3. Detect and segment spot on the ROS detection channel in the
cytoplasm as region of interest (ROI), using a “spot” detection
algorithm similar to the one used for mycobacteria.
4. Measure intensity properties (Mean, Max, Sum) and ROS area
(in pixels) for each ROS spot detected.
5. Calculate all host-related parameters for all cells. Define
“infected” and “non-infected” cell populations and (1) number
of ROS spots and (2) ROS area per cell for the two distinct
cellular populations.
3.31 Image-Based
Analysis
for Quantification
of Oxidative Stress by
Intracellular Nitric
Oxides (NO) Detection
[19]
1. Proceed first to cell and mycobacteria detection and quantification (see steps 1–8 in Subheading 3.26).
2. Define a manual threshold corresponding to the basal level of
NO, using “non-infected” cell population, “NO inhibitor”
and “NO inducer” controls to set intensity thresholds.
3. Detect and segment round shaped spots with a given maximum
radius on the NO detection channel in the cytoplasm as region
of interest (ROI), using a “spot” detection algorithm similar to
the one used for mycobacteria.
4. Measure intensity properties (Mean, Max, Sum) and NO area
(in pixels) for each NO spot detected.
5. Calculate all host-related parameters for all cells
(corresponding to single cell analysis). Define “infected” and
“non-infected” cell populations and (1) number of NO spots
and (2) NO area per cell for the two distinct cellular
populations.
3.32 Image-Based
Analysis
for Quantification
of Zinc Poisoning [10]
1. Proceed first to cell and mycobacteria detection and quantification (see steps 1–8 in Subheading 3.26).
2. Define a manual threshold corresponding to the basal level of
zinc, using “non-infected” cell population, “non-infected cell”
and “infected cell” controls to set intensity thresholds.
3. Detect and segment metal deposits, similarly to the bacteria
segmentation, using a “spot” detection algorithm.
4. Calculate the co-localization between bacteria and metal
deposits using the Mander’s Coefficient, yielding “FluoZin-3
positive” and “negative” bacterial populations.
5. Calculate all host-related parameters. Define “infected” and
“non-infected” cell populations and further cluster according
to the presence of FluoZin-3 deposits (FluoZin-3 positive
versus negative mycobacteria).
6. Image and use the same analysis parameters to detect intracellular Zinc indicator controls (range from 10 to 100 μM).
696
Nathalie Deboosere et al.
cytoplasm as region of interest (ROI), using a “spot” detection
algorithm similar to the one used for mycobacteria.
4. Measure intensity properties (Mean, Max, Sum) and ROS area
(in pixels) for each ROS spot detected.
5. Calculate all host-related parameters for all cells. Define
“infected” and “non-infected” cell populations and (1) number
of ROS spots and (2) ROS area per cell for the two distinct
cellular populations.
3.31 Image-Based
Analysis
for Quantification
of Oxidative Stress by
Intracellular Nitric
Oxides (NO) Detection
[19]
1. Proceed first to cell and mycobacteria detection and quantification (see steps 1–8 in Subheading 3.26).
2. Define a manual threshold corresponding to the basal level of
NO, using “non-infected” cell population, “NO inhibitor”
and “NO inducer” controls to set intensity thresholds.
3. Detect and segment round shaped spots with a given maximum
radius on the NO detection channel in the cytoplasm as region
of interest (ROI), using a “spot” detection algorithm similar to
the one used for mycobacteria.
4. Measure intensity properties (Mean, Max, Sum) and NO area
(in pixels) for each NO spot detected.
5. Calculate all host-related parameters for all cells
(corresponding to single cell analysis). Define “infected” and
“non-infected” cell populations and (1) number of NO spots
and (2) NO area per cell for the two distinct cellular
populations.
3.32 Image-Based
Analysis
for Quantification
of Zinc Poisoning [10]
1. Proceed first to cell and mycobacteria detection and quantification (see steps 1–8 in Subheading 3.26).
2. Define a manual threshold corresponding to the basal level of
zinc, using “non-infected” cell population, “non-infected cell”
and “infected cell” controls to set intensity thresholds.
3. Detect and segment metal deposits, similarly to the bacteria
segmentation, using a “spot” detection algorithm.
4. Calculate the co-localization between bacteria and metal
deposits using the Mander’s Coefficient, yielding “FluoZin-3
positive” and “negative” bacterial populations.
5. Calculate all host-related parameters. Define “infected” and
“non-infected” cell populations and further cluster according
to the presence of FluoZin-3 deposits (FluoZin-3 positive
versus negative mycobacteria).
6. Image and use the same analysis parameters to detect intracellular Zinc indicator controls (range from 10 to 100 μM).
696
Nathalie Deboosere et al.