Yesterday (8/11/22), our research core facility and "new" technology vendors (incl. 10X genomics, illumina) were setting up a 4-hour symposium, titled "Unlock Even More Possibilities with Single Cell Sequencing".
These "new" technologies are combining sequencing and imaging (called "multi-omics with imaging"), providing us users a lot more data and information that were not obtainable before.
There are a few companies competing for this "multi-omics with imaging" niche. For researchers, "available options" in hand tend to prevail or get used. But it is helpful for us to know strengths and shortcomings of different technologies, companies, and platforms.
From the standpoint of PI (Principal Investigator) or technology user, our initial questions for new technologies are actually quite simple, including;
What can they do
What question/hypothesis can they help us to answer
Will they be advantageous in obtaining grants and writing a paper
How much do they cost
How much do I need to budget
How much preparation, manpower or trouble to use them
How much help can I get from the core and vendor to effectively use them
Guess these are quite simple "what's in it for me and for my science" questions and practical questions on implementation.
PIs' have different levels of liking for new technologies. Some are more innovator or early adaptor, others may be more like late majority.
But, as science is "show and tell", in most cases "just do it" is the best approach.
I've used some new technologies, and also have wanted to use other new technologies. The thing is, the new assays may not come with great support for new users (they fix things while flying) and are usually expensive.
When one experiment costs 10k+ dollars, we need to think seriously about the return/outcomes and actively look for the money. Although the outcomes would almost be guaranteed to be published or be useful in some ways, $10k+ is not cheap.
So I listened to the symposium. Presenters are from different corners of medical biology, but all were using the single cell sequencing technology.
The differences in interests were intriguing. But what a technology can do is basically the same. I saw essentially the same kind of presentation over and over. Guess this experience did help me to familiarize myself to the technology.
As technologies advance fast, sometimes it is our understanding that lags behind. Big data reading and interpretation will be increasingly important in the near future.
Will software and AI cover us scientists (or even replace us)? For description and identification segments, it is likely. But we still have a room to contribute in interpretation-hypothesizing-testing segments.
How will the technologies evolve in next 10, 20 or 30 years, I wonder during the meeting. It is good to have time to think about future science.
