Article
Activation of the Notch Signaling Pathway In Vivo
Elicits Changes in CSL Nuclear DynamicsHighlightsd Live imaging reveals that CSL binds very dynamically in
Notch-OFF conditions
d Recruitment of CSL is enhanced in Notch-ON cells, and its
residence time is increased
d Notch locally increases chromatin accessibility to promote
loading of CSL complexes
d Longer CSL dwell times are conferred by the Notch-co-
activator MastermindGomez-Lamarca et al., 2018, Developmental Cell 44, 611–623
March 12, 2018 ª 2018 The Authors. Published by Elsevier Inc.
https://doi.org/10.1016/j.devcel.2018.01.020Authors
Maria J. Gomez-Lamarca,
Julia Falo-Sanjuan, Robert Stojnic, ...,
Kevin O’Holleran, Rhett Kovall,
Sarah J. Bray
Correspondence
sjb32@cam.ac.uk
In Brief
Gomez-Lamarca et al. explore the basis
of Notch signaling responsiveness at the
transcriptional level through in vivo, real-
time analysis of key DNA-binding factor
CSL. CSL is highly dynamic with transient
DNA residence in the Notch-OFF state.
Notch activity increases chromatin
accessibility and confers a longer dwell
time for response sensitivity.
Developmental Cell
ArticleActivation of the Notch Signaling Pathway In Vivo
Elicits Changes in CSL Nuclear Dynamics
Maria J. Gomez-Lamarca,1 Julia Falo-Sanjuan,1,6 Robert Stojnic,1,6 Sohaib Abdul Rehman,2 Leila Muresan,2
Matthew L. Jones,1 Zoe Pillidge,1 Gustavo Cerda-Moya,1 Zhenyu Yuan,3 Sarah Baloul,1 Phillippe Valenti,4
Kerstin Bystricky,5 Francois Payre,4 Kevin O’Holleran,2 Rhett Kovall,3 and Sarah J. Bray1,7,*
1Department of Physiology Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
2Cambridge Advanced Imaging Centre, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK
3University of Cincinnati College of Medicine, Department of Molecular Genetics, Biochemistry and Microbiology,
231 Albert Sabin Way, Cincinnati, OH 45267-0524, USA
4Centre de Biologie du De´veloppement/UMR5547, CBI (Centre de Biologie Inte´grative) University of Toulouse/CNRS, 118 Rte de Narbonne,
31062 Toulouse, France
5LBME/UMR5099, CBI (Centre de Biologie Inte´grative) University of Toulouse/CNRS, 118 Rte de Narbonne, 31062 Toulouse, France
6These authors contributed equally
7Lead Contact
*Correspondence: sjb32@cam.ac.uk
https://doi.org/10.1016/j.devcel.2018.01.020SUMMARY
A key feature of Notch signaling is that it directs
immediate changes in transcription via the DNA-
binding factor CSL, switching it from repression to
activation. How Notch generates both a sensitive
and accurate response—in the absence of any ampli-
fication step—remains to be elucidated. To address
this question, we developed real-time analysis of
CSL dynamics including single-molecule tracking
in vivo. In Notch-OFF nuclei, a small proportion of
CSL molecules transiently binds DNA, while in
Notch-ON conditions CSL recruitment increases
dramatically at target loci, where complexes have
longer dwell times conferred by the Notch co-acti-
vator Mastermind. Surprisingly, recruitment of CSL-
related corepressors also increases in Notch-ON
conditions, revealing that Notch induces cooperative
or ‘‘assisted’’ loading by promoting local increase in
chromatin accessibility. Thus, in vivo Notch activity
triggers changes in CSL dwell times and chromatin
accessibility, which we propose confer sensitivity
to small input changes and facilitate timely
shut-down.
INTRODUCTION
Notch is the receptor in a highly conserved cell-cell signaling
pathway, whose normal function is essential both during devel-
opment and throughout adult life (Borggrefe and Oswald,
2009; Bray, 2016; 2006; Kopan and Ilagan, 2009). In addition, ab-
errations in Notch signaling underpin many diseases and are
causal in some types of cancers (Nowell and Radtke, 2017;
Ntziachristos et al., 2014). Understanding the mechanism trans-
ducing Notch activity is thus of widespread relevance. CanonicalDevelopmental Cell 44, 611–623, M
This is an open access article undNotch signaling results in release of the Notch intracellular
domain (NICD), which directly interacts with a DNA-binding pro-
tein called Suppressor of Hairless in flies (Su(H), also known as
CBF1 or Lag-1 in other species), generally referred to as CSL
and, together with the co-activator Mastermind (Mam), triggers
the transcription of target genes (Bray, 2016; 2006; Kopan and
Ilagan, 2009).
In the absence of Notch, CSL works as a repressor through a
different set of partners, including the co-repressor Hairless
(Barolo et al., 2002; Kulic et al., 2015; Morel et al., 2001). The
prevailing, yet untested, model is that, following Notch activa-
tion, NICD displaces co-repressors, while CSL remains bound
to DNA (Borggrefe and Oswald, 2009). Recent evidence has
challenged this model, as the affinity of NICD for CSL is similar
to that of the co-repressors, making it unclear how it could
displace them (Collins et al., 2014; VanderWielen et al., 2011;
Yuan et al., 2016). In addition, the occupancy level of CSL com-
plexes at target loci differs in Notch-ON versus Notch-OFF
conditions, as deduced from chromatin immunoprecipitation
assays in fixed samples (Castel et al., 2013; Krejci and Bray,
2007; Wang et al., 2014), even though there is no evidence
for any change in DNA affinity or specificity conferred by
NICD (Del Bianco et al., 2010; Wilson and Kovall, 2006). At
some loci with appropriately paired motifs, dimerization be-
tween NICD molecules could contribute to enhanced binding
(Arnett et al., 2010; Hass et al., 2015), but many loci lack the
appropriate sites for this to occur. Existing concepts thus fail
to explain how both levels and duration of NICD signal are
quantitatively integrated to confer proper transcriptional out-
puts (Dallas et al., 2005; Delaney, 2005; Guentchev and McKay,
2006; Mazzone et al., 2010). These considerations argue that
an alternative model is needed and highlight that an under-
standing of Notch signal transduction will require quantifying
the dynamics of Notch nuclear effectors, ideally in an in vivo
system. To tackle this problem, we engineered a comprehen-
sive series of in vivo molecular tools for the live analysis of
Su(H), the Drosophila CSL, to determine its behavior and bind-
ing in Notch-OFF and Notch-ON conditions.arch 12, 2018 ª 2018 The Authors. Published by Elsevier Inc. 611
er the CC BY license (http://creativecommons.org/licenses/by/4.0/).
EC D F
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Figure 1. Su(H) and Hairless Display Fast Nuclear Dynamics
(A and B) Schematic of GFP-tagged genomic rescue constructs for Su(H) (A) and Hairless (B).
(C and D) Salivary glands with nuclear Su(H)WT::GFP (C) and Hairless::GFP (D).
(E–G) Salivary gland with NRE-GFP expression (green) and DNA staining (blue); higher magnifications show NRE-GFP expression in ring cells (F) and single
nucleus with polytene chromosomes (G).
(H) Diagram illustrating wild-type (top) and R266H (bottom) Su(H) co-repressor complexes.
(I and J) FRAP curves obtained for the indicated proteins, following point-bleaching at random positions in the nuclei. Mean ± SEM.
(K) Combinations of residence time and percentage of bound molecules giving best-fit to FRAP data, with grey-blue indicating combinations with%1% error
around the optimal value. Note that Su(H)WT refers to Su(H)WT::GFP in a Su(H) mutant background.
See also Figures S1 and S2.RESULTS
Su(H) Is Transiently Bound to DNA in Notch-OFF
Conditions
As a first step toward visualizing the dynamics of Notch nuclear
effectors in living tissues, we generated EGFP-tagged trans-
genes of both Su(H) and its co-repressor Hairless (Figures 1A
and 1B). Both fusions, here referred to as Su(H)::GFP and Hair-
less::GFP for simplicity, recapitulated endogenous expression612 Developmental Cell 44, 611–623, March 12, 2018and rescued to viability null mutants for the cognate gene
(Figures 1C, 1D, and S1). To analyze the dynamics of Su(H)
and Hairless, we first performed fluorescence recovery after
photobleaching (FRAP) (van Royen et al., 2009), using point-
bleaching directed at a random position in each nucleus. We
took advantage of larval salivary glands that display large nuclei,
where Notch is normally OFF, as shown by the absence of
Notch reporter expression (Figures 1E–1G). Strikingly, FRAP
data showed that CSL repressor complexes are highly dynamic.
BA
D
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Figure 2. SMT Analysis of Su(H) Molecules Show that Few Have Longer Residence Times
(A and B) Motion-blurring experiment. (A) Su(H)::mEOS localizations per nucleus detected with different exposure times after photoconversion remain constant in
fixed samples (black) and decrease in live samples (red), due to blurring of movingmolecules in longer exposure times. (B) Examples of Su(H)::mEOS localizations
detected during 1 s imaging in live samples using different exposure times. n = 7 nuclei/exposure time; mean ± SEM.
(C) Tracks of single Su(H)::mEOS molecules with distinct diffusion coefficients imaged with 10 ms exposure time.
(D) x-t kymographs of molecules in (C).
(E and F) Tables of diffusion coefficients and proportions of molecules belonging to different groups with mean times for each state, calculated as in (Persson
et al., 2013) from 10ms data either with no constraints (E, four states) or constrained to three states (F). Note that the percentage bound in (F) is less than the sum
of B1 and B2 in (E), because high diffusion coefficient outliers assigned to B2 in the four-state system will be assigned to F2 in the three-state model.
(G) Schematic representation of different behaviors from (E), indicating the probabilities of switching states.
