BioPhotonics spoke with Eric Diebold, worldwide vice president of research and development at BD Biosciences, who leads product and technology development for the biosciences business unit within Becton Dickinson. He received his Bachelor of Science degree in electrical engineering and physics from Duke University and his Ph.D. in applied physics from Harvard University. He spoke to us about the existing challenges in the commercialization of flow cytometry systems and how they are being overcome. Flow cytometry has become a standard method for cell sorting in many laboratories. But what have been some of the technical limitations that have prevented it from becoming widespread? Flow cytometry is a powerful and widely used tool for analyzing single cells to better understand health and disease. And you’re right, certain technical limitations have prevented it from becoming even more widespread. Panel design, detector optimization, and complex data analysis make it challenging for biological scientists to become proficient quickly. Sorting cells adds another layer of difficulty on top of that. Cell sorters certainly have limitations to the types of things they can measure from a biological sample, which may impact adoption. And while their ability to search through millions of individual cells one-by-one and separate the cells of interest has steadily improved over the past 50 years — typically by adding more lasers and photodetectors — they have always just measured the total number of proteins on, or inside, single cells. Advancements beyond this hardware innovation paradigm have been made, though. In one product, for example, spectral flow cytometry is now combined with novel sort-capable image analysis, which takes the process much further and allows scientists to easily evaluate more complex traits of cells, such as where those proteins are located within or around a cell, versus whether the protein is simply present or not. With this technology, the user can actually see images of individual cells in real time, and sort them at high speed, using both spectral flow cytometry signals and microscopic imaging information to make the sort decisions. What can be incorporated into system design that has allowed some of these challenges to be overcome? First and foremost, flow cytometers need to be easier to set up so a user can get started collecting data. At our company, we are investing in our data processing capabilities to simplify flow cytometry workflows, including designing experiments, ordering reagents, running experiments, and performing data analysis. Algorithms for photodetector optimization, fluidic stabilization, and data standardization are all necessary to improve the ease of use so that a biologist can answer their biological questions without having to become an expert in physics and electrical engineering first. However, we as engineers need to help them on this experimental journey by designing lower noise electronics, low-cost and high-performance optical components, and reliable yet ergonomic designs for instrumentation. What new applications have been opened to flow cytometry because of these developments? The ability to perform complex experiments with ease allows a broader swath of applications to be addressed using flow cytometry. Further incorporation of technologies into these systems, such as high-speed image-based cell sorting, has allowed novel studies of the genetic origins of protein mislocalization in diseases, such as hereditary dilated cardiomyopathy. It has also enhanced the search for ultrarare cancer cells in patients post chemotherapy treatment and enabled the study of the effects of drug candidates on protein localization within cells to sort them for downstream genomic profiling. We are working with institutions such as Stanford University, the University of Washington, the European Molecular Biology Lab, Novartis, and AstraZeneca, among others, on improving ease of use in flow cytometry to unlock applications in ultrahigh parameter flow cytometry, functional genomics using cell sorting, standardization of flow cytometry data for use in clinical trials, and other advanced applications.