Time-lapse microscopy reveals swimming of wild type S. Typhimurium near cellular surfaces

To study the initial interactions of S. Typhimurium with cellular surfaces, we employed time-lapse microscopy (supplementary Videos S1 and S2). HeLa cells, a commonly used tissue-culture model for studying S. Typhimurium invasion, were infected with S.Tm^Δ4 (SL1344 sopEE2B sipA; Table 1). S.Tm^Δ4behaves like S.Tm^wt in all aspects of the early host-cell interaction (steps i to v), but cannot trigger membrane ruffling or invasion as it lacks the key effector proteins SopE, SopE2, SopB and SipA [8], [9], [30].Therefore, S.Tm^Δ4 allowed us to focus on the initial surface interactions and docking (Fig. 1A).

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Table 1. Strains and plasmids used.

https://doi.org/10.1371/journal.ppat.1002810.t001

First, the initial surface interactions of S.Tm^Δ4were analyzed using time-lapse differential-interference contrast (DIC) microscopy (Fig. 1B).Strikingly, S.Tm^Δ4was swimming along the cellular surface for extended time periods (supplementary Videos S1 and S2). Our subsequent frame-by-frame analysis of the time-lapse videos identified several “stages” of this interaction:

1. “landing”: bacteria leaving the bulk medium to come into close proximity to the host;
2. “near surface swimming” (NSS), defined as a continuous movement along the surface of the host cell;
3. “stopping”:NSS is discontinued and bacteria remain at one particular spot on/near the host cell surface for some time(0.3 s≤stopping time≤remaining length of movie);
4. “take off”: bacteria leaving the cell surface (Fig. 1A,B; supplementary Videos S1, S2).

In most cases, bacteria went through all stages of interaction before taking off again. Please note that some bacteria stopped until the end of the time-lapse movie. In this experiment, we could not distinguish whether these bacteria were “docking” (i.e. bacteria that bound irreversibly, e.g. via the TTSS-1 apparatus [8], [9], [30]), or “stopping” (bacteria transiently stopping but continuing NSS or taking off after the end of the movie).Stopping did not happen randomly but occurred frequently at “obstacles” encountered during NSS. In particular, S. Typhimurium stopped and docked at cells with a round morphology(i.e. a mitotic cell; see supplementary Video S1). This provided a first indication that a transient stop may “preselect” specific sites for subsequent docking of S. Typhimurium. This would be in line with the preferential docking of S. Typhimurium onto mitotic cells observed in earlier studies ([31], [32], see below).

For a quantification of these initial bacteria surface interactions we used S.Tm^Δ4harboring a plasmid conferring constitutive gfp expression (pGFP;Table 1) and time lapse fluorescence microscopy. This allowed precise quantification of all stages of the bacteria surface interaction including landing and take-off, since the fluorescent bacteria moving out of the focus layer appear as “rings” in the movie (see T0s in Fig. 1C). Hence, the “landing” stage was defined as the time between the first detection of a “ring” (followed by a continuous downward movement) and the change of the direction and speed typically observed when NSS started(T0–0.4 s in Fig. 1C). In analogy, the “take off”-stage describes the time between the end of stopping or NSS and the disappearance of the “ring” (T41.9–42.3 s in Fig. 1C). Additionally, we tracked the time spent stopped or engaged in NSS. Take-off and landing occurred within <3.1 and <4.2 s, respectively (median 0.4 s for both; Fig. 1D), while the time engaged in NSS was significantly longer (median 1.5 s; range 0.3–41.5 s; Fig. 1D). Overall, the bacteria covered significant distances swimming along the host cellular surface(6.7–325 µm; see also below).Furthermore, we observed a variety of “behaviors” with respect to stopping.33% of all imaged S.Tm^Δ4(pGFP) bacteria did never stop, while 67% made one or more stops (“NSS and stop”; Fig. 1E). Some bacteria stopped up to 5 times on the cell surface and some S.Tm^Δ4remained stopped (or docked)until the end of the imaging experiment, with the longest observed stop lasting 280 s (Fig. 1F).

Overall, the bacterial density at the cellular surface was significantly increased compared to the overlaying media (Fig. 1G).Accumulation of bacteria at the cellular surfaces was not attributable to gravity, as indicated by a comparison between motile (affected by gravity and NSS) and non-motile bacteria (affected only by gravity; suppl. Fig. S1). Much rather, flagella-driven NSS and ensuing stops accounted for the increased local S. Typhimurium density at the host cell surface. This suggested that the prolonged contact time might contribute to the target site selection by S. Typhimurium.

Fim-adhesins and TTSS-1 do not affect the initial surface interactions, i.e. landing, NSS, stopping or take-off

In order to determine the role of bacterial adhesins in the initial phases of the surface interaction, we analyzed two S. Typhimurium mutants, S.Tm^-T1 (SL1344 invG) and S.Tm^-T1-Fi (SL1344 invG fimD; Tab. 1). These mutants lack one or two surface structures, namely type 1 fimbriae and the TTSS-1 apparatus, which are known to mediate reversible binding and irreversible docking of S. Typhimurium to HeLa and other host cells [8], [9], [30]. However, a role of these adhesins for near surface swimming or stopping had not been addressed.

HeLa cells were infected with S.Tm^-T1(pGFP) and S.Tm^-T1-Fi(pGFP) and transient interactions were monitored by time lapse fluorescence microscopy as in Fig. 1. All analyzed parameters, including the number of stops, were indistinguishable from those of S.Tm^Δ4(Fig. 2A–D; compare to Fig. 1). These results indicated that none of the initial surface interactions were affected by TTSS-1or by fimD(Fig. 2A,C). Strikingly, this also pertained to the transient stops and clearly distinguishes the initial surface interactions from later stages of the infection, i.e. reversible binding and docking. Stopping thus seems to be attributable to a different mechanism.

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Figure 2. S. Typhimurium NSS does neither require adhesins nor chemotaxis.

