Quantitative 3D-imaging for cell biology and ecology of environmental microbial eukaryotes

  1. Sebastien Colin  Is a corresponding author
  2. Luis Pedro Coelho
  3. Shinichi Sunagawa
  4. Chris Bowler
  5. Eric Karsenti
  6. Peer Bork
  7. Rainer Pepperkok
  8. Colomban de Vargas  Is a corresponding author
  1. Centre Nationnal de la Recherche Scientifique, France
  2. Université Pierre et Marie Curie, Sorbonne Universités, France
  3. European Molecular Biology Laboratory, Germany
  4. Paris Sciences et Lettres Research University, France
3 figures and 1 additional file

Figures

Figure 1 with 9 supplements
Environmental high-content fluorescence microscopy (e-HCFM): automated, 3D, and multichannel imaging for aquatic micro-eukaryotes.

(a) e-HCFM workflow applied to Tara Oceans samples: (1) 72 nano-plankton (size range 5–20 μm) samples collected during the Tara Oceans expedition (Pesant et al., 2015) were fixed in …

https://doi.org/10.7554/eLife.26066.003
Figure 1—source data 1

This image acquisition registry details the e-HCFM imaging runs, their metadata, their samples of origin, and associated metadata from the Tara Oceans expedition.

https://doi.org/10.7554/eLife.26066.011
Figure 1—source data 2

List of descriptors computed for each object imaged through e-HCFM.

https://doi.org/10.7554/eLife.26066.012
Figure 1—figure supplement 1
e-HCFM staining strategy is adapted to the environmental protistan biodiversity.

The samples (Tara Oceans expeditions) were fixed on board with a PFA-Glutaraldehyde buffer. They have been kept at 4°C for several years. The specimens were imaged manually from regular e-HCFM …

https://doi.org/10.7554/eLife.26066.004
Figure 1—figure supplement 2
e-HCFM staining strategy is suitable for live imaging.

Four fluorescent channels were recorded: (i) Green, cellular membranes (DiOC6(3)) indicates the core cell bodies and organelles; (ii) Blue, DNA (Hoechst) is used to localize DNA and the nuclei; …

https://doi.org/10.7554/eLife.26066.005
Figure 1—figure supplement 3
e-HTFM strategy for automated screening of planktonic protists.

(a) Sample preparation scheme (<1 hr). Concentrated cells from plankton samples are loaded into an eight-chamber Lab-TekTM II (Nunc 155382, Thermo Fisher Scientific, MA, USA) slide coated with …

https://doi.org/10.7554/eLife.26066.006
Figure 1—figure supplement 4
The image acquisition is coordinated with the image-processing strategy.

(a) Overlapping field of view (fov) reduces detection biases of large planktonic particles. The percentage of overlap between fovs is defined to fit the highest expected cell size. To avoid …

https://doi.org/10.7554/eLife.26066.007
Figure 1—figure supplement 5
A short selection of the biodiversity that was detected by e-HCFM into 5–20 µm samples from the Tara Oceans expeditions.

Alexa-PLL staining clearly improved the full detection of almost all cells and specifically of their extensions and transparent biomineral structures (e.g. diatoms). The staining intensity is …

https://doi.org/10.7554/eLife.26066.008
Figure 1—figure supplement 6
Monitoring the microscope performance over time by measuring the fluorescence intensity of three red fluorescing beads (Inspeck deep red 0.32% [top panel]; 1.41% [middle panel]; 4.10% [lower panel], I-7225, Invitrogen) in the chlorophyll channel (ex633/em680-700).

Each column represents one acquisition (in chronological order; acquisition spans a 10-month period) and the y-axis indicates the intensity value of voxels inside the bead (the data for each …

https://doi.org/10.7554/eLife.26066.009
Figure 1—figure supplement 6—source data 1

Bead intensities for each bead.

This is the source data for Figure 1— figure supplement 6.

https://doi.org/10.7554/eLife.26066.010
Figure 1—video 1
3D-animation of a Diatom (Asterionellopsis sp.) which illustrates the synergy between e-HCFM staining strategy and optical sectioning microscopy (CSLM).

The specimen (surface water, Tara Ocean station 72) was imaged manually from regular e-HCFM sample preparation with a Leica SP8 confocal laser scanning microscope (40X NA1.1 water). Four fluorescent …

https://doi.org/10.7554/eLife.26066.013
Figure 1—video 2
First example of Z-stack animations that e-HCFM method provides for each detected organism (a chain of diatoms).
https://doi.org/10.7554/eLife.26066.014
Figure 1—video 3
Second example of Z-stack animations that e-HCFM method provides for each detected organism (a choanoflagellate).
https://doi.org/10.7554/eLife.26066.015
Figure 2 with 2 supplements
e-HCFM-staining strategy is effective in revealing symbiotic interactions in marine protists.

