Cell Signaling

There are three primary types of cell-cell signaling – characterized by their scale of action. First is contact or juxtacrine signaling, which acts when a cell comes in physical contact with another cell.  The second is paracrine signaling, which acts through emitted soluble signals over the short distance scale(localized to a tissue). This may also allow a cell to act on itself, as the emitted signal may also affect the cell producing it; this is called autocrine signaling.  Finally, there is endocrine signaling, where a cell emits chemical signals which are then distributed throughout the organism.By offering complete control over the microenvironment and the relative position of different heterotypic cells, the combination of optical trapping, microfluidics, and photopolymerizable hydrogel we developed offers an excellent platform for probing cell signaling.


The cell signaling model we want to test is that of Vibro Fischeri, a bacteria which lives in a symbiotic relationship with the squid Euprymna scolopes. When in the open sea, the bacteria do not express the proteins from the lux system – but when inside the light organ of the squid, they express all the genes of the lux system, producing light to help the squid forage and hide from predators.

The bacteria detect that they are in the light organ of the squid using “quorum sensing”, a method by which they detect that there is a sufficient density, a quorum, of bacteria to turn on the genes.  Bacteria detect this by finding that there is a sufficient quantity of acyl-homoserine lactone (AHL) in their surroundings.  This small molecule acts as a paracrine signal, allowing cells to assess the number of other nearby cells.


Following Weiss et al., I broke the lux system up into senders, which produce luxI (AHL producing enzyme) and mRFP under control of the lac operon, and receivers, which produce a degradable form of GFP (GFP-LVA) upon induction with AHL.


Using a 96-well fluorescent plate reader, we calibrated the sender and receiver bacteria to determine their response to different levels of ligand – IPTG in the case of sender bacteria, and AHL in the case of receiver bacteria.  Using these plate measurments, we can clearly see a threshold dependence on ligand concentration, which we will model using the Hill function.


To fully model the bacterial behavior, we used the equations pictured on the right, with the red background indicating the equations used for senders and the green background indicating the equations for receivers.  These are simply ordinary differential equations(ODEs), with a Hill function being used for the ligand threshold dependence.

Parameters are defined as follows: ν represents the rate of cell reproduction(and hence the rate of protein production increase), β is the level of protein production, K is the threshold level of ligand, with n the sharpness of the Hill function.  α is the rate of protein degradation.  γ represents the rate of fluorescent protein oxidation; fluorescent proteins must undergo oxidation after translation in order to be fluorescent and hence detectable.


Then, to model the mass transport of the AHL throughout the system, we implemented a finite-element model (FEM) of our microfluidic device (as pictured to the left).  We defined the convective flow inside the hydrogel as zero, so that the only method of mass transport was diffusion, but outside the hydrogel convection and diffusion both played a role in determining the concentration of AHL.

The convection-diffusion simulation was coupled to the results from the bacterial ODEs in order to determine the time-varying concentration of AHL.

As shown above, the simulation was able to describe the behavior of the experimental system.  On the left an array of sender and receiver bacteria is shown.  As the movie progresses, the red(sender) bacteria begin to fluoresce, which indicates that they are also producing an ever-increasing amount of AHL.  Once the local concentration of AHL is sufficient, the green(receiver) bacteria begin to fluoresce.  The simulation on the right echoes this behavior, with the blue background indicating the calculated time-dependent AHL concentration.

In this controlled microenviornment, we were able to experiment, and found that there was a critical amount of sender bacteria required to generate a sufficient level of AHL for receiver response.

The first video shows a microarray with a single sender position.  Though this sender fluoresces, indicating AHL production, the AHL is transported away too rapidly for the concentration to build up beyond the threshold concentration for receiver response.

In contrast, the second video shows a microarray with four sender positions.  The combination of these senders was sufficient to trigger receiver response.

degrade_graph1aIt is important to note that the receiver bacteria are using a degradable form of GFP, as first outlined in Andersen et al.  This means that, once the AHL concentration drops below the threshold, the fluorescent signal should rapidly decline. This allows a demonstration of the other advantage to microenviornmental control – pulsing of the ligand.  By exogenously applying AHL to the array pictured on the right, then at t=~+5hrs removing the AHL, we can determine how bacteria repsond to turning off these genes.  The values of fluorescence for the different bacteria in the array are plotted on the near left.


By changing the transport characteristics of the system through altering the flow rate over the hydrogel, we can alter the distribution of AHL.  As shown to the left in a 3D array (2x2x3) of bacteria, with senders in the middle positions, by turning up the flow rate, we can remove the AHL faster than it builds up, turning back off the receiver response.  When the flow rate is reduced, the receivers respond again.

This shows that paracrine signaling allows the cells to detect their environment as a sort of sonar – if they feed signal out and it builds up, they know that they are in a confined, static location.  If the signal does not build up, they are in a large or rapid flow environment.