Quantitative insights into actin rearrangements and bacterial target site selection from ...

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Quantitative insights into actin 1

rearrangements and bacterial target site 2

selection from Salmonella Typhimurium 3

infection of micropatterned cells1 4

5

Pascale Vonaesch1, Steven Cardini1, Mikael E. Sellin1, Bruno Goud2, Wolf-Dietrich Hardt1 6

and Kristine Schauer2 7

8 1Institute of Microbiology, ETH Zürich, Wolfgang-Pauli-Str. 12, 8093 Zürich, Switzerland 9 2UMR144 CNRS/ Institut Curie, 75005 Paris, France. 10

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To whom corresponence should be adressed: [email protected]; [email protected] 13

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This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/cmi.12154 A

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Abstract 1

Reorganization of the host cell actin cytoskeleton is crucial during pathogen invasion. We 2

established micropatterned cells as a standardized infection model for cell invasion to 3

quantitatively study actin rearrangements triggered by Salmonella Typhimurium (S. Tm). 4

Micropatterns of extracellular matrix proteins force cells to adopt a reproducible shape 5

avoiding strong cell-to-cell variations, a major limitation in classical cell culture conditions. 6

S. Tm induced F-actin-rich ruffles and invaded micropatterned cells similar to unconstrained 7

cells. Yet, standardized conditions allowed fast and unbiased comparison of cellular changes 8

triggered by the SipA and SopE bacterial effector proteins. Intensity measurements in defined 9

regions revealed that the content of pre-existing F-actin remained unchanged during 10

infection, suggesting that newly polymerized F-actin in bacteria-triggered ruffles originates 11

from the G-actin pool. Analyzing bacterial target sites, we found that bacteria did not show 12

any preferences for the local actin cytoskeleton specificities. Rather, invasion was 13

constrained to a specific “cell height”, due to flagella-mediated near-surface swimming. We 14

found that invasion sites were similar to bacterial binding sites, indicating that S. Tm can 15

induce a permissive invasion site wherever it binds. As micropatterned cells can be infected 16

by many different pathogens they represent a valuable new tool for quantitative analysis of 17

host-pathogen interactions. 18

Introduction 19

Salmonella Typhimurium (S. Tm) is a Gram-negative, flagellated bacterium causing annually 20

more than a billion cases of severe gastroenteritis in humans worldwide (Mead et al., 1999). 21

S. Tm is ingested with contaminated food and water. As shown in tissue culture models, this 22

bacterium employs flagella-driven motility to approach the cell layer and slide along the cell 23

layer’s surface (Misselwitz et al., 2012). This early phase of infection seems to be governed 24

by generic physical forces acting on any particle moving along a surface. Once in close 25

proximity to a cell, S. Tm either attaches reversibly or docks irreversibly (Misselwitz et al., 26

2011) by using adhesins (reversible binding) and the needle of the Salmonella pathogenicity 27

island (SPI)-1 encoded Type 3 secretion system 1 (TTSS-1) (irreversible binding) 28

(Misselwitz et al., 2011, Lara-Tejero et al., 2009). Once irreversibly attached to the host cell, 29

the bacterium injects different effector proteins through this needle. Several of these factors 30

trigger, either directly or indirectly, the formation of pronounced F-actin-enriched membrane 31 Acc

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ruffles (Finlay et al., 1991): SopE and SopE2 act as guanine nucleotide exchange factors 1

(GEFs) for Rac1 and Cdc42 (Hardt et al., 1998, Rudolph et al., 1999, Friebel et al., 2000, 2

Friebel et al., 2001, Cain et al., 2004, Patel et al., 2006, Stender et al., 2000); SipC promotes 3

actin nucleation and bundling (Chang et al., 2005, Chang et al., 2007, Myeni et al., 2010); 4

SipA induces F-actin bundling (Higashide et al., 2002, Galkin et al., 2002, Lilic et al., 2003, 5

Zhou et al., 1999a, Zhou et al., 1999b, McGhie et al., 2004), inhibits F-actin 6

depolymerization and potentiates the action of SipC (McGhie et al., 2001). SopB is an 7

phosphatidylinositol phosphatase that indirectly promotes actin rearrangements: either by 8

producing second messengers acting on Cdc42 and/or RhoG to activate the Arp2/3 complex 9

and to form F-actin-enriched membrane ruffles (trigger mechanism of invasion; (Norris et al., 10

1998, Zhou et al., 2001)) or by activating RhoA resulting in activation of Myosin IIA/B and a 11

stress-fiber-like dependent uptake of the bacteria (Hanisch et al., 2011, Hanisch et al., 2012). 12

The induced F-actin-enriched ruffles facilitate uptake of the bacterium into the host cell 13

(Hayward et al., 2002, Patel et al., 2005, Patel et al., 2006, Schlumberger et al., 2006)). 14

The interaction of S. Tm with its host cells has been extensively studied in the last decades. 15

However, due to the complexity of the host-pathogen interaction, studying invasion on the 16

single cell level has remained challenging. Conventionally grown cells show a high 17

variability in shape, morphology and physiology (Snijder et al., 2009), which makes 18

quantitative analysis of actin cytoskeleton reorganization during S. Tm invasion difficult, thus 19

leaving several important questions unanswered. 20

Up to date, the nature of the cellular source feeding induced actin polymerization remains 21

elusive. Are pre-existing F-actin fibers depolymerized to allow the formation of new 22

structures? Or alternatively, is newly polymerized F-actin formed from the soluble cytosolic 23

G-actin pool? Another question to be investigated is whether the local actin cytoskeleton 24

favors specific target site selection by S. Tm. Recent data suggests that bacterial near-surface 25

swimming is sufficient to explain target site selection in a three dimensional space. This 26

proposed “scanning mechanism” depends on flagella-driven motility and the hydrodynamic 27

entrapment occurring when motile bacteria encounter glass or cellular surfaces (Misselwitz et 28

al., 2012). Are these constraints also important for target finding on single cells? And how 29

does the local cortical actin cytoskeleton affect host cell target site selection? 30

Micropatterned cells that are grown on microfabricated, fibronectin-coated substrates of 31

defined geometry show a stable cellular form and reproducible internal organization of the 32

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actin and microtubule cytoskeleton (Thery et al., 2006) as well as of the trafficking 1

compartments (Schauer et al., 2010). As the cellular organization is constant between 2

independent micropatterned cells, subtle changes in cell morphology upon drug treatment 3

were detected in these cells that remained undiscovered in classical cell culture conditions 4

(Duong et al., 2012, Schauer et al., 2010). Thus, micropatterned cells may be a potent tool to 5

simplify the analysis of bacterial invasion. 6

To quantitatively analyze F-actin rearrangements triggered by S. Tm, we employed 7

micropatterned cells as an infection model for epithelial cell invasion. We found that S. Tm 8

invades micropatterned cells with similar kinetics as classical, unconstrained cultured cells. 9

