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- [Voiceover] What we've been taught for decades
about normal tissues is that the 3.1 billion base pairs
are genetically identical between cells.
And that's fundamentally wrong.
What we now know is that every cell is unique.
Even when a normal cell divides,
it introduces about one or more errors per cell division.
So the challenge now becomes,
how do we study the differences between individual cells,
looking at RNA, or DNA or epigenomes?
And what are the implications of that for biology?
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- So what was one of the biggest surprises
you got when you started doing single cell sequencing?
- So, one of the big surprises was,
as we started to sequence copy number profiles
in single cells, from tumors, we found that
there are a few major populations
that share similar copy number profiles.
And using phylogenetic inference,
we inferred a model of punctuated genome evolution.
In these short punctuated bursts,
you acquired many many mutations.
And then after that burst, the genome
was stable for a long period of time.
And then in this case, there's another punctuated burst
in which you acquire many many mutations.
And then, again, the genome is stable
for most of the lifetime of the tumor.
- So, single cell sequencing has not only
helped us identify the time at which
these cells take up these take up the mutations,
But also which cells have the mutations.
- Right, so we can use this intratumor heterogeneity
within the tumor as a permanent record, so that we can
reconstruct the order and the chronology of the mutations,
or copy number changes, that happened over time.
So we're gonna join Marco now, here,
who is a graduate student in the lab
and he's going to show us how to do
micromanipulation by mouth pipetting to
isolate single cells for sequencing analysis.
So first, Marco's going to take one of the glass pipettes.
He is going to place a plastic tubing
that will connect to his mouth so that he
can have some input and output pressure.
And then he's going to take the glass capillary tube and go
to the solution of cells that's on a microscope slide,
and he's going to find a single cell and hold it there.
And by capillary action, that cell
will get sucked into the tube.
After that, he'll transfer the glass tube over to a
96 well plate and he'll blow in to allow the liquid
to come out and go into a lysis buffer
for the subsequent amplification reactions.
So step two will be taking that cell
and going into a whole genome or
a whole transcriptome amplification reactions
so that you can get lots of DNA and RNA
that we then use to make sequencing libraries.
We then barcode those libraries,
we pull them together and the we do
the next generation sequencing reaction to get data.
So this is the data that we get back
from a typical sequencing run and
we'll take all of this data, we'll align it to
the human genome so that way we'll
know exactly where these sequences
are coming from in the genome.
And then we go through a series of
processing steps and filtering steps
to remove all the technical errors that might have
occurred during the amplification of that single cell.
- So the question now becomes,
how do you make sense out of that?
- So we're gonna take our single cell
large scale data sets and we're going to
upload them to the cluster here at MD Anderson.
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So now we're leaving
the basic science research building,
and we're going to take a little road trip down
to the high performance computing cluster.
- So how long does it take for you to,
let's say, get data from one cell?
- On the cluster, it'll only take about 20 to 30 minutes.
- Wow.
- Because you can't wait a very long time to get
results from patient diagnostic tests or prognostics.
So as soon as we get all of the sequencing data back,
from the sequencing machines, the first thing we do is,
we take those millions and millions of reads,
and we upload them to the cluster, where we're going to
do our initial data processing and analysis.
- So typically, how many cells
would you analyze at one time?
- So it depends on whether we're doing genomes
or exomes, or we're just looking at
targeted gene panels in single cells.
For genomes, it would usually just
be a few, maybe a dozen at most.
Exomes would be a few dozen, and then when we do panels,
we're doing hundreds and hundreds of single cells.
So this is a phylogenetic tree that was created
from single cell copy number profiles
in a triple negative breast cancer.
And one branch of the tree, you can see all the normal cells
they have diploid copy number profiles
and two copies of each chromosome.
Over here, you can see three different tumor populations
indicated in blue, in yellow and in red.
What you can see is that there's a very large
genetic distance between these subpopulations.
Which supports a punctuated model of copy number evolution.
In which you have these bursts of mutations,
and you acquire lots of copy number changes.
Followed by a stable expansion of the population over time.
- I can just imagine that there
must be lots of other applications,
where you can actually apply this technology.
- Over the last five years, we've seen a lot
of broad applications of single cell sequencing,
in many diverse fields of biology.
Including things like microbiology and immunology,
neurobiology, tissue mosaicism, prenatal genetic diagnosis,
which has great clinical applications.
Embryology to study early development,
and things like organogenesis, the development of organs.
And cancer, which is a really big one,
where there are many applications,
both in the cancer research side,
but also potential translational applications.
- This was an amazing day, wasn't it?
- Amazing facility.
- A single cell sequencing actually impacts
every application you can think of.
- Yeah, absolutely, and we finally have a way to
measure the very basic unit of life.
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