Imaging FlowCytobot Research Articles

Morphology and Phylogeny of Prorocentrum texanum sp. nov. (Dinophyceae): A New Toxic Dinoflagellate from the Gulf of Mexico Coastal Waters Exhibiting Two Distinct Morphologies.

A new planktonic species of Prorocentrum is described from the Gulf of Mexico. First observed with the Imaging FlowCytobot, Prorocentrum texanum sp. nov. was characterized using LM, SEM, and TEM along with sequencing of the SSU, LSU, and ITS ribosomal regions and the mitochondrial cob and cox1 regions. P. texanum sp. nov. is a round to oval bivalvate dinoflagellate, with a prominent anterior, serrated solid flange on periflagellar a platelet and an opposing short, flat flange on the h platelet. The periflagellar area consists of 10 platelets. Both left and right valves have shallow round depressions and two-sized valve pores. The anterior ejectosome pore pattern differs between the left and right valve in relation to the periflagellar area and margins. Ten to eleven rows of tangential ejectosome pores are present on each valve. P. texanum sp. nov. has two varieties which exhibit distinct morphotypes, one round to oval (var. texanum) and the other pointed (var. cuspidatum). P. texanum var. cuspidatum is morphologically similar to P. micans in surface markings, but is smaller, and has a serrated periflagellar flange, and is genetically distinct from P. micans. Cytologically, P. texanum has two parietal chlo-roplasts, each with a compound, interlamellar pyrenoid, trichocysts, fibrous vesicles that resemble mucocysts, pusules, V- to U-shaped posterior nucleus, golgi, and tubular mitochondria. No genetic difference was found between the two varieties in the five genes examined. Phylogenetic analysis of the SSU, LSU, and ITS ribosomal regions place P. texanum sp. nov. as a sister group to P. micans. One isolate of P. texanum var. texanum produces okadaic acid.

PHYLOGENETIC ANALYSIS OF BRACHIDINIUM CAPITATUM (DINOPHYCEAE) FROM THE GULF OF MEXICO INDICATES MEMBERSHIP IN THE KARENIACEAE(1).

Brachidinium capitatum F. J. R. Taylor, typically considered a rare oceanic dinoflagellate, and one which has not been cultured, was observed at elevated abundances (up to 65 cells · mL(-1) ) at a coastal station in the western Gulf of Mexico in the fall of 2007. Continuous data from the Imaging FlowCytobot (IFCB) provided cell images that documented the bloom during 3 weeks in early November. Guided by IFCB observations, field collection permitted phylogenetic analysis and evaluation of the relationship between Brachidinium and Karenia. Sequences from SSU, LSU, internal transcribed spacer (ITS), and cox1 regions for B. capitatum were compared with five other species of Karenia; all B. capitatum sequences were unique but supported its placement within the Kareniaceae. From a total of 71,487 images, data on the timing and frequency of dividing cells was also obtained for B. capitatum, allowing the rate of division for B. capitatum to be estimated. The maximum daily growth rate estimate was 0.22 d(-1) . Images showed a range in morphological variability, with the position of the four major processes highly variable. The combination of morphological features similar to the genus Karenia and a phylogenetic analysis placing B. capitatum in the Karenia clade leads us to propose moving the genus Brachidinium into the Kareniaceae. However, the lack of agreement among individual gene phylogenies suggests that the inclusion of different genes and more members of the genus Karenia are necessary before a final determination regarding the validity of the genus Brachidinium can be made.

FIRST HARMFUL DINOPHYSIS (DINOPHYCEAE, DINOPHYSIALES) BLOOM IN THE U.S. IS REVEALED BY AUTOMATED IMAGING FLOW CYTOMETRY1

Imaging FlowCytobot (IFCB) combines video and flow cytometric technology to capture images of nano- and microplankton (∼10 to >100 μm) and to measure the chlorophyll fluorescence associated with each image. The images are of sufficient resolution to identify many organisms to genus or even species level. IFCB has provided >200 million images since its installation at the entrance to the Mission-Aransas estuary (Port Aransas, TX, USA) in September 2007. In early February 2008, Dinophysis cells (1–5 · mL−1) were detected by manual inspection of images; by late February, abundance estimates exceeded 200 cells · mL−1. Manual microscopy of water samples from the site confirmed that D. cf. ovum F. Schütt was the dominant species, with cell concentrations similar to those calculated from IFCB data, and toxin analyses showed that okadaic acid was present, which led to closing of shellfish harvesting. Analysis of the time series using automated image classification (extraction of image features and supervised machine learning algorithms) revealed a dynamic phytoplankton community composition. Before the Dinophysis bloom, Myrionecta rubra (a prey item of Dinophysis) was observed, and another potentially toxic dinoflagellate, Prorocentrum, was observed after the bloom. Dinophysis cell-division rates, as estimated from the frequency of dividing cells, were the highest at the beginning of the bloom. Considered on a daily basis, cell concentration increased roughly exponentially up to the bloom peak, but closer inspection revealed that the increases generally occurred when the direction of water flow was into the estuary, suggesting the source of the bloom was offshore.

The Beat

The Beat

A submersible imaging‐in‐flow instrument to analyze nano‐and microplankton: Imaging FlowCytobot

A fundamental understanding of the interaction between physical and biological factors that regulate plankton species composition requires, first of all, detailed and sustained observations. Only now is it becoming possible to acquire these types of observations, as we develop and deploy instruments that can continuously monitor individual organisms in the ocean. Our research group can measure and count the smallest phytoplankton cells using a submersible flow cytometer (FlowCytobot), in which optical properties of individual suspended cells are recorded as they pass through a focused laser beam. However, FlowCytobot cannot efficiently sample or identify the much larger cells (10 to >100 µm) that often dominate the plankton in coastal waters. Because these larger cells often have recognizable morphologies, we have developed a second submersible flow cytometer, with imaging capability and increased water sampling rate (typically, 5 mL seawater analyzed every 20 min), to characterize these nano‐ and microplankton. Like the original, Imaging FlowCytobot can operate unattended for months at a time; it obtains power from and communicates with a shore laboratory, so we can monitor results and modify sampling procedures when needed. Imaging FlowCytobot was successfully tested for 2 months in Woods Hole Harbor and is presently deployed alongside FlowCytobot at the Martha's Vineyard Coastal Observatory. These combined approaches will allow continuous long‐term observations of plankton community structure over a wide range of cell sizes and types, and help to elucidate the processes and interactions that control the life cycles of individual species.

Automated taxonomic classification of phytoplankton sampled with imaging‐in‐flow cytometry

High‐resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging‐in‐flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom‐built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co‐occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22‐category classifier provides 88% overall accuracy for an independent test set, with individual category accuracies ranging from 68% to 99%. We demonstrate application of this classifier to a nearly uninterrupted 2‐month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.