PS is a recipient of the Bill and Melinda Gates Grand Challenge Grant OPP1141322. PV was supported by a Swiss National Science Foundation Early Postdoc Mobility fellowship and a Roux-Cantarini postdoctoral fellowship. PJS is supported by an HHMI grant and ERC-2013-ADG 339579-Decrypt.

<ol>
	<li>Reineke, L. C., Lloyd, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diversion+of+stress+granules+and+P-bodies+during+viral+infection.">Diversion of stress granules and P-bodies during viral infection.</a> Virology. 436 (2), 255-267 (2013).</li><li>Kedersha, N., Ivanov, P., Anderson, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stress+granules+and+cell+signaling:+more+than+just+a+passing+phase.">Stress granules and cell signaling: more than just a passing phase.</a> Trends Biochem Sci. 38 (10), 494-506 (2013).</li><li>Vonaesch, P., Campbell-Valois, F. -. X., Dufour, A., Sansonetti, P. J., Schnupf, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shigella+flexneri+modulates+stress+granule+composition+and+inhibits+stress+granule+aggregation.">Shigella flexneri modulates stress granule composition and inhibits stress granule aggregation.</a> Cell Microbiol. 18 (7), 982-997 (2016).</li><li>Kolobova, E., Efimov, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microtubule-dependent+association+of+AKAP350A+and+CCAR1+with+RNA+stress+granules.">Microtubule-dependent association of AKAP350A and CCAR1 with RNA stress granules.</a> Exp Cell Res. 315 (3), 542-555 (2009).</li><li>Anderson, P., Kedersha, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stress+granules:+the+Tao+of+RNA+triage.">Stress granules: the Tao of RNA triage.</a> Trends in biochemical sciences. 33 (3), 141-150 (2008).</li><li>Anderson, P., Kedersha, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stressful+initiations.">Stressful initiations.</a> J Cell Sci. 115, 3227-3234 (2002).</li><li>Mohr, I., Sonenberg, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Host+translation+at+the+nexus+of+infection+and+immunity.">Host translation at the nexus of infection and immunity.</a> Cell Host Microbe. 12 (4), 470-483 (2012).</li><li>Hu, S., Claud, E. C., Musch, M. W., Chang, E. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stress+granule+formation+mediates+the+inhibition+of+colonic+Hsp70+translation+by+interferon-+and+tumor+necrosis+factor.">Stress granule formation mediates the inhibition of colonic Hsp70 translation by interferon- and tumor necrosis factor.</a> Am J Physiol Gastointest Liver Physiol. 298 (4), 481-492 (2010).</li><li>Barry, E. M., Pasetti, M. F., Sztein, M. B., Fasano, A., Kotloff, K. L., Levine, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Progress+and+pitfalls+in+Shigella+vaccine+research.">Progress and pitfalls in Shigella vaccine research.</a> Nat Rev Gastroenterol Hepatol. 10 (4), 245-255 (2013).</li><li>Lanata, C. F., Fischer-Walker, C. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+Causes+of+Diarrheal+Disease+Mortality+in+Children.">Global Causes of Diarrheal Disease Mortality in Children.</a> PloS One. 8 (9), 72788 (2013).</li><li>Ashida, H., Ogawa, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shigella+deploy+multiple+countermeasures+against+host+innate+immune+responses.">Shigella deploy multiple countermeasures against host innate immune responses.</a> Curr Opin Microbiol. 14 (1), 16-23 (2011).</li><li>Ashida, H., Ogawa, M., Mimuro, H., Kobayashi, T., Sanada, T., Sasakawa, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shigella+are+versatile+mucosal+pathogens+that+circumvent+the+host+innate+immune+system.">Shigella are versatile mucosal pathogens that circumvent the host innate immune system.</a> Curr Opin Immunol. 23 (4), 448-455 (2011).</li><li>Kedersha, N., Anderson, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mammalian+stress+granules+and+processing+bodies.">Mammalian stress granules and processing bodies.</a> Methods Enzymol. 431, 61-81 (2007).</li><li>Ray, K., Marteyn, B., Sansonetti, P. J., Tang, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Life+on+the+inside:+the+intracellular+lifestyle+of+cytosolic+bacteria.">Life on the inside: the intracellular lifestyle of cytosolic bacteria.</a> Nat Re. Micro. 7 (5), 333-340 (2009).</li></ol>

The authors have nothing to disclose.

