Abstract

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.

© 2017 Optical Society of America

Retraction

This article has been retracted. Please see:
Jianping Li and Zhennan Xu, "Simultaneous dual-color light sheet fluorescence imaging flow cytometry for high-throughput marine phytoplankton analysis: retraction," Opt. Express 25, 20033-20033 (2017)
http://proxy.osapublishing.org/oe/abstract.cfm?uri=oe-25-17-20033

1. Introduction

Imaging flow cytometry (IFC) has become very attractive for phytoplankton analysis because it provides resolution and throughput simultaneously, which can only, in the past, be realized by conventional optical microscopy and flow cytometry (FCM) separately [1,2]. Its ability to extract quantitative information at high speed from statistical measurements of numerous individual cells has brought about many new discoveries and insights to many fundamental oceanographic problems [3–5]. Answering these problems is of crucial importance for marine ecology study and environmental monitoring, and on the other hand, stimulated the development of various IFC instruments and technologies [6–13].

However, taking fast yet accurate measurement of diverse natural phytoplankton with extreme heterogeneity remains challenging for current imaging flow cytometers (IFCs). The dimension of phytoplankton in natural seawater ranges from 0.2μm to 200μm including pico-, nano- to microphytoplankton size groups [14]. Bright-field IFCs (BF-IFC) such as FlowCAM and Imaging FlowCytobot (IFCB) well inherited the advantages of broad cell sizing range and good resolution of conventional bright-field microscopy while greatly improved measurement throughput, which proved to be useful for analyzing larger-sized nanophytoplankton (2-20μm) and microphytoplankton (20-200μm) assemblages in natural seawater samples [6, 7]. Newly emerged digital holographic microscopy and time-stretched microscopy-in-flow technologies (DHM-IFC and TSM-IFC) have demonstrated further enhancement of measurement throughput and provision of extra features for effective classification of relatively large phytoplankton [10–13]. But these non-fluorescence IFCs are essentially difficult to detect small-sized picophytoplankton cells (0.2-2μm) due to their lack of sensitivity and specificity. Fluorescence IFCs based on time-delay-integration spectral imaging (TDI-IFC) are very unique integrating advantages of sensitivity, throughput and spectral resolving capability possessed separately by conventional epifluorescence microscope and flow cytometer (FCM) [15, 16]. But this family of instruments was primarily designed for biomedical applications in laboratory, not exclusively for field analysis of natural phytoplankton samples [1]. Detection of picophytoplankton using TDI-IFC has not been demonstrated. And to avoid defocusing blur, this type of IFCs has adopted hydrodynamic focusing within a narrow flow channel (max 120μm), which would constrain their use for detecting larger-sized diatom and dinoflagellate species frequently encountered in natural seawater samples.

So far, known optical instruments with wide phytoplankton particle sizing ranges include FlowCAM (~2-2000μm) [6], DHM-IFC (~3-300μm) [10] and CytoBuoy (~1-500μm in width and 1000μm in length) [17]. However, to cover such wide ranges, FlowCAM has to mechanically switch among several combinations of flat flow cells with objective lenses with corresponding magnifications and DHM-IFC relies on computation-intensive digital refocusing postprocessing, which are very inconvenient in practice. CytoBuoy is a highly automated flow cytometer exclusively designed for in situ phytoplankton monitoring. It has greatly improved the shortcoming of traditional FCMs in analyzing larger phytoplankton particles by further recoding scattering/fluorescence pulse profiles of every particle (silico-imaging) flowing through the interrogation laser focus. Although silico-imaging does not provide true images of phytoplankton particles, the extra morphological information contained in these pulse shapes can provide more spatial features of the particles than just scattering/fluorescence integral signals for finer taxonomy resolution. Yet CytoBuoy is not an IFC as its primary data source is not from recording images of every individual phytoplankton particles, it has also equipped auxiliary bright-field imaging camera as supplementary, and a trend of integrating both phytoplankton pulse profile and image data for more comprehensive analysis has become more obvious [18].

Recently, Wu et al developed, to our knowledge, the first light sheet-based fluorescence IFC (LSF-IFC) exclusively for phytoplankton analysis [8, 9]. Adopting the excitation-imaging configuration in light sheet fluorescence microscopy (LSFM) [19] with frontal axial fluidic sample introduction, the LSF-IFC has largely overcome blurring problems induced by lateral motion and axial defocusing, and successfully realized high-throughput in-flow 3D tomographic and 2D projection fluorescence imaging of phytoplankton. Especially, its 2D projection imaging mode has achieved a volume flow rate up to 1mL/min covering a cellular size range of ~1-200μm in natural seawater sample measurement [9]. The universal usage of single objective lens, suppression of out-of-focus fluorescence, enhancement of excitation power density and axial integration of more in-focus photons all together facilitate great enhancement in imaging resolution, contrast and sensitivity. Compared with the non-fluorescence IFCs, the LSF-IFC’s higher sensitivity and resolution are beneficial for detecting small picophytoplankton; compared with the TDI-IFCs and traditional FCMs, the LSF-IFC’s wider flow fluidic channel has extended its coverage to larger-sized microphytoplankton or microcolonies. These features are potentially more suitable for rapid analysis of natural phytoplankton samples.

It is well known that spectral analysis of various photosynthetic pigments’ fluorescence by FCM is very helpful and has greatly contributed to the in vivo discrimination and classification of wild phytoplankton [20]. For example, detection of chlorophyll a (Chl a)’s red fluorescence is universally used to discriminate phytoplankton cells from non-phytoplankton particles, while detection of phycoerythrin (PE)’s orange fluorescence is used to differentiate prokaryotic cyanobacteria Synechococcus from Prochlorococcus [21], some small eukaryotes such as Cryptophytes were also shown to exceptionally contain PE and emit distinct orange fluorescence [22]. Differently from FCMs, IFCs extract cell size information from images instead of light scattering. Although they can resolve individual picophytoplankton cells and provide subcellular details and sizing information of larger microphytoplankton cells, IFCs’ resolution is still optically diffraction-limited and far less enough to resolve subcellular features of different picophytoplankton and nanophytoplankton usually appearing coccoid in their images. Therefore, equipment of spectral resolving ability into IFCs would be of great complementary significance to phytoplankton analysis besides high-throughput imaging.

As the previous LSF-IFC can only image Chl a fluorescence, to further extend the potential of this technology, we report in this paper the new development of a dual-color LSF-IFC (DC-LSF-IFC) system that is capable of simultaneous recording Chl a and PE fluorescence spectral images of phytoplankton in-flow. The phytoplankton sample measurement results using this system have, for the first time, evidenced its sensitivity is high enough to detect picophytoplankton at a throughput as high as ~105cells/s, and its dynamic ranges in cell size and fluorescence intensity are wide enough to simultaneously image a phytoplankton size range of 1-300μm. It is also demonstrated the spectral imaging function of the system is very useful and effective for certain PE-containing and PE-lacking species’ discrimination. A newer level of phytoplankton analysis automation improvement based on this technological evolvement is expected to open up more possibilities for laboratory and field studies of aquatic ecology and molecular microbiology.

2. Methods and materials

DC-LSF-IFC system setup

The schematic of the DC-LSF-IFC system is illustrated in Fig. 1. A 445nm blue laser (MLD 445, Cobolt. Max output power: 80mW) and a 532nm green laser (DPL 532, Cobolt. Max output power: 100mW) are used as light sources for excitation of chlorophyll and PE, respectively. Their output beams are firstly combined by DC1 (LM01-466-25, Semrock) and the combined beam is then telescopically expanded by L1 (f1 = −19mm) and L2 (f2 = 50mm) before being transformed into a thin laser-sheet in the XY plane by CL (fcl = 75mm) and a 10 × water dipping objective lens EO (UMPLFLN10XW, NA 0.3, W.D. 3.3mm, Olympus). Along the laser-sheet generation path, M1 through M4 collimate beams, IR blocks scattered laser light and M5 finally deviates the combined beam into EO. The water immersible end of EO is sealed inside a customized chamber, which outputs the laser-sheet to illuminate phytoplankton flowing through a square quartz capillary from 90° aside. The transmitted residual laser light is blocked at the other side of the capillary tip by a laser dump. The optics along the excitation path have been carefully aligned to make two overlapped laser-sheets with appropriate height and Rayleigh length generated covering the whole capillary bore, and their plane coincides with the focal plane of a 40 × detection water dipping objective lens DO (LUMPLFLN40XW, NA 0.8, W.D. 3.3mm, Olympus). The immersible end of DO is also sealed inside the chamber, which collects fluorescence and excitation photons and collimates them into the imaging path. A long-pass filter LP (BLP01-532R-25, Semrock) is then followed to block shorter wavelength excitation photons before the beam enters a home-made dual-channel image splitter.

 

Fig. 1 Schematic of the DC-LSF-IFC system. M1-M5: dielectric flat mirrors; L1-L2: lenses; IR: iris; CL: cylindrical lens; EO: excitation objective lens; DO: detection objective lens; LP: long-pass filter; DC1-DC3: long-pass dichroic filters; BP1-BP2: band-pass filters; ND1-ND2: neutral density filters; TL: tube lens; DL: demagnifying lens.

