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1www.nature.com/nature
doi: 10.1038/nature08781
Supplementary figure 1
end!3
med!1,2
end!1
elt!2
end!3
med!1,2
end!1
elt!2
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Wild
-typ
e (
N2
)
Nu
mb
er
of
tra
nscrip
ts
zu
13
5
Nu
mb
er
of
tra
nscrip
ts
zu
12
9
Nu
mb
er
of
tra
nscrip
ts
zu67
Nu
mb
er
of
tra
nscrip
ts
Supplementary Figure 1: Number of med-1,2, end-3, end-1 and elt-2 mRNAs in individual wild-type (N2)
and skn-1 (zu129, zu135 and zu67) mutant embryos plotted as a function of developmental time (minutes
from the first zygotic cell division). We computed developmental time from the number of nuclei by using
the lineage data collected by Bao et al., Developmental Biology, 318 (1):65-72, 2008: for a particular
number of nuclei, those data allow one to assign a particular developmental time window during which the
embryo contains that many nuclei, and we randomly assigned the time within that window. It is important
to note that Bao et al. lineage data were collected at 20°C rather than 25°C (used in our study), so there
is likely to be a conversion factor between the time indicated on our graphs and the actual time.
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doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
c
Supplementary figure 2
a b
d
Nuclei
DAPI
TMR Cy5
Alexa 594
end-3
end-1 med-1,2
elt-2
Supplementary Figure 2: end-3 and end-1 transcripts are maternally deposited while med-
1,2 are not. a. Image of the nuclei (stained with DAPI) of two C. elegans embryos, one at the
early two-cell stage and one at the three-cell stage. b-d. Images of end-3 (b), end-1 (c) and
med-1,2/elt-2 (d) transcripts in the same two embryos. Images are maximum-projections of
three dimensional microscope data. Scale bars are 5µm long.
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SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
b
a
Supplementary figure 3
Supplementary Figure 3: a. Transcript number vs. number of nuclei for a collection of randomly staged
skn-1(zu67) and skn-1(zu129) mutant early embryos. b. Number of cells expressing end-1 (top) or elt-2
(bottom) within individual skn-1(zu67) and skn-1(zu129) mutant embryos vs. number of nuclei.
skn-1 (zu67) skn-1 (zu129)
Nu
mb
er
of
tra
nscrip
tsN
um
be
r o
f ce
lls e
xp
ressin
g
tra
nscrip
ts
med!1,2
end!1
end!3
elt!2
end!1
elt!2
00
4
8
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8
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Number of nuclei Number of nuclei
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doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
Supplementary figure 4
Fra
ction o
f em
bry
os w
ith m
ore
elt-2
transcripts
than t
hre
shold
Threshold (number of elt-2 transcripts)
0 100 200 300 4000
0.2
0.4
0.6
0.8
1
zu129
zu135
zu67
Supplementary Figure 4: Dependence of the penetrance of the skn-1 mutant alleles (zu129,
blue; zu135, green; zu67, red) upon the threshold in the level of elt-2. For each value of the
elt-2 threshold (x-axis), we calculated the fraction of embryos (between the 80 and 200 cell
stages) with more elt-2 transcripts than the threshold. The dotted line represents a threshold
of 50 elt-2 transcripts, which we used in our calculation of the threshold of the number of end-1
transcripts required to activate elt-2 (supplementary figure 8).
5www.nature.com/nature
SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
b
c
d
e
a
Supplementary figure 5
Wild
-typ
e (
N2
)
Coeffic
ient of variation
zu
12
9zu135
zu67
Num
ber
of
end-1
tra
nscripts
0
200
400
600
Num
ber
of
end-1
tra
nscripts
0
200
400
600
Num
be
r o
f e
nd
-1 t
ran
scrip
ts
0
200
400
600
Nu
mb
er
of
en
d-1
tra
nscrip
ts
0
200
400
600
0
0.5
1.0
1.5
0 50 100 150 200
Time after first cell division (minutes)
Wild-type (N2)
zu129
zu135
zu67
Mean
Supplementary Figure 5: Number of
end-1 transcripts in individual a. wild-
type (N2) and b-d. skn-1 (zu129,
zu135, zu67) mutant embryos plotted
as a function of developmental time
(minutes from the first zygotic cell divi-
sion, computed as per supplementary
figure 1). The cyan line represents the
mean, which we computed using the
Savitsky-Golay method with a data
span of 75%. e. Coefficient of variation
for the strains shown in a-d. We com-
puted the coefficient of variation by first
computing the squared deviation of
each data point from the mean com-
puted in a-d., then smoothing those
values in the same way as the mean to
estimate the variance. While the
values of the coefficient of variation are
artifactually high during the initial rise
and eventual decline of end-1 tran-
script number, the values between 75
minutes and 150 minutes reflect the
actual variation.
