Single Mammalian Cells Compensate for Differences in Cellular …rajlab/pdfs/... · 2015. 4....

Article

SingleMammalianCells Co
mpensate for Differencesin Cellular Volume and DNA Copy Number throughIndependent Global Transcriptional Mechanisms
Graphical Abstract

Highlights

d Transcription scales with cell volume to maintain transcript

concentration

d Cell fusion shows that increasing cellular content can

increase transcription

d Transcriptional burst size changes with cell volume and burst

frequency with cell cycle

d The burst frequency mechanism allows for proper

transcription during early S phase

Padovan-Merhar et al., 2015, Molecular Cell 58, 1–14April 16, 2015 ª2015 Elsevier Inc.http://dx.doi.org/10.1016/j.molcel.2015.03.005

Authors

Olivia Padovan-Merhar,

Gautham P. Nair, ..., Abhyudai Singh,

Arjun Raj

[email protected]

In Brief

Padovan-Merhar et al. combine single-

molecule transcript counting with

computational measurement of cellular

volume, showing that single cells

maintain transcript abundance despite

variability in cell size. Large cells have

greater transcriptional burst size,

whereas burst frequency halves upon

DNA replication.

Accession Numbers

GSE66053

mailto:[email protected]

http://dx.doi.org/10.1016/j.molcel.2015.03.005

Please cite this article in press as: Padovan-Merhar et al., Single Mammalian Cells Compensate for Differences in Cellular Volume and DNA CopyNumber through Independent Global Transcriptional Mechanisms, Molecular Cell (2015), http://dx.doi.org/10.1016/j.molcel.2015.03.005

Molecular Cell

Article

Single Mammalian Cells Compensate for Differencesin Cellular Volume and DNA Copy Number throughIndependent Global Transcriptional MechanismsOlivia Padovan-Merhar,1 Gautham P. Nair,2 Andrew G. Biaesch,2 Andreas Mayer,3 Steven Scarfone,1 Shawn W. Foley,4

Angela R. Wu,5 L. Stirling Churchman,3 Abhyudai Singh,6 and Arjun Raj2,*1Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA2Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA3Department of Genetics, Harvard Medical School, Boston, MA 02115, USA4Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA5Department of Bioengineering, Stanford University, Stanford, CA 94305, USA6Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA*Correspondence: [email protected]


SUMMARY

Individual mammalian cells exhibit large variability incellular volume, even with the same absolute DNAcontent, and so must compensate for differencesin DNA concentration in order to maintain constantconcentration of gene expression products. Usingsingle-molecule counting and computational imageanalysis, we show that transcript abundance corre-lates with cellular volume at the single-cell level dueto increased global transcription in larger cells. Cellfusion experiments establish that increased cellularcontent itself can directly increase transcription.Quantitative analysis shows that this mechanismmeasures the ratio of cellular volume to DNA content,most likely through sequestration of a transcriptionalfactor to DNA. Analysis of transcriptional bursts re-veals a separate mechanism for gene dosage com-pensation after DNA replication that enables propertranscriptional output during early and late S phase.Our results provide a framework for quantitativelyunderstanding the relationships among DNA con-tent, cell size, and gene expression variability in sin-gle cells.

INTRODUCTION

Within a population, individual mammalian cells can vary greatly

in their volume, often independently of their position in the cell

cycle (Bryan et al., 2014; Crissman and Steinkamp, 1973; Tzur

et al., 2009). Biochemical reaction rates, however, depend on

the concentration of reactants and enzymes. Thus, to maintain

proper cellular function, most molecules must be present in the

same concentration despite these volume variations, meaning

that the absolute numbers of molecules would have to scale

roughly linearly with cellular volume (see Marguerat and Bahler,

2012 for an excellent review).

One critical molecule whose concentration need not scale with

cellular volume, however, is DNA. Most mammalian cells have

two or four copies of the genome per cell, and even cells with

the same number of genomes can differ widely in size; thus,

DNA concentration can vary dramatically from cell to cell. This

poses a problem: if two otherwise identical cells with the same

DNA content had different volumes, then the larger cell must

somehow maintain a higher absolute number of biomolecules

despite them being expressed from the same amount of DNA.

Previous efforts to resolve this puzzle have largely focused on

analyzing bulk population measurements of size-altering mu-

tants. A number of such studies have shown that the amount

of both RNA and protein generally scales with cellular volume

(Marguerat and Bahler, 2012; Marguerat et al., 2012; Schmidt

and Schibler, 1995; Watanabe et al., 2007; Zhurinsky et al.,

2010) and ploidy (Wu et al., 2010), with some further finding

that transcription changes in mutants with larger or smaller cell

volumes (Fraser and Nurse, 1979; Schmidt and Schibler, 1995;

Zhurinsky et al., 2010). Most of these studies utilized yeast,

with a few notable exceptions (Miettinen et al., 2014; Schmidt

and Schibler, 1995; Watanabe et al., 2007).

These experiments do not, however, establish a causal rela-

tionship between cellular volume changes and transcript abun-

dance. Causality could change the interpretation of gene expres-

sion measurements because, if cellular volume changes can in

and of themselves change global expression levels, observa-

tions of changes in global expression levels in response to

various perturbations may actually be the indirect consequence

of changes to cellular volume rather than resulting from direct

global transcriptional responses to the perturbations, per se.

Also unclear is how or even whether these mechanisms man-

ifest in individual cells. Much recent evidence has shown that

individual transcripts levels can vary from cell to cell due to sto-

chastic effects in gene expression (Raj and van Oudenaarden,

2008, 2009; Sanchez and Golding, 2013) such as transcriptional

bursts (Chubb et al., 2006; Golding et al., 2005; Raj et al., 2006;

Suter et al., 2011; Zenklusen et al., 2008). Yet it remains unclear

how differences in cellular volume affect the interpretation of

such measurements or whether transcriptional measurements

can reveal further characteristics of homeostatic mechanisms.

Molecular Cell 58, 1–14, April 16, 2015 ª2015 Elsevier Inc. 1

mailto:[email protected]



We used single-molecule RNA imaging and computational

image analysis to measure transcript abundance and cellular

volume simultaneously in individual human cells. Cell fusion

experiments showed that cellular size can directly and globally

affect gene expression by modulating transcription. Quantitative

analysis of these experiments revealed that the mechanism un-

derlying this global regulation does not merely sense cellular vol-

ume but rather integrates both DNA content and cellular volume

to produce the appropriate amount of RNA for a cell of a given

size, consistent with a model in which a factor limiting for tran-

scription is sequestered to the DNA, either through direct titra-

tion by DNA or restriction to the nuclear compartment. We also

provide a quantitative framework for interpreting gene expres-

sion variability in single cells and extended this framework

to genome-wide single-cell RNA-sequencing analysis, showing

that cell-type-specific genes are more variable than ubiquitously

expressed genes.

RESULTS

mRNA Counts Scale with Cellular Volume in SingleMammalian CellsWe first looked at the number of mRNA molecules in individual

primary fibroblast cells (human primary foreskin fibroblasts,

CRL2097) within a population to see whether mRNA counts

scale with cellular volume at the single-cell level. We measured

both mRNA abundance and volume simultaneously using sin-

gle-molecule multicolor mRNA fluorescence in situ hybridization

(RNA FISH; Femino et al., 1998; Raj et al., 2008), which allowed

us to detect the positions of individual mRNAs in three dimen-

sions as fluorescent spots in the microscope (Figure 1A). We

measured the abundance of a particular mRNA (e.g., TBCB)

labeled with one color and then calculated the volume of the

cell using the 3D positions of mRNA from a ‘‘volume guide’’

gene labeled with another color to define the cellular boundary

(Figure 1B; Experimental Procedures). The cell volumes we

measured varied over a 6-fold range, agreeing with other esti-

mates (Bryan et al., 2014; Tzur et al., 2009), and are robust to

choice of guide gene and the fixation procedure itself (Figure S1).

We ultimately measured the abundance of 25 to 30 different

mRNA species in both this primary fibroblast line and a lung can-

cer line (A549).

For most genes, mRNA counts and volumes in single cells ex-

hibited a strongly positive, linear correlation (e.g., Figure 1C; see

Figure S2 for all genes examined). Because larger cells had pro-

portionally more transcripts than did smaller cells, the mRNA

concentration remained relatively constant from cell to cell

despite considerable variation in absolute mRNA numbers. This

scaling property was not confined to high-abundance mRNAs

such as GAPDH and EEF2—genes expressing as few as 10 to

20 mRNA per cell such as ZNF444 and KDM5A scaled similarly,

as did rRNA (Figure S2). We also observed the same behavior

for short-lived mRNA such as UBC and IER2 mRNA, whose

half-lives are 2.9 and 2.2 hr, respectively (Tani et al., 2012).

