (Fig. 4). According to Sukenik et al. [131, 132] DNA content significantly increases in akinetes compared to that of vegetative cells, possibly sustained by phosphate stock from inorganic polyphosphate bodies. Staining with SB showed high fluorescence emission in SB – corresponding channel (Ch7; ex. 405 nm/ collecting filter 457/45) in akinete-like cells. A common marker of reactive oxygen species (ROS) activity in cells is 2,7dichlorodihydro-fluorescein diacetate (H2DCFDA) [133, 134]. Assessment of ROS activity is also commonly used in phytoplankton/cyanobacteria [135]. Several distinctive populations were observed when H2DCFDA was applied to Aphanizomenon sp. culture (Fig. 5): R2 population consisted of H2DCFDA - positive and chlorophyll – fluorescent cells, R3 population contained mostly heterocytes positive for H2DCFDA and lacking chlorophyll fluorescence, whereas R4 population consisted of cell debris with no H2DCFDA signal and almost no chlorophyll fluorescence (Fig. 5). Heterocytes (= heterocysts)1 from R3 population are morphologically and functionally distinct cells specialized for nitrogen fixation in Aphanizomenon sp., and they are vulnerable to ROS toxicity. ROS can be generated by photosystem I and respiratory electron transport in heterocytes [136, 137], and due to permeation of ROS into heterocytes from vegetative cells [138]. However, heterocytes have evolved adaptations such as absence of 1

There are two equivalent names for this type of specialized cells which are used in scientific literature at the moment. Professor Dr. Sc. Jiří Komárek states that the name "heterocyst" is incorrect for these cells because their known function does not correspond to the usual definition of "cyst" [143].

photosystem II, presence of thick envelop and increasing respiration to protect nitrogenase enzyme complex against inactivation by molecular oxygen [139, 140-142]. Calcein AM is a cell-permeant viability dye, which in contrast to SB, penetrates live cells and emits green fluorescence after the hydrolysis by intracellular esterases [144]. When stained with calcein AM, several distinctive populations of Aphanizomenon cells including damaged vegetative cells with intermediate chlorophyll signal and no calcein AM signal (R2), akinetes with strong chlorophyll fluorescence and absent calcein AM fluorescence (R3), and vegetative cells positive for calcein AM and with chlorophyll fluorescence (R4) were found (Fig. 6). Akinetes in R3 population are considered to be thick-walled dormant cells that originate from vegetative cells for survival in unfavorable/stressful environmental conditions [140, 143]. Strong autofluorescence (chlorophyll fluorescence) signal observed in R3 population supports earlier findings related to photosynthetic capacity of akinetes [145]. Akinetes possess the same metabolic processes as vegetative cells, especially preceding germination [143]. Development of vegetative filaments into chains of akinetes like in R3 population is characteristic to the final stage of algal growth, when the culture is old and exhausted [146]. In addition, a within-sample variation in cell size/shape can be captured using the aspect ratio proxy (width to length ratio) of particles analyzed. Fig. 7 demonstrates an example of cell size variation of Aphanizomenon sp. culture based on aspect ratio and area plot, where the gated populations correspond to small round cells (R14) with aspect ratio close to 1, medium length filaments (R13) with low aspect ratio and intermediate area measurement, and long length filaments (R15) with low aspect ratio but larger area. Overall, new applications of Imagestream can be useful for microalgae research, in particular, for biotechnological studies, where metabolic and viability assessment of growing culture is critical. 4. Limitations of IFC technology According to the publications considered in this review, each type of imaging cytometer is more or less frequently used for certain applications. For instance, FlowCam and IFCB are commonly exploited for assessment of heterogeneous natural phytoplankton assemblages in field due to their portability and wide size range of particles analyzed. Furthermore, provided automated continuous sampling by submersible IFCB makes it most suitable for detection and dynamics monitoring of HABs. In contrast, FlowSight and Imagestream are conformed to the

laboratory-based analysis of microalgae, particularly homogenous cultures. The capability of simultaneously displaying cells in bright field and numerous fluorescence channels enables to adopt combinations of fluorescent dyes for assessing physiological properties of microalgae cells. In spite of many advantages of IFC technology, there are still some limitations to overcome. First, taxonomic resolution may be relatively low compared to that of the conventional microscopy [24]. The lower image resolution may also affect the estimation accuracy of cell abundances by counting non-target particles in the sample [24, 30, 32]. Furthermore, in order to assess different phytoplankton size classes, a combination of different magnification objectives and working modes need to be used [64, 66] which can complicate the analysis. Some imaging cytometers, e.g. Imagestream and FlowSight, are not adapted (designed) for analysis of large-sized particles, which limits their use for certain diatom and dinoflagellate species (Fig. 2). As with any flow-based system, sampling efficiency of IFC depends on the sample dilution/concentration, sample volume and analysis time [64]. Alternative algorithms that will infer improved accuracy of phytoplankton classification, sizing, and biovolume estimation must be developed. Also, additional efforts must be undertaken in order to better characterize phytoplankton communities with heterogeneous autofluorescent pigments. Staining of microalgae expressing heterogeneous autofluorescent pigments with different fluorescent dyes may lead to cell-to-cell variations depending on autofluorescent background and artifacts related to the presence of strong autofluorescent signal in the dying cells. Finally, one of the major limitations of the current IFC instrumentation is the absence of the sorting option that has become crucial for many phytoplankton studies. 5. Conclusions and future perspectives The different examples of IFC instrumentation and applications demonstrate that IFC has become a useful experimental tool used in different contexts not only for characterization of phytoplankton diversity, but mainly in algal biotechnology, i.e. for characterization of highly productive algal phenotypes. Over the last 30 years a variety of commercial IFC instruments were developed and employed for different applications in algal research. IFC combines the speed and statistical power of flow cytometry with the morphological insights of microscopy.