The protocol outlined here describes the induction, localization, and analysis of SGs in non-infected cells and cells infected with the cytosolic pathogen S. flexneri in the presence or absence of exogenous stress. Using free imaging software, the protocols allows for the precise qualitative and quantitative analysis of SG formation to identify and statistically address differences in given phenotypes.
There are several critical steps within the protocol for the infection, SG-induction and imaging parts. For the infection, it is important that the bacterial interaction with host cells, such as invasion as outlined protocol here, is synchronized as much as possible. This is particularly important when investigating the effects of bacterial challenge early during the infection process. We previously showed that some phenotypes, such as eIF3b localization after S. flexneri infection, are perturbed early during wild-type S. flexneri challenge while other phenotypes, such as aggregation of G3BP1-containing SGs, are more affected at later time points3. Thus, to precisely describe the temporal effect of bacterial challenge, synchronization of infection is advisable. Notably, to analyze SG formation, cells should be in exponential phase of growth, and thus cell density at the time of stress addition is also an important parameter. It also limits SG analysis to non-confluent cell infection models. Another critical aspect is the co-processing of control and experimental samples during both the processing of the samples for imaging and during image acquisition. For immunofluorescent processing, all samples should be stained with the same antibody working dilutions for the same amount of time and under the same conditions (i.e. RT Vs. 4 &#176;C), while also taking care to treat each coverslip similarly during the washing stages and mounting of the coverslips. This will ensure comparable immunofluorescent stainings that can be analyzed both qualitatively and quantitatively. Differences in mounting medium can have significant effects on bleaching of the samples during image acquisition and therefore should be kept constant. Similarly, image acquisition for all samples to be compared in analysis should be performed within one sitting and with the same settings to minimize differences arising from laser strength or sample set-up.
When using exogenous stresses, it is important to properly store the reagent and to frequently make new working stocks. Extended periods (&#62;4 months for clotrimazole, for example) lead to decreased potency of the drug and will affect SG formation. Reagents should be added to culture medium immediately before addition to the cells; if a reduction in SG formation in control cells is observed or the aggregation of SGs appears aberrant, reagents should be tested for their activity and new stocks should be made.
One limitation is that SG identification using spot detector becomes less reliable when too much background fluorescence is present. While this is not a problem for many SG markers, some, such as eIF3b, which is a clear cytosolic marker under both SG-inducing and non-inducing conditions, are more difficult to analyze as demonstrated in Figure 3. In addition, scales and sensitivities need to be empirically determined for each SG marker. Another limitation is that the analysis through the image analysis software is best with 2D images, and therefore works well on optical slices or collapsed stacks, which however results in a loss of 3D information. The analysis on z-projections therefore tends to overstate the size of SGs and understate the number of SGs. 3D analysis of SGs may be performed with Imaris to give an even more precise spatial characterization, if deemed necessary for a particular phenotype.
SG characterization can be performed quickly and in a standardized manner using the automated spot detector of the image analysis software. In contrast, manual methods to identify and delineate SGs as previously performed4, leave the analysis open to more bias and is considerably more time consuming. SG analysis using spot detector can also be tailored to include or exclude small SG aggregates by selecting different sensitivity and size thresholds, thereby allowing flexibility in evaluating different aspects of SG formation. SG analysis can also be performed in a completely automated manner using an established automated workflow3. Within this workflow, the center of the cell is identified through a DAPI stain and the program then lets a malleable sphere grow outwards to delineate the cell boundaries based on either a cytoplasmic stain (such as eIF3b) or actin. At the same time, using a semi-automated analysis by manually delineating individual cells for analysis, can be advantageous when cells grow in clusters and cell boundaries are difficult for the automated program to delineate clearly. In these circumstances, defining cell boundaries manually can eliminate false positive or negative values.
The protocol may be adapted to other SG-inducing conditions and to other pathogens, including viruses, bacteria, yeast and protozoans. Of particular interest may be other cytosolic pathogens such as Rickettsia spp., Francisella tularensis, Burkholdieria pseudomallei and Listeria spp.14 For each infectious agent, and possibly also different cell lines, the infection protocol will have to be adapted and the sample times of exogenous stresses empirically determined. In addition, SG characterization using image analysis software may also be extended to live-cell imaging in the future. Real-time SG analysis would necessitate the expression of one or more fluorescently-tagged SG markers in the host cells but would thereby allow questions regarding the temporal and special dynamics of SG formation to be addressed under different experimental conditions. Fluorescently-labeled bacteria would then be needed to complement the real-time SG analysis in infected and uninfected cells.