We checked whether the scaling of mRNA count with volume

depended on cell-cycle progression or cell growth. We co-

stained cells with cell-cycle markers (Eward et al., 2004; Lev-

esque and Raj, 2013; Robertson et al., 2000; Whitfield et al.,

2 Molecular Cell 58, 1–14, April 16, 2015 ª2015 Elsevier Inc.

2002) to classify them as being in the G1, S, or G2 phases of

the cell cycle (Figure S3). Cell volume varied as much for cells

in individual phases of the cell cycle as the population overall,

with a shift in the distribution toward G2 cells being larger, and

the linear relationship between mRNA count and volume did

not depend on cell-cycle phase (Figure 1D), showing that

mRNA count did not depend on DNA content of the cell. We

also note that the primary fibroblast cells exhibit normal ploidy

(Levesque and Raj, 2013), so our results are not simply explained

by differences in ploidy. We also found that nuclear size

increased somewhat with cellular volume and that nuclear size

increased in later stages of the cell cycle (Figure S3). To check

whether progression through the cell cycle or cell growth is

responsible for maintaining scaling, we grew the primary fibro-

blasts for 7 days in medium lacking serum, making them quies-

cent. Despite growth and cell cycle arrest, we found that mRNA

count and volume still scaled strongly for all genes examined,

showing that neither progression through cell cycle nor continual

cell growth is required for mRNA count to scale with cellular vol-

ume (Figure 1F). Interestingly, we found that although the mean

count of mRNA decreased in quiescent cells as compared with

proliferating cells, the cells maintained a similar concentration

ofGAPDH and other mRNA between the two conditions (Figures

1G–1H and S1).

We also checkedwhether we could observe similar behavior in

intact organisms. We looked at both RNA and DNA density in the

heads and gonads of adult nematodes, comparing measure-

ments from both wild-type worms and worms with mutations

leading to decreased organismal size but with roughly the

same number of cells (Figures 2A–2B) (Watanabe et al., 2007).

We found that the RNA concentrations were roughly the same

between the two strains and that the number of RNA per mole-

cule of DNA decreased by a factor similar to that of the volume

differences between the strains (Figures 2C–2D), verifying that

our observations can hold in vivo.

It is important to note that although the mRNA abundance is

strongly correlated with cellular volume, the y intercept (a) of a

line fit to the data (mRNA = a + bV) was nonzero, indicating

that mRNA count in individual cells has a volume-independent

component in addition to the volume-correlated component.

Quantifying the relative fractions of mRNA that are volume corre-

lated versus volume independent in a cell of average volume

(Figure 1E) [i.e., a / (a + bVavg) versus bVavg / (a + bVavg)] revealed

a range of values for different genes (Figure S1), although the vol-

ume-dependent fraction was dominant for most genes exam-

ined. Thus, the mRNA concentration is actually somewhat

greater in smaller cells than in larger ones; for most genes, the

smallest cells have anmRNA concentration 1.2 to 3 times greater

than that of the largest cells (Figure S2). We later describe a

mathematical model providing a potential explanation for this

increased concentration based on nuclear volume measure-

ments (see Supplemental Information).

Transcriptional Activity, Not mRNA Degradation, ScalesGlobally with Cellular VolumeThese data show that larger cells have a proportionally higher

number of mRNA than smaller cells, even if they have the

same absolute number of DNA molecules. To maintain this

A C

B

D E

F G H

Figure 1. mRNA from Many Genes Scales with Cellular Volume

(A) Single-molecule RNA FISH. The DAPI stain is in blue, and the TBCB mRNA FISH probe is in white.

(B) Representative outline of a primary fibroblast cell found using our volume calculation algorithm.

(C)mRNA versus volume for EEF2, LMNA, and TBCB. Each point represents one single-cell measurement. Each dataset is a combination of at least two biological

replicates, with at least 30 cells per replicate.

(D)GAPDHmRNA and volume in primary fibroblast cells. Marginal histograms show volume andmRNA distributions. Colors indicate cell-cycle stage determined

by Cyclin A2 (CCNA2) mRNA count. Dashed diagonal line is the best linear fit of RNA versus volume. Vavg indicates the average primary fibroblast volume. We

determined volume-independent and -dependent transcript levels using the linear fit and Vavg. Data are a 15% subset of 1,868 cells spanning >30 biological

replicates.

(E) Fraction of volume-independent and -dependent RNA expression from the linear fit of RNA versus volume for 21 genes in primary fibroblast cells (we omitted

highly variable genes whose volume-independent fractions were less than zero). Data for each gene are a combination of at least two biological replicates, with at

least 30 cells per replicate.

(F)GAPDHmRNA versus volume in cycling and quiescent primary fibroblast cells. Dashed lines are best fit line forGAPDH in cycling cells. Data are an 8% subset

of 1,868 cells spanning >30 biological replicates for cycling cells and 10% subset of 1,105 cells for quiescent. We only analyzed quiescent cells that had less than

20 CCNA2 mRNA.

(G and H) Mean GAPDH mRNA count (G) and concentration (H) in different growth conditions for data from (F).

All error bars represent SEM. See also Figures S1–S3.



A B

C D

Figure 2. mRNA Scales with Volume In Vivo

(A) Images of the two C. elegans strains.

(B) Quantification of the relative sizes of the two strains (n = 24 for N2, n = 20 for

CB502).

(C) Number of mRNA molecules per cell in the gonad region for each type of

worm for genes ama-1 and arf-3. We estimated the number of cells in each

segment by counting nuclei stained with DAPI. Each bar is a compilation of

three biological replicates, with >3 worms per replicate.

(D) Concentration ofmRNA in the gonad region. All scale bars represent 10 mm.

All error bars represent SEM.


proportionality, larger cells must either transcribe more mRNA

from the same number of DNA molecules or degrade those

mRNA more slowly. To distinguish these possibilities, we deter-

mined the rate of mRNA degradation in cells of different sizes by

measuring volumes and mRNA counts for UBC and IER2 after a

period of 4 hr of transcriptional inhibition (transcriptional inhibi-

tion did not affect cell volume; Figure 3D). By measuring mRNA

counts before and after transcription inhibition (Figures 3A–3B,

inset; see Experimental Procedures for details), we calculated

the effective decay constant for each measured cell (Figures

3A–3B).We found that the degradation rate was the same in cells

of all volumes, showing that slower degradation is not respon-

sible for the increased number of mRNA in larger cells.

We next checked whether larger cells transcribe more than

smaller cells (as observed in bulk populations; Fraser and Nurse,

1979; Schmidt and Schibler, 1995; Zhurinsky et al., 2010). We in-

ferred global transcription rate by incorporating a labeled uridine

into all newly synthesized RNA produced during a 60-min time

window (Figure 3C), which we then rendered fluorescent via click

chemistry (Jao and Salic, 2008). The intensity of fluorescence is

equivalent to the global transcription rate. We found that tran-

scription rate is linearly proportional to volume, thus showing

that individual cells vary in their overall transcription (das Neves

et al., 2010), and these variations correlate strongly with volume.

We conclude that larger cells maintain proportionally higher

levels of RNA by increased transcription rather than decreased

degradation as compared to smaller cells. Also, quantification

of fluorescence from probes targeting the internal transcribed

spacer of the rRNA (the ‘‘intronic’’ sequence of rRNA) showed


that transcription of rRNA also scales linearly with volume (Fig-

ure S2), indicating that RNA polymerase I transcription is also

volume dependent.

