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A synthetic multicellular memory device Arturo Urrios, Javier Macia, Romilde Manzoni, Nuria Conde, Adriano Bonforti, Eulàlia de Nadal, Francesc Posas, and Ricard Sole ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.5b00252 • Publication Date (Web): 21 Jul 2016 Downloaded from http://pubs.acs.org on July 26, 2016

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ACS Synthetic Biology

A synthetic multicellular memory device

Arturo Urrios1,¶, Javier Macia2,¶, Romilde Manzoni1,¶, Núria Conde1,2, Adriano Bonforti2,4, Eulàlia de Nadal1, Francesc Posas1,* and Ricard Solé2,3,*

1

Cell Signaling Research Group and 2ICREA-Complex Systems Laboratory. Depar-

tament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra (UPF), E08003 Barcelona, Spain. 3

4

Santa Fe Institute, Santa Fe, New Mexico, NM 87501, USA.

Centre per a la Innovació de la Diabetis Infantil Sant Joan de Déu (CIDI), E-08950 Esplugues de Llobregat, Barcelona, Spain.

¶

These authors contributed equally to this work.

*

Corresponding authors: [email protected]; [email protected]

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Keywords: Synthetic biology, Logic circuits, Multicellular consortia, Biological computation, memory devices.

Abstract (250) Changing environments pose a challenge to living organisms. Cells need to gather and process incoming information, adapting to changes in predictable ways. This requires in particular the presence of memory, which allows to store different internal states. Biological memory can be stored by switches that retain information of past and present events. Synthetic biologists have implemented a number of memory devices for biological applications, mostly in single cells. It has been shown that the use of multicellular consortia provides interesting advantages to implement biological circuits. Here, we show how to build a synthetic biological memory switch using an eukaryotic consortium. We engineered yeast cells able to communicate and retain memory of changes in the extracellular environment. These cells were able to produce and secrete a pheromone and sense a different pheromone following a NOT logic. When both strains were co-cultured they behaved as a double-negative feedback motif with memory. In addition, we showed that memory can be effectively changed by the use of external inputs. Further optimization of these modules and addition of other cells could lead to new multicellular circuits that exhibit memory to a broad range of biological inputs.


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INTRODUCTION One of the fundamental traits of biological systems is their adaptability, their capability of taking complex decisions in response to a changing external world. The rise of biological systems capable of making such decisions represented a major transition of evolution, increasing the complexity of the decisions a cell can trigger1. Two elements were needed: sensing the environment and storing information by means of a cellular memory system. Memory is a crucial element, since it is responsible for predictable and sustained responses to predefined inputs and has been identified in many molecular and cellular systems, from virus life cycles to cell fate2. Not surprisingly, understanding and developing biological memory devices is a major challenge in both systems and synthetic biology due to their potential role as part of complex decision-making systems3-8. Information-storing molecular devices are required, for example, to engineer learning in cells, making them capable of reacting to specific signals in such a way that they are prepared for unpredicted challenge9,10. A repertoire of potential phenotypes can be available if multistability is present, whereas switching from one state to the other under given inputs is the basis of information processing. Several approaches have been followed towards designing memory circuits in biological systems. The minimal motifs required to engineer memory (i.e. the map of regulatory interactions) have been systematically explored and implemented in vivo11 . For systems involving a single component (Figure 1a), memory can be implemented via a positive feedback loop. In this minimalistic scenario, a component A, e.g. a gene or a cell, produces a signal a, e.g. a molecule or a transcription factor, which is not produced constitutively but in response to the input same signal a. The off state of this



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system corresponds to the absence of the signal. Upon a transient external stimuli a is produced, the system evolves toward the on state and remains in this state even when the external input is removed. This minimal system was implemented in yeast based on a transcriptional positive feedback11. However, single-component memory devices have important limitations. The off state corresponds to an unstable fixed point; once the system changes from the off to the on state is not possible to move back to the off state unless the signal is completely removed from the system. Moreover, the system is sensitive to fluctuations and leakiness that can induce the transition from the off to the on state in the absence of external triggers. A more robust architecture that permits memory implementation is the mutual repressor motif (Figure 1b), which involves two components (A and B) mutually repressing each other through the action of their constitutively produced signals (a and b). Two possible stable states are accessible, namely signal a is produced and b repressed or the other way around. A memory device based on this architecture was implemented in vivo by Gardner and co-workers8, by engineering a toggle switch in Escherichia coli with two repressor proteins (LacI and TetR). This system is less sensitive to leakiness and noise-induced transitions since transitions from one stable state to the other are possible by either repressing the dominant signal or externally inducing the expression of the repressed signal. Recently, other mechanisms for memory devices have been explored in vivo, such as the use of DNA recombinases that upon a transient input induce a permanent change on DNA by triggering or blocking the expression of a given gene6. The design and implementation of these memory devices have been obtained in single cells and have served to explore the minimal architecture necessary to achieve stable memory devices. However, the use of single cell circuits and the potential scalability to more complex designs suffers from a number of constrains12 particularly


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in relation with undesired interactions and cross-talk among engineered components. These inevitable outcomes lead to unexpected behaviour and strongly constrain the usage of intracellular wiring molecules. Here, we propose a novel approach for the implementation of synthetic memory devices by using multicellular consortia. The use of consortia has some advantages in relation to single cell circuits13-16. This type of architecture limits the presence of undesired interactions, providing a desirable compartmentalization that limits cross-talk, reduces cellular engineering and facilitates the reuse of molecular components. It has been recently suggested that one class of memory device, the so called flip-flop circuit, can be constructed using a four-cell consortium of yeast cells, under the yeast alpha-factor as wiring molecule17. This was done using in silico simulations based on a detailed, multi parametric model of the pheromone pathway18. Here we present an alternative, simpler design of this memory device by experimentally engineering a consortium including just two cell types. This consortium is capable of implementing a flip-flop circuit following the minimal possible set of design principles. As shown below, because of its modular nature and predictability, our designed circuit provides a baseline for future decision-making circuits where memory elements play a role.