Quantitiative analysis of the initial host-cell interaction byS.Tm^-T1(pGFP), S.Tm^-T1-Fi(pGFP) and S.Tm^ΔcheY(pGFP), lacking either the TTSS-1 apparatus,TTSS-1 and type 1 fimbriae or the four effectors SopE, SopE2, SopB and SipA as well as the protein CheY, essential for chemotaxis. (A), (C), (E)Different stages of NSS were quantified as described for Fig. 1D.The following numbers of bacteria were analyzed: S.Tm^-T1(pGFP), n = 114; S.Tm^-T1-Fi(pGFP),n = 91;S.Tm^ΔcheY(pGFP), n = 39.Time-lapse microscopy data was derived from 2 to5independent experiments. (B), (D), (F) Numbers of stops by an individual bacterium were quantified as described in Fig. 1E.

https://doi.org/10.1371/journal.ppat.1002810.g002

Nevertheless, as indicated by earlier data [8], [9], [30],some of the stops must result in reversible binding and docking. To estimate the relative frequency of stopping and of the docking events, the total number of stops observed on the cell layer ( = the number of potential docking events), during a 5 minute period was calculated: the total number of S.Tm^Δ4bacteria landing on the cell surface during a 5 min period was multiplied by the number of stops using the values determined in as Fig. 1E.After the end of the5-minute real time imaging experiment, the cells were washed, fixed and stained for DNA (DAPI), actin (TRITC-phalloidin) and S. Typhimurium (anti-LPS antibody).This protocol removed all “stopped” bacteria, while “reversibly bound” and “docked” bacteria remained on the cells and were enumerated by fluorescence microscopy [8], [9], [30]. Comparing the results from both types of analysis revealed that no more than 1–2% of the total stops (as detected by time lapse microscopy) resulted in a docking event. Hence, transient stops are approx. 50- to 100-fold more frequent than docking events, at least in the 5-minuteinfection experiments that we have performed, here.

In conclusion, these data established that NSS requires neither TTSS-1nor type I fimbriae. The initial pathogen host cell interaction is thus clearly distinct from subsequent stages, i.e. reversible binding and docking.

S. Typhimurium near surface swimming does not require chemotaxis

Bacterial flagellar movement is guided by chemotaxis. It had remained unclear, if NSS was dependent on chemotaxis or whether non-directed motility would suffice. To address this issue, HeLa cells were infected with S.Tm^{Δ4 cheY}(pGFP) (SL1344 ΔsipAsopBEE2 cheY; Table 1). This isogenic mutant is a “straight-swimmer”, expresses wild type numbers of functional flagella, but cannot swim along chemical gradients. The initial surface interactions were monitored by time lapse microscopy as described in Figs. 1 and 2A–D. All analyzed parameters were indistinguishable from those ofS.Tm^Δ4, S.Tm^-T1 or S.Tm^-T1-Fi(compare Fig. 2E,F to Fig. 1D,E and Fig. 2A–D). Therefore, in our tissue culture assays non-directed motility was sufficient for facilitating the initial surface interactions.

Are physical forces sufficient for explaining the initial surface interactions of S. Typhimurium?

Our data suggested that basic physical principles may explain the initial interactions of S. Typhimurium with host cellular surfaces. In this case, the movement patters on cellular and acellular surfaces should be quite similar. Therefore, we inoculated glass-bottom tissue culture dishes seeded with HeLa cells (or not) with S.Tm^Δ4(pGFP). The bacterial surface interactions were analyzed by time-lapse fluorescence microscopy as described in Figs. 1 and 2. S.Tm^Δ4(pGFP) performed NSS with equivalent speed on glass and on cellular surfaces (approx. 30 µm/s; Fig. 3A). However, the median duration of an episode of NSS(6.3 s on glass vs. 2.95 s on cells; Fig. 3B) and the median distance travelled during this time (221 µm on glass vs. 95 µm on cells; Fig. 3C) were slightly larger on glass than on the cellular surface. These observations were in line with our hypothesis that the initial surface interactions may be governed by equivalent physical principles.

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Figure 3. NSS of S. Typhimurium on cellular and artificial surfaces.

Quantifications of time and distance of S.Tm^Δ4(pGFP) swimming along the bottom of a glass dish, with or without seeded cells. (A–C) Comparison of the speed, the time spent in NSS, or the distance travelled on a cellular and a glass surface, respectively. The figure summarizes 17–22 bacteria per condition from 2 independent experiments. ***: p<0.0001. (D), (E) overlay of all frames (maximum intensity plot; Volocity software) from a 5-minute movie (10 frames/sec; Leica DMI-6000B, 20× air objective 0.7 NA, 2 fold optovar) in the GFP channel of S.Tm^Δ4(pGFP) movement on a glass-or a cellular surface, respectively. Note the shorter and more linear tracks on the cellular surface. Scale bar: 57 µm.

https://doi.org/10.1371/journal.ppat.1002810.g003

Strikingly, the bacteria followed “right-handed” curved tracks on glass and, to a lesser extent, on cellular surfaces (Fig. 3D,E). It is thought that this curvature of the NSS tracks is attributable to the shear force between the flagella-mediated rotation of the bacterial body and the respective surface [28], [33].

So far, our data were in line with the hypothesis that general physical principles are responsible for NSS on cellular and on glass surfaces. In this case, other types of host cells or motile bacteria should yield similar results. Therefore, we have extended our analysis to MDCK cells, a commonly used polarized epithelial cell line, and E. coliNissle which was transformed with the GFP expression plasmid (E.coliNissle (pGFP); Suppl. Fig. S2). Both, E.coliNissle (pGFP) and S.Tm^Δ4 (pGFP) engaged in NSS and displayed similar movement patterns on MDCK, on HeLa cells and on glass surfaces (Fig. 3D,E and suppl. Fig. S2A–D). Nevertheless, the shape of the NSS-tracks may differ slightly. This might be attributable to different morphological features displayed by the different types of surface and represents an interesting topic for future research.

In conclusion, these data lend further support to the notion that initial surface interactions are governed by general physical principles and suggest that near surface swimming might be a general strategy for target selection employed by different flagellated bacteria.

During NSS,S. Typhimurium stops preferentially at topological obstacles

Analysis of our movies so far suggested that during NSS, rounded cells (e.g. dividing cells) represent preferential sites for stopping. We hypothesized that bacterial stopping can be explained by the prominent topological features of these rounded cells. If so, bacteria should also stop at artificial topological obstacles and bacteria should accumulate at such sites. This was tested in two different ways, i.e. in a simplified experimental setup and by a computer simulation (see below).

