October 2015

Surveying the Chromatin Landscape with
Next-Generation Sequencing
Researchers develop novel sequencing methods with the MiSeq® and HiSeq® Systems tounderstand the epigenome and its impact on cancer and immune disease.
Introduction
Every cell in the human body has long strands of deoxyribonucleic
acid (DNA) compactly folded inside its nucleus. That folding is madepossible by chromatin, the complex of macromolecules that package
each cell’s DNA into that small, condensed volume—an architecturenecessary to protect its structure and sequence. Understanding
chromatin and this dynamic architecture are crucial to understanding
how the genome works. Its tightly packed grooves and folds provide
a unique physical landscape for gene transcription—one that has
profound implications for our understanding of gene regulation,
replication, and expression. Scientists are now finding new ways todelve into chromatin’s many biochemical mysteries.
William Greenleaf, PhD, an assistant professor in Stanford University’s
renowned genetics department, is focused on understanding how
the 2 meters of DNA in each cell nucleus are folded and stored.
“About 95% of the genome is folded and sequestered away in thechromatin,” Dr. Greenleaf said. “Only a small percentage is accessible
to the transcription machinery. Deciphering how that all works is
intriguing and important.”
iCommunity spoke with Dr. Greenleaf about his team’s developmentof 2 new next-generation sequencing (NGS) methods to better survey
the enigmatic chromatin landscape: assay for transposase-accessible
chromatin sequencing (ATAC-seq)1 and single-cell ATAC-Seq (scATACseq).2 He believes that these approaches might one day provide
new insights into the development and treatment of cancer andautoimmune disease.
Q: What sparked your interest in applied physics?
William Greenleaf (WG): I was always interested in molecularbiology—particularly DNA and the molecular machinery of the
genome. But as an undergrad, I wanted to avoid chemistry, so I
studied physics instead. I ended up getting my PhD in applied physics
with a focus on single-molecule biophysics, because I was interested
in understanding the mechanics by which individual molecules carryout tasks within the cell. During my postdoc, I was bitten by the highthroughput sequencing bug. We were thinking a lot about new ways
to approach these different complex biological questions. A sequencer
can make hundreds of millions or even billions of measurements
across the genome and that’s what is needed to understand thecomplexity of this biology.
Q: What does high-throughput sequencing provide over the othermethods you used previously?
WG: As a grad student, I performed experiments on individual
molecules. It’s labor-intensive work—and you have to deal with a lotof handcrafted data. After a few years, I wanted to find a different
way. I wanted to do the exact opposite—take an enormous number
For Research Use Only. Not for use in diagnostic procedures.

of measurements very quickly. So we’ve been working to repurposethe infrastructure associated with high-throughput sequencers to do
massive scale biochemistry on nucleic acids.
Q: What inspired you to develop new tools to study chromatin?
WG: We have a great understanding of the structure of DNA—anda good understanding of a single nucleosome. However, that’s where
our high-resolution understanding of the nucleus ends. The questionof how DNA is organized at the kilobase length scale remains afundamental question to be answered. We don’t know all that muchabout how the nucleosomes that bind to DNA tightly are shiftingaround, how the transcription factor binding sites might be competingfor DNA, and how different transcription factors may cooperate tobuild enhancers. These things touch and interact mechanically tomake things happen. We need to understand the logic of the physicalregulatory landscape—the regulome, if you will—to see what makesa gene fire or not.
One of the significant questions is how a cell can mark and use thesedifferent elements to change their biological state. We know that all
the different cells in a body have the same genome effectively, yet they
do incredibly different things. I like to think of chromatin as a physicallandscape that tells the cell which parts of the DNA to use andwhich parts to ignore. In a sense, it’s a major organizational principleof biology.
Q: Has the data from the Encyclopedia of DNA Elements (ENCODE)
Consortium and Epigenetics Roadmap provided a glimpse into
the regulome?
WG: Recent work from the ENCODE consortium and the Epigenomics
Roadmap have tried to illustrate how different elements in DNA
are functional, and how they can be marked and used. That initial

Dr. William Greenleaf is an assistant professor in the Stanford
University Genetics Department.

October 2015
glimpse into the full richness of the epigenomic landscape has beenfascinating. It’s like discovering a new continent. But much of that data
are observational—there’s much less of a functional understanding of
how these elements conspire to allow the development of a single egg
into a full human being. That’s what we’re trying to develop methods
to understand.

