23. The easiest algorithm for a fast cellular or organelle detection is
the threshold-based method, similar to that used for bacteria
detection. However, this method usually cannot yield a list of
individualized objects, especially when objects are too close to
each other, which generally happens at low magnification
(20Â). In this case, a more precise algorithm based on contrast,
shape, and/or intensity has to be applied. The use of already
available algorithms is then recommended, as their de novo
development can be time-consuming.
24. Due to the tendency of mycobacteria to form clumps, the
bacterial area parameter is more reliable than the actual counting of the bacteria. The total bacterial area, expressed in pixels,
is the most useful parameter to determine the extent of bacterial growth.
References
1. Soldati T, Neyrolles O (2012) Mycobacteria
and the intraphagosomal environment: take it
with a pinch of salt(s)! Traffic 13
(8):1042–1052. https://doi.org/10.1111/j.
1600-0854.2012.01358.x
2. Christophe T, Jackson M, Jeon HK,
Fenistein D, Contreras-Dominguez M, Kim J,
Genovesio A, Carralot JP, Ewann F, Kim EH,
Lee SY, Kang S, Seo MJ, Park EJ,
Skovierova H, Pham H, Riccardi G, Nam JY,
Marsollier L, Kempf M, Joly-Guillou ML,
Oh T, Shin WK, No Z, Nehrbass U,
Brosch R, Cole ST, Brodin P (2009) High
content screening identifies decaprenylphosphoribose 2
0 epimerase as a target for
intracellular antimycobacterial inhibitors.
PLoS Pathog 5(10):e1000645. https://doi.
org/10.1371/journal.ppat.1000645
3. Cambier CJ, Falkow S, Ramakrishnan L
(2014) Host evasion and exploitation schemes
of Mycobacterium tuberculosis. Cell 159
(7):1497–1509. https://doi.org/10.1016/j.
cell.2014.11.024
4. Simeone R, Sayes F, Song O, Groschel MI,
Brodin P, Brosch R, Majlessi L (2015) Cytosolic access of Mycobacterium tuberculosis:
critical impact of phagosomal acidification control and demonstration of occurrence in vivo.
PLoS Pathog 11(2):e1004650. https://doi.
org/10.1371/journal.ppat.1004650
5. Philips JA, Ernst JD (2012) Tuberculosis pathogenesis and immunity. Annu Rev Pathol
7:353–384.
https://doi.org/10.1146/
annurev-pathol-011811-132458
6. Conrad C, Wunsche A, Tan TH, Bulkescher J,
Sieckmann F, Verissimo F, Edelstein A,
Walter T, Liebel U, Pepperkok R, Ellenberg J
(2011) Micropilot: automation of fluorescence
microscopy-based imaging for systems biology.
Nat Methods 8(3):246–249. https://doi.org/
10.1038/nmeth.1558
7. Christophe T, Ewann F, Jeon HK, Cechetto J,
Brodin P (2010) High-content imaging of
Mycobacterium tuberculosis-infected macrophages: an in vitro model for tuberculosis
drug discovery. Future Med Chem 2
(8):1283–1293. https://doi.org/10.4155/
fmc.10.223
8. Queval CJ, Song OR, Carralot JP, Saliou JM,
Bongiovanni A, Deloison G, Deboosere N,
Jouny S, Iantomasi R, Delorme V, Debrie AS,
Park SJ, Gouveia JC, Tomavo S, Brosch R,
Yoshimura A, Yeramian E, Brodin P (2017)
Mycobacterium tuberculosis controls phagosomal acidification by targeting CISH-mediated
signaling. Cell Rep 20(13):3188–3198.
https://doi.org/10.1016/j.celrep.2017.08.
101
9. Queval CJ, Song OR, Delorme V, Iantomasi R,
Veyron-Churlet R, Deboosere N, Landry V,
Baulard A, Brodin P (2014) A microscopic
phenotypic assay for the quantification of intracellular mycobacteria adapted for highthroughput/high-content screening. J Vis
Exp 83:e51114. https://doi.org/10.3791/
51114
10. Song OR, Deboosere N, Delorme V, Queval
CJ, Deloison G, Werkmeister E, Lafont F,
Baulard A, Iantomasi R, Brodin P (2017) Phenotypic assays for Mycobacterium tuberculosis
infection. Cytometry A 91(10):983–994.
https://doi.org/10.1002/cyto.a.23129
11. Song OR, Queval CJ, Iantomasi R, Delorme V,
Marion S, Veyron-Churlet R, Werkmeister E,
High-Content Analysis of Host-Pathogen Interactions
701
the threshold-based method, similar to that used for bacteria
detection. However, this method usually cannot yield a list of
individualized objects, especially when objects are too close to
each other, which generally happens at low magnification
(20Â). In this case, a more precise algorithm based on contrast,
shape, and/or intensity has to be applied. The use of already
available algorithms is then recommended, as their de novo
development can be time-consuming.