Cell micropatterning allowed us however to "standardize" the infection process revealing the 10

source of de novo actin polymerization and to statistically compare the actin rearrangements 11

triggered by different S. Tm strains. Furthermore, we localized the docking and invasion sites 12

of S. Tm on micropatterned HeLa cells revealing no influence of the local actin cytoskeleton 13

on target site selection but a preference for a specific cellular height for infection and 14

indicating that S. Tm can induce a permissive entry site wherever it binds. 15

Results 16

Micropatterned cells as a model to study bacterial infection 17

To produce single cells that adopt a similar shape, morphology and physiology, epithelial 18

HeLa cells (clone Kyoto) were grown on coverslips harboring fibronectin-coated patterns. 19

The coverslips were produced by coating the surface of conventional glass coverslips with 20

poly(L-lysine)-g-poly(ethylene glycol) (PLL-g-PEG) and imprinting specific oxidized shapes 21

on this surface using photolithography by deep UV light (Azioune et al., 2011). Upon 22

incubation with fibronectin, the oxidized, “shaped” regions bind the fibronectin, while the 23

rest of the surface does not. Thus, as cells are only able to attach to the fibronectin, the 24

micropatterns force cells into a specific shape. We used the crossbow-shape micropattern that 25

promotes a “polar” intracellular organization of the actin cytoskeleton and endocytic 26

compartments (Thery et al., 2006, Schauer et al., 2010). As previously shown, contractile F-27

actin bundles formed at non-adhesive sides along the “bowstrings” and cortical actin 28

accumulated at the adhesive side along the “extrados” (Figure 1A, middle panel) of non-29

infected control cells. Micropatterned cells were infected with the wild-type strain of S. Tm 30

(Table S1, between 1 to 10 bacteria per cell, constant infection inoculum of ca. 6*106 31 Acc

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bacteria) harboring a plasmid constitutively expressing GFP (pM965; (Stecher et al., 2004)). 1

The infection was stopped at the indicated time points by fixation and compared to 2

“classically” cultured cells that were non-constrained (Figure 1A). To quantitatively measure 3

actin rearrangements induced in micropatterned cells, we averaged the intensity of F-actin 4

staining from many cells at different time points projecting the Maximum Intensity Projection 5

(MIP) of each aligned cell into an average intensity map (Figure 1A, right panel). At 5 min 6

post infection, F-actin-enriched ruffles appeared at the extrados and bowstrings sides, 7

intensified after 20 min of infection and disappeared between 20 and 120 min post infection, 8

similarly to non-patterned cells (reviewed in (Schlumberger et al., 2006)). Although strong 9

variations were observed at the single cell level, average F-actin intensity maps revealed a 10

clear difference between the analyzed time points (Figure 1, right panel). Indeed, average F-11

actin intensity maps showed that actin rearrangements peaked at about 20 min p.i., a fact that 12

was difficult to assess in unconstrained cells. Together our data reveal that S. Tm triggers 13

actin remodeling in micropatterned cells with similar kinetics as observed in classical cell 14

culture conditions (reviewed in (Agbor et al., 2011, Schlumberger et al., 2006)). 15

Additionally, we infected micropatterned cells by bacteria that use the “zipper” mechanism of 16

host cell invasion (Isberg et al., 1987), reviewed in (Steele-Mortimer et al., 2000) (Figure 17

1B). The zipper mechanism is characteristic for e.g. Yersinia pseudotuberculosis and Listeria 18

monocytogenes that express proteins on their surface, which bind to receptors on the host 19

cell’s surface (Cossart et al., 2004, da Silva et al., 2012). As a result, bacteria are taken up 20

through receptor-mediated “wrapping” of the host cell membrane around the bacterium, a 21

process involving much less actin rearrangements than is the case for the trigger mechanism. 22

To follow the entry through the zipper mechanism we infected micropatterned cells with a 23

Salmonella strain lacking a functional TTSS-1 and expressing Invasin from Y. 24

pseudotuberculosis (S. TmΔinvG, Invasin). As expected and similar to classical cell culture 25

conditions, average intensity maps of actin staining revealed that Invasin-coated Salmonella 26

did not trigger pronounced actin rearrangements even 20 min after infection (Figure 1B, right 27

panel). In order to investigate if other cell types that are grown on micropatterned coverslips 28

could be infected as well, we successfully challenged retinal pigment epithelial (RPE-1) cells 29

with S. Tmwt and S. TmΔinvG, Invasin as well as 3T3 fibroblasts with Shigella flexneri (trigger 30

mechanism) and Listeria monocytogenes (zipper mechanism; Figure S1). Together these data 31

showed that “standardized” infection into micropatterned cells can be used as a model for 32 Acc

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different cell types and different invasive bacteria employing either the trigger or the zipper 1

mechanism. 2

Newly polymerized actin in membrane ruffles is recruited from the soluble G-actin pool. 3

Next, we addressed which intracellular actin pool feeds the de novo polymerization within 4

membrane ruffles. Possibly, the actin might originate from the G-actin pool or from 5

depolymerization of distally located F-actin. We selectively analyzed crossbow-shaped, 6

micropatterned epithelial HeLa cells, which were infected along the “extrados” of the cell 7

(Figure S2A and Figure S2B). This allowed us to monitor whether contractile F-actin bundles 8

that accumulate along the “bowstring” site of the cell were disassembled during the course of 9

infection. To quantify changes in the local actin cytoskeleton we compared the normalized 10

intensity of the F-actin signal along the “extrados” (contractile F-actin bundles) and along the 11

“bowstring” (cortical F-actin) at different time points of infection. For this, MIPs of 12

crossbow-shaped cells infected along the “extrados” of the cell were calculated (Figure 2A). 13

The average F-actin fluorescence measured in the specific region was normalized to the 14

average F-actin fluorescence measured in the cytosol (Figure S2C) for each individual cell. 15

Analyses were performed for the indicated time points of infection and the data was plotted 16

for the “extrados” and “bowstring” regions. We found that the normalized intensity of F-actin 17

in the “extrados” region of the cell was significantly increased at 5 and 20 min of infection. 18

This is in line with the triggering of membrane ruffles along the “extrados”. The signal 19

returned to levels of non-infected cells at 2 hours post infection (Figure 2B). In contrast, the 20

normalized intensity of F-actin in the “bowstring” region did not change significantly over 21

the time course of infection (Figure 2C). These data demonstrate that the amount of 22

contractile F-actin fibers along the bowstrings remained unchanged while F-actin 23

polymerized into ruffles along the “extrados” part of the cell. 24

To assess F-actin levels in the “extrados”, we performed a similar analysis by focusing on 25

cells, which were infected only on one side of the “extrados” (Figure S2B and Figure S2C). 26