Visualizing host-pathogen interactions on a cellular level is a powerful method for gaining insights into pathogenic strategies and for identifying key cellular pathways. Indeed, pathogens can be used as tools to pinpoint important cellular targets or structures, as pathogens have evolved to subvert central cellular processes as a strategy for their own survival or propagation. Visualization of cellular components can be achieved by recombinantly expressing fluorescently-tagged host proteins. While this allows for real-time analysis, the generation of cell lines with specifically-tagged host proteins is highly laborious and may result in undesirable side effects. More convenient is the detection of cellular factors using specific antibodies, because multiple host factors can be analyzed simultaneously and one is not limited to a particular cell type. A drawback is that only a static view can be captured as immunofluorescence analysis necessitates host cell fixation. However, an important advantage of immunofluorescence imaging is that it readily lends itself to both qualitative and quantitative analysis. This in turn can be used to obtain statistically significant differences to provide new insights into host-pathogen interactions.
Fluorescent image analysis programs are powerful analytical tools for performing 3D and 4D analysis. However, the high cost of software and its maintenance make methods based on free open source software more widely attractive. Careful image analysis using bio-analysis software is valuable as it substantiates visual analysis and, when assigning statistical significances, increases confidence in the correctness of a given phenotype. Previously, SGs have been analyzed using the free ImageJ software, which necessitates the manual identification of individual SGs4. Here we provide a protocol for the induction and analysis of cellular SG formation in the context of bacterial infections using the free open source bio-image analysis software ICY (http://icy.bioimageanalysis.org). The bio-image analysis software has a built-in spot detector program that is highly suitable for SG analysis. It allows the fine-tuning of the automated detection process in specified Regions Of Interest (ROIs). This overcomes the need for manual analysis of individual SGs and removes sampling bias.
Many environmental stresses induce the formation of SGs, which are phase dense cytosolic, non-membranous structures of 0.2 - 5 &#181;m in diameter5,6. This cellular response is evolutionarily conserved in yeast, plants and mammals and occurs when global protein translation is inhibited. It involves aggregation of stalled translation initiation complexes into SGs, which are considered holding places for translationally-inactive mRNAs, allowing selective translation of a subset of cellular mRNAs. Upon removal of the stress, SGs dissolve and global rates of protein synthesis resume. SGs are composed of translation elongation initiation factors, proteins involved in RNA metabolism, RNA-binding proteins, as well as scaffolding proteins and factors involved in host cell signaling2, although the exact composition can vary depending on the stress applied. Environmental factors that induce SG formation include amino acid starvation, UV irradiation, heat shock, osmotic shock, endoplasmic reticulum stress, hypoxia and viral infection2,7,8. Much progress has been made in understanding how viruses induce and also subvert SG formation, while little is still known about how other pathogens, such as bacterial, fungal or protozoan pathogens, affect this cellular stress response1,7.
Shigella flexneri is a gram-negative facultative cytosolic pathogen of humans and the causative agent of severe diarrhea or shigellosis. Shigellosis is a major public health burden and leads to 28,000 deaths annually in children under 5 years of age9,10. S. flexneri infects the colonic epithelium and spreads cell-to-cell by hijacking the host&#39;s cytoskeletal components11,12. Infection of the epithelium supports the replication of S. flexneri within the cytosol but infected macrophages die through an inflammatory cell death process called pyroptosis. Infection leads to a massive recruitment of neutrophils and severe inflammation that is accompanied by heat, oxidative stress and tissue destruction. Thus, while infected cells are subject to internal stresses induced by infection, such as Golgi disruption, genotoxic stress and cytoskeletal rearrangements, infected cells are also subjected to environmental stresses due to the inflammatory process.
Characterization of the effect of S. flexneri infection on the ability of cells to respond to environmental stresses using a number of SG markers has demonstrated that infection leads to qualitative and quantitative differences in SG composition3. However, little is known about other bacterial pathogens. Here we describe a methodology for the infection of host cells with the cytosolic pathogen S. flexneri, the stressing of cells with different environmental stresses, the labeling of SG components, and the qualitative and quantitative analysis of SG formation and composition in the context of infected and non-infected cells. This method is widely applicable to other bacterial pathogens. In addition, the image analysis of the SG formation may be used for infections by viruses or other pathogens. It can be used to analyze SG formation upon infection or the effect of infection on SG formation in response to exogenous stresses.