RESULTS AND DISCUSSION Design of a multicellular memory device With the aim to create a new type of memory devices, we explored theoretically an optimal configuration for in vivo applications. The basic logic scheme of this circuit is shown in Figure 1c. Our approach takes advantage of natural architectures already present in cells. The system includes a sensor layer and a single signalling pathway similar for both cell components, A and B. In addition, two wiring molecules



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a and b were used to transmit information from A to B and from B to A. These wiring molecules are implemented by means of two orthologous diffusible factors. Thus, the architecture of both components are very similar, however each component has a different engineered sensor layer (i.e. receptor protein) as well as a different output production (i.e. wiring molecule). In this embodiment, component A sense signal b and produce signal a according to a NOT logic, i.e. only in absence of molecule b molecule a will be secreted into medium. Similarly, cells of type B sense a and produce b according to a NOT logic. The NOT logic present in each component is implemented by expressing a protein L that represses the expression of the output signal a or b. Since both components (A and B) are located in different cell types, they can be built using the same repressor protein L, expressed under the control of the same signaling pathway. Under this scheme, only when the external signal is present, the repressor proteins will be produced and the production of the output signal will be inhibited. As shown below this logic scheme supports a bistable dynamical system where two alternative states are available, with a predictable switching behaviour characteristic of a well-defined biological memory device. To determine and characterise the existence of bistable dynamics, the response of our system was analysed with a minimal mathematical model, where the internal details of the underlying network of biological responses were not included. Since the signalling time scales for external cues is very fast compared to protein production, we can apply the rapid equilibrium approximation in the mathematical model19. This approximation considers a separation of time scales, in such a way that the faster process reaches the steady state almost instantaneously compared to the slower process. Hence, our dynamical modelling approach just requires repressor protein production


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and molecular signal secretion. Based on these assumptions, the production of the repressor protein LA in the cellular consortium A can be described by (1)

being δL the degradation rate of the protein. Here, f(b) is a non-linear response function that describes the relationship between the external signal concentration b and the production rate of LA. In this model, we considered a generalised expression for this relation as described by a Hill-like functional form, namely:

(2)

Finally, signal a production is negatively regulated by LA according to

(3)

Here, Na and Nb stand for the cellular populations of A and B cell types according to a standard logistic growth dynamics model, namely (4)



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(5)

In this model, we considered a scenario where degradation of a given signal molecule (either a or b) is actively mediated by the opposite cell type. This particular scenario in our experimental implementation is based on yeast consortia, where a and b are implemented by mating pheromones, which can be actively degraded 20. Analogously, consortium B dynamics is described by the following symmetric expressions: (6)

(7)

(8)

Of note, although the repressor proteins, LA and LB, are depicted as two entities depending on the specific cell type, since they are expressed in different cells, they can actually be implemented experimentally by the same molecule.

Two-cell implementation of a biological memory device To implement a multicellular memory device in vivo, we engineered two types of 8 Environment ACS Paragon Plus

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yeast cells that correspond to the elements A and B of the general model (Figure 1c). One cell type is able to sense the α-factor mating pheromone from Saccharomyces cerevisiae (αSc) while the second type senses the α-factor mating pheromone from Candida albicans (αCa). In addition, each cell responds to a specific pheromone (e.g. αSc) and it is engineered to secrete the alternative pheromone (e.g. αCa) as output. Both cell types respond with a NOT logic (Figure 2a). Therefore, the cell type A (NOT Sc) produces αCa (a in the model) in the absence of αSc (b in the model). In this cell, C. albicans α-factor mating pheromone gene (CaMF(a)1) is constitutively expressed under a modified TEF1 promoter that contains LacI binding sites (PTEF1i). In the presence of LacI repressor (L in the model) the TEF1i promoter is inhibited. This cell type naturally expresses the S. cerevisiae pheromone receptor STE2 (b sensor in our model) and it is able to sense αSc in the media which leads to pheromone pathway activation. LacI repressor is expressed by the pheromone responsive promoter FUS1. Thus, in the presence of αSc, cells trigger FUS1 promoter transcription and LacI is produced and represses the expression of αCa from TEF1i promoter. Conversely, in the absence of αSc, C. albicans α-factor is expressed and secreted into the media (Figure 2b, left). The cell type B (NOT Ca) produces αSc in the absence of αCa. The architecture of this cell is symmetrical to the NOT Sc cell. It expresses constitutively the S. cerevisiae α-factor mating pheromone gene (MF(a)1) under the control of the TEF1i promoter. Moreover, it expresses the C. albicans α-factor pheromone receptor CaSTE2 (a sensor in the model) integrated in the S. cerevisiae STE2 locus. Thus, this cell specifically sense αCa (and not αSc) and activate the pheromone pathway accordingly. As for the NOT Sc cell, LacI repressor is transcribed from the FUS1 promoter. Thus, in the presence of αCa, LacI repressor is produced and represses the



