Application of NanoString nCounter technology to the Validation of
ChIP-Seq datasets in the ENCODE projectCharles B. Epstein1, Alon Goren1,3,4, Melissa Gymrek, Jason Ernst1,2, Noam Shoresh1, Xiaolan
Zhang1, Robbyn Issner 1, Michael Coyne1, Ido Amit1, Aviv Regev1, Manolis Kellis1,2, Bradley E.
Bernstein1,3,4*

1 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
2 MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA.
3 Howard Hughes Medical Institute, Department of Pathology, Massachusetts General Hospital andHarvard Medical School, Boston, Massachusetts, USA
4. Center for Systems Biology and Center for Cancer Research, Massachusetts General Hospital, Boston,
Massachusetts, USA* Correspondence should be addressed to B.E.B. (Bernstein.Bradley@mgh.harvard.edu).

Abstract
Highly multiplexed measurement of the abundances of specific nucleic acid sequences is a main-stay of
the field of genomics. Here we extend the NanoString nCounter technology to the evaluation of fragment
abundances following chromatin immunoprecipitation. We describe the method used to design a custom
NanoString codeset for the purposes of measuring DNA fragment abundances derived from chromatin
immunoprecipitation performed with a diverse collection of antibodies against histone modifications, aswell as RNA Pol II and the factor CTCF. We then employ the custom NanoString array to measurefragment abundances from a total of 13 ChIPs and 3 libraries derived from ChIPs. We present graphicallythe correlation between the quantification of fragment abundances by the two methods. Correlations
ranged from a high of 0.92 (H3K9ac) to a low of 0.09 (whole cell extract). Eleven of the 16 ChIP-seq –NanoString combinations tested had a correlation of at least 0.70. Overall, this report demonstrates thatthe inference of fragment relative abundances from ChIP-Seq can be confirmed by an independent
method, thus validating our ChIP-Seq results in the ENCODE project.

Introduction
The study of gene structure and regulation, and in particular the field of epigenomics, relies upon the
mapping of the genomic locations of specific histone modifications and chromatin associated proteins. To
accomplish this, chromatin is immunoprecipitated with appropriate antibodies, and the recovered DNA
fragments are sequenced using Illumina short read sequencing. The short reads are aligned to a genome
scaffold to discover sites of significant enrichment over background. Although this approach offers many
advantages, including nucleotide scale resolution and the opportunity to do experiments without the prior
design of an array of detectors, it suffers from the disadvantages of relatively high cost and low
throughput.
In order to overcome these disadvantages, we adapted NanoString’s nCounter technology to themeasurement of locus specific enrichments following chromatin immunoprecipitation. In order toaccomplish this, we had to solve two problems. First, we had to develop an experimental strategy for
analyte labeling consistent with nCounter technology. Since nCounter was originally developed to be
used with measurement of RNA species abundance, we adapted the manufacturer’s supplied labelingtechnology to work with DNA fragments. Secondly, we had to design an informative collection of probes
to use with the DNA fragment pools derived by immunoprecipitation of chromatin with antibodies targetinga range of selected histone modifications. Since the particular histone modifications of interest to us areassociated with canonical features of gene structure, we created a probe array that sampled suchfeatures, including enhancers, promoters, transcription start sites, and transcribed or potentially imprinted
regions.
This report will present in detail how the array was designed, explore its baseline behavior in terms ofreproducibility, background, cross hybridization artifacts, and dynamic range, and show the comparison
between results obtained using the routine methods of ChIP-Seq and the novel method of ChIP-String.
Overall, we found excellent correlation between genome scale measurements made using ChIP-Seq and
NanoString, thus validating our ChIP-Seq data in the Encode project.

Methods
Nanostring code set design
Two independent 10 state Chrom-HMM analyses (Ernst and Kellis, Nature Biotech, 2010; Ernst et al,
Nature, 2011) were conducted using ChIP-Seq data from two cell types (K562 and H1 ESCs), using all
ChIP-seq tracks available at the time. The ChIP-Seq tracks employed are summarized in Figure 1. For
each state in each cell type (20 states, total), fifty 400mers were sampled to represent genomic regions
associated with the corresponding state. The sampling was done based on the posterior distribution forthe state, restricted to regions where all 8-25bp intervals in the 400bp interval were uniquely alignable to
the HG18 reference genome. The entire set of one thousand 400mers was supplied to Nanostring
Technologies for probe design, i.e. the process of identification of an optimal 100mer (if any exist) within
the 400mer that could potentially be used as a probe in a Nanostring codeset, satisfying the criteria of