In order to experimentally test our hypothesis, we have analyzed the initial surface interactions of bacteria with small glass beads which were placed as artificial obstacles onto a glass surface. Glass-bottom tissue-culture dishes harboring glass beads (Ø = 500 µm) were therefore inoculated with S.Tm^wt(mCherry) and bacterial movement patterns were analyzed by time-lapse fluorescence microscopy, as described in Figs. 1, 2 and 3. Again, the bacteria were moving for long distances along the glass surface, but were stopping in the immediate vicinity of the glass beads (Fig. 4A, B; suppl. Video S3). 45–70% of these bacteria continued to swim or took off again before the end of the movie, indicating that these were truly stopping (not binding/docking). Thus, topological obstacles can facilitate site-specific stopping of bacteria engaged in NSS.

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Figure 4. Swimming behavior of S. Typhimurium on gelatine coated glass beads.

Gelatine coated glass beads (500 µm diameter) were placed into a glass-bottom dish and the swimming behavior of mCherry expressing bacteria was recorded by time lapse fluorescence microscopy as described in Fig. 3. (A) Illustration of a bacterium stopping at the glass bottom of a coated glass bead (illustration not to scale). (B) Maximum intensity plot (ImageJ) superimposing all frames of a 15 sec movie acquired at 20 frames per second (300 frames total; suppl. Video S3) illustrating the movement of S. Typhimurium (S.Tm^wt(mCherry)) within the vicinity of the bead. Scale bar: 10 µm.

https://doi.org/10.1371/journal.ppat.1002810.g004

In addition, these data allowed a rough estimation of the “altitude” at which S. Typhimurium swims above the glass surface. Based on the geometry of the glass bead, the center of the bacterial cell was 0.43+/−0.07 µm away from the surface. Similar results were obtained for beads with smaller diameters (150 and 30 µm, respectively). The rod-shaped S. Typhimurium cell has a radius of approx. 0.5 µm. Therefore, most of the “distance” is attributable to the bacterial cell and the NSS “altitude” is most likely <150 nm above the glass surface. These observations were in line with earlier work [27], [28], [29], [34] describing bacterial NSS.

Modeling of NSS-driven target site selection by S. Typhimurium

The data presented above indicated that NSS affects target cell selection in two ways: by increasing the local pathogen density on surfaces and by enhancing the probability of surface contacts (stopping) at topological obstacles projecting from this surface. A computer simulation was used to verify whether these two phenomena are sufficient for explaining target site selection.

We modeled the interaction of S. Typhimurium with a three dimensional landscape consisting of a flat surface and one spherical obstacle, partially submerged into the surface (Materials and Methods). The particles (motile, but non-chemotactic “bacteria”) were introduced and moved linearly within the 3D virtual space above the surface. Upon contact with the sphere, the particles were either “stopping” (10% likelihood) or reflected (90% likelihood). Three different scenarios were analyzed with respect to the particles encountering the flat surface. (1) the “random” scenario: particles were reflected back into 3D space and randomly assigned a new direction of movement. (2) the “billiard” scenario: particles were reflected with an angle of reflection identical to the angle of impact. (3) the “NSS” scenario: particles encountering the surface did not leave but followed the surface via NSS. If the sphere was encountered during NSS, the particles stopped at this site with a likelihood of 10%.

In the NSS scenario (scenario 3) but not the random or the billiard scenario, particle accumulation occurred right at the topological obstacle (Fig. 5). Therefore, NSS and stopping at physical obstacles are sufficient for explaining the target site selection observed in simplified model systems (Fig. 4A, B) and tissue culture infection experiments (Fig. 1, [31], [32]).

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Figure 5. Simulation of S. Typhimurium targeting to physical obstacles.

(A) To simulate S. Typhimurium targeting to physical obstacles, a round “cell” (light blue sphere) and linearly moving “bacteria” (orange particles) were modeled in an in silico experiment. If during the simulation a bacterium hits the sphere, it is either deflected or has a 10% chance of remaining at that site (“stopping”, red particles). Behavior of particles on the base of the cage ( = “surface”) and upon hitting the sphere if no docking occurred differed according to the respective scenario: Reflection with a random angle in the “random” scenario, with an angle of reflection identical to the angle of the impact in the “billiard” scenario and no reflection but movements following the surface of the object in the “NSS” scenario. The start of the simulation is depicted in the far left. Inserts show an enlarged view of the area around the sphere.

https://doi.org/10.1371/journal.ppat.1002810.g005

Docking – as observed in standard infection assays – displays the same target site preference as NSS-mediated stopping

So far, our analyses of the target site preference of S. Typhimurium had focused on the initial surface interactions, i.e. landing, take-off, NSS and stopping. Next, it was important to establish how these initial interactions may affect the subsequent steps of the infection process, i.e. docking. If stopping increases the probability of binding and irreversible docking at the respective site, stopping (as observed by time lapse microscopy; Fig. 1, 2) and docking should display an equivalent target site preference.

To assess the target site preference of binding/docking on host cell layers quantitatively, we have employed a well-established “standard” infection protocol [9], [32]. HeLa cells were infected for 6 min with S.Tm^Δ4 at the indicated m.o.i., washed gently, fixed and stained (see Materials and Methods). As binding/docking occurred approximately 50-fold less frequently than stopping (see above), we employed higher multiplicities of infection than in the time lapse microscopy experiments. Visual inspection indicated that S. Typhimurium has a pronounced targeting preference for rounded cells (Fig. 6A, top panel; “mitotic” nuclei with condensed DNA highlighted in yellow). In particular, the bacteria were found to dock to the base of rounded cells (suppl. Fig. S3A). In order to quantify this phenotype, we employed automated fluorescence microscopy, and an automated image-analysis routine (Materials and Methods).

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Figure 6. Motility determines the targeting preference of S. Typhimurium for mitotic cells.