However, none of these instruments can be used for a whole range of phytoplankton taxa and/or applications. Moreover, considering the phenotypical heterogeneity of microalgae, in the near future unbiased cell sorting technology needs to be combined with IFC in one instrument. Such instrumentation platform would be welcomed in laboratory and in the field research alike. In conclusion, despite significant progress in IFC technology, their use by algology community remains limited in comparison with flow cytometers and cell sorters. Recent technological advances combined with future cell sorting capabilities IFC will likely significantly change the field in the next decade. Acknowledgements We are very thankful to Harry Nelson from Imaging Fluid Technologies Inc. and Robert Olson from WHOI for providing images and/or access to the instrumentation. The financial support was provided in part by MES of Republic of Kazakhstan NLA 055 project # 100/14 to N.S.B. and by RFBR # 13-04-40189-H to I.A.V.

References 1. B. Trask, G. Van den Engh, J. Elgerhuizen, Analysis of phytoplankton by flow cytometry, Cytometry A 2 (1982) 258-264. 2. R.J. Olson, S.L. Frankel, S.W. Chisholm, H. Shapiro, An inexpensive flow cytometer for analysis of fluorescence signals in phytoplankton: chlorophyll and DNA distributions, J. Exp. Mar. Biol. Ecol. 68 (1983) 129-144. 3. C.M. Yentsch, P. Horan, K. Muirhead, Q. Dortch, E.M. Haugen, L. Legendre, L.S. Murphy, M.J. Perry, D.A. Phinney, S.A. Pomponi, Flow cytometry and cell sorting: a technique for analysis and sorting of aquatic particles, Limnol. Oceanogr. 28 (1983) 1275-1280. 4. N. Simon, R. Barlow, D. Marie, F. Partensky, D. Vaulot, Characterization of oceanic photosynthetic picoeukaryotes by flow cytometry, J. Phycol. 30 (1994) 922-935. 5. A. Becker, A. Meister, C. Wilhelm, Flow cytometric discrimination of various phycobilincontaining phytoplankton groups in a hypertrophic reservoir, Cytometry 48 (2002) 45-57. 6. S.W. Chisholm, R.J. Olson, E.R. Zettler, R. Goericke, J.B. Waterbury, N.A. Welschmeyer, A novel free-living prochlorophyte abundant in the oceanic euphotic zone, Nature 334 (1988) 340343. 7. C. Courties, A. Vaquer, M. Troussellier, J. Lautier, M. Chrétiennot-Dinet, J. Neveux, C. Machado, H. Claustre, Smallest eukaryotic organism, Nature 370 (1994) 255-255. 8. I. Biegala, F. Not, D. Vaulot, N. Simon, Quantitative Assessment of Picoeukaryotes in the Natural Environment by Using Taxon-Specific Oligonucleotide Probes in Association with Tyramide Signal Amplification-Fluorescence In Situ Hybridization and Flow Cytometry, Appl. Environ. Microbiol. 69 (2003) 5519-5529. 9. N. Crosbie, K. Teubner, T. Weisse, Flow-cytometric mapping provides novel insights into the seasonal and vertical distributions of freshwater autotrophic picoplankton, Aquat. Microb. Ecol. 33 (2003) 53-66. 10. G. Dubelaar, P. Geerders, R. Jonker, High frequency monitoring reveals phytoplankton dynamics, J. Environ. Monit. 6 (2004) 946. 11. M. Cellamare, A. Rolland, S. Jacquet, Flow cytometry sorting of freshwater phytoplankton, J. Appl. Phycol. 22 (2010) 87-100. 12. M. Veldhuis, G. Kraay, K. Timmermans, Cell death in phytoplankton: correlation between changes in membrane permeability, photosynthetic activity, pigmentation and growth, Eur. J. Phycol. 36 (2001) 167-177. 13. L. Peperzak, C. Brussaard, Flow cytometric applicability of fluorescent vitality probes on phytoplankton, J. Phycol. 47 (2011) 692-702. 14. C. Sensen, K. Heimann, M. Melkonian, The production of clonal and axenic cultures of microalgae using fluorescence-activated cell sorting, Eur. J. Phycol. 28 (1993) 93-97.

15. H. Pereira, L. Barreira, A. Mozes, C. Florindo, C. Polo, C.V. Duarte, L. Custódio, J. Varela, Microplate-based high throughput screening procedure for the isolation of lipid-rich marine microalgae, Biotechnol. Biofuels 4 (2011) 61. 16. L. Elliott, C. Feehan, L. Laurens, P. Pienkos, A. Darzins, M. Posewitz, Establishment of a bioenergy-focused microalgal culture collection, Algal Res. 1 (2012) 102-113. 17. M. Cuvelier, A. Allen, A. Monier, J. McCrow, M. Messie, S. Tringe, T. Woyke, R.M. Welsh, T. Ishoey, J. Lee, B.J. Binder, C.L. Dupont, M. Latasa, C. Guigand, K.R. Buck, J. Hilton, M. Thiagarajan, E. Caler, B. Read, R.S. Lasken, F.P. Chavez, A.Z. Worden, Targeted metagenomics and ecology of globally important uncultured eukaryotic phytoplankton, Proc. Natl. Acad. Sci. 107 (2010) 14679-14684. 18. D. Marie, X. Shi, F. Rigaut-Jalabert, D. Vaulot, Use of flow cytometric sorting to better assess the diversity of small photosynthetic eukaryotes in the English Channel, FEMS Microbiol. Ecol. 72 (2010) 165-178. 19. X. Shi, C. Lepère, D. Scanlan, D. Vaulot, Plastid 16S rRNA Gene Diversity among Eukaryotic Picophytoplankton Sorted by Flow Cytometry from the South Pacific Ocean, PLoS ONE 6 (2011) e18979. 20. W. Li, Primary production of prochlorophytes, cyanobacteria, and eucaryotic ultraphytoplankton: Measurements from flow cytometric sorting, Limnol. Oceanogr. 39 (1994) 169-175. 21. S. Duhamel, K. Björkman, D. Karl, Light dependence of phosphorus uptake by microorganisms in the subtropical North and South Pacific Ocean, Aquat. Microb. Ecol. 67 (2012) 225–238. 22. J. Graff, A. Milligan, M. Behrenfeld, The measurement of phytoplankton biomass using flow-cytometric sorting and elemental analysis of carbon, Limnol. Oceanogr.: Methods 10 (2012) 910-920. 23. M. Hildebrand, A. Davis, R. Abbriano, H.R. Pugsley, J.C. Traller, S.R. Smith, R.P. Shrestha, O. Cook, E.L. Sánchez-Alvarez, K. Manandhar-Shrestha, B. Alderete, Applications of imaging flow cytometry for microalgae, In: N.S. Barteneva, I.A. Vorobjev (Eds.), Imaging Flow Cytometry: Methods and protocols, Methods In Molecular Biology 1389, 2016, pp. 47-67. 24. C.K. Sieracki, M.E. Sieracki, C.S. Yentsch, An imaging-in-flow system for automated analysis of marine microplankton, Mar. Ecol. Prog. Ser. 168 (1998) 285-296. 25. N.J. Poulton, FlowCam: Quantification and Classification of Phytoplankton by Imaging Flow Cytometry. In: N.S. Barteneva, I.A. Vorobjev (Eds.), Imaging Flow Cytometry: Methods and protocols, Methods In Molecular Biology 1389, 2016, pp. 237–247. 26. R.J. Olson, H.M. Sosik, A submersible imaging-in-flow instrument to analyze nano- and microplankton: Imaging FlowCytobot, Limnol. Oceanogr.: Methods 5 (2007) 195-203. 27. R.J. Olson, A. Shalapyonok, H.M. Sosik, An automated submersible flow cytometer for analyzing nano- and microplankton: FlowCytobot, Deep-Sea Res. 50 (2003) 301-315.