See also Tables S1 and S2, Figure S3 and Movies S1 and S2.Su(H)::GFP recovery time (t1/2 = 3.6 s) was indeed considerably
faster than that of Forkhead::GFP (Fkh::GFP, t1/2 = 24.4 s), a
lineage-specific transcription factor (Figure 1I). Similar fast dy-
namics were also observed for Hairless::GFP (Figure 1I) but
were not merely a property of repressors, since the unrelated
co-repressor SMRTER exhibited much slower recovery (Fig-
ure S2). Amutation in Su(H) that abrogates its DNA-binding affin-
ity in vitro (Su(H)R266H; Figure S1) led to an even faster recovery
time (t1/2 = 1.9 s) when assayed in vivo (Figure 1J), showing that
the dynamics of wild-type Su(H)/Hairless complexes encompass
DNA-binding events. In contrast, Su(H)::GFP recovery in homo-
zygous mutant background was similar to controls, indicating
that the presence of unlabelled Su(H) has minimal impact.
Thus, in Notch-OFF conditions, Su(H) normally undergoes tran-
sient DNA residency, which must nevertheless be sufficient for
any repression it confers (Barolo et al., 2002; Kulic et al., 2015;
Morel et al., 2001; Morel and Schweisguth, 2000).
FRAP kinetics depend on two distinct parameters: (1) the rela-
tive proportions of diffusible versus DNA-bound molecules and
(2) the time each molecule remains bound to DNA (residencetime). We therefore used a reaction-diffusionmodel to infer these
parameters (see the STAR Methods; Figure S2), first estimating
the diffusion constant of the unbound molecules from the recov-
ery of the non-binding Su(H)R266H (D = 2.2 mm2/s; see the STAR
Methods). Fkh FRAP data were best fit by models where >75%
Fkh molecules were bound to DNA, with a residence time of
30–60 s (Figure 1K). In contrast, optimal models for Su(H) implied
that only 25%–35%molecules were bound, with residence times
of 0.5–2 s (Figure 1K). Hairless residence time was similar to
Su(H), albeit with a higher proportion of free molecules (<20%
bound; Figure 1K). Thus, when compared with Fkh, a relatively
small fraction of Su(H) and Hairless molecules are bound to
DNA at any one time and they have considerably shorter resi-
dence times.
To further investigate Su(H) properties, we performed single-
molecule tracking (SMT) of photo-convertible Su(H)::mEOS in
live salivary glands (Figure 2). Containing the monomeric
mEOS3.2, Su(H)::mEOS can be irreversibly photo-converted
from green to red emission in response to 405 nm light. For our
SMT assays, only a small proportion of the total Su(H)::mEOSDevelopmental Cell 44, 611–623, March 12, 2018 613
population was photo-converted so that single red emitting mol-
ecules could be detected and individually tracked. Freely
diffusing (or very transiently bound) molecules become ‘‘blurred’’
in long exposure times (>50 ms) (Etheridge et al., 2014), while
stationary molecules, such as those bound to DNA, remain
resolved. Indeed we found that a modest proportion (21%) of
photo-converted Su(H)::mEOS molecules stayed unblurred
with 100 ms exposure, consistent with a low abundance of
Su(H) repressor complexes bound to DNA (Figures 2A and 2B).
Analysis of SMT from shorter exposure times (10 ms) further re-
vealed that individual Su(H) complexes exhibit different patterns
of mobility (Figures 2C and 2D; Movies S1 and S2) and when the
characteristics from all the 10 ms tracks were extracted (using
variational Bayes SPT; [Persson et al., 2013]) they revealed
that Su(H) can transition through at least four different states
(Figures 2E and 2G; Table S1). These range from ‘‘freely
diffusing’’ (state F1; 21%, D = 1.89 mm2/s), which had a similar
diffusion constant to that estimated for the non-binding Su(H)
R266H from FRAP, to essentially immobile molecules, likely
engaged in specific DNA binding (state B1; 22% molecules,
D = 0.09 mm2/s). Intermediate states may relate to molecules
whose diffusion is confined by obstacles (e.g., chromosomes;
state F2, D = 0.50 mm2/s) and to those undergoing non-specific
interactions with the chromatin (state B2; D = 0.22 mm2/s).
When themodel was constrained to three states, B1 andB2 coa-
lesced into a single ‘‘bound’’ population that accounted for 32%
of Su(H) molecules (Figure 2F; Table S2), i.e., a value within the
range inferred from FRAP.
SMT results also suggested a very short dwell time (
100 ms)
for Su(H)-bound molecules, below that estimated from FRAP.
However, short exposure SMT is likely to underestimate dwell
times, because of gaps in tracking caused by transient disap-
pearance of molecules and because of photobleaching. Indeed,
we detected some B1 tracks >400 ms, indicative of longer resi-
dence time for some molecules. Furthermore, data extracted
from intermediate exposure times (50 ms) that circumvent
some of those losses led to a mean dwell time of 560 ms, at
the low end of the range inferred from the FRAP (Figure S3).
This suggests that the estimates from the 10 ms data are cur-
tailed by photobleaching. However, as relatively few tracks ex-
hibited stationary behavior throughout (<2%), it is unlikely that
the residence times are greatly in excess of those inferred from
50 ms SMT data, which are approaching those obtained
from FRAP.
Altogether, these in vivo data indicate that one-third, at best, of
Su(H) molecules are specifically bound to DNA and display a
remarkably short dwell time, revealing the very dynamic binding
of Su(H) complexes in Notch-OFF conditions.
Notch-ON Increases the Recruitment and Dwell Time of
Su(H) Complexes
Having established that CSL complexes are highly dynamic in
Notch-OFF cells, we then assayed whether Notch activity influ-
ences their binding properties, as suggested by chromatin
immunoprecipitation experiments in both Drosophila and
mammalian cells (Castel et al., 2013; Krejci and Bray, 2007;
Wang et al., 2014). Hence, we analyzed Su(H) behavior in tissues
supplied with activated Notch. Unexpectedly we found, both by
FRAP and by SMT, that there was no global change in properties614 Developmental Cell 44, 611–623, March 12, 2018of Su(H) (Figures 3A and 3B; Table S3). However, there was a
striking change in Su(H) intra-nuclear distribution in the live-
imaged nuclei, from a general diffuse distribution in Notch-OFF
nuclei to one with a prominent chromosomal band in Notch-ON
nuclei (Figures 3C and 3D; note that several less prominent
bands were also detected, Figure S4). The polytene chromo-
somes of salivary glands contain hundreds of aligned DNA
copies, favoring visualization of transcription factor-binding
events (Lis, 2007). The prominent band observed for Su(H) was
thus likely representing recruitment of Su(H) at a target locus
following Notch activation. Consistently, no such band was
detected with the Su(H)R266H mutant unable to bind DNA (Fig-
ure 3E). These data thus clearly reveal that, in vivo, Notch mod-
ifies the dynamics of Su(H) chromosomal interactions in a way
that is DNA binding dependent.
To further investigate this conclusion, we used complemen-
tary systems to manipulate and visualize a key chromosomal
target of Su(H). In flies, the Enhancer of split-Complex
(E(spl)-C) contains multiple highly responsive Notch target
genes, making this an attractive candidate for the prominent
Su(H) recruitment. To investigate this, we first examined nuclei
bearing an extra transgenic copy of E(spl)-C (Chanet et al.,
2009). These displayed a second strong Su(H) band (Figure S4),
as predicted if the in vivo recruitment of Su(H) is occurring at
E(spl)-C. Second, we implemented a DNA-tagging technique
based on the ParB/int prokaryotic system, which allows visuali-
zation of a given locus in living cells (Saad et al., 2014; P.V.,
M.L.J., S.J.B. and F.P., unpublished data). Briefly, the ParB pro-
tein binds to a small DNA segment (int) and spreads along
adjacent DNA, creating robust ParB foci that can be detected
in fluorescence microscopy. We used CRISPR/Cas9 genome
editing to introduce int within the distal part of E(spl)-C, and
generated transgenic constructs for the targeted expression of
ParB::mCherry. When driven in salivary cells, ParB specifically
bound to the tagged locus, producing a strong signal that
allowed us to specifically visualize the E(spl)-C. The Su(H) band
in Notch-ON nuclei clearly co-localized with the tagged locus
(Figures 3F–3H), demonstrating unequivocally that Su(H) is re-
cruited to E(spl)-C in Notch-ON conditions.
The locus-tag provided us with a powerful way to focus photo-
bleaching specifically on E(spl)-C, so that we could compare the
recovery in Notch-OFF and Notch-ON conditions at a Notch-
regulated target. Strikingly, the FRAP recovery for Su(H)::GFP
was significantly slower in Notch-ON conditions compared
with Notch-OFF (Figure 4A). Modeling the behavior of
Su(H)::GFP around the E(spl)-C locus revealed a dramatic in-
crease in the inferred residence times in Notch-ON nuclei, which
approached 10–15 s (Figure 4B).We note that the overall propor-
tion of bound molecules was largely unaffected, indicating that
only a small fraction of Su(H)::GFP complexes change their
behavior. Similarly, by performing SMT in Notch-ON nuclei
with the tagged E(spl)-C locus we also revealed a striking enrich-
ment for stationary (D = 0.08 mm2/s) Su(H) molecules around the
tagged E(spl)-C locus (Figures 4C–4G). Indeed the proportion
of molecules that had properties of specific binding (B1) were
almost doubled, with 38% B1 tracks at the locus versus 20%
B1 tracks outside this region.
Taken together these data therefore provide compelling evi-
dence that Notch increases the recruitment of Su(H) complexes
C D E
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Figure 3. Notch Enhances Recruitment of Su(H) to E(spl)-C
(A) FRAP curves obtained from half nuclei bleaching of Su(H)WT in Notch-OFF and Notch-ON conditions. Mean ± SEM.
(B) Table of diffusion coefficients and proportions of photo-converted Su(H)::mEOS molecules belonging to different groups with mean times for each state,
calculated as in (Persson et al., 2013) from 10 ms data in Notch-ON nuclei, n > 20,000 tracks.
(C–E) Live imaging of Su(H)::GFP. Chromosomal recruitment of Su(H)WT is stimulated by active Notch (D) (arrowhead), compared to control cells (C), and is
abolished in Su(H)R266H (E). Nact refers to expression of constitutively active Notch, NDecd.
