Transcription coordinates histone amounts and genome content

Abstract

Biochemical reactions typically depend on the concentrations of the molecules involved, and cell survival therefore critically depends on the concentration of proteins. To maintain constant protein concentrations during cell growth, global mRNA and protein synthesis rates are tightly linked to cell volume. While such regulation is appropriate for most proteins, certain cellular structures do not scale with cell volume. The most striking example of this is the genomic DNA, which doubles during the cell cycle and increases with ploidy, but is independent of cell volume. Here, we show that the amount of histone proteins is coupled to the DNA content, even though mRNA and protein synthesis globally increase with cell volume. As a consequence, and in contrast to the global trend, histone concentrations decrease with cell volume but increase with ploidy. We find that this distinct coordination of histone homeostasis and genome content is already achieved at the transcript level, and is an intrinsic property of histone promoters that does not require direct feedback mechanisms. Mathematical modeling and histone promoter truncations reveal a simple and generalizable mechanism to control the cell volume- and ploidy-dependence of a given gene through the balance of the initiation and elongation rates. Accurate regulation of protein concentrations according to changes in cell volume that accompany growth and changes in biosynthetic capacity is an important component of cellular homeostasis. Here, using the model organism S. cerevisiae, the authors show how histone production is quantitatively coupled to genome content through the intrinsic properties of histone promoters.

Full text

ARTICLE
Transcription coordinates histone amounts and
genome content
Kora-Lee Claude1, Daniela Bureik1, Dimitra Chatzitheodoridou1, Petia Adarska1, Abhyudai Singh 2&
Kurt M. Schmoller 1,3 ✉
Biochemical reactions typically depend on the concentrations of the molecules involved, and
cell survival therefore critically depends on the concentration of proteins. To maintain con-
stant protein concentrations during cell growth, global mRNA and protein synthesis rates are
tightly linked to cell volume. While such regulation is appropriate for most proteins, certain
cellular structures do not scale with cell volume. The most striking example of this is the
genomic DNA, which doubles during the cell cycle and increases with ploidy, but is inde-
pendent of cell volume. Here, we show that the amount of histone proteins is coupled to the
DNA content, even though mRNA and protein synthesis globally increase with cell volume.
As a consequence, and in contrast to the global trend, histone concentrations decrease with
cell volume but increase with ploidy. We find that this distinct coordination of histone
homeostasis and genome content is already achieved at the transcript level, and is an intrinsic
property of histone promoters that does not require direct feedback mechanisms. Mathe-
matical modeling and histone promoter truncations reveal a simple and generalizable
mechanism to control the cell volume- and ploidy-dependence of a given gene through the
balance of the initiation and elongation rates.
https://doi.org/10.1038/s41467-021-24451-8 OPEN
1Institute of Functional Epigenetics, Helmholtz Zentrum München, Neuherberg, Germany. 2Department of Electrical & Computer Engineering, University of
Delaware, Newark, DE, USA. 3German Center for Diabetes Research (DZD), Neuherberg, Germany. ✉email: kurt.schmoller@helmholtz-muenchen.de
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Maintaining accurate protein homeostasis despite cell
growth and variability in cell volume is essential for cell
function. Most proteins need to be kept at a constant,
cell-volume-independent concentration. Since the amount of
ribosomes and transcriptional machinery increases in proportion
to cell volume, constant protein concentrations can be achieved
through machinery-limited protein biogenesis, where protein
synthesis depends on the availability of limiting machinery
components and thus increases in direct proportion to cell
volume1,2. While machinery-limited regulation can maintain
constant concentrations of proteins, total mRNA, and individual
transcripts3–6, it poses a conundrum for histones. As components
of nucleosomes, histones are likely needed at a constant protein-
to-DNA stoichiometry, implying that their amount should
increase with ploidy but be independent of cell volume. In other
words, histone concentration, i.e., amount per volume, should
increase with ploidy but decrease with cell volume. Since accurate
histone homeostasis is crucial for fundamental biological
processes7–10 and to avoid toxic effects11–13, cells use several
layers of regulation by translation, transcription and degradation
to tightly coordinate histone production with genome
replication14–16. However, how cells produce histones in pro-
portion to genome content, even though protein biogenesis is
generally linked to cell volume remains unclear.
Here, we use budding yeast as a model to show that histone
protein amounts are coupled to genome content, resulting in a
decrease of histone concentration in inverse proportion with cell
volume, and an increase in direct proportion with ploidy. We find
that this specific regulation of histones is achieved at the tran-
script level and does not necessarily require direct feedback
mechanisms. While our data suggest that 3′-to-5′-degradation by
the nuclear exosome is necessary for the correct decrease of
concentration with cell volume, we show that histone promoters
alone are sufficient to couple transcript amounts to gene copy
number rather than cell volume. Our results suggest that this
differential regulation of histones can be achieved through
template-limited transcription, where mRNA synthesis is limited
by the gene itself and does therefore not increase with cell
volume. This provides a general mechanism by which cells can
couple the amount of a subset of proteins to genome content
while most protein concentrations are maintained constant.
Results
Histone protein concentrations decrease with cell volume and
increase with ploidy. Typically, total protein amounts as well as the
amounts of individual types of protein increase roughly in direct
proportion to cell volume to maintain constant concentrations.
However, such regulation is inappropriate for histones, whose
amount we predicted should be coupled to the cellular genome
content instead. To test if this is the case, we chose the budding yeast
histones HTB1 and HTB2, the two genes encoding for the core
histone H2B, as examples, because they can be fluorescently tagged
without pronounced effects on cell growth. We endogenously tagged
either HTB1 or HTB2 with the fluorescent protein mCitrine in a
haploid strain, and measured cell volume and amount of Htb1/2-
mCitrine over time in cycling cells by microfluidics-based live-cell
fluorescence microscopy17,18. To obtain a large range of cell
volumes, we grew cells on synthetic complete media with 2% gly-
cerol 1% ethanol as a carbon source (SCGE). As expected14,wefind
that Htb1/2 amounts are constant during early G1, rapidly double
during S-phase and reach a plateau before cytokinesis (Fig. 1a). We
then quantified the Htb1/2-mCitrine amounts in new-born cells
directly after cytokinesis and find that the amount of Htb1/2-mCi-
trine is largely constant, independent of cell volume (Fig. 1b, c). To
further test whether histone amounts are coupled to genomic DNA
content rather than cell volume, we next analyzed diploid strains in
which both alleles of either HTB1 or HTB2 are tagged with mCitrine.
Indeed, Htb1/2-mCitrine amounts in diploid cells are approximately
a factor of two higher than in haploid cells (Fig. 1b, c). To more
accurately compare Htb2 concentrations in haploids and diploids of
similar volume, we sought to increase the overlapping range of
observable volumes in both strains. For this purpose, we deleted the
endogenous alleles of the G1/S inhibitor WHI5 and integrated one
copy of WHI5 expressed from an artificial, β-estradiol-inducible
promoter system19 (Fig. 1d). Using this system, we were able to
increase the mean volume of steady-state exponentially growing
populations by up to threefold through overexpression of Whi5
(Fig. 1e) without drastically affecting doubling times, budding
indices or cell-cycle distributions (Supplementary Fig. 1). We repe-
ated the microscopy experiments described above with the
inducible-Whi5 haploid and diploid strains in the presence or
absence of β-estradiol. Again, we find that Htb2-mCitrine amounts
are only very weakly dependent on cell volume, but show a roughly
twofold increase in diploid compared to haploid cells (Supplemen-
tary Fig. 2a). Consistently, we find that the concentration of Htb2-
mCitrine at birth in both haploid and diploid cells decreases strongly
with cell volume (Fig. 1f). To quantify this decrease, we performed a
linear fit to the double-logarithmic data, and defined the slope as the
volume-dependence-parameter (VDP). The observed VDPs of
0:87 ± 0:04 (haploids) and 0:97 ± 0:03 (diploids), respectively,
are close to the value of −1 expected for proteins that are main-
tained at constant amount, resulting in a decrease of concentration
with c~1/V. In contrast, proteins that are maintained at constant
concentration would show a VDP of 0.
In budding yeast, histones are known to be tightly regulated at
several layers. In particular, some histone genes—including
HTB1, but not HTB2—exhibit dosage compensation at the
transcript level20–22. In addition, excess histones are known to be
degraded16. In principle, a coupling of histone amounts to
genomic DNA content could be achieved through such feedback
mechanisms: For example, larger cells may produce histones in
excess, and then degrade the surplus. Alternatively, direct
feedback of histone protein amount on transcription could
ensure that histones are expressed only until the protein amount
matches the genome content. To test whether direct feedback of
histone amounts on transcription, translation, or degradation is
necessary to couple histone production to genome content, we
again focused on Htb2, because it was already shown to not
exhibit dosage compensation at the transcript level21.We
constructed an inducible-Whi5 diploid strain in which we deleted
one of the two HTB2 alleles, while the other allele is tagged with
mCitrine (Fig. 1g). If feedback were responsible for the coupling
of Htb2 amount to genome content, the remaining HTB2-
mCitrine allele should at least partially compensate for the deleted
allele. However, consistent with the absence of any feedback, we
find that Htb2-mCitrine concentrations are reduced by factor of
two in the hemizygous compared to the homozygous diploid
(Fig. 1h, Supplementary Fig. 2b). Moreover, at a characteristic
volume of 60 fL, at which we find both haploid and diploid new-
born cells, the concentration of Htb2-mCitrine in the hemizygous
strain roughly equals the concentration in the haploid (Fig. 1h).
While it seems likely that the reduced concentration of Htb2-
mCitrine is compensated by an increased concentration of the
other H2B, Htb1, our results suggest that no direct feedback is
required to couple Htb2 amounts to genome content. Instead,
Htb2 amounts are intrinsically determined by the HTB2 gene
copy number, independent of ploidy and cell volume.
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5μm 5μm 5μm
0.0
0.5
1.0
1.5
2.0
Normalized Htb2-mCitrine
concentration at 60fL
HTB2-mCitrine
Hemizygotes
HTB2-mCitrine
Homozygotes
HTB2-mCitrine
Haploids
Haploid Diploid
2.5
bca
ef
d
-50 0 50 100
Time relative to bud emergence [min]
0.0
0.5
1.0
1.5
2.0
Htb2-mCitrine amount
[arb.units]
105
S/G2/MG1
-100
Example cell
Selected curves
Mean (n = 145)
16 32 64 128 256
Htb2-mCitrine concentration
at birth [arb. units / fL]
Cell volume at birth [fL]
Haploids (n = 413)
Diploids (n = 512)
64
128
256
512
1024
2048
4096
HTB2-mCitrine Homozygote
(both HTB2 alleles)
HTB2-mCitrine Hemizygote
(one HTB2 allele deleted)
direct feedback no feedback
Htb2-mCitrine
?
g
Concentrations
50 100 150
Cell volume at birth [fL]
0.0
0.5
1.0
1.5
2.0
Htb2-mCitrine amount
at birth [arb.units]
105
Haploids (n = 185)
Diploids (n = 170)
0
Amounts
Whi5
SBF
Start
E-
estradiol
inducible Whi5
}
E-
estradiol / Whi5
concentration
Haploids (n = 57)
Diploids (n = 213)
50 100 150
Cell volume at birth [fL]
1.0
2.0
3.0
4.0
5.0
Htb1-mCitrine amount
at birth [arb. units]
105
0
0.0
Amounts
h
50
100
150
200
250
WT
Ind.-Whi5
not induced
0
Ind.-Whi5
induced WT
Ind.-Whi5
not induced
Ind.- Whi5
induced
Cell volume [fL]
Single Cells
Bulk
Haploid cells Diploid cells
Fig. 1 Htb1/2-mCitrine protein concentrations measured by live-cell fluorescence microscopy decrease with cell volume and increase with ploidy.
aHtb2-mCitrine amounts during the first cell cycle of new-born cells. Red dashed trace highlights data corresponding to the outlined cell shown in the
microscopy images (new-born cell: red outline, its bud: blue outline), brown traces show additional randomly selected example curves and the black line
shows the mean of n¼145 cells. All traces are aligned at the time of first bud emergence (t=0). bHtb2-mCitrine amounts at birth for haploid (blue) and
diploid (green) cells as a function of cell volume. Lines connect binned means with error bars indicating standard errors. cHtb1-mCitrine amounts at birth
for haploid (blue) and diploid (green) cells as a function of cell volume. Lines connect binned means with error bars indicating standard errors. Note that
the fluorescence intensities for Htb2 and Htb1 are not directly comparable due to differences in the microscopy settings. dWhi5 controls cell volume in a
dose-dependent manner. To manipulate cell volume, WHI5 is expressed from a β-estradiol-inducible promoter. Higher β-estradiol concentrations result in
increased mean cell volumes. eDistribution of cell volumes for non-inducible (WT) and inducible haploids (blue) and diploids (green) measured at birth in
HTB2-mCitrine single cells with live-cell fluorescence microscopy, or mean cell volumes in bulk populations of cells with untagged HTB2 measured with a
Coulter counter. Colored boxes highlight the 25- and 75-percentiles, whiskers extend to ±2:7σof the distributions and colored crosses highlight outliers.
