2. Image modification: Top Hat, to adjust the signal-to-noise
ratio and highlight bright objects.
3. Image modification: Fill Dark Hole, to unify the intensity
signal of D. discoideum bodies (cytoplasm to cell membrane).
4. Segmentation: Find Blobs, to segment D. discoideum cells
based on previous image modifications.
5. Filter Mask: remove objects smaller than 50 μm
2
.
6. Grow Objects: to increase cell masks of 5 pixels, to better
correspond to the actual size of D. discoideum bodies. This
corrects the effect of previous image modifications.
M. marinum segmentation based on FITC signal:
1. Image modification: Top Hat, to reduce background and highlight bright objects.
2. Segmentation: Find Blobs, to identify and segment
M. marinum.
3. Filter mask: filters masks (by size) to eliminate small bright dust
artefacts.
Association of masks to analyze M. marinum growth with different parameters:
The Keep or Remove marked objects function is used to create
the following associations:
1. Infected D. discoideum.
2. Non-infected D. discoideum.
3. Intracellular bacteria.
4. Extracellular bacteria.
Several parameters can be exported from these associations,
namely the number of certain objects and integrated intensities.
Various graphs can be made from these parameters, for example:
1. Percentage of infected cells.
2. Growth of D. discoideum: count of D. discoideum over time.
3. Intracellular M. marinum growth: integrated intensity sum (see
Note 17) of bacteria in D. discoideum over time.
4. Extracellular M. marinum growth: integrated intensity sum of
bacteria not in D. discoideum over time.
5. Bacterial growth: integrated intensity sum of bacteria
over time.
All the parameters quantified above can be plotted; Fig. 6
illustrates the fluorescence quantification as a proxy of intracellular
M. marinum growth.
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Manon Mottet et al.
ratio and highlight bright objects.
3. Image modification: Fill Dark Hole, to unify the intensity
signal of D. discoideum bodies (cytoplasm to cell membrane).
4. Segmentation: Find Blobs, to segment D. discoideum cells
based on previous image modifications.
5. Filter Mask: remove objects smaller than 50 μm
2
.
6. Grow Objects: to increase cell masks of 5 pixels, to better
correspond to the actual size of D. discoideum bodies. This
corrects the effect of previous image modifications.
M. marinum segmentation based on FITC signal:
1. Image modification: Top Hat, to reduce background and highlight bright objects.
2. Segmentation: Find Blobs, to identify and segment
M. marinum.
3. Filter mask: filters masks (by size) to eliminate small bright dust
artefacts.
Association of masks to analyze M. marinum growth with different parameters:
The Keep or Remove marked objects function is used to create
the following associations:
1. Infected D. discoideum.
2. Non-infected D. discoideum.
3. Intracellular bacteria.
4. Extracellular bacteria.
Several parameters can be exported from these associations,
namely the number of certain objects and integrated intensities.
Various graphs can be made from these parameters, for example:
1. Percentage of infected cells.
2. Growth of D. discoideum: count of D. discoideum over time.
3. Intracellular M. marinum growth: integrated intensity sum (see
Note 17) of bacteria in D. discoideum over time.
4. Extracellular M. marinum growth: integrated intensity sum of
bacteria not in D. discoideum over time.
5. Bacterial growth: integrated intensity sum of bacteria
over time.
All the parameters quantified above can be plotted; Fig. 6
illustrates the fluorescence quantification as a proxy of intracellular
M. marinum growth.
196
Manon Mottet et al.