Tag: Harshith Bachimanchi

The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward 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 microbial 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 divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

In this mini-symposium organised by Dr. Erik Selander at Lund University, I have spoken about our recent work with marine microplankton, where we have combined holographic microscopy with deep learning to measure the ‘dry’ mass and 3D swimming dynamics of different species of planktons. The article related to this presentation can be found at the following here: Microplankton life histories revealed by holographic microscopy and deep learning.

The presentation was followed by discussions with Prof. Karin Rengefors group at Lund university on the topic of application of AI based methods for various kinds of studies in phytoplankton ecology and evolution.

A droplet of sea water contains an entire ecosytem. There are microscopic plants, the phytoplanktons, which produce oxygen by absorbing carbon dioxide from the atmsphere by the process of photosynthesis. There are microscopic animals, the microzooplankton, which feed on the phytoplankton. In oceanic ecology, phytoplanktons consume around 65 peta grams of carbon annually, producing approximately 50% of oxygen on the Earth. Microzooplankton take on the role of herbivores, and consume about two thirds (40 Pg carbon) of this primary production. Despite their central importance, our understanding of the phytoplankton and microzooplankton in shaping oceanic communities is still much less developed at a single plankton level.
Here, we demonstrate that by combining holographic microscopy with deep learning, we can follow microplanktons through generations, by continuously measuring their three dimensional position and dry mass. The deep learning algorithms circumvent the computationally intensive processing of holographic data, and allow measurements over extended periods of time. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. We exemplify this by detailed descriptions of microzooplankton feeding events, cell divisions, and long term monitoring of single cells from division to division.