Black, horizontal lines indicate the median between single cells for single cell measurements (nWT
haploid ¼185, nnot ind:
haploid ¼120, nind:
haploid ¼108, nWT
diploid ¼170,
nnot ind:
diploid ¼99, nind
diploid ¼243) or the median of population means across seven biological replicates for bulk measurements. Notches indicate the 95%
confidence interval of the median. Haploid cells were induced with 30 nM β-estradiol, diploid cells with 50 nM. fHtb2-mCitrine concentrations of non-
inducible and inducible haploids and diploids as a function of cell volume are shown in a double-logarithmic plot. Individual data points for the different
conditions are shown in blue for haploids (triangles for 0 nM, circles for WT, and stars for 30 nM) and green for diploids (triangles for 0 nM, squares for
WT, and stars for 50 nM). Lines show linear fits to the double-logarithmic data, used to calculate the VDPs. gIllustration of the impact of potential
feedback mechanisms on the concentration of Htb2-mCitrine concentration in a HTB2-mCitrine/htb2Δhemizygous diploid compared to a HTB2-mCitrine
homozygous diploid. hHtb2-mCitrine concentrations at 60 fL, estimated from the linear fit to the double-logarithmic dependence of concentration on cell
volume, for haploids (blue bar, fit through n¼413 cells), HTB2-mCitrine homozygous diploids (green bar, n¼512 cells), and HTB2-mCitrine/htb2Δ
hemizygous diploids (teal bar, n¼266 cells), normalized on the concentration at 60 fL in haploids. Error bars are derived by error propagation of the 95%
confidence interval of the linear fit at 60 fL.
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Histone mRNA concentrations decrease with cell volume. The
fact that the decrease of histone Htb2 protein concentrations with
cell volume is not simply a consequence of feedback, for example
through excess protein degradation, suggests that it might already
be established at the transcript level. To test if this is the case, we
employed the Whi5-overexpression system to measure the cell-
volume-dependence of transcript concentrations (Fig. 2a). Spe-
cifically, we grew wild-type haploid cells, as well as the inducible-
Whi5 haploid cells at three different β-estradiol concentrations (0,
10, and 30 nM), on SCGE media, which led to a roughly fourfold
RNA
extraction
RT-qPCR
6h
16h
24h
YPD
SCGE
E-estradiol
a
e
1.0
0.0
1.5
0.5
2.0
Normalized concentration
at 60 fL [arb. units]
HTB1HTB2
HTB2-Homozygote
HTB2-Hemizygote
b
Log2(rel. conc.) [arb. units]
-10
-9
-8
-7
-6
32 64 128 256
Mean cell volume [fL]
ACT1
VDP = -0.02 ± 0.09
non-ind.
30 nM
0 nM
10 nM
c
-14
-13
-12
-11
-10
32 64 128 256
HTB2
VDP = -1.08 ± 0.09
Log2(rel. conc.) [arb. units]
Mean cell volume [fL]
non-ind.
30 nM
0 nM
10 nM
d
ACT1
ENO2
RPB1
RPB3
HTA1
HTA2
HTB1
HTB2
HHT1
HHT2
HHF1
HHF2
HHO1
0.5
0.0
-0.5
-1.0
-1.5
co
n
s
t
a
nt
co
n
ce
ntr
a
ti
on
cons
t
an
t
a
mount
***
*** ***
*** ***
***
***
***
***
**
***
Volume-Dependence
Parameter (VDP)
f
-2
-1
VDP
-3
0
HTB1 HTB2 HHF1 HHO1ACT1
co
n
s
t
a
nt
c
oncentrat
i
on
c
onstant
a
mount
WT
hir1'
*
g
VDP
-2
-1
-3
0
HTB1 HTB2 HHF1 HHO1ACT1
co
n
s
t
a
n
t
concentrat
i
on
constant
amount
WT
rtt106'
*
h
VDP
constant
concentratio
n
constant
a
moun
t
WT
rrp6'
-2
-1
-3
0
HTB1 HTB2 HHF1 HHO1ACT1
*** *** ***
*
Fig. 2 Histone mRNA concentrations decrease with cell volume and increase with gene copy number. a Experimental procedure for RT-qPCR
measurements. Cells were grown for at least 24 h at the respective β-estradiol concentration before extracting total RNA and performing RT-qPCR. b,cRelative
ACT1 (b)orHTB2 (c) mRNA concentrations (normalized on RDN18) for non-inducible and inducible haploid cells over mean cell volume are shown in a double-
logarithmic plot. Individual data points for the different conditions (down-pointing triangles for 0 nM, circles for non-inducible, diamonds for 10nM, right-
pointing triangles for 30 nM) are shown in gray. Red (b)orblue(c) symbols indicate the mean of the different conditions with error bars indicating the standard
deviations for nACT1
nonind:¼7;nACT1
0¼10;nACT1
10 ¼7;nACT1
30 ¼10 (b), nHTB2
nonind:¼7;nHTB2
0¼11;nHTB2
10 ¼9andnHTB2
30 ¼10 (c) biological replicates. Lines
show linear fits to the double-logarithmic data, with volume-dependence parameters (VDPs) determined as the slope of the fit. dSummary of the VDPs for all
measured genes with error bars indicating the standard error (fitthroughnACT1 ¼34, nENO2 ¼26, nRPB1 ¼26, nRPB 3 ¼25, nHTA1 ¼27, nHTA2 ¼30,
nHTB1 ¼36, nHTB2 ¼37, nHHT1 ¼37, nHHT2 ¼30, nHHF1 ¼31, nHHF2 ¼37, and nHHO1 ¼37 biological replicates); significance that the VDP is different from 0
was tested using linear regressions: **pRPB1 ¼4:1103,***pRPB3 ¼9:0104,***pHTA1 ¼1:5109;***pHTA2 ¼1:81010 , ***pHTB1 ¼7:21014,
***pHTB2 ¼8:01014, ***pHHT1 ¼1:1108;*** pHHT2 ¼5:4106,***pHHF1 ¼2:2109, *** pHHF2 ¼1:2106,***pHHO1 ¼8:31013.eMedian mRNA
concentrations at 60 fL, estimated from the linear fit to the double-logarithmic dependence of concentration on cell volume, for HTB2 (left) and HTB1 (right) in
diploid HTB2 homozygous (green, fit through nHTB2 ¼nHTB1 ¼18 biological replicates) and HTB2/htb2Δhemizygous (teal, fit through nHTB2 ¼nHTB1 ¼18
biological replicates) strains, normalized on the respective median concentration of the HTB2-homozygote. Error bars indicate the 2.5- and 97.5-percentiles
around the median concentration ratio, determined from 10000 bootstrap samples. f–hSummary of VDPs for hir1Δ(f), rtt106Δ(g), as well as rrp6Δ(h)
deletion strains. Error bars indicate the standard error of the VDPs (fit through nhir4
ACT1 ¼30, nhir4
HTB1 ¼28, nhir4
HTB2 ¼30, nhir4
HHF1 ¼30, nhir4
HHO1 ¼30 (f), nrtt1064
ACT1 ¼12,
nrtt1064
HTB1 ¼12, nrtt1064
HTB2 ¼12, nrtt1064
HHF1 ¼12, nrtt1064
HHO1 ¼11 (g), nrrp64
ACT1 ¼17, nrrp64
HTB1 ¼17, nrrp64
HTB2 ¼17, nrrp64
HHF1 ¼17, nrrp4
HHO1 ¼17(h) biological replicates). Significant VDP
deviation from the wild-type VDP (carrying no deletion) was tested using linear regressions; *phir4
HTB1 ¼2:7102(f), *prtt1064
HHF1 ¼2:3102(g),
***prrp64
HTB1 ¼6:8104,***prrp64
HTB2 ¼4:6104,***prrp64
HHF1 ¼2:6104,*prrp64
HHO1 ¼2:0102(h).
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range in mean cell volumes ranging from 39 ± 4 fL to 143 ± 21 fL
(Supplementary Fig. 3a). To ensure steady-state conditions, we
grew cells for at least 24 h at the respective β-estradiol con-
centration, before then measuring cell volume distribution,
extracting total RNA, and performing reverse-transcription-
qPCR (RT-qPCR). First, we measured the concentration of the
ribosomal RNA RDN18 relative to total RNA and found it to be
constant (Supplementary Fig. 4a). This is consistent with the fact
that ribosomal RNA constitutes the large majority of total RNA23,
which itself is expected to increase in direct proportion to cell
volume24, and allows us to now normalize other RT-qPCR
measurements on RDN18.
Next, we quantified the mRNA concentrations of ACT1 and
ENO2, two genes that we expect to be expressed in proportion to
cell volume such that the mRNA concentrations are maintained
constant. Indeed, we find that the VDPs for both transcripts are
not significantly different from 0 (Fig. 2b, d, Supplementary
Fig. 4b). Interestingly, as previously suggested25 we observe a
slight decrease in concentration for the transcripts of the RNA
polymerase II subunits RPB1 and RPB3 with increasing cell
volume (Fig. 2d, Supplementary Fig. 4c). We then quantified the
concentrations of the transcripts of all core histone genes as well
as the H1-like histone HHO1. In budding yeast, all core histone
genes are present as two copies and expressed from bidirectional
promoters controlling pairs of H2A–H2B26 or H3–H427,
respectively. Since the two copies of each core histone show high
sequence similarity, we performed additional tests using deletion
strains where possible to ensure qPCR primer specificity
(Supplementary Table 2). We find that all histone transcripts
show a significant decrease in concentration with cell volume, and
mostly exhibit VDPs close to −1 (Fig. 2c, d, Supplementary
Fig. 3b–d, Supplementary Fig. 4d). Thus, histone mRNA
concentrations decrease with cell volume to ensure constant
amounts—in contrast to global transcription, which increases
with cell volume.
Hir1-dependent feedback is not necessary for cell-volume-
dependence of histone mRNA concentrations. The observation
that histone transcript concentrations decrease with c1=V
suggests that, similar to histone protein amounts (Fig. 1h), also
histone transcript amounts are determined by gene copy number.
We therefore measured the concentrations of representative his-
tone transcripts in inducible-Whi5 diploids homozygous or
hemizygous for HTB2. Again, we find that all histones analyzed
exhibit a VDP close to −1 (Supplementary Fig. 5a), and as
observed for Htb2 protein concentrations (Fig. 1h), the con-
centration of HTB2 transcripts at a characteristic volume of 60 fL
is clearly reduced in hemizygous compared to homozygous
diploids (Fig. 2e). We do not observe a significant overexpression
of HTB1 which is expected to compensate for the reduced HTB2
transcript concentration21 (Fig. 2e). However, given that we see a
roughly 50% overexpression of HTB1 (and HTA1) upon deletion
of HTB2 in a haploid strain (Supplementary Fig. 5b), we note that
we might not be able to resolve the comparably weaker (25%)
overexpression of HTB1 expected to compensate for one missing
HTB2 allele in the diploid strain given our experimental error.
So far, we have shown that in diploid cells with only one HTB2
allele, the concentrations of HTB2 transcript and protein are
reduced compared to wild-type diploid cells. This highlights the
absence of direct feedback mechanisms sensing and controlling
the concentration of Htb2 with cell volume. However, extensive
previous studies have shown that the eight budding yeast core
histone genes show remarkably different modes of regulation.
Specifically, only the gene pair HTA1-HTB1 is known to exhibit
dosage compensation, which is absent for HTA2-HTB220–22.