24. Due to the tendency of mycobacteria to form clumps, the
bacterial area parameter is more reliable than the actual counting of the bacteria. The total bacterial area, expressed in pixels,
is the most useful parameter to determine the extent of bacterial growth.
References
1. Soldati T, Neyrolles O (2012) Mycobacteria
and the intraphagosomal environment: take it
with a pinch of salt(s)! Traffic 13
(8):1042–1052. https://doi.org/10.1111/j.
1600-0854.2012.01358.x
2. Christophe T, Jackson M, Jeon HK,
Fenistein D, Contreras-Dominguez M, Kim J,
Genovesio A, Carralot JP, Ewann F, Kim EH,
Lee SY, Kang S, Seo MJ, Park EJ,
Skovierova H, Pham H, Riccardi G, Nam JY,
Marsollier L, Kempf M, Joly-Guillou ML,
Oh T, Shin WK, No Z, Nehrbass U,
Brosch R, Cole ST, Brodin P (2009) High
content screening identifies decaprenylphosphoribose 2
0 epimerase as a target for
intracellular antimycobacterial inhibitors.
PLoS Pathog 5(10):e1000645. https://doi.
org/10.1371/journal.ppat.1000645
3. Cambier CJ, Falkow S, Ramakrishnan L
(2014) Host evasion and exploitation schemes
of Mycobacterium tuberculosis. Cell 159
(7):1497–1509. https://doi.org/10.1016/j.
cell.2014.11.024
4. Simeone R, Sayes F, Song O, Groschel MI,
Brodin P, Brosch R, Majlessi L (2015) Cytosolic access of Mycobacterium tuberculosis:
critical impact of phagosomal acidification control and demonstration of occurrence in vivo.
PLoS Pathog 11(2):e1004650. https://doi.
org/10.1371/journal.ppat.1004650
5. Philips JA, Ernst JD (2012) Tuberculosis pathogenesis and immunity. Annu Rev Pathol
7:353–384.
https://doi.org/10.1146/
annurev-pathol-011811-132458
6. Conrad C, Wunsche A, Tan TH, Bulkescher J,
Sieckmann F, Verissimo F, Edelstein A,
Walter T, Liebel U, Pepperkok R, Ellenberg J
(2011) Micropilot: automation of fluorescence
microscopy-based imaging for systems biology.
Nat Methods 8(3):246–249. https://doi.org/
10.1038/nmeth.1558
7. Christophe T, Ewann F, Jeon HK, Cechetto J,
Brodin P (2010) High-content imaging of
Mycobacterium tuberculosis-infected macrophages: an in vitro model for tuberculosis
drug discovery. Future Med Chem 2
(8):1283–1293. https://doi.org/10.4155/
fmc.10.223
8. Queval CJ, Song OR, Carralot JP, Saliou JM,
Bongiovanni A, Deloison G, Deboosere N,
Jouny S, Iantomasi R, Delorme V, Debrie AS,
Park SJ, Gouveia JC, Tomavo S, Brosch R,
Yoshimura A, Yeramian E, Brodin P (2017)
Mycobacterium tuberculosis controls phagosomal acidification by targeting CISH-mediated
signaling. Cell Rep 20(13):3188–3198.
https://doi.org/10.1016/j.celrep.2017.08.
101
9. Queval CJ, Song OR, Delorme V, Iantomasi R,
Veyron-Churlet R, Deboosere N, Landry V,
Baulard A, Brodin P (2014) A microscopic
phenotypic assay for the quantification of intracellular mycobacteria adapted for highthroughput/high-content screening. J Vis
Exp 83:e51114. https://doi.org/10.3791/
51114
10. Song OR, Deboosere N, Delorme V, Queval
CJ, Deloison G, Werkmeister E, Lafont F,
Baulard A, Iantomasi R, Brodin P (2017) Phenotypic assays for Mycobacterium tuberculosis
infection. Cytometry A 91(10):983–994.
https://doi.org/10.1002/cyto.a.23129
11. Song OR, Queval CJ, Iantomasi R, Delorme V,
Marion S, Veyron-Churlet R, Werkmeister E,
High-Content Analysis of Host-Pathogen Interactions
701