28. H.M. Sosik, R.J Olson, Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry, Limnol. Oceanogr.: Methods 5 (2007) 204-216. 29. T. George, D. Basiji, B. Hall, D. Lynch, W. Ortyn, D. Perry, M. Seo, C. Zimmerman, P. Morrissey, Distinguishing modes of cell death using the ImageStream® multispectral imaging flow cytometer, Cytometry A 59 (2004) 237-245. 30. E. Alvarez, M. Moyano, Á. Lopez-Urrutia, E. Nogueira, R. Scharek, Routine determination of plankton community composition and size structure: a comparison between FlowCAM and light microscopy, J. Plankton Res. 36 (2014) 170-184. 31. C. Garcia-Munoz, C.M. Garcia, L.M. Lubian, A. Lopez-Urrutia, S. Hernandez-Leon, J. Ameneiro, Metabolic state along a summer north-south transect near the Antarctic Peninsula: a size spectra approach, J. Plankton Res. 36 (2014) 1074–1091. 32. M. Garmendia, M. Revilla, L. Zarauz, Testing the usefulness of a simple automatic method for particles abundance and size determination to derive cost-effective biological indicators in large monitoring networks, Hydrobiologia 704 (2013) 231-252. 33. D. Kamykowski, K.G. Pridgen, J.M. Morrison, A.A. McCulloch, E.S. Nyadjro, C.A. Thomas, G.A. Sinclair, Cold front induced changes on the Florida panhandle shelf during October 2008, Cont. Shelf Res. 54 (2013) 52-66. 34. S. Morillo-García, N. Valcárcel-Pérez, A. Cózar, M.J. Ortega, D. Macías, E. RamírezRomero, C.M. García, F. Echevarría, A. Bartual, Potential Polyunsaturated Aldehydes in the Strait of Gibraltar under Two Tidal Regimes, Mar. Drugs, 12 (2014) 1438-1459. 35. J.H. See, L. Campbell, T.L. Richardson, J.L. Pinckney, R. Shen, N.L. Guinasso, Combining new technologies for determination of phytoplankton community structure in the Northern Gulf of Mexico, J. Phycol. 41 (2005) 305-310. 36. L. Zarauz, X. Irigoien, Effects of Lugol's fixation on the size structure of natural nanomicroplankton samples, analyzed by means of an automatic counting method, J. Plankton Res. 30 (2008) 1297-1303. 37. L. Zarauz, X. Irigoien, J.A. Fernandes, Changes in plankton size structure and composition, during the generation of a phytoplankton bloom, in the central Cantabrian Sea, J. Plankton Res. 31 (2009) 193-207. 38. J. Clough, S. Strom, Effects of Heterosigma akashiwo (Raphidophyceae) on protist grazers: laboratory experiments with ciliates and heterotrophic dinoflagellates, Aquat. Microb. Ecol. 39 (2005) 121-134. 39. H. Liu, M.J. Dagg, S. Strom, Grazing by the calanoid copepod Neocalanus cristatus on the microbial food web in the coastal Gulf of Alaska, J. Plankton Res. 27 (2005) 647-662. 40. P.E. Gribben, J.T. Wright, W.A. O’Connor, M.A. Doblin, B. Eyre, P.D. Steinberg, Reduced performance of native infauna following recruitment to a habitat-forming invasive marine alga, Oecologia. 158 (2009) 733-745.