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The image splitter is of a Mach-Zehnder-like configuration consisting of two flat mirrors M6 and M7, two long-pass dichroic beam splitters DC2 and DC3 (both FF662-FDi01-25 × 36, Semrock), two band-pass filters BP1 and BP2 (FF01-585/40-25 and FF02-685/40-25, Semrock), and two optional neutral density filters ND1 and ND2. DC2 splits the incoming beam into two orthogonal sub-beams at the cutoff wavelength of 660nm. BP1 and BP2 further refines each sub-beam’s spectral composition in corresponding to the fluorescence characteristics of PE and Chl a; only one of ND1 or ND2 with proper optical density is used for intensity balancing between two channels. Afterwards, the transmitted longer wavelengths sub-beam and reflected shorter wavelengths sub-beam are reflected by M6 and M7 towards DC3, respectively, where they are combined and propagate out of the image splitter. Both sub-beams are finally focused by TL (U-TLU, Olympus) and 0.5 × demagnified by DL (U-TV0.5xC-3, Olympus) onto an sCMOS camera (Zyla 5.5, Andor. Max resolution: 2560 × 2160 pixels) for simultaneous two-color imaging. As all of the detectable light is from laser induced fluorescence emission confined in the capillary, the square aperture of the capillary bore acts actually as a field aperture defining the borders of the two sub-images. The whole system’s magnification is equivalent to 20 × , which can produce two sub-images with the same size of ~6.5mm × 6.5mm (up to 1k × 1k pixels). By slightly adjusting M6, M7 and DC3, the two sub-images can be separated side-by-side occupying an area of about 13mm × 6.5mm on the chip of the camera without overlapping. We design the image splitter mechanics to have all its filters being flexibly adjustable and replaceable, so no major physical changes are needed for the DC-LSF-IFC system to adapt for various applications.

The square quartz capillary has a square bore of 300μm × 300μm, an area big enough to pass through most common phytoplankton cells. EO, DO and the capillary are all sealed by corrosion-resistant FKM rubber O-rings with the chamber. The fluidics manipulation can be driven by either a peristaltic pump (9604.000, BOXER) or a syringe pump (NE-300, New Era), and all the containers and the capillary are interconnected with the pump by Ø1mm PTEF tubing. The application of any type of pumps can be easily switched for high flow-rate (>1mL/min) or low flow-rate (<1mL/min) measurements.

During experiment, a negative pressure inside the chamber is generated by a pump through the tubing connected with the capillary. The sample water is then sucked from a container into the chamber to flow through the capillary for optical interrogation. At this time, the camera is turned on to continuously capture raw images under overlapping mode till the end of experiment. Before entering the inlet of the tubing into the chamber, all sample water is passively filtered through 300μm-pitch stainless steel mesh cage to avoid clogging. After detection, the sample water is drained off into a waste container and all the flow channels are cleaned by purified water and alcohol. The image acquisition, storage, processing and control of lasers and pumps of the system are performed by a host computer workstation configured with dual Intel Xeon E5-2620 v2 CPUs, 64GB RAM, 256GB solid-state drive, and 3 × 1TB hard-drives.

Image formation and processing

Figure 2 illustrates the basic principle of the image formation process for each imaging channel of the DC-LSF-IFC system in two cases: imaging phytoplankton particles that are (a) larger than, or (b) smaller than the laser-sheet thickness. During phytoplankton flowing through the laser-sheet, the transition time is proportional to the particle size and inversely proportional to the flow speed. At a nominal volume flow rate of 1mL/min in the current DC-LSF-IFC setup, the flow speed is ~0.2m/s. For a 300μm microphytoplankton, its transition time is <1.6ms; for a 2μm picophytoplankton, its transition time is <100μs. As the exposure time we used in all experiments is 10ms-100ms (viz. 100-10fps under overlapping mode), so in most cases it is much longer than the transition time of the measurable-sized phytoplankton particles. When the phytoplankton particle is larger than the laser-sheet thickness, at every instant during its transition through the laser-sheet, a thin sliced layer of the particle would be excited to emit fluorescence as shown in Fig. 2(a). As the camera exposure time is longer than the transition time, all these sliced images would be superimposed along the imaging axis direction into a 2D projection image. When pico- or nanophytoplankton that are smaller than the laser-sheet thickness flow through the laser-sheet as shown in Fig. 2(b), all of the cell body is excited and its fluorescence would also be integrated during the long exposure time. As the cell abundance of small phytoplankton is usually much higher than big ones, the chances of many cells simultaneously transit the laser-sheet within a time-window that is shorter than the exposure time become much higher. In both cases, the actual time for fluorescence signal acquisition is determined by the particle transition instead of camera exposure. In real measurements, the cell flux and exposure time can be optimized to assure little overlapping of cell images occurs, thus the projection images containing spatially resolvable cells are formed. As the camera exposure is successive under overlapping mode, there would be no “dead time” between any two consecutive frames. So the images of all the particles would be captured without omission.

 

Fig. 2 Schematics of LSF-IFC image formation. (a) Imaging fluorescent particles larger than laser-sheet thickness; (b) Imaging fluorescent particles smaller than laser-sheet thickness.

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At the beginning of every experiment, some images of filtrated seawater were firstly captured and averaged as a blank background to be subtracted from all the subsequently acquired images of real phytoplankton samples. The background-subtracted images are then cropped into two sets corresponding to Chl a and PE channels from two manually selected square regions of interest (ROIs) covering the full mouth of the capillary, and saved in 16-bit greyscale TIF format. Thanks to the high imaging quality provided by the DC-LSF-IFC optics, the contrast of these raw images after background subtraction is already very high for cell segmentation using simple thresholding processing. All the images displayed in this paper are just selected from them without any further processing (only display contrast was adjusted for visual presentation).

For applications where image registration of the two channels is necessary, simple affine transformation processing can be used [23]. The process is briefed as follows: i) manually select three corresponding reference particles from any one of a pair of twain images and identify their centers of mass (COM); ii) calculate the rotation matrices Tr-o (from red- to orange-channel) and To-r (from orange- to red-channel); iii) applying Tr-o and To-r on images from respective channels and perform a mutual verification of twain particles. After registration, different combinations of relatively basic image processing operations can be followed to further process the twain images from both channels for different applications. For example, if microphytoplankton >20μm are targeted for morphology-based species identification or classification, Richardson-Lucy deconvolution can be run using DeconvolutionLab plugin to further restore cellular details upon those images screened to contain big particles as preprocessing [24]. We describe those used for respective experiment in the following section and most of them can be easily found in open source software ImageJ menus [25]. As developing advanced vision and recognition software is not the scope of this work, we just packaged affine transformation, Richardson-Lucy deconvolution and other basic particle analysis operations into an ImageJ plugin for our own convenience to batch process the raw image data acquired in the following experiments.

Phytoplankton samples

Five lab-cultured phytoplankton species were used in the experiments, including three strains of picophytoplankton Synechococcus spp. and Prochlorococcus sp., and two strains of nanophytoplankton Porphyridium sp. and Chlorella sp. Cultures of Synechococcus spp. (CCMA299 and CCMA300), Prochlorococcus sp. (MIT9312) and Porphyridium sp. (CMMA143) were obtained from the Collection Center of Marine Algae, Xiamen University, China. Chlorella sp. (FACHB-9) was provided by the Algae Culture Center of Shenzhen University, China.

Coastal seawater samples were collected near Kowloon Bay (22.3N, 114.2E, Hong Kong, November, 2015) using a 2.5L roped bucket water sampler without any mesh. The samples were shipped back to laboratory and measured by the DC-LSF-IFC system within 3 hours without further fine filtration or enrichment. At the time when each sample was collected, a small part was always separated and fixed with Lugol’s solution for later phycologist’s microscopic inspection as comparison.

3. Results and discussion

DC-LSF-IFC optical characterization

The light sheet and imaging resolution of the DC-LSF-IFC setup were characterized before phytoplankton experiments. For light sheet characterization, a flat mirror is immersed in filtrated seawater inside the chamber and is oriented 45° to the imaging axis. While the mirror is scanned along X, the images of the light sheet cross-section are captured at different X positions as shown in Fig. 3(a). By analyzing these images [26], we obtained 1/e2-radii of ~4-7μm characterizing the two laser-sheets thickness in a 300μm-wide range and found a little misalignment between the waist positions of the two laser beams as shown in Fig. 3(b). The light sheet width was also intentionally expanded much wider than 300μm, not only covering the capillary bore, but also providing a relatively flat laser intensity distribution along Y direction. To characterize imaging resolution, we measured the point spread function (PSF) of both channels by imaging two colors of sub-diffraction fluorescent beads (ThermoFisher Scientific, F8809 and T7280) solution samples while they slowly flow through the focal plane. The lateral resolution is measured to be 0.94μm (horizontal) and 1μm (vertical) for the orange-channel, and 1.11μm (horizontal) and 1.08μm (vertical) for the red-channel as shown in Figs. 3(c) and 3(d), respectively. Compared to the lateral resolution of ~0.75μm achieved in previous work [9], the resolution of ~1μm of the DC-LSF-IFC is slightly degraded due to two-fold decrease in magnification, but it is still fine enough to resolve phytoplankton particles for accurate analysis as demonstrated in the following.

 

Fig. 3 (a) Light sheet cross-section images at various X positions, bar = 100μm; (b) Light sheets thickness variation at different X positions; (c) and (d) Lateral PSFs of the orange and red imaging channels, respectively.

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Cultured phytoplankton sample analysis

After optical performance characterization, the DC-LSF-IFC system was applied to take measurements of selected picophytoplankton and nanophytoplankton cultures as experimental samples. Typical laboratory analysis tasks of cell detection, discrimination, and enumeration were performed and compared with conventional microscopy analysis and Coulter analysis.

