Poulomi Acharya, for people who have used the Applied Biosystems Axiom array plate format for many years, will those plates run on the SwiftArray?
Dr. Acharya: When we were developing this platform and workflow, including the new chemistry and assay, we were cognizant of the need to provide the same kind of content and performance for which people have come to trust the Axiom brand. This platform is developed to be content agnostic, future-proofing applications people might want to bring onto it. We also standardized the process and workflow based on the format. It doesn’t matter what content you’re looking at; the workflow for a 96- or 384-plate format and how the chemistry works remain the same. It allows people to scale and add and subtract content, go back to their usual VIP markers, or use the state-of-the-art content on the new arrays.
Ravi, what will customers who are used to running microarray-based technologies appreciate once they bring the SwiftArray into their laboratories?
Ravi Gupta: We heard from customers about scaling throughput easily, so they will appreciate the features Robert and Poulomi described. The other is turnaround time. Currently it can take four or five days to get results and genotypes; with this platform results are available in as few as 30 hours. This is helpful for decision-making across many applications, especially in clinical research. It will accelerate not only science but also getting results to end users.
Robert, your comment on the same question.
Dr. Balog: I was impressed as the team went through the operational efficiency, including the little pain points. As an example, you used to have to pipette the same reagent into 96 or 384 wells. That takes tips and time. The team developed universal trays; it’s pour and go. What used to take five minutes and included a series of pipette tips now takes 20 seconds, and they move.
Whether it’s a small clinical lab, sophisticated research lab, or large medical center lab, one commonality is the shortage and expense of good staff. Poulomi, tell us how this helps alleviate staffing shortages or customers’ concerns.
Dr. Acharya: It is key for a clinical lab to reduce the number of manual interventions. Because your samples are precious, you do not want to contaminate them or introduce mistakes. This system allows people to prepare a plate, load it onto the instrument, load the reagents, and walk away. It’s a single-touchpoint workflow. Any operator can be trained to do this reliably because the instrument is easy to use, and the workflow is flexible and easily scalable. That said, in an agrigenomics laboratory that’s running high volumes in high season, tens of thousands of samples in a week or two, the workflow can be tweaked and you can do multiple shifts. You’re maximizing the output with the labor you have. You can load everything now and walk away or you can maximize the number of plates that are scanned while reducing your manual touchpoints.
Ravi Gupta: I’d like to add that Thermo Fisher is committed to sustainability. We have reduced the amount of plastics and harmful chemicals. The team spent a lot of time thinking that through and making it a reality on this platform.
Robert, we hear increasingly about multiomics and multiomics research. Give us a few examples of what you regard as multiomics research.
Dr. Balog: We’re finding genomics plays a huge role in how we respond to drugs, how likely we are to develop a particular disease, but that’s generally a static marker. We know other elements are in the genome with things such as epigenetics and methylation patterns and also protein or gene expression. Adding omic measurements can drive better patient risk stratification for disease and therapeutic decision-making.
It’s developing more in oncology and complex diseases. There’s more research on and people interested in the mechanisms of aging, pain management, and neurodegenerative diseases where there are influences from the genome but also other environmental or lifestyle factors that cause a particular disease state. Those can be measured in other omics, like epigenetics, transcriptomics, proteomics.