<table><tbody><tr><td>&lt;strong&gt;Primary Antibodies&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>eIF3b (N20), origin goat&amp;nbsp;</td><td>Santa Cruz</td><td>sc-16377</td><td>Robust and widely used SG marker. Cytosolic staining allows cell delineation. Dilution 1:300</td></tr><tr><td>eIF3b (A20), origin goat&amp;nbsp;</td><td>Santa Cruz</td><td>sc-16378</td><td>Same target as eIF3b (N20) and in our hands was identical to eIF3b (N20). Dilution 1:300</td></tr><tr><td>eIF3A (D51F4), origin rabbit (MC: monoclonal)</td><td>Cell Signaling</td><td>3411</td><td>Part of multiprotein eIF3 complex with eIF3b . Dilution 1:800</td></tr><tr><td>eIF4AI, origin goat&amp;nbsp;</td><td>Santa Cruz</td><td>sc-14211</td><td>Recommended by (Ref # 13). Dilution 1:200</td></tr><tr><td>eIF4B, origin rabbit</td><td>Abcam</td><td>ab186856</td><td>Good stress granule marker in our hands. Dilution 1:300</td></tr><tr><td>eIF4B, origin rabbit</td><td>Cell Signaling</td><td>3592</td><td>Recommended by Ref # 13. Dilution 1:100</td></tr><tr><td>eIF4G, origin rabbit</td><td>Santa Cruz</td><td>sc-11373</td><td>Widely used SG marker. (Ref # 13): may not work well in mouse cell lines. Dilution 1:300</td></tr><tr><td>G3BP1, origin rabbit (MC: monoclonal)</td><td>BD Biosciences</td><td>611126</td><td>Widely used SG marker. Dilution 1:300</td></tr><tr><td>Tia-1, origin goat&amp;nbsp;</td><td>Santa Cruz</td><td>sc-1751</td><td>Widely used SG marker. Can also be found in P bodies when SG are present (Ref # 13). Dilution 1:300</td></tr><tr><td>&lt;strong&gt;Alexa-conjugated Secondary Antibodies&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>A488 anti-goat , origin donkey</td><td>Thermo Fisher</td><td>A-11055</td><td>Cross absorbed. Dilution 1:500</td></tr><tr><td>A568 anti-goat, origin donkey</td><td>Thermo Fisher</td><td>A-11057</td><td>Cross absorbed. Dilution 1:500</td></tr><tr><td>A488 anti-mouse, origin donkey</td><td>Thermo Fisher</td><td>A-21202</td><td>Dilution 1:500</td></tr><tr><td>A568 anti-mouse, origin donkey</td><td>Thermo Fisher</td><td>A10037</td><td>Dilution 1:500</td></tr><tr><td>A647 anti-mouse, origin donkey</td><td>Thermo Fisher</td><td>A31571</td><td>Dilution 1:500</td></tr><tr><td>A488 anti-rabbit, origin donkey</td><td>Thermo Fisher</td><td>A-21206</td><td>Dilution 1:500</td></tr><tr><td>A568 anti-rabbit, origin donkey</td><td>Thermo Fisher</td><td>A10042</td><td>Dilution 1:500</td></tr><tr><td>&lt;strong&gt;Other Reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>&lt;em&gt;Shigella flexneri&lt;/em&gt;</td><td></td><td></td><td>Available from various laboratories by request</td></tr><tr><td>Tryptone Casein Soya (TCS) broth</td><td>BD Biosciences</td><td>211825</td><td>Standard growth medium for Shigella, application - bacterial growth</td></tr><tr><td>TCS agar</td><td>BD Biosciences</td><td>236950</td><td>Standard growth agar for Shigella, application - bacterial growth</td></tr><tr><td>Congo red</td><td>SERVA Electrophoresis GmbH</td><td>27215.01</td><td>Distrimination tool for Shigell that have lost the virulence plasmid, application - bacterial growth</td></tr><tr><td>Poly-L-Lysine (PLL)</td><td>Sigma-Aldrich</td><td>P1274</td><td>Useful to coating bacteria to increase infection, application - infection</td></tr><tr><td>Gentamicin</td><td>Sigma-Aldrich</td><td>G1397</td><td>Selective killing of extracellular but not cytosolic bacteria, application - infection</td></tr><tr><td>HEPES</td><td>Life Technologies</td><td>15630-056</td><td>PH buffer useful when cells are incubated at RT, application - cell culture</td></tr><tr><td>Dulbecco&#039;s Modified Eagle Medium (DMEM)</td><td>Life Technologies</td><td>31885</td><td>Standard culture medium for HeLa cells, application - cell culture</td></tr><tr><td>Fetal Calf Serum (FCS)</td><td>Biowest</td><td>S1810-100</td><td>5% supplementation used for HeLa cell culture medium, application - cell culture</td></tr><tr><td>Non-Essential Amino Acids (NEAA)</td><td>Life Technologies</td><td>11140</td><td>1:100 dilution used for HeLa cell culture medium, application - cell culture</td></tr><tr><td>DMSO</td><td>Sigma-Aldrich</td><td>D2650</td><td>Reagent diluent, application - cell culture</td></tr><tr><td>Sodium arsenite</td><td>Sigma-Aldrich</td><td>S7400</td><td>Potent stress granule inducer (Note: highly toxic, special handling and disposal required), application - stress inducer</td></tr><tr><td>Clotrimazole</td><td>Sigma-Aldrich</td><td>C6019</td><td>Potent stress granule inducer (Note:health hazard, special handling and disposal required), application - stress inducer</td></tr><tr><td>Paraformaldehyde (PFA</td><td>Electron Microscopy Scences</td><td>15714</td><td>4% PFA is used for standard fixation of cells, application - fixation</td></tr><tr><td>Triton X-100</td><td>Sigma-Aldrich</td><td>T8787</td><td>Used at 0.03% for permeabilizationof host cells before immunofluorescent staining, application - permeabilization</td></tr><tr><td>A647-phalloidin</td><td>Thermo Fisher</td><td>A22287</td><td>Dilution is at 1:40, best added during secondary antibody staining, application - staining</td></tr><tr><td>DAPI</td><td>Sigma-Aldrich</td><td>D9542</td><td>Nucleid acid stain used to visualize both the host nucleus and bacteria, application - staining</td></tr><tr><td>Parafilm</td><td>Sigma-Aldrich</td><td>BR701501</td><td>Paraffin film useful for immunofluorescent staining of coverslips, application - staining</td></tr><tr><td>Prolong Gold</td><td>Thermo Fisher</td><td>36930</td><td>Robust mounting medium that works well for most fluorophores , application - mounting</td></tr><tr><td>Mowiol</td><td>Sigma-Aldrich</td><td>81381</td><td>Cheap and robust mounting medium that works well for most fluorophores, application - mounting</td></tr><tr><td>24-well cell culture plate</td><td>Sigma-Aldrich</td><td>CLS3527</td><td>Standard tissue culture plates, application - cell culture</td></tr><tr><td>12 mm&amp;nbsp; glass coverslips</td><td>NeuVitro</td><td>1001/12</td><td>Cell culture support for immunofluorescent applications, application - cell support</td></tr><tr><td>forceps</td><td>Sigma-Aldrich</td><td>81381</td><td>Cheap and robust mounting medium that works well for most fluorophores, application - mounting</td></tr><tr><td>&lt;strong&gt;Programs and Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Prism</td><td>GraphPad Software</td><td></td><td>Data analysisand graphing program with robust statistical test options, application - data analysis</td></tr><tr><td>Leica SP5</td><td>Leica Microsystems</td><td></td><td>Confocal microsope, application - image acquisition</td></tr><tr><td>Imaris</td><td>Bitplane</td><td></td><td>Professional image analysis program, application - data analysis</td></tr><tr><td>Excel</td><td>Microsoft</td><td></td><td>Data analysis and graphing program, application - data analysis</td></tr></tbody></table>

