The paper is about the development of
a very broadly useful technique
which makes use of a technology known as droplet microfluidics.
It's about developing a process.
We're looking at unusual cells and measuring their gene expression.
But simultaneously on thousands of cells.
Ha. So I can try. I'm not very good at concise. But I'll try.
All right. Any cell population that we will look at
will have some variability between cells -- no two cells are identical.
If you just take a hunk of tissue and grind it up and analyze the RNA in there,
you have no idea whether that represents what every cell in that population is doing
or,
it may represent what no cell in that population is doing.
How cells differ
will teach us a lot about the nature of the subpopulation we're looking at.
Imagine if you had a
population of men and women, and you
just assume that everybody in that population was the average
between men and women,
you wouldn't represent a single person in that population.
So one challenge is, can we measure a gene expression
profile of each cell individually, and not do it on one cell at a time but do it on
thousands of cells
so that we can start to develop a picture of how
how the population of cells is embedded in gene expression space.
So, what's so difficult with single cell profiling is,
first of all, is the simple issue of
isolating single cells, and carrying out complex chemical analysis on those single cells.
So, we were thinking about this problem
of how we're going to process large numbers of cells -- how we're going to do so uniformly --
and we became familiar with Dave Weitz's technology for droplet microfluidics.
Droplet Microfluidics. The best way i can explain it
is to imagine doing many many experiments
in the wells a microtiter plate. I replace the well the microtiter plate by
a small drop.
But each drop represents one experiment --
it represents what happens in one well of a microtiter plate
The drops are not limited, and we can do millions to billions of experiments simultaneously.
So the way that this work is conceptually very simple.
You make droplets of water in oil,
and these droplets are quite stable.
So droplet microfluidics allows us to
put cells in droplets, but what we really want to do is to be able to use the
droplets in order to tag each cell using a DNA barcode.
We do it while we assemble this droplet.
We have three channels. We have a channel which
brings the cell in, a channel which brings
the chemical reagents that we need to do the analysis,
and then we have another channel which puts in it
a bead, and that bead is the key to this whole process.
So the approach that we came up with was to use hydrogel beads.
A single bead will contain primers that can be used for a reverse transcription
reaction, which will then subsequently
barcode the contents of the cell inside the droplet.
And we don't make one such gel but we make
a library if such cells. We have approximately 150 thousand different bar codes,
each one of them on a different hydrogel, and now each one of the droplets
receives a different
gel, at random, which insures that each cell
is barcoded differently from its neighbors.
So what we end up with, then, is a collection of
DNA sequences that are copies of the RNA sequences
that are now divided by what drop they came from.
And then, finally, we can break the droplets and treat the entire cell
population as a single bulk sample
knowing that each one other cells has been individually bar-coded.
And now, after sequencing, we get a mixture reads,
each read will contain a different barcode, and we can now group
all of the reads from one cell together and do the same thing for each
one other subsequent cells in our sample.
One wants to know, what is the process that's goin on --
Do you want me to try to get people to stop?
You're going to fix the lighting here? Or the sound???
The challenge is, how do you visualize that data, how do you make
sense of it. We can't look in twenty thousand
gene expression dimensions in order to pick out interesting features.
When we began to explore that, we needed new mathematical tools
that hadn't been applied to these kinds of problems before.
I think anybody who's interested in something
where the cell behavior is not uniform,
which is almost everything, as far as I can tell,
is going to use this process to understand the diversity among the cells.
So we're really excited by the questions that this technology is
now opening up for us.
There's really a large number questions where we're thinking to apply this and where
some people are already approached us to to ask whether we can help them.
So it could be for cancer, it could be for tumors, it could be for
almost anything that there's likely to be a diversity in the population of cells.
So I think a very important part of the work is to actually stimulate
mathematicians, computer scientists, to actually
develop new ideas about how to interrogate
this data and get new information out of it.