uniqueness in the genome and mutual independence from the other probes in the design set. This led tothe design of 903 candidate probes, with zero or one coming from each of the original 1000 regions.
We took the set of 903 candidate probes, and for each available ChIP-Seq dataset in 8 cell types (some90 datasets in all), we tabulated the ChIP-Seq scaled intensity over all 90 datasets. The 8 cell types
included the 2 used in the original HMM, plus six other cell types used in the ENCODE project (HepG2,
HMEC, HUVEC, NHEK, NHLF, GM12878). We then analyzed the 90 x 903 matrix to identify an optimallysensitive and diverse set of 490 probes, using the following method.
To overcome the challenge posed by the fact that many of the 903 regions showed little or no enrichmentin our prior ChIP-Seq results (hence were expected to yield only background readings in a Nanostring
assay, regardless of the antibody employed in the ChIP), we ranked the regions and selected the 490
regions expected to give the strongest detection over the full range of epitopes employed in assay design.
The ranking was performed as follows: For each of the 9 ChIP-Seq epitopes consistently represented inour 90 datasets, we computed the average ChIP-Seq signal over the 8 cell types. Using the average
performance over the 8 cells types, we ranked each probe with a number from 1(high signal) to 903 (lowsignal) for each epitope. We then computed the minimum value of the ranks for each probe over the 9epitopes. We then identified within each HMM class the 24 or 25 probes having the lowest minimum
ranks, indicating that these were the best probes to detect the strongest possible signal from one or moreof the ChIP-Seq antibodies used in prior data. Note, to preserve the diversity of genomic regions
represented on the array, we retained nearly equal numbers (24 or 25 members) of each of the 20 HMM
states identified in the initial step. This was necessary because some HMM classes are intrinsically “dim”,
meaning they correspond to regions that were not particularly enriched in any known ChIP-Seq datasetsat the time the analysis was conducted.
An additional 7 probes were designed to encompass regions representing Zinc finger genes and known
sites of H3K9me3 enrichment, such as the KCNQ1 locus. Finalization of probe selection was
accomplished in collaboration with Nanostring technologies based on further constraints on probe
performance discovered during synthesis and testing. The final resulting Nanostring array had 487 probes(in the first synthesis batch) and 489 probes in subsequent batches, from the initial 497 designs. Oneprobe (B24) was omitted from all analyses when it was found to correspond to a repetitive DNA element,
hence the binning analysis described below employed 486 probes. The sequence coordinates of the fullset of 487 probes are found in the accompanying Excel spread sheet.
Figure 2 illustrates the average ChIP-Seq intensity over all 50 originally designed probes associated with aparticular HMM class, for each epitope, from prior ChIP-Seq data available during probe design andselection. As anticipated from the figure, probes derived from certain HMM classes, such as B and L(from H1 cells and K562 cells, respectively) gave rise to bright signals in H3K4me3, H3K4me2, andH3K9ac ChIPs, whereas probes derived from other HMM classes, such as H and R, at most weakly detectsignals in nearly all assays.

Labeling sample for measurement

ChIP DNA (5 to 100 ng) was brought to a volume of 5ul and thermally denatured at 95C for 5’, then
immediately transferred to ice. DNA was supplemented with 10 ul of Reporter CodeSet and 10 ul ofHybridization Buffer (supplied by Nanostring Technologies). When ready, 5 ul of Capture ProbeSet wasadded, and the sample was immediately placed at 65C overnight (> 12 hours). Following overnightincubation, samples are automatically processed in a Nanostring Prep Station following the manufacturer’s
protocol. The processed sample is then imaged in a Nanostring Imaging station.

Analysis of Nanostring assay results
Data are linearly rescaled (normalized) using the supplied exogenous positive controls, employing theconvention that the 6 positive controls sum to a value of 60,000 after rescaling. Typical scale factors arein the range from 0.5 to 5, and the median scale factor was 1.74 over the first 200 assays run in our lab.
To explore graphically the correspondence between NanoString results and ChIP-Seq results, thefollowing procedure was followed: For each ChIP-Seq data set, numerical values were derived for each of
486 genomic regions in the NanoString codeset, and divided by the genome wide average value of aregion of equivalent size from the same dataset, to approximate the fold enrichment above background ofeach region. The 486 values were then placed in “bins” based on the following bin – range boundaries:
Under 2 fold enrichment, 2 – 5 fold enrichment, 5 – 10 fold enrichment, 10 – 20 fold enrichment, 20 – 40
fold enrichment, and > 40 fold enrichment. The NanoString assay values for the probes corresponding toeach bin were statistically summarized as 5th, 25th, median, 75th, and 95th percentiles, and the statisticalrange of NanoString assay results were plotted against the bin identifier. The Y axis was re-scaled so thatthe median value of the bin corresponding to < 2 fold enrichment = 100 arbitrary units. The resulting
graphs are attached.