HeLa cells were infected with S.Tm^wt or S.Tm^ΔfliGH at the indicated m.o.i. for 6 min either without centrifugation (A, C) or including a centrifugation step for 10 min at 500 g (B, D), followed by staining of nuclei, extracellular bacteria (anti-LPS antibody) and automated microscopy. Using an automated analysis algorithm, nuclei and individual bacteria were detected and mitotic nuclei identified. (A, B). Representative images are shown for infections at an m.o.i. of 125. Nuclei are shown in blue, extracellular bacteria in green. The red arrows indicate mitotic nuclei. For selected parts of the image (white boxes)the automated detection of nuclei and bacteria is shown (see inserts). Please note, that our image analysis algorithm is not able to distinguish all bacteria in highly crowded regions (S.Tm^Δ4 and centrifugation, not shown); therefore, docking onto mitotic cells might be underestimated and our quantitative analysis below would be a conservative estimation. Scale bar: 40 µm. (C, D) Infection efficiency at the respective m.o.i. with respect to the number of docked bacteria per infected cell (non-mitotic cells, light grey; mitotic cells, dark grey). Each bar represents the median. The error bars represent the standard deviation of 6 replicas from 3 independent experiments. Approximately 5000 cells per replica were analyzed. *p<0.05, **p<0.01 (Mann-Whitney-U test). For the immotile strain(S.Tm^{Δ4 fliGHI}; no centrifugation), we did not detect enough bacteria on mitotic cells for a reliable statistical analysis.

https://doi.org/10.1371/journal.ppat.1002810.g006

Rounded cells were targeted (docked) with significantly higher efficiency than non-dividing cells (Fig. 6C, left panel).This targeting preference for rounded cells was also observed by manual quantification (S.Tm^Δ4; suppl. Fig. S3B) and in infection experiments with S.Tm^wt (data not shown). These findings were quite similar to our observations during the initial surface interactions (Figs. 1 and 2).Rounded mitotic cells seem to represent topological obstacles within the cellular landscape. In fact, the rounded mitotic cells were significantly “higher” than the interphase cells (12±2 µm vs. 5.4±1.1 µm; n = 20 each; p<0.0001, Mann-Whitney-U test). Taken together, NSS-driven stopping and binding/docking displayed equivalent preferences for topological obstacles like rounded cells.

Next, we have addressed the role of flagellar-driven motility. It is well established that flagella are required for cellular invasion of S. Typhimurium [15], [16], [17], [18], [19]. The data presented above suggested that flagella-driven NSS might determine the target site preference. In order to study the role of bacterial motility in the targeting preference of binding/docking, we analyzed the host-cell interaction patterns of the non-motile mutant S.Tm^{Δ4 fliGHI} (SL1344 fliGHI) which does not express flagella. This mutant and other non-motile S. Typhimurium mutants did not dock efficiently at all (Fig. S4). Importantly, the few HeLa cells that were infected harbored just one bacterium, even at high m.o.i. (Fig. 6A, bottom panel; Fig. 6C, right panel). In line with our hypothesis, S.Tm^{Δ4 fliGHI} did not display a targeting preference for rounded cells (Fig. 6A,C).

In order to increase the chances of a host cell encounter by S.Tm^{Δ4 fliGHI}, equivalent infection experiments were performed applying mild centrifugal force (500 g for 10 min) to increase the collision rate between non-motile S.Tm^{Δ4 fliGHI} and the host cells (Materials and Methods). This strategy is commonly used for “rescuing” invasion defects of non-motile S. Typhimurium mutants, (e.g. [16], [35]). As expected, centrifugation increased the number of binding/dockingS.Tm^{Δ4 fliGHI} in our assay (Fig. 6B, data not shown). Nevertheless, S.Tm^{Δ4 fliGHI} did not display any target preference for the rounded (mitotic) cells, even at the highest m.o.i., tested (Fig. 6D; right panel). This was in line with our hypothesis that flagella-driven NSS determines not only the targeting preference of “stopping”, but also that of binding/docking.

Finally, we analyzed the role of chemotaxis, i.e. directed motility along chemotactic gradients. S.Tm^{Δ4 cheY} is an isogenic and motile mutant incapable of chemotaxis (compare Fig. 2E, F). This mutant yielded equivalent results as S.Tm^Δ4including a highbinding/docking efficiency and a pronounced targeting preference for rounded cells (data not shown). Taken together, these results suggested that preferential infection of rounded cells is attributable to simple non-directed motility and that chemotaxis was not required, at least in this simple tissue culture model.

Altering the host cellular morphology affects the initial surface interactions and docking

During NSS, stopping and docking occurred preferentially at rounded cells. If targeting was indeed dictated by topological obstacles, manipulation of the host cell morphology should affect both, the initial surface interactions and docking by S. Typhimurium. To test this hypothesis, we manipulated the host cellular morphology with cytochalasin D (Cyt. D). This drug depolymerizes actin filaments causing the cells to round up.

First we analyzed the effect of cytochalasin D treatment on binding/docking. HeLa cells, pretreated with the indicated concentration of cytochalasin D for one hour, were infected for 12 minutes with S.Tm^Δ4(pGFP), S.Tm^-T1(pGFP) or S.Tm^-T1-Fi(pGFP) at the indicated m.o.i..Afterwards, cells were fixed and stained and we analyzed the actin-based cytoskeleton and bacterial docking (Materials and Methods). Since the automated evaluation now focused on the percentage of infected cells (not the number of individual bacteria per infected cell), overlapping but somewhat higher m.o.i.s were used than in Fig. 6. As shown in Fig. 7A, HeLa cells were partially rounded at 2 µM and fully rounded at 10 µM cytochalasin D. All three S. Tm strains bound/docked to the rounded cells with an increased efficiency (Fig. 7B). Equivalent results were obtained with another actin-disrupting drug (latrunculin B; suppl. Fig. S5). This verified that the host cell topology has a profound effect on the target site preference of binding/docking by S. Typhimurium.

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Figure 7. Cellular morphology affects docking of S. Typhimurium.

(A) Cells were incubated with the indicated concentration of cytochalasin D for one hour prior to infection withS.Tm^Δ4(pGFP) at an m.o.i. of 125 for 10 min. Afterwards, cells were fixed as described in Fig. 6 and stained with DAPI and TRITC-phalloidin (stains actin). Stacks of confocal images were acquired in the actin channel (grey) and the bacteria channel (green). An extended focus projection and a reconstruction of a zx-layer are shown. Scale bar, upper images: 20 µm, zx-layer: 5 µm. (B)Cells were pre-treated with the indicated concentration of cytochalasin D for 1 hour and infected with the respective S. Typhimurium strain for 12 min. After washing, fixing and staining S. Typhimurium docking was quantified by an automated microscopy based docking assay [9]. The curves shown summarize 3 independent experiments. Error bars: standard deviation.

