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• W4307856285 abstract "Article Figures and data Abstract Editor's evaluation eLife digest Introduction Results Discussion Methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract 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. Editor's evaluation This paper presents a valuable new method combining holographic microscopy and deep learning to track the behavior and growth of individual plankton. The paper illustrates the method with compelling data from two applications, zooplankton feeding behavior and diatom cell division. This paper will be of interest to plankton ecologists and ocean ecosystem modelers. The results obtained from this method will provide new insights into the trophic strategies of ocean plankton and important constraints for global ocean models. https://doi.org/10.7554/eLife.79760.sa0 Decision letter eLife's review process eLife digest Picture a glass of seawater. It looks clear and empty, but in reality, it contains one hundred million bacteria, about one hundred thousand other single-celled organisms, and a few microscopic animals. In fact, the majority of life in the ocean is microscopic and we know relatively little about it. Nevertheless, these microbes have a major impact on our lives. Microscopic algae known as phytoplankton, for example, produce half of the oxygen we breathe. For animals, birds and other large organisms in the ocean, we have a good understanding of who eats who and where the material ends up. However, for phytoplankton and other microbes, we depend on bulk measurements and averages of large groups. Bachimanchi et al. developed a method to follow individual microbes living in seawater and to observe how they move, grow, consume each other and reproduce. The team combined holographic microscopy with artificial intelligence to follow multiple planktons, diatoms and other microbes throughout their life span and continuously measured their three-dimensional location and mass. This made it possible to estimate how fast the organisms were growing and moving, and to observe what they ate. The experiments revealed new insights into how micro-zooplankton, diatoms and other microbes in the ocean interact with each other. This new method may be useful for researchers who would like to track the movements and whereabouts of microscopic planktons, bacteria or other microbes for extended periods of time. It is also a rapid method for counting, sizing, and weighing cells in suspension. The hardware used in this method is relatively cheap, and Bachimanchi et al. have shared all the computer code with examples and demonstrations in a public database to enable other researchers to use it. Introduction The role of herbivores in structuring plant communities is well established in terrestrial ecology. Already Darwin, in his foundations on evolutionary biology (Darwin, 2004), noted how excluding herbivores from a heath land transformed it into a forest of pine trees with an altogether different species composition. Single-celled micro-zooplankton take on the role of herbivores in the ocean, consuming approximately two thirds (40 Petagrams (Pg) carbon) of the primary production (Calbet and Landry, 2004). In oceanic ecology, the primary production is dominated by unicellular phytoplankton, which produce around 50 Pg of carbon annually, quantitatively slightly exceeding the production of terrestrial plants (Behrenfeld and Falkowski, 1997; Field et al., 1998). Selective grazing shapes the plankton community and drives large-scale processes such as harmful algal bloom formation and carbon export (Irigoien et al., 2005; Selander et al., 2019). Despite its importance, our understanding of the role of micro-zooplankton in shaping oceanic communities is still much less developed than that of macro-organisms, which can more readily be observed at the individual level (Glibert and Mitra, 2022). In fact, rates and fluxes in the oceanic microbial food web are still mainly inferred from indirect measurements or ensemble averages, leaving us with a limited mechanistic understanding. Quantitative estimates of primary production are mostly inferred from satellite images of ocean color (chlorophyll) using moderate resolution spectroradiometers calibrated against in situ isotope incorporation experiments (Hu et al., 2012). Ensemble-level biomass transitions during grazing events by microscopic zooplankton are calculated from dilution experiments (Landry and Hassett, 1982), where the grazer density is manipulated by dilution, and the corresponding net increase in primary production is approximated. While