(F–G00) Su(H) recruitment—green in (F) and (G), white in (F00) and (G00)—is localized to E(spl)-C, labeled by ParB::RFP—red in (F) and (G), white in (F0) and (G0)
(see yellow arrowheads).
(H) Quantification of Su(H)::GFP intensity across the E(spl)-C locus. n = 11; mean ± SEM.
See also Figure S4.to E(spl)-C target genes. Furthermore, our results demonstrate
that Notch activity can change the dynamics of Su(H)-complex
binding at target loci.
Differential Effects of NICD and Mam on Su(H)
Recruitment and Dwell Times
We next sought to investigate the mechanisms underlying the
increased Su(H) recruitment and dwell times in response to
Notch activity. As a first step, we assessed whether Su(H)
recruitment at E(spl)-C directly relies on its interaction with
NICD, by engineering a Su(H) variant (NBM) in which five critical
NICD-binding residues were mutated. While these mutations do
not impinge on Hairless interaction (Yuan et al., 2016), they elim-
inate binding to NICD as assayed both in vitro and in vivo (Fig-
ure S5). Recruitment of Su(H)NBM to E(spl)-C was greatly
compromised in Notch-ON nuclei, as evident from decreased
GFP intensity and faster FRAP recovery (Figures 5B, 5C, 5E,and 5J). Indeed, the behavior of Su(H)NBM in Notch-ON cells
resembled that of un-mutated Su(H) in Notch-OFF cells, with
residence times of <1 s (Figure 5K). Despite its reduced resi-
dence, a low level of residual recruitment of Su(H)NBM to
E(spl)-C was still evident in Notch-ON nuclei. We hypothesize
that this is an indirect effect of NICD brought to the locus by
endogenous untagged Su(H), as discussed further below.
Second, as a means to assay whether co-activators could
stabilize Su(H) occupancy upon Notch activation, we co-ex-
pressed a dominant negative form of Mam (MamDN; Figure 5A).
Mam normally recruits p300/CBP and other factors, forming a
higher-order complex necessary to promote transcription (Fryer
et al., 2002; Wallberg et al., 2002), although it is not essential to
stabilize the Su(H)-NICD complex in Drosophila as it is in
mammalian cells (Contreras et al., 2015). As in other contexts,
when expressed in the salivary gland MamDN interfered with
endogenous Mam, preventing polymerase II recruitment andDevelopmental Cell 44, 611–623, March 12, 2018 615
BA
C D E
F
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Figure 4. Notch Increases Su(H) Dwell Times at E(spl)-C
(A and B) FRAP curves obtained from focused point bleaching of Su(H)WT specifically at E(spl)-C in the conditions indicated (A). Mean ± SEM. Combinations of
residence time and percentage of bound molecules giving best-fit to FRAP data, with grey-blue indicating combinations with %1% error around the optimal
value (B).
(C–G) SMT tracks in Notch-ON nuclei in relation to E(spl)-C locus, labeled by ParB::GFP (white) and indicated by brackets in (C) and (D). (C) Distribution of SMT
tracks of indicated types relative to E(spl)-C locus, with (D) indicating locations of specific binding tracks only. (E and F) Higher magnification of locus region
and locations of freely diffusing molecules (red) and specific binding molecules (blue). (G) Proportion of tracks with characteristics of specific DNA binding
(D = 0.08 mm2/s) in the locus region versus elsewhere in Notch-ON nuclei (n > 7,000 tracks).blocking transcriptional activation of E(spl) genes (Figures 5I
and S6) (Fryer et al., 2002; Helms et al., 1999; Nam et al.,
2006; 2003; Wallberg et al., 2002; Wilson and Kovall, 2006). Un-
expectedly, live imaging revealed that MamDN did not reduce
levels of Su(H) at E(spl)-C (Figures 5B, 5D, and 5E), indicating
that the increased recruitment of Su(H) per se is not dependent
on a transcriptionally competent complex. Strikingly however,
MamDN led to a faster recovery time of Su(H) after photo-
bleaching at E(spl)-C in Notch-ON nuclei (Figure 5J), with the
best fit models indicating residence times of <1 s, resembling
those of Notch-OFF despite the presence of NICD (Figure 5K).
Similar alterations in Su(H) dynamics were obtained when Mam
levels were depleted by RNAi (Figure S6). Functional Mam is
therefore necessary for the increased dwell time of Su(H) in
Notch-ON conditions, arguing that it does not rely on NICD
alone, but Mam appears dispensable for the enhanced
recruitment.
NICD Promotes Chromatin Opening and Assisted
Loading of Su(H) Complexes
The fact that Su(H)NBM but not MamDN (or Mam RNAi) reduced
the amount of Su(H)::GFP recruited, argues that NICD also ex-
erts an effect that is independent of Mam. If our interpretation616 Developmental Cell 44, 611–623, March 12, 2018that the residual Su(H)NBM enrichment in Notch-ON nuclei relies
on endogenous untagged Su(H) that retains the capability to bind
NICD is correct, then it implies that NICD exerts an indirect effect
on Su(H) recruitment. This model would predict that one Su(H)-
NICD complex can affect the behavior of another Su(H) mole-
cule(s), whether or not bound to NICD. One way that NICD could
evoke an indirect effect on Su(H) recruitment, is by promoting a
more open chromatin conformation, as suggested by its effects
on histone modifications in other cell types (Oswald et al., 2016;
Skalska et al., 2015;Wang et al., 2014). In agreement, the volume
occupied by the locus tag in Notch-ON nuclei was expanded, as
expected for less-compact chromatin (Figure S6). Levels of
H3K27 acetylation and H3K4 monomethylation, two histone
modifications associated with active enhancers, were also
significantly increased at E(spl)-C locus in Notch-ON nuclei (Fig-
ures 6A–6H). To investigate further we measured the openness
of chromatin at E(spl)-C using the assay for transposase-
accessible chromatin (ATAC) method. A substantial increase
in accessibility was detected at several regions across E(spl)-C
locus in Notch-ON nuclei (Figures 6I and 6J). Thus, one conse-
quence of NICD recruitment is a localized chromatin remodeling,
which makes the region more accessible and favors Su(H)
binding.
JB C D
A
I
F G H
K
E
Figure 5. Effects of NICD and Mam on Su(H) Recruitment and Dwell Times
(A) Diagram of co-activator complexes, control (left), Su(H)NBM (middle), or MamDN (right).
(B–D) Live imaging of Su(H)WT (B and D) and Su(H)NBM (C) in Notch-ON cells. Su(H)NBM has reduced recruitment (C) (arrowhead) compared with Su(H)WT (B)
(arrowhead). MamDN does not reduce Su(H)WT recruitment (D) (arrowhead).
(E) Relative fluorescence of Su(H) bands in the indicated genotypes (n > 35). Box and whiskers min to max; ****p < 0.0001, unpaired two-tailed t test.
(F–H) Live imaging of Su(H)WT (F and G) or Su(H)NBM (H) in the conditions indicated.
(I) As detected by qPCR of salivary gland samples, increased mRNA levels of three E(spl) genes in Notch-ON cells is prevented when MamDN is co-expressed.
n = 3; mean ± SEM; *p < 0.05, **p < 0.0332, one-way ANOVA.
(J) FRAP curves obtained from focused point-bleaching of Su(H)WT specifically at the E(spl)-C locus in the conditions indicated. Mean ± SEM. Note: Notch-OFF
and Notch-ON are the same as in Figure 4A.
(K) Combinations of residence time and percentage of bound molecules giving best-fit to FRAP data, with grey-blue indicating combinations with%1% error
around the optimal value.
See also Figures S5 and S6.If NICD promotes Su(H) recruitment by changing chromatin
accessibility, it would not discriminate against Su(H) that is com-
plexedwith other factors, including the Hairless co-repressor. Asopposed to current views, our model thus makes the surprising
prediction that Hairless recruitment to target loci should also be
enhanced following Notch activation. We therefore quantifiedDevelopmental Cell 44, 611–623, March 12, 2018 617
MK K’ K’’
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Figure 6. Notch Induces Chromatin Opening and Promotes Assisted Loading
(A–D00) Levels of H3K27ac—green in (A) and (B), white in (A00) and (B00)—and H3K4me1—green in (C) and (D), white in (C00) and (D00)—histone modifications at
E(spl)-C (arrowheads) labeled by ParB::RFP—red in (A)–(D), white in (A0)–(D0)—in Control (A and C) and Notch-ON cells (B and D). Higher magnifications, insets in
(A)–(D), show unchanged flanking loci (yellow asterisk) compared with increase at E(spl)-C locus (arrowheads).
(E–J) Quantification of the indicated histone modifications across the E(spl)-C locus, both H3K27ac and H3K4me1 are increased in Notch-ON cells. n > 25;
mean ± SEM. (I–J) Chromatin accessibility across E(spl)-C (I) measured by enrichment of fragments to transposon tagging with ATAC (J); positions of primers
used in (J) are indicated in (I) relative to the gene models (dark blue) and Su(H)-binding profile in Kc cells (cyan). Rab11 intron and Eip78C EcR are predicted open
chromatin control regions, while Negative1 and Mst87F are closed chromatin control regions, none of which are subject to Notch regulation. Shown is the fold
enrichment compared with the Neg1 control region. n = 3; mean ± SEM.
(K–L00) Increased Hairless-GFP recruitment—green in (K) and (L), white in (K00) and (L00)—at E(spl)-C—red in (K) and (L), white in (K0) and (L0)—in Notch-ON cells (L)
(arrowhead) compared with Notch-OFF cells (K) (arrowhead).