To demonstrate the sensitivity of the DC-LSF-IFC system, a set of three picophytoplankton samples of Synechococcus spp. and Prochlorococcus sp. were firstly used in this experiment, because their sizes are known to be as small as ~1μm. The excitation power of both lasers was set to 50mW and the exposure time for their imaging was 20ms (50fps).The imaging results are shown and compared in Figs. 4(a)-4(h).

 

Fig. 4 (a) and (b) Bright-field and epifluorescence microscopy images of Synechococcus CCMA299, bar = 50μm; (c) and (d) Red- and orange-channel DC-LSF-IFC images of Synechococcus CCMA299; (e) and (f) Red- and orange-channel DC-LSF-IFC images of Synechococcus CCMA300; (g) and (h) Red- and orange-channel DC-LSF-IFC images of Prochlorococcus sp. (i) Bright-field microcopy images of Chlorella sp. (top row) and Porphyridium sp. (bottom row), bar = 5μm; (j) and (k) Red- and orange-channel DC-LSF-IFC images of Chlorella sp. and Porphyridium sp. mixed sample; (l) Pseudo-color composite image of (j) and (k). For DC-LSF-IFC captured images, frame size = 300μm × 300μm, and red frames correspond to Chl a-channel and orange frames correspond to PE-channel, respectively.

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Figures 4(a) and 4(b) shows Synechococcus (CCMA299) images captured by conventional bright-field microscopy and epifluorescence microscopy on a glass slide with 50 × magnification, respectively. It is obvious the bright-field image lacks of contrast for possible detection and counting at such small cell sizes. For epifluorescence image, the signal is from Chl a fluorescence and the contrast is much better for detection. But the background halo coming from outside the shallow focal plane still makes the image blurry and the fluorescence is easy to bleach after long time exposure, so it remains troublesome for accurate cell quantification. Prominently different from conventional microscopy images, the images obtained by the DC-LSF-IFC shown in Figs. 4(c)-4(e) and 4(g) display very sharp contrast and good signal-to-noise ratio (SNR). Compared to previous work, the increase in excitation power has further boosted the detection sensitivity, so that a volume flow rate of 100μL/min was reached to when imaging Synechococcus spp. as shown in Figs. 4(c)-4(e). For even dimmer fluorescent Prochlorococcus sp., very good imaging contrast can still be reserved by slowing down the volume flow rate to 20μL/min, as can be seen from Fig. 4(g).

With the addition of green laser, the PE inside phytoplankton can be better excited as 530nm is closer to its excitation spectrum peak. So, the PE-rich Synechococcus CCMA299 can be clearly imaged in the orange-channel of the DC-LSF-IFC as shown in Fig. 4(d). As the DC-LSF-IFC can record twain spectral images of the same cell from its Chl a and PE emissions in one shot, comparing Figs. 4(d) with 4(c), one-to-one correspondence for all of the bright particles can be easily identified by image registration. On the contrary, the PE-lacking Synechococcus CCMA300 and Prochlorococcus sp. does not contain PE, so they only show up in the red-channel images in Figs. 4(e) and 4(g), and nothing appears in their orange-channel images in Figs. 4(f) and 4(h).

To further demonstrate this spectral discrimination ability, two nanophytoplankton, Porphyridium sp. and Chlorella sp., are mixed and analyzed, too. The results are shown in Figs. 4(i)-4(l). Porphyridium sp. is a red microalgae containing PE and Chlorella sp. belongs to Chlorophyta that only contains chlorophyll. Bright-field images of Porphyridium sp. and Chlorella sp. shown in Fig. 4(i) have better contrast than those of picophytoplankton, because nanophytoplankton has much larger cellular volume that contains more absorptive pigments. But the two types of cells look too alike in shape, color and texture to distinguish even for experienced phycologist. While in DC-LSF-IFC imaging as shown in Figs. 4(j) and 4(k), more fluorescence is emitted than that from picophytoplankton cells, so the volume flow rate can be speeded up to 3mL/min with the excitation power of both lasers remains 50mW and the exposure time remains 20ms (50fps). By image registration processing, Porphyridium sp. and Chlorella sp. cells can be discriminated. Figure 4(l) exemplifies a resultant image after discrimination that is composited from Figs. 4(j) and 4(k) with purple indicating Porphyridium sp. and green indicating Chlorella sp., respectively.

On the basis of good detection and discrimination, very accurate quantification of phytoplankton becomes easier through processing of the DC-LSF-IFC images. To demonstrate the counting accuracy, one species of Synechococcus sample and a mixture sample of Porphyridium sp. and Chlorella sp. were quantified with each sample being diluted into different densities. The cell counting of Synechococcus sp. just used simple global thresholding and particle analysis processing upon their two-channel raw images even without registration. The discrimination and quantification of Porphyridium sp. and Chlorella sp. from their mixture included more image processing steps. First, image registration was performed to associate the spatial mapping between the twain images from both channels. Second, grey-scale opening function was applied upon all images from both channels to remove tiny particles obviously too small (<2μm) to be possibly any one of the two nanophytoplankton species. Then global thresholding was applied to the registered twain images to obtain binary masks of each particle. Next, particle masks in the red-channel images were discriminated into PE-containing and PE-lacking classes by comparison between their positions in both channels. Last, particle analysis of the masks in the red-channel images for each species were obtained. As the imaging frame rate and volume flow rate were also recorded, the total sample volume can be obtained by their multiplication, and the resultant cell density can be calculated by dividing the cell numbers counted from all images of an experiment with the corresponding water volume.

Figure 5 shows the quantitation results of both samples. It can be seen that both results exhibit very good linearity (R2>0.99) along their dilution series, proving excellent quantitation reliability and reproducibility. Figure 5(a) further reveals that this Synechococcus sp. belongs to the PE-containing strain CCMA299, as the number of cells counted from PE-channel is so close to that obtained from Chl a-channel. In addition, it can be estimated that a highest cell counting rate of ~1.3 × 105cells/s has been reached to, which is already comparable to the detection speed of many non-imaging FCMs. The counting results from Porphyridium sp. and Chlorella sp. mixture measurements shown in Fig. 5(b) indicate that each individual nanophytoplankton species can be well quantified, further demonstrating the importance of cell discrimination as prerequisite for correct enumeration. The number of cells at each concentration has very good numerical consistency and the highest cell counting throughput is calculated to be ~5000 cells/s, comparable with the commercial TDI-IFC [27].

 

Fig. 5 Cell quantitation results of (a) Synechococcus CCMA299 and (b) mixture of Porphyridium sp. and Chlorella sp. by DC-LSF-IFC analysis.

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To further demonstrate counting accuracy, comparison experiments were performed on quantifying Porphyridium sp. by conventional manual counting, Coulter counting and DC-LSF-IFC counting, respectively. In manual counting, two experienced phycologists used standard hemocytometer and bright-field microscope to count a subsample from five FOVs and averaged their results to reduce subjective bias. In Coulter counting, a subsample was immersed in electrolyte and measured through a 100μm aperture tube by a Coulter analyzer (Multisizer 4, Beckman-coulter). The last piece of subsample was measured by the DC-LSF-IFC system at volume flow rate of 3mL/min and the image processing is similar to the previous used for nanophytoplankton mixture quantification. The counting results from manual, Coulter and IFC analyses are 1.25 × 106cells/mL, 1.83 × 106 cells/mL, and 1.14 × 106cells/mL, respectively. It turned that the counting results by manual and IFC analyses are very agreeable, but the number counted by Coulter analyzer is much larger. This overestimate is known to be caused by Coulter analyzer’s inability to differentiate phytoplankton from non-phytoplankton particles. The latter could be unavoidable inorganic impurities and cell debris that mixed in the samples, which can be directly neglected in IFC analysis as it only detects the intrinsic Chl a fluorescence as signal. So we believe the counting accuracy of IFC is much more reliable than Coulter counting.

Different from serial detection of single cells that are lined up flowing through the laser focus in FCM, the acceleration of detection throughput in our method is attributed to more efficient usage of spatial multiplex of wide-field imaging. The cell counting throughput reported here is not the cap of the DC-LSF-IFC system. Its ultimate limit of the fastest sample volume flow rate is mainly dependent on imaging sensitivity, while its highest cell counting throughput can be limited by additional influences from sample physical properties (cell size and concentration), imaging resolution, camera speed, and cell transition time as well. Given appropriate experimental settings, the cell counting throughput can be further promoted at the premise of accuracy assurance. But compared with extra electrolyte expense and complex sample filtration and enrichment in Coulter and FCM analysis, the sample pretreatment requisite for using DC-LSF-IFC is almost negligible, which would provide great operational convenience for practical applications.

Natural coastal seawater sample exploring

According to the visual microscopy inspection of the backup samples in advance, the DC-LSF-IFC was set to work with a moderate volume flow rate of 1mL/min under excitation of 50mW for both lasers when we performed natural seawater sample measurements. As the phytoplankton density of natural sample is much lower than the cultured samples, the camera frame rate was also slowed down to 10fps (100ms exposure time). Figure 6 shows a selected collection of typical raw images of phytoplankton particles obtained in this experiment.

 

Fig. 6 Selected collection of raw images of natural coastal seawater sample acquired by the DC-LSF-IFC system. Frame size = 300μm × 300μm, and red frames correspond to Chl a-channel and orange frames correspond to PE-channel, respectively.