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expression of αSc. In the absence of αCa, S. cerevisiae α-factor is expressed and secreted into the media (Figure 2b, right). See Figure S1 and Supporting Information for the logic functions of each cell and graphical notation. Once the consortia (A and B) act together, the NOT Ca and NOT Sc mutually inhibit each other. Given this architecture, two extreme scenarios are possible: αCa is produced by NOT Sc and inhibits αSc production from the NOT Ca (high αCa, low αSc; state 1) or the opposite scenario where αSc inhibits αCa produced by NOT Sc (low αCa, high αSc; state 0). This architecture allows, in principle, the implementation of a memory switch device. To assess the proper computational output of our system as a logic circuit, we measured the production of αCa. High or low level of αCa indicates the memory state of the device (in a Boolean language, state 1 or state 0). We therefore engineered a third cell able to sense the presence of αCa in the media (BUF Ca; Figure 2c). This cell was designed to produce GFP in the presence of αCa according to a Buffer logic. Briefly, this cell expresses the C. albicans pheromone receptor (CaSTE2) from the ScSTE2 locus to specifically sense αCa in the media. Also, it expresses GFP under the endogenous FUS1 promoter (FUS1::GFP). Therefore, in the presence of αCa, activation of the pheromone pathway leads to induced GFP expression. Similarly, we built a cell able to sense αSc (BUF Sc). Then, we characterised both buffer cells (BUF Ca and BUF Sc) for their ability to respond to their respective input in different conditions (Figure S2). BUF Ca cells showed high specificity towards its input (αCa) and did not respond to αSc (thus crosstalk can be neglected). Of note, the presence of αSc did not affect the response to αCa when both inputs were simultaneously added. Furthermore, when BUF Ca cells were incubated with αCa together with a protease that specifically degrades αCa (C. albicans Bar1 protease, CaBar1), response to the


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input pheromone was produced (Figure S2a). Similar data were obtained when BUF Sc cells were treated with S. cerevisiae Bar1 protease (Figure S2b). The genotype and the graphical notation of the logic function performed by each BUF cell are depicted in Figure S1 and Supporting Information. To characterise each of the NOT cells of the circuit, we coupled each one to their respective BUF cells and assessed their response after addition of either Sc or Ca pheromones (input) (Figure 3a). The resulting output was quantified by measuring the fluorescence produced by the BUF cells using flow cytometry. BUF cells can be specifically analysed because they also express constitutive mCherry fluorescence (ENO1::mCherry), which allows distinguishing them from NOT cells. GFP fluorescence of BUF cells was calculated as mean GFP fluorescence (in arbitrary units, see Materials and Methods section). In the absence of input, the corresponding pheromone is produced by the TEF1i promoter and secreted into the media (black bars). In contrast, in response to input pheromone, the LacI repressor is induced and represses the production of the alternative pheromone (white bars). Both NOT Sc and NOT Ca cells responded as expected to the specific input with clear separation between 0 and 1 logic states (Figure 3a). As designed, our system presents an intrinsic memory due to the activation of a repressor with a specific half-life. Briefly, after LacI induction by the input and until LacI degradation, cells maintain a one state outcome. In this case, memory is not established by the consortia of the two NOT cells, but rather to an intrinsic propriety of the system that needs to be taken into account to assess the outcome of the memory device. To quantify this effect, NOT cells were treated with the specific pheromone input (Figure 3b, white bars) during different times: 0, 1, 2, 3 and 4 hours and then 16 hours after washing the input, cells were incubated with their respective BUF cell for



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4 hours. As shown in Figure 3b, the input pre-treatment induces LacI expression which maintains the repression of the TEF1i promoter for 4 hours (i.e. intrinsic memory). When cells were grown for 16 hours in the absence of the input after the initial exposure (grey bars), the TEF1i promoter was able to recover from LacI inhibition and induced pheromone expression. These results show that if we measure the circuit output after 16 hours, the intrinsic memory contribution is negligible. The transfer functions of each of the cells, BUF cells and NOT cells (i.e. the relationship between different input concentrations and output production) were fitted by using least squares (Figure S3). The transfer function of BUF cells was assessed by measuring the response to increasing concentrations of synthetic pheromone and fluorescence was quantified by flow cytometry. The transfer function of NOT cells was also evaluated by incubating them with their corresponding BUF cells and output fluorescence of BUF cells was upon exposure to increasing levels of pheromone input. Despite the advantages of designing synthetic devices from a multicellular framework, a potential challenge is to maintain a balanced growth rate of the different cell types present. To explore this problem, we characterized cell growth rates for the two NOT cells. We first measured growth rate separately and found no major differences among cell types (Figure S4a). Then, we tagged NOT Sc cells with a constitutive fluorescence signal (ENO1::mCherry), and incubated with an initial 1:1 ratio with NOT Ca cells for three days in a competition assay. The percentage of cells of each type present in the consortia was then measured using flow cytometry. As shown in Figure S4b the percentage of both cell types within the consortium was constant for at least three days. All together these results suggest that a potential problem of an unbalanced growth can be neglected in our experimental setup.


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The multicellular memory device triggers a bistable dynamic response An essential requirement to implement a memory device is the existence of bistability, i.e. two alternative, well defined stable states. Both states must be accessible depending on the initial conditions (e.g. external inputs). Once one of these states has been reached, proper memory behaviour requires that the system remains in this state even if the initial external input is removed. Initially, we explored experimentally the existence of these two potential stable states using our engineered NOT cells (Figure 4a). The consortia containing the NOT Sc and NOT Ca cells were incubated with one input (e.g. αCa) to assess whether the system could establish a steady state (e.g. high αCa, low αSc; state 1) and maintain it along the time (i.e. the multicellular consortia is able to store information). Briefly, once the external αCa pheromone is added to the cell mixture, αCa inhibits the production of αSc from NOT Ca by secreting more αCa, thus maintaining the previous state. The production of αCa in the absence of the input will indicate the system behaves as a memory device. To test this prediction, NOT Sc and NOT Ca cells were mixed and treated with synthetic αCa for 4 hours. After washing, the multicellular consortium was incubated without the input for 4, 16 and 48 hours. The presence of αCa pheromone in the supernatant of these samples was assessed by their incubation with BUF Ca. After an initial incubation with synthetic αCa to fix an initial state (state 1), this was maintained for 48 hours (Figure 4b, white bars and Figure 4c, upper panel). Therefore, the multicellular consortia kept memory of its previous state. Similar data where obtained when αSc was added to the consortia (Figure 4b, black bars and Figure 4c, lower panel). These experimental results showed that this multicellular consortia is able to implement a bistable memory device.