“We need to understand thelogic of the physical regulatory
landscape—the regulome,
if you will—to see what makesa gene fire or not.”
Q: What methods were used previously to assess the epigenetic
information encoded in the chromatin structure?
WG: Certainly, there has been some deep, important foundational
work at the level of individual loci. For more than 35 years, researchers
have used DNase hypersensitivity and run resulting patterns onpolyacrylamide gels. That work has formed the foundation for our
understanding of how DNA accessibility changes within the chromatin
and how these individual loci function. There have been otherbiochemical assays of transcription factors that provided information
on what’s necessary and sufficient in very defined systems for themto bind.
Q: What are the limitations of those older methods and how can
the tools you’re developing answer those challenges?
WG: Most of the limitations of previous methods have to do withthroughput and sensitivity. We’re trying to understand comprehensively
how these different markings and genetic elements are used acrossthe whole genome. We want to follow these things over time inunperturbed native states. For example, high-throughput sequencing
is effectively a single molecule measurement. It’s a digital measurementthat can have ultimate sensitivity if used in the appropriate way.
Current sequencers allow you to read out billions of sequences at
a time, and you can make a billion measurements at a time for areasonable cost.
The old methods are brilliant methods, really. What’s wonderfulis that they, too, can be moved into this high-throughput realm withincredible sensitivity and broad application across the genome ina straightforward manner.
Q: What sparked the development of your new method, ATAC-seq?
WG: Early on, I was interested in pushing sensitivity for epigenomic
methods to the single cell level so we could ask questions about
populations versus individual cells and how individual cells within evena biologically similar population might vary. We’ve been interested intransposase because it has an ability to insert sequencing adapters ina single step. It captured our imagination and Jason Buenrosto,
a member of my lab, thought it was an interesting reagent and wanted
to see if we could use it to read out open chromatin. He got it goingvery quickly—and I credit Jason and my colleague, Professor Howard
Chang, and members of his lab for working to make it happen.

For Research Use Only. Not for use in diagnostic procedures.

Q: How can the transposase, Tn5, be used to identify regions inaccessible chromatin?
WG: Tn5 is a hyperactive transposase that’s meant to more or lessinsert sequencing adapters, or adapters that can be used to makea sequencing library, everywhere in naked DNA. It’s an efficient andsimple way to generate a sequencing library for whole-genome
sequencing. But we thought if only a few percent of the genome is“open” in a nucleus, and therefore accessible to the Tn5 transposase,then likely these regions would be the only ones that would beaccessible. So it acts, in some sense, like a test transcription factor
and it generates reads from regions that are accessible. The lengthof the fragments it generates also tells you about some of the finer
structure of the chromatin. For example, we see that there’s anenrichment for reads that are on the order of 200 base pairs long—reads that are being protected by a single nucleosome. You candetermine whether the reads that you’re getting are from nucleosomal
or nucleosome-free regions based on the predicted length.3 So not
only do the reads tell you something, but also the length of thefragments provides information about the local structure.

“Technical reproducibility between
replicates using ATAC-seq wasvery high—with an R valueof ~0.93.”
Q: What can we identify when sequencing the DNA fragments
at locations of open chromatin using ATAC-seq?
WG: There are 3 different things that you can get out of the ATAC-seq
primary data analysis. The first is information about the regions of open
chromatin, those regions that are accessible to the machineryof transcription. The second is information about whether thoseregions of increased accessibility have nucleosomes that are boundto them. Finally, in an analogy to DNase hypersensitivity methods, you
can see when a transcription factor is tightly bound to a specific locus
because we see a depletion of reads where that transcription factor
protects the DNA to which it is bound. Using the footprinting observed
from the binding of specific transcription factors, you can infer thepresence of a factor that binds to that specific DNA motif. It’s valuable
because it’s giving you a sense of what the molecular factors are thatare potentially driving gene regulation. We and others have also beenworking on algorithms that use information about the distributionof fragments to call a chromatin state, or determine whether regions
are poised or in active transcription.
Q: How much of a sample was required to perform this method?
WG: Most of our replicates in the initial ATAC-seq study weregenerated from about 50,000 cells. In comparison, most publishedprotocols for DNase require 10 million cells or more. We’ve also now
published data sets that have been generated from about 250 cells
in our single-cell protocols. How many cells you need depends on
your goal and the type of data analysis you want to perform. We’ve
created some relatively deep, complex libraries from only 50,000 cells.