https://doi.org/10.1371/journal.ppat.1002810.g007

Next, we analyzed the effects of the cytochalasin D treatment on the initial surface interactions. HeLa cells pretreated with 10 µm cytochalasin D were infected with S.Tm^Δ4(pGFP), S.Tm^-T1(pGFP) or S.Tm^-T1-Fi(pGFP) and landing, NSS, stopping and take-off were analyzed by time-lapse microscopy as described in Fig. 1 and Fig. 2. Strikingly, on the cytochalasin D-treated cells, nearly all bacteria engaging in NSS made at least one stop. Only very few bacteria displayed uninterrupted NSS (“NSS only”; Fig. 8A, D, G). This was quite different from our data on untreated HeLa cells where 33% of all bacteria displayed uninterrupted NSS (see Fig. 1E). Furthermore, on cytochalasin D-treated cells, the time spent at each stop was significantly longer and some of these bacteria “stopped” for the entire course of the 5-min experiment (Fig. 8B,E,H). In a similar analysis, the fraction of time thatS.Tm^Δ4(pGFP), S.Tm^-T1(pGFP) or S.Tm^-T1-Fi(pGFP) spent stopping at the host-cell surface was significantly higher than in untreated HeLa cells. In fact, on the cytochalasin D-treated cells, a majority of the bacteria spent most of their time “stopping” (“only stoppers”; Fig. 8C,F,I). Again, no differences were observed between S.Tm^Δ4(pGFP), S.Tm^-T1(pGFP) and S.Tm^-T1-Fi(pGFP), confirming that “classical” adhesins do not significantly affect these transient initial bacteria-host cell interactions. In conclusion, the shape of host cells has a profound effect on initial surface interactions and binding/docking. This provides further evidence that NSS leads to a target site preference for physical obstacles.

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Figure 8. Host cell morphology affects the NSS pattern of S. Typhimurium.

HeLa cells were either treated with 10 µM cytochalasin D or mock-treated and infected with S.Tm^Δ4(pGFP), S.Tm^-T1(pGFP) or S.Tm^-T1-Fi(pGFP), as indicated at an m.o.i. of 1.5. The infection process was imaged for 5 min by time-lapse fluorescence microscopy. Data for bacteria without cytochalasin D were taken from Fig. 1D–F and Fig. 2A–D and are displayed for comparison. (A), (D), (G) Fraction of bacteria making no stops or the indicated number of stops on the cell layer (compare Fig. 1E). The effect of cytochalasin D on the number of bacteria with no stops vs. one or more stops was statistically significant for all 3 strains (Fisher's exact test). (B), (E), (H) Cumulative time a single bacterium spent stopped over the entire duration of the contact with the cell layer. (C), (F), (I) Fraction of the contact time that a given bacterium spent stopped; the remaining percentage of time was spent in NSS. *: p<0.05; **: p<0.01; ***: p<0.0001.

https://doi.org/10.1371/journal.ppat.1002810.g008

Membrane ruffles represent physical obstacles mediating stopping, binding and docking

Membrane ruffles triggered by the TTSS-1 virulence system represent a well-known topological obstacle encountered on infected cell layers [36], [37], [38], [39]. We hypothesized that membrane ruffles might enhance local stopping, binding and docking at pre-existing ruffles, thus leading to cooperative invasion.

To study the targeting of ruffles, we have focused on binding and docking. These two steps of the infection process can be analyzed using “standard” fluorescence microscopy assays [9], [32]. First, we employed a co-infection strategy using a “helper strain” and a “reporter strain”. S.Tm^SopE (without plasmid) was chosen as the helper strain, since its effector SopE is able to trigger pronounced membrane ruffling [5], [32]. S.Tm^SopE carries deletions of the effectors sipA, sopB and sopE2, thus eliminating confounding pleiotropic actions of these effectors on the host cell. SopE-induced ruffles appeared within 5 minutes, are known to have a prominent shape, and represent a large physical obstacle on an otherwise much flatter cellular surface (Fig. 9A; [40]). S.Tm^Δ4 (pGFP), which does not trigger ruffles itself, was used as a “reporter” to assess docking to the ruffles. In a time lapse microscopy experiment employing DIC and fluorescence imaging both strains engaged in NSS and stopped frequently at ruffles (Fig. 9B; supplementary VideoS4; data not shown).

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Figure 9. Ruffles are a site of cooperative invasion.

(A) 3D-reconstruction of ruffles on HeLa cells infected for 6 min with S.Tm^SopE(pGFP) at an m.o.i. of 250. The actin channel (phalloidin stain) is shown in grey, S. Typhimurium in green (GFP) and extracellular bacteria in red (anti-Salmonella LPS stain before permeabilization). 3 views of a typical, large ruffle (ruffle 1) and details of another ruffle (ruffle 2) are depicted. Scale bar: 10 µm. (B) HeLa cells were infected with a 1∶1 mixture of S.Tm^SopE and S.Tm^Δ4(pGFP)at an m.o.i. of 5 and a movie was acquired via DIC imaging (see also supplementary VideoS4). A snapshot of the movie is depicted. The tracks of 2representative bacteria and their estimated position in the z-axis are indicated by the colored ellipsoids. Both bacteria stop at the ruffle and stay for the remaining observation time (blue track: 4.95 s, green track: 1.24 s). Scale bar: 9 µm. (C) Quantification strategy for analyzing S. Typhimurium docking onto ruffles. HeLa cells were infected for 6 min with a 1∶1 mixture S.Tm^Δ4(pGFP) (green; reporter strain), shown in green and the S.Tm^SopE as a helper strain which did not express gfp at an m.o.i. of 62.5 for each strain. After infection, cells were fixed and stained for actin (TRITC-phalloidin, red) and extracellular S. Typhimurium (indirect immunofluorescence using an anti-Salmonella antibody; blue; stained before permeabilization). Three types of S. Typhimurium can be distinguished: Extracellular reporter S.Tm^Δ4 (labeled green and blue), extracellular helper S.Tm^SopE (only blue), and intracellular reporter S.Tm^Δ4 (only green). Intracellular helper S.Tm^SopE is non-fluorescent and cannot be detected. Scale bar: 10 µm.(D, E) HeLa cells were infected for 6 min with a 1∶1 mixture of a helper strain (either S.Tm^Δ4 or S.Tm^SopE) and the reporter strain S.Tm^Δ4(pGFP) at the indicated m.o.i.. Cells were stained for actin and extracellular bacteria were stained with anti-LPS antibodies. In the control scenario (helper strain S.Tm^Δ4, only non-ruffling cells) bound bacteria were quantified for the area of a whole cell (grey bars); in the ruffling scenario (helper strain S.Tm^SopE) bacteria were quantified over the area of a ruffle as explained in panel (B,C).Even with a complex 3D structure, the surface of a ruffle should be much smaller than the surface of a whole cell. Therefore, if anything, our approach should underestimate the specific recruitment of bacteria onto ruffles. Extracellular bacteria of the reporter strain and the helper strain were quantified separately ((D): reporter strain, expresses gfp; (E): helper strain; no gfp). The bars summarize 170–220 cells/ruffles from two independent experiments. ***: p<0.0001.

