APPLICATION NOTE

TranscriptionPath™: A Method for RNA-free Transcription Profiling
Brian Egan

Active Motif’s TranscriptionPath™ Assay is an alternate approach to RNA-based gene expression methods. The
TranscriptionPath method utilizes Chromatin Immunoprecipitation (ChIP) to measure transcription rates as a functionof RNA Polymerase II (RNAPII) occupancy across the genome. The assay can be combined with Next-Generation
sequencing (TranscriptionPath-Seq) for a genome-wide readout, or it can be used with qPCR for a gene-specificreadout. TranscriptionPath offers several advantages when compared to RNA-based approaches including theability to look at early time points that are not accurately measured with mRNA approaches and the ability tomeasure gene expression in the same sample as was used to measure transcription factor (TF) binding by ChIP. In
this study, TranscriptionPath data was compared to mRNA data to provide evidence that RNAPII occupancy is areliable measure of transcription rates, and to show that TranscriptionPath data is rich in additional informationsuch as locations of alternative transcription start sites (TSS) and identification of unannotated genes.

Introduction
Gene expression is a complex and tightly regulated processcomplex (PIC) formation or pausing and transcriptionthat is influenced by both transcription rates and mRNA
rates. Described below are the methodology and data
degradation rates. For many genes the main regulatoryinterpretation, as well as the benefits of using thismechanism of gene expression is at the level of transcripapproach rather than RNA-based approaches.
tion. This process can be describedby a cycle of successive steps that
7$FWki[ZWdZJhWdiYh_X[Z8$DejFWki[ZWdZJhWdiYh_X[Zincludes: 1) the recruitment of an
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6) elongation, and 7) termination.
Even though so many different
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most approaches still measure onlymRNA steady state levels, which
gives no information regarding
9$FWki[ZWdZDejJhWdiYh_X[Z:$DejFWki[ZWdZDejJhWdiYh_X[Zthe mechanistic regulation ofDei_]dWbWYheiij^[][d[Dei_]dWbWYheiij^[][d[gene expression.

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understanding of gene regulation
can be obtained by focusing on
the actual process of transcription.
This can be achieved by measuring RNAPII occupancy across the
genome using chromatin immunoprecipitation. Using this approach,
information can be collected onstart site selection, pre-initiation
Active Motif Application Note

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Dnajb9Figure 1: TranscriptionPath data reveals four states of RNAPII occupancy.
TranscriptionPath-Seq was performed using chromatin from mouse neuroblastoma cells. Four patterns of RNAPII occupancy
were detected; examples are shown above. (A) Promoter Paused and Transcribed – Transcription is rate-limited post-initiation.
(B) Not Paused and Transcribed – Transcription is rate-limited by RNAPII recruitment. (C) Promoter Paused and Not Transcribed– These genes are poised for activation, but are not transcribed. In this example, the poised pattern shown is inducible. (D) Not
Paused and Not Transcribed – These genes are inactive.

Page 1

Results
The Method and Data Readout
TranscriptionPath is based on the ChIP method utilizing
an antibody that targets RNAPII. Four distinct patterns
are observed when using ChIP-Seq to analyze the immunoprecipitated DNA (Figure 1, previous page). These are:
A. Paused and Transcribed – This pattern is defined by asignificant peak at the TSS followed by a lower, more evensignal across the gene body. The peak at the TSS is indicative of either formation of the PIC or pausing following
RNAPII release from the PIC. The elevated signal acrossthe gene body is indicative of transcription of the gene. B.
Not Paused and Transcribed – This pattern is defined byeven signal across the gene body with no peak at the TSS.
C. Paused and Not Transcribed – This pattern is definedby a peak at the TSS and no signal in the gene body. These
genes have assembled the PIC and are poised for transcription, however the genes are not being transcribed. D. Not
Paused and Not Transcribed – This pattern is defined by
no recruitment of RNAPII to the gene body or TSS. In
other words, these genes are inactive. The different patterns
provide information on the mechanisms of transcription ofeach gene. Patterns A and C, which have RNAPII peaks at
the TSS, have rate-limiting steps that occur after RNAPII
recruitment. Pattern B, which does not display a RNAPII
peak at the promoter, is rate-limited by the recruitment
of RNAPII.
Some additional features of the TranscriptionPath signalthat are worth noting concern RNAPII occupancy at the
3´ end of genes. RNAPII occupancy is strongly correlatedwith gene annotations, except at the 3´ end where signal;b[lWj[Zi_]dWbWj) [dZ