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The memory device has two outputs. Since one output is the inverse of the other we considered it as a two-input-one-output device, using 〈Ca as the output. Nevertheless, to further characterize the circuit, we wanted to measure the levels of αSc as a secondary readout of the memory state. Initial characterization involved incubating NOT Ca cells in the presence or absence of synthetic αCa. After 4 hours the supernatants were collected and incubated with BUF Sc cells for 4 hours. Fluorescence of BUF Sc cells was measured by flow cytometry. We observed that in the absence of external αCa, NOT Ca cells produce αSc which can be clearly measured by an increase in BUF Sc cells fluorescence. Addition of synthetic αCa reduces the production of αSc that is reflected by a reduced BUF Sc fluorescence (Figure S9, white bars). These results indicate that, under these conditions, αSc can be measured via incubation wit BUF Sc cells. We then simulate a scenario much more similar to the set up of the memory device: NOT Ca coexisting with another cell population that senses αSc. For this purpose we mixed NOT Ca cells and BUF Sc cells together and co-cultured them in the presence (or absence) of synthetic αCa. After 4 hours we incubated the supernatants of this mix with new BUF Sc cells. Fluorescence of BUF Sc cells was measured by flow cytometry. (Figure S9, black bars). Surprisingly, we could not observe an increase in BUF Sc fluorescence neither in the absence nor in the presence of external input. The nature of αSc as well as an effect of BUF Sc threshold response may impair the measurement of αSc. This simple experiment points out that an indirect quantification of αSc will not allow us to define the state of the memory device.

Memory state changes can be externally triggered


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For a deeper mathematical analysis of the memory device, we fitted the model parameters to reproduce the experimental results and describe the device response upon addition of αCa and αSc. The model considers the two pheromones a (〈Ca) and b (〈Sc) as wiring molecules, and three different cell types: NOT Sc, NOT Ca and the BUF Ca as a reporter cell. The description of the whole system should include equations (1)-(8) and the response of the BUF Ca upon different 〈Ca concentration in the steady state. This response is described by a Hill function

The parameters were determined by using a standard non-linear least squares method (Table S1). The output of the model adjusted correctly with the experimental data (Figure S5). Once the parameters were obtained, we performed a nullcline analysis in the phase plane αCa versus αSc to analyse model stability. This analysis is performed considering that the system has reached the steady state for all their components. At the steady state, equations (1)-(8) satisfy the conditions:

(9)



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If we do this for a as a function of b and also b in terms of a, we obtain the so called nullclines, i.e. the two following curves:

(10)

and

(11)

(here at steady state we have NA=NB=1). The fixed points of the system are located at the intersection points of nullclines21. By defining these null clines we can actually describe functional dependencies among variables. When both nullclines are represented in the (a,b)-phase plane (Figure 5), we find three crossing points. The system described by equations (10) and (11) cannot be solved analytically. As a consequence, only a numerical approximation to the values of the fix points can be calculated. Numerical solutions using parameters shown in Table S1 reveal the existence of tree fixed points corresponding to E1={a=6.2·10-7 nM, b=0.74 nM, LA=2.18 nM, LB=1.4·10-7 nM}, I={a=6.1·10-4 nM, b=4.02·10-2 nM, LA=1.3·10-2 nM, LB=3.45·10-7 nM} and E2={a=7.04 nM, b=1.6·10-9 nM, LA=4·10-8 nM, LB=13.85 nM


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} (see figure 5 and Supporting information). In order to characterise the stability of our fixed points, a numerical study of different trajectories in the (a,b)-phase plane has been performed. These numerical results, as shown in Figure S8, indicate that fixed points E1 and E2 are stable whereas the fixed point I is unstable. It is worth mentioning that there is a clear asymmetry between both basins of attraction (Figure S8a), being the basin of attraction of E1 significantly smaller than the one associated to E2. These results confirm that the circuit built with the consortia containing NOT Sc and NOT Ca responds as a properly defined memory switch device. One of the advantages of using a multicellular consortium to construct a logic device is the possibility to interact externally with the device and modulate the levels of the wiring molecules (a and b). A transient modulation of the wiring can trigger a switching from one stable state to the alternative state. Such wiring molecule levels could be modulated in vivo by: (i) the reduction of the levels of the dominant molecule (negative modulation) or by (ii) increasing the levels of the less represented molecule (positive modulation). When the levels of the dominant wiring molecule are below a critical level, the internal state of the device defined by the concentration of the repressor proteins LA and LB would change. This change can trigger a transition from one stable state to the other. Once the external perturbation is removed, the system would then remain in the new stable state. To consider this potential scenario, equations (3) and (8) need to be modified to include an addition term that accounts for the mechanism used to reduce the levels of the wiring molecule not depending on cell concentration as follows:

(12)



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(13)

where ρa describes the effect of the external mechanism used to reduce the levels of wiring molecules. Similarly, if the external addition of the non-dominant wiring molecule overcomes a critical value, the same effect is observed (Figure S6). Thus, the mathematical model predicts that a memory device created with a multicellular consortia is susceptible to external tuning and that state transitions can be reversed by a proper exogenous signal. This specific prediction has also been tested.