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The top row of images in Fig. 6 are from the red-channel and their twain images from the orange-channel are listed in the bottom row. From the red-channel images, some large micro- or nanophytoplankton cells can be found with strong Chl a fluorescence. But their emergence frequency is much lower than the picophytoplankton particles. This is because usually the abundance of picophytoplankton is much higher than that of the larger phytoplankton in the nature. Enumerating the cells in these images has produced a total cell concentration >5100 cells/mL, which is consistent with the previous observation and much larger than the historical data of 1500-4500cells/mL reported by the Hong Kong Environmental Protection Department [9].

Similar to those obtained by the single-channel LSF-IFC, the images of relatively larger cells found in the Chl a-channel display good image quality in terms of resolution, contrast, and sharpness even without further deconvolution processing, which can provide rich features for identification. From comparative bright-filed microscopy observation of the fixed sample backups, quite a few common genus of diatoms and dinoflagellates inhabiting in the South China Sea can be identified by the phycologists. The provision of cellular PE presence/absence information from the orange-channel of the DC-LSF-IFC system would be very helpful to complement the identification judgement that based only on morphology features from Chl a counterpart images. For example, the cells displayed in Figs. 6(a) and 6(b) are identified as common diatoms of Thalassiosira and Coscinodiscus based on their morphology and none-emission of PE fluorescence. The cell in Fig. 6(c) is highly possible to be a dinoflagellate judging from its profile and partial emission of PE fluorescence [22]. The cell in Fig. 6(d) is very likely to be from Cryptophyta due to its cellular size and strong fluorescence emission of both Chl a and PE [21]. At the same time, picophytoplankton cells were found to widely exist and most of them also showed up in the orange-channel images. This indicates the abundance of prokaryotic Synechococcus around the sampling site and the difference between twain images from both channels could reflect the quantity of possible eukaryotic picophytoplankton.

Very occasionally, some large phytoplankton >200μm can be found in the red-channel images due to their extremely low density. Figure 7 shows two examples of such big microphytoplankton cells with sizes closer to 300μm. The cell shown in Fig. 7(a) is identified to be a diatom of Coscinodiscus based on its morphology and internal chloroplasts distribution. The long needle-like cell in Fig. 7(b) is also very likely to be a diatom considering its shape and absence of PE fluorescence. Both cell images shown in Fig. 7 provide direct evidence to prove the new DC-LSF-IFC has broadened cell size coverage up to 300μm, a hundred micron wider than previously achieved in the single channel LSF-IFC [9]. In addition, Fig. 7(b) also demonstrates the cell morphology preservation advantage of adopting laminar flow for imaging long filamentous, chained and colony cells with fragile structures. Compared with hydrodynamically focused streams used in many other flow-through detection methods, in which the fragile phytoplankton particles are prone to be torn apart due to large shear force gradient between sample flow and sheath flow at high flow speed, the shear force being imposed on the cells in laminar flow at similar speed is much milder. If these large phytoplankton are selectively targeted in certain investigations, their imaging throughput can be further increased by accelerating the flow rate (e.g., to several mL per minute) not only because they can emit more fluorescence signals than smaller cells, but also because their morphology can be well preserved during this high-speed interrogation process.

 

Fig. 7 Selected large phytoplankton cell images captured by the DC-LSF-IFC system from natural coastal seawater samples. Frame size = 300μm × 300μm and red frames correspond to Chl a-channel.

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Discussion

Phytoplankton analysis is of fundamental significance for ecological and environmental study in aquatic sciences. There are many laboratory and field application scenarios need to have all-sized natural phytoplankton suspension analyzed as rapidly as possible. As demonstrated in the experiment results, the multispectral imaging function has been well incorporated into the DC-LSF-IFC system and played important role for phytoplankton analysis. This would enable IFC’s function closer to FCMs and has well filled the gap of current IFCs’ lack of sensitivity in measuring smaller-sized phytoplankton classes. In the meantime, the high resolution and high throughput features of LSF-IFC have also been well inherited, which are very advantageous for very low density microphytoplankton screening and make up the deficiencies of FCM and TDI-IFC in their sparse cell detection limit in this larger size range.

The simultaneous possession of high sensitivity, high throughput and spectral resolving would enable the DC-LSF-IFC to analyze very wide size range of phytoplankton by using just one set of instrument, which is very appealing for natural seawater sample analysis. For example, live pico- and nanophytoplankton cells are smaller than 20μm and most species appear spherical shape with similar color under moderate optical lighting, magnification and resolution. So, short of morphological features make their identification and discrimination very difficult through conventional bright-field microscopy. Although quantitative phase information provided by DHM has been demonstrated to provide new features for successful discrimination of nanophytoplankton around 5μm [11], lack of sensitivity still makes this technology hard to detect and discriminate even smaller picophytoplankton down to ~1μm. Using fluorescence signature of photosynthetic pigments to discriminate these tiny ball-like cells is more straightforward and intuitive than phase features. The equipment of multicolor excitation/detection in DC-LSF-IFC is an analogy to FCM but realizes spectral resolving via imaging. The concept and instrumentation of the DC-LSF-IFC system might be another option for small-sized phytoplankton analysis, such as discriminating and quantifying important cyanobacteria Synechococcus and Prochlorococcus in many field investigations for marine ecology study. After all, it would be more economic and robust to bring just one instrument with these scientific expeditions than using two separate sets of machines to handle different-sized phytoplankton classes.

On the other hand, adding multispectral imaging capability is also beneficial for expanding the application range of IFCs for measuring and analyzing artificially labeled planktonic samples. With the development of FCM technology in the past decades, there have emerged a range of fluorochromes (e.g. SYBR-Green I, SYTO-13, Nile Red, BODIPY, etc.) that can dye cellular components (e.g. DNA, lipid, etc.) of microorganisms to fluoresce various colors with good quantum yield [28–31]. The marriage of multispectral imaging with fluorophore color and specificity enrichment would make IFC reach to its full potential. Beyond conventional analysis just based on cell geometric information, more molecular microbiology applications such as HAB probing and cell viability and metabolic activity study can be carried out [1]. Moreover, fluorescence staining will also enable spectral IFCs to simultaneously measure phytoplankton and other planktonic microorganisms without natural fluorescent pigments such as bacterioplankton [28, 29] and protist [32]. This would open up more possibilities for studying planktonic interplay in much larger communities in the same water habitat.

The DC-LSF-IFC system has prominent feature of versatility and flexibility in its hardware and software. Thanks to its simple frontal optofluidic configuration, it eliminates the need of complex hydrodynamic flow focusing to confine cells flowing across the interrogation volume as used in most FCMs and IFCs [7, 12, 13, 16, 17, 33]. With the development in microfluidics technology, such configuration can even be readily integrated into very small lab-on-chip devices [34]. It can also keep various sized cells in focus during imaging by using just one high magnification objective lens, eliminating the necessity of inconvenient mechanical switching between combinations of objective lenses with corresponding flow channels for different magnifications. In comparison, the control and configuration of TDI-IFCs is much more complex. On the one hand, critical synchronization between cell movement and TDI camera pixel shift has to be precisely controlled via dedicated flow velocity monitoring subsystem. One the other hand, the defocusing problem has not been completely eliminated, as it was not specially designed for natural phytoplankton sample analysis; the indispensable sheath flow still limits the measurable cell size range and the postprocessing-based depth-of-field extension would also be very computation-intensive and time-consuming [35]. Apparently, these issues increased system complexity and cost, and prevent their extensive application in natural phytoplankton sample analysis.

Although the spectral resolving ability of the DC-LSF-IFC system is not as powerful as that of the commercial TDI-IFCs, which can support up to 12 spectral imaging channels [27], it can be seen from the results that imaging two fluorescence colors is already very informative for a variety of phytoplankton analysis applications. As the lasers and filters inside the DC-LSF-IFC setup can be easily replaced, this system design is readily modifiable to suit for detecting other fluorophores such as phycocyanin (PC) for freshwater cyanobacteria monitoring [36]. Of course, it can also be adapted to measure single cells or multicellular particles other than phytoplankton for many other biomedical applications, e.g. drug discovery and toxicology studies [37]. The simple hardware configuration, control, and straightforward image acquisition and processing would facilitate the DC-LSF-IFC system to be developed into laboratory instrument, its working principle and advantages in phytoplankton flow imaging also hold promise for in situ applications on various field platforms [14]. Compared with CytoBuoy, the imaging nature of DC-LSF-IFC should gain in providing more morphological details of phytoplankton particles and higher detection throughput, but at a price of more image data redundancy and higher energy consumption. To circumvent these bottlenecks for field use, relevant big image data handling and energy usage strategies for specific applications need to be investigated; on the other hand, new technologies for image compressing, transferring, processing and storage, instrumental power consumption reduction and power supply increase have to be further developed. With the rapid development of nowadays computation and new energy technologies, such solution can be anticipated in the near future.