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Figure 2: A representative TranscriptionPath gene profile.
TranscriptionPath-Seq was performed using chromatin from mouse neuroblastoma
cells. Most genes will display an RNAPII peak at the promoter indicative of the paused
state. RNAPII occupancy in the gene body tends to be fairly even across the gene body.
Occupancy then increases toward the 3´ end of the gene and often extends beyond the3´ gene boundary. The increased occupancy at the 3´ end is associated with pausing, astranscription is terminated. Red arrows mark the start and end of the annotation andserve to highlight these features in relation to the TranscriptionPath data.

Page 2often extends beyond where the 3´ end of the maturemRNA will be. This extended transcription is a known
phenomenon and “extra” 3´ sequence will be cleaved duringmRNA processing to yield the mature transcript1. Additionally, signals will often be elevated at the 3´ end of thetranscribed region relative to the gene body. This patternindicates slowed extension or polymerase pausing astranscription is terminated (Figure 2).
Accessing Early Time Points
Many gene expression studies are initiated in an effort tounderstand how gene regulation is being altered in response
to a stimulus such as a drug. Gene expression in these studies is often measured at time points beyond 24 hours, with
six hours being considered an early measurement. However,gene expression changes at 24 hours are not primary events.
The primary events occur within minutes of treatment,
often with the induction of transcription factors that aretranscribed, translated and imported back into the nucleus
resulting in a second wave of transcriptional changes. In
order to truly understand a cellular response it is importantto understand the primary response, that is, the changes
that are happening within minutes of treatment2. MeasuringmRNA at these early time points has limitations, especiallywhen studying transcripts derived from longer genes(i.e. genes over 100 Kb)3. Transcripts over 100 Kb willtake over one hour to be fully transcribed, processed andexported to the cytoplasm where they can be detected. As
a result, many changes in gene expression will be missed
when using mRNA-based methods at early time points.
The TranscriptionPath method can detect gene expression changes that are missed by mRNA detection methods
because it measures RNAPII occupancy at DNA in realtime. The occupancy of the RNAPII on the DNA is a surrogate readout for transcription rates and the TranscriptionPath data presented in this paper support this assertion.
Additionally, studies by others support these findings andprovide evidence that RNAPII occupancy is a reliable readout for gene transcription4.
In order to demonstrate the ability of TranscriptionPath toassess gene expression at early time points and to compare it
to mRNA-based methods, studies were initiated in a mousepancreatic islet cell line treated with forskolin to stimulatechanges in gene expression. TranscriptionPath DNA andmRNA from these cells were used to analyze specific genesof varying lengths. For TranscriptionPath, RNAPII ChIP
was performed followed by qPCR using primers that were
designed approximately 1500 bp 3´ of the TSS of each gene.
Because the polymerase extends at a rate of approximately
1500 bp/min in vivo5, this method enables us to detectchanges in RNAPII occupancy within these genes at one
minute after treatment. Figure 3 (next page) shows that forTranscriptionPath™: A Method for RNA-free Transcription Profiling7$?]\'h"=[d[B[d]j^3(.'"*''Xf,

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Mouse Min6 cells were treated with forskolin to induce changes in gene expression. Cells were fixed, chromatin was prepared and TranscriptionPath analysis was performed using qPCR primers
specific to sequences approximately 1500 bp 3´ of the transcription start site (TSS). mRNA was measured by preparing cDNA and designing primers that span splice junctions. (A) Igf1r, (B) Irs2and (C) Egr1 were all induced after treatment. For all of these genes the peak in transcription is detectable before the peak in mRNA, and the data shows that the longer a gene is, the farther
the peak in mRNA lags behind the peak in transcription. Earlier detection and improved responsiveness is also observed with down-regulated genes. (D) TranscriptionPath analysis of Slc2a2
shows the greatest decrease in transcription at 30 minutes compared to three hours for RT-qPCR measurements.

