As technical director of a shared-resource laboratory at New York University Langone Health, I strive to help users generate the best data from their flow cytometry experiments. To this end, our lab recently acquired two next-generation cell analyzers to provide better services to our users. Some of our users have taken to the new instruments quickly, but some have hesitated to use the new technology, preferring platforms and software already familiar to them.
A lab manager’s role in advancing science is not just providing access to the best instruments, but equipping and persuading users to take advantage of the lab’s new capabilities. Our lab has applied a few core principles to invite hesitant scientists to adopt new technology that would benefit their applications. Here are five approaches we have found to be successful in transitioning reluctant users:
1. Align purchases with anticipated needs
We purchased two Bio-Rad ZE5™ cell analyzers in anticipation of researchers’ evolving scientific needs. Since the late 1990s, most typical flow cytometry experiments have used four or five cellular characteristics, requiring instruments capable of detecting only a handful of colors or parameters. However, as the field of immunology began to rapidly develop, some groups began designing experiments to parse out rarer and rarer cells by flagging a dozen or more characteristics, thus demanding more parameters. The ZE5 cell analyzer was perfectly aligned with this growing need, as it can analyze up to 30 parameters at once.
Although immunology has long been the traditional realm of flow cytometry users, scientists in other fields, including developmental biology, microbiology, and stem cell biology, have started using our facilities, and the ease of use of the ZE5 has enabled us to easily introduce novice users to high-end flow cytometry.
2. Persuade scientists with data
Getting a user comfortable by providing example data is key to persuading them to try a new platform. We present or supply sample data sets either generated in-house or obtained from vendors that demonstrate performance, particularly in resolution and background noise. Doing so allows us to illustrate how studies will vary from machine to machine. For instance, a 10-color instrument can run a 10-color assay, but a 10-color assay designed for a 20-color instrument can achieve higher resolution. When researchers see that their data will look as good, and likely better, on our new instruments, we can usually convince to them to take a closer look.
3. Arrange an instrument meet and greet
Getting a researcher in front of a new machine to explore the software and build an experiment—to play with it—can help with transitions as well. Software on high-end instruments has become increasingly user-friendly, and in some cases, walks users step-by-step through the process of building a protocol. This can be a departure from older platforms, but it is one that new users tend to pick up quickly. Once users are comfortable navigating a machine, they will be more receptive to formal training.
4. Institute comprehensive training
Although my team can quickly show users how to run an experiment, we have made it standard practice to mandate rigorous, one-on-one, two-hour training sessions before letting researchers use the machines on their own. Comprehensive training provides users a complete understanding of the software’s capabilities.
This training is a necessity in our lab. Our lab serves a diverse mix of users spanning a wide range of areas, from the NYU Langone Health campus and the NYU downtown campus to nearby academic centers and industry scientists throughout the metropolitan area. Some of our instruments are operated in a satellite facility down the block from our lab. We have access to remote desktop control to assist with some troubleshooting when we’re off-site, but we have found it more reliable to cultivate a lab culture in which every user is confident enough to run instruments with minimal staff support.
5. Provide a comfortable user experience
Our final approach in new-instrument adoption is to provide technology that aids the scientists themselves, not just their data. The researchers we work with frequently request instruments with automated plate loaders or other high-throughput systems so they can comfortably sit back and watch the instrument run, even if their experiment is a simple analysis that could be performed on another instrument. We usually try to accommodate them; I’d be remiss to refuse after so much effort to gain their interest.
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