Results
1. Distribution and reproducibility of background values
As a preliminary experiment, we wished to measure the intrinsic signal associated with each probe in theabsence of input DNA. We performed a labeling assay with no input DNA, and measured (with four fold
replication) the distribution of probe counts (intensities). We found that the median normalized assay
value was 15.7, which is appropriately low, considering that a true signal in the assay can be in the rangeof several thousands. However, we also found that the background values for each probe reproducibly fellin some range, and that two probes at the high end of the range (L43 and B40) were high enough to be ofsome concern, giving mean assay values of 119 and 95, respectively (Figure 3). The very weak, but
reproducible, signals in the absence of true signal must be taken into account in large scale studies that
employ this platform, insofar as spurious inferences of similarity could be drawn if multiple assays wererun using samples having essentially no signal. We also found a typical inverse relation between assay

value and CV, as shown in Figure 4. Thus, while for unknown reasons, some probes have a reproducibly
high background, this background is still trivial compared to the values obtained in a true assay.

2. Reproducibility of the assay
We used the ChIP-String assay to make replicate measurements of 5 ng of ChIP DNA, derived using
K562 cells and H3K4me3 antibody. We found that ChIP-String assay values were highly reproducible
between assays, in spite of the very low amount of input DNA employed, giving a correlation (r squared) of0.94; correlation plot is shown in Figure 5. The average assay values were 112 and 232, and the maximal
assay values were 2550 and 6612. One probe (B24) was found to give a very high value in all assays ofhuman DNA and is omitted from this and all further calculations. This probe was later found to interact
with a repetitive DNA element, in spite of the otherwise successful effort to exclude such probes during thedesign of the probe array.

3. Correlation of Nanostring results from assays of a ChIP and a library derived
from that ChIP
We compared 5 ng of H3K4me3 ChIP DNA to 33 ng of library DNA derived from that ChIP, using simple
correlation analysis (Figure 6). We found excellent agreement between the two, with an overall R squaredof 0.89. The assay of the library gave much higher ChIP-String counts than the assay of the ChIP,
consistent with the greater amount of input DNA. Thus, we have demonstrated, in a highly multiplexedassay, the overall agreement between fragment abundances in a ChIP, and in a library derived from that
ChIP. We repeated the comparison of NanoString assays of ChIP and library using H3K9ac (Fig. 7),
H3K27ac (Fig. 8), H3K27me3 (Fig. 9), and H3K36me3 (Fig. 10). As expected, the agreements aresuperior for the marks having higher signal to background ratios, but overall, the agreement between
corresponding ChIP and library is extremely good. The agreement between ChIP and library, which is afoundation of the accuracy of the ChIP-Seq method, implies that one does not introduce significant bias
when generating libraries from ChIPs. While heretofore this has been demonstrated using comparisons of
ChIP-on-chip and ChIP-seq, for low resolution, and using qPCR of ChIPs and libraries, for high resolution,
we are not aware of any prior high resolution demonstrations of the validity of ChIP-seq which approach
the scale of the present report.