immunofluorescence analysis of stress granule formation after bacterial challenge of mammalian cells

Fluorescent imaging of cellular components is an effective tool to investigate host-pathogen interactions. Pathogens can affect many different features of infected cells, including organelle ultrastructure, cytoskeletal network organization, as well as cellular processes such as Stress Granule (SG) formation. The characterization of how pathogens subvert host processes is an important and integral part of the field of pathogenesis. While variable phenotypes may be readily visible, the precise analysis of the qualitative and quantitative differences in the cellular structures induced by pathogen challenge is essential for defining statistically significant differences between experimental and control samples. SG formation is an evolutionarily conserved stress response that leads to antiviral responses and has long been investigated using viral infections1. SG formation also affects signaling cascades and may have other still unknown consequences2. The characterization of this stress response to pathogens other than viruses, such as bacterial pathogens, is currently an emerging area of research3. For now, quantitative and qualitative analysis of SG formation is not yet routinely used, even in the viral systems. Here we describe a simple method for inducing and characterizing SG formation in uninfected cells and in cells infected with a cytosolic bacterial pathogen, which affects the formation of SGs in response to various exogenous stresses. Analysis of SG formation and composition is achieved by using a number of different SG markers and the spot detector plug-in of ICY, an open source image analysis tool.

To explain and demonstrate the protocol described in this manuscript, we characterized the image of clotrimazole-induced SGs in HeLa cells infected or not with the cytosolic pathogen S. flexneri. An outline of the procedure is presented in Figure 1, and includes virulent and avirulent S. flexneri streaked on Congo Red plates, bacteria preparation, infection, addition of environmental stress, sample fixation and staining, sample imaging and quantitation, as well as image analysis. A number of different stresses may be used to induce SG formation and a variety of SG markers are available for interrogation. eIF3b is a canonical SG marker that also clearly stains the cytoplasmic compartment of the cell and can be used to identify cell edges. G3BP1 is a widely-used SG marker that aggregates into SGs without any background staining (Figure 2A, B &#38; D). Cells are best imaged with a confocal microscope, and the stacks covering the whole cell depth are loaded onto the imaging analysis freeware ICY (Figure 2A - C). To better identify infected cells and to put cells into either the infected or non-infected group for later analysis, it is useful to increase the intensity of the nucleic acid stain (Figure 2D). Within the imaging analysis software, the spot detector is then used to identify SGs within each of the designated ROIs (Figure 2E) and a binary image is generated that displays all the identified SGs (Figure 2F).
SG analysis needs to be tailored to each SG marker analyzed and the appropriate setting for analysis must be carefully chosen. Focusing on the SG marker G3BP1, changing the size requirement of the spot detected or changing the sensitivity of the detection parameters will lead to varying results, as demonstrated in Figure 3A - C. The scale selection (1 - 3, although scales can be added) is based on pixel size and thus at higher scales, smaller SGs will not be counted and the numbers of SGs will decrease (Figure 3C). Sensitivity is measured from 1 - 100, with 100 being the most sensitive. By increasing the sensitivity, the number of SGs detected increases (Figure 3C). Thus, the correct settings must be found to minimize false positives and false negatives. This is best done by carefully analyzing the visuals obtained for each setting. For G3BP1, a scale of 2 with a sensitivity of 100 (2 - 100, highlighted in red) gave the best result. A scale of 2 with a lower sensitivity (50 or 25) left small SGs uncounted (see orange arrows). Similarly, a scale of 3 left small SG unaccounted while a scale of 1 oversampled. Importantly, additional scales can be added to focus only on large SGs, if this is warranted. For eIF3b, a scale of 2 with a sensitivity of 55 (2 - 55) gave the best result for eIF3b (highlighted in red) although red arrows highlight either under or oversampling in a scale 2 - 50 &#38; 2 - 55, respectively.
Once the parameters for SG analysis are properly set, the data can be analyzed in many ways (Figure 4). The spot detector gives the number of SGs detected within each ROI such that the uninfected and infected cells can be compared (Figure 4A). Similarly, the size, in this case number of pixels, is given from which the surface area can be calculated (Figure 4B). Frequency distribution plots are also useful to highlight shifts in the size of SGs found in different cell populations. Here, a complete absence of large SGs in S. flexneri infected cells, as well as a definite shift in the distribution becomes clearer (Figure 4C). In addition, the intensity (shown here is minimum and maximum intensity) provides information on the quality of the SGs analyzed. For S. flexneri infected cells, SGs are significantly less intense. These analyses provide statistically relevant information on both the qualitative and quantitative nature of SGs formed in response to exogenous stress when cells are infected with or without S. flexneri.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55536/55536fig1.jpg" /> 
Figure 1: Outline of the Experimental Procedure to Infect Cells with S. Flexneri and to Analyze the Effect of Infection SG Formation. A Congo-red colony, and a nonvirulent Congo-red-negative colony, are picked and grown O/N in tryptic soy broth. The overnight culture is subcultured to the late exponential phase before bacteria are coated with PLL to promote adherence. Host cells grown on glass coverslips in 12-well plates are infected with bacteria for 30 min. Control cells are not infected. Cells are washed and treated with stress inducers to induce SG formation either by replacing the medium with stress-containing medium or subjecting the cells to stress conditions, such as heat. Cells are then fixed, permeabilized, stained with immunofluorescent SG-specific markers, and imaged using confocal microscopy. Z-projections are made using ImageJ and analyzed using the spot detector of the image analysis software. The data is then analyzed. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55536/55536fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55536/55536fig2.jpg" /> 
Figure 2: Spot Detector Analysis of HeLa Cells Infected with S. Flexneri for 1.5 h before the Addition of Clotrimazole for 1 h. A. Immunofluorescent image of a z-projection showing cells stained with the SG markers eIF3b and G3BP1, DAPI and actin. B. Same image as in (A) but without the actin stain to highlight the use of eIF3b to delineate cell boundaries. C. Screen shot of the image analysis software with the spot detector module. D. Gating of the infected and non-infected cells as seen using different channels. To see all bacteria, the intensity is increased. Cells with more than 1 bacterium present in the cell are labeled with a red star. E. Output of the spot detector analysis of individual ROIs, indicating the ROI name, the number of spots and their location. F. Image of spots detected in the ROIs. Scale bar = 30 &#181;m. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55536/55536fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55536/55536fig3v2.jpg" /> 
Figure 3: Comparisons of Different Scale and Sensitivity Settings of Spot Detector for the Detection of SGs through Different SG Markers. A. Zoom-in to HeLa cells infected or not with S. flexneri and stained with DAPI and the SG marker G3BP1, and analyzed at different scales (pixel size cut off, 1 - 3) and sensitivity (1 - 100). Graphical representation of SG numbers in uninfected and infected cells analyzed at different scales (B) and sensitivities (C). Visuals (D) and graphical representation (E) of SG number detection of the SG marker eIF3b for the same image when changing the sensitivity limit without changing the spot size cut-off. Red writing and red symbols indicate the best parameters. Red arrows point towards false positives and orange arrows to a lack of SG detection. Values include mean with standard deviation. Best results are highlighted in red. Selected statistical analysis included for clarity using the Wilcoxon rank sum test p-values on the left and the variance F-test on the right. * = &#60;0.05, ** = &#60;0.01, *** = &#60;0.001, ns = nonsignificant. Scale bar = 10 &#181;m. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55536/55536fig3v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55536/55536fig4.jpg" /> 
Figure 4: Analysis of Spot Detector Generated SG Data Obtained from Clotrimazole-treated HeLa Cells Infected with S. Flexneri. A. Number of G3BP1 SGs per cell in infected and uninfected HeLa cells. B. Surface area of G3BP1-SGs in infected and uninfected cells, and their percent distribution frequency (C). D. Minimum and maximum intensity values (arbitrary) of G3BP1-SGs. Values include mean with standard error. Selected statistical analysis included for clarity using the Wilcoxon rank sum test p-values on the left and the variance F-test on the right. * = &#60;0.05, ** = &#60;0.01, *** = &#60;0.001, ns = nonsignificant. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55536/55536fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Immunofluorescence Analysis of Stress Granule Formation After Bacterial Challenge of Mammalian Cells at JoVE.com

