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

Abstract

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

Pollen analysis using multispectral imaging flow cytometry and deep learning

“…Multispectral imaging flow cytometry in combination with deep learning has recently been demonstrated as a tool for phytoplankton identification and quantification (Dunker et al ., 2018; Dunker, 2019), allowing algae traits to be described in detail and enabling new research regarding the roles of functional traits in shaping patterns of coexistence and diversity in this important functional group of species (Hofmann et al ., 2019). Here, we propose a similar application for pollen, aiming primarily for fast and accurate identification and counting of pollen grains.…”

Temperature and stoichiometric dependence of phytoplankton traits

“…At the first and the final day, flow cytometric measurements were performed to capture cellular traits to test all of the hypotheses. Image‐based flow cytometric measurements of each well were recorded for up to 1,000 events with the special order ImageStreamX MK II (AMNIS, Luminex Corporation, Austin, Texas, USA) (Dunker ). Images of every single cell measured were recorded at 400‐fold magnification at a velocity of 110 mm/s and with Dulbecco's PBS (Biowest, Nuaillé, France) as sheath fluid.…”

“…At the start and end of each experiment, we measured three categories of traits related to growth rate, light use, and morphology. Specifically, we quantified single‐cell morphological and physiological traits, such as cellular length, width, area, and chl a fluorescence, which were measured with image‐based flow cytometry (Dunker and Wilhelm , Dunker ). This novel application of image‐based flow cytometry allowed for highly detailed measurements coupling morphological and physiological trait variation that could be verifiably attributed at the species level and enabled creation of composite traits such as chl a to volume ratio, as well as computation of volume‐associated functions such as sinking rate, surface area to volume ratio, and predation defense.…”

Methodological Approaches for Monitoring Five Major Food Safety Hazards Affecting Food Production in the Galicia–Northern Portugal Euroregion

“…Certified monitoring procedures for phytoplankton analysis, currently performed by reference laboratories, are based on international standard methods [ 42 ] that require the taxonomic identification, counting, and calculation of phytoplankton biovolumes by light microscopy. However, other methods, such as image analysis, pigment analysis by high-performance liquid chromatography (HPLC), flow cytometry, molecular methods, and remote satellite sensing, are also used [ 43 , 44 , 45 ]. Microscopy and image analysis are technically demanding, time consuming, costly, and predominantly affected by human error as the identification relies on the expertise of taxonomists able to distinguish the morphological differences between different species of phytoplankton [ 44 , 46 ].…”