4. Validation of ENCODE project ChIP-Seq data by comparison of ChIP-Seq
results with ChIP-String results

In order to validate the ChIP-Seq data generated by our group in the ENCODE project, we derived new
ChIPs from an independent culture of K562 cells, and performed ChIP-String assays on each ChIP. We
then compared the resulting ChIP-String data to the previously obtained ChIP-Seq data. We explored
several methods for comparing ChIP-String and ChIP-Seq data; in each case, we compared a derived
metric (explained below) to the ChIP-Seq integrated tag count from the genomic region corresponding toeach Nanostring probe. The following derived metrics were evaluated:(a) ratio of Nanostring measurements of ChIP to Nanostring measurements of input control;(b) ratio of Nanostring measurements of ChIP to Nanostring measurements of input control, with afloor value of 20 substituted when necessary; and(c) normalized Nanostring measurements of ChIP used without any adjustment based on input
control.
We found that input controls and ChIPs were uncorrelated in Nanostring measurements, so methods (a)and (b) were noisy compared to method (c). All the comparison plots referenced below used method (c),which simply compares the normalized Nanostring measurement, probe by probe, to the integrated ChIPSeq tag count in the region covered by the probe.
Comparisons were done using both dot plots (i.e. graphs with one points per probe), and using a statisticalsummary of the range of Nanostring assay values obtained on a quantile by quantile basis, where eachquantile corresponds to a range of ChIP-Seq fold enrichments (signal over background). Results for
epitopes are found in the attached figures, numbered as summarized in the first two columns of thefollowing table. The table also shows what type of DNA was analyzed by NanoString (ChIP or Illumina
sequencing library derived from ChIP), the amount of DNA input to the NanoString assay, the unique
identifier for the NanoString assay (for purposes of establishing correspondence with the tabulated
NanoString data supplied in the accompanying spread sheet), and the correlation (r) between ChIP-Seq
and NanoString over the entire 486 region code set.

Table 1: Index to Graphs and other statistics

printed 1/3/2012

ChIP-Seq data sets employed in Chrom HMM

slide 1Average ChIP-Seq values from 10 chromatinmarks computed from 8 cell types,
corresponding to each of the 20 HMM states

printed 1/3/2012class WCE - AVG H3K4me1 - AVG H3K4me2 - AVG H3K4me3 - AVG H3K9me3 - AVG H3K36me3 - AVG H3K27me3 - AVG H3K9ac - AVG H3K27ac - AVG H4K20me1 - AVG
A
2.6
8.2
18.9
14.7
3.1
1.9
15.8
9.4
8.5
5.0
B
2.3
6.0
40.5
64.7
0.8
1.9
2.6
39.0
37.0
3.4
C
1.7
7.3
9.5
5.7
0.7
2.3
1.6
3.7
5.2
3.6
D
1.9
6.8
5.3
2.8
1.0
2.3
1.7
3.1
6.2
2.8
E
2.1
6.2
5.5
3.6
0.8
4.5
1.2
3.5
4.7
6.4
F
1.7
3.2
2.2
1.8
0.8
5.5
0.8
1.7
2.3
3.8
G
1.5
1.6
1.6
2.0
0.6
1.6
1.3
1.4
1.5
2.2
H
1.5
1.7
1.6
1.9
3.6
2.0
1.6
1.2
1.1
2.0
I
1.7
3.4
3.7
2.9
0.8
1.6
7.5
3.0
3.8
3.5
J
2.0
4.6
4.0
2.3
1.0
2.4
1.5
2.0
2.5
3.1
K (A)
1.8
5.8
11.6
11.1
0.8
1.9
2.3
5.8
5.1
2.7
L (B)
2.2
9.2
28.3
35.1
0.7
3.1
1.6
23.2
21.1
3.6
M (C)
1.9
8.7
5.3
3.3
0.6
3.0
1.3
4.1
7.0
2.6
N (D)
1.8
5.5
4.5
2.4
0.8
2.0
2.4
2.4
4.4
2.7
O (E)
2.3
7.3
5.5
3.2
0.9
6.0
1.4
3.5
5.2
6.6
P (F)
1.7
3.2
2.3
1.4
0.6
2.4
1.4
1.5
2.0
3.0
Q (G)
1.9
2.5
1.5
1.5
0.8
7.2
0.8
1.7
2.5
3.6
R (H)
1.4
1.4
0.9
1.1
0.8
1.1
1.2
0.8
0.8
1.9
S (I)
1.7
3.2
4.5
3.7
0.8
1.2
4.0
1.6
1.5
2.8
T (J)
2.1
3.8
3.1
1.9
0.8
1.6
2.1
1.7
1.5
3.5

HMM classes A through J are based on the HMM inferred from H1 cell data, andHMM classes K through T are based on the HMM inferred from K562 cells. Blue
corresponds to low values, yellow to intermediate values, and red corresponds tohigh values. HMM class R corresponds to regions that are low intensity in nearly
every ChIP (horizontal blue band).