October 2015
Q: What was the quality of the results for ATAC-seq?
WG: Technical reproducibility between replicates using ATAC-seq wasvery high—with an R value of ~0.93. We also had a correlation with
2 other DNase hypersensitivity data sets. Correlation with those data
sets was similar to the correlation of the 2 data sets with each other.
That said, it’s going to take a lot more data from many different cells tocompare to the DNase hypersensitivity bulk data to fully characterize
how many peaks ATAC-seq observes and DNase doesn’t, and whatpeaks DNase observes and ATAC-seq doesn’t, and why that might be
the case.

“ATAC-seq allows you to ask
questions about the epigenetic
variability in complex or raretissues and epigenomic landscapein populations of cells that haven’t
been observable at the genomewide level before.”
Q: What did the ATAC-seq paired-end reads provide regarding
nucleosome packaging and positioning information?
WG: There’s no reason not to do paired-end sequencing whengenerating ATAC-seq libraries. Both ends tell you the insertion pointof a transposase—so it really doubles your data. It also tells you thelength of each of the fragments generated from your library. When
you align each of the ends to the genome, you can determine howlong that fragment is. That length can be used to identify, for example,
nucleosome positions. We recently reported a method for using thelength distribution of the ATAC-seq fragments to call nucleosome
positions at high resolution.
The fragment length distribution provides another dimension of
information. You have both positions where the insertions occur andthe lengths of the fragments generated. That length distribution can bevaluable in understanding the local chromatin structure.
Q: What does ATAC-seq offer that other methods can’t?
WG: ATAC-seq gives you a bit of a hybrid between DNase
hypersensitivity and MNase concentrated in regions of accessibility
that are likely regulatory regions. It can be applied to relatively low
numbers of cells—and the workflow is simple. ATAC-seq allows you
to ask questions about the epigenetic variability in complex or raretissues and epigenomic landscape in populations of cells that haven’t
been observable at the genome-wide level before.
Q: How does the multidimensional portrait of gene regulation that
ATAC-seq provides help us to understand disease?
WG: One of the interesting applications of this methodology is tolook at open chromatin landscapes over time or in a developmental
hierarchy. This gives you an understanding of what DNA elements arechanging or becoming active during an immune response or during
the differentiation of blood cells. It gives you a map of the landscape,

For Research Use Only. Not for use in diagnostic procedures.

a picture of DNA elements that are controlling the biological state of
the cell. When you pair that with RNA-Seq information, you can makeinferences about regulatory elements that are potentially controlling
RNA expression. This allows us to ask questions about what factors
are driving chromatin changes that result in disease phenotypes.
We can use ATAC-seq on a few cells, which you can get from astandard blood draw, and assay their epigenomic signatures.4 If there’s
a patient with an autoimmune problem, we can look at what molecularfactors are driving gene expression of a particular cytokine responsible
for the faulty immune response. We can then rationally nominate drugs
that might antagonize those molecular factors. One day, it might help
us nominate a first course of treatment in a clinical context.
Q: Why did you choose the MiSeq and HiSeq Systems to develop
this method?
WG: The MiSeq System is great for method development becauseof its relatively low cost per run and rapid turnaround time. You cansequence something in a day—get an answer and then optimize yourprotocols. The HiSeq System provided the least expensive cost forthe bases sequenced and, like the MiSeq System, offered the ability toperform paired-end reads. Illumina sequencing systems generally can’t
be matched in terms of raw throughput or cost.

“The MiSeq System is great
for method development becauseof its relatively low cost per run
and rapid turnaround time.”
Q: Why did you choose the Nextera® DNA Library Prep Kit forthis method?
WG: The Tn5 aspect of the Nextera DNA Library Prep Kit was themost important thing. This Tn5 enzyme provided us with the assay
and the library prep step all in one. The assay is identifying regions of
the genome that are accessible and then the transposase, with its cut
and paste mechanism, gives you a sequenceable library after some
PCR steps. The Nextera kit is also commercially available, we felt like itwould be a method that others could perform on their own.
Q: What led you to the development of scATAC-seq?
WG: There have been powerful methods for assessing cell-to-cellvariation in both the genomic sequences and the transcriptome. This
has led to a better fundamental understanding of standing variation insubsets of mammalian cells. Much single-cell imaging work suggeststhat that transcription factors and regulatory proteins that bind to DNA
and initiate transcription are dynamically expressed at varying levels or
dynamically localized into the nucleus. The question is, how do theseobserved heterogeneities couple into the chromatin itself and how
does this affect gene expression? In addition, if you had the ability tolook at cell chromatin at the individual cell level, you might also be able
to take a complex tissue and build a regular hierarchy understandingof the underlying elements performing gene regulation by trying tounderstand what cells look like other cells—and then building up yourunderstanding of the population from there.