three genes that were up regulated in response to treatment(Igf1r, Irs2 and Egr1), TranscriptionPath was able to detectchanges that could not be detected using RT-qPCR at earlytime points. The most dramatic example was Igf1r, a genethat is 281,411 bp long. For this gene, TranscriptionPath
was able to detect a three-fold increase in transcription at
10 minutes and a five-fold increase at 30 minutes. In contrast, RT-qPCR only detected a two-fold difference at threehours (Figure 3A). These results are as expected as RNAPII
would require roughly three hours to fully transcribe a geneof this length. In contrast, as the Egr1 gene is only 3750 bp
the full-length mRNA should be detectable within
2.5 minutes of treatment. Even so, the TranscriptionPathdata shows earlier and more robust detection of gene
expression for this shorter gene than RT-qPCR (Figure 3C).
For genes that are down regulated, the TranscriptionPathmethod can also detect changes earlier than mRNA-based
approaches. This is illustrated by data on the Slc2a2 gene,which shows a 50% reduction in TranscriptionPath signal at
10 minutes and 80% reduction at 30 minutes (Figure 3D).
Active Motif Application Note

It took three hours, or 18 times longer, to detect the same
50% reduction when measuring expression with RT-qPCR.
The observed decrease in Slc2a2 mRNA over time is areflection of the half-life of the mRNA since transcription ofthis gene, as shown by TranscriptionPath, was largely shut
down. These data demonstrate the ability of the TranscriptionPath method to detect changes in gene expression thatare not detectable with RNA methods at important earlytime points.
Combining TranscriptionPath with ChIP Reveals
Functional Consequences of Transcription Factor Binding
ChIP is widely used to detect TF binding or histone
modifications. When ChIP is combined with Next-Gen
sequencing (ChIP-Seq), TF binding and modified histone
occupancy can be mapped across the entire genome.
However, mapping binding sites lack contextual information because the process of TF binding does not always
lead to transcriptional activation or repression. In manycases binding results in a mixed response, with some
genes being activated, some being repressed and somePage 3

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Figure 4: Induced TF binding correlates with TranscriptionPath-measured gene expression.
ChIP-Seq was performed using chromatin from control and estrogen-treated MCF-7 cells using antibodies directed against RNA Pol II and the estrogen-inducible transcription factor SRC3.
Performing ChIP-Seq using an antibody against RNA Pol II produces a genome-wide profile of gene transcription rates and the TranscriptionPath ChIP-Seq data shows induced transcriptionof the RET gene after estrogen treatment (bottom 2 panels). Induced transcription of the RET gene correlates with the induced binding of SRC3 in the promoter and gene body of the
RET gene (copper arrows in top 2 panels).

not changing their transcriptional response at all. Thesevariable expression patterns are a reflection of the differences in the recruitment of other transcription factors andco-factors at each gene. In order to more fully understandthe importance and/or function of individual TF binding
events it is necessary to examine the effects of TF bindingon transcription of each bound gene6. This type of analysis
can be achieved by integrating ChIP data with gene expression data. The integration of TranscriptionPath data with
ChIP-Seq data is advantageous in that A) the TranscriptionPath method can be performed on the identical chromatin
sample that was used for TF ChIP, B) the TranscriptionPathdata output is the same as the TF ChIP data output, thus
simplifying the analysis, and C) the TranscriptionPathmethod is a direct readout of the effect of TF binding ontranscription. This is in contrast to RNA measurements,
which are influenced by other aspects, such mRNA half-life.
TranscriptionPath experiments were performed in parallel with ChIP-Seq to identify changes in SRC3 binding in
response to estrogen treatment of MCF-7 cells. SRC3 binding events were identified that correlated with increasedtranscription of associated genes, decreased transcription
and no change in transcription. Each of these three groupsof genes were associated with different gene ontologies thus
offering greater insight into the role SRC3 plays in estrogen
responsiveness. A single example of SRC3 binding correlating with induced gene expression is presented in Figure 4,
Page 4

