Hi, I'm Steve McCarroll.
And I'm Evan Macosko, a post-doctoral fellow
in Steve's lab.
And I'm Ani Basu, a post-doc with Aviv Regev and [INAUDIBLE]
and with David Weitz at Harvard SEEDs.
Three years ago, we started working on a new genomics
technology to make single cell gene expression profiling
fast and straightforward.
Complex tissues like the brain contain
many different populations of functionally specialized cells.
These cells use the same genome in different ways.
There is so much to learn about the ways in which specific cell
types assume unique cell shapes, accomplish feats of physiology,
and respond to mutations and other stimuli.
What is the range of cell types out there?
We wanted to develop a method to systematically characterize
brain cell diversity.
If we could get enough gene expression profiles
from single cells, we thought we might start getting answers.
When we started this project, gene expression analysis
was really good at measuring gene levels
in a single homogenized sample.
So it's really like a fruit smoothie,
which has all the different fruits
commingled and homogenized, which
is still pretty delicious.
But what we really wanted was the fruit salad,
where you could taste every fruit one at a time.
What this technique does is measure
the genome-wide expression cell by cell.
We started thinking about trying to make a system that
would let us make thousands and thousands of single cell RNA
seq libraries in parallel, inexpensive, easy experiments.
We had been using droplets to study
many things in molecular biology,
such as the expression and copy number
variation of individual genes.
Droplets provide a way of scaling molecular reactions,
so that one can perform hundreds of thousands of them
in a tiny microcentrifuge tube.
What the microfluidic technology brings to the table
is really the scalability.
We can do around 10,000 to 20,000 cells at every go.
And we can really look at complex systems
now, like the brain or the immune system.
The challenge with droplets is that they
don't stay in one place and they don't have stable addresses.
We needed a way to put a DNA barcode under the RNA
from each cell and to have that barcode be
different than the barcode that was applied
to the RNA of each other cell.
Evan came up with a cool way of doing this.
We used beads to deliver DNA barcode into droplets.
We synthesize the oligos directly on the beads.
To make the barcodes, we split the beads at random
into groups, add a different DNA base to each group,
and then pool the beads again.
Repeating this split and pool process 12 times,
we produce 16 million different barcoded beads.
Oni designed microfluidic devices
that could deliver these beads and cells together
into droplets.
We call this technology Drop-seq.
But how can we really know if we're
making single cell libraries?
It could have been we were just making
a lot of really tiny smoothies.
So we came up with a simple test.
Let's put human and mouse cells together in our system.
If each bead or cell barcode associates
with a mixture of human and mouse transcripts,
we've ended up with smoothies.
But if each bead has captured transcripts
from just one species, then we've
preserved the information about individual cells.
In the first series of experiments we did,
many of the cell bar codes we identified
had mixtures of human and mouse transcripts.
We continued improving the design of our lysis buffer.
We increased the stability of the droplets
we made and adjusted the concentration of cells
and beads used in our experiments.
Eventually, we were able to achieve very high organisms
specificity for the great majority of the cell
barcodes we sequenced, proving that our libraries came
from single cells.
So we had Drop-seq in hand.
And now we could finally do what we wanted to do,
which was to use it to study complex tissue
like the nervous system.
We chose to start with the mammalian retina, because so
much is known about the different cell types that
exist in the retina, their functions,
and there are molecular markers for many of them.
We generated 44,808 single cell profiles for mouse retina.
It took four days to do with Drop-seq.
Rahul Satija developed computational approaches
for clustering these 45,000 single cell transcriptional
profiles into clusters or groups.
We identified 39 transcriptionally distinct
populations of cells in the retina.
We found cell populations that didn't have markers before.
And most reassuringly, we found a lot of types
that we did know about.
A single scientist can come in and make 10,000 single cell
libraries every day.
And the reagent costs are small, on the order
of 6 and 1/2 cents per cell.
There is so much exciting work to do,
and we hope that scientists enjoy
using Drop-seq to do that work.