these methods provide good estimates of the magnitude of biomass fluxes, they do not resolve the small-scale individual interactions that drive the large-scale processes. Moreover, indirect measurements of processes such as micro-zooplankton grazing rest on assumptions that are not always fulfilled. For example, feeding rates and growth rates of both predators and prey need to be unaffected by dilution, which is often not true (Dolan et al., 2000). In addition, the dilution technique is based on chlorophyll measurements and does not account for consumption of non-chlorophyll-bearing particles, which leads to underestimation of carbon transfer (Stoecker et al., 2017). Currently, the biomass of individuals is often inferred from volume-to-carbon relationships developed over time for different trophic groups of planktons (Strathmann, 1967; Menden-Deuer and Lessard, 2000), which require cell counting and sizing followed by elemental analysis, but do not allow continuous measurements of the same individual. However, these regression relations are not very precise: the average deviation of individual data points to the regressed expression exceeds 50% (Menden-Deuer and Lessard, 2000). In addition, single cells of the same volume can differ by a factor two in dry mass, which is not possible to detect by volume-to-carbon relationships. To go beyond the current level of detail in marine microbial food webs, we need complementary techniques that can follow individual microplanktons over extended periods, while continuously monitoring their growth rate and predation events. Continuous measurements can be realized using microscopy techniques. For example, holographic microscopy can record holograms of cells under investigation in the form of interference patterns containing phase and amplitude information. The information in the holograms can be used to extract the three-dimensional position of microplanktons as well as their mass (Zangle and Teitell, 2014). Holographic imaging has already found applications in microbial studies, especially for in situ measurements of particle size distributions and their identity (Nayak et al., 2021). However, its full potential has not yet been exploited, namely for the quantitative investigation of the growth and feeding patterns of individual planktons over prolonged times. Arguably, this is because the data acquisition and processing pipelines are very computationally expensive. Here, we solve this problem by employing a technique that combines holography with deep learning. The deep-learning algorithms circumvent the long computational times and, once trained, allow rapid determination of three-dimensional position and dry mass of individual microplanktons over extended time periods. We evaluate this method on nine plankton species belonging to different trophic levels and representing the major classes of microplankton. We highlight that unlike other methods, our approach makes it possible to follow and weigh single cells throughout their lifetime, being especially useful to detail micro-zooplankton and mixotrophic life histories as feeding events can be quantitatively measured. Furthermore, the estimated dry mass can be tagged to single planktons detected in the experiments. We can track and identify both prey and predator cells and closely follow the transfer of mass from cell to cell. Finally, we observe the growth and cell divisions in diatoms by their long-term monitoring over more than one cell cycle. Results Experimental setup and deep-learning data analysis Figure 1 shows an overview of the holographic microscopy experimental setup and the deep-learning data analysis pipeline to estimate the position and dry mass of the planktons. We use an inline holographic microscope in a lens-less configuration (see details in Methods, ‘Holographic imaging’). A monochromatic LED light source illuminates the sample suspension that contains the planktons under investigation. As the light passes through the sample, it acquires a complex amplitude that depends on the optical properties of the materials it traverses, generating inline holograms (Figure 1—figure supplement 1), which encode the three-dimensional position of the planktons as well as their size and refractive index. A CMOS camera located on the opposite side of the sample acquires the holograms for further analysis with a frame rate of 10⁢fps, and an exposure time of 8⁢ms. Figure 1 with 3 supplements see all Download asset Open asset Experimental setup and deep-learning data analysis. (a) Holographic microscope: Planktons suspended in a miniature sample well are imaged with an inline