https://doi.org/10.1371/journal.ppat.1002810.g009

To analyze a preference of S. Typhimurium for cellular ruffles in a quantitative manner, HeLa cells were infected with a 1∶1 mixture of S.Tm^SopE andS.Tm^Δ4(pGFP) at a high or a lower m.o.i for 6 min. In control experiments (no ruffles), HeLa cells were infected with a mixture of S.Tm^Δ4 and S.Tm^Δ4(pGFP). Subsequently, cells were fixed followed by staining of extracellular bacteria (using an anti-Salmonella antibody; Materials and Methods). After permeabilization of the cell membrane, the actin cytoskeleton was stained. The data evaluation strategy is depicted in Fig. 9C.In the experiments using S.Tm^SopE as the helper strain (ruffling occurs), we determined the number ofS.Tm^Δ4 (pGFP) (Fig. 9D, red bars) and S.Tm^SopE(Fig. 9E, red bars) residing on the respective ruffle. In the negative controls (no ruffling; co-infection with S.Tm^Δ4 and S.Tm^Δ4 (pGFP)) we quantified all bacteria located on the respective cell(Fig. 9D, D grey bars). Comparing targeting to a ruffle as opposed to targeting to a whole cell (control w/o ruffle) is a conservative strategy for detecting ruffle-specific target site preferences, since the whole cell has a much larger area than an individual ruffle. Furthermore, it should be noted that the anti-Salmonella antibody was applied before permeabilization (Materials and Methods). This allowed us to discern internalized and external reporter bacteria.

In all experiments, S.Tm^SopE and S.Tm^Δ4(pGFP) docked more efficiently to ruffles than to non-ruffling cells (Fig. 9D, E compare red and grey bars). This was true for the reporter strain (Fig. 9D; S.Tm^Δ4 (pGFP)) as well as for the helper strain (Fig. 9E; i.e. S.Tm^SopE). A similar effect was also observed in 12-min infection experiments (data not shown). Therefore, ruffling stimulated docking of bacteria to cellular ruffles. Moreover, ruffling facilitated internalization of S.Tm^Δ4(pGFP).Internalized S.Tm^Δ4 (pGFP) was detected in all co-infections with S.Tm^SopE (1.4 and 1.6 bacteria per ruffle on average for the experiment in Fig. 9D for m.o.i.s of 62 and 250, respectively). No internalizedS.Tm^Δ4 (pGFP) was detected in the negative controls (no ruffles; S.Tm^Δ4 as helper strain). Equivalent data were obtained in an analogous experiment using automated image acquisition and analysis methods (suppl. Fig. S6). In another control we tested immotile bacteria (S.Tm^{Δ4 fliGHI} (pGFP)). As expected, these bacteria did not attach to ruffles or normal cells (Fig. S7). Again, cellular attachment was rescued upon centrifugation; however, this did not lead to any targeting preference for ruffles vs. non-ruffling cells (Fig. S7). Finally, we explored the targeting to ruffles by E.coli Nissle, a non-invasive motile bacterium. In co-infection experiments with S.Tm^wt, E.coli Nissle displayed a targeting preference for host cellular ruffles (Fig. S8).

Taken together, the effector protein SopE is required for triggering ruffles. Once the ruffles are formed, they seem to represent prominent physical obstacles which facilitate stopping, binding, docking and internalization of motile pathogens. However, please note that we cannot exclude that other factors, besides ruffle topology (e.g. altered membrane structure or composition in the ruffle) might also contribute to S. Typhimurium targeting to these sites.

Cooperative invasion at ruffles

Finally, we reasoned that S. Typhimurium recruitment onto ruffles might lead to cooperative invasion. If multiple bacteria dock to the same ruffle, more effector proteins are delivered, thus increasing the ruffle size and enhancing the chance for stopping, binding and docking of additional bacteria. To test this hypothesis, HeLa cells were infected for 9 min with S.Tm^SopE(pGFP) at increasing multiplicities of infection, fixed and stained. We first quantified the fraction of ruffling cells (Fig. 10A, B, left panel).Next we determined(at each m.o.i.)the number of intracellular and extracellular bacteria residing in an individual ruffle (Fig. 10A, B, middle panel). Finally, we determined the number of “invaded S. Tm^SopE” per cell (Fig. 10 A, B, right panel). This was achieved by multiplying the percentage of ruffling cells with the number of intracellular bacteria per ruffle. All these parameters increased as a function of the m.o.i.

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Figure 10. Cooperative invasion at membrane ruffles.

(A) Scheme explaining our quantification strategy for cooperative invasion. (B) HeLa cells were incubated with S.Tm^SopE(pGFP) at the indicated m.o.i. for 9 min, followed by washing, fixation and staining of actin and extracellular bacteria (anti-LPS antibody; staining before permeabilization). Left panel: quantification of the fraction of cells carrying ruffles. Middle panel: quantification of the number of “inside” and “outside” S.Tm^SopE(pGFP) per individual ruffle. Right Panel: number of invaded S.Tm^SopE(pGFP) per cell, calculated by multiplying the number of intracellular bacteria per ruffle with the fraction of ruffling cells. The black line indicates the estimated number of invaded S.Tm^SopE(pGFP) assuming independent invasion events, extrapolated from experimental data of ruffles with only one associated bacterium at the lowest m.o.i.. Each data point summarizes 4×150 cells for the analysis of cellular ruffling and 4×25 cells for inside and outside bacteria from 2 independent experiments. Error bars: standard deviation.

https://doi.org/10.1371/journal.ppat.1002810.g010

Next we wanted to determine the invasion efficiency mediated by a single bacterium without “support” from other bacteria. We therefore focused at low m.o.is., where typically one bacterium was observed per ruffle. The few ruffles with two associated bacteria were excluded from the subsequent analysis. The single bacterium was either located outside or within the host cell (Fig. 10B, m.o.i. 4 and 8). To estimate invasion efficiency by this single bacterium alone without possible support by other bacteria, this invasion efficiency was extrapolated to higher m.o.i., assuming a linear increase of invasion efficiency with increasing m.o.i. (Fig. 10B, right panel, black line). Strikingly, this extrapolated invasion efficiency was much lower than the observed invasion efficiency at higher m.o.i. (Fig. 10B, right panel, red line).This indicated that S. Typhimurium invasion occurred in a cooperative fashion, most likely by provoking stopping, binding and docking of additional bacteria engaged in NSS at sites of membrane ruffling.