4. Conclusion

In conclusion, we have successfully developed a new DC-LSF-IFC system for high throughput phytoplankton analysis. With the simultaneous dual-color excitation and spectral imaging-in-flow capability, this new IFC can discriminate PE-containing and PE-lacking phytoplankton species groups based on photosynthetic pigments fluorescence signatures. The experimental results on measuring various cultured and natural phytoplankton samples have demonstrated the system has good sensitivity and resolution to detect picophytoplankton down to ~1μm, high throughput of 1.3 × 105cells/s and 5 × 103cells/s at 100μL/min and 3mL/min 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 extension of analyzing ability towards both ends of a size range of 1-300μm has further empowered the light-sheet based fluorescence IFC for natural phytoplankton sample analysis. On the basis of accuracy and high-throughput, such technology is expected to be developed into scientific instruments and integrated into field observation platforms for oceanographic sciences research and aquatic environmental applications such as marine HAB species identification and freshwater quality monitoring.

Funding

Research Grants Council of the Hong Kong SAR of PR. China (HKBU12201214); Hong Kong Baptist University (FRG2/14-15/098); National Key Research and Development Program of China (2016YFC1400700).

Acknowledgments

The authors thank Dr. Robert K.Y. Chan from Hong Kong Baptist University for his generous loan of the green laser, Mr. Mark Luk and Mr. Tsz Kwong Mak from Hong Kong Baptist University for their technical assistance, Miss Jing TONG, Ms. Weijun JIAN and Dr. Huirong Chen from Shenzhen University for phycology expertise supporting, and the State Key Laboratory of Marine Environmental Science of Xiamen University and College of Life and Oceanography Sciences of Shenzhen University for kind provision of phytoplankton samples. In memorial, JP LI would specially acknowledge Ms. Connie Wong from Hong Kong Baptist University for her continuous help and support.

References and links

1. V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017). [CrossRef]   [PubMed]  

2. 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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016). [CrossRef]   [PubMed]  

3. J. E. Cloern, “Phytoplankton bloom dynamics in coastal ecosystems: a review with some general lessons from sustained investigation of San Francisco Bay, California,” Rev. Geophys. 34(2), 127–168 (1996). [CrossRef]  

4. G. T. Evans, “A framework for discussing seasonal succession and coexistence of phytoplankton species,” Limnol. Oceanogr. 33(5), 1027–1036 (1988). [CrossRef]  

5. J.-J. Wang and D. L. Tang, “Phytoplankton patchiness during spring intermonsoon in western coast of South China Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr. 101, 120–128 (2014). [CrossRef]  

6. C. K. Sieracki, M. E. Sieracki, and C. S. Yentsch, “An imaging-in-flow system for automated analysis of marine microplankton,” Mar. Ecol. Prog. Ser. 168, 285–296 (1998). [CrossRef]  

7. R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007). [CrossRef]  

8. J. Wu, J. Li, and R. K. Y. Chan, “A light sheet based high throughput 3D-imaging flow cytometer for phytoplankton analysis,” Opt. Express 21(12), 14474–14480 (2013). [CrossRef]   [PubMed]  

9. J. Wu and R. K. Y. Chan, “A fast fluorescence imaging flow cytometer for phytoplankton analysis,” Opt. Express 21(20), 23921–23926 (2013). [CrossRef]   [PubMed]  

10. C. Yourassowsky and F. Dubois, “High throughput holographic imaging-in-flow for the analysis of a wide plankton size range,” Opt. Express 22(6), 6661–6673 (2014). [CrossRef]   [PubMed]  

11. E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014). [CrossRef]  

12. C. Lei, T. Ito, M. Ugawa, T. Nozawa, O. Iwata, M. Maki, G. Okada, H. Kobayashi, X. Sun, P. Tiamsak, N. Tsumura, K. Suzuki, D. Di Carlo, Y. Ozeki, and K. Goda, “High-throughput label-free image cytometry and image-based classification of live Euglena gracilis,” Biomed. Opt. Express 7(7), 2703–2708 (2016). [CrossRef]   [PubMed]  

13. Q. T. K. Lai, K. C. M. Lee, A. H. L. Tang, K. K. Y. Wong, H. K. H. So, and K. K. Tsia, “High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton,” Opt. Express 24(25), 28170–28184 (2016). [CrossRef]   [PubMed]  

14. J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012). [CrossRef]   [PubMed]  

15. D. Basiji, “Multispectral Imaging Flow Cytometry,” in Biomedical Imaging: From Nano to Macro (2007), pp. 1100–1103. [CrossRef]  

16. T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004). [CrossRef]   [PubMed]  

17. G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999). [CrossRef]   [PubMed]  

18. K. v. Hecke, “New camera upgrade package yields impressive results” (2016), http://www.cytobuoy.com/company/news/show/article/new-camera-upgrade-package-yields-impressive-results/.

19. E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015). [CrossRef]   [PubMed]  

20. D. A. Phinney and T. L. Cucci, “Flow cytometry and phytoplankton,” Cytometry 10(5), 511–521 (1989). [CrossRef]   [PubMed]  

21. J. L. Collier, “Flow cytometry and the single cell in phycology,” J. Phycol. 36(4), 628–644 (2000). [CrossRef]  

22. R. J. Olson, E. R. Zettler, and O. K. Anderson, “Discrimination of eukaryotic phytoplankton cell types from light scatter and autofluorescence properties measured by flow cytometry,” Cytometry 10(5), 636–643 (1989). [CrossRef]   [PubMed]  

23. C. V. Open, 2.4.13.2 documentation, “Affine Transformations” (2017), http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.html.

24. D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017). [CrossRef]   [PubMed]  

25. M. D. Abràmoff, P. J. Magalhães, and S. J. Ram, “Image processing with ImageJ,” Biophoton. Int. 11, 36–42 (2004).

26. A. P. Singh, J. W. Krieger, J. Buchholz, E. Charbon, J. Langowski, and T. Wohland, “The performance of 2D array detectors for light sheet based fluorescence correlation spectroscopy,” Opt. Express 21(7), 8652–8668 (2013). [CrossRef]   [PubMed]  

27. Merk Millipore, “ImageStream®X Mark II Imaging Flow Cytometer”, (2017), http://www.merckmillipore.com/CN/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/imagestreamx-Mark-ii-imaging-flow-cytometer/VaSb.qB.QokAAAFLzRop.zHe,nav.

28. P. Lebaron, N. Parthuisot, and P. Catala, “Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems,” Appl. Environ. Microbiol. 64(5), 1725–1730 (1998). [PubMed]  

29. M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999). [CrossRef]  

30. W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009). [CrossRef]   [PubMed]  

31. T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012). [CrossRef]   [PubMed]  

32. M. V. Zubkov, P. H. Burkill, and J. N. Topping, “Flow cytometric enumeration of DNA-stained oceanic planktonic protists,” J. Plankton Res. 29(1), 79–86 (2007). [CrossRef]  

33. R. J. Olson, A. Shalapyonok, and H. M. Sosik, “An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot,” Deep Sea Res. Part I Oceanogr. Res. Pap. 50(2), 301–315 (2003). [CrossRef]  

34. P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016). [CrossRef]   [PubMed]  

35. W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007). [CrossRef]   [PubMed]  

36. H. Schubert, U. Schiewer, and E. Tschirner, “Fluorescence characteristics of cyanobacteria (blue-green algae),” J. Plankton Res. 11(2), 353–359 (1989). [CrossRef]  