41. K. Ide, K. Takahashi, A. Kuwata, M. Nakamachi, H. Saito, A rapid analysis of copepod feeding using FlowCAM, J. Plankton Res. 30 (2008) 275-281. 42. U. Cotano, X. Irigoien, E. Etxebeste, P. Alvarez, L. Zarauz, J. Mader, L. Ferrer, Distribution, growth and survival of anchovy larvae (Engraulis encrasicolus L.) in relation to hydrodynamic and trophic environment in the Bay of Biscay, J. Plankton Res. 30 (2008) 467-481. 43. C.L. Meunier, F.M. Hantzsche, A.Ö. Cunha-Dupont, J. Haafke, B. Oppermann, A.M. Malzahn, M. Boersma, Intraspecific selectivity, compensatory feeding and flexible homeostasis in the phagotrophic flagellate Oxyrrhis marina: three ways to handle food quality fluctuations, Hydrobiologia 680 (2012) 53-62. 44. R.M. Kudela, J.Q. Lane, W.P. Cochlan, The potential role of anthropogenically derived nitrogen in the growth of harmful algae in California, USA, Harmful Algae 8 (2008) 103-110. 45. A.C. Garcia, S. Bargu, P. Dash, N.N. Rabalais, M. Sutor, W. Morrison, N.D. Walker, Evaluating the potential risk of microcystins to blue crab (Callinectes sapidus) fisheries and human health in a eutrophic estuary, Harmful Algae 9 (2010) 134–143. 46. С.F. Breier, E.J. Buskey, Effects of the red tide dinoflagellate, Karenia brevis, on grazing and fecundity in the copepod Acartia tonsa, J. Plankton Res. 29 (2007) 115-126. 47. M.A. Brzezinski, S.B. Baines, W.M. Balch, C.P. Beucher, F. Chai, R.C. Dugdale, J.W. Krause, M.R. Landry, A. Marchi, C.I. Measures, D.M. Nelson, A.E. Parker, A.J. Poulton, K.E. Selph, P.G. Strutton, A.G. Taylor, B.S. Twining, Co-limitation of diatoms by iron and silicic acid in the equatorial Pacific, Deep Sea Res. Part II: Topical Stud. Oceanogr. 58 (2011) 493-511. 48. B.A. Stauffer, J.I. Goes, K.T. Mckee, H.D.R. Gomes, P.J. Stabeno, Comparison of springtime phytoplankton community composition in two cold years from the western Gulf of Alaska into the southeastern Bering Sea, Deep Sea Res. Part II: Topical Stud. Oceanogr. 109 (2014) 57– 70. 49. A. Reul, M. Muñoz, B. Bautista, P. Neale, C. Sobrino, J. Mercado, M. Segovia, S. Salles, G. Kulk, P. León, W. Van de Poll, E. Pérez, A. Buma, J. Blanco, Effect of CO2, nutrients and light on coastal plankton. III. Trophic cascade, size structure and composition, Aquat. Biol. 22 (2014) 59–76. 50. M. Koski, L. Yebra, J. Dutz, S. Jόnasdόttir, C. Vidoudez, H. Jakobsen, G. Pohnert, J.C. Nejstgaard, The effect of egg versus seston quality on hatching success, naupliar metabolism and survival of Calanus finmarchicus in mesocosms dominated by Phaeocystis and diatoms, Mar. Biol. 159 (2012) 643-660. 51. P. Lavrentyev, M. McCarthy, D. Klarer, F. Jochem, W. Gardner, Estuarine Microbial Food Web Patterns in a Lake Erie Coastal Wetland, Microb. Ecol. 48 (2004) 567-577. 52. C. Llewellyn, D. White, V. Martinez-Vincente, G. Tarran, T. Smyth, Distribution of Mycosporine-Like Amino Acids Along a Surface Water Meridional Transect of the Atlantic, Microb. Ecol. 64 (2012) 320-333.

53. K. Natunen, J. Seppälä, D. Schwenk, H. Rischer, K. Spilling, T. Tamminen T, Nile Red staining of phytoplankton neutral lipids: species-specific fluorescence kinetics in various solvents, J. Appl. Phycol. 27 (2014) 1161-1168. 54. R. Sorichetti, I. Creed, C. Trick, Evidence for iron-regulated cyanobacterial predominance in oligotrophic lakes, Freshw. Biol. 59 (2013) 679-691. 55. D. Stoecker, J. Nejstgaard, R. Madhusoodhanan, G. Pohnert, S. Wolfram, H. Jakobsen, S. Šulčius, A. Larsen, Underestimation of microzooplankton grazing in dilution experiments due to inhibition of phytoplankton growth, Limnol. Oceanogr. 60 (2015) 1426-1438. 56. P. Venail, M. Alexandrou, T. Oakley, B. Cardinale, Shared ancestry influences community stability by altering competitive interactions: evidence from a laboratory microcosm experiment using freshwater green algae, Proc. R. Soc. Lond., B, Biol. Sci. 280 (2013) 20131548. 57. C. Vidoudez, J. Nejstgaard, H. Jakobsen, G. Pohnert, Dynamics of Dissolved and Particulate Polyunsaturated Aldehydes in Mesocosms Inoculated with Different Densities of the Diatom Skeletonema marinoi, Mar. Drugs, 9 (2011) 345-358. 58. W. Balch, D. Drapeau, B. Bowler, E. Lyczskowski, L. Lubelczyk, S. Painter, A. Poulton, Surface biological, chemical, and optical properties of the Patagonian Shelf coccolithophore bloom, the brightest waters of the Great Calcite Belt, Limnol. Oceangr. 59 (2014) 1715-1732. 59. M. Bartley, W. Boeing, D. Daniel, B. Dungan, T. Schaub, Optimization of environmental parameters for Nannochloropsis salina growth and lipid content using the response surface method and invading organisms, J Appl. Phycol. 28 (2015) 15-24. 60. H. Campos, W.J. Boeing, B.N. Dungan, T. Schaub, Cultivating the marine microalga Nannochloropsis salina under various nitrogen sources: Effect on biovolume yields, lipid content and composition, and invasive organisms, Biomass Bioenerg. 66 (2014) 301–307. 61. I. Cetinić, M. Perry, E. D'Asaro, N. Briggs, N. Poulton, M. Sieracki, C. Lee, Optical community index to assess spatial patchiness during the 2008 North Atlantic Bloom, Biogeosci. Discuss. 11 (2014) 12833-12870. 62. S.R Laney, H.M. Sosik, Phytoplankton assemblage structure in and around a massive underice bloom in the Chukchi Sea, Deep Sea Res. Part II: Topical Stud. Oceanogr. 105 (2014) 30-41. 63. K.R. Arrigo, D.K. Perovich, R.S. Pickart, Z.W. Brown, G.L. van Dijken, K.E. Lowry, M.M. Mills, A. Palmer, W.M. Balch, F. Bahr, N.R. Bates, C. Benitez-Nelson, B. Bowler, E. Brownlee, J.K. Ehn, K.E. Frey, R. Garley, R. Laney, L. Lubelczyk, J. Mathis, A. Matsuoka, B.G. Mitchell, G.W.K. Moore, E. Ortega-Retuerta, S. Pal, C.M. Polashenski, R.A. Reynolds, B. Schieber, H.M. Sosik, M. Stephens, J.H. Swift, Massive phytoplankton blooms under Arctic Sea ice, Sci. 336 (2012) 1408. 64. E. Alvarez, Á. Lopez-Urrutia, E. Nogueira, S. Fraga, How to effectively sample the plankton size spectrum? A case study using FlowCAM, J. Plankton Res. 33 (2011) 1119-1133. 65. L. Zarauz, X. Irigoien, A. Urtizberea, M. Gonzalez, Mapping plankton distribution in the Bay of Biscay during three consecutive spring surveys, Mar. Ecol. Prog. Ser. 345 (2007) 27-39.