Moreover, three out of four core histone gene pairs, not including
HTA2-HTB2, show negative feedback regulation of transcript
concentration upon replication stress14,28. This feedback regula-
tion is thought to be mediated by the HIR complex and to be
dependent on HIR1 and RTT10629–31. Thus, to test if HIR-
dependent sensing and feedback regulation of histone transcript
concentration may also be responsible for the cell-volume-
dependence of HIR-regulated histone genes, we measured the
cell-volume-dependence of representative histone genes (HTB1,
HTB2,HHF1, and HHO1)inhir1Δand rtt106Δstrains. While we
observed a significantly weaker cell-volume-dependence for
HTB1 upon deletion of HIR1,wefind that neither Hir1 nor
Rtt106 are essential for a decrease of concentration with cell
volume for any of the tested histone transcripts (Fig. 2f, g,
Supplementary Fig. 5c, d).
3′-to-5′-degradation by the nuclear exosome is not necessary
for cell-volume-dependence of histone mRNA concentrations.
The fact that the correct dependence of histone transcript con-
centration on cell volume does not necessarily require direct
feedback suggests that instead it is an intrinsic property of either
transcription rate or mRNA degradation. To test if degradation
from the 3′-end by the nuclear exosome is required, we analyzed
the cell-volume-dependence of histone transcript concentrations
in strains where we deleted RRP6, a component of the nuclear
exosome exonuclease32,33. As shown in Fig. 2h, we find that also
in rrp6Δcells, histone transcript concentrations decrease with cell
volume. Surprisingly, due to increased transcript concentrations
in small cells (Supplementary Fig. 5e), this decrease with a VDP
close to −2 is significantly stronger than in wild-type cells, sug-
gesting that the volume-dependence of histone transcripts is
modulated by Rrp6-dependent degradation. Thus, while degra-
dation by the nuclear exosome is not needed for the volume-
dependent decrease of histone transcript concentrations, it may
contribute to achieve the correct VDP of −1.
Histone promoters are sufficient for cell-volume-dependence
of transcript concentrations. Given that degradation from the 3′-
end does not seem to be crucial for the cell-volume-dependent
decrease of histone transcript concentration, we next asked
whether the promoter alone is sufficient to establish this cell-
volume-dependence. To address this, we created strains that carry
additional copies of either the ACT1 or a histone promoter
(HTB1,HTB2 or HHF1) driving expression of the fluorescent
protein mCitrine, regulated by the identical ADH1 terminator
(Fig. 3a). We first confirmed that the additional promoter does
not affect the VDPs of the endogenous histone and ACT1 genes
(Fig. 3b). Strikingly, we then find that the dependence of mCitrine
transcript concentration on cell volume is determined by the
promoter: If driven by the ACT1 promoter, the VDP of mCitrine
resembles that of endogenous ACT1; if driven by histone HTB1,
HTB2,orHHF1 promoter, it resembles that of the endogenous
histone.
To verify this result using a different method, we made use of
the fact that the fluorescent reporter mCitrine enables a fast
experimental readout using flow cytometry (Fig. 3a). We analyzed
the cell-volume-dependent fluorescence of mCitrine expressed
from either the ACT1 or a histone promoter (HTB1,HTB2,or
HHF1). Consistent with the qPCR-based result, we find that all
histone promoters tested show significantly negative VDPs in
haploid and diploid cells, which confirms that flow cytometry can
be used to qualitatively distinguish the distinct volume-
dependences (Fig. 3c–e, Supplementary Fig. 6).
Histones not only need to be maintained at cell-volume-
independent amounts, leading to a decrease of concentration with
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1=V, but also need to increase in proportion to cell ploidy (Fig. 1).
This is in contrast to most other genes, which are maintained at a
ploidy-independent concentration34. To test if the histone
promoters are also sufficient to establish this distinct ploidy-
dependence, we compared the expression level of the single
mCitrine copy in diploid versus haploid cells. For ACT1, which
needs to be maintained at a ploidy-independent concentration,
we expect that a single gene allele in a diploid should produce half
of the protein compared to a homozygous diploid or haploid of
similar volume2. Indeed, for the ACT1 promoter we find that at a
given cell volume, the concentration of mCitrine expressed from a
single additional promoter is 50% lower in diploids compared to
8
16
32
64
128
32 64 128 256
Cell volume [fL]
mCitrine concentration
[arb. units]
Haploid
Diploid
+ ACT1prom-mCitrine
VDPH. = -0.28 ± 0.08
VDP
D.
= -
0
.
2
5
±
0
.
08
Haploid
Diploid
+ HTB1prom-mCitrine
8
16
32
64
4
32 64 128 256
Cell volume [fL]
2
VDPH. = -0.81 ± 0.18
VDP
D.
= -
1
.
1
5
±
0
.
21
de
a
Promoter
mCitrine
ADH1term
+
RT-qPCR
Flow Cytometry
1N or 2N
1 copy of
b
0.0
0.2
1.2
0.8
0.4
0.6
1.0
c
Diploid
/c
Haploid
of mCitrine at 60 fL
+ ACT1prom
-mCitrine
+ HTB1prom
-mCitrine
+ HTB2prom
-mCitrine
+ HHF1prom
-mCitrine
1.4
f
c
-1.5
-0.5
0.0
-1.0
VDP of mCitrine
Haploid
Diploid
+ ACT1prom
-mCitrine
+ HTB1prom
-mCitrine
+ HTB2prom
-mCitrine
+ HHF1prom
-mCitrine
Flow Cytometry
ACT1 mCitrine ACT1 mCitrineHTB2 HHF1
ns
ns
ns
ns
ns ns
-1.5
-0.5
0.5
0.0
-1.0
-2.0
-1.5
-0.5
0.5
0.0
-1.0
-2.0
WT
WT + ACT1promoter-mCitrine
*
*
VDP
ACT1 mCitrine ACT1 mCitrineHTB1
ns
ns ns
WT
WT + HTB1promoter-mCitrine
RT-qPCR
VDP
WT
WT + HTB2promoter-mCitrine
WT
WT + HHF1promoter-mCitrine
mCitrine concentration
[arb. units]
Fig. 3 Histone promoters are sufficient for cell-volume- and ploidy-dependence of transcript concentrations. a Illustration of haploid (1N) or diploid (2N)
strains carrying a single additional copy of a promoter of interest, driving the expression of the fluorescent reporter mCitrine regulated by the ADH1 terminator.
RT-qPCR or flow cytometry were used to analyze expression of the fluorescent reporter. bSummary of VDPs determined with RT-qPCR for the genes ACT1,
mCitrine,andHTB1,HTB2,orHHF1 for a haploid strain carrying an additional ACT1 promoter (red circles), and haploid strains carrying an additional HTB1,HTB2,
or HHF1 promoter (blue circles) in comparison to a wild-type strain (black circles). VDPs were determined as the slope of the linear fit to the double-logarithmic
dependence of concentration on cell volume (fit through nACT1prom
ACT1 ¼36, nACT1prom
mCitrine ¼36, nHTB1prom
ACT1 ¼18, nHTB1prom
HTB1 ¼17, nHTB1prom
mCitrine ¼18, nHTB2prom
ACT1 ¼27,
nHTB2prom
HTB2 ¼27, nHTB2prom
mCitrine ¼27, nHHF1prom
ACT1 ¼27, nHHF1prom
HHF1 ¼27, nHHF1prom
mCitrine ¼27 biological replicates), with error bars indicating the standard error of the
VDPs. Significant VDP deviation between two genes was tested using linear regressions; *pHTB1prom
ACT1 ¼1:0102,*pHTB1prom
HTB1 ¼4:6102.cSummary of VDPs
determined with flow cytometry for different strains in haploid (filled circles) and diploid (open squares) cells. VDPs were determined as the slope of the linear
fit to the double-logarithmic dependence of concentration on cell volume (fit through nACT1prom
haploid ¼12, nHTB1prom
haploid ¼12, nHTB1prom
diploid ¼8, nHTB2prom
haploid ¼12,
nHTB2prom
diploid ¼8, nHHF1prom
haploid ¼12, nHHF1prom
diploid ¼8 biological replicates), with error bars indicating the standard error of the VDPs. d,emCitrine concentration, driven
by an additional copy of the ACT1 (d)orHTB1 (e) promoter in haploid (filled circles) and diploid (open squares) cells, shown as a function of cell volume in a
double-logarithmic plot. Lines show linear fits to the double-logarithmic data with volume-dependence parameters (VDPs) determined as the slope of the fit,
with respective standard error. fConcentration of mCitrine, estimated from the linear fit to the double-logarithmic dependence of concentration on cell volume,
in diploid cells compared to the concentration in haploid cells at 60 fL. Error bars indicate the 2.5- and 97.5-percentiles around the median concentration ratio,
determined from 10,000 bootstrap samples.
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haploids (Fig. 3d, f). In contrast, for each of the three histone
promoters tested, we observe that the concentration in diploids is
considerably higher than 50% of that in haploids of comparable
volume, with a ratio close to 1 for the HTB1 promoter (Fig. 3e, f,
Supplementary Fig. 6). This demonstrates that in addition to
setting the cell-volume-dependent decrease in concentration,
regulation by the histone promoters also largely accounts for the
fact that histones are needed in proportion to ploidy.
Transcript concentrations during S-phase account for cell-
volume-dependence of histone promoters. So far, we have
shown that for mCitrine transcripts expressed from histone pro-
moters, average concentrations in asynchronous populations
decrease with average cell volume. It is well established that his-
tone synthesis is temporally linked to DNA replication, restricting
histone expression to late G1 and S-phase. While also protein and
mRNA degradation contribute to this cell-cycle-dependence,
regulation mediated by the promoter is sufficient for increased
expression in S-phase14,35,36. Indeed, using live-cell microscopy
we confirmed that all our histone promoter constructs exhibit a
peak of mCitrine synthesis after bud emergence, roughly corre-
sponding to S-phase (Fig. 4a–c). Thus, two possible scenarios
could account for the decreased mCitrine transcript concentration
in large compared to smaller cells. One explanation is that during
the period in which mCitrine is expressed, the transcript con-
centration decreases in larger cells. However, another explanation
would be that the mCitrine transcript concentration during S-
phase is constant independent of cell volume, but S-phase dura-
tion decreases in large cells. In an asynchronous cell population,
this would then lead to a decrease in the fraction of cells that
express mCitrine, which in turn would result in a cell-volume-
dependent decrease of the average transcript concentration.
In the live-cell microscopy experiments described above, we
did not observe a relevant decrease in the mCitrine production
period with cell volume (Supplementary Fig. 7a–c). To gain
further insight into the potential contribution of a cell-volume-
dependent cell-cycle-dependence, we performed single-molecule
fluorescence in situ hybridization (smFISH), which allows us to
count mCitrine mRNA molecules in single cells. At the same
time, we can use bright-field microscopy to estimate cell volume
and –together with a DAPI-stain to visualize the nucleus—
classify cells as being in G1, S, or G2/M phase (Fig. 4d).
Consistent with our live-cell microscopy result, we again observe
an increased expression during S-phase for all histone promoters
analyzed (Fig. 4e–g, Supplementary Fig. 7d–f). In contrast, the
ACT1 promoter results in more uniform expression throughout
the cell cycle (Fig. 4h, Supplementary Fig. 7g). To test whether
expression during S-phase accounts for the overall cell-volume-
dependence, we analyzed the concentration of transcripts in S-
phase as a function of cell volume. Strikingly, we find that all
three histone promoters (HTB1,HTB2, and HHF1 promoters)
but not the ACT1 promoter lead to a significant decrease of
transcript concentration with cell volume (Fig. 4i–l).
Different cell-volume and ploidy dependences can be explained
by competition of promoters for limiting transcriptional
machinery. Taken together, our results suggest that while most
genes are transcribed at a rate proportional to cell volume to
maintain constant concentrations, transcripts controlled by his-
tone promoters are instead transcribed at a cell-volume-
independent rate to couple histone production to DNA content.