37. E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017). [CrossRef]   [PubMed]  

References

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  1. V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
    [Crossref] [PubMed]
  2. 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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
    [Crossref] [PubMed]
  3. J. E. Cloern, “Phytoplankton bloom dynamics in coastal ecosystems: a review with some general lessons from sustained investigation of San Francisco Bay, California,” Rev. Geophys. 34(2), 127–168 (1996).
    [Crossref]
  4. G. T. Evans, “A framework for discussing seasonal succession and coexistence of phytoplankton species,” Limnol. Oceanogr. 33(5), 1027–1036 (1988).
    [Crossref]
  5. J.-J. Wang and D. L. Tang, “Phytoplankton patchiness during spring intermonsoon in western coast of South China Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr. 101, 120–128 (2014).
    [Crossref]
  6. C. K. Sieracki, M. E. Sieracki, and C. S. Yentsch, “An imaging-in-flow system for automated analysis of marine microplankton,” Mar. Ecol. Prog. Ser. 168, 285–296 (1998).
    [Crossref]
  7. R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007).
    [Crossref]
  8. J. Wu, J. Li, and R. K. Y. Chan, “A light sheet based high throughput 3D-imaging flow cytometer for phytoplankton analysis,” Opt. Express 21(12), 14474–14480 (2013).
    [Crossref] [PubMed]
  9. J. Wu and R. K. Y. Chan, “A fast fluorescence imaging flow cytometer for phytoplankton analysis,” Opt. Express 21(20), 23921–23926 (2013).
    [Crossref] [PubMed]
  10. C. Yourassowsky and F. Dubois, “High throughput holographic imaging-in-flow for the analysis of a wide plankton size range,” Opt. Express 22(6), 6661–6673 (2014).
    [Crossref] [PubMed]
  11. E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
    [Crossref]
  12. C. Lei, T. Ito, M. Ugawa, T. Nozawa, O. Iwata, M. Maki, G. Okada, H. Kobayashi, X. Sun, P. Tiamsak, N. Tsumura, K. Suzuki, D. Di Carlo, Y. Ozeki, and K. Goda, “High-throughput label-free image cytometry and image-based classification of live Euglena gracilis,” Biomed. Opt. Express 7(7), 2703–2708 (2016).
    [Crossref] [PubMed]
  13. Q. T. K. Lai, K. C. M. Lee, A. H. L. Tang, K. K. Y. Wong, H. K. H. So, and K. K. Tsia, “High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton,” Opt. Express 24(25), 28170–28184 (2016).
    [Crossref] [PubMed]
  14. J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
    [Crossref] [PubMed]
  15. D. Basiji, “Multispectral Imaging Flow Cytometry,” in Biomedical Imaging: From Nano to Macro (2007), pp. 1100–1103.
    [Crossref]
  16. T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
    [Crossref] [PubMed]
  17. G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
    [Crossref] [PubMed]
  18. K. v. Hecke, “New camera upgrade package yields impressive results” (2016), http://www.cytobuoy.com/company/news/show/article/new-camera-upgrade-package-yields-impressive-results/ .
  19. E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015).
    [Crossref] [PubMed]
  20. D. A. Phinney and T. L. Cucci, “Flow cytometry and phytoplankton,” Cytometry 10(5), 511–521 (1989).
    [Crossref] [PubMed]
  21. J. L. Collier, “Flow cytometry and the single cell in phycology,” J. Phycol. 36(4), 628–644 (2000).
    [Crossref]
  22. R. J. Olson, E. R. Zettler, and O. K. Anderson, “Discrimination of eukaryotic phytoplankton cell types from light scatter and autofluorescence properties measured by flow cytometry,” Cytometry 10(5), 636–643 (1989).
    [Crossref] [PubMed]
  23. C. V. Open, 2.4.13.2 documentation, “Affine Transformations” (2017), http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.html .
  24. D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
    [Crossref] [PubMed]
  25. M. D. Abràmoff, P. J. Magalhães, and S. J. Ram, “Image processing with ImageJ,” Biophoton. Int. 11, 36–42 (2004).
  26. A. P. Singh, J. W. Krieger, J. Buchholz, E. Charbon, J. Langowski, and T. Wohland, “The performance of 2D array detectors for light sheet based fluorescence correlation spectroscopy,” Opt. Express 21(7), 8652–8668 (2013).
    [Crossref] [PubMed]
  27. Merk Millipore, “ImageStream®X Mark II Imaging Flow Cytometer”, (2017), http://www.merckmillipore.com/CN/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/imagestreamx-Mark-ii-imaging-flow-cytometer/VaSb.qB.QokAAAFLzRop.zHe,nav .
  28. P. Lebaron, N. Parthuisot, and P. Catala, “Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems,” Appl. Environ. Microbiol. 64(5), 1725–1730 (1998).
    [PubMed]
  29. M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999).
    [Crossref]
  30. W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
    [Crossref] [PubMed]
  31. T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012).
    [Crossref] [PubMed]
  32. M. V. Zubkov, P. H. Burkill, and J. N. Topping, “Flow cytometric enumeration of DNA-stained oceanic planktonic protists,” J. Plankton Res. 29(1), 79–86 (2007).
    [Crossref]
  33. R. J. Olson, A. Shalapyonok, and H. M. Sosik, “An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot,” Deep Sea Res. Part I Oceanogr. Res. Pap. 50(2), 301–315 (2003).
    [Crossref]
  34. P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016).
    [Crossref] [PubMed]
  35. W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
    [Crossref] [PubMed]
  36. H. Schubert, U. Schiewer, and E. Tschirner, “Fluorescence characteristics of cyanobacteria (blue-green algae),” J. Plankton Res. 11(2), 353–359 (1989).
    [Crossref]
  37. E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
    [Crossref] [PubMed]

2017 (3)

V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
[Crossref] [PubMed]

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
[Crossref] [PubMed]

2016 (4)

P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016).
[Crossref] [PubMed]

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

C. Lei, T. Ito, M. Ugawa, T. Nozawa, O. Iwata, M. Maki, G. Okada, H. Kobayashi, X. Sun, P. Tiamsak, N. Tsumura, K. Suzuki, D. Di Carlo, Y. Ozeki, and K. Goda, “High-throughput label-free image cytometry and image-based classification of live Euglena gracilis,” Biomed. Opt. Express 7(7), 2703–2708 (2016).
[Crossref] [PubMed]

Q. T. K. Lai, K. C. M. Lee, A. H. L. Tang, K. K. Y. Wong, H. K. H. So, and K. K. Tsia, “High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton,” Opt. Express 24(25), 28170–28184 (2016).
[Crossref] [PubMed]

2015 (1)

E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015).
[Crossref] [PubMed]

2014 (3)

C. Yourassowsky and F. Dubois, “High throughput holographic imaging-in-flow for the analysis of a wide plankton size range,” Opt. Express 22(6), 6661–6673 (2014).
[Crossref] [PubMed]

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

J.-J. Wang and D. L. Tang, “Phytoplankton patchiness during spring intermonsoon in western coast of South China Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr. 101, 120–128 (2014).
[Crossref]

2013 (3)

2012 (2)

T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012).
[Crossref] [PubMed]

J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
[Crossref] [PubMed]

2009 (1)

W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
[Crossref] [PubMed]

2007 (3)

M. V. Zubkov, P. H. Burkill, and J. N. Topping, “Flow cytometric enumeration of DNA-stained oceanic planktonic protists,” J. Plankton Res. 29(1), 79–86 (2007).
[Crossref]

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007).
[Crossref]

2004 (2)

M. D. Abràmoff, P. J. Magalhães, and S. J. Ram, “Image processing with ImageJ,” Biophoton. Int. 11, 36–42 (2004).

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

2003 (1)

R. J. Olson, A. Shalapyonok, and H. M. Sosik, “An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot,” Deep Sea Res. Part I Oceanogr. Res. Pap. 50(2), 301–315 (2003).
[Crossref]

2000 (1)

J. L. Collier, “Flow cytometry and the single cell in phycology,” J. Phycol. 36(4), 628–644 (2000).
[Crossref]

1999 (2)

M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999).
[Crossref]

G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
[Crossref] [PubMed]

1998 (2)

C. K. Sieracki, M. E. Sieracki, and C. S. Yentsch, “An imaging-in-flow system for automated analysis of marine microplankton,” Mar. Ecol. Prog. Ser. 168, 285–296 (1998).
[Crossref]

P. Lebaron, N. Parthuisot, and P. Catala, “Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems,” Appl. Environ. Microbiol. 64(5), 1725–1730 (1998).
[PubMed]

1996 (1)

J. E. Cloern, “Phytoplankton bloom dynamics in coastal ecosystems: a review with some general lessons from sustained investigation of San Francisco Bay, California,” Rev. Geophys. 34(2), 127–168 (1996).
[Crossref]

1989 (3)

D. A. Phinney and T. L. Cucci, “Flow cytometry and phytoplankton,” Cytometry 10(5), 511–521 (1989).
[Crossref] [PubMed]

R. J. Olson, E. R. Zettler, and O. K. Anderson, “Discrimination of eukaryotic phytoplankton cell types from light scatter and autofluorescence properties measured by flow cytometry,” Cytometry 10(5), 636–643 (1989).
[Crossref] [PubMed]

H. Schubert, U. Schiewer, and E. Tschirner, “Fluorescence characteristics of cyanobacteria (blue-green algae),” J. Plankton Res. 11(2), 353–359 (1989).
[Crossref]

1988 (1)

G. T. Evans, “A framework for discussing seasonal succession and coexistence of phytoplankton species,” Limnol. Oceanogr. 33(5), 1027–1036 (1988).
[Crossref]

Abbriano, R.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Abràmoff, M. D.

M. D. Abràmoff, P. J. Magalhães, and S. J. Ram, “Image processing with ImageJ,” Biophoton. Int. 11, 36–42 (2004).

Alderete, B.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Anderson, O. K.

R. J. Olson, E. R. Zettler, and O. K. Anderson, “Discrimination of eukaryotic phytoplankton cell types from light scatter and autofluorescence properties measured by flow cytometry,” Cytometry 10(5), 636–643 (1989).
[Crossref] [PubMed]

Barteneva, N. S.

V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
[Crossref] [PubMed]

Basiji, D. A.

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Bassi, A.

P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016).
[Crossref] [PubMed]

Beeker, A. E.

G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
[Crossref] [PubMed]

Bragheri, F.

P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016).
[Crossref] [PubMed]

Buchholz, J.

Burkill, P. H.

M. V. Zubkov, P. H. Burkill, and J. N. Topping, “Flow cytometric enumeration of DNA-stained oceanic planktonic protists,” J. Plankton Res. 29(1), 79–86 (2007).
[Crossref]

Bux, F.

T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012).
[Crossref] [PubMed]

Catala, P.

P. Lebaron, N. Parthuisot, and P. Catala, “Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems,” Appl. Environ. Microbiol. 64(5), 1725–1730 (1998).
[PubMed]

Chan, R. K. Y.

Charbon, E.

Chen, W.

W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
[Crossref] [PubMed]

Cloern, J. E.

J. E. Cloern, “Phytoplankton bloom dynamics in coastal ecosystems: a review with some general lessons from sustained investigation of San Francisco Bay, California,” Rev. Geophys. 34(2), 127–168 (1996).
[Crossref]

Collier, J. L.

J. L. Collier, “Flow cytometry and the single cell in phycology,” J. Phycol. 36(4), 628–644 (2000).
[Crossref]

Cook, O.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Courties, C.

M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999).
[Crossref]

Cucci, T. L.

D. A. Phinney and T. L. Cucci, “Flow cytometry and phytoplankton,” Cytometry 10(5), 511–521 (1989).
[Crossref] [PubMed]

Dashkova, V.

