Painting with bacteria: Smart templated self assembly using motile bacteria

External control of the swimming speed of `active particles' can be used to self assemble designer structures in situ on the micrometer to millimeter scale. We demonstrate such reconfigurable templated active self assembly in a fluid environment using light powered strains of Escherichia coli. The physics and biology controlling the sharpness and formation speed of patterns is investigated using a bespoke fast-responding strain.

pairs. These release their energy stepwise along an electrochemical potential ladder of respiratory enzymes located in the inner cell membrane, generating a PMF of ≈ −150 mV. The electron pair ultimately passes to and reduces O 2 to water. Thus, with no O 2 , PMF = 0 and swimming ceases [17]. If cells under anaerobic conditions can express proteorhodopsin (PR) [3], a green-photon-driven proton pump [20], then they will swim only when suitably illuminated: these are living analogues of synthetic light-activated colloidal swimmers. [9,10,11,12] We show below that the speed with which such cells respond to changes in illumination is crucial for successful bacterial STASA. For this work, we constructed a PR-bearing mutant (AD10) that stops much faster when illumination ceases than previously-reported [3,21], by deleting the unc gene cluster [22] encoding the F 1 F o -ATPase membrane protein complex (see SI §1.1), so that these enzymes cannot act in reverse in darkness to continue to export protons and sustain a PMF [23].
We suspended cells in phosphate motility buffer at optical density OD 8 (cell-body volume fraction ≈ 1.1%) [17] and sealed 2 µL into 20 µm high flat capillaries, where cells swim in two dimensions but have enough room to 'overtake' each other in all three spatial dimensions. Differential dynamic microscopy (DDM) [24] returned an averaged speedv ≈ 30 µm s −1 and β ≈ 20% of non-motile organisms at OD=1 under fully-oxygenated conditions. (Note that 'non-motile' = cells that can never swim; 'stationary' = non-swimming cells capable of motility when illuminated.) Motile cells were allowed to swim until O 2 was depleted andv dropped abruptly to zero after a few minutes [17] (see SI §1.2 and Fig. S1(a)). After these cells were left in the dark for ≈ 10 min, green illumination was turned on (510 -560 nm, intensity I ≈ 5 mW cm −2 at the sample). The stationary cells accelerated uniformly before saturating, Fig. 1. Fitting the data tov(t) =v sat t/(t+τ on ) givesv sat = 9.5 µm s −1 , τ on = 30 s. When illumination ceased,v dropped within τ off 1 s, but never quite to zero -it is unclear why a few cells (< 1%) continued to swim.v sat increased with I, Fig. 1 (inset), up to 27 µm s −1 . {v, β, v sat , τ on } changed over hours as cells aged.
The discharging of the PMF through the membrane (capacitance C 10 −14 F) and rotary motors (total resistance R 10 14 Ω) upon cessation of illumination should take RC ∼ 1 s (see SI §1.3 for details), which explains the observed τ off . Consistent with this interpretation, τ off is approximately independent of the starting speed of decelerating cells (see SI §1.2 and Fig. S2(a)). The observed τ on ≈ 30 s is likely controlled by the rate constant [25] for stator units to come on and off motors, k stator ≈ 0.  Our STASA protocol uses a spatial light modulator (SLM) to project patterns of illumination onto a uniform field of cells rendered stationary by O 2 exhaustion. We first projected a positive mask with bright features on a dark background, Fig. 2a (inset). The width of the features, 90 µm, is comparable to the persistence length (i.e. the length scale over which the cell's motion can be approximated as a straight line) of smooth-swimming E. coli [26]. Over time, the mask pattern was replicated in the cell population with increasing clarity: cells swim out of illuminated areas, stop within τ off ∼ 1 s, and accumulate at the edges just outside the boundary of these areas, Fig. 2b (also see SI movie M1). Next we abruptly switched to a negative mask (dark pattern against bright background), Fig. 2c (inset). Edge-accumulated cells start swimming, about half of which arrive inside the dark features immediately and stop within τ off , rapidly outlining the inside edge of the masked areas, Fig. 2c.
Thereafter, the dark areas progressively filled up: bacteria continually arrive from the outside, which acts as an essentially infinite reservoir of swimming cells, Fig. 2d-e (also see SI movies M2 and M3). Other patterns can be 'painted' using this method, Fig. 2g. In contrast to static 'bacterial painting' recently demonstrated [27], our method is dynamic and reconfigurable in real time within our liquid medium.
To understand how fast such reconfigurations of spatial patterns can take place, we experimented with square masks, Fig. 2f, using two initial conditions: a (dark) field of stationary cells (v(0) = 0, i.e., diffusive), and a field of (illuminated) cells that have reached speed saturation (v(0) =v sat ). We studied the evolution of cell density (see Methods and SI §1.5) and speed inside bright squares against a dark background. Starting with diffusive cells,v(t) =v sat t/(t + τ on ) ≈ at with a =v sat /τ on for t τ on . The time to empty an L × L square should scale as The measured cell density evolution, ρ tot (t), in illuminated squares of different sizes is shown in Fig. 3a, from which we extracted t D , the time for ρ tot to drop to 1/e of its decay. As expected, t D (L) ∼ L 0.5 , Fig. 3c. Fitting Eqn.
(1) gives a = 0.05 µm s −2 , which is a reasonable average acceleration over the first ∼ 100 s for the data in On the other hand, starting with cells all swimming (on average) atv sat , a square should empty in From the measured ρ tot (t) in this case, Fig. 3b, we deduced a 'box emptying time' and find, Fig. 3c between bright and dark areas acts as an absorber of swimmers arriving with an effective diffusive process. Due to conservation of mass the diffusive propagation of the peak is thus expected, although quantitative aspects remain to be elucidated.
Finally, we seek to understand what controls the sharpness of the self-assembled features using our method.
A stark demonstration of the answer comes from projecting a step-function pattern of light-dark illumination on initially uniformly swimming cells (see SI §1.7 for details). Figure 4 shows the resulting density of cells (from the intensity variance, see SI §1.6) for two strains, AD10, with τ stop 1 s, and AD57, a strain lacking the unc cluster deletion and having τ off > 1 min (see Fig. S7 for images). In the case of AD10, the illumination pattern is clearly replicated in cell density within 1 min, and the steady state shows a sharp feature with half width 10 µm. For AD57, it takes 1 min to establish a corresponding density pattern, which, in the steady state, is 50 µm wide.
Thus, a short enough τ off is necessary for the fast establishment of sharp features using our STASA protocol.  In sum, we have demonstrated how to perform smart templated self assembly using a PR-bearing strain of E. coli with unc cluster deletion dispersed in anaerobic motility buffer. Spatial patterns of light are reproduced sharply in cell density, with the time scale of pattern formation controlled by the 'on time' and the sharpness of the features controlled by the 'off time'. This opens up many opportunities. Thus, e.g., it should now be possible to test a fundamental prediction that the product of density and speed is constant in a system with spatiallydependent activity [28]. Practically, we can go beyond a recent demonstration [13] of the control of lithographed micro-devices using light-activated bacteria: STASA allows the assembly and real-time reconfiguration of the devices themselves using the same bacteria [16]. On the other hand, if cell density is used to trigger the secretion of adhesive biopolymers, bacterial STASA offers a route for manufacturing bespoke 10-100 µm microstructures, which remains a challenging length scale for current 3D printing.