where 2 inducible SRC3 binding sites in the RET genecorrelate with increased RET transcription.
Transcription Factors in Pathways
The cellular response to stimulation is a complex processthat begins with the ability of the cell to sense changes in its
extracellular environment and transmit the signal throughthe cell membrane to the cytoplasm. Intracellular signalingcascades then relay this information to the nucleus resulting
in changes in chromatin structure and ultimately changes
in gene regulation. The initial response of altered gene
expression will include induced expression of transcription factors (for example, the well-characterized immediate
early genes Fos, Jun, Myc and Egr1) that will ultimately betranslated into proteins. These will be imported back into
the nucleus and, through DNA binding, initiate a second
wave of stimulated transcription7. These cycles of TF binding, which subsequently induce the expression of additional
TFs, increase the complexity of temporal gene expression
patterns. With this framework in mind, it is easy to see thatgene expression profiles following treatment can be dramatically divergent when comparing one-hour treatments
to 24-hour treatments. In order to illustrate the influenceof timing and TF expression cycles on gene expression, thelevels of transcription, TF binding and mRNA expression
were each measured in a simplified model of a transcriptional network. In this example, MIN6 cells were treatedwith forskolin and the transcription of the immediate earlyTranscriptionPath™: A Method for RNA-free Transcription Profilinggene Egr1 was measured using TranscriptionPath. mRNAlevels were determined using RT-qPCR. The data showsthat Egr1 transcription peaks at 10 minutes (Figure 5A)
while the peak in Egr1 mRNA occurs one time point later at
30 minutes (Figure 5B). The Egr1 message is translated into
protein and imported to the nucleus where it binds DNA,
resulting in the induction of a new set of genes. We identified Nab1 as one of the target genes of Egr1 using ChIP-Seq.
Therefore Egr1 binding to the Nab1 promoter was studied
further by performing Egr1 ChIP-qPCR on chromatinfrom all time points. Nab1 transcription levels were also
determined using the TranscriptionPath-qPCR assay. The
data shows that the timing of Egr1 binding to the Nab1
promoter occurs on the same time frame as Egr1 mRNA
expression (Figures 5C and 5B) and that the timing of transcription of the Nab1 gene correlates nicely with Erg1 binding in the Nab1 promoter (Figures 5D and 5C). Lastly, Nab1mRNA was measured by RT-qPCR and showed induction
only at the three-hour time point (Figure 5E). Nab1 is aknown transcriptional repressor8 and the data show that its
influence on transcription does not come into play until atleast three hours after treatment. This detailed example ofthe timing of induced TF expression illustrates the impor-'(&

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Mathematical Modeling Shows TranscriptionPath is a
Reliable Measure of Transcription
The data presented above shows that there is a clear
correlation between TranscriptionPath data, TF binding and mRNA expression. To provide further evidence
that TranscriptionPath accurately measures transcription
rates, a mathematical model was developed that uses themeasured TranscriptionPath data to predict mRNA levelsin the cell. If the TranscriptionPath data is accurate, thepredicted mRNA levels should overlap with the experimentally derived mRNA data points. This approach was used
to model c-Fos mRNA levels using the TranscriptionPathexperimentally determined transcription rates at 10, 30,
60 and 180 minutes after treatments along with publishedinformation on c-Fos half-life. A model assuming the c-Fos
mRNA half-lives of 15 minutes9 or 30 minutes10 was used.
If the half-life of c-Fos is set to 30 minutes (Figure 6A, nextpage), the predicted c-Fos mRNA levels nicely overlap theobserved RT-qPCR data points. If the c-Fos half-life is set to

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tance of selecting appropriate time points for analysis andalso demonstrates how induced TF expression continually
influences gene expression.

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DWX'fh_c[hiDWX'][d[Figure 5: A detailed analysis of gene regulation using TranscriptionPath, TF-targeted ChIP and RT-qPCR.
Mouse MIN6 cells were treated with forskolin to induce gene expression changes. At 10, 30, 60 and 180 minutes after treatment either the cells were fixed and chromatin was prepared for ChIP,
or total RNA was isolated for RT-qPCR. (A) TranscriptionPath was used to show a maximal increase in transcription of the Egr1 gene at 10 minutes. (B) When measured with RT-qPCR, the Egr1
mRNA peaked one time point later at 30 minutes. Egr1 ChIP-Seq identified Nab1 as a target gene for Egr1; (C) Egr1 ChIP-qPCR showed maximal induction of Erg1 binding to the Nab1 promoter at
30 minutes. (D) The timing of the transcription of the Nab1 gene mirrored the timing of Egr1 binding and also peaked at 30 minutes. (E) When measured with RT-qPCR, the induced expression of
the Nab1 transcript was only detectable at three hours.