The scalingof transcriptionwith cellular volumecouldbedue to

global factors regulating transcription of all genes in a volume-

correlated manner, or it could be that gene regulatory networks

sense deviations in each particular gene’s protein concentration

and modulate transcription to restore concentration. In the latter

case, reducing protein concentration of any one gene would

result in increased transcription to compensate, whereas in the

global scenario, reducing the concentration of any one gene

would not appreciably affect the cellular volume, thus leaving

the gene’s transcription unchanged. We tested this by reducing

the level of lamin A/CmRNA and protein in the cell via small inter-

fering RNA (siRNA) (Figures 3E and 3F); we chose lamin A/C

because its expression scales strongly with volume (Figure 1C)

and is thought to be tightly regulated (Swift et al., 2013). To mea-

sure the transcriptional response, we took advantage of the fact

that transcription occurs in bursts (Chubb et al., 2006; Dar et al.,

2012; Golding et al., 2005; Suter et al., 2011; Vargas et al., 2005;

Zenklusen et al., 2008), and genes that are actively undergoing a

transcriptional burst have bright accumulations of nascent RNA

at the site of transcription itself (Levesque and Raj, 2013; Levsky

et al., 2002; Raj et al., 2006; Zenklusen et al., 2008) (note that

siRNA does not affect nuclear RNA; Maamar et al., 2013). We

measuredboth the averagenumber of active laminA/C transcrip-

tion sites per cell and the intensity of those transcription sites,

finding bothmetrics unchanged upon reduction of lamin A/C pro-

tein levels (Figure 3G).We conclude that increasedmRNAcounts

in larger cells result froma global difference in transcription rather

than the activity of a particular gene network that regulates the

concentration of lamin A/C. There may be other situations in

which mRNA levels are regulated by specific networks.

A Diffusible trans Factor Sensing DNA Content andVolume Links Cellular Volume and TranscriptionWhat links volume and transcription? One possibility is that the

total cellular content itself exerts a global influence on transcrip-

tion, thus making transcription scale with cellular volume; alter-

natively, transcription may affect cellular volume. To distinguish

these possibilities, we fused a small human melanoma cell that

expressed GFP mRNA (WM983b-GFP-NLS) at constant density

to the larger fibroblasts that do not express GFP (Figure 4A) to

form heterokaryons (Pomerantz et al., 2009). We found that ab-

solute GFP mRNA counts increased in fused cells as compared

to the original small cells (Figure 4B), showing that increasing to-

tal cellular content is by itself sufficient to increase absolute

mRNA abundance. Moreover, the GFP mRNA counts scaled

with heterokaryon volume (Figure 4C), suggesting that the rate

of GFP transcription scaled with the ultimate volume of the fused

cell. The fact that the nucleus from the WM983b-GFP-NLS cell

could change its overall transcriptional activity showed that the

modulation occurs via the activity of a diffusible trans factor.

How might such a factor transmit volume information to the

GFP gene in order to increase its transcription concordantly

with the increase in cellular volume? There are two broad cate-

gories of mechanism: (1) the factor acts as a ‘‘volume sensor’’

and does not know about the amount of DNA in the cell. An

A C

B D

E F G

Figure 3. Cells Exhibit Global Volume-Dependent Transcriptional Control over mRNA Abundance

(A andB)We inhibited transcription in primary fibroblast cells using actinomycin D for 4 hr and allowedUBC (A) and IER2 (B) mRNA to degrade. Inset showsmRNA

before and after inhibition. Each point represents a single-cell measurement.We calculated the decay constant for each cell using the best-fit line before inhibition

(see Experimental Procedures). Blue line shows fit if degradation were volume-dependent; red line shows fit if transcription were volume-dependent. Data

represent one of two biological replicates.

(C) We fluorescently labeled nascent RNA produced in 1 hr using the Click-iT eU assay in primary fibroblast cells and quantified the total fluorescence intensity by

imaging the nuclei of single cells. Inset shows raw micrograph data. Blue line shows fit for volume-dependent degradation; red line shows fit for volume-

dependent transcription. Data shown are from quiescent cells and are one of three biological replicates.

(D) Distribution of cell volumes before and after transcription inhibition.

(E) We performed siRNA treatment for 72 hr in primary fibroblast cells using either a control siRNA (left) or an siRNA targeting LMNA mRNA (right). DAPI stain is

shown in purple, and LMNA mRNA FISH probe is shown in white. White arrows indicate active transcription sites.

(F) Quantification of cytoplasmic LMNA mRNA knockdown by RNA FISH. Inset shows protein knockdown.

(G) Comparison of the number of LMNA transcription sites and transcription site intensity in siRNA control and LMNA knockdown conditions. We detected

transcription sites through intron/exon colocalization using RNA FISH. All error bars represent SEM. Data in (D) and (E) are a combination of two biological

replicates, n = 323 cells for control siRNA and 284 cells for LMNA siRNA.


example could be amodifiable global transcription factor protein

whose degree of modification/activity is proportional to cellular

size (Figure 4D, left). Or (2) the factor acts as a ‘‘volume/DNA

sensor’’ whose activity depends on both cellular volume and

DNA content. One such mechanism is the existence of a general

transcription factor of limiting abundance relative to the number

of binding sites in the DNA (limiting factor). Here, the DNA

‘‘counts’’ how big the cell is by binding all available factor mole-

cules (Figure 4D, right), thus increasing transcription in bigger

cells as more factor binds to DNA. Another possibility is


A

D

B CPrediction for volumesensor hypothesis

Figure 4. A trans-Acting Limiting Factor Links Gene Expression to Volume

(A) Representative image of fused cells (heterokaryon, left) and unfused cells (WM983b, primary fibroblast, right). DAPI stain is in orange, GFP mRNA is in green,

and GAS6 mRNA is in white. White arrows indicate transcription sites.

(B) Quantification of GFP mRNA in unfused and fused cells. Box extends to first and third quartiles, and whiskers extend to the maximum-distance points within

1.5 interquartile ranges of the box. Data are a combination of two biological replicates.

(C) GFP versus volume for fused and unfused cells. Upper dashed line represents fit for unfused cells. Lower dashed line has a slope that is half of the upper fit line.

(D) Schematic of transcriptional output of fused cells if the scaling of expression with volumeweremediated by a volume sensor or a volume/DNA sensor. All error

bars represent SEM.


sequestration of the factor to the nucleus, which can also

achieve the same volume/DNA-sensing behavior if nuclear vol-

ume is only weakly dependent on cellular volume (see Supple-

mental Information).

We distinguished these two alternatives by comparing the

concentration of GFP mRNA in the fused and unfused cells (Fig-

ure 4C). In the volume sensor scenario, the fusion cell will have

the same concentration of GFP mRNA as the original small cells

because the factor transmits the volume information to the GFP

gene independent of the number of nuclei in the cell. In the vol-

ume/DNA sensor scenario, the fusion cell will have half the con-

centration of GFP mRNA because the factor senses both the

increased volume and the two nuclei (for example, a limiting

factor would be diluted between the two nuclei in the fused

cell). We found that the concentration of GFP mRNA in the fused

cells is strictly less than and very close to half the concentration

in unfused cells. We conclude that the factor responsible for

increased transcription in smaller cells is not a volume sensor

but responds to both the size and DNA content. These results

also suggest that perturbations that change cell size will indi-

rectly change global transcript counts per cell through this

generic mechanism.


Transcriptional Burst Size Increases in Larger CellsWe next sought to characterize this mechanism further by

examining the relationship between volume and transcription

of individual genes. mRNA is produced in bursts, marked by

bright accumulations of nascent mRNA at the site of transcrip-

tion. We characterize this bursting behavior through burst size

(how much RNA is produced during a single burst) and burst

fraction (how often a gene is actively transcribing, which is

related to burst frequency; Levesque and Raj, 2013). To quantify

burst size, we measured the intensity of transcription sites for

four genes in our fibroblasts and found higher intensity tran-

scription sites in larger cells (Figure 5A). We verified that the in-

tensity of transcription sites reflected the degree of transcrip-

tional activity by treating primary fibroblast cells with 100 nM

triptolide, which targets RNA polymerase II for degradation

(Bensaude, 2011), reducing its levels (Figure 5D). After 1 hr,

we saw a reduction of bright transcription sites for two different

genes, showing that transcription site intensity depends directly

on the amount of transcriptional machinery available. This inten-

sity is often proportional to burst size (Levesque and Raj, 2013;

Senecal et al., 2014). Interestingly, transcription site intensity

did not depend on cell-cycle stage (Figure 5B). We concluded

A

B

C

D

Figure 5. Transcriptional Burst Size In-

creases in Larger Cells

(A) Transcription site intensity and volume in pri-

mary fibroblast cells for genes UBC, MYC, EEF2,

and TUSC3. Each data point represents the mean

transcription site intensity per cell for a quartile of

cells classified by volume or GAPDH. We detected

transcription sites through intron/exon colocaliza-

tion using RNA FISH. We calculated volume for

EEF2 data using EEF2 as a guide and volume for

MYC data using GAPDH. We use GAPDH as a

proxy for volume for UBC and TUSC3.

