Transcriber: Trang Thu Dao Reviewer: Ido Dekkers
I want to tell you a bit about the problem namely: detecting viruses.
And there’s a lot of situation when we might want to detect a virus.
I’ve shown them organized in this slide,
on a couple of different potential use cases.
So in red, you can see places where we currently test for viruses at the moment.
These are places like hospitals, doctor’s offices and things like that
We’re starting to expand to some of the light blue areas;
things like self-testing and drive-through.
But we really haven’t quite gotten to the point
where we’re able to do testing at scale with fast turnaround;
which is really what’s needed in order to really address this problem.
So, what I’m going to tell you a bit about today is some of our work to try
and move us up into the right on this graph to get us
from these kind of red and blue areas up into the purple.
To start, I just want to give you a brief reminder on what CRISPR is.
It’s this system that bacteria use to defend themselves against bacteriophages.
And the way that works.
It starts with this CRISPR array that gets transcribed into a guide RNA
that can recognize a specific RNA or DNA virus that is infecting the bacterium.
And it does so by complementary base pairing
with the help of this effector protein
and one very famous effector protein is Cas9.
And for those of you who are watching this at home and aren’t familiar with Cas9,
I’d suggest you go and pause this video and look up what Cas9 is;
because it's really quite incredible.
And Cas9 is able to cut double strand DNA that’s complementary to a guide RNA.
But I’m not going to talk about Cas9 today.
I’m actually going to talk about a different CRISPR effector protein,
namely Cas13, which cuts RNA instead of DNA.
And like Cas9, Cas13 uses a guide RNA
to direct itself to particular RNA sequences of interest,
and then it starts going haywire.
It really cuts many, many times once it is bound to its target.
And that’s a hallmark of Cas13
and something that we actually take advantage of in a diagnostic sense.
The first step is really Cas13 and the CRISPR RNA
binding to the target molecule that then activates the enzyme,
which allows it to start cutting.
And with the help of this reporter molecule,
which contains a fluorescent group and
a quencher that kind of blocks the fluorescence of that molecule,
we’re able to read out Cas13′s cleavage activity
because those two moieties are held together by this short piece of RNA
that Cas13 can cut and so Cas13 starts cutting
and then boom!
The molecule falls apart,
and that produces a fluorescent signal that you can detect very easily.
This is really great and really nice and sensitive,
but there’s actually a key challenge associated with a lot of diagnostics
that some of you in this room may be familiar with.
That is, the sampling required to make them work is really invasive,
and that limits their use.
So things like NP swabs or blood draws
really are getting in the way of people doing testing more routinely.
So one of the first questions we actually tried to address
was that initial kind of sample processing.
Can we use non-invasive specimen types, things like saliva,
urine or these anterior nares swabs that go just a short way up your nose?
And indeed, we showed that you can use saliva and urine
to detect Zika virus noninvasively.
This was work that was done a while ago back when Zika was all the rage.
You can kind of see that I've been doing this work for a while,
given what's been going on recently.
But I want to also talk about another mosquito-born virus.
This is dengue virus, and dengue is a really brutal infection.
It has no known cure, and it affects many, many people around the world.
I’m showing you on this map the countries
in the world where dengue is found, it affects over a billion people,
and it would be really great if we had a better way of detecting dengue,
particularly one that could be used all around the world
in various different countries.
And so we developed a way of detecting dengue more easily
where we’ve now taken that fluorescent reporter and convert it into one
that can be read out on a paper strip.
And so now you can see band showing up
in saliva taken from dengue patients
I’m showing you three patients on the slide, as well as
one healthy saliva control where you don’t see a band.
And so that is simplifying our ability to detect viruses.
It would be very hard to imagine doing PCR in this kind of circumstance
because that requires a laboratory infrastructure and lots of other things.
We've removed a lot of those requirements
and have really tried to take this technology one step further.
But we didn't just stop at viruses
because of lots of other things you might actually want to detect
using the power of CRISPR.
And one of those is snips are single nucleotide polymorphisms.
These are small genetic changes that we all have,
and they're slight variations in sequence. Only a single letter varies.
Some snips are associated with human disease
things like sickle cell anemia, cystic fibrosis and whatnot.
And so be really great if we had an easy way to detect those mutations
to let people know what they’ve got,
because that could potentially let you start treatment much sooner.
It’s currently still challenging to diagnose
many of these rare genetic diseases as they show people show up
with very strange symptoms, and it’s often quite an odyssey to get to that diagnosis
of what is making someone sick.
We took our CRISPR approach
and used it to detect single nucleotide polymorphisms
by targeting each of the illegal with a CRISPR RNA.
And what I mean by that is we have one CRISPR RNA designed to detect the mutation
The other to detect the non mutated or wild type sequence.
And based on the relative levels of activity of those CRISPR RNAs,
we can then tell whether a patient has a mutation or not.
As I'm showing you on this slide where the patient does not have the mutation.