To better understand how the transcription rate of one specific
promoter depends on cell volume and ploidy context, we revisited
a minimal model for transcription that was in essence proposed
by Heldt et al.37 (Fig. 5a). We further simplified the model by not
explicitly modeling the dynamics of cell growth. Instead, we
assume that transcription can be described by a single (limiting)
component of the transcriptional machinery, TM, whose amount
increases in proportion to cell volume. In other words, its con-
centration cTM stays constant. We then considered two classes of
promoters, a specific promoter of interest, p, present as a single
copy, and a general pool of promoters, g, which are present as
nh¼6000 in haploids or nd¼12000 copies in diploids. Each
promoter is competing for the transcriptional machinery, and is
modeled as a single binding site for TM. Initiation, i.e., binding of
the machinery, occurs at a rate kp
on or kg
on, respectively. Further-
more, we assume that all other steps of transcription can be
summarized in a single rate-limiting step, occurring at a rate kp
off
or kg
off , respectively. Each transcript is then degraded with the
same rate kdeg ¼1. Depending on the parameters chosen for the
specific promoter, the model predicts qualitatively different
dependences of transcript concentration on cell-volume and
ploidy (Fig. 5b–e)37.
For example, at a given kp
off , a high on-rate kp
on (kp
on kg
on) can
result in histone-promoter-like behavior, i.e., cell volume-
dependent but ploidy-independent transcript concentration. This
can be understood considering that due to the higher on-rate
compared to the general pool of promoters, the promoter of
interest is already saturated with transcriptional machinery at
very small cell volumes. A further increase of the available
machinery at larger cell volumes does therefore not result in a
higher occupancy with transcriptional machinery, leading to a
constant, cell-volume-independent transcription rate. Thus, the
transcript concentration obtained from a single promoter of
interest is independent of ploidy but decreases with cell volume.
A homozygous diploid carrying two of the promoters of interest
will therefore show a twofold higher concentration than a haploid
with the same volume. In contrast, at lower kp
on (kp
on kg
on)we
observe actin-promoter-like behavior, i.e., cell volume-
independent but ploidy-dependent transcript concentration. In
this regime, both the specific and the general promoters compete
equally for the transcriptional machinery. As long as not all
promoters are saturated, transcription rate therefore increases
with the amount of available transcriptional machinery, and thus
cell volume. In diploid cells, the single promoter of interest
competes with twice the number of general promoters, leading to
a roughly twofold reduction of the transcription rate compared to
haploids with equal cell volume.
Interestingly, at very low kp
on (kp
on kg
on) we observe a third
type of behavior, in which transcript concentration increases with
cell volume. Because the affinity of the promoter of interest to the
transcriptional machinery is very low compared to that of the
general promoters, the occupancy is very low as long as the
general promoters are not saturated. Only once the general
promoters approach saturation, machinery becomes available for
the promoter of interest, then leading to a nonlinear increase of
transcription rate with cell volume.
Histone-promoter truncations lead to a switch from histone-
like to actin-like behavior. One key prediction of this model is
that if all other parameters are fixed, reducing kp
on for a histone-like
promoter should eventually shift its behavior to that of an actin-
like promoter (Fig. 5d). To experimentally test this prediction, we
aimed to decrease the initiation rate kp
on of the HHF1 and HTB1
promoters by creating series of haploid and diploid strains with
increasingly shorter fragments of the promoters, each truncated
from the 5′-end (Fig. 6a). Again, we used flow cytometry to analyze
mCitrine expression driven by these additional, endogenously
integrated promoter fragments. For both promoters we observe a
decrease of mCitrine expression once part of the known upstream
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activating sequences (UASs)35 are truncated (Fig. 6b, Supple-
mentary Fig. 8a). Fully consistent with the model, for both pro-
moters, and for haploids and diploids, this drop in expression
coincides with a change of the VDP toward 0 (Fig. 6b, c, Sup-
plementary Fig. 8b, c). At the same time and also consistent with
the model, the ratio of the mCitrine concentration at a given
volume in diploid compared to haploid cells decreases from close
to 1 toward 0:5 (Fig. 6c). Thus, our analysis shows that for both the
HHF1 and HTB1 promoter truncation series, a transition from
histone-like to actin-like behavior occurs between the 450 and 300
bp truncations.
While we consistently observe the same qualitative trend in
flow cytometry measurements, we found that the exact VDP
measured with flow cytometry depended on the flow cytometry
settings, which need to be adjusted depending on the observed
cell-volume range. Thus, to quantitatively confirm our results, we
repeated the experiment for the 450 and 300 bp truncations of the
HTB1 and HHF1 promoters using RT-qPCR. Again, we observe a
change in the VDP toward 0, and a decrease of the ratio of the
mCitrine concentration between diploid and haploid cells from
close to 1 to close to 0.5 (Fig. 6d).
To test that this switch in behavior is not due to a disruption of
the cell-cycle-dependence, we analyzed mCitrine expression from
the histone-promoter truncations with live-cell microscopy. As
expected, total mCitrine intensity strongly decreases in the 300 bp
compared to the 450 bp truncations. However, while the peak of
mCitrine synthesis seems to be delayed for the 300 bp truncations
of the HTB1 and HHF1 promoters, they both still show a clear
-50 0 50 100
Time relative to bud emergence [min]
150
mCitrine synthesis
[arb. units / 3 min]
800
200
0
400
600
-200
1000
-50 0 50 100
Time relative to bud emergence [min]
150
mCitrine synthesis
[arb. units / 3 min]
200
-200
0
-100
100
300
400
500
-50 0 50 100
Time relative to bud emergence [min]
150
mCitrine synthesis
[arb. units / 3 min]
-200
200
600
1000
1800
1400
bca
d
*** ***
G1 S G2M
Cell cycle stage
mCitrine mRNA
concentration [1 / fL]
0.8
0.4
0.0
1.2
1.6
2.0
e
HTB1prom-mCitrine
*** ***
G1 S G2M
Cell cycle stage
mCitrine mRNA
concentration [1 / fL]
f
HTB2prom-mCitrine
***
G1 S G2M
Cell cycle stage
mCitrine mRNA
concentration [1 / fL]
g
HHF1prom-mCitrine
h
***
G1 S G2M
Cell cycle stage
mCitrine mRNA
concentration [1 / fL]
ACT1prom-mCitrine
klj
S-phase (n = 69)
VDP = -0.88 ± 0.13
Cell volume [fL]
16 32 64 128
1.0
2.0
0.25
0.5
0.125
mCitrine mRNA
concentration [1 / fL]
S-phase (n = 49)
VDP = -0.44 ± 0.13
Cell volume [fL]
16 32 64 128
1.0
2.0
0.25
0.5
mCitrine mRNA
concentration [1 / fL]
S-phase (n = 48)
VDP = -0.57 ± 0.18
Cell volume [fL]
16 32 64 128
1.0
2.0
0.25
0.5
0.125
mCitrine mRNA
concentration [1 / fL]
HTB1prom-mCitrine HTB2prom-mCitrine HHF1prom-mCitrine
i
Cell volume [fL]
16 32 64 128
1.0
2.0
0.25
0.5
0.125
mCitrine mRNA
concentration [1 / fL]
S-phase (n = 48)
VDP = 0.20 ± 0.29
ACT1prom-mCitrine
n = 58
HTB1prom-mCitrine
n = 28
HTB2prom-mCitrine
n = 121
HHF1prom-mCitrine
0.8
0.4
0.0
1.2
1.6
2.0
0.8
0.4
0.0
1.2
1.6
2.0
0.8
0.4
0.0
1.2
1.6
2.0
5μm
Bright-field DAPI
Promoter
mCitrine
ADH1term
+
2N
1 copy of
mCitrine
mRNA
dye-labeled
probes
Quasar®-670
5μm5μm
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Fig. 4 Cell-cycle-dependence does not account for the cell-volume-dependence of expression from histone promoters. a–cmCitrine synthesis rate
measured by live-cell fluorescence microscopy during the first cell cycle of new-born diploid cells, when expressed from an additional HTB1 (a), HTB2 (b),
or HHF1 (c) promoter. Traces represent the mean of the moving averages over three frames of the single cell traces and are shown for the time span during
which at least ten single cell traces were included in the average. All traces are aligned at the time of first bud emergence (t=0). dIllustration of the
smFISH experiments. Quasar®-670-labeled probes were used to count mCitrine mRNA spots in diploid cells carrying an additional promoter driving mCitrine
expression. DAPI-staining of nuclear DNA and bright-field microscopy were used to classify cells as G1, S, or G2/M phase and to estimate cell volumes.
Multiple images were taken per condition and at least two independent biological replicates were measured on different days. Example images show
maximum intensity z-projections of diploid cells carrying an additional HTB1 promoter; contrast was adjusted for visualization. e–hmCitrine mRNA
concentration in G1-, S-, or G2/M-phases, estimated as the number of mRNA spots detected with smFISH in the whole cell including the bud and divided
by the cell volume, for diploid cells expressing mCitrine from an additional HTB1 (e), HTB2 (f), HHF1 (g), or ACT1 (h) promoter. Colored boxes highlight the
25- and 75-percentiles, whiskers extend to ±2:7σof the distributions and colored crosses highlight outliers. Black, horizontal lines indicate the median
between single cells for nG1 ¼158;nS¼69;nG2M ¼57 (e), nG1 ¼77;nS¼49;nG2M ¼25 (f), nG1 ¼113;nS¼48;nG2M ¼21 (g), and nG1 ¼
151;nS¼48;nG2M ¼38 (h), with notches indicating the 95% confidence interval. Significances were tested using a two-tailed, two-sample t test at a
confidence level α=0.05, where applicable (between G1 and S-phase cells for (f), between all populations for (g,h)), or a Kruskal–Wallis test at a
confidence level α¼0:05 otherwise; ***pG1vs S ¼1:01011;***pS vs G2M ¼1:2109(e), ***pG1vs S ¼8:01021 ;***pS vs G2M ¼6:5107(f),
***pG1vs S ¼3:51016;***pS vs G2M ¼5:4104(g). A diploid strain carrying no mCitrine allele was used as a control to test that smFISH signal is specific
(Supplementary Fig. 7h). i–lmCitrine mRNA concentration in S-phase cells, expressed from an additional ACT1 (i)orHTB1 (j), HTB2 (k), or HHF1 (l)
promoter, shown as a function of cell volume in a double-logarithmic plot. Solid lines show linear fits to the double-logarithmic data, dashed lines represent
the 95% confidence intervals of the fit. Volume-dependence parameters (VDPs) were determined as the slope of the fit, with respective standard error.
limiting component ~ volume
general pool
of promoters g
n
haploid
= 6000, n
diploid
= 12000
k
on
g
k
off
g
k
on
g
k
off
g
k
on
g
k
off
g
k
on
p
k
off
p
single promoter
of interest p
0.5 1.0 1.5 2.0 2.5 3.0
Cell volume
0
1
2
3
4
5
6
7
Concentration
k
on
p
k
on
= 20, histone-like
p
k
on
= 0.05, third behavior
p
k
on
= 1, actin-like
p
0.5 1.0 1.5 2.0 2.5 3.0
Cell volume
0.0
0.2
0.4
0.6
0.8
1.0
c
Diploid
/c
Haploid
k
on
p
c
Diploid
/c
Haploid
at V
0
VDP
0.5
0.6
0.7
0.8
0.9
1.0
0.4
-0.5
0.0
0.5
1.0
1.5
-1.0
Log( )
k
on
p
-2.0 -1.0 0.0 2.01.0
a
cb
d
c
Diploid
/c
Haploid
at V
0
VDP
0.5
0.6
0.7
0.8
0.9
1.0
0.4
-0.5
0.0
0.5
1.0
1.5
-1.0
Log( )
k
off
p
-1.0 0.0 2.01.0
e
Fig. 5 Minimal model for the dependence of transcription rate of one specific promoter of interest on cell volume and ploidy. a The model includes two
classes of promoters: the general pool of promoters gand the specific promoter of interest pwith their respective initiation rates kp
on or kg
on, describing the
binding of the limiting machinery and off-rates kp
off or kg
off, summarizing all other steps of transcription. b–eThe model predicts that tuning kp
on or kp
off while
keeping all other parameters fixed (cTM ¼2000;kg
on ¼1;kg
off ¼kp
off ¼3;for tuning kp
on;or cTM ¼2000;kg
off ¼3;kg
on ¼kp
on ¼1;for tuning kp
off)
results in a qualitative change of the cell volume-dependence of transcript concentration obtained from the specific promoter (b), as well as a change in the
ratio between the concentration in diploid cells and the concentration in haploid cells (c). d,eModel prediction for the VDP (right, black) and for the ratio
between the concentration in diploid cells and the concentration in haploid cells at a characteristic volume V0¼1 (left, orange) as a function of kp
on (d)or
kp
off (e).