(B) Transcription site intensity and cell-cycle stage

in primary fibroblast cells.We determined cell-cycle

stage by Cyclin A2 and the histone 1H4E mRNA

counts (see Experimental Procedures and Fig-

ure S3). For intensity measurements, data for UBC,

MYC, and EEF2 are from one of two biological

replicates (EEF2: n = 190, UBC: n = 202, and MYC:

n = 103 transcription sites). Data for TUSC3 are

combined from two biological replicates (n = 255

transcription sites).

(C) Western blot analysis reveals that >99% of the

C-terminal domain hyperphosphorylated form of

RNA polymerase II (IIO) is present in the chromatin

fraction. The hypophosphorylated form of Pol II (IIA)

is captured in all cellular fractions. We generated

subcellular lysates from the same batch of primary

fibroblast cells and probed with the F-12 antibody

(Santa Cruz Biotechnology) that is directed against

the N-terminal region of RPB1, the largest subunit

of RNA polymerase II. We adjusted sample volumes

so that western blot signals of the subcellular

fractions are comparable.

(D) Quantification of transcription site intensity

before and after treatment with 100 nM triptolide for

1 hr. p value represents the probability of randomly

finding the distributions of bright transcription

sites (values to the right of the black line) in each

condition.

All error bars represent SEM. See also Figures S3

and S4.


that the factor connecting gene expression and cellular volume

affected mRNA abundance through modulation of transcrip-

tional burst size.

Molecular Cell 58

What might this factor be? In order to

formalize the possibilities, we developed

a mathematical model for how this factor

may act (see Supplemental Information),

assuming that the factor is almost purely

nuclear and that the total amount of factor

in the cell is proportional to cellular volume.

Our model of perfect linear scaling of tran-

scription with volume requires that either

(1) the volume of the nucleus remains fixed

between cells irrespective of cytoplasmic

volume or (2) the factor has such a high af-

finity for DNA that it is essentially titrated by

DNA (or both). In both scenarios, either the

nucleus or the DNA ‘‘counts’’ the amount

of factor in the cell to modify transcription,

thusmaking transcription proportional to the total amount, rather

than concentration, of the factor. Further, in scenario 1, it predicts

that if nuclear volume changes with cell size, it will manifest as a

, 1–14, April 16, 2015 ª2015 Elsevier Inc. 7


deviation frompure linear scalingof transcriptionwith cellular vol-

ume. Our data cannot distinguish these two possibilities, but our

analysis did predict that in scenario 1, the slight increase in nu-

clear volume we saw in larger cells (Figure S3) could lead to a

slight decrease inmRNAconcentration,manifesting as anonzero

intercept when fitting RNA abundance to volume, as observed in

Figure 1D. Indeed, the quantitative magnitude of the increased

concentration at low cell volumes matches what our model

predicts based on our measured relationship between nuclear

volume and cellular volume (see Supplemental Information),

perhaps favoring the model in which transcriptional scaling

results from sequestration of this factor to the nucleus over a

pure titration mechanism. It is possible that the factor is some

component of the general transcriptional machinery, which is

94% nuclear based on fractionation (Figure 5C) (A.M. and

L.S.C., unpublished data).

A DNA-Linked cis-Acting Factor Reduces TranscriptionFrequency, Not Burst Size, Immediately after DNAReplicationWe have shown that cells in the G1 and G2 phases of the cell cy-

cle can have the same volumeand samemRNAdensity, although

cells inG2 have twice theDNAcontent as those inG1. How, then,

do twocells of the same size produce the sameamount ofmRNA,

despite having different amounts of DNA?

We already found that transcriptional burst size does not

changedramatically betweenphases of the cell cycle (Figure 5B),

so we now measured burst fraction. To measure transcriptional

burst fraction, we counted active transcription sites in each cell

and divided by the total number of gene copies for each stage

of the cell cycle (two copies in G1, four copies in G2). For all of

the genes we measured, the number of active sites per gene

copy in G2 was approximately half of that in G1 (Figure 6A).

This is not due to repression of replicated copies of DNA, as

we observed G2 cells with four transcription sites (Figure S4).

This showed that the cell has a mechanism to precisely reduce

transcription fraction in G2 to keep overall transcription constant

across the G1 and G2 phases of the cell cycle. Burst fraction did

not depend on cell volume (Figure 6B), showing that the mecha-

nism is distinct from the volume-compensating mechanism

described above.

We were surprised that the mechanism for cell cycle was

different from the one compensating for volume. In principle,

limiting factor models that may be responsible for volume con-

servation would also compensate for increased gene copy num-

ber due to DNA replication. However, these models predict an

inappropriate boost in transcription for genes that replicate early

in S phase because a limiting factor would distribute itself over all

the DNA, essentially ‘‘double counting’’ the small percentage of

genes that replicate considerably earlier than themajority of DNA

(Figure 6C). To see whether this was the case, we measured

transcriptional burst fraction for early-replicating genes in S

phase (EEF2, MYC, UBC; see Figure S4 for replication timing).

These genes all showed the same number of active transcription

sites per gene copy in S and G2, ruling out the possibility that a

factor simply gets diluted between copies of replicated DNA.

This leaves two alternatives for burst fraction reduction

between G1 and G2. Transcription fraction could universally


decrease by a factor of two upon the initiation of S phase,

but this would potentially cause the opposite problem—genes

that replicate late in S phase would be under-transcribed for

themajority of S phase. Alternatively, transcription fraction could

decrease on a gene-by-gene basis (i.e., in cis) immediately upon

DNA replication. To test between these alternatives, we imaged

transcription of a gene that replicates very late in S phase

(TUSC3; Figure S4). If transcription fraction was universally

reduced by a factor of two at the beginning of S phase, this

gene would have the same transcription fraction in S and G2.

However, we found that this late-replicating gene maintains G1

levels of transcription through S phase and does not reduce tran-

scription fraction until G2. Therefore, we conclude that there ex-

ists a mechanism whereby transcription fraction is reduced by a

factor of two immediately upon replication of that gene, with

different timing for different genes. Candidates for a mechanism

include the partitioning or modification of DNA-linked factors,

such as histones with particular modifications, upon DNA repli-

cation, resulting in half the transcriptional burst fraction as before

replication.

Together, our data demonstrate the existence of two separate

transcriptional mechanisms that allow cells to maintain RNA

concentration homeostasis despite changes in DNA content

and cellular volume. Cells modulate transcriptional burst size

through a trans mechanism to allow larger cells to produce

more mRNA from the same amount of DNA and modulate burst

frequency over the cell cycle in cis to maintain RNA concentra-

tion despite changes in DNA content.

Single-Cell RNA Sequencing Reveals that Cell-Type-Specific Genes Are ‘‘Noisier’’ Than UbiquitouslyExpressed GenesOur RNA FISH data revealed that, although the expression of

most genes was consistent with a volume-dependent transcrip-

tion rate, many of the genes also showed strong variability in

transcript concentration from cell to cell (Li and Xie, 2011; Raj

and vanOudenaarden, 2009; Raj et al., 2008; Sanchez andGold-

ing, 2013).

To accurately quantify gene expression noise while account-

ing for cell volume, we developed a metric we call volume-cor-

rected noise measure (Nm; Figure 7A), defined as (Bowsher

and Swain, 2012; Johnston et al., 2012; Swain et al., 2002)

follows:

Nm=CV2m �

�bhVi

a+bhVi��

Covðm;VÞhmihVi

�;

where

CVm =sm

hmi;

and a and b indicate the intercept and slope of a best-fit line for

mRNA and volume,

hmi= a+bhVi:

Volume-corrected noise measure is in principle similar to the

squared coefficient of variation of mRNA concentration but ac-

counts for volume-independent transcription (Figure S5).

A

B

C

Figure 6. A cis-Acting Factor Decreases

Transcription Frequency Immediately upon

DNA Replication

(A) Number of transcription sites by cell-cycle stage

in primary fibroblast cells. We determined cell-cycle

stage by Cyclin A2 and histone 1H4E mRNA counts.

Dashed lines represent half the number of tran-

scription sites in G1. We normalize all G1 data to two

gene copies and all G2 data to four gene copies. For

EEF2, MYC, and UBC (early replicators), we

normalize S-phase data to four gene copies. For

TUSC3 (late replicator), we normalize S-phase data

to two gene copies.