V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
[Crossref] [PubMed]

Davis, A.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Di Carlo, D.

Donati, L.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Dubelaar, G. B.

G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
[Crossref] [PubMed]

Dubois, F.

C. Yourassowsky and F. Dubois, “High throughput holographic imaging-in-flow for the analysis of a wide plankton size range,” Opt. Express 22(6), 6661–6673 (2014).
[Crossref] [PubMed]

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

El Mallahi, A.

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

Erickson, J. S.

J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
[Crossref] [PubMed]

Evans, G. T.

G. T. Evans, “A framework for discussing seasonal succession and coexistence of phytoplankton species,” Limnol. Oceanogr. 33(5), 1027–1036 (1988).
[Crossref]

Fortun, D.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Frost, K.

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

Gardner, R.

E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
[Crossref] [PubMed]

George, T. C.

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Gerritzen, P. L.

G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
[Crossref] [PubMed]

Goda, K.

Govender, T.

T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012).
[Crossref] [PubMed]

Gualda, E. J.

E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
[Crossref] [PubMed]

Guiet, R.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Hall, B. E.

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Hashemi, N.

J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
[Crossref] [PubMed]

Hildebrand, M.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Hu, Q.

W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
[Crossref] [PubMed]

Huisken, J.

E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015).
[Crossref] [PubMed]

Ito, T.

Iwata, O.

Jonker, R. R.

G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
[Crossref] [PubMed]

Kobayashi, H.

Krieger, J. W.

Kromkamp, J. C.

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

Lai, Q. T. K.

Langowski, J.

Lebaron, P.

M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999).
[Crossref]

P. Lebaron, N. Parthuisot, and P. Catala, “Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems,” Appl. Environ. Microbiol. 64(5), 1725–1730 (1998).
[PubMed]

Lee, K. C. M.

Lei, C.

Li, J.

Liang, L.

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

Ligler, F. S.

J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
[Crossref] [PubMed]

Lynch, D. H.

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Magalhães, P. J.

M. D. Abràmoff, P. J. Magalhães, and S. J. Ram, “Image processing with ImageJ,” Biophoton. Int. 11, 36–42 (2004).

Maki, M.

Malashenkov, D.

V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
[Crossref] [PubMed]

Manandhar-Shrestha, K.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Martins, G. G.

E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
[Crossref] [PubMed]

Meysman, F. J. R.

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

Moreno, N.

E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
[Crossref] [PubMed]

Morrissey, P. J.

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Nozawa, T.

Okada, G.

Olson, R. J.

R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007).
[Crossref]

R. J. Olson, A. Shalapyonok, and H. M. Sosik, “An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot,” Deep Sea Res. Part I Oceanogr. Res. Pap. 50(2), 301–315 (2003).
[Crossref]

R. J. Olson, E. R. Zettler, and O. K. Anderson, “Discrimination of eukaryotic phytoplankton cell types from light scatter and autofluorescence properties measured by flow cytometry,” Cytometry 10(5), 636–643 (1989).
[Crossref] [PubMed]

Ortyn, W. E.

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Osellame, R.

P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016).
[Crossref] [PubMed]

Ozeki, Y.

Paiè, P.

P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016).
[Crossref] [PubMed]

Parthuisot, N.

P. Lebaron, N. Parthuisot, and P. Catala, “Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems,” Appl. Environ. Microbiol. 64(5), 1725–1730 (1998).
[PubMed]

Pereira, H.

E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
[Crossref] [PubMed]

Perry, D. J.

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Peychl, J.

E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015).
[Crossref] [PubMed]

Phinney, D. A.

D. A. Phinney and T. L. Cucci, “Flow cytometry and phytoplankton,” Cytometry 10(5), 511–521 (1989).
[Crossref] [PubMed]

Poulton, N.

V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
[Crossref] [PubMed]

Pugsley, H. R.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Ram, S. J.

M. D. Abràmoff, P. J. Magalhães, and S. J. Ram, “Image processing with ImageJ,” Biophoton. Int. 11, 36–42 (2004).

Ramanna, L.

T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012).
[Crossref] [PubMed]

Rawat, I.

T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012).
[Crossref] [PubMed]

Reynaud, E. G.

E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015).
[Crossref] [PubMed]

Sage, D.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Sánchez-Alvarez, E. L.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Schiewer, U.

H. Schubert, U. Schiewer, and E. Tschirner, “Fluorescence characteristics of cyanobacteria (blue-green algae),” J. Plankton Res. 11(2), 353–359 (1989).
[Crossref]

Schmit, G.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Schubert, H.

H. Schubert, U. Schiewer, and E. Tschirner, “Fluorescence characteristics of cyanobacteria (blue-green algae),” J. Plankton Res. 11(2), 353–359 (1989).
[Crossref]

Seitz, A.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Seo, M. J.

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Servais, P.

M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999).
[Crossref]

Shalapyonok, A.

R. J. Olson, A. Shalapyonok, and H. M. Sosik, “An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot,” Deep Sea Res. Part I Oceanogr. Res. Pap. 50(2), 301–315 (2003).
[Crossref]

Shrestha, R. P.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Sieracki, C. K.

C. K. Sieracki, M. E. Sieracki, and C. S. Yentsch, “An imaging-in-flow system for automated analysis of marine microplankton,” Mar. Ecol. Prog. Ser. 168, 285–296 (1998).
[Crossref]

Sieracki, M. E.

C. K. Sieracki, M. E. Sieracki, and C. S. Yentsch, “An imaging-in-flow system for automated analysis of marine microplankton,” Mar. Ecol. Prog. Ser. 168, 285–296 (1998).
[Crossref]

Singh, A. P.

Smith, S. R.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

So, H. K. H.

Sommerfeld, M.

W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
[Crossref] [PubMed]

Song, L.

W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
[Crossref] [PubMed]

Sosik, H. M.

R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007).
[Crossref]

R. J. Olson, A. Shalapyonok, and H. M. Sosik, “An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot,” Deep Sea Res. Part I Oceanogr. Res. Pap. 50(2), 301–315 (2003).
[Crossref]

Soulez, F.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Sullivan, J. M.

J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
[Crossref] [PubMed]

Sun, X.

Suzuki, K.

Tang, A. H. L.

Tang, D. L.

J.-J. Wang and D. L. Tang, “Phytoplankton patchiness during spring intermonsoon in western coast of South China Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr. 101, 120–128 (2014).
[Crossref]

Tangen, K.

G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
[Crossref] [PubMed]

Tiamsak, P.

Tomancak, P.

E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015).
[Crossref] [PubMed]

Topping, J. N.

M. V. Zubkov, P. H. Burkill, and J. N. Topping, “Flow cytometric enumeration of DNA-stained oceanic planktonic protists,” J. Plankton Res. 29(1), 79–86 (2007).
[Crossref]

Traller, J. C.

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Troussellier, M.

M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999).
[Crossref]

Tschirner, E.

H. Schubert, U. Schiewer, and E. Tschirner, “Fluorescence characteristics of cyanobacteria (blue-green algae),” J. Plankton Res. 11(2), 353–359 (1989).
[Crossref]

Tsia, K. K.

Tsumura, N.

Ugawa, M.

Unser, M.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Venkatachalam, V.

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

Vonesch, C.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Vorobjev, I.

V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
[Crossref] [PubMed]

Wang, J.-J.

J.-J. Wang and D. L. Tang, “Phytoplankton patchiness during spring intermonsoon in western coast of South China Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr. 101, 120–128 (2014).
[Crossref]

Weidemann, A. D.

J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
[Crossref] [PubMed]

Wohland, T.

Wong, K. K. Y.

Wu, J.

Yentsch, C. S.

C. K. Sieracki, M. E. Sieracki, and C. S. Yentsch, “An imaging-in-flow system for automated analysis of marine microplankton,” Mar. Ecol. Prog. Ser. 168, 285–296 (1998).
[Crossref]

Yourassowsky, C.

C. Yourassowsky and F. Dubois, “High throughput holographic imaging-in-flow for the analysis of a wide plankton size range,” Opt. Express 22(6), 6661–6673 (2014).
[Crossref] [PubMed]

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

Zetsche, E.-M.

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

Zettler, E. R.

R. J. Olson, E. R. Zettler, and O. K. Anderson, “Discrimination of eukaryotic phytoplankton cell types from light scatter and autofluorescence properties measured by flow cytometry,” Cytometry 10(5), 636–643 (1989).
[Crossref] [PubMed]

Zhang, C.

W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
[Crossref] [PubMed]

Zimmerman, C. A.

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

Zubkov, M. V.