Active Motif Application Note

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CeZ[b[ZcHD7b[l[biFigure 6: Gene expression models show that TranscriptionPath is an accurate measureof transcription rates.
Mathematical models of gene expression were developed at Thomas Jefferson University
by Daniel Zak. (A) A model that assumes a c-Fos half-life of 30 minutes and using ourTranscriptionPath data as input predicts mRNA levels that overlap precisely with our
RT-qPCR data points. (B) A model that assumes a shorter c-Fos half-life of 15 minutes
predicts mRNA levels that are shifted to the left of the actual RT-qPCR data points.

15 minutes (Figure 6B), the calculations are still valid anddemonstrate peak mRNA expression occurring after the
peak in the TranscriptionPath data, however the predicteddata does not completely overlap the actual RT-qPCR data.
These models support our assertion that TranscriptionPath is measuring actual transcription rates, as using the
published c-Fos mRNA half-life of 30 minutes results in thepredicted data precisely overlapping the experimental data.
Identification of Alternate Transcription Start Sites andUnannotated Genes
30-50% of all human genes use alternate TSSs11. The use ofalternate TSSs not only contributes to genetic diversity andis needed for tissue-specific expression of different protein
isoforms, but there is also evidence that aberrant use of
TSSs of tumor suppressors and oncogenes is functionallylinked to cancer12. mRNA detection using microarrays
does not provide information of alternative TSSs because
mRNA detection is typically based on hybridization to
Page 6probes complimentary to the 3´ end of the message. Using
the TranscriptionPath method for gene expression studies has the added advantage of providing information on
TSSs, as Hao et al. demonstrated by detecting and confirming an alternate TSS for the Mef2c gene used in retinal rod
photoreceptors13. The TranscriptionPath-Seq data show
many examples of transcription initiation outside of theannotated TSS (Figure 7, next page). These alternate TSSs
can occur within the existing annotation or far upstream of
the annotated start site. Often the identified alternate TSS
can be verified by searching additional gene databases. One
further advantage that TranscriptionPath offers is the ability
to detect transcription of unannotated genes. These two
examples illustrate ways in which the TranscriptionPathdata is rich with additional biological information beyond
measuring transcription rates.

Discussion
Understanding any cell system, whether it is related todevelopment, disease or response to treatment, requires
an in-depth knowledge of gene regulation. Methods suchas Nuclear Run-ons, Northern Blotting, PCR, Differential
Display, SAGE, microarrays and RNA-Seq have evolved
over the years to measure changes in gene expression. It
is important to note that most of these methods measuresteady-state RNA, which is influenced both by transcription
rates and mRNA degradation rates. TranscriptionPath, on
the other hand, measures the actual process of transcription. Therefore the information gained is more mechanistic,
yielding a more direct readout of cellular response.
One advantage of looking at gene expression using TranscriptionPath is its increased sensitivity at short time points.
The average human gene is 27 KB14 and, assuming atranscription rate of 1500 bp/min, changes in mRNA
expression for the average gene will not be detected until
18 minutes after treatment. Many genes are more than
100 Kb, thus requiring more than one hour until mRNAchanges can be detected. The ability to look directly attranscriptional changes within minutes simplifies pathway
analysis because it can be used to examine the immediate
and direct effects of a treatment without the influence of
secondary and tertiary effects that may occur when looking
at later time points. In addition, the assay can be performed
across multiple species since the carboxy terminal domainof the RNA polymerase is well conserved. To date, we haveused TranscriptionPath in human, mouse, rat, D. melanogaster, A. thaliana, C. elegans, Z. mays and S. cerevisiae.
In addition, the assay may also be advantageous insituations where mRNA integrity is compromised or
mRNA isolation is difficult or impossible. For example,
TranscriptionPath™: A Method for RNA-free Transcription Profilingin FFPE samples the mRNA has typically been degraded,
so RNA-based methods are not reliable. However, recent
studies have shown that it is possible to isolate chromatinfrom FFPE samples and that the chromatin can be used in
ChIP15. Therefore, TranscriptionPath could potentially be aconsistent method by which gene expression studies could
be performed using FFPE samples. A second application forTranscriptionPath, where mRNA analysis is not an option,is in conjunction with methods that allow for the isolationof cellular sub-populations from complex heterogeneous
tissues. These protocols result in the isolation of nuclei,
not whole cells, and TranscriptionPath would provide ameans of gene expression profiling from these populationsof nuclei16.
TranscriptionPath was developed as an alternate approach
to RNA-based methods of gene expression and provides anumber of advantages that include the ability to 1) perform