(B) Number of transcription sites per gene copy

classified by volume in primary fibroblast cells. Each

data point represents the mean number of tran-

scription sites for a quartile of cells classified by

volume. We calculated volume for EEF2 data using

EEF2 as a guide and volume for MYC data using

GAPDH. We use GAPDH as a proxy for volume for

UBC and TUSC3. For frequency measurements,

data for EEF2, UBC, and TUSC3 are a combination

of two biological replicates (EEF2: n = 516, UBC: n =

332, and TUSC3: n = 255 transcription sites). Data

for MYC is from one of two biological replicates

(MYC: n = 103 transcription sites).

(C) Schematic depicting different potential mecha-

nisms for changing gene expression with cell cycle.

All error bars represent SEM. See also Figures S3

and S4.


We evaluated Nm for all of the genes we measured by RNA

FISH. A standardmeasure of gene noise is the coefficient of vari-

ation (CV; standard deviation divided by mean), which takes into

account only the spread and the mean of the expression data.

With such a measurement, most of the genes we measured by

RNA FISH would be deemed ‘‘noisy,’’ or far from Poisson noise

levels (Figure S5). However, when we instead measured Nm

for each of our RNA FISH genes, we saw that many of them

Molecular Cell 5

displayed levels of variability near to or

indistinguishable from Poisson (Figure 7B)

once we accounted for measurement error

(upper bound of approximately 15%) (Raj

et al., 2006).

To quantify this variability genome-wide,

we performed single-cell RNA sequencing

(Brennecke et al., 2013; Grun et al., 2014;

Shalek et al., 2013) on human foreskin

fibroblast cells, calibrating the sequencing

data using our RNA FISH results (Figures

7C and S6) and adding synthetic RNA at

known concentrations to estimate cell

volume. Briefly, we used the ratio of

RNA-sequencing reads mapping to the

transcriptome to the reads mapping to

spiked-in RNA from the External RNA

Controls Consortium (ERCCs) (Devonshire

et al., 2010) to estimate the total relative

amount of RNA in the cell (Marinov et al.,

2014; Wu et al., 2014), dropping a small

number of cells that were qualitatively inconsistent with the

rest (Figure S7). We used the measured relationship between

GAPDH mRNA count and cellular volume from RNA FISH to

convert total RNA to relative cellular volume, matching this to

our measured volume distribution to obtain cellular volumes.

We then used the correlation between FPKM (fragments per kilo-

base per million fragments mapped) and RNA FISH counts for

our gene panel to provide estimates of absolute RNA counts

8, 1–14, April 16, 2015 ª2015 Elsevier Inc. 9

A B

C

D E F

Figure 7. Connection between Single-Cell RNA Sequencing and RNA FISH Reveals that Cell-Type-Specific Genes Exhibit Higher Noise

Levels

(A) RNA FISH data. TBCBmRNA abundance and volume in primary fibroblast cells. Each point represents a single-cell measurement. Histogram indicates mRNA

distribution. Arrow indicates volume-corrected noise measure. Gray line is best linear fit.

(B) Volume-corrected noise measure values for different genes in primary fibroblast cells. Each data point represents a collection of single-cell measurements for

one gene. The straight gray line represents the Poisson limit. The curved gray line is the Poisson limit plus our experimental noise limit, a combination of the

Poisson limit and a 15% measurement error. Data for each gene is a combination of at least two biological replicates, with at least 30 cells per replicate.

(C) Pipeline for converting FPKM from single-cell sequencing to RNA-FISH-equivalent counts and cellular volume in picoliters.

(D) Qualitative comparison of count versus volume from RNA FISH and single-cell RNA sequencing. Example low-Nm (GAPDH) and high-Nm genes (MYC).

(E) Comparison between Nm calculated from RNA FISH data and single-cell RNA-seq data. Each point represents a single gene. Nm is calculated by boot-

strapping.

(F) Breakdown of high- and low-noise genes into ubiquitously expressing genes and genes that express in a cell-type-dependent manner.

All error bars represent 95% confidence intervals as calculated by bootstrapping. See also Figures S5–S7.


for all genes in individual cells. (It is important to note, however,

that there is substantial variance in this relationship and that the

relationship is nonlinear.) We found that both RNA FISH and sin-

gle-cell mRNA sequencing yielded similar results for noisy and

non-noisy genes (Figures 7D and 7E).

Upon quantifying Nm for the genes in our RNA FISH experi-

ments in both human foreskin fibroblast and lung cancer cells,

we noticed that three of the four genes (ICAM1, LUM, and

ACTA2) with the strongest degree of cell-type specificity were

also the three genes with the highest noise measure of all the

genes in our study (Figure S7). To see whether this trend held

more generally, we used our single-cell RNA-sequencing data

to explore noise measure across all genes. We selected genes

with high or low noise measures and looked for enrichment in

genes exhibiting cell-type-specific expression between human

lung cancer cell and foreskin fibroblast cell data. We found that


the set of high noise measure genes contained a significantly

higher proportion of cell-type-specific genes than did the set of

low noise genes (Figure 7F; see Figure S7 for classification de-

tails). Such findings mirror those showing that more ubiquitously

expressed ‘‘housekeeping’’ genes typically exhibit lower levels

of noise than other types of genes, although the notion of cell-

type specificity is more difficult to relate to studies performed

in single-celled organisms (Bar-Even et al., 2006; Newman

et al., 2006; Taniguchi et al., 2010).

DISCUSSION

We have shown that individual cells globally control transcription

to compensate for variability in the ratio of DNA to cellular

content. Our results point to two independent mechanisms:

one that compensates for cell size fluctuations and one that


compensates for DNA content changes during the cell cycle.

These compensatory mechanisms help to maintain the concen-

tration of mRNA in the cell, which is presumably useful from the

perspective of the cell because the rates of most chemical reac-

tions in the cell depend on concentration rather than absolute

number. Importantly, the fact that these mechanisms seem to

be global in nature, and not gene-specific, means that other

specific forms of transcriptional regulation, for instance, during

development or in response to particular cues, can still function

properly in both large and small cells without the need for a

complex interplay between the specific regulation and the

mechanism governing concentrational homeostasis; indeed,

the expression of most genes is likely not specifically compen-

sated to maintain concentration (Springer et al., 2010). This is

not to say that the concentration of most gene expression prod-

ucts is arbitrary and unregulated. Rather, these global mecha-

nismsprovide ameansbywhichanysuch regulationmayoperate

to achieve said concentrationwithout having to take into account

differences inDNAcontent due to cellular volumeorDNAcontent

differences. This is important in a number of biological contexts

such as development and embryogenesis, in which rapid cell di-

visions lead to an exponential decrease in individual cell volume,

but the organism must maintain the concentration of most pro-

teins while still enabling dynamic transcriptional programs to

occur (Nair et al., 2013).

Our work also highlights the importance of taking cellular vol-

ume into account when interpreting gene expression data and

points to the significance of global factors in studying single

cell expression in general (Elowitz et al., 2002; das Neves

et al., 2010; Volfson et al., 2006). In particular, our cell fusion ex-

periments show that changing the amount of cellular content in

and of itself can lead to changes in total RNA abundance,

whereas previous experiments largely relied on cell-size mutants

that make inferences of cause and effect more difficult (Fraser

and Nurse, 1979; Miettinen et al., 2014; Schmidt and Schibler,

1995; Zhurinsky et al., 2010). These cell fusion experiments

directly establish that any perturbation that changes overall

cellular volume may result in global changes in overall transcript

abundance as a secondary rather than primary effect per the

generic mechanism of a diffusible trans factor that senses an

increased ratio of volume to DNA. Thus, we believe one must

take care in interpreting experiments showing global changes

in transcript abundance (Lin et al., 2012; Nie et al., 2012), both

from the perspective of establishing causal relationships, given

that cellular volume/content can by itself change transcription

rates, and in the interpretation of the functional significance,

given that the concentration of many transcripts will remain

roughly the same despite these overall changes. Our study

does not, however, address the question of why cells have

different volumes and how expression plays a role in that hetero-

geneity—such questions necessarily involve the examination of

mechanisms regulating cell growth and proliferation. Rather,

our results show how cells may globally cope with such changes

to maintain biomolecule concentration.

We do not yet know the factor (or factors) linking the ratio of

cellular volume to DNA content to the amount of transcription.

One potential candidate is some element of the general tran-

scription machinery, such as the RNA polymerase II holoen-

zyme. We have shown that RNA polymerase II is almost com-

pletely nuclear, and it is required for transcription; indeed, its

reduction changes burst size, much as reduced volume does.