holographic microscope. The (cropped) example holographic image features two different plankton species: Oxyrrhis marina and Dunaliella tertiolecta (full image in Figure 1—figure supplement 1). (b) Deep-learning network 1: A regression U-Net (RU-Net, see details in Figure 1—figure supplement 2), trained on simulated holograms, uses individual holograms to predict output maps containing the segmentation of the planktons, their z-position, their dry mass m, and the distances Δ⁢x and Δ⁢y from the closest plankton for each pixel (to be used for the accurate localization of planktons). (c) Plankton 3D position and dry mass: The information obtained by the RU-Net permits us to reconstruct the 3D position of the planktons along with their dry mass (color bar). (d) Plankton sequences: Using the plankton positions obtained by the RU-Net, we extract sequences of 64×64-pixel holograms centered on an individual plankton. (e) Deep-learning network 2: The sequences are then used by a weighted-average convolutional neural network (WAC-Net, see details in Figure 1—figure supplement 3), trained on simulated data, to refine the estimations of m and z. (f) Dry mass time series: Example of a refined dry mass prediction in picograms (pg) for a micro-zooplankton (Oxyrrhis marina, orange line) and a phytoplankton (Dunaliella tertiolecta, blue line) obtained by the WAC-Net. In order to measure the position and dry mass of the planktons, the recorded holograms are analyzed by a regression U-Net (RU-Net, Figure 1b and Figure 1—figure supplement 2, see details in Methods, ‘RU-Net architecture and training’). The RU-Net is a deep-learning architecture based on a modified U-Net, with two parallel arms in the upsampling path. The output of the RU-Net is a five-channel image where each channel corresponds to a heat map containing: a segmentation of the planktons from the background used to obtain a rough estimate of their x⁢y (in-plane) position; their estimated z (axial) position; the plankton estimated dry mass m; and the distances Δ⁢x and Δ⁢y from the closest plankton for each pixel (used to improve the in-plane localization). This RU-Net is implemented and trained on simulated input–output image pairs (4000 samples) using the Python software package DeepTrack 2.0 (Midtvedt et al., 2021a). The output heat maps are finally processed to obtain a prediction of the plankton three-dimensional position and their dry mass, as shown in Figure 1c. In order to increase the accuracy of the dry mass estimations, we extract time sequences of holographic images cropped around an individual plankton (Figure 1d and Figure 1—figure supplement 3) and further analyze them with a second deep-learning network. This is a weighted-average convolutional neural network (WAC-Net, Midtvedt et al., 2021b), Figure 1e and Figure 1—figure supplement 3, see details in Methods, ‘WAC-Net architecture and training’. The WAC-Net determines a single estimated value of the equivalent spherical radius, as well as a more accurate value of the dry mass of the plankton in the sequence, through a weighted average of the latent representation of various holograms with learnable weights. The number of frames in the sequence is limited to 15 frames for training the WAC-Net. For inference, the length of the sequence is dependent on the application. For example, when analyzing feeding events we aim to capture dry mass dynamics on short time scales, and the sequence length is therefore restricted to a single frame. For the division events, the sequence length is 15 frames, as they occur over longer times ranging from hours to days with more recorded frames. Also the WAC-Net is implemented and trained with simulated data (4000 15-frame sequences of 64⁢px×64⁢px images) using DeepTrack 2.0 (Midtvedt et al., 2021a). Figure 1f shows an example of the dry mass output of the WAC-Net in picograms (pg)when applied on a sliding window over a sequence of holograms corresponding to a micro-zooplankton (Oxyrrhis marina) and a phytoplankton (Dunaliella tertiolecta). Dry mass estimates The combination of RU-Net and WAC-Net permits us to measure the dry mass of each plankton at any point in time. For example, Figure 2a shows a portion of an inline hologram of the micro-zooplankton species, O. marina, tracked by the RU-Net (circles). Individual O. marina cells are then traced for 30 frames and their holograms are further processed with WAC-Net to obtain an estimation of the dry mass for each cell. The orange histogram in Figure 2b shows the dry mass distribution estimated by WAC-Net. Figure 2 with 1 supplement see all Download asset Open asset Dry mass