External switching between stable states We performed several experiments to validate the previous outcomes predicted by the mathematical model. Specifically, we analysed both the effect of increasing the rate of pheromone degradation by adding a specific protease and the addition of external pheromone into the system (Figure 6a). The consortium containing the NOT Sc and NOT Ca cells was treated with synthetic αCa for 4 hours, washed and incubated without the input for 4 or 16 hours respectively (as in Figure 4). After 16 hours, CaBar1 was added to the culture for a period of 6 hours and the outcome of the circuit was analysed after 12 and 24 hours (Figures 6b and 6c). The presence of αCa in the supernatant was assessed with the BUF Ca reporter cell. The treatment with synthetic αCa inhibited the production of αSc from NOT Ca and, as a consequence, NOT Sc cells produced αCa themselves maintaining a state of high αCa and low αSc (state 1) (Figure 6b,c left). Notably, as predicted by the model, the addition of CaBar1 resulted in a decrease in αCa which was maintained also after CaBar1 removal. Thus, the system changed its state to low αCa and high αSc (state 0) and maintained it for at least 18 Environment ACS Paragon Plus

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24 hours (Figure 6b,c, right, lower panel). Figure S6a illustrates the similarities between experimental results and theoretical predictions. Similar data were obtained when ScBar1 was added to the memory device previously treated with αSc (Figure S7a). Thus, the external manipulation of the wiring properties serves indeed to change the state of the memory device. A second prediction from the mathematical model was the ability to change the state of the device by the addition of wiring molecules. Starting from a state of high αCa and low αSc (state 1) the system was predicted to switch to a state of low αCa and high αSc (state 0) through the addition of synthetic αSc. To test this prediction, we repeated the previous experiment but adding synthetic αSc once the system was stabilized after 16 hours. Cells were incubated with αSc for a period of 6 hours and after washing the synthetic αSc (pheromone removal) the circuit was then analyzed after 4 and 16 hours, respectively. The supernatant of these samples was incubated with BUF Ca to measure the amount of αCa present in the media. As shown in Figure 6b and 6c, the addition of αSc established a new steady state (state 0) and inhibited αCa production by NOT Sc cells. As a consequence, in the absence of αCa inhibition, NOT Ca cells produced αSc and inhibited the synthesis of αCa. Thus, as the model predicted, at 16 hours after αSc addition, the production of αCa was clearly inhibited and thus maintained the switch to state 0 induced by counter-input addition. Figure S6b shows the similarities between model predictions and experimental results. Similar data were obtained when αCa was added to the consortium previously treated with αSc (Figure S7b). Therefore, as it was shown by the pulsed manipulation of the wiring molecule by degradation, the addition of an external input also served to change the status of the memory device as predicted by the mathematical model.



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Implementation of a memory device in a microfluidic platform. We transformed NOT Sc cells with a short-live fluorescent reporter (i.e. UBIYdkGFP*) under the control of the pheromone responsive promoter (i.e. FUS1p). In this way, the levels of the counter-output (i.e. αSc) and the behavior of the memory device were followed by a direct real time fluorescent measurement. We performed a time-lapse experiment using a microfluidic platform to characterized NOT Sc GFP* (Figure S10).

NOT Sc GFP* cells were placed in a microfluidic platform and challenged for 3 hours with 50nM αSc followed by a period in the absence of input. We observed an increase in fluorescence when external αSc is added. Once the input is removed from the media the signal decayed and returned to basal levels in 2 – 3 hours (Figure S10). NOT Sc GFP* and NOT Ca cells were mixed and placed in a microfluidic platform (Figure 7a). When αSc was added for 3h, NOT Sc GFP* increased its fluorescence (Figure 7b). Once the input was removed from in the media the signal was sustained for at least 3h. When ScBar1 was added to the mix, NOT Sc GFP* fluorescence rapidly decreased (Figure 7b, green). These results suggested that, in the absence of external αSc, NOT Ca cells kept producing αSc. However NOT Ca cells could not counteract the effect of external protease addition (Figure 7b). After 3h without input, cells were exposed for an additional 3h to different conditions. First, in the absence of the input (light blue), the signal was maintained suggesting that NOT Ca cells produced αSc and could sustain the state up to 6 hours. Second, ScBar1 addition (dark blue) induced a rapid signal decay, suggesting that external protease addition counteracted NOT Ca αSc production. As well as for ScBar1, a 6-fold increase in the flow rate (violet) induced a rapid signal decay. These results suggest that an extremely high flow


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rate in the chamber washed away the αSc produced, impaired the communication between the two populations and disrupted the memory state. Finally the addition of 50nM αCa (red), reduced the signal. In these conditions, NOT Ca cells are repressed and can no longer produce αSc, which is washed away by the basal flow rate. All these experiments suggests, that when both cells are mixed and challenged with αSc, the state of high αSc is set and maintained for at least 6h in the absence of external αSc. The state of high αSc is maintained by the NOT Ca consortia. Therefore, by repressing the NOT Ca cells, either by actively degrading αSc or via increasing the flow rate, the communication between the two populations is lost and the memory state is erased. Additionally, we tested the opposite scenario: αCa was added for 3h, followed by a period of 3h without input. Then αSc was added for 3h, followed by a period without input. We observed that during αCa addition and after 3 hours there was a basal fluorescence of NOT Sc GFP* cells. However after addition of αSc the signal changed to a high state and it was maintained for more than 3 hours. Previous experiments following αCa showed that when mixing NOT Sc and NOT Ca cells they behaved as a memory device. The circuit could be set to low or high levels of αCa, which were kept in the absence of the input. Similarly, microfluidics experiments following αSc with a direct readout showed that low or high levels of αSc can be set and kept in the absence of the input, a behavior that is opposite to the αCa levels.

Discussion Synthetic biology offers a unique opportunity both to interrogate natural systems22 and to develop novel strategies for biomedical research23,24. Because cellular functions can be understood in terms of some sort of computation25-30, one obvious ques-



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tion that is often formulated involves the limits of biological design rules: how complex can engineered circuits be? Memory devices are one of the fundamental components of standard electronic circuits, and their simplest versions have been implemented using genetic regulatory motifs as well as with molecular, cell-free circuits. In most cases, such devices have been engineered (or proposed as in silico designs) in single cells, but computational approaches based on cellular consortia might provide a parallel solution14,15,31’33. Here we proposed a memory device that can be implemented using multicellular consortia. This choice allows us to build cells that are easier to engineer. Additionally, the modularisation of our system resulting from the use of cells as parts of the circuit allows an extensive reuse of molecular components (e,g, the pheromone pathway). Beyond these building advantages, the successful implementation of our memory device suggests that the synthesis of decision-making biological circuits can strongly benefit from the multicellular approach taken here. Since the memory state is maintained in the media (and not within a single cell), affecting the media could modify the memory state without the need of interfering with the cells engineering. Addition of external proteases or flow rate modification in a microfluidic platform are just two examples of how to externally interfere with the memory state. Also, we have a system where two cells communicate through pheromones and are able to establish and maintain the asymmetry in the levels of one pheromone. Taking advantage of this propriety we could develop memory devices with cells that are able to produce pheromones in response to a desired input. In this scenario the circuit memory could be set and maintained by a broad range of inputs. Still, further investigation on consortia proprieties has to be performed to get and optimized and predictable behavior.