The “extrados” of these cells was divided into an “uninfected” and an “infected” area. We 27

then analyzed the intensity of the F-actin stain in the non-infected area. The intensity of the 28

F-actin in the non-infected “extrados” part remained constant over the time course of 29

infection (Figure 2D). This was in line with the data obtained for the contractile F-actin 30

bundles. 31 Acc

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In order to verify if indeed depolymerization of pre-existing F-actin is dispensable for the 1

formation of S. Tm-induced ruffles, we treated cells with the filament stabilizing agent 2

Jasplakinolide. Jasplakinolide is known to inhibit the depolymerization of F-actin within 3

minutes (Cramer, 1999). However, in vivo, high-dosed Jasplakinolide can also lower the 4

critical concentration of actin and promote nucleation of new filaments (Bubb et al., 2000). 5

Therefore, we treated HeLa cells either with 0.05 % DMSO or with increasing concentrations 6

of Jasplakinolide for 3 min. The cells were then infected with S. Tmwt and incubated for 7

another 5 min. The actin rearrangements induced were first studied in non-constrained cells 8

(Figure S3). At Jasplakinolide concentrations between 0.03 and 0.125 µM, the actin 9

cytoskeleton of uninfected cells remained unchanged. However, at higher concentrations of 10

Jasplakinolide, accumulation of aberrantly polymerized actin could be observed which 11

intensified in a dose-dependent manner (Figure S3). In cells treated with up to 0.125 µM of 12

Jasplakinolide (that did not overly alter the morphology of the actin cytoskeleton), S. Tmwt 13

was still able to induce pronounced actin ruffles (Figure S3). Interestingly, this was even true 14

at higher doses (Figure S3). Similar results were obtained in micropatterned cells treated with 15

0.06 µM Jasplakinolide (Figure S4). We found that bacteria infected Jasplakinolide-treated 16

cells induced pronounced actin ruffles as visualized by average intensity maps of many cells 17

(Figure S4B) and the significant increase of normalized intensity of F-actin in the cellular 18

“extrados” region 5 min post infection (Figure S4C). Hence our combined data indicated that 19

distally located pre-existing F-actin structures do not need to be disassembled in order to 20

permit S. Tm-induced ruffling, thus favoring the conclusion that the soluble G-actin pool is 21

the source for pathogen- triggered actin polymerization. 22

Net recruitment of soluble G-actin upon S. Tm-induced ruffling would predictively lead to a 23

lower total G-actin content. To test this prediction, we used a fractionation assay based on the 24

selective retention of cytoskeletal filaments, and release of their soluble protomers upon cell 25

permeabilization (Sellin et al., 2012). HeLa cells were infected with S. Tm, or for comparison 26

treated with Latrunculin B for 15 min, followed by gentle permeabilization and separation of 27

supernatant and cell pellet fractions (Figure S5). Latrunculin B is a G-actin-binding drug that 28

depolymerizes F-actin. The analysis revealed that S. Tm infection causes a significant 29

decrease in soluble actin levels (31.1±1.3% of total actin in supernatant vs. 43.5±0.8% in 30

control-treated cells). In contrast, a brief treatment with Latrunculin B resulted in the opposite 31

effect (59.3±3.2% of actin in supernatant). Thus, while lacking the single cell- and spatial 32

resolution of the present microscopy-based approach, a bulk biochemical assay supports the 33 Acc

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conclusion that S. Tm-triggered ruffles are formed by actin recruitment from the soluble G-1

actin pool. 2

Global actin rearrangements triggered by different S. Tm strains. 3

The scale of actin cytoskeletal rearrangements induced by different invasive S. Tm strains in 4

cultured cells is difficult to compare, as F-actin organization is strongly interrelated with the 5

cell morphology. Thus, we asked if micropatterned cells allowed an unbiased quantitative 6

comparison of actin rearrangements triggered by different strains of S. Tm which were 7

proficient in or displayed well defined defects in TTSS-1 triggered invasion (see 8

Supplementary Table S1). First we calculated the average intensity maps after 5 min of 9

infection (Figure 3, left panel). Based on these F-actin data, the cells could be divided into 10

three groups: Whereas non-infected cells and cells infected with a Salmonella strain 11

harboring no TTSS (SB161, S. TmΔinvG) or none of the four key effector proteins (M566, S. 12

TmΔ4) showed almost no actin ruffles, cells infected with a strain harboring SipA as the only 13

of the four key effector proteins (M516, S. TmSipA) showed slight but detectable actin 14

rearrangements. The wildtype strain (SB300, S. Tmwt) and a strain harboring only SopE out 15

of the four key effector proteins (M701; S. TmSopE) triggered pronounced actin 16

rearrangements (corresponding host-cell bound bacteria are shown in Supplementary Figure 17

S6). Therefore, the strains induced the same actin rearrangement activity in micropatterned 18

cells as observed earlier in non-constrained cells ((Ehrbar et al., 2002, Hardt et al., 1998, 19

Perrett et al., 2009, Higashide et al., 2002, Zhou et al., 1999b), validating our model as a 20

“standardized” alternative to study actin rearrangements. 21

In order to statistically compare rearrangements of the actin cytoskeleton induced by different 22

bacteria, the average intensity maps were transformed into density maps (Figure 3B) that can 23

be compared by a multivariate, two-sample test which was recently developed (Duong et al., 24

2012). Briefly, fluorescence signals from the actin staining were transformed into a cloud of 25

coordinate points by segmentation. Then, coordinates were replaced by Gaussian functions 26

(kernels) that were summed, revealing the underlying density of the F-actin throughout the 27

cell (Figure 3B). The red contours represent areas in which F-actin was most concentrated 28

and the yellow contours represent areas with the least F-actin concentration. Thus density 29

maps corresponded well to the average intensity maps. Density maps were compared with a 30

novel black-box statistical test (Duong et al., 2012). P-values of pairwise comparisons 31

confirmed the results obtained with the average intensity maps: The F-actin morphology of S. 32

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TmΔinvG and S. TmΔ4 did not differ significantly from non-infected cells. In contrast, S. TmSipA 1

significantly differed from non-infected cells (P-value for significance < 0.05; see Table 1). 2

Interestingly, the strain S. TmSopE also triggered strong actin ruffling, however these 3

rearrangements differed significantly from those induced by the wildtype strain S. Tmwt. 4

Thus, our analysis showed that SopE is not sufficient to trigger wildtype levels of ruffling. 5

Micropatterned cells therefore allow detailed analysis of actin rearrangements induced by the 6

presence of different effector proteins. 7

In a next step, we quantified the distribution of Rac1, a direct host cell target of SopE (Hardt 8

et al., 1998, Rudolph et al., 1999, Friebel et al., 2001). Average intensity maps of Rac1 were 9

determined in uninfected cells and in cells 5 min post infection with the wildtype strain S. 10