66. H.H. Jakobsen, J. Carstensen, FlowCAM: Sizing cells and understanding the impact of size distributions on biovolume of planktonic community structure, Aquat. Microb. Ecol. 65 (2011) 75-87. 67. E. San Martin, X. Irigoien, R.P. Harris, Á. López-Urrutia, M. Zubkov, J.L. Heywood, Variation in the transfer of energy in marine plankton along a productivity gradient in the Atlantic Ocean, Limnol. Oceanogr. 51 (2006) 2084-2091. 68. S.A. Spaulding, D.H. Jewson, R.J. Bixby, H. Nelson, D.M. McKnight, Automated measurement of diatom size, Limnol. Oceanogr.: Methods 10 (2012) 882-890. 69. V.R.D. Vaillancourt, C.W. Brown, R.R.L. Guillard, W.M. Balch, Light backscattering properties of marine phytoplankton: relationships to cell size, chemical composition and taxonomy, J. Plankton Res. 26 (2004) 191-212. 70. F.H. Chang, E.C. Marquis, C.W. Chang, G.C. Gong, C.H. Hsieh, Scaling of growth rate and mortality with size and its consequence on size spectra of natural microphytoplankton assemblages in the East China Sea, Biogeosciences 10 (2013) 5267-5280. 71. C. Chang, T. Miki, F. Shiah, S. Kao, J. Wu, A. Sastri, C. Hsieh, Linking secondary structure of individual size distribution with nonlinear size–trophic level relationship in food webs, Ecology 95 (2014) 897-909. 72. P. Echeveste, S. Agustí, J. Dachs, Cell size dependence of additive versus synergetic effects of UV radiation and PAHs on oceanic phytoplankton, Environ. Pollut. 159 (2011) 1307-1316. 73. P. Ho, C. Chang, C. Hsieh, F. Shiah, T. Miki, Effects of increasing nutrient supply and omnivorous feeding on the size spectrum slope: a size-based nutrient-phytoplankton-zooplankton model, Popul. Ecol. 55 (2013) 247-259. 74. E. Alvarez, Á. Lopez-Urrutia, E. Nogueira, Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM, J. Plankton Res. 34 (2012) 454-469. 75. L.T. Nielsen, H.H. Jakobsen, P.H. Hansen, High resilience of two coastal plankton communities to twenty-first century seawater acidification: Evidence from microcosm studies, Mar. Biol. Res. 6 (2010) 542-555. 76. C. Schmoker, J. Arístegui, S. Hernández-León, Planktonic biomass variability during a late winter bloom in the subtropical waters off the Canary Islands, J. Mar. Systems. 95 (2012) 24-31. 77. S.C. Sharpe, J.A. Koester, M. Loebl, A.M. Cockshutt, D.A. Campbell, A.J. Irwin, Z.V. Finkel, Influence of Cell Size and DNA Content on Growth Rate and Photosystem II Function in Cryptic Species of Ditylum brightwellii, PLoS ONE 7 (2012) e52916. 78. E.A. Moberg, H.M. Sosik, Distance maps to estimate cell volume from two-dimensional plankton images, Limnol. Oceanogr.: Methods 10 (2012) 278-288. 79. M.L. Brosnahan, L. Velo-Suárez, D.K. Ralston, S.E. Fox, T.R. Sehein, A. Shalapyonok, H.M. Sosik, R.J. Olson, D.M. Anderson, Rapid growth and concerted sexual transitions by a

bloom of the harmful dinoflagellate Alexandrium fundyense (Dinophyceae), Limnol. Oceanogr. 60 (2015) 2059-2078. 80. R.M. Errera, L. Campbell (correction), Osmotic stress triggers toxin production by the dinoflagellate Karenia brevis, Proc. Natl. Acad. Sci. 109 (2011) 17723-17724. 81. E.J. Buskey, C.J. Hyatt, Use of the FlowCAM for semi-automated recognition and enumeration of red tide cells (Karenia brevis) in natural plankton samples, Harmful Algae 5 (2006) 685-692. 82. P. Lehman, K. Marr, G. Boyer, S. Acuna, S. the, Long-term trends and causal factors associated with Microcystis abundance and toxicity in San Francisco Estuary and implications for climate change impacts, Hydrobiologia 718 (2013) 141-158. 83. P. Lehman, C. Kendall, M. Guerin, M. Young, S. Silva, G. Boyer, S. Teh, Characterization of the Microcystis Bloom and Its Nitrogen Supply in San Francisco Estuary Using Stable Isotopes, Estuaries coasts 38 (2015) 165-178. 84. E. Siswanto, J. Ishizaka, S. Tripathy, K. Miyamura, Detection of harmful algal blooms of Karenia mikimotoi using MODIS measurements: A case study of Seto-Inland Sea, Japan, Remote Sens. Environ. 129 (2013) 185-196. 85. L. Campbell, R.J. Olson, H.M. Sosik, A. Abraham, D.W. Henrichs, C.J. Hyatt, E.J. Buskey, First harmful Dinophysis (Dinophyceae, Dinophysiales) bloom in the U.S. is revealed by automated imaging flow cytometry, J. Phycol. 46 (2010) 66-75. 86. L. Campbell, D.W. Henrichs, R.J. Olson, H.M. Sosik, Continuous automated imaging-inflow cytometry for detection and early warning of Karenia brevis blooms in the Gulf of Mexico, Environ. Sci. Pollut. Res. 20 (2013) 6896-6902. 87. V.D.W. Henrichs, H.M. Sosik, R.J. Olson, L. Campbell, Phylogenetic analysis of Brachidinium capitatum (Dinophyceae) from the Gulf of Mexico indicates membership in the Kareniaceae, J. Phycol. 47 (2011) 366–374. 88. L.B. Harred, L. Campbell, Predicting harmful algal blooms: a case study with Dinophysis ovum in the Gulf of Mexico, J. Plankton Res. 36 (2014) 1434–1445. 89. E.C. Wert, M.M. Dong, F.L. Rosario-Ortiz, Using digital flow cytometry to assess the degradation of three cyanobacteria species after oxidation processes, W. Res. 47 (2013) 37523761. 90. H.Z. Triki, M. Laabir, O.K. Daly-Yahia, Life history, excystment features, and growth characteristics of the Mediterranean harmful dinoflagellate Alexandrium pseudogonyaulax, J. Phycol. 51 (2015) 980–989. 91. M. Maier, T. Peterson, Observations of a Diatom Chytrid Parasite in the Lower Columbia River, Northwest Sci. 88 (2014) 234-245. 92. T. Tanoi, M. Kawachi, M. Watanabe, Effects of carbon source on growth and morphology of Botryococcus braunii, J. Appl. Phycol. 23 (2010) 25-33.

