Microplankton life histories revealed by holographic microscopy and deep learning
Harshith Bachimanchi, Benjamin Midtvedt, Daniel Midtvedt and, Erik Selander, Giovanni Volpe

TL;DR
This paper introduces a novel approach combining holographic microscopy and deep learning to track individual microplanktons, enabling detailed insights into their growth, interactions, and role in the oceanic carbon cycle.
Contribution
The study presents a new method for real-time, high-resolution monitoring of microplankton life histories using holographic microscopy coupled with deep learning algorithms.
Findings
Enabled continuous tracking of microplankton growth and division.
Measured trophic interactions and predation events.
Provided detailed insights into micro-zooplankton feeding and life cycles.
Abstract
The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of the ocean, however, is biased towards its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the oceanic food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three dimensional position and dry mass. The deep learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell…
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Taxonomy
TopicsMicrobial Community Ecology and Physiology · Isotope Analysis in Ecology · Coral and Marine Ecosystems Studies