Tmwt or the “SopE-only” strain S. TmSopE. Both S. Tm strains showed relocalization of Rac1 11

to the cell periphery in response to bacterial infection (Figure S7A and S7B). Statistical 12

analysis of the corresponding Rac1 density maps confirmed that infection with S. Tmwt or S. 13

TmSopE modified the localization of Rac1 significantly as compared to non-infected cells 14

(Figure S7C). However, we did not detect any significant difference between Rac1 15

relocalization by S. Tmwt or S. TmSopE, suggesting that the Rac1 redistribution is mostly 16

attributable to SopE. 17

S. Tm docks at a specific “height” to host cells where it induces a permissive environment 18

for invasion. 19

Finally, we employed anisotropic micropatterned cells to investigate if local actin 20

cytoskeleton characteristics may affect S. Tm entry at specific cellular sites. Epithelial, 21

crossbow-shaped micropatterned HeLa cells were infected with S. Tmwt constitutively 22

expressing GFP (pM965) and the infection was stopped at the indicated time points of 23

infection (5, 20 or 120 min p.i.) by fixation. The cells were then aligned and MIPs of the 24

bacteria bound to the cells were made. The MIPs revealed that the bacteria accumulated at the 25

cell periphery without any apparent site preference around the entire cell (Figure 4A, left 26

panel). However, because of the high density of bacteria, MIPs may have failed to reveal 27

subtle site preferences. Thus, we used a density-based approach to reveal areas, in which 28

bacteria were concentrated during docking and subsequent invasion. Bacteria were 29

automatically detected in the 3D stack images taken from each infected cell and their point 30

coordinates were extracted using segmentation analysis. Again, point coordinates were 31

replaced by kernels and summed, revealing density maps of bacteria, in which red contours 32

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represent areas in which bacteria were most concentrated and the yellow contours represent 1

areas with least bacterial concentration (Figure 4A, middle panel). To our surprise, the 2

bacteria docked at the “bowstring” and the “extrados” with the same efficiency. Small 3

preferences were only observed for the edges. The latter is probably explained by the 4

spontaneous formation of small ruffles in these regions of the micropatterned cells. Such pre-5

existing ruffles are known to facilitate docking (Misselwitz et al., 2012). Additionally, we 6

calculated 3D density maps. The 50 % probability contour that represents the smallest 7

volume containing 50 % of all observed bacteria showed that bacteria always docked at the 8

same height of about 0.7 µm above the surface of the coverslip (see Figure 4A, right panel). 9

It has been shown recently that target site selection of S. Tm on cell layers is determined by 10

flagella-driven near-surface swimming (Misselwitz et al., 2012). In order to test if the 11

observed constant “binding height” on single patterned cells was linked to flagella-driven 12

motility, we compared the 3D density maps of the wildtype strain with that of different 13

mutants: A fully motile strain harboring no TTSS-1 (S. TmΔinvG, Figure 4B) was used to 14

investigate a possible role in binding site selection by the TTSS-1. To test flagella-driven 15

motility, we analyzed the S. Tm mutant strain S. TmΔinvGΔflgK that lacks the TTSS-1 and the 16

flgK gene (Figure 4C). S. TmΔinvGΔflgK is therefore devoid of functional flagella and is hence 17

non-motile. 3D analysis of the cellular distribution of the three strains revealed that although 18

the S. TmΔinvG strain showed a less constrained binding than the wildtype strain, most bacteria 19

were found around 0.7 µm above the cover slide. In contrast, S. TmΔinvGΔflgK did not show any 20

site preference on the host cell. Instead it was evenly distributed across the entire surface of 21

the host cell. Finally, we also analyzed the strain S. TmΔinvG, Invasin missing a TTSS-1 but 22

expressing flagella and Invasin from Y. pseudotuberculosis (Figure 4D). Using this strain, we 23

addressed the question if targeted binding at a specific height was dependent on receptor 24

availability on the cell or if it was simply due to swimming constraints and independent of the 25

receptor bound as well as the subsequent invasion mechanism (trigger vs. zipper mechanism). 26

The Yersinia-like S. Tm strain bound at the same specific height and with no site preference 27

to the cells (See Figure 4A and Figure 4D). Together these results suggest that target site 28

selection on individual host cells depends mostly on bacterial swimming. 29

Finally, we assessed the bacterial trafficking patterns after host cell invasion using time 30

course experiments. Analyzing bacterial localization of S. Tmwt and S. TmΔinvG, Invasin at 20 31

min after infection, we observed that bacteria moved towards the center of the cell (Figure 32 Acc

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4E). Interestingly, the preferred accumulation at cell edges was still evident at 20 min post-1

infection. Again, we did not observe any preference for cell regions either containing 2

contractile F-actin fibers or cortical F-actin. Within the first 20 min of infection, the bacteria 3

remained at the same height, i.e. 0.7 µm above the surface. Only later (2h post infection) they 4

showed a broader density distribution and localized near the nucleus (Figure 4E). Hence 5

bacteria seem to invade at the same focal plane as they dock and later move towards 6

particular juxtanuclear positions. 7

Discussion 8

The field of cellular microbiology has made tremendous progress in the last decades and the 9

invasion mechanisms of many different pathogens have been unraveled at appreciable detail. 10

Of particular importance for this achievement has been the use of microscopy to visualize the 11

processes, which take place upon bacterial entry (Ehsani et al., 2009). However, due to high 12

cell-to-cell variability in cell morphology, quantitative analysis of subtle changes and spatial 13

distributions of proteins and bacteria have remained challenging. In this study we used for the 14

first time micropatterned cells as a tool to study host-pathogen interactions in a quantitative 15

way. We show that micropatterned cells are invaded by bacteria using either the zipper 16

mechanism (Yersinia pseudotuberculosis, Listeria monocytogenes) or the trigger mechanism 17

(Salmonella Typhimurium, Shigella flexneri) and that the kinetics of infection are similar to 18

whose found in non-patterned cells. Thus, micropatterned cells represent an interesting 19

infection model, which allows quantitative analysis of bacteria-induced changes. 20

Furthermore, standardization of host cells allowed us to address questions in the infection 21

process of S. Tm, which could not be answered by conventional cell culture techniques. 22

Upon contact with its host cell, S. Tm induces extensive actin-containing membrane ruffles 23

((Finlay et al., 1991) reviewed in (Schlumberger et al., 2006)). Actin, which is polymerized 24

in these ruffles, could derive directly from the cellular G-actin pool, or alternatively, from 25

depolymerization of pre-existing distal F-actin structures. Upon Shigella flexneri infection, 26

bulk measurement of the ratio of G-actin versus total actin content has suggested that the 27

soluble G-actin pool decreases at early time points of infection. The authors therefore 28

concluded that newly formed actin filaments derive from de novo polymerization of the G-29

actin pool (Clerc et al., 1987). However, in this study, only the total and the soluble G-actin 30

could be measured. Therefore no spatial or single cell information of the different pools could 31 Acc