93. T. Tanoi, M. Kawachi, M. Watanabe, Iron and glucose effects on the morphology of Botryococcus braunii with assumption on the colony formation variability, J. Appl. Phycol. 26 (2014) 1-8. 94. T. Shuman, G. Mason, M. Marsolek, Y. Lin, D. Reeve, A. Schacht A, An ultra-low energy method for rapidly pre-concentrating microalgae, Bioresour. Technol. 158 (2014) 217-224. 95. M.L. Brosnahan, S. Farzan, B.A. Keafer, H.M. Sosik, R.J. Olson, D.M. Anderson, Complexities of bloom dynamics in the toxic dinoflagellate Alexandrium fundyense revealed through DNA measurements by imaging flow cytometry coupled with species-specific rRNA probes, Deep Sea Res. Part II: Topical Stud. Oceanogr. 103 (2014) 185-198. 96. E.E. Peacock, R.J. Olson, H.M. Sosik, Parasitic infection of the diatom Guinardia delicatula, a recurrent and ecologically important phenomenon on the New England Shelf, Mar. Ecol. Prog. Ser. 503 (2014) 1-10. 97. C. Dapena, I. Bravo, A. Cuadrado, R.I. Figueroa, Nuclear and Cell Morphological Changes during the Cell Cycle and Growth of the Toxic Dinoflagellate Alexandrium minutum, Protist 166 (2015) 146-160. 98. I.T.D. Cabanelas, M.V.D. Zwart, D.M. Kleinegris, M.J. Barbosa, R.H. Wijffels, Rapid method to screen and sort lipid accumulating microalgae, Bioresour. Technol. 184 (2015) 47–52. 99. J.C. Traller, M. Hildebrand, High throughput imaging to the diatom Cyclotella cryptica demonstrates substantial cell-to-cell variability in the rate and extent of triacylglycerol accumulation, Algal Res. 2 (2013) 244-252. 100. H. Utermöhl, Zur Vervollkommnung der quantitativen Phytoplankton-Methodik. Mitt. Int. Ver. Theor. Angew. Limnol. 9 (1958) 1–38. 101. A. Sournia, Phytoplankton Manual, UNESCO, Monographs on Oceanographic methodology, Volume 6, 1978. 102. N.J. Poulton, J.L. Martin, Imaging flow cytometry for quantitative phytoplankton analysis—FlowCAM, in: B. Karlson, C. Cusack, E. Bresnan (Eds.), Microscopic and molecular methods for quantitative phytoplankton analysis, Intergovernmental Oceanographic Commission of UNESCO, 2010, pp. 49–54. 103. C. García-Muñoz, L.M. Lubián, C.M. García, Á. Marrero-Díaz, P. Sangrà, M. Vernet, A mesoscale study of phytoplankton assemblages around the South Shetland Islands (Antarctica), Polar Biol. 36 (2013) 1107-1123. 104. M.B. Blaschko, G. Holness, M.A. Mattar, D. Lisin, P.E. Utgoff, A.R. Hanson, H. Schultz, E.M. Riseman, M.E. Sieracki, W.M. Balch, B. Tupper, Automatic in situ identification of plankton, Proc. WACV/MOTION’05 1 (2005) 79–86. 105 P. Drews-Jr, R.G. Colares, P. Machado, M.D. Faria, A. Detoni, V. Tavano, Microalgae classification using semi-supervised and active learning based on Gaussian mixture models, J. Braz. Comput. Soc. 19 (2013) 411–422.