TF ChIP and get gene expression data using the exact same
sample; 2) look at changes in gene expression at short time
points after treatment; 3) directly measure transcription
without the influence of mRNA half-life; and 4) detect
alternately used TSSs and unannotated genes. In addition,
this technology may prove to be useful in gene expression
studies in FFPE samples and in gene expression studies
where nuclear isolation is required.

References
1.

Kim H., Erickson B., Luo W., Seward D., Graber J.H., Pollock
D.D., Megee P.C., Bentley D.L. (2010) Gene-specific RNAPII
phosphorylation and the CTD code. Nat Struct Mol Biol. 17:1279-86.

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Michaelson J.J., Trump S., Rudzok S., Gräbsch C., Madureira D.J.,
Dautel F., Mai J., Attinger S., Schirmer K., von Bergen M., Lehmann
I., Beyer A. (2011) Transcriptional signatures of regulatory and toxic
responses to benzo-[a]-pyrene exposure. BMC Genomics. 12:502.

3.

Singh J., Padgett R.A. (2009) Rates of in situ transcription and splicing
in large human genes. Nat Struct Mol Biol. 16:1128-33.

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WY[cXboÅFigure 7: Alternate transcription start sites (TSSs) revealed by TranscriptionPath.
TranscriptionPath-Seq was performed using chromatin from a mouse neuroblastoma cell line. The data presented above is from two genes with transcription initiation in areas of the genomethat do not correspond to their respective start sites in the RefSeq gene database (purple gene annotations). (A) Comparison to an alternate gene database (mgcGenes, green gene annotation)
reveals a known alternative transcription start site for Fam49b that corresponds to the transcription start site detected with TranscriptionPath. (B) Comparison to the RefSeq and mgcGenes
databases shows the same annotations for Cbx7, but comparison to a third gene annotation database (Acembly Genes, blue gene annotation) reveals a known alternative start site for Cbx7
that corresponds to the transcription start site detected with TranscriptionPath. The red arrows show that the start sites from the alternate gene databases correspond precisely with locationof paused RNAPII.

Active Motif Application Note

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4.

Gilchrist D.A., Fargo D.C., Adelman K. (2009) Using ChIP-chip and
ChIP-seq to study the regulation of gene expression: genome-wide
localization studies reveal widespread regulation of transcription
elongation. Methods. 48:398-408.

5.

Izban M.G., Luse D.S. (1992) Factor-stimulated RNAPII transcribes
at physiological elongation rates on naked DNA but very poorly on
chromatin templates. J Biol Chem. 267:13647-55.

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Mokry M., Hatzis P., Schuijers J., Lansu N., Ruzius F.P., Clevers H.,
Cuppen E.(2012) Integrated genome-wide analysis of transcription
factor occupancy, RNAPII binding and steady-state RNA levels
identify differentially regulated functional gene classes. Nucleic Acids
Res. 40:148-58.

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Pérez-Cadahía B., Drobic B., Davie J.R. (2011) Activation andfunction of immediate-early genes in the nervous system. Biochem
Cell Biol. 89:61-73.

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Swirnoff A.H., Apel E.D., Svaren J., Sevetson B.R., Zimonjic D.B.,
Popescu N.C., Milbrandt J. (1998) Nab1, a corepressor of NGFI-A(Egr-1), contains an active transcriptional repression domain. Mol Cell
Biol. 18:512-24.

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Page 8TranscriptionPath™: A Method for RNA-free Transcription Profiling