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be derived. Using micropatterned cells we studied the changes in the actin cytoskeleton 1

during infection with S. Tm in a spatially defined and quantitative manner. Our data reveal 2

that in epithelial HeLa cells the content of contractile F-actin fibers as well as of cortical F-3

actin in non-infected regions remain unaffected during the time course of infection while 4

pronounced F-actin structures (ruffles) were formed in the infected areas. Using a 5

biochemical assay, we could also confirm that the total G-actin pool decreases during the 6

time course of infection. Therefore, our combined data indicate that newly polymerized actin 7

in ruffles is recruited from the soluble G-actin pool. 8

S. Tm expresses several effector proteins that trigger actin rearrangements during invasion, 9

including SopE, SopE2, SopB and SipA. A mutant harboring neither SopE/E2 nor SopB (S. 10

TmSipA) cannot produce ruffles (Mirold et al., 2001, Zhou et al., 2001). However, SipA was 11

reported to interact directly with actin and to form F-actin foci (McGhie et al., 2001, McGhie 12

et al., 2004, Lilic et al., 2003, Zhou et al., 1999a, Zhou et al., 1999b). The average intensity 13

maps and statistical comparison of cells infected with S. TmSipA or the wildtype strain 14

demonstrated that SipA, in the absence of SopB, SopE and SopE2, induces small and local, 15

but clearly detectable actin rearrangements. Furthermore, we found that a strain harboring 16

SopE, but missing SopE2, SopB and SipA (S. TmSopE) induced strong actin rearrangements 17

that differed from those triggered by the wildtype strain, suggesting that different effectors 18

act together in a coordinated way during bacterial invasion. Micropatterned cells allow 19

studying even very subtle actin rearrangements and quantifying them by statistical methods, 20

opening the door to novel aspects of pathogen-triggered intracellular changes. We have here 21

shown that this analysis is not limited to actin, but can also be employed to quantify the 22

relocalization of other host factors such as Rac1. To the best of our knowledge, this is the 23

first time that Rac1 relocalization upon infection could be shown in a quantitative way, 24

paving way for a plethora of applications in the analysis of host-pathogen interactions, e.g. 25

the role of different host cell proteins in protein trafficking during pathogen invasion. As cells 26

can be transfected with plasmids or siRNA prior to patterning as well as treated with 27

inhibitors or stimulators after seeding their use in host-pathogen interaction studies can also 28

be extended to a wide array of applications. Fast and robust statistical analysis for the 29

distribution of host proteins and/or bacteria may enable whole-genome screening for host 30

proteins targeted by bacteria as well as bacterial effectors involved in host cell invasion and 31

subversion. Furthermore, micropatterned cells could be employed to characterize in detail the 32

function of candidate proteins from such screens. 33 Acc

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Further, we investigated target site selection of S. Tm. on micropatterned cells. Crossbow-1

shaped, micropatterned cells show a “standardized” anisotropy, as several proteins and 2

compartments are stereotypically localized (Supplementary Figure S8A and S8B). In addition 3

to the strong differences in the actin cytoskeleton along the “extrados” and the “bowstring” 4

sides, several organelles are organized in a polar fashion. The front of the crossbow shaped 5

cell (indicated in blue in Supplementary Figure S8A) harbors the Golgi apparatus and 6

multivesicular bodies/lysosomes that are orientated towards the adhesive “extrados”. 7

Furthermore, Rab6-containing secretory vesicles concentrate at adhesive areas (Schauer et 8

al., 2010). The cell rear (indicated in red in Figure S8A) harbors the nucleus (Thery et al., 9

2006). It has been described earlier that the exocyst complex is important to provide new 10

membrane for the formation of the actin ruffle induced upon S. Tm entry (Nichols et al., 11

2010). One would therefore predict that bacteria invade preferentially in the vicinity of the 12

exocyst complex. Surprisingly, we found no preference for S. Tm docking and entry either 13

for the cell front/rear or for regions harboring contractile F-actin bundles/cortical F-actin. 14

However, our data showed that S. Tm bound at a specific height. This site preference was 15

strongly dependent on the presence of functional flagella but independent of invasion 16

mechanisms: bacteria triggered their uptake into host cells through a TTSS-1 at a similar 17

height as bacteria using a zipper-mediated invasion mechanism, such as Yersinia 18

pseudotuberculosis, which binds to integrins (Isberg et al., 1987). Our data therefore strongly 19

suggests that physical constraints given by surface forces induced upon swimming are solely 20

responsible for docking site selection, thus enforcing recent findings from non-constrained 21

cell layers (See Supplementary Figure S8B; (Misselwitz et al., 2012)). 22

Similar to host cell binding, S. Tm did not show a site preference for invasion at the 23

“extrados” or the “bowstring” regions of the cell. Our data suggest that S. Tm employs 24

swimming motility in combination with the TTSS-1 in order to induce a permissive site 25

regardless of the underlying cytoskeletal structures. These data indicate that S. Tm can 26

subvert any part of the anisotropic cell cortex and transform it into a permissive site for 27

invasion. This remarkable property, together with the fact that S. Tm is able to target cells 28

due to near-surface swimming might to some extent explain the broad host cell and species 29

specificity observed for this pathogen. 30

Taken together, we could extend the current model of S. Tm invasion into HeLa cells (Figure 31

5): S. Tm swims in the medium, randomly hitting the bottom of the culture dish and then 32 Acc

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sliding along the surface by flagella-driven physical forces. Due to this sliding, S. Tm 1

encounters cells at a specific height where it docks, regardless of the underlying F-actin 2

structures, irreversibly via its Type III secretion system. S. Tm subsequently induces F-actin-3

rich ruffles by recruiting the soluble G-actin pool of the cell, leaving distally located F-actin 4

structures unchanged. The TTSS-1 dependent effector proteins work in a coordinated manner 5

to induce “wildtype-like ruffles”. S. Tm cannot only induce ruffles independent of the 6

underlying F-actin structures as well as the organization of the endoyctic system, but 7

remarkably can also induce a permissive entry site wherever it binds. 8

To the best of our knowledge, this is the first time that micropatterned cells were used for the 9

analysis of host-pathogen interactions. Our approach discloses a significant potential for 10

addressing topological and quantitative questions for the interaction of bacteria, viruses and 11

parasites with their host cells. 12

Material and Methods 13

Bacterial strains and plasmids 14

The S. Typhimurium strains used were isogenic derivatives of SL1344 (SB300) of S. enterica 15

subspecies I serovar Typhimurium (Supplementary Table S1; (Hoiseth et al., 1981)). Strains 16