106. D.H. Cushing, A difference in structure between ecosystems in strongly stratified waters and in those that are only weakly stratified, J. Plankton Res. 11 (1989) 1-13. 107. L. Legendre, J.L. Fèvre, From individual plankton cells to pelagic marine ecosystems and to global biogeochemical cycles, In: S. Demers (Ed.), Particle Analysis In Oceanography, Vol. 27, Springer Berlin Heidelberg, 1991, pp. 261–300. 108. E. Marañón, P.M. Holligan, R. Barciela, N. González, B. Mouriño, M.J. Pazó, M. Varela, Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments, Mar. Ecol. Prog. Ser. 216 (2001) 43-56. 109. C. Marrasé, C. Duarte, D. Vaqué, Succession patterns of phytoplankton blooms: directionality and influence of algal cell size, Mar. Biol. 102 (1989) 43-48. 110. J.M. Blanco, F. Echevarría, C.M. García, Dealing with size-spectra: some conceptual and mathematical problems, Sci. Mar. 58 (1994) 17–29. 111. J. Rodriguez, M.M. Mullin, Relation between biomass and body weight of plankton in a steady state oceanic ecosystem, Limnol. Oceanogr. 31 (1986) 361-370. 112. R.A. Quinones, T. Platt, J. Rodríguez, Patterns of biomass-size spectra from oligotrophic waters of the Northwest Atlantic, Prog. Oceanogr. 57 (2003) 405-427. 113. M. Mullin, P. Sloan, R. Eppley, Relationship between carbon content, cell volume, and area in phytoplankton, Limnol Oceanogr. 11 (1966) 307-311. 114. P. Verity, C. Robertson, C. Tronzo, M. Andrews, J. Nelson, M. Sieracki, Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton, Limnol Oceanogr. 37 (1992) 1434-1446. 115. D.J.S. Montagnes, J.A. Berges, P.J. Harrison, F.J.R. Taylor, Estimating carbon, nitrogen, protein, and chlorophyll a from volume in marine phytoplankton, Limnol. Oceanogr. 39 (1994) 1044-1060. 116. S. Menden-Deuer, E.J. Lessard, Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton, Limnol. Oceanogr. 45 (2000) 569-579. 117. L. Naselli-Flores, J. Padisák, M. Albay, Shape and size in phytoplankton ecology: do they matter? Hydrobiologia 578 (2007) 157-161. 118. P.H. Burkill, Analytical flow cytometry and its application to marine microbial ecology, In: Microbes in the Sea, Ed Sleigh MA, Wiley, Chichester, 1987, pp.139-166. 119. M.J.W. Veldhius, G.W. Kraay, Application of flow cytometry in marine phytoplankton research: current applications and future perspective, Sci. Mar. 64 (2000) 121-134. 120. C.P.D. Brussaard, D. Marie, R. Thyrhaug, G. Bratbak, Flow cytometric analysis of phytoplankton viability following viral infection, Aqua. Microb. Ecol. 26 (2001) 157-166. 121. D.E. Pettijohn, Structure and properties of the bacterial nucleoid, Cell, 30 (1982) 667-669.

122. C. Robinow, E. Kellenberger, The bacterial nucleoid revisited, Microbiol. Rev. 58 (1994) 211–232. 123. T. Debenest, F. Gagne, A.N. Petit, M. Kohli, P. Eullafroy, C. Blaise, Monitoring of a flame retardant (tetrabromobisphenol A) toxicity on different microalgae assessed by flow cytometry, J. Environ. Monit. 12 (2010) 1918-1923. 124. V. Dashkova, E. Segev, D. Malashenkov, R. Kolter, I. Vorobjev, N.S. Barteneva, Microalgal cytometric analysis in the presence of endogenous autofluorescent pigments, Algal Res. Submitted. 125. J.F. Kerr, A.H. Wyllie, A.R. Currie, Apoptosis: a basic biological phenomenon with wideranging implications in tissue kinetics, Br. J. Cancer 26 (1972) 239e257. 126. K. Lewis, Programmed death in bacteria, Microbiol. Mol. Biol. Rev. 64 (2000) 503–514. 127. K.D. Bidle, P.G. Falkowski, Cell death in planktonic, photosynthetic microorganisms, Nat. Rev. Microbiol. 2 (2004) 643–655. 128. C. Jimenez, J.M. Capasso, C.L. Edelstein, C.J. Rivard, S. Lucia, S. Breusegem, T. Berl, M. Segovia, Different ways to die: cell death modes of the unicellular chlorophyte Dunaliella viridis exposed to various environmental stresses are mediated by the caspase-like activity DEVDase, J. Exp. Bot. 60 (2009) 815–828. 129. W. Zheng, U. Rasmussen, S. Zheng, X. Bao, B. Chen, Y. Gao, X. Guan, J. Larsson, B. Bergman, Multiple modes of cell death discovered in a procaryotic (cyanobacterial) endosymbiont, PLoS ONE 8 (2013) e66147. 130. I. Vermes, C. Haanen, H. Steffens-Nakken, C. Reutellingsperger, A novel assay for apoptosis Flow cytometric detection of phosphatidylserine expression on early apoptotic cells using fluorescein labelled Annexin V, J. Immunol. Methods 184 (1995) 39-51. 131. A. Sukenik, O. Hadas, S. Stojkovic, N. Malinsky-Rushansky, Y. Viner-Motzini, J. Beardall, Fluorescence microscopy reveals variations in cellular composition during formation of akinetes in the cyanobacterium Aphanizomenon ovalisporum, Eur. J. Phycol. 44 (2009) 309-317. 132. A. Sukenik, R.N. Kaplan-Levy, J.M. Welch, A.F. Post, Massive multiplication of genome and ribosomes in dormant cells (akinetes) of Aphanizomenon ovalisporum (Cyanobacteria), ISME J. 6 (2012) 670–679. 133. D. Bass, P. Olbrantz, P. Szejda, M. Seeds, C. McCall, Subpopulations of neutrophils with increased oxidative product formation in blood of patients with infection. J. Immunol. 136 (1986) 860-866. 134. H. Possel, H. Noack, W. Augustin, G. Keilhoff, G. Wolf, 2,7-Dihydrodichlorofluorescein diacetate as a fluorescent marker for peroxynitrite formation, FEBS Lett. 416 (1997) 175-178. 135. N. Mallick, F.H. Mohn, Reactive oxygen species: response of algal cells, J. Plant Physiol. 157 (2000) 183–193.