6www.nature.com/nature
doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
ba
Supplementary figure 6N
um
ber
of
em
bry
os
Wild
-typ
e (
N2
)zu
12
9zu135
zu67
Number of end-3 transcripts
12-20 cell stage 20-30 cell stage 65-120 cell stage 120-180 cell stage
end-3 end-1
Number of end-1 transcripts
0 100 200 300 4000
8
0 100 200 300 4000
15
0 100 200 300 4000
11
0 100 200 300 4000
22
0 100 200 300 4000
16
0 100 200 300 4000
54
0 100 200 300 400 5000
14
0 100 200 300 400 5000
7
0 100 200 300 400 5000
5
0 100 200 300 400 5000
10
0 100 200 300 400 5000
11
0 100 200 300 400 5000
14
0 100 200 300 4000
17
0 100 200 300 4000
46
0 100 200 300 400 5000
14
0 100 200 300 400 5000
12
Supplementary Figure 6: Histograms of end-3 and end-1 transcript numbers in wild-type (N2)
and skn-1 mutant embryos (zu67, zu135 and zu129). a. We generated end-3 expression histo-
grams for embryos containing between 12 and 20 nuclei (left) or 20 and 30 nuclei (right). b. We
generated end-1 expression histograms for embryos containing between 65 and 120 nuclei
(left) or 120 and 180 nuclei (right). The purpose of examining the end-3 expression distribu-
tions was to see if differences in end-3 expression could explain the different penetrances of
the various mutant alleles. Our data indicate that this is unlikely, since the levels of end-3
expression are generally higher during the 10-20 cell stage in the more penetrant zu67 and
zu135 alleles. The expression of end-3 is somewhat higher in the less penetrant zu129 allele
during the 20-30 cell stage, but the cumulative level of END-3 protein is likely to be dominated
by the differences in transcript number during the earlier time window.
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SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
Supplementary figure 7
Supplementary Figure 7: Transcript number vs. number of expressing cells for both end-1 and
elt-2 in all the strains used in this study. We computed the number of mRNA per cell using a least
squares fit to compute the slope of the best fit line to the data; we computed the errors by bootstrap-
ping.
skn-1(zu135); HDA-1(RNAi)
skn-1(zu135)
skn-1(zu129)
Wild-type (N2)
skn-1(zu67)
end-3 (ok1448)
Nu
mb
er
of
en
d-1
tra
nscrip
ts
Nu
mb
er
of
elt-2
tra
nscrip
ts
0
200
400
Nu
mb
er
of
en
d-1
tra
nscrip
ts
Nu
mb
er
of
elt-2
tra
nscrip
ts
0
200
400
Nu
mb
er
of
en
d-1
tra
nscrip
ts
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mb
er
of
elt-2
tra
nscrip
ts
0
200
400
Nu
mb
er
of
en
d-1
tra
nscrip
ts
Nu
mb
er
of
elt-2
tra
nscrip
ts
0
200
400
Nu
mb
er
of
en
d-1
tra
nscrip
ts
Nu
mb
er
of
elt-2
tra
nscrip
ts
0
200
400
Nu
mb
er
of
en
d-1
tra
nscrip
ts
Nu
mb
er
of
elt-2
tra
nscrip
ts
0
200
400
Number of expressing cells
45.1±1.4
mRNA per cell
32.9±2.0
mRNA per cell
35.4±2.2
mRNA per cell
34.5±2.4
mRNA per cell
36.8±1.1
mRNA per cell
27.7±1.7
mRNA per cell
0 4 80 4 8
Number of expressing cells
0 4 80 4 8
Number of expressing cells
0 4 80 4 8
Number of expressing cells
0 4 80 4 8
Number of expressing cells
0 4 80 4 8
Number of expressing cells
0 4 80 4 8
8www.nature.com/nature
doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
0
0.1
0.2
0.3
0.4
0.5
0 100 200 3000
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200
300
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200
300
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300
Supplementary figure 8
Supplementary Figure 8: a. We estimated the number of end-1 transcripts in each cell in
embryos (6-60 nuclei) in which two cells were expressing end-1, assigning mRNAs to the near-
est nucleus. The x axis is the number of end-1 nuclei in the one of the cells, and the y axis is
the number of cells in the other cell (randomly chosen). The red line, y=x, represents equal
expression in both cells; hence, deviation from this line represents variability. b. To quantify the
variability, we computed (for each embryo) the difference in transcript number between the two
cells divided by the mean transcript number in the two cells and calculated the root-mean-
square deviation across all embryos in (a) (y axis). We computed the error bars by bootstrap-
ping. In computing the variability, we excluded embryos in which either cell contained fewer
than 50 end-1 transcripts because there were few N2 embryos in that region. Despite potential
errors in our cell-assignment method, we found significantly increased variability in both the
zu67 and zu135 mutant alleles as compared to the wild type.