So his wild type and the wild type, or WT CRISPR RNA
has a lot more activity than the mutant one, as you can sort of see
And so this is something we would love to apply in a lot more context.
This is just a single example. But what happens when a new virus emerges?
And this is not just a hypothetical scenario, as you can see,
over the last 20 or so years, epidemics and pandemics have occurred
with increasing regularity.
And so we asked ourselves, could we rapidly adapt our system
in the face of a new sequence or target?
And the answer is yes. It takes about a week or so to retarget our assays.
On the first day, we designed a set of new sequences,
CRISPR RNAs and primers in order to detect that new sequence
that we’re trying to find.
It then takes a couple of days, at least
for a company to make those sequences and ship them to us.
That's actually the slow part in the process.
And then in this particular example, on the sixth day,
we were able to do testing and verify that our design worked.
And as I like to joke on the seventh day, we rested.
(Laugh)
Speaking of viruses that are known to cause pandemics,
I want to talk to you a bit about some of our work that we’ve been doing
to look at COVID 19 using CRISPR based diagnostics.
And there's a lot on this slide.
But the key concept is really that we tried to simplify the assay even further.
We tried to combine multiple steps into one as much as possible,
simplify the sample treatment so that we're starting with either a swab
or even a saliva sample and then simplified the readout as well.
So we both have that paper strip readout that I showed you earlier,
as well as a fluorescent in tube read out where the tube itself starts to glow.
And you can see on the side of the slide some examples of that an action
where we’re able to detect patient samples using this method,
either with that intube fluorescent readout or with the paper strip readout.
And overall, we compared those results with the gold standard, which is PCR,
and we saw a pretty good performance.
The sensitivity was about 90 percent or so and it was 100 percent specific,
so there were no false positives. This is not perfect.
We’re still actively working to improve the method,
but it’s really quite encouraging.
So I just talked about one big part of the diagnostic problem,
which is trying to make testing as easy as possible for anyone to perform.
But there’s actually a whole other side of the problem that is when we do testing
in a lab or in a hospital.
We have this very old fashioned approach. We only test, for one thing, at a time.
And that is super frustrating as someone with a technology background.
I want to enable us to test comprehensively to figure out what we've
got more quickly when we become sick.
And so we developed a massively multiplexed approach to detecting viruses
and other nucleic acid sequences using CRISPR.
And the way that works is by miniaturizing
the individual reactions down to the nanometre scale from the micrometer scale.
And what that does is it lets us perform many, many reactions simultaneously.
And we've come up with some very clever ways that I won't quite get into.
But if you're interested, you can read our paper where we figured out
a way to comprehensively test sets of samples for sets of different CRISPR RNA
as we form all of the different pairwise combinations.
And that lets us comprehensively test a set of samples for
a set of different pathogens.
And as it turns out, and as I'll show you on the next slide,
that’s a really powerful approach, particularly when combined with
automated sequence design that can allow us to do this at scale.
And so to test out our approach, we designed a one hundred and sixty nine
flex panel going after all of the known human-associated viruses
that had at least 10 available nucleic acid sequences that had been published.
And that was sort of our criteria
for saying there's enough information about this virus to design a test,
and you can see we tested all of them out on this slide.
And what I’m showing you is basically the cross-reactivity
or lack thereof of the assay.
In particular, there’s this really nice diagonal line,
which is indicating that almost all of our designs about ninety four percent
to be precise, are detecting only the thing they're supposed to detect
and nothing else.
And keep in mind, this is after only one round of optimization.
I'm sure if we did more work, we could get it to work even better.
This was all done at the species level, so
it’s sort of every virus you’ve heard of and many you probably haven’t
unless you’re a virologist, in which case that’s great.
(Laugh)
We also did some work at the subspecies level, and here we were,
differentiating different subtypes or strains of influenza virus,
and we picked H1 through 16 and N1 through 9
because those are the subtypes of influenza that infect birds,
so-called bird flu or avian influenza that some of you
may have heard about in the news.
And as you can see again, we’ve got some really nice diagonal lines
indicating we can indeed differentiate those different subtypes.
Our method also works well on clinical samples.
And so it’s probably hard for you to read the legend of
what I’m showing you on the slide.
But what you can probably tell, even just looking at the slide from a distance,
is that there are two groups of samples that clearly come out of our analysis
and these are from patients.
The samples that we identified were either H1N1 or H3N2,
which are the two subtypes of influenza that infect people.
And so we're actively working to improve these methods.
We're hoping to try and both improve the scalability of them.
That is their ability to detect many, many samples at once,
as well as reducing that turnaround time so that we can get answers
faster and faster.
And with that, I’d just like to thank all of the people that made this possible.
This was a huge team effort, including many folks
in my post-doc advisor Pardis Sabeti’s lab
She's phenomenal as well as folks in my new lab at Princeton who are pictured
on our lab kayaking trip. Thank you so much.
(Applause)