Bacterial strains

Published bacterial strains are listed in Table 1. The construction of additional strains is described, below.

Time lapse microscopy

Cells were seeded into glass-bottomed culture dishes (Mat Tek) in DMEM (Invitrogen), 10% FCS (Omnilab) containing streptomycin (50 µg/ml), 24 hours prior to the experiment at 300 000 cells (HeLa Kyoto, if not mentioned otherwise) per 35 mm well; experiments were performed in HBSS (Invitrogen), 10% FCS, 20 mM Hepes (Invitrogen), pH 7.2–7.5 (Invitrogen). After exchange of media to HBSS, cells were incubated at 37°C, 5% CO₂ and infected with the indicated S. Typhimurium strain (derivative of SL1344, [53]) carrying plasmid pCJLA-GFP at an estimated m.o.i. of 1.5.

If not stated otherwise, movies were acquired on a Leica DMI-6000B microscope, either using the differential interference contrast mode or the fluorescence mode. DIC-movies were acquired using a 63× oil objective (HCX PLAN Apochromat from Zeiss, NA 1.4); for fluorescence imaging, a 20×-objective (HC PLAN Apochromat from Zeiss, NA 0.75) with a 2-fold optovar was used. If not indicated otherwise, movies were acquired at 10 frames per second for 5 minutes. Movies were analyzed using the program Volocity (Improvision, UK) and the manual tracking mode.

For Fig. 4, we used a Zeiss Axio Observer equipped with a spinning disc confocal head and a 100× objective (oil NA1.4,). For suppl. Fig. S1, we used a Zeiss Axiovert 200 m inverted microscope equipped with an Ultraview confocal head (Perkin Elmer) and a krypton-argon laser (643-RYP-A01 Melles Griot, The Netherlands) and a 20× objective, 0.75NA (Optovar 1.6). In these experiments estimating the influence of gravity, glass-bottom culture dishes containing HBSS but no cells were inoculated with bacteria as described for the other time lapse experiments;

For testing of bacterial near surface swimming on MDCK cells, cells were seeded in glass-bottom culture dishes in DMEM, supplemented with 10% FCS, grown to confluence and let polarize for five days. Infection with S.Tm^Δ4 was done exactly as described for HeLa cells. Movies were acquired on a Leica DMI-6000B microscope using a 20×-objective with a 1.6 optovar. The movie was acquired for 5 min at 10 frames per second and an overlay of the movie is shown.

S. Typhimurium strains were grown before infection as described [32]. E. coli Nissle was grown identically except that normal Luria-Bertani media was used.

Quantitative analysis of movie sequences

For quantification of the contact of S. Typhimurium with the cells, the interaction was divided into 4 phases: 1) landing: the time of appearance of a S. Typhimurium until interruption of the continuous downward movement, indicated by changes in focus, direction and speed. 2) stopping: episodes without changes in any direction for at least 3 frames. 3) take off: the continuous upward movement until disappearance. 4) NSS: a continuous movement in the xy-direction other than 1) or 3). S. Typhimurium that left the field of view during quantification, as well as a few cases with ambiguities were excluded.

For quantification of the number of S.Tm^Δ4(pGFP) within the focus depth, either at the cell surface or >100 um above the surface (“in solution”), 40 time points from 2 independent experiments were analyzed. The same time points were used for each movie analyzed and they were spaced throughout the duration of the movie. For each bacterium within focus depth at the specific time point, the entire contact time spent on the surface was analyzed. Each bacterium was thus either counted as engaged in “NSS” or “NSS and stopping”. For the quantification of bacteria swimming in solution, any non-motile bacteria were excluded.

For quantification of the distance, time and speed of S.Tm^Δ4(pGFP) on the glass and cell surface, 20 bacteria from 2 independent experiments were manually tracked from their first contact with the cell to their last contact with the cell (i.e. NSS quantification excluding the landing and take-off stages) or until they left the field of view. S.Tm^Δ4 were selected at random from those arriving in the center of the field of view. The time indicated is NSS-time only and does not include any intermittent time spent stopping at the surface.

High-resolution microscopy

For high-resolution images, HeLa cells were seeded on glass cover slips for 24 hours and infected with S. Typhimurium carrying plasmid pM965 for constitutive gfp expression at the indicated m.o.i.. After fixation, S. Typhimurium were stained by indirect immunofluorescence using an anti-LPS antibody (Difco) and goat anti-rabbit-Cy5 (Jackson) as a secondary antibody; the actin cytoskeleton was stained by tetra methyl rhodamine isothiocyanate (TRITC)-phalloidin after 5′ permeabilization with 0.1% Triton Tx100. Images were taken on a Zeiss Axiovert 200 m inverted microscope equipped with an Ultraview confocal head (PerkinElmer) and a krypton argon laser (643-RYP-A01, Melles Griot) using a 100× oil immersion objective (PLAN-Apochromat Zeiss with an NA of 1.4). Stacks of 0.2 µm were acquired; deconvolution was performed in the actin channel with the program Volocity and a calculated point spread function. 3D-reconstruction and reconstruction of zx-layers was done using Volocity.

Automated analysis of S. Typhimurium docking

Docking experiments were done as described [9]. In brief: cells were seeded in 96-well micro-clear plates, (half size, Greiner), at 6000 cells per well 24 hours prior to the experiment and infected with the indicated S. Typhimurium strain followed by fixation, staining of nuclei and bacteria using DAPI and an anti-Salmonella antibody (Difco), respectively. Images were acquired on a MD-Image Xpress microscope (Molecular Devices) using a 4×-objective in the DAPI- and Salmonella-channel. Images were analyzed using the open source program CellProfiler [58] and customized Matlab-scripts, available upon request.