M701 (S. TmSopE; (Muller et al., 2009)), M566 (S. TmΔ4 (Ewen et al., 1997)), SB161 (S. 17

TmΔinvG; (Kaniga et al., 1994)), M516 (S. TmSipA; (Schlumberger et al., 2007)) and M2424 (S. 18

TmΔinvG, ΔflgK; (Hoffmann et al., 2010)) have been described previously. Plasmids pM965 19

(Stecher et al., 2004) and pEGFP-C3/Rac1WT (Hage et al., 2009) have been described 20

previously. pCJLA-GFP and pHR355 (Invasin expression) were kindly provided by C. Jacobi 21

and H. Rüssmann, respectively. 22

Cell culture and bacteria growth conditions 23

HeLa cells (clone Kyoto) and 3T3 fibroblast cells were grown in DMEM (Gibco) 24

supplemented with 10% FCS (Omnilab) and 50 mg/l Streptomycin (AppliChem); retinal 25

pigment epithelial (RPE-1) cells (Invitrogen) were grown in DMEM/F12 (Invitrogen) 26

supplemented with 10% FCS and 50 mg/l Streptomycin (AppliChem) at 37 °C and 5% CO2. 27

The GFP-Rac1 HeLa Kyoto cells were stably transfected with plasmid pEGFP-C3/Rac1WT 28

and cultured in DMEM (Gibco) supplemented with 10% FCS (Omnilab) and 500 mg/l 29 Acc

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Neomycin (AppliChem) at 37 °C, 5% CO2. For the infection of HeLa/RPE-1/T3T cells, S. 1

Tm was grown in lysogenic broth (LB) supplemented with 0.3 M NaCl (Sigma) and 50 mg/l 2

Streptomycin (Appli-Chem) for 12 h at 37 °C and subcultured for 4 h. Shigella flexneri strain 3

M90T (Sansonetti et al., 1986) was cultured in BTCS medium. Listeria monocytogenes 4

EGDe strain (Buchrieser et al., 2003) was prepared as described (Dai et al., 1997). 5

Preparation of micropatterned coverslips, cell seeding, drug treatment and infection 6

The micropatterned coverslips were fabricated according to (Azioune et al., 2011). They 7

were incubated for 1 h with a mix of 5 µg/µl Fibronectin (Invitrogen) and 0.05 µg/µl 8

Fibrinogen-Alexa-647 (Invitrogen). The coverslips were then washed with fresh medium and 9

40’000 cells/coverslip were seeded onto them. After cell attachment for 20 min and washing 10

to remove unbound cells, adherent cells were incubated for full spreading for an additional 11

3.5 h prior to infection. For the experiments using Jasplakinolide (Enzo Life Sciences), cells 12

were treated 3 min prior to infection with the indicated concentrations of Jasplakinolide 13

dissolved in DMSO. 0.05 % DMSO was used as control that corresponded to the highest 14

amount of DMSO present in the Jasplakinolide titration. Cells were infected with bacteria at 15

different multiplicities of infection (to obtain between 1 and 10 bound bacteria per 16

micropatterned cell) for the indicated time points (5 min, 20 min and 120 min for S. Tm, 15 17

min for L. monocytogenes and S. flexneri) before fixation. Non-patterned HeLa cells were 18

seeded on glass coverslips 1 day before infection and infected in the same way as described 19

above. 20

Fluorescence staining 21

HeLa, RPE-1 or 3T3 cells were fixed with 4% (wt/vol) paraformaldehyde (PFA, Sigma) 22

supplemented with 4% sucrose for 15 min and permeabilized with 0.1% Triton X-100 23

(Sigma) for 5 min. Cells were blocked with 3% BSA (PAA) supplemented with 4% sucrose 24

(Sigma) and incubated with either TRITC Phalloidin (0.5 μg/ml, Sigma) or Alexa647 25

Phalloidin (3.5 μg/ml, Invitrogen) and DAPI (1 μg/ml, Sigma). The coverslips were mounted 26

with Mowiol (VWR International). 27

Image acquisition 28 Acc

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Microscopic acquisition of images was performed either with a Zeiss Axiovert 200M inverted 1

microscope equipped with an Ultraview confocal head (Perkin Elmer) and a krypton-argon 2

laser (643-RYP-A01 Melles Griot, The Netherlands) using a 100x oil immersion objective 3

(PLAN-Apochromat Zeiss) and z intervals of 0.2 µm or with a Zeiss 200M (inversed) 4

PALM-Microdissection widefield microscope with a 63x oil immersion objective (Zeiss) 5

with z intervals of 0.3 µm, followed by image deconvolution. 6

Image analysis 7

Average F-actin intensity maps were obtained by a modified ‘Reference Cell’ analysis (Thery 8

et al., 2006). The F-actin signals of 3D stacks of each cell were first reduced to a 2D 9

representation using Maximum Intensity Projection of all slides of the stack. All 2D images 10

of single cells from one condition were then assembled into one stack and aligned using the 11

corresponding fluorescent micropattern for each cell. Then, the average intensity of each 12

pixel over the assembled stack of aligned cells was calculated by an Average Intensity 13

Projection. A heat map (Fire Lookup Table) was applied to the Z-projected image to facilitate 14

examination and interpretation of the experimental results (the region with the most F-actin 15

being displayed in yellow). 16

The measurement of the fluorescence ratio of the actin-stain in different cell areas was 17

performed on cells that were only infected from the adhesive “extrados” side. Maximum 18

Intensity Projections were generated and the average intensity of the F-actin signal was 19

measured in specific, non-overlapping regions, namely the i) “extrados” side, ii) the 20

“bowstrings” side, iii) the center of the cell and iv) the non-infected “extrados” side (see 21

Supplementary Figure S2C). The ratios of the average F-actin fluorescence in the “extrados” 22

versus cell center and “bowstrings” versus center were calculated for each individual cell. 23

To visualize 2D bacterial distributions the 3D stacks obtained by microscopy were first 24

reduced to 2D using Maximum Intensity Projections. All 2D images of bacteria from one 25

condition were assembled into one stack and aligned using the fluorescent micropattern of 26

each cell. Then, a Maximum Intensity Projection of all slides of the aligned, assembled stack 27

was performed. 28

Density map analysis of F-actin structures and bacteria were performed according to (Schauer 29

et al., 2010). For the analysis of F-actin, average F-actin intensity maps (see above) were 30 Acc

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segmented with the multidimensional image analysis (MIA) interface running under 1

MetaMorph (Universal Imaging Corporation) based on wavelet decomposition (Racine et al., 2