estimates. (a) Phytoplankton species Oxyrrhis marina as detected by RU-Net on a portion of experimental hologram (see Figure 1—figure supplement 1 for the complete hologram). (b) Dry mass distributions for O. marina (illustrated in the inset) obtained by applying weighted-average convolutional neural network (WAC-Net) to the experimental holograms (orange) and by volume-to-carbon relationships (gray, Menden-Deuer and Lessard, 2000). The red line is the value of the average mass estimate obtained from elemental analysis. (c) Comparison of the dry mass estimations obtained by WAC-Net and by the volume-to-carbon method for nine different species of diatoms (Thalassiosira pseudonana, Thalassiosira weissflogii), phytoplantons (Isochrysis galbana, Rhodomonas salina, Dunaliella tertiolecta), and micro-zooplanktons (Oxyrrhis marina, Kryptoperidinium triquetrum, Alexandrium minutum, Scrippsiella acuminata). The two measurements have a correlation coefficient of ρ=0.988. The dashed line represents the best fit and the error bars show the standard deviations of the distributions. The insets illustrate each species. To benchmark the dry mass measurements, we used the volume-to-carbon relationships from Menden-Deuer and Lessard, 2000 followed by an extrapolation of elemental composition using extended Redfield ratios (Anderson, 1995 see Methods, ‘Dry mass estimation by volume-to-carbon relationships’). The gray histogram in Figure 2b shows the results for the case of O. marina. The dry mass predicted by the volume-to-carbon relationships (394 ± 123 pg, the uncertainty represents the standard deviations of the distribution) agrees well with the dry mass estimated by our technique (338 ± 126 pg, orange histogram). Importantly, in contrast to the volume-to-carbon relation method, the dry mass estimated by our approach can be tagged to individual cells in the image. This additional feature can be used to study the dry mass evolution of single cells (e.g., in the following sections, we will exploit this possibility in two exemplary studies of feeding and cell division events). We repeated this analysis for nine species of planktons belonging to different taxonomic groups and trophic levels in the marine ecosystem (see Methods, ‘Plankton cultures’): phytoplankton species (Isochrysis galbana, Rhodomonas salina, Dunaliella tertiolecta); micro-zooplankton species (Kryptoperidinium triquetrum, Alexandrium minutum, Scrippsiella acuminata, along with Oxyrrhis marina which is used in the above discussion); and diatomic species (Thalassiosira weissflogii, Thalassiosira pseudonana). These results are summarized in Figure 2c. The data points and error bars represent the means and standard deviations of the dry mass distributions estimated by our method and the volume-to-carbon method. The two estimates correlate very well (correlation coefficient ρ=0.988). A detailed dry mass distribution comparison (along with equivalent spherical radius distribution comparison) for different species can be seen in Figure 2—figure supplement 1. As a further independent test, we also estimated the dry mass from the elemental analysis of carbon and nitrogen content in O. marina (extrapolated to the other fundamental elements hydrogen, oxygen, and phosphorous through Redfield ratios, Anderson, 1995, see Methods, ‘Dry mass estimation by elemental analysis’). The resulting dry mass (453 pg, indicated with a red line in Figure 2) also confirms that our method arrives at realistic numbers. The average value indicated by the red line in Figure 2b lies within the distributions predicted by holographic estimate. Feeding events We use the phytoplankton species D. tertiolecta and the micro-zooplankton species O. marina as the prey and predator, respectively. Figure 3a–c shows the 3D traces of prey (blue) and predator (orange) during a feeding event (see 3D movie of the feeding event in Video 1). In the pre-feeding phase Figure 3a, corresponding to about 10⁢s or 100⁢frames (see also Figure 3d), the predator explores the sample volume in a random fashion. It passes the prey cell closely on a couple of occasions before it makes contact (see Videos 1 and 2 and , Figure 3—figure supplement 2, and Figure 3—figure supplement 3). In the feeding phase (Figure 3b, lasting for about 20⁢s or 200⁢frames), the predator makes contact with the prey and performs a localized swirling motion about a fixed location for 16⁢s while handling the prey. In the post-feeding phase (Figure 3c, last 10⁢s or 100⁢frames, see also Figure 3d), the predator returns back to its normal swimming behavior and carries on its search for new prey. Figure 3 with 3 supplements see all Download asset Open asset Feeding events. 