These seven cells, fixed on board Tara and kept at 4°C for several years, were imaged manually using the e-HCFM workflow (Figure 1). Each cell is illustrated by two panels: the left side overlays …

https://doi.org/10.7554/eLife.26066.016
Figure 2—figure supplement 1
e-HCFM reveals an unreported epibiose involving the diatom Chaetoceros simplex and an unidentified nano-flagellate from the 5–20 µm samples of the Tara Oceans expeditions.

The two first rows of the plate illustrate how the epiphytic nano-flagellate cells are attached on the diatom frustule. They live in small tubes (lorica) which are bound at the base of the setae …

https://doi.org/10.7554/eLife.26066.017
Figure 2—video 1
3D-animation of a Diatom (Corethron sp.) which illustrates how the e-HCFM method supports investigation about microbial interactions.

The specimen (surface water, Tara Ocean station 137) was imaged manually from regular e-HCFM sample preparation with a Leica SP8 confocal laser scanning microscope (40X NA1.1 water). Four …

https://doi.org/10.7554/eLife.26066.018
Figure 3 with 4 supplements
Analysis of e-HCFM images and their descriptors.

(a) Overview of the training set as a hierarchical pie chart. The size of the slices scales with the number of elements in the training set (details in Figure 3—source data 1). Accuracy values (%) …

https://doi.org/10.7554/eLife.26066.019
Figure 3—source data 1

Organization of the hierarchical classification scheme for the automated classification, the training set categories abundance and the recall value for each category of the four levels (four tables).

https://doi.org/10.7554/eLife.26066.028
Figure 3—source data 2

Confusion matrix generated by the classifier at the classification level 4.

https://doi.org/10.7554/eLife.26066.029
Figure 3—source data 3

Relative abundance of each taxon in each sample.

The relationship between sample label and sampling location is provided in ‘Figure 1—source data 1.’.

https://doi.org/10.7554/eLife.26066.030
Figure 3—source data 4

Assignment of stations to oceanic provinces.

https://doi.org/10.7554/eLife.26066.031
Figure 3—source data 5

Object counts (normalized to seawater volume) per taxonomic group (panel d).

https://doi.org/10.7554/eLife.26066.032
Figure 3—source data 6

Measured PO₄ concentrations (panel d).

https://doi.org/10.7554/eLife.26066.033
Figure 3—source data 7

Values of N, Spearman correlation (rho), and number of samples (N) for each sub-group (panel d).

https://doi.org/10.7554/eLife.26066.034
Figure 3—figure supplement 1
Binary confusion matrix showing how classification errors are typically within the same broad taxonomical group.

Both rows and columns represent the 155 categories. Cells are filled in whenever there is at least one labeled object of the category indicated in the row classified into the category indicated by …

https://doi.org/10.7554/eLife.26066.020
Figure 3—figure supplement 2
Tradeoff between accuracy and recall.

Shows the accuracy as a function of the fraction of high-confidence objects that are kept, results are displayed at the most-detailed, fourth level (left, 155 categories) or the intermediate third …

https://doi.org/10.7554/eLife.26066.021
Figure 3—figure supplement 2—source data 1

Classification results for all objects in the training data at the fourth (finest) resolution level presented in in left panel (obtained by cross-validation).

https://doi.org/10.7554/eLife.26066.022
Figure 3—figure supplement 2—source data 2

Classification results for all objects in the training data at the third resolution level presented in right panel (obtained by cross-validation).

https://doi.org/10.7554/eLife.26066.023
Figure 3—figure supplement 3
Ordination of eHCFM-derived taxonomic abundances reveals regional clustering and tests technical reproducibility of eHCFM-taxonomic profiling.

Principal component analysis of relative abundances of living single cells. Colors indicate ocean basin (IO: Indian Ocean, SAO: South Atlantic Ocean, SO: Southern Ocean, SPO: South Pacific Ocean, …

https://doi.org/10.7554/eLife.26066.024
Figure 3—figure supplement 3—source data 1

Derived principal component values (original data is Figure 3—source data 2).

https://doi.org/10.7554/eLife.26066.025
Figure 3—figure supplement 4
Classification accuracy as a function of the number of features.

Accuracy was estimated by cross-validation, using a feature selection step prior to classification. Source data are available in the file Figure 3—figure supplement 4—source data 1.

https://doi.org/10.7554/eLife.26066.026
Figure 3—figure supplement 4—source data 1

Accuracy of classification (estimated by cross validation) using a limited number of features (from 5 to 480, in increments of 5).

https://doi.org/10.7554/eLife.26066.027

Additional files

Download links