136. K. Asada, The water–water cycle in chloroplasts: scavenging of active oxygens and dissipation of excess photons, Annu. Rev. Plant Physiol. Plant Mol. Biol. 50 (1994) 601–639. 137. M. Banerjee, P.S. Raghavan, A. Ballal, H. Rajaram, S.K. Apte, Oxidative stress management in the filamentous, heterocystous, diazotrophic cyanobacterium, Anabaena PCC7120, Photosyn. Res. 118 (2013) 59-70. 138. W. Zhao, Z. Ye, J. Zhao, RbrA, a cyanobacterial rubrerythrin, functions as a FNRdependent peroxidase in heterocysts in protection of nitrogenase from damage by hydrogen peroxide in Anabaena sp. PCC 7120, Mol. Microbiol. 66 (2007) 1219–1230. 139. C.P. Wolk, A. Ernst, J. Elhai, Heterocyst metabolism and development, In: D.A. Bryant (Ed.), The Molecular Biology of Cyanobacteria, Dordrecht: Kluwer Academic Press, 1994, pp. 769–823. 140. D.G. Adams, P.S. Duggan, Heterocyst and akinete differentiation in cyanobacteria, New Phytol. 144 (1999) 3–33. 141. T. Thiel, Nitrogen fixation in heterocyst-forming cyanobacteria, In: W. Klipp, B. Masepohl, J. R. Gallon, and W. E. Newton (Eds.), Genetics and regulation of nitrogen fixing bacteria, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2004, pp. 73-110. 142. K. Kumar, R.A. Mella-Herrera, J.W. Golden, Cyanobacterial Heterocysts, Cold Spring Harb. Perspect. Biol. 2 (2010) a000315-a000315. 143. J. Komárek, Phenotypic Characters of Heterocytous Cyanobacteria, In: Süßwasserflora von Mitteleuropa, Bd. 19/3: Cyanoprokaryota, Springer-Verlag Berlin Heidelberg, 2013. 144. D. Bratosin, L. Mitrofan, C. Palii, J. Estaquier, J. Montreuil, Novel fluorescence assay using calcein-AM for the determination of human erythrocyte viability and aging, Cytometry A 66 (2005) 78-84. 145. A. Sukenik, J. Beardall, O. Hadas, Photosynthetic characterization of developing and mature akinetes of Aphanizomenon ovalisporum (cyanoprokaryota), J. Phycol. 43 (2007) 780– 788. 146. F.C. Sili, A. Ena, R. Materassi, M. Vincenzini, Germination of desiccated aged akinetes of alkaliphilic cyanobacteria, Arch. Microbiol. 162 (1994) 20-25.

Legends to figures Fig. 1. Imaging flow cytometers with corresponding examples of image galleries derived from analysis: A, D - submersible imaging FlowCytobot and corresponding image gallery (images: R. Olson, WHOI and McLane Research Laboratories, Inc.); B, E - benchtop FlowCam VS series and corresponding image gallery (images: H. Nelson, Fluid Imaging Technologies, Inc.); C, F Imagestream X Mark II and corresponding image gallery (images: Merck-Amnis, Inc. and V. D., Nazarbayev University).

Fig. 2. Particle size range (bright field) of different imaging flow cytometers. Size range by fluorescence for Imagestream X Mark II starts from tenths of nanometers. Fig. 3. Discrimination of dead (left) and live (right) cell populations of Aphanizomenon sp. culture stained with nucleic acid dye SB and analyzed using Imagestream X Mark II imaging cytometer. Two images of each cell population are shown in bright field (BF), SB– corresponding (Ch7; ex. 405 nm/collecting filter 457/45), chlorophyll – corresponding (Ch11; ex. 658 nm laser/ collecting filter 702/85) and merged BF, SB and chlorophyll channels. Fig. 4. Subpopulations of cells found in Aphanizomenon sp. culture stained with both AnnexinV and nucleic acid dye Sytox Blue (SB) and analyzed using ImagestreamX Mark II imaging cytometer. Top row - live cells (no AnnexinV and no SB signal); middle row - apoptotic or dying cells (positive for Annexin V and no SB signal), bottom row – dead cells (positive for AnnexinV and SB). Representative images of each cell population are shown in bright field (BF), AnnexinV – corresponding (Ch2; ex. by blue 488 nm laser/ collecting filter 528/65), SB – corresponding (Ch7; ex. 405 nm/ collecting filter 457/45), chlorophyll – corresponding (Ch11; ex. 658 nm laser/ collecting filter 702/85) and merged BF, AnnexinV, SB and chlorophyll (merge) channels. Fig. 5. Analysis of Aphanizomenon sp. cells stained with ROS indicator dye H2DCFDA using Imagestream X Mark II imaging cytometer. R1 population was gated on the histogram displaying all cells and was visualized on dot plot H2DCFDA intensity vs. Chlorophyll intensity. Three distinct populations R2, R3 and R4 were gated and examined using corresponding images. Representative images of each cell population are shown in bright field (BF), H2DCFDA – corresponding (Ch2; ex. by blue 488 nm laser/ collecting filter 528/65), chlorophyll – corresponding (Ch11; ex. 658 nm laser/ collecting filter 702/85) and merged BF, H2DCFDA and chlorophyll channels. Fig. 6. Analysis of Aphanizomenon sp. cells stained with, esterase activity-specific dye Calcein AM using Imagestream X Mark II imaging cytometer. R1 population was gated on the histogram displaying all cells and was then visualized on dot plot Calcein AM intensity vs. Chlorophyll intensity. Six distinct populations R2, R3, R4, R5, R6 and R7 were gated and examined using corresponding images. Representative images of each cell population are shown in bright field (BF), Calcein AM – corresponding (Ch2; ex. by blue 488 nm laser/ collecting filter 528/65), chlorophyll – corresponding (Ch11; ex. 658 nm laser/ collecting filter 702/85) and merged BF, Calcein AM and chlorophyll channels. Fig. 7. Size distribution analysis of Aphanizomenon sp. cells stained with Calcein AM using Imagestream X Mark II imaging cytometer. Three populations R13, R14, R15 were gated on Aspect Ratio vs. Area dot plot and visualized using corresponding images. Representative images of each cell population are shown in bright field (BF), Calcein AM – corresponding (Ch2; ex. by blue 488 nm laser/ collecting filter 528/65), chlorophyll – corresponding channels (Ch11; ex. 658 nm laser/ collecting filter 702/85).

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Graphical Abstract

Highlights •

•

• •

IFC for phytoplankton analysis allows the assessment of phytoplankton communities` abundance and composition, size structure determination, biovolume estimation, detection of harmful algal bloom (HAB) species, evaluation of viability and metabolic activity and other applications. IFC represents an advantageous technology for phytoplankton analysis as it combines the speed and statistical power of flow cytometry with the morphological insights of microscopy. Capabilities and limitations of the different IFC instrumentation determine the extent of their exploitation for particular phytoplankton applications. We present examples of viability and metabolic activity assessment of phytoplankton using imaging cytometer Imagestream X and fluorescent dyes.