3D recording of a feeding event where (a) a predator micro-zooplankton (Oxyrrhis marina, orange traces) approaches a prey phytoplankton (Dunaliella tertiolecta, blue traces), (b) feeds on it, and (c) finally moves away (see Video 1 and Figure 3—figure supplement 2). The 2D projection of traces is superimposed on the holographic images in the bottom (see also Figure 3—figure supplement 1). (d) Dry mass time series of predator (orange trace) and prey (blue trace) estimated by weighted-average convolutional neural network (WAC-Net) in the three different phases. (e) The pre-feeding dry mass distributions of the predator Oxyrrhis marina (O. m) and the prey Dunaliella tertiolecta (D. t), and the post-feeding dry mass distribution of predator are represented in the box plots. The dry mass increase between pre- and post-feeding phases of the predator is indicated in the plot. The post-feeding dry mass increment of the predator (O. m) matches the dry mass of the prey (D. t). (f) There is a high correlation (ρ=0.794) between dry mass increments of predators and dry mass of prey for 26 feeding events. The dashed line represents the best fit. Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Feeding event 1. Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Feeding event 2. Figure 3d shows the dry mass time series of prey and predator during the feeding event. As the feeding events happen on a short time scale compared to the frame rate of the camera, we use WAC-Net with a sliding window of only one frame, maximizing the available temporal resolution of the dry mass estimation. The dry mass distributions of the prey and predator in pre- and post-feeding phases are shown by the box plots (Figure 3e) to the right hand side. In the pre-feeding phase: the prey dry mass is measured to be 26 ± 1 pg (blue box plot) and the predator 204 ± 5 pg (orange box plot). The uncertainties represent the standard error of the mean. The post-feeding dry mass distribution of the predator is 234 ± 5 pg. The difference in predator dry mass post- and pre-feeding closely matches the prey dry mass (Figure 3e). This indicates that the predator has fully consumed its prey, thus providing a direct measurement of the amount of the dry mass consumed during each individual feeding event. In Figure 3f, we report the results of the dry mass increase in 26 feeding events. The increase in the predator dry mass in the post-feeding phase correlates well with the pre-feeding dry mass of the prey (correlation coefficient ρ=0.794). The slope of the best fit line (with slope, α=0.97) also indicates that on average 97% of prey is consumed by the predator in a feeding event. Thus, it is possible to quantify individual feeding rates and, if predator cells are followed over time, also gross growth efficiency, that is, how much of the consumed biomass is converted into predator biomass. Life history of a plankton The technique we have developed can follow the entire life histories of planktons, over time scales from hours to days. To demonstrate this, we use a diatom species, T. weissflogii, which is autotrophic and nonmotile. Over a preriod of 8 hr (Figure 4), we image a T. weissflogii and two generations of its daughter cells, continuously assessing the changes in their dry mass using the WAC-Net, which we already used to estimate the dry mass of T. weissflogii in Figure 2c (see Methods, ‘Holographic imaging’). We place a low-density (1000⁢cells⁢ml-1) culture of diatoms in the sample well, which we illuminate with a white light source (5⁢W, 60⁢Hz warm light source bulb, aligned not to affect the holographic imaging sensor) to aid the cell growth. Figure 4 with 1 supplement see all Download asset Open asset Growth and cell division of a diatom. Different life stages of a diatom (Thallasiosira weissflogii) and its daughter cells: (a) the parent cell (blue), (b) divides into two daughter cells (orange); (c) the daughter cells continue to grow, (d, e) until another cell division occurs (green). (f) Dry mass time series through generations estimated by weighted-average convolutional neural network (WAC-Net) (see also Video 3 and Figure 4—figure supplement 1). Each cell dry mass is estimated when it has at least 3.6 μm (40⁢px) of empty space around it to ensure optimal performance of the WAC-Net; the corresponding times are indicated by the gray dashed lines. A drop in the dry mass values can be noticed with the daughter cells in subsequent divisions. (g) Correlation plot showing the relation between the sum of the dry masses of the daughter cells and the dry mass of