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Although we have limited our approach to two closely similar cell types, it can be easily generalised (at least computationally) to different kinds of cells provided they can communicate through the basic feedback loop described here. Moreover, the potential for switching the system states through an externally provided signal opens many interesting possibilities. We can store different states in a given consortium and that might be generalised to multiple states beyond the bistable scenario presented here. Along with the switching behaviour defined by the nonlinearities intrinsic to the model, a potential control from the outside is obviously relevant if the designed device has to play functional tasks. This might correspond to engineered cells within bioreactors requiring multiple potential states to be achieved under given conditions or thresholds. Similarly, our synthetic multicellular design might be part of an “organin-a-chip” or even an artificial organoid within a living organism. One particularly interesting possibility concerns the extension of our implementation to the human microbiome, by engineering microbial consortia to perform memory-dependent functionalities. The microbiome is linked to a wide variety of diseases, including those related to metabolism, autoimmune responses and cancer. Here the approach of synthetic biology has become a major potential path to harnessing these naturally commensal microorganisms to prevent infections, deliver desired molecules targeting given diseases34. Of special interest for our context, the gut microbiome35 defines a multi species layer of information processing where a community of symbionts can sense a broad repertoire of signals, but also remember and report on their past history. Here too engineering memory elements has been successfully tested35 showing that reliable designs allow synthetic strains to sense and record exposure to a chemical cue, ATC, in the mammalian gut. Such design can be easily extended, using our distributed ap-



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proach, to other more complex circuits involving associative learning (Macía and Solé, in preparation). This type of system combines memory and learning components that can be safely separated in two (or more) types of cells thus effectively defining a division of labor where memory and response are segregated in two different (comunicating) cellular strains. Similarly, it has been recently suggested36 that a proper engineering of interactions among natural and engineered strains in an ecological context can help fighting catastrophic shifts in degraded ecosystems37. The approach is in part inspired in the similarities between complex ecosystems and the ecology of the gut microbiome38 . The distributed nature of our approach provides useful paths to safely engineering ecological interactions by designing controllable consortia (and not just single strains). The success of the designed memory devices in the gut microbiome context offers some clues to exploit similar approaches -but in a multicellular consortium context- to monitor and store signals in the field that could help endangered components of the ecosystem to properly respond to external uncertainties. In either case, our system provides both a source of informational complexity (a diversity of alternative cell states) as well as a controllable, switchable device whose state can be modified by using probiotics, specific drugs or suitable chemicals. Future work should explore the previous scenarios both in vitro and in silico. and how these nonstandard designs might allow redefining the nature of major innovations in biology39.


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METHODS Engineered yeast cell library and cell growth conditions Yeast W303 cells (ade2-1 his3-11,15 leu2-3,112 trp1-1 ura3-1 can1-100) were genetically modified to: produce a fluorescent protein (i.e. GFP) in response to αSc (BUF Sc) or αCa (BUF Ca), repress the constitutive expression of αCa (NOT Sc) or αSc (NOT Ca) in the presence of αSc or αCa respectively, or secrete S. cerevisiae Bar1 protease (i.e. Bar1) or C. albicans Bar1 like protease (i.e. CaBar1) when galactose is used as carbon source inducer. Schematic genotypic characteristics of each cell and plasmid used are summarized in Supporting information, Figure S7, and Tables S2 and S3. Cells were grown overnight in selective media and diluted to mid exponential phase in rich media at 30 °C. Memory switch experiments were performed at 25 ºC.

Protease purification Yeast W303 cells stably expressing S. cerevisiae BAR1 protease gene (ScBar1 in this study) or C. albicans Bar1-like protease gene, SAP30, (CaBar1 in this study) under the control of the GAL1 promoter (pRS406-PGAL1-Bar1 and pRS406-PGAL1-CaBar1) were grown in synthetic media containing 2% raffinose. Cells were diluted at OD660nm = 0.8 in synthetic media containing 2% galactose to induce protease expression. After 4 hours the supernatant was collected and concentrated using 10 KDa Centrifugal Filter Units (Amicon® Ultra). Filtered products were stored at -20ºC.

Synthetic pheromones



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The synthetic pheromones used in this study are C. albicans α-factor like mating pheromone (GFRLTNFGYFEPG) and S. cerevisiae α-factor mating pheromone (WHWLQLKPGQPMY). αSc and αCa were synthesized by the peptide synthesis facility (UPF) in free dithiol form. The peptides were diluted in H2O2 to a final concentration of 3 mM and stored at -20 ºC.

Output detection by flow cytometry in single cells All experiments requiring flow cytometry were analyzed as follows; Samples were diluted in PBS with cyclohexamide 1x and analyzed using flow cytometry (BD LSRFortessaTM). A total of 10.000 cells were collected from each sample. Constitutive mCherry fluorescence of BUF Ca and NOT Sc cells was used to differentiate them from other cells and calculate the frequency of the population in the sample. Specific emission in the fluorescence channel was measured versus autofluorescence (PerCPCy5-5-A channel for GFP and PerCP-Cy7 channel for mCherry). Mean GFP fluorescence of the BUF cells population was calculated using FlowJo or BD FACSDiva software. Data were expressed as mean fluorescence (in arbitrary units).