Its transcription also scales with volume (Figure S4). Other

studies (Marguerat and Bahler, 2012; Zhurinsky et al., 2010)

have speculated that RNA polymerase II holoenzyme may act

as a limiting factor titrated by DNA, but various studies (Kimura

et al., 1999, 2002) show that RNA polymerase II is directly asso-

ciated with DNA only for short periods of time. Thus, we believe

that the scenario in which the factor remains in the nucleus and

nuclear volume scales only weakly with cell size is themost plau-

sible given our current evidence (see Supplemental Information).

Indeed, our model shows that the weak scaling of nuclear vol-

ume with cell size we observe can also explain why we observed

higher mRNA concentrations in smaller cells, sometimes by a

factor of two or more, further supporting this view, although

more experiments will be required to establish this model.

Regardless of the origin of the effect, it is clear that mRNA con-

centration is typically higher in smaller cells. We do not know the

consequences of this effect, in particular on cell growth, or how it

may vary in different cell types and contexts. One practical

consequence of this finding is that the time-honored practice

of normalizing transcript levels to GAPDH mRNA abundance,

although largely sound, does not fully account for differences

in mRNA concentration between small and large cells. This

suggests that new strategies may be required for measuring

cellular volume when interpreting single-cell RNA-sequencing

experiments.

We found it striking that the volume compensationmechanism

is distinct from the one that compensates for changes in DNA

content as the cell cycle progresses. We found that the burst fre-

quency appears to decrease uponDNA replication for each gene

rather than at a particular time in the cell cycle for all genes. One

plausible explanation for this feature is to ensure proper tran-

scriptional output regardless of whether a gene is replicated

early or late in S phase, which can proceed for many hours.

The molecular underpinnings of this mechanism remain unclear,

although our results demonstrate that it must be a factor that re-

mains bound to DNA and changes in character during DNA repli-

cation. A likely candidate may be a DNA or histone modification

that completely coats the DNA during G1 but that is diluted by a

factor of two upon DNA replication in S phase.

Together, these findings provide a deeper quantitative under-

standing of single-cell gene expression and its role in main-

taining cellular homeostasis. Further work may elucidate how

these homeostatic mechanisms for maintaining biomolecule

concentration manifest themselves in biological contexts and

whether they are an important point of dysregulation in disease

processes.

EXPERIMENTAL PROCEDURES

Cell Culture

We grew primary human foreskin fibroblast cells (CCD-1079Sk, ATCC CRL-

2097) and A549 cells (human lung carcinoma, A549, ATCC CCL-185)

in Dulbecco’s Modified Eagle Medium supplemented with 10% FBS and

50 U/mL penicillin and streptomycin (Pen/Strep). To create quiescent cells,

we grew primary fibroblast cells in DMEM with Pen/Strep, without FBS for

7 days. We cultured WM983b-GFP-NLS cells (melanoma cell line from the



lab of Meenhard Herlyn) in Tu 2% media. The WM983b-GFP-NLS contains

EGFP fused to a nuclear localization signal driven by a cytomegalovirus pro-

moter that we stably transfected into the parental cell line.

RNA Fluorescence In Situ Hybridization and Imaging

We performed single-molecule RNA FISH on the samples as described previ-

ously (Femino et al., 1998; Raj and Tyagi, 2010; Raj et al., 2008). We co-stained

the actin cytoskeleton with Phalloidin-Alexa 488 (Life Technologies) to detect

cell boundaries.

We used Cyclin A2 mRNA to specifically label cells in S, G2, and M phases

(Eward et al., 2004). To distinguish cells in S phase from those in G2 (Robertson

et al., 2000;Whitfield et al., 2002), we labeled histoneH4mRNA (see Figure S3).

Table S1 lists all sequences of oligonucleotide probes.

We imaged the cells with a Nikon Ti-E equipped with appropriate filter sets.

We took a series of optical z sections, each 0.2–0.35 microns high, that

spanned the vertical extent of the cell.

Image Analysis and Quantification

We manually identified cell boundaries and counted and localized RNA spots

using custom software written in MATLAB (Raj and Tyagi, 2010; Raj et al.,

2008). We estimate the technical error in our RNA count determination to be

at most 15%.

To compute the volume of a cell, we used the 3D positions of a highly abun-

dant mRNA found by RNA FISH to define the outline of the cell, applying cor-

rections for bias in volume estimation. The volume computation did not

depend on the number of spots identified nor on the choice of mRNA (Fig-

ure S1). We limited ourselves to the cytoplasmic volume by removing a vertical

cylinder corresponding to the nuclear outline.

We identified transcription sites through intron/exon probe colocalization.

We manually annotated transcription sites by visually inspecting images of

intron and exon probes to determine instances of colocalized signal and

computationally determined their intensity.

RNA Degradation

Wemeasured UBC and IER2mRNA degradation by inhibiting transcription for

4 hr by applying actinomycin D at 1 ug/ml. We interpreted the data using

models of volume-dependent or -independent degradation.

LMNA siRNA Knockdown

We incubated primary fibroblast cells with an siRNA targeting LMNA for 72 hr,

verifying protein knockdown via western blot.

Heterokaryon Formation

We created heterokaryons by pelleting cultures of primary fibroblast cells

and WM983b-GFP-NLS cells and resuspending in PEG for 2 min. We

added media over the course of 5 min to allow cells to fuse, and then we

plated the cells onto two-well chambered coverglasses (Lab-Tek) and fixed

the cells after 12 hr.

Fractionation and RNA Polymerase II Western Blot

We performed cell fractionation and blotting as described in (Bhatt et al., 2012)

and based on (Wuarin and Schibler, 1994) with modifications.

Triptolide

We degraded RNA polymerase II in primary fibroblast cells by incubating cells

in 100 nM triptolide for 1 hr and then fixed cells in methanol.

Repli-seq Analysis

We accessed Repli-seq data fromHansen et al. (2010) using the UWRepli-seq

track on the UCSC Genome Browser.

Bulk RNA Sequencing

We sequenced total RNA from primary fibroblast cells. We used the NEB Next

Ultimate Library Preparation Kit for Illumina and the Ribo-Zero Magnetic Gold

Kit. We used 50 b single-end reads and sequenced each of two replicates at a

depth of 10–15 M reads. We aligned reads to hg19 using STAR’s included

annotation. We quantified reads per gene using HTSeq and a RefSeq hg19


annotation. We calculated FPKM for each gene using R. All sequencing data

are available at GEO accession number GSE66053.

Single-Cell RNA Sequencing

We prepared 96 cells for RNA sequencing on a Fluidigm C1 Single-Cell Auto

Prep System using a large-size chip. We added ERCC (External RNA Controls

Consortium) RNA controls, Mix 1 (Ambion 4456740) at a concentration of

1:10,000before loadingandpreparedcDNA librariesasper theFluidigm instruc-

tions.Weobtained75bpaired-end reads toadepthof�1–2Mreadspersample.

We quantified reads per gene using HTSeq and a RefSeq hg19 annotation and

transformed the data to obtain an estimate of molecule count per cell. All

sequencing data are available at GEO accession number GSE66053.

C. elegans Growth and Imaging

We grew N2 (wild-type) and CB502 (sma-2 mutant) C. elegans at 20�C under

standard conditions. We performed RNA FISH and analyzed the data as pre-

viously described (Raj et al., 2008), determining the volume of each analyzed

region computationally.

ACCESSION NUMBERS

The GEO accession number for the single-cell RNA sequencing (primary fibro-

blast cell line) and the bulk RNA sequencing (primary fibroblast and lung can-

cer cell lines) data reported in this paper is GSE66053.

SUPPLEMENTAL INFORMATION

Supplemental Information includes Supplemental Experimental Procedures,

seven figures, and one table and can be found with this article online at

http://dx.doi.org/10.1016/j.molcel.2015.03.005.

ACKNOWLEDGMENTS

We thank members of the Raj lab for many helpful suggestions. We thank

Jeff Carey for suggesting the LMNA knockdown experiment and Sydney

Shaffer for the WM983b-GFP-NLS cell line. We thank Jan Skotheim for useful

discussions about mechanism and John I. Murray for pointing out that the

simple trans factor model would lead to overtranscription in early S phase.

