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Plankton Analysis via Automated Imaging Flow Cytometry

Learn about transforming a specialized research tool into a broadly accessible instrument for studying plankton dynamics and community structures. Explore the technology, partnerships, and data generated through Imaging FlowCytobots.

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Plankton Analysis via Automated Imaging Flow Cytometry

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  1. Plankton Analysis by Automated Submersible Imaging-in-Flow Cytometry:Transforming a Specialized Research Instrument into a Broadly Accessible Tool Heidi M. Sosik Robert J. Olson

  2. Flow Cytometry in the Lab Flow Cytometry in situ Microphytoplankton Picophytoplankton Long-term goal and strategy • Understand regulation of seasonal to interannual plankton dynamics • Time series observations are key • New sampling and analysis systems must be developed

  3. 2008 NOPP-funded project “Sensors for Measurement of Biological, Bio-Optical or Chemical Properties of the Ocean” Topic Three-way partnership Woods Hole Oceanographic Institution University of Washington Cytopeia, Inc. Two main objectives • Transition of Imaging FlowCytobot to commercially viable status Promote access for the broader oceanographic and environmental monitoring communities  User-tested pre-commercial units • Extending Imaging FlowCytobot’s target size range Enhance the technology for a next generation instrument  Research prototype

  4. Overview • Imaging FlowCytobot • Description, readiness, motivation for commercialization • Why should this community be interested? • Partnerships • Cytopeia • University of Washington • Proposed work • Iterative design optimization strategy • Target subsystems • Current Status

  5. FlowCytobot Principals from conventional flow cytometry (but automated and submersible) Optimized for “small” cells (~1-15 mm) Olson et al. 2003 Imaging FlowCytobot Derived from FlowCytobot design Optimized for large cells (~10-300 mm) Olson and Sosik 2007 • Automated features for extended deployment • Standard analysis • Biofouling control • Realtime humidity sensing & intake valve control 6-month deployments routine

  6. Imaging FlowCytobot Data example Nano/microplankton -Associated images (all same scale) Chl fluoresence Light scattering Individual particle measurements

  7. Martha’s Vineyard Coastal Observatory Remote sensing Air-side observations SeaPRISM shortwave radiation, winds, etc. In water observations T,S, currents, fluorescence, backscattering, oxygen, flow cytometry and cell imaging Bottle samples chlorophyll, absorption, etc. • Operational since 2001 • 24/7 power and data • Open to new users • Realtime public data access

  8. The Phytoplankton Community at MVCO Imaging FlowCytobot Microplankton FlowCytobot Picoplankton

  9. Picoplankton to Microplankton event to seasonal to interannual scales Diatoms Which ones are diatoms? > 130 million images to date

  10. 50 mm Automated image analysis and classification 22 categories (16 phytoplankton genera) 88% overall accuracy Image processing Supervised machine learning algorithm Statistical error correction Sosik and Olson 2007

  11. Major contributors: 6 Diatom taxa Taxonomic resolution winter / spring 2007 2007 Total for all images January – July

  12. Seasonality in phytoplankton biomass – Two views Extracted pigment analysis • Chl • fall / winter peaks • diatom blooms … Carbon budget cell image / scattering ↓ cell volume ↓ cell carbon ∑(C cell-1) Flow cytometry

  13. Biomass and community structure How is this C distributed across size classes? Microplankton fall / winter Picoplankton summer Nanoplankton all year

  14. Biomass and community structure HPLC-based (Vidussi et al. approach) Proportion pico v. micro + nano How does this result compare to other methods?

  15. Biomass and community structure HPLC-based (Vidussi et al. approach) Proportion nano … How does this result compare to other methods?

  16. Texas Coast Winter 2008 - First ever DSP event Auto * Manual Port Aransas, TX Imaging FlowCytobot 3 – The early warning! Shellfish recalled & harvest closed within days Olson, Sosik & Campbell

  17. Partnerships University of Washington • Development of position sensitive detector (PSD) Cytopeia, Inc. • Influx – high speed cell sorter, open architecture • Large bio-medical market ($9M in 2007 sales) • Specialized Influx Mariner for oceanographic users • Contributing engineering and fabrication at no cost Founder Ger van den Engh Experienced R&D team

  18. Evaluation by outside users ~ ~ Commercial units Commercial Transition Design Optimization WHOI / Cytopeia Iterative process Build on strengths at WHOI & Cytopeia Leverage MVCO access and existing research prototypes Expand to select outside users Evaluation at WHOI / MVCO

  19. Design optimization targets • Opto-mechanical system • Fixed modules for stability • Fluidics system • Custom syringe pump to reduce size and power • Illumination for imaging • LEDs to replace Xenon strobe • Signal detection • Improved electronics, digital signal processing • Control system • Integration • Control software • Integration and user-friendliness

  20. Current Status Opto-mechanical components Existing collection of off-the-shelf components to be replaced by Cytopeia’s custom rigid assembly Model 1 Design complete Fabrication complete Under evaluation at WHOI Cytopeia’s rigid fixed assembly detector module Imaging FlowCytobot fluidics and optics

  21. Current Status Fluidics system Existing off-the-shelf HPLC syringe pump to be replaced by Custom unit, modified from MBARI design Model 1 Design acquired Fabrication complete Under evaluation at WHOI  20% reduction in overall power consumption custom syringe pump

  22. Imaging FlowCytobot – commercial transition Applications Ecological research HAB warning PFT algorithms / validation Cell size class algorithms / validation Carbon budgets Design goals Increase ease of manufacture & use Reduce size, power consumption Expand dynamic range Adapt analysis methods THANK YOU!

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