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peak of mCitrine synthesis after bud emergence (Fig. 7a, b). In
addition, we did not observe a dependence of the mCitrine
production period on cell volume for any of the HTB1 and HHF1
promoter truncations investigated (Supplementary Fig. 9a–d).
This suggests that even though the 300 bp truncations of the
HTB1 and HHF1 promoters have an effect on the level and exact
timing of mCitrine expression, its cell-cycle dependence remains
largely intact.
To further test that the switch in promoter behavior is caused
by a change in expression during S-phase rather than a change in
the cell-cycle-dependence, we performed smFISH to quantify
mCitrine transcripts expressed from the 450 and 300 bp HTB1
promoter truncations. Consistent with the live-cell microscopy
results, we find that both promoter truncations show a peak of
expression during S-phase (Fig. 7c, d, Supplementary Fig. 9e, f).
Moreover, we find that the transcript concentration in S-phase
cells significantly decreases with cell volume for the 450 bp—but
a
d
b
+
1N / 2N
1copy of
Full promoter mCitrine ADH1term
300bp mCitrine ADH1term
mCitrine ADH1term
450bp
k
on
?
p
promoter strength
c
450 bp
300 bp
HTB1prom-mCitrine
RT-qPCR
Haploid
Diploid
c
Diploid
/c
Haploid
at 60 fL
0.0
-1.0
-2.0
1.0
2.0
HHF1prom-mCitrine
450 bp
300 bp
3.0
VDP
Norm. concentration
at 60 fL
Full
Promoter
750 bp
600 bp
450 bp
300 bp
150 bp
0.5
1.0
0.0
-1.0
-0.5
-1.5
0.0
Flow Cytometry
1.5
HTB1prom-mCitrine
HHF1prom-mCitrine
VDP
Full
Promoter
450 bp
300 bp
Full
Promoter
450 bp
300 bp
c
Diploid
/c
Haploid
at 60 fL
Flow Cytometry
HTB1prom-mCitrine HHF1prom-mCitrine
0.0
-0.5
-1.0
0.0
0.5
1.0
Haploid
Diploid
VDP
Fig. 6 Reducing the strength of a histone promoter shifts its behavior from histone-like to actin-like. a Illustration of a series of haploid and diploid strains
carrying a single additional copy of increasingly shorter fragments of promoters driving mCitrine expression, each truncated from the 5′-end. bmCitrine
concentration at 60 fL normalized on maximum concentration of the respective promoter (upper panel) and VDP of mCitrine (bottom panel) determined by
flow cytometry for the respective promoter truncations of the HTB1 promoter (dark blue circles) and the HHF1 promoter (light blue squares) driving mCitrine
expression, integrated in haploid cells. Concentrations were estimated from a linear fit to the double logarithmic dependence of concentration on cell volume,
VDPs were determined as the slope of the linear fit(fit through nHTB1prom
full ¼12, nHTB1prom
750bp ¼15, nHTB1prom
600bp ¼15, nHTB1prom
450bp ¼15, nHTB1prom
300bp ¼15, and
nHHF1prom
full ¼12, nHHF1prom
600bp ¼15, nHHF1prom
450bp ¼15, nHHF1prom
300bp ¼15, nHHF1prom
150bp ¼15 biological replicates). Error bars in the upper panel are derived by error
propagation of the 95% confidence interval of the linear fit at 60 fL. In the bottom panel, error bars show the standard error of the VDPs. cVDP of mCitrine in
haploid (blue filled circles) and diploid (green open squares) cells (upper panel) and mCitrine concentration at 60 fL in diploids compared to the concentration
in haploids (bottom panel) determined by flow cytometry. Left shows results for the HTB1 promoter truncations, right shows results for the HHF1 promoter
truncations. Concentrations were estimated from a linear fit to the double logarithmic dependence of concentration on cell volume, VDPs were determined as
the slope of the linear fit(fitthrough nHTB1prom
full;haploid ¼27, nHTB1prom
full;diploid ¼18, nHTB1prom
450bp;haploid ¼27, nHTB1prom
450bp;diploid ¼27, nHTB1prom
300bp;haploid ¼27, nHTB1prom
300bp;diploid ¼27, and
nHHF1prom
full;haploid ¼27, nHHF1prom
full;diploid ¼18, nHHF1prom
450bp;haploid ¼27, nHHF1prom
450bp;diploid ¼18, nHHF1prom
300bp;haploid ¼27, nHHF1prom
300bp;diploid ¼17 biological replicates). Error bars in the upper
panels show the standard error of the VDPs. In the bottom panel, error bars indicate the 2.5- and 97.5-percentiles around the median concentration ratio,
determined from 10,000 bootstrap samples. dVDP of mCitrine in haploid (blue filled circles) and diploid (green open squares) cells (upper panel) and mCitrine
mRNA concentration at 60 fL in diploids compared to the concentration in haploids (bottom panel) determined by RT-qPCR for HTB1 and HHF1 promoter
truncations driving mCitrine expression. Concentrations were estimated from a linear fit to the double logarithmic dependence of concentration on cell
volume, VDPs were determined as the slope of the linear fit(fit through nHTB1prom
450bp;haploid ¼15, nHTB1prom
450bp;diploid ¼14, nHTB1prom
300bp;haploid ¼16, nHTB1prom
300bp;diploid ¼12, and,
nHHF1prom
450bp;haploid ¼12, nHHF1prom
450bp;diploid ¼12, nHHF1prom
300bp;haploid ¼11, nHHF1prom
300bp;diploid ¼12 biological replicates). Error bars in the upper panel show the standard error. Error
bars in the bottom panel indicate the 2.5- and 97.5-percentiles around the median concentration ratio, determined from 10,000 bootstrap samples.
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not for the 300 bp—promoter truncation (Fig. 7e, f). In summary,
while we cannot fully exclude that differences in the cell-cycle
dependences might contribute to the switch in behavior of the
transcript concentrations, this would appear unlikely to fully
account for the observed change.
Transcriptional feedback might contribute to the cell-volume-
dependent regulation by the HTB1 promoter. In summary, our
analysis of the histone-promoter truncations suggests that
decreasing promoter strength can shift the volume- and ploidy-
dependence of the histone promoters to an actin-like behavior, as
predicted by a minimal model. Consistent with such a picture,
both the HTB1 and the HHF1 promoters include well-
characterized UASs partially located in the 150 bp sections that
are lost between the 450 bp and 300 bp truncations (Supple-
mentary Fig. 8a). These UAS elements act as binding sites for the
transcription factor Spt10, which activates histone transcription
during S-phase36. It is therefore plausible that the partial loss of
the UAS elements causes the reduction in promoter strength
observed for the 300 bp truncations, which in the model is
described as a reduced initiation rate. However, in the case of the
-50 0 50 100
Time relative to bud emergence [min]
150
normalized mCitrine
synthesis [arb. units / 3 min]
Full, n = 121
450bp, n = 55
300bp, n = 25
HHF1prom-mCitrine
-50 0 50 100
Time relative to bud emergence [min]
150
HTB1prom-mCitrine
Full, n = 58
450bp, n = 161
300bp, n = 27
*** ***
G1 S G2M
Cell cycle stage
mCitrine mRNA
concentration [1 / fL]
1.2
0.8
1.6
2.0
0.4
0.0
*** ***
G1 S G2M
Cell cycle stage
mCitrine mRNA
concentration [1 / fL]
b
c
a
ef
d
450bp HTB1prom-mCitrine 300bp HTB1prom-mCitrine
S-phase (n = 54)
VDP = -0.69 ± 0.14
Cell volume [fL]
16 32 64 128
1.0
2.0
0.25
0.5
0.125
450bp HTB1prom-mCitrine
0.0625
mCitrine mRNA
concentration [1 / fL]
0.015625
S-phase (n = 52)
VDP = -0.43 ± 0.37
Cell volume [fL]
16 32 64 128
mCitrine mRNA
concentration [1 / fL]
300bp HTB1prom-mCitrine
1.0
2.0
0.25
0.5
0.125
0.0625
0.015625
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
normalized mCitrine
synthesis [arb. units / 3 min]
1.2
0.8
1.6
2.0
0.4
0.0
Fig. 7 Change in behavior of truncated histone promoters is not due to a disruption of the cell-cycle-dependence. a,bmCitrine synthesis rate measured
by live-cell fluorescence microscopy during the first cell cycle of new-born diploid cells, when expressed from HHF1 (a)orHTB1 (c) promoter truncations.
Traces represent the mean of the moving averages over three frames of the single cell traces and are shown for the time span during which at least ten
single cell traces were included in the average. All traces are aligned at the time of first bud emergence (t=0) and normalized to the maximum mean value
of mCitrine synthesis for the full promoter. c,dmCitrine mRNA concentration in G1-, S-, or G2/M-phases, estimated as the number of mRNA spots detected
with smFISH in the whole cell including the bud and divided by the cell volume, measured for diploid cells expressing mCitrine from an additional 450 bp (c)
or 300 bp (d)HTB1 promoter truncation. Colored boxes highlight the 25- and 75-percentiles, whiskers extend to ±2:7σof the distributions and colored
crosses highlight outliers. Black, horizontal lines indicate the median between single cells for nG1 ¼160;nS¼54;nG2M ¼66 (c) and nG1 ¼131;nS¼
52;nG2M ¼55 (d), with notches indicating the 95% confidence interval. Significances were tested using a Kruskal–Wallis test at a confidence level
α¼0:05; ***pG1vs S ¼2:51014, ***pS vs G2M ¼2:91012 (c), ***pG1vs S ¼5:21013 , ***pS vs G2M ¼1:6108(d). e,fmCitrine mRNA concentration
in S-phase cells, expressed from an additional 450 bp (e) or 300 bp (f)HTB1 promoter truncation, shown as a function of cell volume in a double-
logarithmic plot. Solid lines show linear fits to the double-logarithmic data, dashed lines represent the 95% confidence intervals of the fit. Volume-
dependence parameters (VDPs) were determined as the slope of the fit, with respective standard error.
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HTB1 promoter, the section lost for the 300 bp truncation also
includes the NEG element38,39, which is necessary for HIR-
dependent negative feedback40 (Supplementary Fig. 8a). While
our smFISH and live-cell microscopy results (Fig. 7e, f, Supple-
mentary Fig. 9c, d) suggest that the cell-volume-dependence of
HTB1 promoter driving transcription is not due to a change in
the S-phase duration, transcriptional feedback sensing the
amount of histone protein could in principle still account for the
cell-volume-dependence if it acts uniformly throughout S-phase
while DNA is replicated. In this case, loss of the NEG element
provides an alternative explanation for the change to actin-like
behavior observed for the truncated HTB1 promoter.
To examine a potential role of NEG-mediated feedback, we
deleted HIR1 in the strain carrying the additional HTB1
promoter. Similar to the effect on the endogenous HTB1 (Fig. 2f),
we found that deleting HIR1 results in a significantly weaker
decrease of mCitrine transcript concentration with cell volume
(Supplementary Fig. 10a, b). Surprisingly, even though the HTB2
promoter does not include an NEG element and is therefore not
thought to be subject to HIR-dependent regulation, we also
observed a similar effect on the cell-volume-dependence of
mCitrine expression when we repeated the experiment in the
strain carrying the additional HTB2 promoter (Supplementary
Fig. 10c, d). Thus, while transcriptional feedback regulation might
contribute to the cell-volume-dependence mediated by the HTB1
promoter, we cannot exclude that the observed weaker cell-
volume-dependence is due to an indirect effect of deleting HIR1.
By deleting HTB2 in the haploid strain that carries the
additional HTB1 promoter driving expression of mCitrine,we
then further tested whether the HTB1 promoter exhibits
transcription-based dosage compensation. As before (Supple-
mentary Fig. 5b), we observe a significant overexpression of
endogenous HTB1 upon deletion of HTB2 (Fig. 8a). However, we
do not observe a significant increase of mCitrine transcript
concentration (Fig. 8b), which was surprising given that an HTB1
promoter reporter construct containing the Htb1 N-terminus was
reported to show dosage compensation upon deletion of the full
HTA2-HTB2 locus21. Taken together, our results indicate a
contribution of HIR1-dependent regulation on the cell-volume-
dependence of histone expression, but suggest that even in the
case of the HTB1 promoter, the observed decrease of transcript
concentration with cell volume is not fully due to feedback
regulation on the transcript level.