October 2015
Q: What kind of cell-to-cell variation do you see using scATAC-seq?
WG: With scATAC-seq, we saw variation in accessibility associatedwith the binding sites of specific transcription factors—and thetranscription factors that were associated with this noise varied fromcell type to cell type. We also observed that regions of the genome
tended to be correlated in their accessibility noise if they were in thesame compartments of chromatin organization. Regions that looked
to be close together in three-dimensional space tended to be open
together as well.

“The HiSeq System provided
the least expensive cost for thebases sequenced and the ability
to perform paired-end reads.”
Q: Would scATAC-seq information be useful in a clinical setting?
WG: This is one of the things we’re excited about. If we could scale
up our single cell methods to more than 100 cells at a time, it would
be exciting to try to link transcription start sites to their promotersbased on correlated accessibility. There’s an ongoing question ofwhat the linkage between enhancers in the genome and the specificgenes they regulate might be. Because each individual cell we look at
is an independent measurement, we might expect that transcription
start sites that are open together in a correlated manner might befunctionally linked. That is, the genetic element might be regulatingthat gene of interest. That wiring is not only interesting from abasic biological perspective, it also offers the possibility of clinicalapplications. Even if there are relatively rare circulating tumor cells, forexample, we might be able to capture those and assess the regulatory
landscape. That might allow us to nominate specific molecular drivers
that drive that dysfunction and provide hypotheses for drug targeting
downstream. It’s an exciting possibility.

Q: What’s next for your lab?
WR: We’re interested in applying these methods to specific biologicalcontexts. We want to understand the differences in the epigenomic
landscape in cancers, and also in immune response and autoimmune
disorders. There are many directions we can go in here. With thesetechniques, we can draw a blood sample and interrogate the genomeof specific subsets of cells to learn more about the general biology ofthe genome, and also the pathology involved in disease states. It’s an
exciting time to be studying biology.

References
1.

2.

3.

4.

Buenrostro JD, Giresi PG, Zaba LC, et al. Transposition of native chromatin
for fast and sensitive epigenomic profiling of open chromatin, DNA-binding
proteins and nucleosome position. Nat Methods. 2013; 10(12): 1213-1218.
Buenrostro JD, Wu B, Litzenburger UM, et al. Single-cell chromatin
accessibility reveals principles of regulatory variation. Nature. 2015;
523(7561): 486-490.
Schep AN, Buenrostro JD, Denny SK, et al. Structured nucleosome
fingerprints enable high-resolution mapping of chromatin architecture within
regulatory regions. Genome Res. 2015; August 27, pii: gr. 192294. 115.
Qu K, Zaba LC, Giresi PG, et al. Individuality and variation of personal
regulomes in primary human T cells. Cell Syst. 2015; 1(1):51–61.

Learn more about the Illumina products and systems
mentioned in this article:
MiSeq System
www.illumina.com/systems/miseq.html
HiSeq System
www.illumina.com/systems/hiseq_2500_1500.html
Nextera DNA Library Prep Kit
www.illumina.com/products/nextera_dna_library_prep_kit.html

Q: How did using Illumina sequencing systems help you
develop scATAC-seq?
WG: We’ve used the MiSeq System to pioneer a lot of these studies. It
enables a rapid assessment of whether the libraries we generate are ofhigh quality and are ready to be sequenced deeper. We can multiplex
samples onto a MiSeq flow cell and, for a reasonable cost, obtain arapid turnaround on the library quality. We then use the HiSeq System
to sequence deeper when needed.

Illumina • 1.800.809.4566 toll-free (US) • +1.858.202.4566 tel • techsupport@illumina.com • www.illumina.com
For Research Use Only. Not for use in diagnostic procedures.
© 2015 Illumina, Inc. All rights reserved. Illumina, HiSeq, MiSeq, Nextera, and the pumpkin orange color are trademarks of Illumina, Inc. and/or its affiliate(s) in the U.S. and/or other countries. Pub. No. 1070-2015-003 Current as of 22 October 2015