(M) Quantification of Hairless::GFP intensity across E(spl)-C. n > 25; mean ± SEM.Hairless levels at E(spl)-C in Notch-OFF versus Notch-ON condi-
tions. Indeed, like Su(H),wemeasured aclear increase inHairless
levels upon Notch activation (Figures 6K–6M). Furthermore, the
residual recruitment of Su(H)NBM observed in Notch-ON nuclei
was abolished when Hairless was knocked down (Figures 5F
and 5H), suggesting that it is Hairless-boundSu(H)NBM that is re-
cruited.We noted, however, that the levels of Su(H) were strongly
reduced in these conditions (Figures 5G and 5H), arguing that618 Developmental Cell 44, 611–623, March 12, 2018Hairless is necessary for Su(H) stability as reported recently
(Yuan et al., 2016), which may have compromised our ability to
detect any binding. Nevertheless, recruitment of un-mutated
Su(H) was still obvious even in the Hairless-depleted Notch-ON
nuclei (Figure 5F). Together the data indicate that NICD triggers
a substantial change in the accessibility of its E(spl)-C target lo-
cus, and likely other target loci too, which favors recruitment of
Su(H), affecting both the repressor and the activator complexes.
I J
K
E
A B C D
F G H
L
M
Figure 7. CBP and Trithorax-Related Are Required for Enhanced Su(H) Recruitment in Notch-ON Cells
(A–C) Live imaging of Su(H)WT in Notch-ON cells treatedwith DMSO (A), triptolide (10 mM) (B), or C646CBP inhibitor (30 mM) (C). Yellow arrowheads indicate Su(H)
recruitment.
(D) Live imaging of Su(H)WT in Notch-OFF cells treated with E(z) RNAi.
(E) Relative fluorescence of Su(H) bands in the indicated genotypes. n > 30; box and whiskers 5–95 percentile; **p < 0.0332, unpaired two-tailed t test.
(F and G) FRAP curves obtained from focused point-bleaching of Su(H)WT specifically at the band region in the conditions indicated. Mean ± SEM.
(H) Effects of the indicated treatments on up-regulation of E(spl) gene mRNA levels in Notch-ON cells, measured by RT-qPCR.
(legend continued on next page)
Developmental Cell 44, 611–623, March 12, 2018 619
CBP and Trithorax-Related Contribute to NICD-Induced
Changes in Su(H) Dynamics
We next asked whether the change in Su(H) recruitment elicited
by NICD was an indirect consequence of increased transcription
initiation, by testing the effects of triptolide, a potent inhibitor of
the TFIIH complex required for promoter opening (Krebs et al.,
2017) Indeed, triptolide treatment abolished the NICD-induced
expression of E(spl)-C mRNAs (Figure 7H). However, triptolide
had no effect on the levels of Su(H) recruitment or FRAP recovery
in Notch-ON cells (Figures 7A, 7B, and 7E–7F), indicating that
these events are independent of transcription initiation. Earlier
steps in pre-initiation complex formation involve the Mediator
complex and the acetyltransferase CBP/p300, which is also
responsible for H3K27 acetylation and which has been shown
to interact with Mam (Fryer et al., 2002; Wallberg et al., 2002).
To test whether CBP was necessary for the change in Su(H) dy-
namics, we treated Notch-ON tissues with C646, a potent inhib-
itor of CBP catalytic activity that causes its dissociation from
chromatin (Boija et al., 2017). Although CBP inhibition resulted
in a significant decrease in the expression of E(spl)-C mRNAs
(Figure 7H), it produced only a modest decrease in recruitment
of Su(H) (Figures 7C and 7E). The C646 treatment also slightly
modified Su(H) FRAP recovery kinetics in Notch-ON tissues,
indicating that the Su(H)-binding dynamics were altered (Fig-
ure 7G), but the effects were less pronounced than occurred
with MamDN. Similar change in FRAP occurred when Mediator
function was perturbed by depleting levels of its Med7 subunit,
suggesting that Mediator contributes to the increase in dwell
time (Figure S7). These results, together with the fact that there
is still a clear band in Notch-ON cells treated with C646, demon-
strate that CBP activity is not sufficient to account for all the
changes in Su(H) behavior in Notch-ON cells, and they suggest
it is likely to have an important role at subsequent steps in Notch
induced transcription.
Key factors involved in the interplay between active and inac-
tive enhancers include members of Polycomb (repressive) and
Trithorax (activating) complexes (Schuettengruber et al., 2017).
Reasoning that activity of these complexes might be important
for Su(H) recruitment in Notch-ON cells, we examined the
consequences of their RNAi-mediated depletion. First, we
asked whether loss of repressive complexes could phenocopy
Notch-ON conditions, i.e., facilitate Su(H) recruitment in Notch-
OFF nuclei. However, depletion of repressive Polycomb group
members (Enhancer of zeste [E(z)], Polycomb, polyhomoetic
proximal, and pipsqueak) was not sufficient to promote a detect-
able Su(H) ‘‘band’’ in Notch-OFF cells (e.g., Figure 7D), nor could
E(z) depletion further enhance recruitment in Notch-ON condi-
tions (data not shown). Second, we tested the converse hypoth-
esis, i.e., that chromatin regulators of the activating Trithorax
group (e.g., ash1, Trithorax-like, and trithorax-related) would be(I and J) Live imaging of Su(H)WT in Notch-ON cells co-expressing control (I) or
(K) Percentage of nuclei with measurable Su(H) bands in the indicated genotypes.
whiskers min to max.
(L) Chromatin accessibility across the E(spl)-C locus in the conditions indicate
normalized to Neg1. n = 3; mean ± SEM.
(M) Model summarizing the changes in DNA binding of CSL complexes at targ
amplification. CSL, blue; co-repressor, gray; NICD, green; Mam, pink.
See also Figure S7.
620 Developmental Cell 44, 611–623, March 12, 2018required for Su(H) recruitment in Notch-ON nuclei. Of those
tested, only depletion of Trithorax-related (Trr), the Drosophila
orthologue of MLL3/4 H3K4 mono-methylase (also known as
KTM2D), produced a discernible effect. Strikingly, the Su(H)
enrichment in Notch-ON cells was abolished in the Trr-depleted
nuclei (Figures 7I–7K and S7) as was the increase in E(spl)-C
mRNA expression, which resembled that in Notch-OFF condi-
tions (Figure 7H). Likewise, residual chromosomal Su(H) recruit-
ment still occurred in Trr-depleted cells, even though the
enriched ‘‘band’’ was lost (Figures 7J and S7). Finally, ATAC as-
says indicated that the chromatin accessibility at E(spl)-C in
Notch-ON cells was also reduced in the absence of Trr (Fig-
ure 7L), more resembling that in Notch-OFF tissue (Figure 6J),
a result that fits well with evidence that MLL3/4 promotes chro-
matin opening at enhancers in mammalian cells (Dorighi et al.,
2017) and positively regulates Notch outputs in pre-T cells
(Oswald et al., 2016).
DISCUSSION
Until recently, most existing models have portrayed CSL as a
molecule with long DNA residence that serves as a static plat-
form for exchange between NICD and co-repressors. Our anal-
ysis, using a combination of FRAP and SMT to measure Su(H)
dynamics, reveals a very different story and highlights two
important characteristics. First, in Notch-OFF conditions, Su(H)
normally undergoes very transient DNA residency, despite the
fact that it is important for repression of the target loci. This im-
plies that prolonged binding is not a prerequisite for repression.
It also argues against a model where co-factors are exchanged
while CSL remains bound to DNA. Second, in Notch-ON condi-
tions, there is a striking enrichment of Su(H) at E(spl)-C, its pri-
mary target locus, where its dwell time is significantly increased.
These changes in CSL-binding dynamics, can enable a sensitive
and accurate response to NICD at its target sites.
Here we find that NICD enhances both Su(H) recruitment
and residence time at its target locus E(spl)-C, via a combination
of mechanisms. One key step is that NICD-Su(H) com-
plexes induce local changes in chromatin, which requires Trr
(MLL3/4), a long-range co-activator that can contribute to chro-
matin opening (Herz et al., 2012). Notably, the consequence of
NICD-induced chromatin opening is that it renders the target en-
hancersmore accessible for additional complexes, regardless of
whether they contain NICD or Hairless. Since binding of Hairless
and NICD to Su(H) are mutually exclusive, it is likely that these
represent discrete activator (Su(H)-NICD) and repressor (Su(H)-
Hairless) complexes, although we have not formally shown Hair-
less recruitment relies on Su(H). This enhanced recruitment by
NICD resembles that described for the glucocorticoid receptor
(Voss et al., 2011) and other factors (Madsen et al., 2014),Trr (J) RNAi. Arrowheads indicate Su(H) recruitment in control.
Three independent experiments, 12 nuclei scored in each experiment; box and
d, measured by enrichment of fragments to transposon tagging with ATAC,
et loci between Notch-OFF and Notch-ON nuclei that can lead to signaling
referred to as ‘‘assisted loading,’’ whereby the binding of one
protein complex helps the binding of another. We propose that
the localized chromatin remodeling brought about by Su(H)-
NICD reduces obstacles (e.g., moves nucleosomes) to facilitate
DNAbinding, i.e., effectively increasing KON (Figure 7M). Such in-
direct cooperativity would render the response very sensitive to
signal levels (Koshland et al., 1982; Sneppen et al., 2008; Zhang
et al., 2013).
A second aspect helps explain how the transiently bound
Su(H)-NICD complexes can successfully activate transcription.
Although at genomic locations with paired binding motifs
the dimerization of NICD could enhance binding (Arnett et al.,
2010; Hass et al., 2015), our data argue that the presence of
Mam itself confers a longer dwell time to the activator complex,
most likely by favoring contacts with additional chromatin-asso-
ciated factors, such as Mediator complex. One candidate to
mediate these effects was CBP, a histone acetyltransferase
that interacts with Mam and is necessary for its ability to stimu-
late transcription (Fryer et al., 2002; Wallberg et al., 2002). How-
ever, inhibiting CBP or depleting theMediator subunit Med7 only
slightly modified the Su(H) dynamics, suggesting that each
makes at best a modest contribution to the change in its
behavior. As neither manipulation fully replicated the effects of
Mam inhibition/depletion, despite preventing transcriptional
activation, it is likely that they also act at a later step in the initi-
ation process. Thus Mam is likely to exert its early effects on
Su(H) recruitment through a combination of other chromatin fac-
tors besides CBP. The interaction of the tripartite Su(H)-NICD-
Mam complex with these chromatin factors, although still
transient, could confer a probabilistic switch between an inactive
state and an active state, by leaving a longer-lastingmodification
or reorganization of the chromatin template or initiation complex
(Coulon et al., 2013; Lickwar et al., 2012).