2007). The segmentation was performed in 2D and the watershed function was applied to cut 3

long F-actin structures into several coordinates. For the analysis of bacteria, the fluorescence 4

signals recorded in the 3D stack of each cell were segmented in 3D. Fluorescent objects were 5

detected as fluctuations that are 100-fold larger than noise. The watershed function was 6

applied to separate bacteria in clusters. The coordinates of segmented bacteria were aligned 7

relative to the center of the corresponding micropattern. 8

The obtained coordinates from segmentation analysis (in 2D for F-actin and in 3D for 9

bacteria) were used to calculate corresponding density maps with a nonparametric, unbinned 10

kernel density estimator (Simonoff, 1996) programmed in the ks library (Duong, 2007) ks: 11

Kernel smoothing, R package version 1.8.10) in the open-source R programming language (R 12

Development Core Team. R: A Language and Environment for Statistical Computing. 13

Vienna, Austria). In brief, each coordinate was replaced by a normal distribution (kernel) and 14

summed over the entire population to be analyzed. For visualizing kernel density estimates 15

probability contours were used (Bowman, 1993, Hyndman, 1996). Contours represent the 16

smallest area (for 2D) or volume (for 3D) in which a percentage of all structures are found. 17

E.g. the 50 % contour represents the smallest area/volume in which 50% of all structures are 18

localized. Graphical representation in three dimensions was achieved using the rgl library 19

(Adler, D. & Murdoch, D. rgl: 3D visualization device system (OpenGL). R package version 20

0.84) and the misc3d library (Feng, D. & Tierney, L. misc3d: Miscellaneous 3D Plots. R 21

package version 0.6–1). To ensure robust statistics of bacterial entry sites, bacterial 22

coordinates were mirrored along the x-axis resulting in aggregated density maps for one half 23

of a symmetric cell that was duplicated to facilitate visualization. 24

P-value calculations 25

P-values between pairwise comparisons of different conditions were calculated according to 26

(Duong et al., 2012) using a completely automatic test programmed in the ks library (Duong, 27

2007) ks: Kernel smoothing, R package version 1.8.10) in R (R Development Core Team. R: 28

A Language and Environment for Statistical Computing. Vienna, Austria). 29

Actin partitioning in permeabilized cells 30 Acc

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HeLa cells were infected with S. Tm (MOI 200) or treated with 10µM Latrunculin B 1

(Calbiochem) for 15 minutes, washed, and subsequently permeabilized at 37⁰C in 10mM 2

phosphate/137mM NaCl/3mM KCl/1mM EDTA/0.2% saponin buffer supplemented with 3

protease inhibitor cocktail (Roche). The supernatant fraction was immediately transferred to 4

cold acetone (67% final concentration) and precipitated at -20⁰C. Supernatant (S) and cell 5

pellet (P) fractions were resuspended in equal volumes of western blot sample buffer, loaded 6

on 15% SDS-PAGE, and transferred onto nitrocellulose filter according to manufacturer's 7

recommendations (Bio-Rad). Western blot detection was performed with antibodies directed 8

against Beta-actin (AC40, SIGMA), Op18 (anti-SLEEIQ, see Sellin ME 2012), Calnexin (C-9

20, Santa Cruz Biotechnology), and appropriate secondary reagents. Autoradiogram band 10

intensities were quantified using Alphaview SA 3. 11

Acknowledgements 12

Many thanks to Tarn Duong for assistance with density calculations and Csaba Balazs of the 13

Light Microscopy Center Zürich as well as Jacques Laville and his team for help with the 14

data handling and analysis at ETH. We thank the participants of the EMBO Microscopy 15

course 2011 for help with Shigella flexneri and Listeria monocytogenes infection, Jost 16

Enninga for helpful discussions and critical reading of the manuscript and Alessandro Pioda 17

for help with the figures. P.V. and W.D.H were supported by the Swiss National Science 18

Foundation (310030-132997) and a grant (InfectX) to W.D.H from the Swiss SystemsX.ch 19

initiative, evaluated by the Swiss National Science Foundation. M.E.S. was supported by a 20

Swedish Research Council post-doctoral fellowship. K.S. received funding from the 21

Fondation pour la Recherche Médicale en France and Association pour la Recherche sur le 22

Cancer. This project was further supported by grants from Agence Nationale de la Recherche 23

(#2010 BLAN 122902), the Centre National de la Recherche Scientifique and Institut Curie. 24

Conflict of interest statement: A patent has been filed on the density-based two sample test. 25

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1

2

Figure legends 3

Figure 1: Infection of micropatterned cells with S. Tm follows the same kinetics as in non-4

patterned cells. A is depicting non-patterned (left panel) or patterned (middle panel) cells, which 5

were infected with the wildtype strain of S. Tm (S. Tmwt) and then fixed at the indicated time post 6

infection. The nucleus is depicted in dark blue, the actin cytoskeleton in red and the bacteria in green. 7

The average intensity maps of the F-actin distribution for N cells (right panel) were obtained by 8

projecting the Maximum Intensity Projection (MIP) of each aligned cell into an average intensity map 9

for the different time points post infection. The average F-actin distribution is represented with a heat 10

map, where the regions containing most F-actin are showing the brightest color (yellow). In B, cells 11

were infected with a Salmonella strain lacking a functional Type III secretion system 1 and 12

constitutively expressing Invasin from Yersinia pseudotuberculosis (S. TmΔinvG, Invasin). Infection in 13

non-patterned (left panel) and patterned (middle panel) cells as well as average F-actin maps (right 14

panel) for 5 and 20 min post infection are shown. Scale bars: 10 µm. 15

16

Figure 2: Newly polymerized actin in membrane ruffles is not recruited from pre-existing 17

distally located F-actin. In A, crossbow shaped micropatterned cells were infected for 5, 20 or 120 18

minutes prior to fixation. Infected cells were chosen, which were only infected from the “extrados” of 19

the cell (see experimental setup in Fig. S2). The average intensity maps of the F-actin distribution for 20

N micropatterned cells where obtained for non-infected cells and cells at different time points post 21

infection. The F-actin distribution is represented with a heat map, where the regions containing most 22

F-actin are showing the brightest color (yellow). In B-D, the quantifications of the F-actin intensity 23

changes in different regions of the cells are shown. The ratio between the average F-actin intensity in 24

“extrados” versus the average F-actin intensity in the cytoplasm (B) and the average F-actin intensity 25

in the “bowstring” part versus the average F-actin intensity in the cytoplasm (C) were calculated for 26

every cell for N cells at different time points post infection. In D, the average F-actin intensity of the 27

non-infected side of the “extrados” versus the F-actin intensity in the cytoplasm are compared to the 28

ratio of the average F-actin intensity of the “extrados” in non-infected cells to their cytoplasm. The 29

exact regions measured are depicted in Supplementary Figure S2. 30

31 Acc

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Figure 3: Actin rearrangements induced by different Salmonella strains at early time points of 1

infection. In A, the actin rearrangements in crossbow shaped, micropatterned cells were determined at 2