Methods Cells
The Escherichia coli used in this work (AD10 & AD57) are smooth-swimming mutants of AB1157 transformed by a plasmid expressing SAR86 γ-proteorhodopsin (a gift from Jan Liphardt, UC Berkley), similar to the original study [3]. To achieve better light-switching response, we also deleted the unc gene complex, thus removing the coupling between PMF and ATP [22].
Overnight cultures were grown aerobically in 10 mL Luria-Bertani Broth (LB) using an orbital shaker at 30 • C and 200 rpm. A fresh culture was inoculated as 1:100 dilution of overnight-grown cells in 35 mL tryptone broth (TB) and grown for 4 h (when an optical density of OD 600 ≈ 0.2 was reached). The production of proteorhodopsin (PR) was induced by adding arabinose to a concentration of 1 mmol as well as the necessary cofactor all-transretinal to 10 µmol in the growth medium. Cells were incubated under the same conditions for a further hour to allow protein expression to take place and then transferred to motility buffer (MB, pH = 7.0, 6.2 mM K 2 HPO 4 , 3.8 mM KH 2 PO 4 , 67 mM NaCl and 0.1 mM EDTA). Cells were re-suspended in MB after a single filtration (0.45 µm HATF filter, Millipore), yielding high-concentration stock solutions (OD ≈ 8 to 10), which were then diluted with MB as required.
Suspensions were loaded into 2 µL sample chambers (SC-20-01-08-B, Leja, NL, 6 mm×10 mm×20 µm), giving a quasi-2D environment for E. coli swimming. The chamber was sealed using vaseline to stop flow and oxygen supply, and placed onto a microscope for video recording. E. coli initially swims under endogenous metabolism, which depletes the oxygen within the sample cells [17]. Once oxygen is depleted, the cells stop swimming unless illuminated by green light.