We thank Hyun Youk and Uschi Symmons for a careful reading of the manu-

script. We thank the Herlyn lab for providing the WM983b cell line. A.R. ac-

knowledges support from an NSF CAREER award, NIH New Innovator Award

1DP2OD008514, and a Burroughs Wellcome Fund Career Award at the Scien-

tific Interface. G.P.N. is a Howard Hughes Medical Institute Fellow of the Life

Sciences Research Foundation (http://www.lsrf.org). A.M. was supported by

Long-Term Postdoctoral Fellowships of the Human Frontier Science Program

(LT000314/2013-L) and EMBO (ALTF858-2012). L.S.C. acknowledges support

from anNIH grant (NHGRI R01HG007173), a Damon Runyon Cancer Research

Foundation Frey Award, and a Burroughs Wellcome Fund Career Award at the

Scientific Interface.

Received: September 22, 2014

Revised: January 16, 2015

Accepted: March 4, 2015

Published: April 9, 2015

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Molecular Cell

Supplemental Information

Single Mammalian Cells Compensate for Differences

in Cellular Volume and DNA Copy Number through

Independent Global Transcriptional Mechanisms

Olivia Padovan-Merhar, Gautham P. Nair, Andrew G. Biaesch, Andreas Mayer, Steven

Scarfone, Shawn W. Foley, Angela R. Wu, L. Stirling Churchman, Abhyudai Singh, and

Arjun Raj

Supplemental Figure 1

01000200030004000

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Figure S1, related to Figure 1.A and B. We monitored primary fibroblast cells on the microscope throughout the process of fixation. We took measurements of the same cells live, after fixing in 4% formaldehyde for 10 minutes, and after permeabilizing in ethanol for 30 minutes.A. We measured the areas of the cells through brightfield images.B. We measured the height of the cells by coating the cells with fluorescent beads. These measurements indicate that the cells remain roughly the same size throughout the fixation and permeabilization process.C. To demonstrate the robustness of the volume calculation algorithm, we calculated volume for the same cells using all the GAPDH mRNA spot coordi-nates as detected by RNA FISH, or using only half of the points, chosen randomly. Both methods result in approximately the same volume, suggesting that the number of points we use is sufficient to calculate the volume accurately.D. We calculated volume using a different gene, EEF2. On average, EEF2 has an abundance that is less than half that of GAPDH (mean EEF2 = 1079 mRNA/cell, mean GAPDH = 2673 mRNA/cell). Volume calculated using EEF2 is systematically lower than that calculated using GAPDH, but the values are similar.Black lines in C, D indicate a fit with intercept = 0 and slope = 1.E. mRNA concentration is similar in cycling and quiescent cells. We calculated the average mRNA concentration for 17 genes in both the cycling and quiescent state in human foreskin fibroblast cells. Each data point represents one gene. Each gene had a minimum of 2 biological replicates, with at least thirty cells per replicate. Line has intercept 0 and slope 1. Error bars represent standard error.F,G. We compared volume-dependent and -independent abundance for cycling and quiescent cells. Both volume-independent and volume-dependent expression are lower in quiescent cells. All error bars represent confidence intervals of the slope or intercept of the fit, normalized to the scale of the plot. In C and D, we omitted error bars that extended below zero. Each gene had a minimum of two biological replicates, with at least 30 cells per replicate. We omitted highly variable genes with intercept terms less than zero.

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ACTA2 ACTN4 BABAM1 DNMT1 EEF2 FTL

FUCA1 GAA GAPDH GAS6 ICAM1 IER2

KDM5A KDM5B LMNA LUM MYC PABPC1

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Figure S2, related to Figure 1.A, B. mRNA count (A) and concentration (B) measurements for all genes in cycling fibroblast cells. Each data point is an individual single cell measure-ment. In count plots, red line indicates best linear fit to the data. In concentration plots, red line indicates mean mRNA concentration. Each data set is a combination of at least two biological replicates.C. We measured ribosomal RNA by quantifying total fluorescence intensity in the cytoplasm from an rRNA FISH probe in cycling fibroblast cells.D. We measured the rRNA ITS (the rRNA “intron”) by quantifying total fluorescence intensity in the nucleus from an ITS RNA FISH probe.rRNA and the rRNA ITS both scale with volume to some degree, suggesting that the production of ribosomal RNA scales with volume. We have shown that mRNA scales with volume, so a similar scaling of rRNA is not inconsistent with the production of protein to scale with volume as well. The data shown for rRNA is one of three biological replicates.

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ACTA2 ACTN4 BABAM1 DNMT1 EEF2 FTL

FUCA1 GAA GAPDH GAS6 ICAM1 IER2

KDM5A KDM5B LMNA LUM MYC PABPC1

RBM3 RND3 SLC1A5 SUPT5H TBCB UBC

USF2 ZNF444

0500100015002000

0500

10001500

0255075100125

0100200

0100020003000

025005000750010000

01020

01020304050

02000400060008000

0300600900

020406080

0100200

01020304050

020406080

0500

1000

0400800

1200

050100150200

0250500750

0200400

0500

10001500

0100200300400

050

100

050100150200

0500

10001500

050

100150

010203040

0.0

2.5

5.0

7.5

0.0

2.5

5.0

7.5

Volume (picoliters)

mR

NA

Cou

nt

A

B

●

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0.0e+00

2.5e+07

5.0e+07

7.5e+07

0 1 2 3 4Volume (picoliters)

rRN

A IT

S In

tens

ity (a

.u.)

●

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0.0e+00

5.0e+08

1.0e+09

1.5e+09

2.0e+09

0 1 2 3 4Volume (picoliters)

rRN

A In

tens

ity (a

.u.)

DC


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0

500

1000

1500

0 100 200 300 400Cyclin mRNA

His

tone

mR

NA

0.0

0.1

0.2

0.3

0.4

2.5 5.0 7.5Volume (picoliters)

Frac

tion

of c

ells

G1G2

G1 G2S

A B

0.0000

0.0025

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100 200 300 400 500 600

Frac

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0.0 2.5 5.0 7.5Cellular volume (picoliters)

Nuc

lear

Are

a (µ

m2 )

Nuclear Area (µm2)

G1SG2

G1SG2

C D

Figure S3, related to Figures 1, 5, 6.A. We simultaneously measured CCNA2 and HIST1H4E mRNA by RNA FISH to precisely determine cell cycle position. Each data point is a single cell measurement. CCNA2 is highly expressed in S and G2, but not G1. HIST1H4E is highly expressed only in S phase. Cells with low CCNA2 and HIST1H4E are in G1 (cutoff = 20 CCNA2 mRNA), cells with mid-range CCNA2 and high HIST1H4E are in S, and cells with high CCNA2 and low HIST1H4E are in G2 (cutoff = 230 CCNA2 mRNA). We determined thresholds for all samples using this method. Data shown are from one of four biologi-cal replicates.B. Volume distributions in G1 and G2. We determine cell cycle position using CCNA2. We note that G2 cells are larger than G1 cells, but only 10% larger on average, possibly due to non-linearities in growth over the course of the cell cycle. n = 841 cells in G1, 191 cells in G2. C. Nuclear area vs. cytoplasmic volume. We measure cytoplasmic volume using our standard method. We measure nuclear area using the DAPI stain. We note that we only measure nuclear area and not volume.D. Density plot of nuclear area across cell cycle stages. While nuclear area generally scales with cytoplasmic volume, there is considerable spread in the data (R2 = 0.358). n = 1866 cells.


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Control 100nMTriptolide Concentration

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n nu

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r of t

rans

crip

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s pe

r cel

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Control 100nMTriptolide Concentration

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n nu

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r of t

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r cel

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G2WS

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E MYC UBC TUSC3

Figure S4, related to Figures 5 and 6.A. Quantification of RNA Polymerase II mRNA (quantified by RNA FISH) vs. cytoplasmic volume in A549 cells. Data shown is from a single biological replicate.B,C.We treated primary fibroblast cells with 100nM triptolide (100nM) or simply replaced the media on cells (Control) and fixed in methanol after one hour. For genes UBC and MYC, we identified active transcription sites through intron/exon colocalization and quantified the number of active transcription sites per cell with and without triptolide. Bars represent mean of cells, error bars are SEM. The data shown here is from two replicates, for a total of 282 cells for UBC and 257 cells for MYC. For both genes, the change in frequency between conditions is small compared to the change in intensity (Fig. 5).D. UBC mRNA in a CRL2097 cell. RNA FISH probe in white, DAPI stain in purple. White arrows indicate transcription sites. We detect transcription sites through intron/exon colocalization by RNA FISH. This cell is in G2 and has four transcription sites, demonstrating that all gene copies are transcriptionally competent after replication.E. Tracks from UCSC genome browser displaying UW Repli-Seq data in GM12878 (lymphoblastoid), K562 (chronic myelogenous leukemia), HeLa (cervical cancer), HepG2 (liver carcinoma), and HUVEC (human umbilical vein endothelial) cell lines. The track displays data for different points in the cell cycle: G1, S1 (early S phase), S2 (middle-early S phase), S3 (middle-late S phase), S4 (late S phase), and G2. WS represents a wavelet-smoothed transform of the six other tracks. This data was generated by sequencing newly-replicated DNA in each point in the cell cycle. Darkness of track corresponds to read density. Each track shown corresponds to entire length of each gene. Data is shown for early replicating genes EEF2, MYC, UBC, and a late replicating gene, TUSC3.