Discussion
Taken together, we identified a mechanism that allows cells to
deal with a fundamental challenge—how to quantitatively couple
histone production to DNA content even though total biosyn-
thetic capacity is linked to cell volume instead (Fig. 8c). We found
that this coordination is already achieved at the transcript level.
This finding was recently also confirmed by independent RNA-
seq analysis of differently sized cell populations obtained by a
combination of centrifugal elutriation with a synchronous release
from a G1 arrest41. While mRNA degradation and feedback
mechanisms contribute to histone homeostasis, our results sug-
gest that competition for potentially limiting transcriptional
machinery can be sufficient to achieve differential regulation of
histone and other transcript concentrations with cell volume and
ploidy. Specifically, if transcription is limited by the availability of
limiting machinery, larger cells with more machinery will pro-
duce proportionally more mRNA, maintaining constant tran-
script concentrations. Since each gene will compete for the
limiting machinery, transcription rate per gene decreases in
inverse proportion with ploidy. Since the number of gene copies
increases with ploidy, the total transcription rate is then
independent of ploidy at a given cell volume. If transcription is
instead limited by the gene itself, transcript concentrations will
decrease with cell volume but will be proportional to ploidy
because each individual gene copy will exhibit a transcription rate
independent of ploidy context.
It was recently proposed that mRNA degradation in budding
yeast is modulated dependent on cell volume25. While mRNA
degradation is well known to contribute to histone homeostasis, it
seems unlikely that degradation is responsible for the cell-volume
dependence we observe for histone promoters expressing mCi-
trine. This is because we observe different cell-volume depen-
dences for the 300 bp promoter truncations compared to the full
promoters of HTB1 and HHF1. This excludes the possibility that
degradation controlled by the 5′-untranslated regions of the
histone genes, which is included in all promoter truncation
constructs we studied, is responsible for the histone-specific
coupling of transcriptional output to DNA content rather than
cell volume. However, we cannot fully exclude the possibility that
the untranscribed part of the promoter indirectly controls mRNA
degradation through an “imprinting”mechanism42.
Our work identifies a general mechanism that can be sufficient
to couple histone amounts to DNA content. However, it also
suggests that the exact regulation varies between the individual
histone genes. Specifically, our results indicate that for the HTB1
promoter, feedback regulation at the transcriptional level might
contribute to the coupling of histone homeostasis to DNA con-
tent. Future work will therefore be needed to disentangle the
specific contributions to the regulation of each individual
histone gene.
In addition to histones, other proteins will require differential
regulation with cell volume. For example, the G1/S inhibitors
Whi5 in yeast18 and Rb in mammalian cells43 have recently been
shown to decrease in concentration with cell volume, enabling
cells to sense and control their size. Along those lines, a recent
study suggested that many cell-cycle regulators show differential
transcriptional regulation with cell volume44. The simplicity of
template-limited transcription therefore suggests that it may be
broadly employed across species to differentially regulate the
concentrations of larger subsets of proteins, in particular to
couple the amount of DNA-binding proteins to DNA content.
Moreover, in addition to the ideal template- or machinery-limited
regimes, cells can achieve a large variety of cell volume- and
ploidy dependences, which importantly can be decoupled from
the expression level of a given gene by independently tuning its
initiation and elongation rates. Specific regulation of mRNA and
protein degradation provides yet another level of control that cells
can employ to tune the dependence of protein concentrations on
cell volume and ploidy. In fact, our observation that the cell-
volume-dependence of histone transcripts is even stronger in rrp6
deletion cells cannot be explained by our simple model and
suggests that such additional regulation contributes to cell-
volume-dependent histone homeostasis in budding yeast. To
quantitatively understand the cell volume- and ploidy-
dependence of protein homeostasis on a genome wide level, it
will therefore be crucial to identify the rate-limiting steps of
transcription and mRNA degradation as well as the corre-
sponding rate-limiting molecules.
Methods
Yeast strains. All yeast strains used in this work are based on W303 and were
constructed using standard methods. Full genotypes of all strains are listed in
Supplementary Table 1.
Inducible-Whi5 strain. In order to increase the range of observable cell volumes,
we used strains with β-estradiol-inducible WHI5, similarly described in previous
works18,45. For this purpose, we deleted the endogenous alleles of the G1/S inhi-
bitor WHI5 and integrated one copy of WHI5 expressed from an artificial,
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β-estradiol-inducible promoter system19. Specifically, this inducible promoter
system consists of a β-estradiol-dependent, artificial transcription factor, which can
bind an artificial promoter. This promoter is then used to induce WHI5 expression.
To ensure that β-estradiol addition itself has no effect on cell growth, we grew
cell cultures of a non-inducible WHI5 haploid strain and cell cultures of a whi5Δ
haploid strain, containing the β-estradiol-dependent, artificial transcription factor,
but no copy of WHI5. We then added β-estradiol to those cultures and quantified
the mean cell volumes after 24 h of growth in the presence of β-estradiol, by
measuring the cell volume distributions using a Coulter counter (Beckman Coulter,
Z2 Particle Counter). Finally, we compared the mean cell volumes to the mean cell
volumes obtained from cell populations without β-estradiol addition
(Supplementary Fig. 3a). In addition, we performed reverse-transcription-qPCR
(RT-qPCR) on cell populations with and without β-estradiol addition and
compared the obtained mean values for several genes (Supplementary Fig. 3b, c).
For the non-inducible WHI5 haploid strain, we could not identify a significant
deviation of the population means between the cell populations with and without
β-estradiol addition. For the whi5Δhaploid strain, containing only the β-estradiol-
dependent, artificial transcription factor, we observed a slight but significant
reduction of the relative mean mRNA concentrations of HTA2, HHF2 and HHO1
at 30 nM compared to 0 nM β-estradiol, which was consistent with a slightly
increased mean cell volumes at 30 nM β-estradiol. In contrast, performing the same
experimental procedure on cell cultures of an inducible WHI5 haploid strain leads
to much stronger changes of mean cell volumes and relative mean mRNA
concentrations for all histone genes, demonstrating that the observed decrease of
histone mRNA concentrations is specific to the Whi5-dependent cell volume
increase (Supplementary Fig. 3a, d).
Live-cell fluorescence microscopy. Cultures (3 mL) were grown at 30 °C in
synthetic complete media containing 2% glycerol and 1% ethanol (SCGE) for at
least 6 h in a shaking incubator at 250 rpm (Infors, Ecotron). Appropriate β-
estradiol concentrations were then added to inducible cells (0 nM and 30 nM for
haploids or 50 nM for diploids) and the cultures were grown for at least 24 h to
ensure steady-state conditions. Optical densities were measured using a spectro-
photometer (Perkin Elmer, Lambda Bio+) and OD600 <1:0 was maintained
through appropriate dilutions during culture growth. For imaging, 1 mL of cells
(OD600 <1:0) was spun down at 10k g-force for 1 min (Thermo Fisher Scientific,
Pico 17), resuspended in 200 µL SCGE and sonicated for 5 s (Bandelin electronics,
HD2070 and UW2070). 100 µL of this cell suspensio n was then introduced in a
Cellasic microfluidics Y04C (haploids and non-induced diploids) or Y04D
(induced diploids) plate. Note that no β-estradiol was used in the microfluidic
device during the microscopy experiments, resulting in a gradual decrease of cell
volume of induced cells after the start of the experiment.
Live-cell fluorescence microscopy experiments were performed on a Zeiss LSM
800 microscope (software installed: Zen 2.3, blue edition) with additional
b
c
a
HTB1 ***
WT htb2∆
Normalized relative
concentration
2.5
1.5
0.5
2.0
1.0
0.0 + HTB1prom-mCitrine
mCitrine
ns
WT htb2∆
Normalized relative
concentration
2.5
1.5
0.5
2.0
1.0
0.0
Cell volume
Ploidy
1N
(Haploid)
2N
(Diploid)
histone production
≈ constant
histone
production
increases
k
on
concentration
decreases
limiting component of
transcriptional machinery
~ volume
General promoter pool
Histone promoter
+ HTB1prom-mCitrine
Fig. 8 Histone promoters can couple gene expression to genome content. a,bRelative HTB1 (a) and mCitrine (b) mRNA concentrations (normalized on
RDN18) for a wild-type haploid strain carrying an additional HTB1 promoter driving mCitrine expression, and a htb2Δin the same background, measured by
RT-qPCR. Concentrations are normalized on the respective median concentration in the wild-type. Biological replicates are represented as colored data
points (circles, n=9), colored boxes highlight the 25- and 75-percentiles and whiskers extend to the minimum and the maximum of the distributions.
Black, horizontal lines indicate the median of the biological replicates, notches indicate the 95% confidence interval. Significances were tested using a two-
tailed, two-sample ttest at a confidence level α¼0:05; ***pHTB1 ¼3:5104.cIllustration of the mechanism identified in this study. Through template-
limited transcription, cells can quantitatively couple histone production to DNA content even though total biosynthetic capacity is linked to cell volume.
This results in a decrease of histone concentration with increasing cell volume and an increase with increasing ploidy.
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epifluorescence setup using a Cellasic microfluidics device to ensure constant
media (SCGE) flow in the microfluidics plate throughout the experiment.
Experiments ran for 12 h with images being taken every 3 min using an automated
stage (WSB Piezo Drive Can), a plan-apochromat 40×/1.3 oil immersion objective
and an axiocam 506 camera. Phase-contrast images were taken at an illumination
voltage of 4.5 V and an exposure time of 30ms. mCitrine images were taken using
the Colibri 511 LED module at 25% power and an exposure time of 10 ms. For each
condition, at least two independent biological replicates were measured on different
days. For experiments performed on cells with fluorescently tagged HTB1 and cells
carrying an additional promoter driving mCitrine expression, a microscope
maintenance service had to be performed between imaging of biological replicates,
which resulted in increased illumination intensities. Imaging parameters for the
mCitrine channel were adjusted to avoid photo toxicity: images were taken using
the Colibri 511 LED module at 5% power and an exposure time of 100 ms.
To correct for inaccuracies of the x–y-stage between time points, movies were
first aligned using a custom Fiji46 script. Then, cell segmentation and quantification
of the fluorescent signal as well as subtraction of background fluorescence and cell-
volume-dependent autofluorescence (determined from control strains not
expressing a fluorescent protein), and determination of time points of cell birth,
bud emergence, and cytokinesis were performed with MATLAB 2017b using
previously described methods17,18,47. For our analyses, we only included cells born
during the experiment. Total fluorescence intensity after background- and
autofluorescence correction was used as a proxy for total protein amount.
In order to determine total protein concentrations as total protein amounts
divided by cell volume, we calculated cell volumes based on phase-contrast images.
Briefly, after segmentation, cell areas were aligned along their major axis. We then
divided the cells into slices perpendicular to their major axis, each 1 pixel in width.
To estimate cell volume, we then assumed rotational symmetry of each slice around
its middle axis parallel to the cell’s major axis, and summed the volumes of each
slice to obtain total cell volume. This allowed us to analyze protein amounts and
protein concentrations as a function of cell volume.
Estimation of cell-cycle phases and histone production period using live-cell
microscopy. To test whether the decrease of histone concentrations with cell
volume could be explained by a decrease in the S-phase duration, and thus a
shorter time period during which histones are produced, we aimed to estimate the
duration of the histone production period (H-period; referred to as mCitrine
production period for strains in which a histone promoter is driving mCitrine
expression) from the mCitrine fluorescent intensity traces. For each single cell, we
first performed a constant linear fit in each of the two plateaus of the fluorescence
intensity, linked to G1- or G2/M-phase, respectively, and denoted them as P1and
P2.P1was obtained by performing the linear fit through the data points of the
fluorescent intensity trace from cell birth to first bud emergence, P2was obtained
by performing the linear fit through the last 30 min of the fluorescent intensity
trace. We then set a threshold of 5%, determined the last time point for which
ImCitrine <P
1þ0:05 P1, and defined this time point as the beginning of the H-
period. Similarly, we defined the first time point for which ImCitrine >P
20:05 
P2as the end of the H-period. Finally, the duration of the H-period was calculated
as the difference between those two time points. We defined G1-phase duration as
the time from cell birth to first bud emergence, and G2/M duration as the time
between the end of the H-period and cytokinesis. Cells for which this approach
failed where excluded from the cell-cycle phases analysis.