The fact that the Su(H)-NICD activator complex also enhances
recruitment of Hairless co-repressor complexes was entirely un-
expected based on prevailingmodels, and has several important
consequences. First, it will bring opposing enzymatic activities
(e.g., both histone acetyl-transferases and histone deacety-
lases), which could create a covalent modification cycle with
switch-like properties (Ferrell and Ha, 2014; Koshland et al.,
1982; Sneppen et al., 2008), potentially further sensitizing re-
sponses to Notch. Second, enhanced recruitment of Hairless
would ensure that genes are rapidly turned off after the signal de-
cays, the switch operating in the converse direction when NICD
levels decrease. Such ‘‘facilitated repression,’’ where transcrip-
tional activators promote global chromatin decondensation to
facilitate loading of repressors, has also been described during
circadian gene regulation where it operates as an amplitude
rheostat (Zhu et al., 2015).
In conclusion, our in vivo analysis of the mechanisms under-
lying the transcriptional response to Notch signaling reveal the
fundamental importance of changes in DNA-binding dynamics
and highlight how different mechanisms combine to enhance
Su(H) recruitment and dwell time at E(spl)-C in Notch-ON
cells. Whether both mechanisms operate at all Notch-regu-
lated loci remains to be established, but they will likely be
relevant for most genes where CSL occupancy was found to
increase in Notch-ON conditions (Castel et al., 2013; Krejci
and Bray, 2007; Wang et al., 2014). Furthermore, this newinsight into Notch signaling leads us to propose that similar
changes in the dynamics of nuclear effectors may also oper-
ate to deliver proper transcriptional outputs of other key
signaling pathways.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
d KEY RESOURCES TABLE
d CONTACT FOR REAGENT AND RESOURCE SHARING
d EXPERIMENTAL MODEL AND SUBJECT DETAILSB Experimental Animals
B Fly Stocks
B Generation of GFP Tagged Su(H) and Hairless Flies
B Locus Tag
d METHOD DETAILS
B Salivary Gland Cultures and Drug Treatments
B Live Imaging and FRAP Setup
B FRAP Modeling
B PALM Imaging Using Su(H)-mEOS and SMT Analysis
B Immunostainings
B Immuno-Precipitation and Western Blots
B mRNA Extraction and Quantitative RT-PCR
B Measuring Chromatin Accessibility by ATAC
B Isothermal Titration Calorimetry Measurements
B Band Assay
B Enrichment of Marks at the E(spl) Locus
B Statistical Analyses
SUPPLEMENTAL INFORMATION
Supplemental Information includes seven figures, five tables, and two movies
and can be found with this article online at https://doi.org/10.1016/j.devcel.
2018.01.020.
ACKNOWLEDGMENTS
We thankmembers of the Bray Lab and of theNotch community for helpful dis-
cussions and Bill Harris for comments on the manuscript. This work was sup-
ported by grants from theMedical Research Council to S.J.B. (MR/L007177/1)
and to K.H. (MR/K015850/1), from Wellcome Trust (L.M.; 099130/Z/12/Z) to
CAIC, from NIH to R.K. (CA178974), from ANR (ChronoNet) and CNRS to
F.P. and P.V., and by studentships from the Wellcome Trust for J.F.-S. and
M.J. (099744/Z/12/Z), from EPSRC for S.A.R. (EP/L015455/1), and from
BBSRC for Z.P. (BB/J014540/1). S.B. was an Amgen Scholar.
AUTHOR CONTRIBUTIONS
M.J.G.-L., J.F.-S., R.S., K.O., R.K., and S.J.B. designed the experiments.
M.J.G.-L., J.F.-S., S.A.R., Z.P., S.B., and Z.Y. performed the experi-
ments. M.J.G.-L., J.F.-S., R.S., S.A.R., L.M., K.O., R.K., Z.P., and S.J.B.
analyzed the data. G.C.-M., M.L.J., P.V., K.B., and F.P. generated essential re-
agents. M.J.G.-L., J.F.-S., R.S., F.P., R.K., and S.J.B. wrote the manuscript.
DECLARATION OF INTERESTS
The authors declare no competing interests.
Received: June 2, 2017
Revised: November 27, 2017
Accepted: January 23, 2018
Published: February 22, 2018Developmental Cell 44, 611–623, March 12, 2018 621
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STAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
Rabbit anti-PolII Abcam Cat#5095; RRID: AB_304749
Rabbit anti-H3K27ac Abcam Cat# ab4729; RRID: AB_2118291
Rabbit anti-H3K4me1 Abcam Cat# ab8895; RRID: AB_306847
Rabbit anti-Trr (Herz et al., 2012) N/A
Goat anti-Su(H) Santa Cruz Biotechnology Cat# sc-15813; RRID: AB_672840
Rabbit anti-Su(H) Santa Cruz Biotechnology Cat# sc-28713; RRID: AB_2179304
Rabbit anti-GFP ThermoFisher Scientific Cat# A-11122; RRID: AB_221569
Mouse anti-NICD Developmental Studies Hybridoma Bank Cat# c17.9c6; RRID: AB_528410
Rabbit anti-Hairless (Maier et al., 1999) N/A
Donkey anti-Rabbit FITC Jackson ImmunoResearch Laboratories, Inc Cat# 711-095-152; RRID: AB_2315776
Goat anti-Rabbit HRP BioRad/ AbD Serotec Cat# 170-6515; RRID: AB_11125142
Goat anti-Mouse HRP BioRad/ AbD Serotec Cat# 170-6516; RRID: AB_11125547
Chemicals, Peptides, and Recombinant Proteins
Shield and Sang M3 insect medium Sigma Aldrich Cat#: S3652
Fetal Bovin Serum Sigma Aldrich Cat#: F9665
Antibiotic-antimicotic ThermoFisher Scientific Cat#: 15240062
Methyl-cellulose Sigma Aldrich Cat#: M0387
Triptolide Sigma Aldrich Cat#: T3652
C646 Sigma Aldrich Cat#: SML0002
TRIzol ThermoFisher Scientific Cat#: 15596026
Oligo(dT)15 primers Promega Cat#: C1101
M-MLV reverse transcriptase Promega Cat#: M1701
NEBNext High-Fidelity 2x PCR Master Mix New England Biolabs Cat#: M0541S
SYBR green ThermoFisher Scientific Cat#: S7563
Hoechst 33258 SigmaAldrich Cat#: 94403-1ML
Vectashield mounting medium Vector laboratories Cat# H-1000; RRID:
AB_2336789
cOmplete Protease Inhibitor Cocktail Roche (SigmaAldrich) Cat#: 11697498001
G-agarose beads Santa Cruz Biotechnology Cat# sc-2002; RRID: AB_10200697
Amersham ECL Western Blotting
Detection Reagent
GE Healthcare Life Sciences Cat#: RPN2109
Glutathione-Sepharose column GE Healthcare Life Sciences Cat#: 17528201
PreScission Protease GE Healthcare Life Sciences Cat#: 27084301
Critical Commercial Assays
Ambion’s DNA-free DNA removal kit ThermoFisher Scientific Cat#: AM1906
LightCycler 480 SYBR Green I Master PCR kit Roche Cat#: 04707516001
Nexera DNA Library preparation kit Illumina Cat#: FC-121-1030
MinElute PCR purification kit Qiagen Cat#: 28004
DNeasy Blood and Tissue Kit Qiagen Cat#: 69504
Experimental Models: Organisms/Strains
D. melanogaster 1151-Gal4 Laboratory of Lingadahalli S. Shashidhara FBti0007229
D. melanogaster UAS-NDECD (Fortini et al., 1993; Rebay et al., 1993) N/A
D. melanogaster UAS-Hairless-RNAi Bloomington Drosophila Stock Center FBst0027315
D. melanogaster UAS-Trr-RNAi Bloomington Drosophila Stock Center FBst0036916
(Continued on next page)
e1 Developmental Cell 44, 611–623.e1–e7, March 12, 2018
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
D. melanogaster UAS-Mam-RNAi Bloomington Drosophila Stock Center FBst0028046
D. melanogaster UAS-MamDN (Helms et al., 1999) N/A
D. melanogaster NRE-GFP (Housden et al., 2012) N/A
D. melanogaster Dup E(spl)d-8 (Chanet et al., 2009) N/A
D. melanogaster UAS-nlsGFP Bloomington Drosophila Stock Center FBst0065402
D. melanogaster Fkh::GFP Bloomington Drosophila Stock Center FBst0043951
D. melanogaster SMRTR::YFP Kyoto Stock Center FBst0324767
D. melanogaster yw Bloomington Drosophila Stock Center FBst0001495
D. melanogaster vas-phiC31;; 3xP3-
RFP.attP-86Fb
Bloomington Drosophila Stock Center FBst0024749
D. melanogaster vas-phiC31; 3xP3-
RFP.attP-51D
Bloomington Drosophila Stock Center FBst0024483
D. melanogaster Su(H)AR9 Null allele Bloomington Drosophila Stock Center FBst0030477
D. melanogaster Su(H)SF8 Null allele Kyoto Stock Center FBst0300297
D. melanogaster HairlessP8 Null allele (Maier et al., 1999) N/A
D. melanogaster Hairless1 Null allele Bloomington Drosophila Stock Center FBst0000515
D. melanogaster UAS-ParB1-mCherry This paper N/A
Oligonucleotides
Oligonucleotides for mRNA levels measurement
and ATAC qPCR
This paper Table S5
Recombinant DNA
pHD-DsRed Addgene Cat#: 51434
Software and Algorithms
FRAP parameter estimation code Github https://github.com/rstojnic/suh_frap
ImageJ v1.48c (Schneider et al., 2012) N/A
GraphPad Prism 7 GraphPad Software, Inc. N/A
Origin Software OriginLab N/ACONTACT FOR REAGENT AND RESOURCE SHARING
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Sarah J.