Immunofluorescence Analysis of Stress Granule Formation After Bacterial Challenge of Mammalian Cells

We describe a method for the qualitative and quantitative analysis of stress granule formation in mammalian cells after the cells are challenged with bacteria and a number of different stresses. This protocol can be applied to investigate the cellular stress granule response in a wide range of host-bacterial interactions.

Fluorescent imaging of cellular components is an effective tool to investigate host-pathogen interactions. Pathogens can affect many different features of ...

immunofluorescence-analysis-stress-granule-formation-after-bacterial

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

10.1K Views.  Institut Pasteur. We describe a method for the qualitative and quantitative analysis of stress granule formation in mammalian cells after the cells are challenged with bacteria and a number of different stresses. This protocol can be applied to investigate the cellular stress granule response in a wide range of host-bacterial interactions.

Video: Immunofluorescence Analysis of Stress Granule Formation After Bacterial Challenge of Mammalian Cells

Invasion of Human Cells by a Bacterial Pathogen

Here we will describe how we study the invasion of human endothelial cells by bacterial pathogen Staphylococcus aureus . The general protocol can be applied to the study of cell invasion by virtually any culturable bacterium. The stages at which specific aspects of invasion can be studied, such as the role of actin rearrangement or caveolae, will be highlighted. Host cells are grown in flasks and when ready for use are seeded into 24-well plates containing Thermanox coverslips. Using coverslips allows subsequent removal of the cells from the wells to reduce interference from serum proteins deposited onto the sides of the wells (to which S. aureus would attach). Bacteria are grown to the required density and washed to remove any secreted proteins (e.g. toxins). Coverslips with confluent layers of endothelial cells are transferred to new 24-well plates containing fresh culture medium before the addition of bacteria. Bacteria and cells are then incubated together for the required amount of time in 5% CO2 at 37&deg;C. For S. aureus this is typically between 15-90 minutes. Thermanox coverslips are removed from each well and dip-washed in PBS to remove unattached bacteria. If total associated bacteria (adherent and internalised) are to be quantified, coverslips are then placed in a fresh well containing 0.5% Triton X-100 in PBS. Gentle pipetting leads to complete cell lysis and bacteria are enumerated by serial dilution and plating onto agar. If the number of bacteria that have invaded the cells is needed, coverslips are added to wells containing 500 &mu;l tissue culture medium supplemented with gentamicin and incubation continued for 1 h, which will kill all external bacteria. Coverslips can then be washed, cells lysed and bacteria enumerated by plating onto agar as described above. If the experiment requires direct visualisation, coverslips can be fixed and stained for light, fluorescence or confocal microscopy or prepared for electron microscopy.

A general protocol for the study of invasion of host cells by a bacterial pathogen, focusing on Staphylococcus aureus and human endothelial cells.

Here we will describe how we study the invasion of human endothelial cells by bacterial pathogen Staphylococcus aureus . The general protocol can be ...

Stress-induced Antibiotic Susceptibility Testing on a Chip

We have developed a rapid microfluidic method for antibiotic susceptibility testing in a stress-based environment. Fluid is passed at high speeds over bacteria immobilized on the bottom of a microfluidic channel. In the presence of stress and antibiotic, susceptible strains of bacteria die rapidly. However, resistant bacteria survive these stressful conditions. The hypothesis behind this method is new: stress activation of biochemical pathways, which are targets of antibiotics, can accelerate antibiotic susceptibility testing. As compared to standard antibiotic susceptibility testing methods, the rate-limiting step -&#160;bacterial growth -&#160;is omitted during antibiotic application. The technical implementation of the method is in a combination of standard techniques and innovative approaches. The standard parts of the method include bacterial culture protocols, defining microfluidic channels in polydimethylsiloxane&#160;(PDMS), cell viability monitoring with fluorescence, and batch image processing for bacteria counting. Innovative parts of the method are in the use of culture media flow for mechanical stress application, use of enzymes to damage but not kill the bacteria, and use of microarray substrates for bacterial attachment. The developed platform can be used in antibiotic and nonantibiotic related drug development and testing. As compared to the standard bacterial suspension experiments, the effect of the drug can be turned on and off repeatedly over controlled time periods. Repetitive observation of the same bacterial population is possible over the course of the same experiment.