end-1
tra
nscript num
ber
(cell
2)
Variabili
ty in e
xpre
ssio
n
end-1 transcript number (cell 1)
zu67
zu67
zu135
zu135
N2
6-60 nuclei
N2
zu129
zu129
a b
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SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
Supplementary figure 9
skn-1(zu135)
skn-1(zu129)
Wild-type (N2)
65-90 nuclei
skn-1(zu67)
Num
ber
of elt-2
tra
nscripts
in c
ells
expre
ssin
g e
nd-1
Number of end-1 transcripts in cells expressing end-1
Supplementary Figure 9: Number of elt-2 vs. end-1 transcripts in individual cells expressing
end-1. We used embryos containing between 65 and 90 nuclei in the wild type as well as the
skn-1 mutant alleles. We counted the number of mRNA in individual cells by assigning each
mRNA to the closest nucleus (whose locations we identified manually).
0 100 2000
50
100
0 100 2000
50
100
0 100 2000
50
100
0 100 2000
50
100
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doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
Supplementary figure 10
Supplementary Figure 10: Number of
elt-2 vs. end-1 transcripts in zu67,
zu135 and zu129 mutant embryos con-
taining between 65 and 120 nuclei with
1 through 8 (top to bottom) cells
expressing end-1 (left); number of elt-2
vs. average number of end-1 transcripts
per cell in zu67, zu135 and zu129
mutant embryos containing between 65
and 120 nuclei with 1 through 8 (top to
bottom) cells expressing end-1 (right).
The threshold of end-1 required to acti-
vate elt-2 (in the entire embryo) was
perhaps somewhat independent of the
number of cells expressing, although
our data are somewhat sparse to make
this conclusion.
1 cell
2 cells
3 cells
4 cells
5 cells
6 cells
7 cells
8 cells
end-1 transcript number end-1 transcript number
(mean per cell)
elt-2
tra
nscript num
ber
elt-2
tra
nscript num
ber
00 0200 1004000
350
0
350
0
350
0
0
0
0
0
350
350
350
350
350
1 cell
2 cells
3 cells
4 cells
5 cells
6 cells
7 cells
8 cells
end-1 transcript number end-1 transcript number
(mean per cell)
0 0200 1004000
350
0
350
0
350
0
0
0
0
0
350
350
350
350
350
end-1 transcript number end-1 transcript number
(mean per cell)
0200 1004000
350
0
350
0
350
0
0
0
0
0
350
350
350
350
350
zu67 (65-120 cells) zu135 (65-120 cells)
zu129 (65-120 cells)
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SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
0 50 100 1500
50
100
150
0 50 100 1500
50
100
150
0 50 100 1500
50
100
150
0 50 100 1500
50
100
150
0 50 100 1500
50
100
150
200
0 100 200 3000
50
100
150
Supplementary figure 11
Supplementary Figure
11: a. In order to check if
high expression of end-1
lead to expression of
elt-2, we examined inter-
cellular variation in end-1
expression in embryos in
which a proper subset of
cells expressing end-1
also expressed elt-2. In
such embryos, we
assigned each end-1
mRNA to the cell whose
nucleus was the closest
in three-dimensional
space and then com-
puted the mean expres-
sion in those cells with
elt-2 expression to those
without elt-2 expression,
as plotted in b. The blue
line, y=x, represents the
null hypothesis that
end-1 transcript number
is unrelated to elt-2
expression, and the
colors correspond to the
total number of nuclei in
the embryos examined.