Bray (sjb32@cam.ac.uk).
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Experimental Animals
Species: Drosophila melanogaster. Flies were grown and maintained on food consisting of the following ingredients: Glucose 76g/l,
Cornmeal flour 69g/l, Yeast 15g/l, Agar 4.5g/l, Methylparaben 2.5ml/l
Animals of both sexes were used for this study.
Fly Stocks
For genetic manipulations the Gal4 driver line 1151-Gal4 (Lingadahalli S. Shashidhara, Centre for Cellular andMolecular Biology, Hy-
derabad, India) was used and combined with UAS-NDECD to provide constitutively active Notch (Fortini et al., 1993; Rebay et al.,
1993). These were combined with RNAi lines as listed in Table S4 and STAR Methods key resources table, including UAS-Hair-
less-RNAi (Bloomington Drosophila Stock Center, BL-27315), UAS-Mam-RNAi (BL 28046), UAS-Trr-RNAi (BL36916) or with UAS-
MamDN to block Mam activity (Helms et al., 1999). Crosses were maintained at 25C. Other lines used include NRE-GFP (Housden
et al., 2012), DpE(spl)v-8 (Chanet et al., 2009), UAS-nls-GFP (Bloomington 65402), Fkh::GFP (Bloomington 43951), SMRTR::YFP
(DGRC 115513) (Lowe et al., 2014) and yw (Bloomington 1495). Further details are provided in Table S4.
Generation of GFP Tagged Su(H) and Hairless Flies
To generate a genomic Su(H)::GFP construct, AttB plasmids containing the genomic region 2L:15038840-15045039 were con-
structed, with the coding sequences of eGFP (eGFP CloneTech; here referred to as Su(H)::GFP for simplicity) inserted at positionDevelopmental Cell 44, 611–623.e1–e7, March 12, 2018 e2
446 of the Su(H) transcript, (end of 5’ UTR, 2L-15039933), to generate a protein fusion at the N-terminus. This plasmid was injected
into a strain containing phiC31 integrase and AttP site in position 86F8 in chromosome 3 (Bloomington 24749) to generate transgenic
Su(H)::GFP flies. Identical strategy was used to produce Su(H)::mEOS3.2 (here referred to as Su(H)::mEOS for simplicity) (M. Zhang
et al., 2012). The functionality of the tagged proteins was assessed by their ability to rescue Su(H)AR9/Su(H)SF8 flies; the viability of the
mutant flies was fully rescued indicating that the plasmid confers functional Su(H) activity. Using site-directed mutagenesis, we
generated two mutated versions of Su(H)::GFP, one with impaired DNA binding, Su(H)R266H (R266H, G to A substitution in position
413 in exon 3) and another with impaired binding to NICD, Su(H)NBM (F309A, TT to GC in position 541 in exon 3, V311A, T to C in
position 548 in exon 3, E446R, GA to CG in position 71 in exon 4, R470E, CGC to GAG in position 143 in exon 4 and E473R, GA
to CG in position 152 in exon 4). Transgenic flies were then generated in the same way as for Su(H)WT. All of the tagged Su(H) proteins
were expressed at similar level to the endogenous (Figure S1)
The genomic Hairless::GFP AttB plasmids contained the genomic region 3R 20621141- 20628985 with the coding sequences of
eGFP (eGFP CloneTech) inserted at position 5648, to generate a protein fusion at the C-terminus. This plasmid was injected into a
strain containing phiC31 integrase and AttP site in position 51D in chromosome 2 (Bloomington stock 24483) to generate transgenic
Hairless::GFP flies. The functionality of the protein was assessed by its ability to rescue Hairless mutants; the transgene fully rescued
the viability ofHP8/H1mutant flies to adulthood and the phenotypes of homozygousHP8mutant clones in the wing disc indicating that
the plasmid confers functional Hairless activity. Levels of expression were also similar to endogenous (Figure S1). We note however
that the rescued HP8/H1 adult flies were not fully fertile, most likely because the genomic fragment lacked regulatory sequences
required for germ-line expression.
Locus Tag
Int1 sequences were inserted into an intergenic region in E(spl)-C via a two-step method using CRISPR/Cas9 genome editing and
PhiC31 integrase mediated transgenesis. An AttP site was first integrated via Cas9/CRISPR mediated homology directed repair
(HDR) using a single guide RNA (Sequence: AGAACCCTCAAGATTTGTAA, Chromosome 3R 26038865:26038884) and a template
plasmid with 
1kb homology directly adjacent to the guideRNA cut site on both sides and a fluorescent marker (pHD-DsRed,
AddGene). Int1 sequences were then introduced by standard phiC1mediated integration and recovered using themini-whitemarker.
HDR injections used a molar ratio of 1 guide RNA: 2 Homology Directed Repair template and all injections used a final concentration
of 1mg/ml. attP and Int1 insertions were genotyped by PCR. Strains containing UAS-ParB1-mcherry inserted into AttP.86Fb were
generated by standard phiC1 mediated integration. Further details available on request.
METHOD DETAILS
Salivary Gland Cultures and Drug Treatments
Salivary glands of early third-instar larvae were dissected in dissecting media [Shields and Sang M3 insect medium (Sigma, S3652),
supplemented with 5% FBS (Sigma, F9665) and 13 Antibiotic-Antimycotic (Gibco, 15240-062)]. Unharmed gland pairs were
placed in a Poly-L-lysine treated observation chamber (Aldaz et al., 2010). The chamber was made with a double layer of double
side tape (Sellotape acid free perforated using a hole puncher of 9-mm diameter hole). The tape was attached to a 22 3 50-mm
coverslip. The coverslip was then attached to a metal slide with a cut-out panel. Finally, the chamber/hole was filled with
medium and the discs placed in it and then covered with the semipermeable membrane and covered with viscous media [dissecting
media + 2.53 wt/vol methyl-cellulose (Sigma-Aldrich)]. For PALM imaging of fixed samples, salivary glands were fixed in 4% Form-
aldehyde for 15min, washed 3x 15min in PBT (PSB+ 0.3% Triton X-100) then left in PBT at 4C overnight before mounting as for live
samples.
For the inhibitor treatments, salivary glands of early third-instar larvae were dissected in dissecting media and incubated for 1h in
the presence of DMSO (10 ml), Triptolide (10mM) or C646 (30mM). If salivary glands were to be used for imaging, were mounted in an
observation chamber in viscous media containing the inhibitors or DMSO. For mRNA extraction, salivary glands were immediately
transfer to TriZOL and proceed as explained below.
Live Imaging and FRAP Setup
Image acquisition was performed using a Leica TCS SP8 confocal microscope equipped with 403/1.30 NA oil and 60X/1.40 NA oil
HC PL APO CS2 objective lens. For live imaging of Su(H)::GFP and Hairless::GFP nuclei the pinhole was set to 3-Airy and Z-stacks
spaced 0.5mmwere taken to cover the volume of the nucleus using 512x512 resolution and scanning speed 600Hz. Two or four frame
averages were used to increase the signal-to-noise ratio as indicated. >20 nuclei were imaged for each genotype, unless indicated,
and were usually taken from 3 or more salivary glands.
For FRAP experiments, the pinhole was set to 3-Airy and single plane images were obtained using a scanning speed of 1400Hz.
10 images were acquired at 0.098s intervals before bleaching and then point-bleaching, directed to a single point in the nucleus, was
performed for 1s, using 100% 488-nm laser power. These conditions were selected after different point bleaching conditions were
tested to identify those that produced bleaching through the whole nucleus, yielding a column that had a similar depth of bleaching
throughout (Figure S2). 300 images were then acquired at 0.098s intervals after bleaching followed by a further 100 images at 1s in-
tervals. For the band FRAP experiments, 300 post-bleaching images were acquired at 0.115s intervals followed by 100 images at 2s
intervals. In Figure 3A, half of the nucleus was bleached rather than a point. For each experiment, we measured fluorescencee3 Developmental Cell 44, 611–623.e1–e7, March 12, 2018
intensities in total nucleus (T), bleached (B), unbleached (UB) and background (BG) regions over time using ImageJ. >15 nuclei were
imaged for each genotype and were usually taken from 3 or more salivary glands.
To obtain the recovery curves, we perform a double normalization, where the signal in B is normalized to the average prebleach
signal and, at the same time, considers the loss of total signal due to the bleach pulse and bleaching during post-bleach imaging. For
that, we use the formula:
(Tpre-BG) x (Bt-BG) / (Tt-BG) x (Bpre-BG)
where BG=Is the mean BG along the experiment, Tpre is the mean fluorescence in T before bleaching, Bpre is the mean intensity in
B before bleaching, Tt is the fluorescence in T over time and Bt is the fluorescence in B over time (Phair et al., 2004).
FRAP Modeling
The fluorescent recovery after photobleaching (FRAP) was modelled using the reaction-diffusion model, following the approach of
(Beaudouin et al., 2006). The partial differential equations describing the movement of molecules were simulated using the finite dif-
ference approach. The simulation was performed separately for each nucleus replicate by initialising it with the real nucleus shape
and bleaching profile derived by comparing the pre- and post-bleach images. To reduce the noise and speed up the simulations, we
smoothed the images using a 5-pixel Gaussian kernel and then reduced the resolution of the images by averaging over 10x10 pixels.
We also simulated higher resolution nuclei by averaging over 5x5 pixels, but this did not alter the results significantly (Figure S2). It is
known that the initialisation step has a large influence on the accuracy of parameter estimation (Mueller et al., 2008). To reduce the
noise in the initialisation, we further averaged the first three post-bleach images to create a single imagewhichwe used to initialise the
simulations using the approach from (Beaudouin et al., 2006). We also scaled the total fluorescence within the bleached area by a
number close to 1 so that the binned fluorescence within the binned bleached area exactly match the fluorescence within the original
(full-resolution) bleach area. Wemonitored the recovery at two distinct places in the nucleus: at the circular bleaching location, and at
a circular location at the opposite end of the nucleus. To improve the precision of parameter estimation we simultaneously fitted the
recovery at both locations (van Royen et al., 2009).