We have developed a microfluidic platform for rapid antibiotic susceptibility testing. Fluid is passed at high speeds over bacteria immobilized on the bottom of a microfluidic channel. In the presence of stress and antibiotic, susceptible strains of bacteria die rapidly. However, resistant bacteria can survive these stressful conditions.

We have developed a rapid microfluidic method for antibiotic susceptibility testing in a stress-based environment. Fluid is passed at high speeds over ...

Bioengineering

Visualizing the Actin and Microtubule Cytoskeletons at the B-cell Immune Synapse Using Stimulated Emission Depletion (STED) Microscopy

B cells that bind to membrane-bound antigens (e.g., on the surface of an antigen-presenting cell) form an immune synapse, a specialized cellular structure that optimizes B-cell receptor (BCR) signaling and BCR-mediated antigen acquisition. Both the remodeling of the actin cytoskeleton and the reorientation of the microtubule network towards the antigen contact site are essential for immune synapse formation. Remodeling of the actin cytoskeleton into a dense peripheral ring of F-actin is accompanied by polarization of the microtubule-organizing center towards the immune synapse. Microtubule plus-end binding proteins, as well as cortical plus-end capture proteins mediate physical interactions between the actin and microtubule cytoskeletons, which allow them to be reorganized in a coordinated manner. Elucidating the mechanisms that control this cytoskeletal reorganization, as well as understanding how these cytoskeletal structures shape immune synapse formation and BCR signaling, can provide new insights into B cell activation. This has been aided by the development of super-resolution microscopy approaches that reveal new details of cytoskeletal network organization. We describe here a method for using stimulated emission depletion (STED) microscopy to simultaneously image actin structures, microtubules, and transfected GFP-tagged microtubule plus-end binding proteins in B cells. To model the early events in immune synapse formation, we allow B cells to spread on coverslips coated with anti-immunoglobulin (anti-Ig) antibodies, which initiate BCR signaling and cytoskeleton remodeling. We provide step-by-step protocols for expressing GFP fusion proteins in A20 B-lymphoma cells, for anti-Ig-induced cell spreading, and for subsequent cell fixation, Immunostaining, image acquisition, and image deconvolution steps. The high-resolution images obtained using these procedures allow one to simultaneously visualize actin structures, microtubules, and the microtubule plus-end binding proteins that may link these two cytoskeletal networks.

Visualizing the Actin and Microtubule Cytoskeletons at the B-cell Immune Synapse Using Stimulated Emission Depletion STED Microscopy

We present a protocol for using STED microscopy to simultaneously image actin structures, microtubules, and microtubule plus-end binding proteins in B cells that have spread on coverslips coated with antibodies to the B-cell receptor, a model for the initial phase of immune synapse formation.

B cells that bind to membrane-bound antigens (e.g., on the surface of an antigen-presenting cell) form an immune synapse, a specialized cellular structure ...

A Multi-well Format Polyacrylamide-based Assay for Studying the Effect of Extracellular Matrix Stiffness on the Bacterial Infection of Adherent Cells

Extracellular matrix stiffness comprises one of the multiple environmental mechanical stimuli that are well known to influence cellular behavior, function, and fate in general. Although increasingly more adherent cell types&#39; responses to matrix stiffness have been characterized, how adherent cells&#39; susceptibility to bacterial infection depends on matrix stiffness is largely unknown, as is the effect of bacterial infection on the biomechanics of host cells. We hypothesize that the susceptibility of host endothelial cells to a bacterial infection depends on the stiffness of the matrix on which these cells reside, and that the infection of the host cells with bacteria will change their biomechanics. To test these two hypotheses, endothelial cells were used as model hosts and Listeria monocytogenes as a model pathogen. By developing a novel multi-well format assay, we show that the effect of matrix stiffness on infection of endothelial cells by L. monocytogenes can be quantitatively assessed through flow cytometry and immunostaining followed by microscopy. In addition, using traction force microscopy, the effect of L. monocytogenes infection on host endothelial cell biomechanics can be studied. The proposed method allows for the analysis of the effect of tissue-relevant mechanics on bacterial infection of adherent cells, which is a critical step towards understanding the biomechanical interactions between cells, their extracellular matrix, and pathogenic bacteria. This method is also applicable to a wide variety of other types of studies on cell biomechanics and response to substrate stiffness where it is important to be able to perform many replicates in parallel in each experiment.

We have developed a multi-well format polyacrylamide-based assay for probing the effect of extracellular matrix stiffness on bacterial infection of adherent cells. This assay is compatible with flow cytometry, immunostaining, and traction force microscopy, allowing for quantitative measurements of the biomechanical interactions between cells, their extracellular matrix, and pathogenic bacteria.

Extracellular matrix stiffness comprises one of the multiple environmental mechanical stimuli that are well known to influence cellular behavior, ...

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

We present a freely available workflow built for rapid exploration and accurate analysis of cellular bodies in specific cell compartments in fluorescence microscopy images. This user-friendly workflow is designed on the open-source software Icy and also uses ImageJ functionalities. The pipeline is affordable without knowledge in image analysis.

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in ...

Multi-scale Analysis of Bacterial Growth Under Stress Treatments

Analysis of the bacterial ability to grow and survive under stress conditions is essential for a wide range of microbiology studies. It is relevant to characterize the response of bacterial cells to stress-inducing treatments such as exposure to antibiotics or other antimicrobial compounds, irradiation, non-physiological pH, temperature, or salt concentration. Different stress treatments might disturb different cellular processes, including cell division, DNA replication, protein synthesis, membrane integrity, or cell cycle regulation. These effects are usually associated with specific phenotypes at the cellular scale. Therefore, understanding the extent and causality of stress-induced growth or viability deficiencies requires a careful analysis of several parameters, both at the single-cell and at the population levels. The experimental strategy presented here combines traditional optical density monitoring and plating assays with single-cell analysis techniques such as flow cytometry and real time microscopy imaging in live cells. This multiscale framework allows a time-resolved description of the impact of stress conditions on the fate of a bacterial population.