5 minutes post infection for different Salmonella strains using average intensity maps of the F-actin 3

distribution for N micropatterned cells. The F-actin distribution is represented with a heat map, where 4

the regions containing most F-actin are showing the brightest color (yellow). Average intensity maps 5

are ordered according to the degree of actin rearrangement from top (no F-actin rearrangements, 6

indicated in light green) to middle (little actin rearrangement, indicated in dark green) to bottom 7

(extensive actin rearrangements, indicated in light blue). In B, corresponding density maps are shown. 8

Density contours are shown from 10% (red) to 90% (yellow) and represent the smallest regions that 9

contain the percentage of analyzed structures. The colors for actin rearrangements are as in A, with an 10

additional group (dark blue) for cells infected with the wildtype strain showing specific actin 11

rearrangements which differ from cells infected by mutant strains. The groups indicated in B are 12

based on the P-value obtained by pairwise comparison of cells infected with different S. Tm strains 13

(see Table 1). 14

15

Figure 4: S. Tm binding and entry sites in micropatterned cells. In A-D, binding sites of S. Tm 16

wildtype strain (S. Tmwt, A) and strains lacking a functional type III secretion system 1 (S. TmΔinvG, B) 17

and either lacking a functional flagella (S. TmΔinvGΔflgK, C) or expressing Invasin from Yersinia 18

pseudotuberculosis (S. TmΔinvG, Invasin, D) were analyzed. Maximum Intensity Projections of N cells 19

were performed (left panels), n indicating the number of all bacteria bound to N analyzed cells. 20

Corresponding 2D density maps of the bacteria were calculated (middle panels). Density contours are 21

shown from 10% (red) to 90% (yellow) and represent the smallest regions containing the 22

corresponding percentage of all bacteria. Density maps were also calculated for 3D (right panels). The 23

50% density contour is shown visualizing the smallest volume in which 50 % of all bacteria are. In E 24

and F, the localization of wildtype bacteria (E) and the “Invasin” mutant (F) are shown. Composite 25

images of MIPs of bacteria at different time points (left panels) are shown. The corresponding 2D 26

(middle panels) and 3D density maps (right panels) show bacterial localization at different time points 27

post infection. The 50% density contour shown represents the smallest region/volume containing 50 28

% of all bacteria. Each time point is represented by a different color (see color legend). 29

30

Figure 5: A refined model of S. Tm host cell invasion as revealed by micropatterned HeLa cells. 31

S. Tm swims in the medium, eventually hitting the bottom of the culture dish and then sliding along 32

the surface by flagella-driven physical forces. Due to this sliding, S. Tm encounters cells at a specific 33 Acc

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height of about 0.7 µm. Bacteria harboring a TTSS-1 (a) dock irreversibly regardless of the 1

underlying actin cytoskeleton (cortical F-actin or contractile F-actin bundles) and induce actin-rich 2

ruffles from the soluble G-actin pool (indicated as blue dots) of the cell. The TTSS-1 dependent 3

effector proteins work in a coordinated manner to induce “wildtype-like ruffles” from the soluble G-4

actin pool, leaving distally located actin structures unchanged. Bacteria that do not harbor a TTSS-1 5

(b) bind at the same height via their fimbriae, with no preference for the “bowstring” or the “extrados” 6

regions, but do not trigger actin rearrangements. At later time points of infection, the F-actin pool 7

returns to a basal state, bacteria move to a juxtanuclear position and establish a Salmonella containing 8

vacuole (SCV). 9

10

Table 1: Comparison of the average F-actin density distribution of the different strains analyzed 11

in Figure 3. The P-values are calculated according to a density-based method as described in Duong 12

et al, 2012. 13

Sample 1 Sample 2 P-value Significance non infected S. TmΔT1 (SB161) 0.0549 ns non infected S. Tm∆4 (M566) 0.0860 ns non infected S. TmSipA (M516) 0.0001 **** non infected S. TmSopE (M701) 3.2238E-42 **** non infected S. Tmwt (SB300) 0 **** S. TmΔT1 (SB161) S. Tm∆4 (M566) 0.0852 ns S. TmΔT1 (SB161) S. TmSipA (M516) 0.6338 ns S. TmΔT1 (SB161) S. TmSopE (M701) 8.3434E-18 **** S. TmΔT1 (SB161) S. Tmwt (SB300) 9.8035E-160 **** S. Tm∆4 (M566) S. TmSipA (M516) 0.0033 **** S. Tm∆4 (M566) S. TmSopE (M701) 8.1568E-123 **** S. Tm∆4 (M566) S. Tmwt (SB300) 0 **** S. TmSipA (M516) S. TmSopE (M701) 1.2749E-21 **** S. TmSipA (M516) S. Tmwt (SB300) 0 **** S. TmSopE (M701) S. Tmwt (SB300) 3.0059E-80 ****

14

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N = 157

N = 153

N = 170

5 m

in p

i20

min

pi

120

min

pi

non-

infe

cted

A

B

N = 82

N = 133

5 m

in p

i20

min

pi

N = 188 min

max

N = 164

non- constrained micropatterned average of N cells

bowstr

ings

bowstr

ings

bowstrings

extrados

cmi_12154_f1.eps

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cmi_12154_f2.tif

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N = 126

non

infe

cted

S. T

mΔ

4S.

Tm

ΔT1

S. T

mSi

pAS.

Tm

wt

S. T

mSo

pE

N = 164

N = 93

N = 105

N = 80

N = 80

A Bmin

max

cmi_12154_f3.eps

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n = 250

n = 350

n = 1250

n = 1056

n = 250

n = 350

n = 1250

N = 94

N = 104

N = 137

n = 1056N = 156

n = 250

n = 350

n = 1250

N = 94

N = 104

N = 137

n = 1056N = 156

5 min20 min120 min

5 min20 min

A

F

S. TmΔT1, Invasin

S. TmΔT1

S. Tmwt

S. TmΔT1, flgK

S. Tmwt

S. TmΔT1, Invasin

B

C

D

50%

5 min20 min

50%

5 min20 min

n = 1056N = 156

N 104

n = 250N = 94

350

n = 1250N = 137

50%

5 min20 min120 min

50%

5 min20 min120 min

E

cmi_12154_f4.eps

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contractile actin bundles

cortical actin fibres

newly polymerized F-actin

Swimming and target finding- depends on physical forces

Binding and docking- is independent of underlyingpre-existing F-actin structures

Induction of ruffles- newly polymerized F-actin comes from G-actin pool- ruffling independent of underlying pre-existing F-actin structures

Uptake of the bacterium- bacteria invade without site preference

nucleus

nucleusnucleus nucleus

soluble G-actin

a)

wildtype (TTSS-1, Fimbriae)

TTSS, Fimbriae

nucleus

a)

a)

b)

a)

SCV

nucleus

top view

side view a)b)

a)

Δ

binding height of S. Tm

soluble G-actin

cmi_12154_f5.eps

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