I'm Albert Falk. I'm a professor at the University of Washington in Seattle, and I work on the broad area of biomes, which stands for biomedical micro electromechanical systems. And we work on microfluidics and we focus on applications in cell biology, primarily in in neuroscience.
One of the focuses of the lab is to try to overcome the limitations of traditional cell culture technology. If you look at the, the, the way cell biologists study cells in, in vitro in a, in a Petri dish, you will notice immediately that the cells are bathed in a homogeneous cell culture medium, and they're also sitting on a, on a surface that is homogeneous. However, in the body cells are not on a homogeneous surface.
They're not bathed in, in a homogeneous medium. They're actually exposed to a variety of, of signals that change in space and time, often the form of gradients, and they are surrounded by a, by a gel type of matrix. There are also some practical limitations when you study cells in a Petri dish, you need an incubator, which is a, a large and expensive piece of equipment.
Typically, biologists want to study a bunch of different conditions. They want to study cells in a, let's say in a cell culture A and cell culture B.There's a lot of variability in how cells respond to these compounds. So typically the biologists put the, put the cells on different petri dishes and they, maybe they do it in triplicate and, and then they study a, a range of conditions to, so they end up using a lot of cell culture, medium, a lot of supplies, a lot of dishes, and that takes a lot of space.
So we trying to miniaturize that, and that's one aspect of it. Also, the exchanging this and putting the cells and exchanging the cell culture medium, that requires a lot of pipetting. That's, that's very expensive in terms of the amount of time and, and personnel that it, that it involves, and also the amount of fluids that need to be placed in and out of the, of the Petri dish.
Another limitation is that in order to fill all the surfaces with cells, you have to use a large number of cells, and that often requires sacrificing a lot of animals. And also it can be, it can be expensive just because of the nu the sheer numbers. So the bottom line is that the traditional techniques end up costing a lot of money.
They're very expensive, both in terms of, of time and personnel required to use it to implement the any experiment and also the, the yields in practice, biologists end up compromising on the number of surfaces there are, or conditions are being studied. And so it deals very statistically weak data. Conclusions in a typical biological study are very qualitative by engineering standards, and so it end, it ends up that all these studies are not scalable.
So, and that's becoming a big problem now with the, with the genome area era, where people want to study large numbers of conditions, a lot of variables, and to, to find new variables, new cell phenomenon that can only be revealed by studying large numbers of cells and conditions. So micro fabrication techniques give us several advantages that we exploit for different reasons. There are advantages first from a, from a fundamental point of view, the same way that in microelectronics transistors work best be, work better because they are smaller.
Here also we have, we build devices that access scales that are on the order of the object being studied, which which are cells. The micro fabrication technology can overcome these limitations, not in one way, but in, in, in many ways for once. It allows you to probe single cells in very large numbers.
So that gives you immediately very quantitative conclusions. Also, it allows you to make cheap experiments. Also, these systems are very inexpensive to fabricate in the sense that you can make many devices for the price of one.
Also, they're cheap to operate because they're easy, they're very amenable to automation. Then these, these advantages of low cost of fabrication and low cost of operation result in a, in very successful coer, commercial, commercial implementations. And finally, also, these are very, these systems are very amenable to quantitative design.
This is very important because this way we can model the behavior of let's say a microfluidic device or fabrication of a of a surface and know exactly how it's gonna work. And then we go back, we, that's done by modeling. We don't go to the, to the lab yet.
And then after everything works in theory, then we go and, and we go to the lab and we implement it and we save a lot of time doing. So one of the main technologies that we use in our lab is called soft lithography and it's techniques of family, of sister techniques that developed by George Whiteside at Harvard since the early nineties. And they're based on the replication and the molding of a material, a transparent elastomer called Polymethyl Sloane, or PDMS biologists know it by its brand name is S Guard 180 4 fabricated by do Corning.
And it's essentially transparent rubber is a, is a device. You can bend it and it's, as I said, it can be molded, it can be in repeatedly from one same mold with what's expensive to fabricate is the mold. And, but the, the material, we buy it by the bucket.
It's, it's, you know, it's not very expensive. Then also it's very biocompatible. It's, you can even seed cells on it, on it.
It's used in implants. And also another big advantages advantage is that it's transparent and that's very, very important for, for a biological microscopy, which as you know, is a, a very important method of analysis in biological studies. And the, the, the molding procedure, as you can see in the, in the videos from the lab, is very, it's very easy, very straightforward.