Although the data are
noisy (likely due to errors
in mRNA assignment),
we found that the wild-
type (N2) strain, the
skn-1 mutants zu67 and
zu129, and the end-3
deletion all exhibited
higher end-1 expression in elt-2 positive cells than in elt-2 negative cells (i.e., above the blue line) at particular
developmental stages (40-60 nuclei for N2; 60-90 for skn-1 mutants, consistent with the developmental delay in
end-1 expression; 40-70 for end-3 deletion). This shows that decision of whether or not to express elt-2 is not
made when there is just one E cell, with that early decision propagating to all subsequent E lineage cells. More-
over, in the skn-1 mutants, whenever two of the three or four end-1 positive cells expressed elt-2, those two cells
were invariably next to each other, indicating that the decision to express elt-2 was made one cell-cycle before
and the decision was carried over through one division. This may imply that the decision window occurs when
there are two E cells. The skn-1(zu135); hda-1(RNAi) data did not exhibit a discernible pattern, perhaps
because of variability in the onset of the decision window.
skn-1(zu135); hda-1(RNAi)
skn-1(zu135)
skn-1(zu129)
Wild-type (N2)
skn-1(zu67)
end-3 (ok1448)
Me
an
nu
mb
er
of
en
d-1
tra
nscrip
ts in
ce
lls e
xp
ressin
g e
lt-2
(p
er
em
bry
o)
Me
an
nu
mb
er
of
en
d-1
tra
nscrip
ts in
ce
lls e
xp
ressin
g e
lt-2
(p
er
em
bry
o)
Me
an
nu
mb
er
of
en
d-1
tra
nscrip
ts in
ce
lls e
xp
ressin
g e
lt-2
(p
er
em
bry
o)
Mean number of end-1 transcripts in cells
not expressing elt-2 (per embryo)
Mean number of end-1 transcripts in cells
not expressing elt-2 (per embryo)
end-1
expression
Mean end-1 expression in
these two cells ! y-axis
Embryo
40-50 cells
40-50 cells
40-50 cells
40-50 cells
40-50 cells
70-80 cells
70-80 cells70-80 cells
50-60 cells
50-60 cells
50-60 cells
50-60 cells
50-60 cells
50-60 cells
60-70 cells
60-70 cells
60-70 cells
60-70 cells
80-90 cells
80-90 cells
80-90 cells
80-90 cells
80-90 cells
90-100 cells
90-100 cells
90-100 cells
90-100 cells
90-100 cells
100-140 cells
100-140 cells
100-140 cells
100-140 cells
100-140 cells
Mean end-1 expression in
these two cells ! x-axis
elt-2
expression
a
b
12www.nature.com/nature
doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
Supplementary figure 12
Num
ber o
f elt-
2 tra
nscr
ipts
Number of end-1 transcripts
zu129
65-120 cell stage
zu67
zu135
Supplementary Figure 12: Estimation of the threshold number of end-1 transcripts required for elt-2 activation in skn-1 mutant alleles (zu129, zu67, zu135). We fit our data to the Hill equation y = a + b*xn/(kn+xn), where x is the number of end-1 transcripts, y is the number of elt-2 transcripts, a and b are the maximum and minimum of the equation and k is the location of the half-maximum. We fit the data by first smoothing and then using a non-linear least squares error minimization; we obtained values for k of 199±40, 253±27 and 250±34 and values of n of 11.5±6.5, 9.0±4.2 and 12.3±5.3 for the zu129, zu67, zu135 alleles, respectively. We computed the error on k by fitting bootstrap resampled data. The high values of n indicate a sharp threshold behavior. Note that the values for k are different than the thresholds given in the main text; there, we chose the threshold to be where we started to get values of elt-2 greater than 50 (our threshold for elt-2 being considered ON), which will typically be less than k. Our analysis here is mainly intended to statistically quantify the differences in threshold and the threshold-like nature of the dose-response relation between end-1 and elt-2.
