Shana Kelley

And I think as we take this technology and we scale it and we multiplex it and we're, you know, right now looking at two things, but then we're looking at 10 things, we're looking at 50 things over a time scale of weeks, right?

This is an incredibly complex data set.

And, you know, my dream, I'm sure you've seen these charts of like, you know, the metabolic everything in the human body, right?

The Krebs cycle and all of this different stuff

But it's all kind of textbook-y, right?

You know, in the human body, things are a lot more complicated.

And with sensors, right, if we have the ability to have this real-time data, that complicated scheme really comes to life in terms of what it means for human physiology.

But I do think that having the computational models really driving that will be essential.

Sure.

So my labs over the years were really not the type of folks that just work on one problem.

We're really technology developers.

I was trained as a chemist at Caltech, not the molecule making kind of chemist, but like the molecule measuring kind of chemist.

and we really just you know we look for white space we look for unsolved problems unmet needs and then you know the beginning of our project is always standing at a whiteboard and saying how can we measure this and if we could measure this what could we do with it so we started a project a few years ago where we wanted a way to be able to search for rare cells in the blood and

much faster.

You know, you can use a flow cytometer, but to get through a milliliter of blood is going to take you weeks and weeks and weeks.

The cells will be dead by then.

So we developed a high throughput technology for cellular analysis.

And we ended up being able to find immune cells in the blood that had been inside of a tumor and had come out.

So these are tumor reactive lymphocytes.

And what we learned is that we could isolate enough of those