We fitted the FRAP model where the behaviour of the protein is described by a reaction-diffusion reaction which a diffusion con-
stant D, rate of binding to DNA kon and unbinding from DNA koff. Following the approach of (Beaudouin et al., 2006) we simulated the
system using the finite difference approach where we tile the imaged 2D plane into discrete tiles (bins) and simulate their dynamics.
We denote the free molecules along the spatial steps i and j at time t as F(i, j, t) and the molecules bound to DNA asC(i, j, t). This leads
to the following equations:
vFði; j; tÞ
vt
=
D
p2i
ðFði  1; j; tÞ+Fði + 1; j; tÞ  2Fði; j; tÞÞ+ D
p2j
ðFði; j  1; tÞ+Fði; j + 1; tÞ  2Fði; j; tÞÞ  k1ði; jÞFði; j; tÞ+ koffCði; j; tÞvCði; j; tÞ
vt
= k1ði; jÞFði; j; tÞ  koffCði; j; tÞ
The rate k1 (i, j) depends on kon and an unknown proportionality coefficient A that can be estimated from the steady-state image
following the approach of Beaudouin et al. (2006, Appendix A) (written here in the matrix form):
k1 =
koff
Free
 
ist
istavg
 Free
!
where Free is the average number of free molecules in the nucleus, ist is the matrix of fluorescence intensities over the spatial grid,
and istavg is its average value. This equation assumes that the nucleus is in equilibrium and that therefore the fluorescence can be ex-
pressed as a sum of uniformly distributed free molecules plus non-uniformly distributed molecules bound to DNA.
In the situation when there is no binding, the equation simplifies considerably by settingC (i, j, t) = 0, which allows us to directly infer
the distribution of free molecules before and after bleaching by looking at the fluorescence intensity.
We simulated recovery for a grid of parameter values to find the parameter combination that explains the data the best. To sum-
marise the results over the replicates, we summed up the mean square errors (comparing observed and predicted recovery) for each
of the replicates. We highlight the parameter combinations that have the lowest global error rate, i.e. whose deviation from the
observed recovery is on average, over the whole recovery curve, within 1% of the best model. This is equivalent to the maximum
likelihood estimate of the parameters assuming normal errors.
The code for the FRAP parameter estimation is available on github: https://github.com/rstojnic/suh_frap.
PALM Imaging Using Su(H)-mEOS and SMT Analysis
Cultured salivary gland cells were imaged using an inverted microscope frame customised for localisation microscopy. The output
beams of a laser combiner (Omicron LightHUB) were collimated to a 12mm diameter using a reflective collimator and then circularly
polarised using an achromatic quarter wave plate. The excitation (561nm) and activation (405nm) beamswere then demagnified onto
the sample through a 250mm tube lens and 60x silicone oil immersion objective (Olympus UPLSAPO60XS2, 1.3 NA). This resulted inDevelopmental Cell 44, 611–623.e1–e7, March 12, 2018 e4
an illumination area at the sample plane with a diameter of 96 mm. Fluorescence was collected through the same objective lens,
filtered (Chroma-ZT405/488/561/640rpc) to remove scattered and reflected excitation or activation light before being focused
onto a sCMOS camera (Hamamatsu, Flash 4.0). A final band-pass filter (Semrock-FF01 600/52-25) was placed before the camera
to ensure high attenuation of any remaining laser light.
For each experiment, we imaged a 50350 mm region, sufficient to observe one nucleus, with continuous excitation beam (150-
250 W/cm2) and regular bursts of activation beam (3-9 W/cm2). 5-8 cells were imaged at exposure times of 10, 50, 100, 200
and 500 ms.
Images were analysed using undecimated wavelet transform for detection of fluorescent proteins. Subsequent tracking of these
diffusing protein molecules was carried out using multiple hypothesis tracking algorithm. For a conservative approach, tracks with
detections in each consecutive frame were included in the analysis i.e. no detection gaps were allowed inside a track. Detection and
tracking analysis was performed using icy-plugins based on (Chenouard et al., 2013; Olivo-Marin, 2002). Different diffusive states,
their dwell times and transition probabilities were computed using variational Bayesian treatment of Hidden Markov models based
on (Persson et al., 2013). The analysis was performed on tracks longer than three time points.
To benchmark the vbSPT analysis software in the context of our biological data, we simulated trajectories of randomwalk with four
diffusion states, in a discretised cell geometry (with 15 nm resolution) as discussed in (Persson et al., 2013). Given the simulated sin-
gle molecule positions we generated images by assuming a 2D Gaussian point spread function (s = 120 nm). Poisson and Gaussian
noise (s = 1.1 e-, matching the readout noise of the camera used in our experiments) was subsequently added to the images. A repre-
sentative experimental and simulated image is shown in Figure S3. Spot detection, tracking and analysis were performed using the
procedure discussed above and results from ten data sets are shown in Figure S3. The vbSPT software successfully differentiated
between different diffusion states for all the data sets, albeit yielding very slight variations in the diffusion coefficients due to the added
noise (for D1-D3) and the confinement induced by cell boundaries (D4). For a more detailed discussion on the performance of vbSPT
under different conditions see (Persson et al., 2013).
We used fixed samples to estimate the localization errors under the experimental conditions. The covariance of the computed lo-
calisations of the same fixedmolecule over time gives ameasure of the precision for our imaging system of approximately 55 nm (with
the Spot Tracking plug-in of icy) and 33nm (with a custom written localisation software), within the range estimated in other SMT
studies (e.g. 70nm, Izeddin et al., 2014). The average jump distance for a molecule with slowest diffusion coefficient in our experi-
ments was D=0.08um^2/sec) suggesting that signal to noise ratio puts a limit on the slowest diffusion coefficient that can be
measured from the images. We note that the estimated peak signal to noise ratio (PSNR) of the fixed sample images is
19dB, which
is lower than for the unfixed samples where the estimated PSNR is 
29 dB. This suggests that the localisation precision for the
tracked molecules is better than 55nm. The PSNR of simulated images is 
32.8 dB.
Immunostainings
Salivary glands or wing discs of early third-instar larvae were dissected in PBS and fixed in 4% formaldehyde for 15min. Glands were
washed in PBS-T (0.3% Triton X-100 in PBS) and blocked in BBT (PBS-T + 1% BSA) before incubation with primary antibody over-
night at 4C. These included Rb anti-PolII (1:500, Abcam 5095), Rb anti-H3K27ac (1:500, Abcam 4729), Rb anti-H3K4me1 (1:1000,
Abcam 8895), Rb anti-Trr (1:100, Herz et al., 2012). After several washes in PBS-T, glands were incubated with FITC secondary anti-
body, (Jackson ImmunoResearch Laboratories, Inc), for 2–4h at Room Temperature (RT) followed by washing in PBS-T and labelling
with Hoechst 33258 (Sigma). Samples were mounted in Vectashield (Vector Laboratories) for imaging.
Immuno-Precipitation and Western Blots
Extracts were prepared by lysing and homogenizing 20-40 third-instar larval heads in 100ml IP buffer (50mM Tris-HCL pH8, 150mM
NaCl, 10% Glycerol, 0.5% Triton X-100, and proteinase inhibitor cocktail). After 30min on ice, debris was pelleted by centrifugation
(13,000 rpm, 4C for 15min) and 220mg of protein extract was diluted to final volume of 500ml in IP buffer then incubated with Goat
anti-Su(H) (1:100, Santa Cruz Tech sc-15813) or Rabbit anti-GFP (1:1.000, Invitrogen A11122) overnight at 4C. 30ml protein
G-agarose beads were added and samples incubated for 3h at 4C before the beads were pelleted, washed 5x in IP buffer, and re-
suspended in SDS-sample buffer (130mM Tris-Cl, pH8, 20% Glycerol, 4% SDS, 0.02 Bromophenol blue, 2% b-mercaptoethanol).
Samples were analyzed on 10%acrylamide SDS-PAGE gels (Bio-Rad) before transfer to Nitrocellulosemembrane.Membraneswere
blocked in TBTM (TBS, + 0.05% tween, 3% milk) for 1h and incubated overnight at 4C in primary antibodies (Rb anti-GFP 1:500,
Invitrogen A11122; Rb anti-Su(H) 1:400 Santa Cruz sc-28713; Mo anti-NICD 1:100, Developmental Studies Hybridoma Bank
C17.9C6; Rb anti-Hairless 1:1000, Maier et al., 1999). Following 3x 15min washes at RT (TBS + 0.05 Tween) membranes were incu-
bated 1hwith secondary antibodies (1:2000, HRP conjugated Goat anti-Mouse andGoat anti-Rabbit, BioRad) thenwashed 4x 15min
in TBT. For detection, Amersham ECL Western Blotting Detection Reagent (GE Healthcare Life Sciences) was used.
mRNA Extraction and Quantitative RT-PCR
RNA from 20 dissected third instar salivary gland pairs per condition was extracted. Glands were dissected in dissecting media
(Shields and Sang M3 insect medium + 5% FBS + 13 Antibiotic-Antimycotic), quickly washed in PBS and incubated in TriZOL for
10min. Then, we added chloroform and incubated for another 10min before spining for 10min at 4C. The supernatant containing
RNA was precipitated in isopropanol overnight at -20C. After a wash with Ethanol, the pellet was resuspended in DEPC-treated
water. Samples were treated with Ambion’s DNA-free kit (#AM1906) to eliminate genomic DNA, following manufacturer instructions.e5 Developmental Cell 44, 611–623.e1–e7, March 12, 2018
We synthesized cDNA using Oligo(dT)15 Primers (Promega C1101) andM-MLV reverse transcriptase (PromegaM531A). Quantitative
PCR was then performed using LightCycler 480 SYBR Green I Master PCR Kit (Roche 04707516001) with a Roche Light Cycler.