0 100 200 300 4000
100
200
300
0 100 200 300 4000
100
200
300
0 100 200 300 4000
100
200
300
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SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
5651k5652k5653k5654k5655k5656k5657k5658k5659k5660k5661k
Chromosome IV
T19E7.2a
skn-1 (T19E7.2a)
T19E7.2b
skn-1 (T19E7.2b)
T19E7.2c
skn-1 (T19E7.2c.1)
skn-1 (T19E7.2c.2)
skn-1 (T19E7.2c.3)
skn-1 (T19E7.2c.4)
skn-1 (T19E7.2c.5)
skn-1 (T19E7.2c.6)
skn-1 (T19E7.2c.7)
zu129 zu135zu67
Supplementary figure 13
Activation domains DNA binding domain
Supplementary Figure 13: Gene model of skn-1. skn-1 encodes many isoforms, all of which contain a
DNA binding domain (red) and several activation domains (green; as described in Walker et al., J. Biol
Chem, 2000, 275(29): 22166-71). The mutant alleles described in this paper (zu129, zu67 and zu135)
are all premature stop codons in the open reading frame of the mRNA, located as indicated. All of the
premature stop codons remove the DNA binding domain. Surprisingly, the least penetrant allele (zu129)
removes the greatest portion of the protein coding sequence of all the mutant alleles. This may indicate
a dominant negative effect for the zu67 and zu135 alleles. It is also possible the translational
readthrough of the premature stop codon leads to some production of full-length SKN-1 protein; the
efficiency of this readthrough could be sequence specific. We believe that this is not the case, though,
because the number of skn-1 transcripts is larger in the more penetrant zu135 strain than in the zu129
strain (see Supplelementary Figure 14). (Image generated by www.wormbase.org)
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doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
Wild
-typ
e (
N2
)zu
12
9zu
13
5
end!3
end!1
skn-1
end!3
end!1
skn-1
end!3
end!1
skn-1
b
c
a
Supplementary figure 14N
um
be
r o
f tr
an
scrip
tsN
um
be
r o
f tr
an
scrip
tsN
um
be
r o
f tr
an
scrip
ts
200
400
600
00
200
400
600
0
500
1000
1500
2000
0
50 100 150 200
Number of nuclei
200
400
600
00
200
400
600
0
500
1000
1500
2000
0
50 100 150 200
Number of nuclei
200
400
600
00
200
400
600
0
500
1000
1500
2000
0
50 100 150 200
Number of nuclei
Supplementary Figure 14: Number of
skn-1, end-3 and end-1 transcripts in indi-
vidual wild-type (N2) and skn-1 (zu129
and zu135) mutant embryos plotted as a
function of the number of nuclei. We
labeled the skn-1 mRNAs with a probes
conjugated to the Cy5 dye, while we
labeled end-1 and end-3 with TMR and
Alexa 594, respectively. Owing to poor
signal quality for the skn-1 transcripts,
some of the variability in skn-1 transcript
abundance is probably methodological;
however, the differences we observed
between strains are likely to be real. The
purpose of examining differences in skn-1
transcript abundances was to see if they
could account for the differences in pen-
etrance between the different mutant
alleles. Our data indicate that this is
unlikely, because skn-1 transcripts are
actually more abundant in the more pen-
etrant zu135 allele. The two skn-1 mutant
alleles possess premature stop codons in
different locations within the mutant tran-
script, and we attribute variation in skn-1
transcript abundance to differences in the
efficiency with which a particular prema-
ture stop codon will activate the
nonsense-mediate mRNA degradation
pathway. It is possible that the large
differences in skn-1 transcript numbers do
not correspond with the observed differ-
ences in penetrance because the tran-
scripts may be translationally silenced in
the somatic lineages.
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20-36 nuclei10-20 nuclei
end-3 transcript number
end-
1 tra
nscr
ipt n
umbe
r
zu13
5(m
ore
pene
trant
)zu
67(m
ore
pene
trant
)zu
129
(less
pen
etra
nt) WT (N2)
skn-1 mutant
WT (N2)skn-1 mutant
WT (N2)skn-1 mutant
Supplementary Figure 15
0
200
400
0 200 4000
200
400
0 200 400
0
200
400
Supplementary Figure 15: Scatter plots of end-3 and end-1 transcript numbers in wild-type (N2; blue) and skn-1 mutant embryos (red) containing between 10 and 20 nuclei (left) or 20 and 36 nuclei (right). We analyzed different mutant alleles of skn-1 (zu67, zu135 and zu129; top, middle, bottom, respectively). We were interested in seeing whether the residual expres-sion of end-3 was correlated with the variablility in end-1 expression levels. Between the 10 and 20 cell stages, there is no real correlation beween end-1 and end-3 expression, and between the 20 and 36 cell stages, there is a only a mild correlation. These data argue against the possibility that variability in residual levels in end-3 propagate to variability in end-1 expres-sion, although it is possible that END-3 protein levels do correlate with end-1 expression; our methodology is unable to test that possibility, however.