This protocol allows a time-resolved description of bacterial growth under stress conditions at the single-cell and the cell population levels.

Analysis of the bacterial ability to grow and survive under stress conditions is essential for a wide range of microbiology studies. It is relevant to ...

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

The quantification of gene expression at the single cell level uncovers novel regulatory mechanisms obscured in measurements performed at the population level. Two methods based on microscopy and flow cytometry are presented to demonstrate how such data can be acquired. The expression of a fluorescent reporter induced upon activation of the high osmolarity glycerol MAPK pathway in yeast is used as an example. The specific advantages of each method are highlighted. Flow cytometry measures a large number of cells (10,000) and provides a direct measure of the dynamics of protein expression independent of the slow maturation kinetics of the fluorescent protein. Imaging of living cells by microscopy is by contrast limited to the measurement of the matured form of the reporter in fewer cells. However, the data sets generated by this technique can be extremely rich thanks to the combinations of multiple reporters and to the spatial and temporal information obtained from individual cells. The combination of these two measurement methods can deliver new insights on the regulation of protein expression by signaling pathways.

Two complementary methods based on flow cytometry and microscopy are presented which enable the quantification, at the single cell level, of the dynamics of gene expression induced by the activation of a MAPK pathway in yeast.

The quantification of gene expression at the  single cell level uncovers novel regulatory mechanisms obscured in measurements  performed at the population ...

Biology

Visualization of G3BP Stress Granules Dynamics in Live Primary Cells

SGs can be visualized in cells by immunostaining of specific protein components or polyA+ mRNAs. SGs are highly dynamic and the study of their assembly and fate is important to understand the cellular response to stress. The deficiency in key factors of SGs like G3BP (RasGAP SH3 domain Binding Protein) leads to developmental defects in mice&#160;and alterations of the Central Nervous System. To study the dynamics of SGs in cells from an organism, one can culture primary cells and follow the localization of a transfected tagged component of SGs. We describe time-lapse experiment to observe G3BP1-containing SGs in Mouse Embryonic Fibroblasts (MEFs). This technique can also be used to study G3BP-containing SGs in live neurons, which is crucial as it was recently shown that these SGs are formed at the onset of neurodegenerative diseases like Alzheimer&#39;s disease. This approach can be adapted to any other cellular body and granule protein component, and performed with transgenic animals, allowing the live study of granules dynamics for example in the absence of a specific factor of these granules.

Stress granules (SGs) are cytoplasmic RNA granules containing stalled ribonucleoprotein particles (RNPs), and important in cellular response to various stresses. Dynamics of SGs can be followed in live cells by visualizing the localization of a tagged component of SGs in transfected primary cells after stress.

SGs can be visualized in cells by immunostaining of specific protein components or polyA+ mRNAs. SGs are highly dynamic and the study of their assembly ...

Methods to Classify Cytoplasmic Foci as Mammalian Stress Granules

Cells are often challenged by sudden environmental changes. Stress Granules (SGs), cytoplasmic ribonucleoprotein complexes that form in cells exposed to stress conditions, are implicated in various aspects of cell metabolism and survival. SGs modulate cellular signaling pathways, post-transcriptional gene expression, and stress response programs. The formation of these mRNA-containing granules is directly connected to cellular translation. SG assembly is triggered by inhibited translation initiation, and SG disassembly is promoted by translation activation or by inhibited translation elongation. This relationship is further highlighted by SG composition. Core SG components are stalled translation pre-initiation complexes, mRNA, and selected RNA-binding Proteins (RBPs). The purpose of SG assembly is to conserve cellular energy by sequestering translationally stalled housekeeping mRNAs, allowing for the enhanced translation of stress-responsive proteins. In addition to the core constituents, such as stalled translation preinitiation complexes, SGs contain a plethora of other proteins and signaling molecules. Defects in SG formation can impair cellular adaptation to stress and can thus promote cell death. SGs and similar RNA-containing granules have been linked to a number of human diseases, including neurodegenerative disorders and cancer, leading to the recent interest in classifying and defining RNA granule subtypes. This protocol describes assays to characterize and quantify mammalian SGs.

Stress Granules (SGs) are nonmembranous cytoplasmic structures that form in cells exposed to a variety of stresses. SGs contain mRNAs, RNA-binding proteins, small ribosomal subunits, translation-related factors, and various cell signaling proteins. This protocol describes a workflow that uses several experimental approaches to detect, characterize, and quantify bona fide SGs.

Cells are often challenged by sudden environmental changes. Stress Granules (SGs), cytoplasmic ribonucleoprotein complexes that form in cells exposed to ...

An Immunofluorescence Assay to Characterize and Quantify Mammalian Stress Granules

Source: Aulas, A. et al., <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/55656">Methods to Classify Cytoplasmic Foci as Mammalian Stress Granules.</a>&#160;J. Vis. Exp. (2017)
This video demonstrates an assay for characterizing and quantifying mammalian stress granules, which consist of mRNAs, RNA-binding proteins (RBPs), and various other proteins.

Test immunofluorescencyjny do charakterystyki i ilościowego oznaczania granulek stresu ssaków

1. Cell Preparation

	Add autoclaved coverslips to 12 wells of a 24-well plate, as indicated in&#160;Figure 1A; this can be done using a sterile Pasteur ...