Optical setup
The samples were observed using a Nikon TE2000 inverted microscope with a phase contrast objective (PF 10×/0.3).
Time series of ≈ 40 s-movies (100 frames per second) were recorded using a fast CMOS camera (MC 1362, Mikrotron). A long-pass filter (RG630, Schott Glass) was introduced into the bright-field light path to ensure that the light required for imaging did not activate PR. The light controlling the swimming behavior of the bacteria was provided by an LED light source (Sola SE II, Lumencor) filtered to a green wavelength range (510 -560 nm) to overlap with the absorption peak of PR [3]. This light was reflected off a digital mirror device (DLP6500, Texas Instruments), which was imaged onto the microscope sample plane using a demagnifying relay telescope, leading to a resolution of 2.7 µm per pixel. The light was coupled into the transmission path by introducing a dichroic mirror in between the long working distance phase contrast condenser and the sample.
This 'trans' geometry allowed us to illuminate uniformly a circular area of ≈ 2.9 mm in diameter, larger than the objective's field of view. Computer control of the digital mirror device gave us precise spatial and temporal control of the illumination pattern.
Motility and density measurements.
The motility of the samples was quantified using differential dynamic microscopy (DDM) [29], which yields the average swimming speedv and fraction of non-motile bacteria β from low magnification movies [24,30,17]. By comparing the amplitude of the DDM signal to that of a reference sample, it can also give the relative bacteria density ρ/ρ 0 (see SI §1.5 for details). However, the spatial resolution of this method of density measurement is very limited. In experiments where we need to measure density profiles with sharp spatial variations, we used instead the local variance of the image intensity to provide estimates of local cell density (see SI §1.6 for details).  AD10 was constructed using our laboratory stock of E.coli AB1157 [31]. All of the genes present within the operon encoding the ATP synthase complex (atpI, atpB, atpE, atpF, atpH, atpA, atpG, atpD, atpC) were deleted in a single step by allelic replacement using a recombinant pTOF24 vector [32] containing 400bp homology arms flanking the deletion on each side. This resulted in the ∆unc strain AD10 with a deletion of 7504bp (corresponding to position 3915552-3923056 of the E.coli K12 MG1655 chromosome). P1 transduction was used to delete the cheY gene in both this strain and the parental strain (to generate AD57) using JW1871 (BW25113 cheY) as donor [33]. Diagnostic PCR reactions were performed to confirm genotypes. Both strains were transformed with plasmid pBAD-His C (a kind gift from Jan Liphardt, UC Berkley) encoding the SAR86 γ-proteobacterial photorhodopsin variant [21].

Saturation, startup and stopping behaviour
Here we give further information and detailed data supporting the claims made in the main text about the dependence of average swimming speed,v, on light intensity (I), startup time (τ on ), and stopping time (τ off ).  Fig. S1(a). Figure S1: (a) Swimming speed of AD10 (sample OD = 1) during oxygen depletion and for different light intensities: Within ≈ 7 min of sealing the sample oxygen was depleted and swimming ceased. Green light was switched on at ≈ 20 min. Illumination intensity was changed at various points throughout this experiment to provide data for the speed vs. intensity plot in the main manuscript (inset, Fig. 1). (b) A light-sensitive strain with unc-cluster deletion (AD10) shows a rapid decrease in swimming speed as soon as the illumination is stopped (black symbols). However, for strains which do not have the unc gene cluster deleted (AD57) a significant fraction of the population (see inset showing the motile fraction α = 1 − β, lines are guides to the eye) continues to swim at reduced speed for several minutes, giving a much slower decay inv (red symbols; solid red line: exponential fit, giving τ ≈ 60 s).
We claimed in the main text that deleting the unc gene cluster responsible for encoding the ATP synthase complex significantly reduced τ off . Figure S1(b) shows the behaviour of two strains of proteorhodopsin-powered E. coli: AD57 and AD10, without and with unc gene cluster deletion respectively. When illumination was switched off after the cells have reached steady state in constant illumination, the average swimming speed of both strains showed a sharp drop. In the case of AD10, this sharp drop was from ∼ 17 to 4 µm s −1 over 1 s.
The drop was much smaller for AD57, from ∼ 14 to 10 µm s −1 , again over 1 s. Thereafter, a significant fraction of AD57 cells kept on swimming with their average speed decreasing exponentially with a time constant of ∼ 60 s.
A simple resistor-capacitor equivalent circuit for the cell membrane and flagella motor was used in the main text to explain the observed τ off ≈ 1 s. We claimed that this was supported by the observation that τ off was independent of the starting speed from which cell decelerated. Figure S2(a) shows evidence for this claim.
In the main text, we suggested that the long τ on of cells that have been kept in prolonged darkness was controlled by the time needed to reassemble the motor from individual stator units. We supported this claim by saying that τ on was much shorter for cells that had not been in low illumination for very long. Figure S2(b) shows evidence for this. When illumination alternated between high and low with a period of 20 s, we find τ 1 s, comparable to τ off . Figure S2: (a) Average swimming speed measured with DDM as green light is switched off (at t = 0). There is a very rapid speed drop τ off ≈ 1 s as the light is switched off, which is independent of the initial swimming speed (at the resolution limit of our technique). (b) AD10 bacteria adapt their swimming speed within 1s as the green light is alternated between low and high intensity. In this case τ on ≈ τ off . Figure S3: Equivalent circuit of the bacterial PMF generation and dissipation: R mot = resistance of flagella motors, C mem = membrane capacitance, V res = PMF generated by respiratory enzymes, R res = variable resistor modelling the effect of oxygen concentration on respiratory enzymes, V PR = proteorhodopsin-generated PMF, R PR = variable resistor modelling the effect of light on PR. (Redrawn after [3].)