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doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
end!3
b
c
a
Supplementary figure 16
Supplementary Figure 16: Number
of pop-1, end-3 and end-1 transcripts
in individual wild-type (N2) and skn-1
(zu129 and zu135) mutant embryos
plotted as a function of the number of
nuclei. We labeled the pop-1 mRNAs
with a probes conjugated to the Alexa
594 dye, while we labeled end-1 and
end-3 with TMR and Cy5, respec-
tively. The number of pop-1 tran-
scripts was virtually identical in all
these strains.
zu135
zu129
Wild-type (N2)
Nu
mb
er
of
tra
nscrip
ts
Nu
mb
er
of
tra
nscrip
ts
pop-1
end!1
end!3
pop-1
end!1
00
50 100 150 200 0 50 100 150 200
200
400
600
0
200
400
600
0
1000
2000
3000
4000
0
1000
2000
3000
4000
0
200
400
600
0
200
400
600
Number of nuclei Number of nuclei
0 50 100 150 200
0
1000
2000
3000
4000
0
200
400
600
0
200
400
600
Number of nuclei
17www.nature.com/nature
SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
b
a
Supplementary figure 17
Supplementary Figure 17: a. Transcript number vs. number of nuclei for a collection of randomly
staged end-3(ok1448) and skn-1(zu135); hda-1(RNAi) mutant early embryos. b. Number of cells
expressing end-1 (top) or elt-2 (bottom) within individual end-3(ok1448) and skn-1(zu135); hda-1(RNAi)
mutant embryos vs. number of nuclei. Up until the 4E stage, skn-1(zu135); hda-1(RNAi) mutant early
embryos exhibited some ectopic expression of end-1, presumably in the MS cell, as found by Calvo et
al. EMBO J 20(24):7197-208 (2008)
skn-1(zu135); HDA-1(RNAi)end-3 (ok1448)
Nu
mb
er
of
tra
nscrip
tsN
um
be
r o
f ce
lls e
xp
ressin
g
tra
nscrip
ts
med!1,2
end!1
end−3
elt!2
end!1
elt!2
00
4
8
0
4
8
0
4
8
0
4
8
50 100 150 200 0 50 100 150 200
00
50 100 150 200 0 50 100 150 200
200
400
600
0
200
400
600
0
200
400
600
0
200
400
600
0
200
400
600
0
200
400
600
0
200
400
600
Number of nuclei Number of nuclei
18www.nature.com/nature
doi: 10.1038/nature08781 SUPPLEMENTARY INFORMATION
Supplementary figure 18
Supplementary Figure 18: Scatter plots of end-1 vs. elt-2 transcript numbers in wild-type (N2; blue)
and skn-1(zu135); hda-1(RNAi) (top) and end-3(ok1448) (bottom) mutant embryos (red). Plots on the
left represent embryos containing between 65 and 120 nuclei, while plots on the right represent embryos
containing between 120 and 180 embryos. The thresholding mechanism still applies to the skn-
1(zu135); hda-1(RNAi) embryos, which shows that the removal of hda-1 does not increase elt-2 expres-
sion intrinsically but rather does so by increasing the expression of end-1. We also see some indica-
tions that a thresholding mechanism applies to in the end-3(ok1448) strain (consistent with our data
from the skn-1 mutant strains), although there are relatively few data points at the lower levels of end-1
expression.
skn-1(zu135); HDA-1(RNAi)
end-3 (ok1448)
end-1 transcript number
elt-2
tra
nscript num
ber
elt-2
tra
nscript num
ber
120-180 nuclei65-120 nuclei
120-180 nuclei65-120 nuclei
00
200
200
400
400
00
200
200
400
400
00
200
200
400
400
00
200
200
400
400
WT (N2)
skn-1(zu135);
HDA-1(RNAi)
WT (N2)
end-3(ok1448)
19www.nature.com/nature
SUPPLEMENTARY INFORMATIONdoi: 10.1038/nature08781
Supplementary figure 19
Supplementary Figure 19: Transcript number vs. number of nuclei
for a collection of randomly staged end-1(ok558) mutant embryos.
end-1 (ok558)
Number of nuclei
Nu
mb
er
of
tra
nscrip
ts
med!1,2
end!3
elt!2
0 50 100 150 2000
200
400
600
0
200
400
600
0
200
400
600