Normalization of single cell fluorescent intensity traces after microscope
maintenance service. In order to pool experimental data from two biological
replicates imaged before and after maintenance service, respectively (Figs. 1c, 4a–c,
7a, b, Supplementary Fig. 7a, b, Supplementary Fig. 9a–d), intensities of single cell
traces for the experiments taken before maintenance service were normalized to the
intensities of experiments performed after maintenance service. For this purpose,
the mean P1of all single cell traces before and after maintenance service was
calculated and a normalization factor a;determined as:
a¼PAfter
1;mean=PBefore
1;mean ð1Þ
Single cell traces before maintenance service were then multiplied with aand
those normalized single cell traces then pooled with single cell traces obtained after
maintenance service.
Estimation of mCitrine synthesis peak using live-cell microscopy. To char-
acterize the cell-cycle-dependence of transcription from full and truncated histone
promoters, we estimated the mCitrine synthesis rates from the mCitrine fluores-
cence intensity traces. For this purpose, we calculated the difference in mCitrine
intensity between frame xnþ1and frame xn, for each frame of the single cell traces,
which corresponds to the mCitrine synthesis as a function of time. To remove
measurement noise, we then calculated moving averages over three frames for the
mCitrine synthesis curves. Finally, we calculated the mean of those smoothed single
cell curves and show the mean for the time span during which at least ten single
cell traces were included in the average.
RNA extraction and RT-qPCR. Cultures (25 mL) were grown at 30 °C in yeast
peptone media containing 2% glucose (YPD) for at least 6 h in a shaking incubator
at 250 rpm, before being washed and transferred to SCGE. The cultures were grown
for at least 16 h before appropriate β-estradiol concentrations were added to
inducible cells (0, 10, and 30 nM). The cultures (final volume of 50 mL) were then
grown for at least 24 h in order to ensure steady-state conditions. During culture
growth, OD600 <1:0 was maintained through appropriate dilutions. Cell-volume
distributions of the cultures were measured with a Coulter counter after sonication
for 5 s.
Remaining cell cultures were spun down at 4000 rpm for 5 min and the cell
pellet resuspended in 50 µL nuclease-free water (Qiagen). Total RNA was extracted
using a hot acidic phenol (Sigma-Aldrich) and chloroform (Thermo Fisher
Scientific) extraction method adapted from an established protocol48. Yield of RNA
was increased by precipitation in 100% ethanol (Merck Millipore) at −20 °C
overnight, followed by a second precipitation in 100% ethanol at −80 °C for 2–4h.
As a quality check for total RNA extraction, agarose gel electrophoresis (1% agarose
gel, run 30 min at 100 V) was performed to check for the presence of the 25, 18,
and 5.8 s ribosomal RNA bands. Concentration and purity of the RNA samples
were measured with a spectrophotometer (Thermo Fisher Scientific, NanoDrop
2000) at 260 nm and 280 nm. cDNA was then obtained from 800 ng total RNA in a
PCR cycler (Applied Biosystems, ProFlex PCR system 3 × 32-well) using random
primers and a high-capacity cDNA reverse-transcription kit following the included
protocol (Thermo Fisher Scientific).
Quantitative PCR (qPCR) measurements were carried out on a LightCycler 480
Multiwell Plate 96 (Roche) using a DNA-binding fluorescent dye (BioRad,
SsoAdvanced Universal SYBR Green Supermix) and mRNA sequence specific
primers (Sigm a-Aldrich). The qPCR was performed with 2 µL of a 1:10 dilution of
the cDNA for the genes ACT1,HHO1,HTB2 and mCitrine, or a 1:100 dilution for
all other genes. Melting curve data were analyzed to verify primer specificity. Each
sample was measured in technical duplicates and the mean value CGene
Pwas used
for further analyses if σCGene
P<0:5. Relative concentrations, normalized on the
reference gene RDN18 were calculated using the equation:
log2relative concentration
ðÞ
¼CGene
PCRDN18
P

ð2Þ
In order to analyze relative concentrations as a function of cell volume, the
mean cell volumes were determined from the measured cell volume distributions.
For each condition measured, the RT-qPCR experiments were performed at least
three times on different days.
Test for qPCR primer specificity. To test the specificity of the qPCR primer used
to quantify histone mRNA concentrations, we analyzed deletion strains, where
possible, for their respective deleted gene to check for unspecific primer binding.
For example, we performed a qPCR measurement with the HHO1 primers on a
hho1Δstrain and compared the obtained CPvalues with the CPvalues obtained in
the reference strain MS63-1 (Supplementary Table 1). We constructed deletion
strains for the genes HHO1,HTB2,HHF1,HHF2,HHT1, and HHT2, for which we
obtained viable colonies without dramatic growth defects. RNA was extracted as
described above, and 1 µg of total RNA was reverse-transcribed using the above
mentioned high-capacity cDNA synthesis kit. The qPCR was performed with 2 µL
of a 1:10 dilution of each cDNA sample, and measured in 3 or 6 technical repli-
cates. CPvalues and melting curve data were analyzed to verify primer specificity.
Results are shown in Supplementary Table 2, deletion strains used are listed in
Supplementary Table 1, a list of all qPCR primers used can be found in Supple-
mentary Table 3.
Flow cytometry. Cultures (2–5 mL) were grown in YPD for at least 6 h in a
shaking incubator (30 °C, 250 rpm) before being washed and transferred to SCGE
and grown for at least 16 h. Appropriate β-estradiol concentrations were then
added to inducible cells (0 nM and 30 nM for haploids or 50 nM for diploids), and
the cultures grown for at least 24 h in a final volume of 3–5 mL. During cell growth,
OD600 <1:3 was maintained through appropriate dilutions.
Cell-volume distributions of cultures were measured with a Coulter counter
after sonication for 5 s. Cells were fixed using a 37% formaldehyde solution (Sigma-
Aldrich) by pipetting 100 µL of formaldehyde into 900 µL of cell cultures in order
to achieve a final formaldehyde concentration of 3.7%. Cultures were incubated at
room temperature on a rotator (VWR International, Tube Rotator) for 15 min,
spun down at 10 k g-force for 3min and subsequently washed and resuspended in
100–1000 µL 100 mM potassium phosphate (pH 7.5). Samples were then stored on
ice until being used for flow cytometry.
Flow Cytometry measurements were carried out on a benchtop flow cytometer
with octagon and trigon detector arrays (BD Biosciences, LSR II, software installed:
BD FACSDiva 8.0.1). Strains expressing the fluorescent protein mCitrine were
excited with a 488 nm coherent sapphire solid-state laser paired with a 530/30 nm
filter set. Side-scatter voltage was set to 220 V for all measurements, voltages for
forward-scatter and photomultiplier tubes were adjusted depending on whether
haploid or diploid cells or both were being measured. However, identical settings
were used for replicate experiments. After removing obvious outliers or potential
doublets through standard gating strategies (Supplementary Fig. 11), at least 10.000
cells were measured in the final stopping gate. For each experiment, cells not
expressing mCitrine were measured to determine the cell-volume-dependent
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autofluorescence background which was subtracted from the mean fluorescence
intensity of each sample measured in the same experiment. In order to calculate
fluorescence concentrations, mean cell volumes were determined from the cell
volume distributions measured with the Coulter counter. Mean fluorescence
concentrations were then calculated by dividing the mean fluorescence intensity of
each sample by its mean cell volume, allowing us to analyze mCitrine fluorescence
concentrations as a function of cell volume. For each condition measured, the flow
cytometry experiments were performed at least three times on different days.
Cell-cycle analysis using flow cytometry. To get insights into the distributions of
cell-cycle phases in cell populations of non-inducible and inducible-WHI5 haploid
and diploid strains, we performed cell-cycle analysis using flow cytometry. For this
purpose, cell cultures (5 mL) were grown in YPD for at least 6 h in a shaking
incubator (30 °C, 250 rpm), before being washed and transferred to SCGE, where
appropriate β-estradiol concentrations were added (10 nM or 30 nM for haploid
cells, 50 nM for diploid cells). The cultures were then grown for at least 24 h,
assuring OD600 <1:3 during culture growth through appropriate dilutions. Cell-
volume distributions of cultures were measured with a Coulter counter after
sonication for 5 s. To fixate the cells and subsequently stain the DNA, we followed
an already established protocol49. Specifically, 1 mL of each cell culture was
pipetted into 9 mL of cold 80% ethanol and incubated at 4 °C on a rotator over-
night. The cultures were then spun down at 4000 rpm for 2 min and washed twice
in 50 mM Tris-HCl (pH =8.0). Cells were then successively treated with a 1 mg/
mL RNase A (Thermo Fisher Scientific) solution for 40 min at 37 °C, a 20 mg/mL
Proteinase K (Promega) solution for 1 h at 37 °C and a 10x SYBR Green I (Sigma-
Aldrich) solution for 1 h at room temperature. Between each treatment, cells were
washed twice with 50 mM Tris-HCl and resuspended in 50 mM Tris-HCl. After the
last treatment, cells were sonicated for 5 s. Flow Cytometry measurements were
carried out on the benchtop flow cytometer described above, using the same laser,
filter sets and side-scatter voltage. Settings for forward-scatter and photomultiplier
tubes were adjusted depending on the condition measured. To estimate cell-cycle
fractions, imaged DNA content frequency histograms were analyzed using Watson
modeling. However, we noticed that for cell populations with large cell volumes
(i.e., high β-estradiol concentrations), the DNA content distributions showed
pronounced tails at large cell volumes that were not fit by the model. We speculate
that this tail represents an increased mitochondrial DNA content in large cells50,
which suggests that a fraction of G1 cells would be wrongly identified as S phase.
Thus, we decided to limit our analysis to classifying cells as either G1/S-phase or
G2/M-phase (Supplementary Fig. 1c). Using this approach, we did not find a
drastic influence of the β-estradiol concentration used for Whi5 induction on the
cell-cycle distributions. For each condition measured, the experiments were per-
formed two times on different days.
Single-molecule fluorescence in situ hybridization (smFISH). To detect indi-
vidual mRNA molecules in single cells, we used commercially available Stellaris®
FISH probes. The custom probe set for the coding sequence of mCitrine was
designed using the Stellaris®FISH Probe Designer (Biosearch Technologies,
available online at www.biosearchtech.com/stellarisdesigner) and consisted of 27
probes, each 18 nucleotides long and labeled with the fluorophore Quasar®—670
(Biosearch Technologies).
smFISH was carried out according to the Stellaris®RNA FISH Protocol for S.
cerevisiae, available online at www.biosearchtech.com/stellarisprotocols. Cultures
(5 mL) were grown in YPD for at least 6 h in a shaking incubator (30 °C, 250 rpm)
before being washed and transferred to SCGE. Those cultures were then grown
overnight to reach OD600 0:25 0:4 and fixed the next morning by adding 5
mL of 37% formaldehyde to 45 mL of cell culture (final concentration 3.7%) and
incubating at room temperature for 45 min. After washing the cells twice with ice-
cold fixation buffer (1.2 M sorbitol (Sigma-Aldrich), 0.1 M K2HPO4 (Sigma-
Aldrich), pH 7.5), they were digested at 30 °C in 1 mL fixation buffer containing
6.25 µg zymolyase (Biomol). Progression of cell digestion was monitored using
bright-field microscopy (VWR International, VisiScope BL114) and digestion was
continued until most of the cells appeared dark, which was mostly the case after 55
min of incubation. The digested cells were then washed with ice-cold fixation
buffer and stored at 4 °C in 70% EtOH overnight. For hybridization, 300 μLof
digested cells were centrifuged, resuspended in 100 µL hybridization buffer
(Stellaris®RNA FISH Hybridization Buffer (Biosearch Technologies) with 10% v/v
formamide (VWR International)) with a final Stellaris®FISH probe concentration
of 125 nM and hybridized overnight at 30 °C. Afterwards, cells were washed with
wash buffer A (Stellaris®RNA FISH 1X wash buffer A (Biosearch Technologies)
with 10% v/v formamide), incubated in 1 mL of a DAPI solution (5 ng/mL DAPI in
wash buffer A) at 30 °C for 30 min to stain the nuclear DNA and washed with
Stellaris®RNA FISH wash buffer B. For image acquisition, cell samples were
mounted between glass microscopes slides (Thermo Fisher Scientific, Superfrost
plus, 25 × 75 × 1 mm) and cover slides (VWR International, 18 × 18 mm No. 1)
using Vectashield®Mounting Medium (Vector Laboratories) and were allowed to
settle overnight. Cells were imaged on a Zeiss LSM 800 microscope with additional
epifluorescence setup using a 63×/1.4 oil immersion objective and an axiocam 506
camera. Stacks composed of 20 z-slices (0.24 µm step size) were acquired to cover
the entire depth of cells. For each condition, multiple images were taken per
experiment and at least two independent biological replicates were measured on
different days. Before a microscope maintenance service, mCitrine images were
taken using the Colibri 630 LED module at 55% power and an exposure time of 5 s.