Characterization of the engineered cells through transfer function We followed standard electronics for defining a positive signal from a circuit as described. A proper characterization of the library of engineered cells is necessary to analyze the so-called Transfer Function, i.e. the cellular response with respect to different input levels. Figures S2 shows the full set of transfer functions for each cell. This procedure allows characterization of cellular behavior: all these curves exhibit the proper shape to be logic blocks for a multicellular implementation. BUF cells


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were grown to mid exponential phase, diluted at OD660nm = 0.1 and then incubated for 4 hours at 30 ºC with different amounts of pheromone. Fluorescence was analyzed by flow cytometry. NOT cells were grown to mid exponential phase, washed, diluted at OD660nm = 0.4 and mixed at 4:1 ratio with the respective BUF cells. Mixtures were incubated at 30 ºC for 4 hours with different amounts of the corresponding input and analyzed

by

flow

cytometry.

Constitutive

fluorescence

of

BUF

cells

(ENO1::mCherry) was used to differentiate the two cell populations. The transfer functions represent the mean and standard deviation of three independent experiments.

Characterization of the engineered cells For crosstalk analysis, BUF cells were grown to mid exponential phase, diluted to OD660nm = 0.2 and then treated with different inputs individually (synthetic S. cerevisiae α-factor, synthetic C. albicans α-factor, Bar1 protease or CaBar1 protease) or in combination. Samples were incubated for 4 hours at 30 ºC and analyzed using flow cytometry. For growth rate measurements, cells were grown in YPD liquid media for 20 hours at 30 ºC. Absorbance at 660nm was measured every hour by Synergy H1, Biotek. For competition assay, NOT Ca and NOT Sc cells were mixed and grown in liquid media at 30 ºC for 3 consecutive days. Samples were taken every day and analyzed

by

flow

cytometry.

Constitutive

fluorescence

of

NOT

Sc

cells

(ENO1::mCherry) was used to differentiate the two cell populations. Data are expressed as percentage of mCherry positive cells.

Characterization of the intrinsic memory of the device



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NOT cells were grown to mid exponential phase and pre-treated with 50 nM of the specific pheromone for different time: 0, 1, 2, 3 and 4 hours. Cells were washed, diluted to OD660nm = 0.4 and divided in two samples. One sample was mixed at 4:1 ratio with the specific BUF cells in the absence (black) or presence (white) of 50 nM of the corresponding input. Mixtures were incubated for 4 hours and analyzed by flow cytometry. The other sample (grey) was grown for 16 hours in media without input. Cells were washed, diluted to OD660nm = 0.4 and mixed at 4:1 ratio together with the corresponding BUF cells. Mixtures were incubated at 30 ºC for 4 hours and analyzed by FACS. Constitutive fluorescence of BUF cells (ENO1::mCherry) was used to differentiate the two cellular populations.

In vivo bistability analyses NOT Sc and NOT Ca cells were grown overnight in selective media. Cells were washed, diluted in rich media to OD660nm = 0.4 and mixed together. The mixture was incubated for 4h in the presence of 50 nM of αSc or αCa. After washing, the multicellular consortium was diluted to OD660nm = 0.4 and incubated without the input for 4, 16 and 48 hours. The supernatants of these samples were incubated with BUF Ca to measure the amount of αCa produced. BUF Ca cells were grown in rich media, incubated with the samples for 4 hours at 30 ºC and analyzed by FACS.

Experimental validation of the transitions between the stable states NOT Sc and NOT Ca cells were grown overnight in selective media. Cells were washed, diluted in rich media to OD660nm = 0.4 and mixed together. The mixture was incubated for 4 hours in the presence of 50 nM of αCa or αSc. After washing, the multicellular consortium was diluted to OD660nm = 0.4 and incubated without the input


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for 4 and 16 hours. After 16 hours cells were washed, diluted to OD660nm = 0.4 in rich media and split in three different independent conditions: absence of input, presence of 50 nM of αSc or αCa and presence of 50 nM of CaBar1 or ScBar1. Samples were washed after 6 hours of incubation, diluted to OD660nm = 0.4 in rich media without input and incubated at 25 ºC. Supernatants were taken at indicated time points and incubated with BUF Ca to measure the amount of αCa produced. BUF Ca cells were grown in rich media, incubated with the samples for 4 hours at 30 ºC and analyzed by FACS.

Theoretical estimation of GFP fluorescence levels The model described by equations (1)-(6) allowed estimating the levels of αCa and αSc. Experimental levels of αCa were measured using the BUF Ca cells which senses αCa and produces GFP. The BUF Ca cells transfer function was determined at the steady state. The relationship between αCa and GFP levels fits to the following Hill function experimentally determined:

This empirical relationship was used in order to compare theoretical and experimental results. Microfluidic setup and image quantification CellASIC® ONIX Control System and Cellasics Y04C yeast plates were used as a microfluidic platform. Plates were pressurized and flowed for 30 minutes with a blocking buffer, containing 1% solution of bovine serum albumin (BSA) in PBS.



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Yeast cells were cultured in low fluorescence synthetic media and were loaded in the microfluidic plates together with the inputs according to manufacturer's protocol. Temperature was kept at 30ºC and experiments were run at 0.5psi. Brightfield and fluorescence images were collected with a Nikon Eclipse Ti Microscope using NIS elements Software (Nikon) and were analyzed with CellProfiler software using a custom-made pipeline.

Acknowledgements We thank Carlos Rodriguez-Caso, L. Subirana and A. Fernandez for technical support. AU is a recipient of a “La Caixa” fellowship. This work was supported by an ERC Advanced Grant Number 294294 from the EU seventh framework program (SYNCOM) to RS and FP, the Santa Fe Institute and AGAUR to RS, and funding from “la Caixa” Foundation in collaboration with “Centre per a la Innovació de la Diabetis Infantil Sant Joan de Déu (CIDI)”. FP and RS laboratories are also supported by Fundación Botín, by Banco Santander through its Santander Universities Global Division. The laboratory of FP and EN is supported by grants from the Spanish Government (BFU2015-64437-P and FEDER to FP; BFU2014-52333-P and FEDER to EN) and the Catalan Government (2014 SGR 599). EN and FP are recipient of an ICREA Acadèmia (Generalitat de Catalunya).