Circuit model
The effect of proteorhodopsin (PR) on the proton motive force (PMF) can be modelled using a simple equivalent circuit, Fig. S3 [3]. We use this model for our experiments by interpreting R res as the effect of oxygen concentration on the respiratory circuit: R res → ∞ when oxygen is exhausted. The effect of light on PR is modelled as a variable resistor, R PR , which Walters et al. have argued to be related to the incident light intensity I by R PR (I) = R ∞ PR (I + K)/I (with K a constant). Using the assumption that the average swimming speed is proportional to PMF, this leads to a similar intensity dependence of the speedv sat (I) =v max I/(I + I 1/2 ) under complete oxygen starvation. Fitting this to our experimental data gives a maximum swimming speed v max = 28.4(7) µm s −1 and an intensity to reach half this speed of I 1/2 = 10.7(4) mW cm −2 , suggesting that our PR is significantly more efficient than the one used by Walter et al. (for which I 1/2 = 60 mW cm −2 ) [3].
We suggest that the stopping time is controlled by the discharge of the PMF of the equivalent capacitance of the membrane through the equivalent resistance of the flagella motor, i.e. τ off = R mot C mem . The specific capacitance of bacterial membranes is ≈ 0.6 µF cm −2 [34]. A 2 µm×1 µm spherocylindrical E. coli cell has 2π µm 2 surface area, giving C mem ≈ 4 × 10 −14 F. We have previously estimated [17] that the proton current through a motor is ≈ 2 × 10 4 H + s −1 , which is driven by a PMF of ≈ −150 mV, giving R mot ≈ 0.5 × 10 14 Ω per motor. On average, each cell has, say, ≈ 5 motors. These constitute resistors in parallel, so that R mot ≈ 0.1 × 10 14 Ω. Thus, τ off ∼ R mot C mem 1 s, as claimed in the main text.

Additional data for cells leaving illuminated squares
In the main text we reported measurements of how long it took to empty an illuminated square (side L) of cells, starting from swimming or stationary cells. The emptying time scales as L and L 1/2 respectively. This is the scaling expected for random ballistic swimmers leaving a box of size L. This is appropriate for our experiments, as the persistence length of our smooth swimming bacteria is comparable to the box size. Here we report the average speed of cells remaining in the illuminated square,v r , as a function of time in the two cases. Starting from swimming cells, our simplistic model presented in the main manuscript assumed a constant swimmming speedv r =v sat , whereas the actually measured speedv r decreased with time, Fig. S4

Differential dynamic microscopy
Differential dynamic microscopy (DDM) is a fast, high-throughput technique to measure the dynamics of particle suspensions such as colloids [29] or swimming microorganisms [24,30,17]. For swimming bacteria, DDM measures motility parameters averaged over ∼ 10 4 cells from ≈ 30 s long movies via the differential image correlation function g( q, τ ), i.e. the power spectrum of the difference between pairs of images delayed by time τ . Under appropriate imaging conditions and for isotropic motion, g( q, τ ) is related to the intermediate scattering function f (q, τ ), which is the q th Fourier component of the density temporal autocorrelation function, via Here, B(q) relates to the background noise and A(q) is the signal amplitude. Fitting f (q, τ ) to a suitable swimming model of E. coli, we obtain the average,v, and width, σ, of the speed distribution P (v), the fraction of non-motile bacteria β and their diffusion coefficient D [30]. Provided the system is dilute enough such that its structure factor S(q) ≈ 1, A(q) is proportional to the particle density [35,36]. Thus, in two otherwise identical samples with different cell densities ρ 1,2 , We verified this using bacteria suspensions of different cell concentrations (measured by optical density), Fig. S5.