slide 2

Correlation of replicate assays with no input DNA
L43

printed 1/3/2012

B40

slide 3

B40
printed 1/3/2012

L43

slide 4

Correlation of Replicate Assays of ChIP DNA
10000

y = 0.4559x + 6.442
R2 = 0.9361

1000

printed 1/3/2012

100

10

1
1

10

100

1000

10000

100000slide 5

Correlation of Assays of ChIP and Library DNA

printed 1/3/2012

NanoString assay of Library

H3K4me3R2 = 0.89

NanoString assay of ChIP
slide 6

Correlation of Assays of ChIP and Library DNA

printed 1/3/2012

NanoString assay of Library

H3K9acR2 = 0.76

NanoString assay of ChIP
slide 7

Correlation of Assays of ChIP and Library DNA

printed 1/3/2012

NanoString assay of Library

H3K27ac
R2 = 0.81

NanoString assay of ChIP
slide 8

Correlation of Assays of ChIP and Library DNA

printed 1/3/2012

NanoString assay of Library

H3K27me3
R2 = 0.56

NanoString assay of ChIP
slide 9Correlation of Assays of ChIP and Library DNA

printed 1/3/2012

NanoString assay of Library

H3K36me3
R2 = 0.66

NanoString assay of ChIP
slide 10printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K27ac ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 11

K562 H3K27ac ChIP
10000.0

100.0

printed 1/3/2012

Nanostring

1000.0

10.0

1.0
1.0

10.0

100.0

1000.0

ChIP-Seq

slide 12

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K9ac ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 13

K562 H3K9ac ChIP
10000.0

100.0

printed 1/3/2012

Nanostring

1000.0

10.0

1.0
1.0

10.0

ChIP-Seq

100.0

1000.0slide 14printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K27me3 ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 15

K562 H3K27me3 ChIP

100.0

10.0

printed 1/3/2012

Nanostring

1000.0

1.0
1.0

10.0

100.0

ChIP-Seq

slide 16printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K36me3 ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 17

K562 H3K36me3 ChIP

100.0

10.0
printed 1/3/2012

Nanostring

1000.0

1.0
1.0

10.0

100.0

ChIP-Seq

slide 18

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K4me1 ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 19

K562 H3K4me1 ChIP

100.0

10.0
printed 1/3/2012

Nanostring

1000.0

1.0
1.0

10.0

100.0

ChIP-Seq

slide 20

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K4me2 ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 21

K562 H3K4me2 ChIP
10000.0

100.0

printed 1/3/2012

Nanostring

1000.0

10.0

1.0
1.0

10.0

100.0

1000.0

ChIP-Seq

slide 22

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K4me3 ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 23

K562 H3K4me3 ChIP
10000.0

100.0

printed 1/3/2012

Nanostring

1000.0

10.0

1.0
1.0

10.0

100.0

1000.0

ChIP-Seq
slide 24

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H4K20me1 ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 25

K562 H4K20me1 ChIP
1000.0Nanostring

100.0

printed 1/3/2012

10.0

1.0
1.0

10.0

100.0

ChIP-Seq
slide 26

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 CTCF Library

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 27

K562 CTCF Library

100000.0

1000.0

100.0

printed 1/3/2012

Nanostring

10000.0

10.0

1.0
1.0

10.0

100.0

1000.0

ChIP-Seq

slide 28

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 RNA Pol II ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 29

K562 RNA Pol II ChIP
1000.0Nanostring

100.0

printed 1/3/2012

10.0

1.0
1.0

10.0

100.0

1000.0

ChIP-Seq

slide 30

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 whole cell extract – chromatin prep

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10
10 – 20
20 – 40 > 40

slide 31

K562 whole cell extract – chromatin prep
1000.0Nanostring

100.0

printed 1/3/2012

10.0

1.0
1.0

10.0

100.0

ChIP-Seq

slide 32

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 EZH2 library DNA

N=Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10 10 – 20 20 – 40 > 40
slide 33

printed 1/3/2012

K562 EZH2 library DNA

slide 34

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H2A.Z library DNA

N=Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10 10 – 20 20 – 40 > 40
slide 35

printed 1/3/2012

K562 H2A.Z library DNA

slide 36

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K79me2 library DNA

N=Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10 10 – 20 20 – 40 > 40
slide 37

printed 1/3/2012

K562 H3K79me2 library DNA

slide 38

printed 1/3/2012

Statistical range of normalized Nanostring assay results

K562 H3K9me3 ChIP

N=
Fold Enrichment from ChIP-Seq (normalized intensity)<2
2–5
5 – 10 10 – 20 20 – 40 > 40
slide 39

printed 1/3/2012

K562 H3K9me3 ChIP

slide 40