Samples were normalized using the Rpl32 gene as control. Primers used were
ma (GCAGGAGGACGAGGAGGATG and GATCCTGGAATTGCATGGAG)
mß (GCTGGACTTGAAACCGC and AGAAGTGAGCAGCAGCC),
m3 (AGCCCACCCACCTCAAC and GTCTGCAGCTCCATTAGTC)
Rpl32 (ATGCTAAGCTGTCGCACAAATG and GTTCGATCCGTAACCGATGT).
Measuring Chromatin Accessibility by ATAC
ATAC was performed largely as described in (Buenrostro et al., 2015), that is: Ten pairs of salivary glands were dissected from wan-
dering third instar larvae of each genotype in PBS. Glands were transferred in PBS to LoBind microcentrifuge tubes (Eppendorf)
treated with 1% BSA to avoid sticking. Glands were washed again in PBS by pelleting at 500xg for one minute and resuspending
in 1mL PBS. A two-step lysis was performed where fat and debris were removed in the first step and the salivary gland nuclei
were released in the second: First, glands were pelleted and resuspended in 50mL lysis buffer (10mM Tris-HCl, pH7.4,
10mM NaCl, 3mM MgCl2, 0.1% NP-40). After five minutes on ice, the liquid was pipetted up and down but glands always remained
intact with fat cells coming unattached. Glands were pelleted again (fat remained floating) and resuspended in 50mL of the same
buffer with the addition of 1mL of 10%NP-40 to bring the final concentration to 0.3%. After fiveminutes on ice and vigorous pipetting,
the released nuclei were pelleted at 100xg for five minutes at 4C. The nuclei were resuspended in 30mL TD buffer (Illumina #FC-121-
1030) and 5mL was used to observe the nuclei under a microscope to check the lysis had worked.
The tagmentation reaction was performed by transferring the remaining 25mL of nuclei to PCR tubes containing TD buffer andmix-
ing with 22.5mL nuclease-free water and 2.5mL of Tagment DNA enzyme (Illumina #FC-121-1030) by gentle pipetting. The reaction
was incubated at 37C for 30 minutes, after which the DNA was immediately purified using the Qiagen MinElute PCR purification
kit and eluted in 10mL EB.
CustomNextera PCR primers were used for PCR amplification of DNA (see below, Table S5). 10mL tagmented DNAwas combined
with 10mL water, 2.5mL of each primer at 25mM concentration, and 25mL NEBNext High-Fidelity 2x PCRMaster Mix (NEB #M0541S).
Nextera PCR primer 1 was always used with a different Nextera PCR primer 2 for each sample. Five PCR cycles were performed as
follows: 72C, 5mins; 98C, 30 secs; five cycles (98C, 10 secs; 63C, 30 secs; 72C, 1min). After five cycles, the reactions were kept
on ice while qPCR using a Roche Lightcycler 480 II system was performed with a 5mL sample, in order to determine the number of
additional cycles for each sample. To a single well of a 384 multiwell plate (Roche #04 729 749 001), 5mL of the PCR reaction was
combined with 5mL NEBNext High-Fidelity 2x PCR Master Mix, 2mL SYBR green at 4.5x (diluted from 10,000x, Invitrogen #S7563)
and 1.5mL of each primer at 4.2mM concentration. 20 qPCR cycles were performed and the software was used to determine how
many additional cycles corresponded to the fluorescence reaching closest to one quarter of the maximum. This number of cycles
was then performed with the remaining 45mL PCR reaction (always between six and nine additional cycles): 98C, 30 secs; cycles
(98C, 10 secs; 63C, 30 secs; 72C, 1 min). Finally, the DNA was once again purified with the Qiagen MinElute PCR purification
kit and eluted in 20mL EB.
The DNA was diluted five-fold before quantification by standard qPCR using SYBR green 2x master-mix (Roche #04707516001).
Primers used are listed in Table S5 Fold enrichment of open chromatin was calculated by comparing Cp values obtained from tag-
mented DNA to those obtained from genomic DNA extracted from salivary glands using the Qiagen DNeasy Blood and Tissue Kit
(Qiagen #69504), and by comparing all primer regions tested to the negative control closed chromatin region. In other words, 2 to
the power of [(Cptest,naked – Cptest,ATAC) – (Cpnegative,naked – Cpnegative,ATAC)].
Nextera PCR Primers for DNA Amplification:
Nextera PCR primer 1 AATGATACGGCGACCACCGAGATCTACACTCGTCGGCAGCGTCAGATGTG
Nextera PCR primer 2.1 CAAGCAGAAGACGGCATACGAGATTCGCCTTAGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.2 CAAGCAGAAGACGGCATACGAGATCTAGTACGGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.3 CAAGCAGAAGACGGCATACGAGATTTCTGCCTGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.4 CAAGCAGAAGACGGCATACGAGATGCTCAGGAGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.5 CAAGCAGAAGACGGCATACGAGATAGGAGTCCGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.6 CAAGCAGAAGACGGCATACGAGATCATGCCTAGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.7 CAAGCAGAAGACGGCATACGAGATGTAGAGAGGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.8 CAAGCAGAAGACGGCATACGAGATCCTCTCTGGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.9 CAAGCAGAAGACGGCATACGAGATAGCGTAGCGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.10 CAAGCAGAAGACGGCATACGAGATCAGCCTCGGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.11 CAAGCAGAAGACGGCATACGAGATTGCCTCTTGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.12 CAAGCAGAAGACGGCATACGAGATTCCTCTACGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.13 CAAGCAGAAGACGGCATACGAGATATCACGACGTCTCGTGGGCTCGGAGATGT
Nextera PCR primer 2.14 CAAGCAGAAGACGGCATACGAGATACAGTGGTGTCTCGTGGGCTCGGAGATGTDevelopmental Cell 44, 611–623.e1–e7, March 12, 2018 e6
Nextera PCR primer 2.15 CAAGCAGAAGACGGCATACGAGATCAGATCCAGTCTCGTGGGCTCGGAGATGT
Primers Used in qPCR Analysis:
Rab11 intron ACTGAAAATGGGCCGTTTCG AGGAGTGGTAATCGACGGTC
Eip78C EcR AGAAGTAGGGGCCGTCAAGT GTGTAAGACCCGTCGCATTT
Negative 1 GCATTTTTGTGGCAGAGGCA CTCTTTCGGTGTCGCCTTCT
Mst87F ATCCTTTGCCTCTTCAGTCC; AATAATGATACAAAATCTGGTTACGC
mb gene AGAAGTGAGCAGCAGCCATC GCTGGACTTGAAACCGCACC
mb peak AGAGGTCTGTGCGACTTGG GGATGGAAGGCATGTGCT
ma peak AAGCCAGTGGACTCTGCTCT TGATCTCCAAGCGGAGTATG
ma gene GCAGGAGGACGAGGAGGATG GATCCTGGAATTGCATGGAG
m3 peak ACACACACAAACACCCATCC CGAGGCAGTAGCCTATGTGA
m3 gene CGTCTGCAGCTCAATTAGTC AGCCCACCCACCTCAACCAG
m8 gene CAATTCCACGAAGCACAGTC GAGGAGCAGTCCATCGAGTT
Isothermal Titration Calorimetry Measurements
Recombinant Su(H) (98-523) and NICD (1762-2142) proteins were overexpressed and purified from bacteria as GST-fusion proteins.
Bacteria were harvested by centrifugation and lysed by sonication, and subsequently loaded onto a glutathione-Sepharose column
(GE Healthcare). The columnwaswashedwith PBS and the GST-fusion proteins were eluted using reduced glutathione. The GST tag
was cleavedwith Precision Protease (GEHealthcare) per themanufacturer’s protocol. An additional GST affinity column removed the
GST moiety. Su(H) and NICD constructs were further purified to homogeneity using cation exchange and size exclusion
chromatography.
ITC experiments were carried out using a MicroCal VP-ITC microcalorimeter. All Su(H)-NICD and Su(H)-DNA experiments were
performed at 25C and 10C, respectively, in a buffer composed of 50 mM sodium phosphate pH 6.5 and 150 mM NaCl. Su(H)
and NICD proteins were degassed and buffer-matched using dialysis and size exclusion chromatography. A typical Su(H)-DNA bind-
ing experiment contained 10 mMSu(H) in the cell and 100 mMDNA in the syringe. A typical Su(H)-NICD binding experiment contained
50 mM Su(H) in the syringe and 5 mM NICD in the cell. The data were analyzed using ORIGIN software and fit to a one-site bind-
ing model.
Band Assay
In the experiments where the band intensity was quantified, we used ImageJ (Schneider et al., 2012) to draw a Region Of Interest
(ROI) around the band and a control ROI in the same chromosome, and measure the average fluorescence intensity. We expressed
the band intensity as a ratio of mean fluorescence intensity in the band and the control region.
Enrichment of Marks at the E(spl) Locus
To show the enrichment of marks across the E(spl) locus, we used ImageJ (Schneider et al., 2012) to draw a rectangular ROI of a fixed
length (3 to 5 microns, as indicated in each figure) and variable width (to cover the whole chromosomal width) in each nucleus. We
measured the profile of fluorescence intensity across the width of the ROI in the locus tag channel and the corresponding mark. We
normalized the values of each nucleus so that they range from 0 to 1 (minimum value=0, maximum value=1) for better comparison
between nucleus and experiments. Finally, we aligned all measurements so that the max intensity of the locus tag channel is in the
middle of the profile, before averaging all replicates.
Statistical Analyses
The statistical comparisons of different genotypes were done by student t-test or One-Way ANOVA, depending on the number of
comparisons. Error bars in all graphs except box and whiskers, represent the standard error of the mean. p-values are indicated
as follows: * 0.05-0.01, ** 0.01-0.001, *** 0.001-0.0001, ****<0.0001. The box and whiskers plots show minimum to maximum values.
All details of statistical analyses, including n values, are found in the figure legends.e7 Developmental Cell 44, 611–623.e1–e7, March 12, 2018