Automated analysis of individual docked bacteria onto mitotic and interphase cells

For automated analysis of individual docked bacteria (Fig. 1) the analysis consisted of three major steps. First, cells and nuclei were detected using the program CellProfiler [58] and cellular properties, such as morphology, texture and intensity were extracted. Second, single bacteria were identified using the “a trous” wavelet transform [59] and every bacterium was assigned to the corresponding host cell. In a third step, mitotic cells were distinguished from interphase cells using supervised machine learning technique. For this purpose, the “Advanced Cell Classifier program” was applied and the classification was performed using the extracted cellular features with an artificial neural network classifier [60]. Finally the average amount of bacteria on mitotic and interphase cells was calculated.

Manual quantification of S. Typhimurium docking and cooperative invasion

Docking onto mitotic cells: HeLa cells were seeded in 96-well Microclear plates (full size, Greiner) at 6000 cells per well 24 hours prior to infection. After infection and fixation, extracellular S. Typhimurium was stained using an anti-Salmonella LPS antibody and a Cy5-labelledsecondary antibody (Fig. 9). Due to the bright staining of extracellular bacteria, the unique bacterial shape and the high resolution used for quantification bacterial staining was clearly distinguishable from actin staining. For illustrative purposes (Fig. 9C), in one experiment bacteria were stained using a Cy3-labelled secondary antibody. Subsequently, nuclei and actin were stained using DAPI and TRITC-phalloidin, respectively. Finally, bound S. Typhimurium bacteria were manually quantified using a 40×-objective (EC Plan-Neofluar objective with a NA of 1.3).Docking onto ruffles: The gfp-labeled S. Typhimurium strain and the helper strain were mixed at equal dilutions prior to infection. Staining and fixation was done as described for mitotic cells. Testing of cooperativity: S.Tm^SopE-carrying plasmid pM965 was incubated at the indicated concentration with HeLa cells; fixation and staining was done as described. To calculate invasion efficiency of S. Typhimurium without cooperativity, invasion was calculated at the lowest m.o.i., excluding ruffles with more than one associated S. Typhimurium. At higher m.o.i., this number was assumed to increase proportionally with the number of added S. Typhimurium.

Modeling of S. Typhimurium targeting

In our calculations we modeled a three-dimensional environment within a cubic space. A round sphere was placed into this space; the radius of the sphere was chosen to be 1/10 of the length of the surrounding cube. The sphere was partially submerged into the bottom surface of the cube and 75% of the height of the sphere remained within the cube. In addition, 100 single particles were modeled into the cubic space. For simplicity the size of the particles was assumed to be zero in all dimensions. The whole volume of the cube was assumed to be accessible to the particles except the interior of the sphere. All particles were assigned the same constant speed but a randomly chosen vector of movement. The positions of the particles where integrated after travelling a fraction of 1.2*10⁻⁵ of the length of the square. Upon hitting the bottom surface, particles followed the rules according to the chosen scenario:1) Reflection at a randomly chosen angle in the “random” scenario, where the new vector of movement was randomly chosen (“impossible” movements, for instance into the surface were excluded). 2) Reflection with an angle identical to the angle of infliction in the “billiard” scenario, the vector of “take off” thus mirroring the vector of “landing” or 3) particles started swimming along the surface in the “NSS” scenario. In this scenario the z-vector of movement was set to zero (particles thus following the surface); the x and y-direction of movement remaining unchanged. When encountering the remaining 5 limiting surfaces of the cube, particles were simply reflected. Upon hitting the limits of the sphere, particles had a 10% chance of being attached (simulating stopping/docking); otherwise particles were simply reflected. Particles hitting the sphere during NSS also stopped/docked to the sphere with a likelihood of 10%. Upon each change of direction the movement vectors were adjusted to achieve a constant overall velocity. After an identical number of calculated increments of particle movements the simulation was interrupted and screenshots were acquired.Scatter3D is implemented in C++ and has previously been used to simulate light scattering [61]. Calculations were performed on a desktop computer. Further details are available upon request.

Construction of strains and plasmids

Strains S.Tm^{Δ4 fliGHI}, S.Tm^{-T1 fliGHI}, S.Tm^{-T1 -Fi fliGHI} were constructed by P22 transduction [62] of the tetracycline allele of SB245 (sipABCDsptP::aphT, fliG/H::Tn10, K. Kaniga and J.E. Galan, unpublished data) into strains S.Tm^Δ4, S.Tm^-T1 and S.Tm^{-T1 -Fi}, respectively. flgK was deleted in SL1344 (SB300) as described in [35]. Strains S.Tm^{Δ4 flgK}, S.Tm^{-T1 flgK}, S.Tm^{-T1 -Fi flgK} were constructed by P22 transduction of the chloramphenicol resistance containing the flgK deletion into the respective host strain. MotA/B were deleted in SL1344 (SB300) using the method of Datsenko and Wanner [63] by insertion of a chloramphenicol resistance cassette that was amplified using the forward primer: ATGCTTATCTTATTAGGTTACCTGGTGGTTATCGGTACAGTGTAGGCTGGAGCTGCTTC and the reverse primer: TCACCTCGGTTCCGCTTTTGGCGATGTGGGTACGCTTGCATGGGAATTAGCCATGGTCC. S.Tm^{Δ4 motAB}, S.Tm^{-T1 -motAB} and S.Tm^{-T1 -Fi motAB} were obtained by P22 transduction of the chloramphenicol resistance into the respective host strain. Lack of motility of the respective strains was tested on motility agar.

pM2120 (expressing mCherry constitutively) was constructed by PCR amplification of the mCherry gene using forward primer: CGCGGATCCCCCGGGCTGCAGGAATTCAGGAAACAGTATTCATGGTGAGCAAGGGCGAGGAG (BamHI) and reverse primer: GGGAAGCTTGATATATCGGAATTCTTACTTGTACAGCTCGTCCATG (HindIII). Subsequently the PCR product as well as plasmid pM975 [22] was digested using BamHI/HindIII and ligated.

List of gene ID numbers of S. Typhimurium genes (SL1344)

fliG (11765205), fliH (11765206), fliI (11767468), flgK (11767368), motA (11765169), motB (11765168), cheY (11765165), sopE (11765807), sipA (11765948), sopE2 (11768039), sopB (1252609), invG (11765959), fimD (11764167).