Author contributions All authors shared all the phases and topics of the work. JM and RS developed the theoretical framework. Circuits were designed and implemented by AU, JM, RM, EN, RS and FP. RM, JM, AU, EN, FP and RS wrote the paper.


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Conflict of interest The authors declare no conflict of interest.

Supporting Information This section contains a full description of each yeast strain and plasmid used in this study. It also provides parameters for model fitting and transfer function, as well as additional experiments related to characterization of cells behavior that are referenced in the manuscript.



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Figures Legends

Figure 1. Schematic representation of a memory device. (a-b) Minimal motif able to implement a memory device involving one (a) or two (b) components. (c) Schematic representation of the internal architecture of two different cell types, A and B, for multicellular implementation of a two-component memory device. The internal architecture of both cell types is identical. They differ only in the sensor protein (a sensor or b sensor) and in the genes encoding the output signal a or b. The presence of an external signal, a or b, activates a cellular signal pathway. In both cells, the NOT logic is implemented by means of a repressor protein LA or LB that prevents output production when the signal pathway is activated.

Figure 2. Experimental implementation of the memory device. (a) Schematic of NOT Sc and NOT Ca cells inhibiting each other by the production of two different pheromones. (b) NOT Sc cells produce and secrete C. albicans α-factor mating pheromone. In the presence of αSc in the extracellular media, NOT Sc cells activate the pheromone pathway which leads to the internal accumulation of LacI and to the repression of αCa production. NOT Ca cells produce and secrete S. cerevisiae α-factor mating pheromone. In the presence of αCa in the extracellular media, NOT Ca cells activate the pheromone pathway which leads to the internal accumulation of LacI and to the repression of αSc production. (c) BUF Ca cells senses αCa present in the media and produce GFP according to buffer logic.


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Figure 3. Characterization of NOT cells. (a) Quantification of single cell computational output coupling each NOT cells with the corresponding BUF cells. NOT Ca cells (left) were mixed with BUF Sc cells in the absence (black) or in the presence (white) of 50 nM αCa. NOT Sc cells (right) were mixed with BUF Ca cells in the absence (black) or in the presence (white) of αSc. After 4 hours of computation output results were obtained by measuring BUF cells mean GFP fluorescence (in arbitrary units; a.u.) using flow cytometry. (b) NOT cells were pre-treated with 50 nM of the specific pheromone for different times: 0, 1, 2, 3 and 4 hours. After washing, cells were either grown for 16 hours in media without the input (grey), or treated (white) or not treated (black) with 50 nM of the corresponding input. After incubation, cells were mixed with the corresponding BUF cells and incubated at 30 ºC for 4 hours. BUF cells mean GFP fluorescence (in arbitrary units, a.u.) was analyzed by FACS. Data represent mean and standard deviations of three independent experiments.

Figure 4. Implementation of a bistable multicellular memory device. (a) Schematic diagram of the elements involved in a multicellular memory device with bistable dynamics. (b, c) NOT Sc and NOT Ca cells were mixed together and incubated for 4 hours in the presence of 50 nM of αSc (black) or αCa (white). After washing, the multicellular consortium was incubated without the input for 4, 16 and 48 hours. Supernatants were taken at indicated time points and incubated with BUF Ca which fluorescence intensity was analyzed by FACS. Data are expressed as mean GFP fluorescence arbitrary units, a.u. (b) and as GFP intensity relative to autofluorescence in a pseudocolor plot (c). Data represent mean and standard deviations of three independent experiments.



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Figure 5. Phase plane analysis. The red line represents the nullcline associated to dαSc/dt=0, and the blue one corresponds to dαCa/dt=0. Due to the differences in the scales, the central plot is a qualitative representation of both nullclines shapes. The other plots are quantitative representation of the areas near the fixed points. E1 and E2 (squares) are stable fixed points, whereas I (circle) is the unstable one.

Figure 6. Experimental validations of the transitions between stable states. (a) Schematic diagram of the elements involved in the transitions between stable states of the multicellular memory device. (b, c) NOT Sc and NOT Ca cells were mixed together and treated as in Figure 4. After 16h cells mixture was washed and diluted in rich media without input, or with 50 nM of either αSc or CaBar1. Samples were washed and incubated without the input. Supernatants were taken at indicated time points and mixed with BUF Ca which fluorescence intensity was analyzed by FACS. Data are expressed as mean GFP fluorescence a.u. (b) and as GFP intensity relative to autofluorescence in a pseudocolor plot (c). Data represent mean and standard deviations of three independent experiments.

Figure 7. Implementation of the memory device in a microfluidic platform. (a) Schematic diagram of cells (NOT Sc GFP* and NOT Ca) in a microfluidic platform (Cellasics Y04C). (b) Initially 50nM αSc was added for 3h. Then the consortia were challenged with a period of 3h in the absence of any input or in the presence of ScBar1 (green), followed by different conditions: no input (light blue), ScBar1 addition (dark blue), 6-fold increase in flow rate (violet) or 50nM αCa addition (red). (c)


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NOT Sc GFP* and NOT Ca were initially treated for 3h with 50nM αSc (red) or 50nM αCa (blue) followed by a 3h without input. Then, cells were exposed to 3h with 50nM αSc (blue) or 50nM αCa (red) followed by a 3h without input. Single cell fluorescence was analyzed with CellProfiler software, and the mean and standard deviation of 3 independent fields were plotted.



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FIGURE 1


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FIGURE 2



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FIGURE 3


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FIGURE 4



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FIGURE 5


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FIGURE 6



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FIGURE 7


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A Synthetic Multicellular Memory Device - ACS Publications

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