Estimating cell density using intensity variance
Although DDM in the form of Eq. 4 is useful for extracting average cell densities over large uniform areas, its strictly limited spatial resolutions makes it unsuitable for assessing cell densities that vary rapidly in space, such as from images shown in Fig. 2 of the main text to illustrate STASA, or the band of high density bacteria shown Figure S5: Cell density deduced from DDM ( §1.5) and intensity variance (section §1.6) measurements for a series of 7 samples starting at a measured optical density of OD = 5.6 and diluted successively by factors of 2. Both analysis methods were applied to the same movies and yield consistent results in quantitative agreement with the prepared sample densities throughout the measured range. Both data sets have been calibrated by assuming the data point for OD 1.4 was measured correctly.
in Fig. S6(a). In the latter case, neither the intensity along a line nor the average of such line profiles, Fig. S6(b) and (c) respectively, bears much resemblance to the cell density variation visible to direct inspection. This is related to the nonlinearities at high phase shifts and artifacts inherent to phase contrast imaging [37].
Interestingly, the single line profile, Fig. S6(b), shows that the intensity fluctuates more in the high-density region. Indeed, the averaged horizontal variance profile, Fig. S6(d), shows qualitative agreement with the visuallyobserved density profile of the band, suggesting that the variance (with background subtracted) could be used to quantify local density. Figure S5 shows that, indeed, densities of uniform samples deduced from variance measurements agree quantitatively with densities deduced from DDM using Eq. 4 and from optical density measurements. At least within the low density limit, this scaling of intensity variance with cell density is not surprising: a (dark) cell on a uniform (light) background will lower the mean intensity slightly and introduce a characteristic amount of variance relative to this background. As more cells are added they initially increase the variance by the same characteristic amount (as the mean background hardly changes), leading to the observed proportionality. For phase contrast imaging, phase-dark object are often surrounded by a lighter halo, so that the net effect of cells on mean intensity is rather small while at the same time leading to a significant characteristic variance per object.

STASA for AD10 vs AD57
We demonstrate the effect of τ stop on STASA by projecting a bright-dark intensity step onto cells that had been uniformly illuminated for ≈ 10 min (so that the population had reached saturation speed,v AD10 = 17.5 µm s −1 andv AD57 = 11.8 µm s −1 ). Therefore there is initially very little effect on the cell density, and only after a much longer time a far more gradual pattern emerges, as demonstrated by the profiles of the intensity variance (reproduced from the main text in Fig. S7). Figure S7: Spatial control of a) AD10 strain compared to b) AD57 strain. Phase contrast images (Nikon PF 20x/0.5 objective) of the samples just before (0 min) as well as 1 min and 16 min after blocking the green light on the right half of the image (boundary position indicated by green lines). The corresponding variance profiles (normalised by the mean variance under uniform illumination) are shown underneath the images. The numerically smoothing (bold lines; using a binomial smoothing filter with n = 201) of the raw data (faint lines) highlights the underlying change in cell density. For the AD57 strain the density difference develops much more slowly and shows a much more gradual transition, making it less suitable for STASA.

Movies
Movie M1: 40 s movie showing the initial formation of the 'UoE' pattern. This corresponds to the run-up to fig. 2a in the main manuscript. The frame-rate has been reduced from the 100 fps of the original movie to 2 fps and is played back at 5× the real speed. One pixel corresponds to 1.4 µm.
Movie M2: Movie showing initial 40 s just after the inversion of the 'UoE' pattern. This corresponds to the transition between fig. 2c and d in the main manuscript. Frame-rate adjusted as for movie M1.
Movie M3: Timelapse image series of the UoE pattern being formed and inverted. This illumination pattern is switched on at time '0:00' when the sample is still uniform (corresponding to movie M1) and becomes clearer over time. After 9 min the pattern is inverted (corresponding to movie M2) and again becomes more pronounced over time. The outline of the letter 'o' is marked to highlight the switching of the boundary as the pattern is inverted.