Microfluidics Solutions to Proteomics Problems

Microfluidic platforms are becoming commonplace across biological science. To find out more about how microfluidic technology is revolutionizing protein analysis, we spoke to Tuomas Knowles, Founder and Chief Scientific Officer, Fluidic Analytics.

Ruairi Mackenzie (RM):  Could you summarize the technique that your Fluidity One platform uses to characterize proteins?

Tuomas Knowles (TK): Fluidity One is a microfluidic platform that enables the measurement of protein size under conditions that do not perturb a protein’s native state. Protein size can reveal all sorts of useful information, such as their molecular weight, physical state and how they aggregate and interact with other proteins.

Fluidity One measures a protein’s size according to its rate of diffusion in solution – a very well understood process that can generate very accurate sizing information. Here, the key to obtaining reliable diffusion data is to suppress other fluid dynamic effects (such as the propensity for miscible fluids to mix) by using very small solution volumes. With Fluidity One, we introduce an aqueous stream of proteins into a microfluidic chamber alongside a second laminar flow. Because there is no convective mixing in the microfluidic chamber, the only way proteins can migrate into the auxiliary stream is by diffusion, the rate of which will depend on the size of the protein. After leaving the chamber, the proteins are labelled using an amine-reactive fluorogenic dye that enables detection. The ratio of fluorescence between the two streams gives us the hydrodynamic radius of the proteins, and their sum yields the concentration.

TK: The biggest advantage of characterizing proteins using Fluidity One is that the molecules are studied in their native state. Most existing bioanalysis techniques characterize proteins by removing them from their native environment, either by attaching them to matrices or surfaces for detection (such as biosensing techniques like ELISA immunoassays), or by transferring them to the gas phase for analysis (such as mass spectrometry). However, in doing so, you fundamentally change the protein’s structure and behavior, impacting on the validity of the conclusions drawn from these measurements. By characterizing proteins in solution using microfluidic diffusional sizing (MDS) under native conditions, it’s possible to access information on protein aggregation, folding and interactions in a more biologically-relevant context.

Currently, the most common way to measure protein size in solution is by performing light scattering experiments. However, these typically require large sample volumes and high concentrations and, due to the poor light scattering properties of small proteins, such measurements are extremely sensitive to contamination by larger molecules. The fact that Fluidic Analytics’ technology does not rely on light scattering allows scientists to work at lower concentrations. This is not only more physiologically relevant but also less sensitive to contamination from aggregated species as the technique only measures protein content.

RM: The Fluidity One platform is for characterizing single proteins – is there any way this technology might be applied to address questions at the level of the proteome?

TK: Understanding biological systems at the level of the proteome relies on characterizing proteins in highly heterogeneous environments. Fluidic Analytics’ goal is to extend our Fluidity One platform to enable other fundamental protein characteristics to be studied in solution, beyond protein size. We’re currently developing microfluidic technologies that will allow us to separate proteins based on charge and isoelectric point on a single chip. By combining these techniques in a single integrated system, along with our existing MDS technologies, we will be able to achieve a higher degree of separation. This will allow us to distinguish between different protein isoforms and study protein physical state and behavior in a very fine level of detail.

Ultimately, our aim is to help scientists answer complex biological questions in any area where proteins play a role. We’re keen to bring these technologies into the research and clinical setting as soon as possible to support diagnostics. For example, this might include applications where scientists are trying to use clinical samples to identify useful protein biomarkers and evaluate their propensity to interact with other proteins, which is particularly important for immunology research. We’re also excited to apply this type of characterization technology to support the development of new protein therapeutics and even evaluate the efficacy of existing ones.

RM: Even just using MS tools, proteomics data is often heterogenous between labs and papers, which produces reproducibility and data aggregation issues. Is this something that could be addressed using your technology?

TK: There certainly are challenges around reproducibility in proteomics, and many of these issues extend to protein science more generally. Fundamentally, it reflects the challenge of undertaking quantitative measurements using very complex systems. Loss of measurement reproducibility in protein characterization essentially comes from three main sources: the inherent variability of the technology itself, the interactions of users, and the impact of consumables. Fluidity One addresses all of these issues.

Every technique has its limitations, and the issues of reproducibility associated with many existing techniques for protein characterization are extremely complex. Approaches such as protein microarrays, for example, require specific probe molecules (such as capture antibodies) for each protein. Ensuring specificity using techniques like these can be very challenging. Fluidity One is an orthogonal approach to conducting these measurements that isn’t constricted by the same limitations as techniques that study proteins outside of their native state. The better tools we have, the more consistent protein characterization data we can obtain.

One of the key advantages of Fluidic Analytics’ Fluidity One platform in terms of measurement consistency is the fact that it’s highly automated. The platform eliminates several manual separation and labelling steps that could otherwise introduce human variability. Furthermore, the fluidic handling occurs on a disposable microfluidic device, which means there should be no cross-contamination between measurements.

RM: Are there other applications for microfluidic technologies in protein research that you are excited about?

TK: Many of the most pressing questions in life science will only be solved by building a better understanding of protein structure and behavior. It’s a fact that reflects the fundamental role proteins play in biology. Fluidic Analytics’ goal is to help scientists collect physiologically-relevant characterization data in any research field where understanding protein behavior in their native environment is key. This could range from the characterization of purified proteins used in research or produced as therapeutic products, through to understanding the behavior of proteins in clinical samples. It could even extend to determining the state of a biological system by having a global view of the proteins and their behavior within it. Ultimately, by taking protein measurements under conditions that more closely reflect those in nature, we will be able to draw more accurate conclusions and better answer these complex questions.

Microfluidic technologies are poised to make a real impact on human health and we’re extremely excited about their potential in drug development, especially their capability for probing protein–protein interactions. We want to help scientists characterize protein-based drugs in a more physiologically-relevant environment and study their behavior as they would occur in the patient. Using better protein characterization tools, we hope to accelerate the development of the next generation of safe and effective treatments to patients.

Tuomas Knowles was speaking to Ruairi J Mackenzie, Science Writer for Technology Networks