the parent cell for 11 different division events (ρ=0.857). The dashed line represents the best fit. Video 3 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Division event. Figure 4a–e shows the growth and division of a diatom imaged over a small portion of the sample. The parent cell (highlighted in Figure 4a) initially divides into two daughter cells, approximately 0.14 hr into the experiment (Figure 4b). Note that the biomass does not divide equally between the daughter cells. Asymmetric division in terms of cell size had already been shown in both bacteria and diatoms; our experiments now show that the daughter cells receive unequal proportions of the biomass from the mother cell. Then, the two daughter cells move slightly apart (Figure 4c) and the cell with the largest biomass of the two divides again at 4.86 hr (Figure 4d, e). Figure 4f shows the dry mass of the parent and daughter cells as the experiment proceeds. We remark that, while the dry mass of these cells is continuously monitored, the WAC-Net estimates the most reliable values when the cells are isolated. Therefore, we consider the reference dry mass measurements as those when the cells have at least 3.6 μm (40⁢px) of empty space around them before or after each division; these times are indicated by the gray dashed lines in Figure 4f. The initial parent cell dry mass (measured at 1.1 hr) is estimated at 433 ± 2 pg. The dry mass of its two daughter cells (measured at 2.7 hr, as soon as the two daughter cells move sufficiently apart) is 326 ± 1 pg and 110 ± 1 pg, whose sum is close to the dry mass of the parent cell. As the experiment proceeds, one of the daughter cells divides again producing a second generation of daughter cells (Figure 4d), whose dry masses are 225 ± 3 pg and 93 ± 1 pg (at 5.48 hr, Figure 4e). Again, their sum is close to the mass of their parent cell. The uncertainty in the dry mass value represents the standard error of the mean computed for ±5 frames around the measurement point (gray dashed lines in Figure 4). We have repeated this experiment with various cell densities with independently cultured samples, collecting multiple division events. Figure 4g shows the high correlation (ρ=0.857) between the parent cell dry mass and the sum of the daughter cells’ dry masses for 11 cell divisions. It is interesting to note that the division events of T. weissflogii occur when the parent cell weighs between 310 pg and 436 pg, with a mean value of ≈ 378 pg. This kind of a tip-off value prediction in dry mass for a division event is achieved for the first time thanks to this method and is another example of the type of information that can be acquired by employing a nonintrusive technique that can continuously measure single cells throughout the cell cycle. Discussion The main advantage of combining holographic microscopy with deep-learning algorithms lies in the ability to monitor position and dry mass of individual plankton cells over extended time periods. The method is nondestructive and minimally invasive, and allows quantitative assessment of trophic interactions such as feeding and biomass increase throughout the cell cycle, providing unprecedented detail to the life histories of marine microorganisms. The standard methods to determine the biomass of cells entail either performing elemental analysis on cells harvested from single species cultures or estimating the biomass from volume-to-carbon relationships drawn from multiple elemental analyses of similar plankton organisms of different sizes (Strathmann, 1967; Menden-Deuer and Lessard, 2000). Elemental analysis has the advantage of providing detailed measurements of individual elements, typically carbon, nitrogen, and hydrogen; however, it is destructive and cannot provide individual cell resolution. Volume-to-carbon relationships can provide biomass estimates of individual living cells as long as the volume of the cells can be measured accurately (Menden-Deuer and Lessard, 2000); yet, the variability around the relationship is substantial (e.g., the estimated value for O. marina used in Menden-Deuer and Lessard, 2000 is 30% higher than that measured by elemental analysis). Moreover, the volume-to-carbon relationships do not account for the nutritional status of the cell (e.g., as discussed in Results, ‘Dry mass estimates’, similarly sized cells of the same species can indeed differ" @default.
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• W4307856285 title "Author response: Microplankton life histories revealed by holographic microscopy and deep learning" @default.
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