M. V. Zubkov, P. H. Burkill, and J. N. Topping, “Flow cytometric enumeration of DNA-stained oceanic planktonic protists,” J. Plankton Res. 29(1), 79–86 (2007).
[Crossref]

Anal. Chem. (1)

J. S. Erickson, N. Hashemi, J. M. Sullivan, A. D. Weidemann, and F. S. Ligler, “In situ phytoplankton analysis: there’s plenty of room at the bottom,” Anal. Chem. 84(2), 839–850 (2012).
[Crossref] [PubMed]

Appl. Environ. Microbiol. (1)

P. Lebaron, N. Parthuisot, and P. Catala, “Comparison of blue nucleic acid dyes for flow cytometric enumeration of bacteria in aquatic systems,” Appl. Environ. Microbiol. 64(5), 1725–1730 (1998).
[PubMed]

Biomed. Opt. Express (1)

Biophoton. Int. (1)

M. D. Abràmoff, P. J. Magalhães, and S. J. Ram, “Image processing with ImageJ,” Biophoton. Int. 11, 36–42 (2004).

Bioresour. Technol. (1)

T. Govender, L. Ramanna, I. Rawat, and F. Bux, “BODIPY staining, an alternative to the Nile Red fluorescence method for the evaluation of intracellular lipids in microalgae,” Bioresour. Technol. 114, 507–511 (2012).
[Crossref] [PubMed]

Cytometry (3)

R. J. Olson, E. R. Zettler, and O. K. Anderson, “Discrimination of eukaryotic phytoplankton cell types from light scatter and autofluorescence properties measured by flow cytometry,” Cytometry 10(5), 636–643 (1989).
[Crossref] [PubMed]

D. A. Phinney and T. L. Cucci, “Flow cytometry and phytoplankton,” Cytometry 10(5), 511–521 (1989).
[Crossref] [PubMed]

G. B. Dubelaar, P. L. Gerritzen, A. E. Beeker, R. R. Jonker, and K. Tangen, “Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters,” Cytometry 37(4), 247–254 (1999).
[Crossref] [PubMed]

Cytometry A (3)

T. C. George, D. A. Basiji, B. E. Hall, D. H. Lynch, W. E. Ortyn, D. J. Perry, M. J. Seo, C. A. Zimmerman, and P. J. Morrissey, “Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer,” Cytometry A 59(2), 237–245 (2004).
[Crossref] [PubMed]

W. E. Ortyn, D. J. Perry, V. Venkatachalam, L. Liang, B. E. Hall, K. Frost, and D. A. Basiji, “Extended depth of field imaging for high speed cell analysis,” Cytometry A 71(4), 215–231 (2007).
[Crossref] [PubMed]

E. J. Gualda, H. Pereira, G. G. Martins, R. Gardner, and N. Moreno, “Three-dimensional imaging flow cytometry through light-sheet fluorescence microscopy,” Cytometry A 91(2), 144–151 (2017).
[Crossref] [PubMed]

Deep Sea Res. Part I Oceanogr. Res. Pap. (1)

R. J. Olson, A. Shalapyonok, and H. M. Sosik, “An automated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot,” Deep Sea Res. Part I Oceanogr. Res. Pap. 50(2), 301–315 (2003).
[Crossref]

Deep Sea Res. Part II Top. Stud. Oceanogr. (1)

J.-J. Wang and D. L. Tang, “Phytoplankton patchiness during spring intermonsoon in western coast of South China Sea,” Deep Sea Res. Part II Top. Stud. Oceanogr. 101, 120–128 (2014).
[Crossref]

FEMS Microbiol. Ecol. (1)

M. Troussellier, C. Courties, P. Lebaron, and P. Servais, “Flow cytometric discrimination of bacterial populations in seawater based on SYTO 13 staining of nucleic acids,” FEMS Microbiol. Ecol. 29(4), 319–330 (1999).
[Crossref]

J. Microbiol. Methods (1)

W. Chen, C. Zhang, L. Song, M. Sommerfeld, and Q. Hu, “A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae,” J. Microbiol. Methods 77(1), 41–47 (2009).
[Crossref] [PubMed]

J. Phycol. (1)

J. L. Collier, “Flow cytometry and the single cell in phycology,” J. Phycol. 36(4), 628–644 (2000).
[Crossref]

J. Plankton Res. (2)

M. V. Zubkov, P. H. Burkill, and J. N. Topping, “Flow cytometric enumeration of DNA-stained oceanic planktonic protists,” J. Plankton Res. 29(1), 79–86 (2007).
[Crossref]

H. Schubert, U. Schiewer, and E. Tschirner, “Fluorescence characteristics of cyanobacteria (blue-green algae),” J. Plankton Res. 11(2), 353–359 (1989).
[Crossref]

Lab Chip (1)

P. Paiè, F. Bragheri, A. Bassi, and R. Osellame, “Selective plane illumination microscopy on a chip,” Lab Chip 16(9), 1556–1560 (2016).
[Crossref] [PubMed]

Limnol. Oceanogr. (1)

G. T. Evans, “A framework for discussing seasonal succession and coexistence of phytoplankton species,” Limnol. Oceanogr. 33(5), 1027–1036 (1988).
[Crossref]

Limnol. Oceanogr. Methods (2)

E.-M. Zetsche, A. El Mallahi, F. Dubois, C. Yourassowsky, J. C. Kromkamp, and F. J. R. Meysman, “Imaging-in-Flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms,” Limnol. Oceanogr. Methods 12(11), 757–775 (2014).
[Crossref]

R. J. Olson and H. M. Sosik, “A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot,” Limnol. Oceanogr. Methods 5(6), 195–203 (2007).
[Crossref]

Mar. Ecol. Prog. Ser. (1)

C. K. Sieracki, M. E. Sieracki, and C. S. Yentsch, “An imaging-in-flow system for automated analysis of marine microplankton,” Mar. Ecol. Prog. Ser. 168, 285–296 (1998).
[Crossref]

Methods (2)

V. Dashkova, D. Malashenkov, N. Poulton, I. Vorobjev, and N. S. Barteneva, “Imaging flow cytometry for phytoplankton analysis,” Methods 112, 188–200 (2017).
[Crossref] [PubMed]

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy,” Methods 115, 28–41 (2017).
[Crossref] [PubMed]

Methods Mol. Biol. (1)

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, and B. Alderete, “Applications of imaging flow cytometry for microalgae,” Methods Mol. Biol. 1389, 47–67 (2016).
[Crossref] [PubMed]

Nat. Methods (1)

E. G. Reynaud, J. Peychl, J. Huisken, and P. Tomancak, “Guide to light-sheet microscopy for adventurous biologists,” Nat. Methods 12(1), 30–34 (2015).
[Crossref] [PubMed]

Opt. Express (5)

Rev. Geophys. (1)

J. E. Cloern, “Phytoplankton bloom dynamics in coastal ecosystems: a review with some general lessons from sustained investigation of San Francisco Bay, California,” Rev. Geophys. 34(2), 127–168 (1996).
[Crossref]

Other (4)

K. v. Hecke, “New camera upgrade package yields impressive results” (2016), http://www.cytobuoy.com/company/news/show/article/new-camera-upgrade-package-yields-impressive-results/ .

D. Basiji, “Multispectral Imaging Flow Cytometry,” in Biomedical Imaging: From Nano to Macro (2007), pp. 1100–1103.
[Crossref]

Merk Millipore, “ImageStream®X Mark II Imaging Flow Cytometer”, (2017), http://www.merckmillipore.com/CN/en/life-science-research/cell-analysis/amnis-imaging-flow-cytometers/imagestreamx-Mark-ii-imaging-flow-cytometer/VaSb.qB.QokAAAFLzRop.zHe,nav .

C. V. Open, 2.4.13.2 documentation, “Affine Transformations” (2017), http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.html .

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Figures (7)

Fig. 1
Fig. 1 Schematic of the DC-LSF-IFC system. M1-M5: dielectric flat mirrors; L1-L2: lenses; IR: iris; CL: cylindrical lens; EO: excitation objective lens; DO: detection objective lens; LP: long-pass filter; DC1-DC3: long-pass dichroic filters; BP1-BP2: band-pass filters; ND1-ND2: neutral density filters; TL: tube lens; DL: demagnifying lens.
Fig. 2
Fig. 2 Schematics of LSF-IFC image formation. (a) Imaging fluorescent particles larger than laser-sheet thickness; (b) Imaging fluorescent particles smaller than laser-sheet thickness.
Fig. 3
Fig. 3 (a) Light sheet cross-section images at various X positions, bar = 100μm; (b) Light sheets thickness variation at different X positions; (c) and (d) Lateral PSFs of the orange and red imaging channels, respectively.
Fig. 4
Fig. 4 (a) and (b) Bright-field and epifluorescence microscopy images of Synechococcus CCMA299, bar = 50μm; (c) and (d) Red- and orange-channel DC-LSF-IFC images of Synechococcus CCMA299; (e) and (f) Red- and orange-channel DC-LSF-IFC images of Synechococcus CCMA300; (g) and (h) Red- and orange-channel DC-LSF-IFC images of Prochlorococcus sp. (i) Bright-field microcopy images of Chlorella sp. (top row) and Porphyridium sp. (bottom row), bar = 5μm; (j) and (k) Red- and orange-channel DC-LSF-IFC images of Chlorella sp. and Porphyridium sp. mixed sample; (l) Pseudo-color composite image of (j) and (k). For DC-LSF-IFC captured images, frame size = 300μm × 300μm, and red frames correspond to Chl a-channel and orange frames correspond to PE-channel, respectively.
Fig. 5
Fig. 5 Cell quantitation results of (a) Synechococcus CCMA299 and (b) mixture of Porphyridium sp. and Chlorella sp. by DC-LSF-IFC analysis.
Fig. 6
Fig. 6 Selected collection of raw images of natural coastal seawater sample acquired by the DC-LSF-IFC system. Frame size = 300μm × 300μm, and red frames correspond to Chl a-channel and orange frames correspond to PE-channel, respectively.
Fig. 7
Fig. 7 Selected large phytoplankton cell images captured by the DC-LSF-IFC system from natural coastal seawater samples. Frame size = 300μm × 300μm and red frames correspond to Chl a-channel.

Metrics