It just involves mixing two components. And you know, anyone can do that. It's just like cooking and putting it in the oven.
Even my son who's four years old, he came to the lab and actually made rubber. And so it's really straightforward. Microfluidics is the study of the behavior of fluids.
In, in small channels, as you know, you cannot throw a stone underwater. That's because you, the, the forces, the, the friction forces between the stone and the water are very large compared to the force that you impart on it. Something a little similar happens in small ch in a small channel where the, for the, the, the situation is on reverse.
The, the water is the, the object that is moving and the, and the walls around it are, IM imparting a lot of friction on them because of the high surface to volume ratio in, in microchannels, the walls have a very strong effect. And so these inertial forces are very small compared to the viscous forces and the friction caused by the walls. So because the viscous forces dominate, turbulence never occurs in, in microchannels you, so what we have is a regime of flow known as laminar flow because the fluid flows in, in sheets.
And an example is shown here where different streams are not able to mix because they flowing side by side. They, there's no turbulence that accelerates the mixing like it happens when you steer cup of coffee. Now because the viscous forces dominate and it's very hard to have turbulence in the microchannel, that means that two streams that merge into one channel will not mix, so will only mix by diffusion very slowly.
Now we take advantage of this property to expose different parts of a cell population or even a single cell to different fluids. And the reason why we do that is because that's how it happens in vivo. Inside the organism, cells are exposed to gradients of substances and a lot of times very transiently.
So because we know exactly we can model even the behavior of the fluids in the, the, the diffusion of the different substances in the fluid stream, we know what constant, what cons, what concentration the cell is exposed to and what portion of the cell is exposed to what concentration. So that allows us to do very quantitative studies on the exposure of cells to a particular substance. As an example of how laminar flow can be used to study cells, we have a project in the lab, we're actually trying to, where we're pulling a muscle cell into thinking that it's being inated by a neuron.
So during development, what happens is that the nerve at some point arrives at the muscle and it secretes substance called arin that is involved in the, the birth of the synapse. And so what we are doing is something actually conceptually very simple. We put muscle cells in a device and we expose it to a flow that contains rin only at the center portion of the, of the flow.
So that allows us to pretend that the device is the nerve and the muscle cells are in the device are the real muscle during development. Another example where microfluidics is, is very powerful is in the study of axon guidance. Here, the, the idea is to use the fact that we know, we can predict very well how fluid is diffused in a, in, in small volumes to produce gradients of substances.
And during development, gradients are responsible, partially responsible for how nerve cells find their targets. So we trying to reproduce that inside a Petri dish. Inside a microchannel, a sense of smell is a fascinating sensor system.
Neurons in the nose, let's say of mice have each one expresses only one type of olfactory receptor each. There's about about a thousand different olfactory receptor genes in m in mice. And each receptor binds to a, a range of odorants and each odin binds to several receptors.
And so that we believe that the way we detect odors is by, in a combinatorial way, traditional faction research it is difficult to find matches between large number of odorants and a given receptor or any odorant and a large number of receptors. And that's because it's very hard to expose any given neuron or factor sensory neuron to a range of odorants and, and vice versa, given any odorant is very hard to expose all the neurons in the olfactory epithelium. So our approach has been to take the olfactory epithelium and chop it up into dissociated into single cells and put them on a, on an array in a large microarray of micro wells, one cell per micro.
Well, and in a field of view we typically have tens of thousands of neurons that we image with calcium imaging and we look at the patterns of activation of these neurons, of this large amount of neurons to odorants and groups of ods. And that allows us to be confident that for any given odor and that we choose, we are looking at the whole olfactory receptor space. There's, you know, at least one or a few receptors being represented on that array.
And this way we know we can ask questions such as, you know, how many of the, the cells that are smelling banana are also smelling lemon, for example. These microfluid and micro patterning techniques are actually quite straightforward and the reason why they haven't been adopted more widely by cell biology, I believe is because of a cultural difference between engineers and biologists. Biologists are usually a little bit reluctant if not allergic to, to new technology and engineers are, are usually not very proficient at cell biology or biology in general.
And what we'll see I believe in the near future is biologists that themselves will not need to knock at the, at the, at the door of someone like me. They will be able to design a simple device and take the design to a, to a foundry like alu foundry and they will be FedExed back the device in, in a day or two and it will be, be very easy for them to implement the experiment themselves.