DAPI images were taken using the Colibri 385 LED module at 30% power and an
exposure time of 130 ms. After microscope maintenance, mCitrine images were
taken at 30% power and an exposure time of 5 s to roughly match the intensities in
the images taken before. DAPI images were taken at 20% power and an exposure
time of 80 ms. Bright-field images were consistently taken at an illumination
voltage of 3 V and an exposure time of 100 ms.
Quantification of mRNA spots with smFISH. To analyze the smFISH images and
quantify single mRNA spots, we used FISH-quant v351. Briefly, we first segmented
the cells in the FISH-quant interface by manually tracing the outlines of the cells
and the nuclei with the help of bright-field and DAPI images, respectively. In order
to calculate cell volumes from the cell outlines, we then used the same method as
described above by aligning the cells along their major axis, dividing them into
slices perpendicular to their major axis, each 1 pixel in width, and then assuming
rotational symmetry of each slice around its middle axis parallel to the cell’s major
axis. Total cell volume was then obtained by summing the volumes of each slice.
To quantify single mRNA spots in the imaged cells, we used the batch
processing tool of FISH-quant. First, we defined the ideal image filtering settings,
resulting in images with little background and bright, localized spots, for each
experiment by applying a Laplacian of Gaussian filter on one example image of
each experimental condition. Quality of the filtered image was confirmed by visual
inspection. Second, we performed a pre-detection of mRNA spots in this filtered
example image to define the best intensity thresholds to use for the spot detection
in the batch processing. We aimed to use example images containing at least one S-
phase cell (with high number of mRNA spots). Finally, we analyzed all images
belonging to the same experimental condition via FISH-quant batch processing.
mRNA spots were detected and then fit with a 3D Gaussian on the raw, unfiltered
images, allowing us to set different maximum thresholds for the spot sizes in xy
and z, as well as a minimum threshold for the amplitude and intensity of the
detected spots in order to differentiate background spots from real mRNA spots.
Using this approach, most spots detected in a wild-type strain carrying no mCitrine
allele were excluded, and we thus neglected the contribution of background in the
mRNA mCitrine concentration for further analysis (Supplementary Fig. 7h).
mCitrine mRNA concentration was estimated as the number of mRNA spots
detected with FISH-quant in the whole cell including the bud, divided by the total
cell volume including the bud.
Classification of cell-cycle stages with smFISH. To classify cells in G1, S, or G2/
M phase, we used the cell and nucleus segmentations performed with the help of
bright-field and DAPI images. Cells having nuclear signal and no bud were clas-
sified as G1 cells, cells having a nuclear signal and a bud without nuclear signal
were classified as S phase. However, if the ratio of bud area divided by mother area
was greater than 0.3, the cells were classified as G2M cells instead. For cells having
nuclear signal and a bud that also had nuclear signal, we ensured that they were still
in G2M (rather than two separate G1 cells) by inspecting the bright-field and DAPI
images.
Volume-dependence parameter. Analyzing protein and mRNA concentrations as
a function of cell volume reveals a decrease of concentration with increasing cell
volume for histones. In order to quantify this decrease, we performed a linear
regression on the double-logarithmic data and define the slope of the fit as the
VDP:
log2cðÞ¼log2ðc0ÞþVDP log2VðÞ ð3Þ
The VDP gives us a quantitative measure for the relation of protein and mRNA
concentrations with cell volume: a negative VDP indicates a decrease of
concentration with increasing cell volume. The special case of VDP ¼1
corresponds to a decrease of concentration with c1=V, and therefore signifies a
constant amount of protein or mRNA with increasing cell volume. A positive VDP
indicates an increase of concentration with increasing cell volume, and VDP ¼0
corresponds to a constant concentration c0.
Statistical analyses
Significance of VDPs. To test for a significant deviation of the VDP from 0, we
performed two-tailed one-sample ttests on the regression coefficients of the linear
fit at a confidence level of α¼0:05:Our null hypothesis H0assumes the
respective coefficient to be equal to 0. In order to test for the significance of the
VDP, we are interested in the slope of the linear fit: for a pvalue smaller than α,we
reject H0and consider the slope, i.e., the VDP, to be significantly different from 0.
To test whether the VDPs of two different conditions significantly deviate from
each other, we used a general linear regression model with a categorical variable,
Type, to differentiate between the two conditions analyzed:
log2cðÞ¼log2ðc0ÞþVDP0log2VðÞþδ1Type þδ2Type log2VðÞ ð4Þ
with c0and VDP0corresponding to the reference condition ðType ¼0Þ,δ1
describing the average difference in the intercepts of the linear fits between the two
conditions, and δ2describing the change in the slopes (VDPs) between the two
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conditions. In order to test for a significant difference between the two VDPs, we
perform a two-tailed one-sample ttest on δ2;with the null hypothesis H0assuming
δ2=0, at a confidence level of α¼0:05. For a pvalue smaller than α, we reject H0
and consider the change between the two slopes to be significant, i.e., we consider
the two VDPs to be significantly different from each other.
Error estimation of concentrations at 60 fL. To calculate concentrations at a char-
acteristic cell volume of 60 fL with respective error estimates, we evaluated the
linear fits to the double-logarithmic data at 60 fL and estimated the 95% confidence
intervals of the fit at 60 fL. When normalizing the concentration to a chosen value
x, errors were calculated using error propagation:
4y¼yffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
4c2
c2

2
þ4x2
x2

2
sð5Þ
with ybeing the new normalized concentration and cthe previously calculated
concentration.
To estimate the error associated with the ratio between the concentrations at 60 fL
in haploids and diploids, we used bootstrap analysis. Specifically, we treated the
measurements of protein or mRNA concentration and corresponding cell volume as a
set of linked variables, both for haploid and diploid cells. We then resampled n=
10,000 populations of same size by random sampling with replacement from this
experimental two-dimensional population. Next, we performed a linear regression on
the double-logarithmic data for each of the resampled populations and estimated the
concentration at 60 fL, giving us a distribution of n=10,000 concentrations at 60 fL
for both haploid and diploid cells. Finally, we randomly selected a concentration in
each of those distributions, and divide the concentration for diploids by the
concentration for haploids. We repeated this process 10,000 times with replacement
to obtain a distribution of n=10,000 concentration ratios, for which we calculate the
median and the 2.5- and 97.5-percentiles.
Comparison of population means. When comparing distributions of mean cell
volumes determined with a Coulter counter, or mRNA concentrations determined
with either RT-qPCR or smFISH, we performed the following statistical tests to assess
whether the population means were significantly different from each other. First, we
performed a Shapiro–Wilk test at a confidence level of α¼0:05 to test whether the
distributions were normally distributed. For normal distributions, we then performed
a Bartlett test at a confidence level of α¼0:05 to test whether equal variances of the
distributions could be assumed. If we could assume equal variances, we performed a
two-tailed, two-sample ttest assuming equal variances with the null hypothesis H0
assuming equal means, at a confidence level of α¼0:05. If we could not assume
equal variances, we performed a two-tailed, two-sample ttest assuming unequal
variances. For a pvalue smaller than α,werejectH0and consider the means of the
distributions to be significantly different from each other.
If we could not assume normal distributions, after performing the
Shapiro–Wilk test, we performed a Kruskal–Wallis test with the null hypothesis H0
assuming that our distributions are from the same population, at a confidence level
of α¼0:05. For a pvalue smaller than α, we reject H0and consider the
distributions to not be from the same population.
Minimal model. To obtain mechanistic insight on how the transcription rate of
one specific promoter depends on cell volume and ploidy context, we sought to
build a minimal model. Similar to Heldt et al.37 we consider transcription being
limited by one component of the transcriptional machinery, potentially a subunit
of the RNA polymerase. In addition, we assume transcript degradation to be the
same for all transcripts, and set the corresponding degradation rate k
deg
=1, i.e., all
other rates are normalized with respect to k
deg
. Note that in the case of stable
transcripts, k
deg
also describes dilution of transcripts by cell growth.
To account for the competition of different promoters for a finite number of the
limiting component of the transcriptional machinery (TM), our model
distinguishes two classes of promoters—a general pool of promoters, g, with nh
6000 (haploids) or nd12000 (diploids), and a single promoter of interest, p,
present as a single copy. We describe each promoter as one single binding site for
TM and denote the number of TM bound to general promoters as Rg. Binding of
TM at the single promoter of interest is described by Rp, which can assume values
between 0 (not bound) and 1 (bound). Moreover, Rfdenotes the number of free
TM. We assume that the total number of TM (free and bound) scales
proportionally to cell volume Vand is given by
RgþRpþRf¼cTMVð6Þ
with cTM being the total TM concentration.
Assuming that the arrival of TM at promoters is proportional to the
concentration of free TM, the change in number of bound general promoters over
time is given by following equation:
dRg
dt ¼kg
on nh=dRg

Rf
Vkg
off Rgð7Þ
where kg
on is the rate at which transcription is being initiated at each general
promoter, nh=dRgare the number of general promoters not bound to TM in
haploids or diploids, respectively, and kg
off models the rate at which bound TM
complete transcriptional elongation.
Similarly, the change in binding of TM to the single promoter of interest over
time is given by:
dRp
dt ¼kp
on 1Rp
ðÞ
Rf
Vkp
off Rpð8Þ
with parameters kp
on and kp
off representing transcriptional initiation and elongation,
respectively, at the promoter of interest.
Solving (7) and (8) at steady-state ðdRg
dt ¼dRp
dt ¼0Þ, constraints the number of
bound TMs via the following nonlinear equations
kg
on nh=dRg

Rf
V¼kg
off Rgð9Þ
kp
on 1Rp
ðÞ
Rf
V¼kp
off Rpð10Þ
Finally, the steady-state concentration of transcripts produced from the single
promoter of interest is equal to kp
off Rp=V.
Given a set of parameters cTM;kg
on;kg
off ;kp
on kp
off , numerically solving Eqs. (6),
(9) and (10) allows to calculate the transcript concentration, generated by the single
promoter of interest as a function of cell volume V. We set cTM ¼2000;kg
on ¼
1;kg
off ¼kp
off ¼3 and calculate the steady-state concentration in haploids and
diploids over cell volume for kp
on ¼0:01;100
½
.
In order to determine the VDP as a function of kp
on, we calculated the
concentration for each value of kp
on over a cell volume range of V¼1
3;3

and
performed a linear regression fit on the logarithm of the concentration as a
function of the logarithm of the cell volume, with cell volumes being equally spaced
on the log scale. The VDP is then determined as the slope of the linear fit.
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
The data that supports this study is available from the corresponding author upon
reasonable request. Source data are provided with this paper.
Received: 10 August 2020; Accepted: 18 June 2021;
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Acknowledgements
We thank Matthew Swaffer and Anika Seel for sharing strains, Thomas Hofer and
Elfriede Nößner from the HMGU-Immunoanalytics-Core Facility for support with flow
cytometry, and the Institute of Functional Epigenetics and the Skotheim lab for dis-
cussions. We thank Matthew Swaffer, Amanda Amodeo, and Jan Skotheim for com-
ments on the manuscript. This work was supported by the DFG through project
SCHM3031/4-1, by the Human Frontier Science Program (career development award to
K.M.S.) as well as the Helmholtz Gesellschaft. A.S. was supported by NIH grants
5R01GM124446 and 5R01GM126557.
Author contributions
K.-L.C., D.B., D.C., P.A., and K.M.S. designed and performed experiments and analyzed
the data. A.S. developed the mathematical model. All authors interpreted the results.
K.-L.C, D.C., and K.M.S wrote the paper, receiving input from all authors.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41467-021-24451-8.
Correspondence and requests for materials should be addressed to K.M.S.
Peer review information Nature Communications thanks the anonymous reviewers for
their contribution to the peer review of this work.
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