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IER2

KDM5A

KDM5B

LMNA

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5H

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ZNF444

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corre

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se M

easu

re

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1

2

ACTA2

FUCA1

ICAM

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MMYC

RND3

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me-

corre

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se M

easu

reC


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5 10 15 20 25mRNA Halflife (hours)

VROXPHïFRUUH

FWHG�

Noi

se M

easu

re

BA

Figure S5, related to Figure 7.A. TBCB mRNA concentration vs. volume. These data are the same as in (Fig. 7A), but each is normalized by volume. Histogram indicates distribution of mRNA concentration. Gray line indicates average concentration. Data are from a combination of two biological replicates.B. We compared volume-corrected noise measure and mRNA half-life. We obtained half-life values from Tani et al., Genome Res. (2012). We find that volume-corrected noise measure does not depend strongly on half-life. Each data point represents one gene. For each gene, we have at least two biolog-ical replicates with at least 30 cells per replicate. Error bars represent 95% confidence intervals, calculated by bootstrapping.C. mRNA CV, concentration CV, and volume-adjusted CV for cycling primary fibroblast cells. Inset shows genes that exhibit higher cell-to-cell variability in RNA, and had values too high for main axes. Generally, mRNA CV is highest, followed by concentration CV and volume-adjusted CV. Error bars represent 95% confidence intervals by bootstrapping.

CV2 RNA CountCV2 RNA ConcentrationNm

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�Hï�� 1e+01 1e+03ERCC reference concentration (attomoles/microliter)

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n FP

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0 2500 5000 7500Total RNA per cell equivalent

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0

2.5

5

7.5

0 2000 4000 6000 8000GAPDH mRNA count (FISH)

Volu

me

(pic

olite

rs)

Total GAPDH by FISHNormalized Genomic/ERCC FPKM ratio

FPKM cutoff


DC

BA

y = 1.07x-532

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100

10000

10 100 1000Mean molecule count by RNA FISH

Mea

n FP

KM

Figure S6, related to Figure 7.A. Mean FPKM and known concentrations for each of the ERCC reference transcripts. Each point represents a single ERCC transcript and is an average over all 96 samples. Below 10 FPKM, we begin to see significant dropouts, and therefore choose and FPKM of 10 as our cutoff for “reliable” measure-ments. All other data in the manuscript is taken from genes having greater than 10 FPKM.B. Mean count as measured by RNA FISH vs. mean FPKM from single-cell RNA sequencing. Each point represents a single gene and is an average over 44 single cells for single-cell sequencing, and an average over at least two biological replicates with at least 30 cells apiece for RNA FISH. These data suggest that an FPKM of 1 corresponds to approximately 23.2 transcripts per cell, as measured by RNA FISH in our cells, although the relationship between RNA FISH counts and FPKM scales nonlinearly (FPKM ~ (FISH)1.7, see Methods). We used this fitted relationship between RNA FISH count and FPKM to transform FPKM into transcript counts. Error bars represent SEM.C. Comparison of “total RNA” distributions from single-cell sequencing and RNA FISH. Data represent a collection of total RNA measurements from single cells. We assume that total GAPDH mRNA counts by RNA FISH are proportional to total RNA. For sequencing data, we use the ratio of reads mapped to genomic loci to reads mapped to ERCCs as a proxy for total RNA. We scaled this ratio to have the same mean as the distribution of total RNA by RNA FISH. After scaling, the distributions are similar, suggesting that our method for measuring total RNA via the ratio of genomic to ERCC transcripts is sound.D. Mapping between total RNA count (here, total GAPDH mRNA in single cells) and volume, as measured by RNA FISH. Each point represents a single cell. We use this mapping to convert total RNA from sequencing experiments to actual volume. The red line is the best fit, as computed by principle components analysis.

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0e+00

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0 30 60 90 120Genomic/ERCC count ratio ("volume")

GAP

DH

FPK

M *

coun

t rat

io ("

coun

t")

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0

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0e+00 1e+06 2e+06 3e+06Summed genomic counts

Sum

med

ER

CC

cou

nts "Good” cells

Eliminated cells


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0.1

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0.1 1.0 10.0Count CV2 (RNA FISH)

Cou

nt C

V2 (s

ingl

e-ce

ll R

NA

sequ

enci

ng)

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r = 0.5898 r = 0.6449

Count CV2 (RNA FISH)

FPKM

CV2

(sin

gle-

cell

RN

A se

quen

cing

)BA C D

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ï�

0

1

1 2 3 4Log 10 (mean FPKM)

Log

10 (T

rans

form

ed N

m)

Fibroblast-specific genesUbiquitously expressed genes

Cutoff for high Nm genes

Cutoff for low Nm genes

IH

G

Figure S7, related to Figure 7.A. “Count” vs. “volume” for GAPDH from single-cell sequencing data. We define “volume” as the ratio between genomic reads and ERCC reads for each cell. This quantity is more representative of total RNA, which we know to be roughly proportional to volume, although the relationship is not exactly proportional due to volume-independent transcription (see main text Figure 1). We observed two clearly distinct classes of cells, those with a volume range that matches what we see by imaging and RNA FISH and those that have very low volumes. For unknown reasons, these cells ended up with a considerably higher ratio of ERCC reads than genomic reads, and we eliminated them from our subsequent analyses.B. ERCC counts vs. genomic counts for the cells that we kept and those we eliminated.C. Correspondence between CV2 of RNA FISH counts and CV2 of inferred counts from single-cell sequencing (transforming FPKM as in Supplemental Figure 6 and Methods). Each data point represents a single gene. D. Same as C, except using CV2 of FPKM from single-cell sequencing instead. Correlation is slightly higher than in C.For C and D, error bars represent 95% confidence intervals, calculated by bootstrapping.E. Average mRNA counts in cycling primary fibroblasts and A549 cells, calculated using RNA FISH. Gray line indicates a 1:1 correspondence. Error bars represent standard error of the mean.F. Volume-corrected noise measure in cycling primary fibroblast and A549 cells, calculated using RNA FISH. Gray line indicates a 1:1 correspondence. Nm calculated by bootstrapping; error bars represent 95% confidence interval. Data in E and F for each gene is a combination of at least two biological replicates, with at least 30 cells per replicate.G. FPKM measurements from bulk RNA-sequencing in primary fibroblast and A549 cells. Each point represents one gene. We classified genes as “ubiquitously expressed” if they had >5 FPKM in both cell types and differed by less than a factor of 2 in FPKM across the two cell types. We considered genes “fibroblast specific” if they had >5 FPKM in fibroblasts and their FPKM was greater than five times higher in fibroblasts that A549 cells.H. Single-cell RNA-sequencing data in primary fibroblast cells. Each point represents one gene. We used the method described in Supplemental Figure 6 and Methods to calculate Nm for each gene. We observe that higher abundance genes typically have lower Nm values. Red line indicates best fit line.I. The same data as in H, but transformed to remove the abundance dependence from Nm. Red line here is transformed fit line from H. We use this transformed data to select abundance-matched “low” and “high Nm” genes using a cutoff of Nm=0.5 and Nm=-0.5, respectively. We selected 307 high Nm genes and 257 low Nm genes. Note that these high Nm genes actually have a higher mean abundance (FPKM=196.5) than the low Nm genes (FPKM=55.4), thus showing that the observed differences in noise levels are not due to the overall increase in noise in genes of low abundance.

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●● ●0.1

10.0

0.110.0

Volume-corrected noise measure (CRL2097)

Volu

me-

corre

cted

noi

se m

easu

re

(A54

9)

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10

1000

10 1000Mean mRNA (CRL2097)

Mea

n m

RN

A (A

549)

LUM

ACTA2

ICAM1

ACTA2

ICAM1LUM

GAS6

FE

Supplemental Experimental Procedures Cell culture: We grew primary human foreskin fibroblast cells (CCD-1079Sk, ATCC CRL-2097™) and A549 cells (human lung carcinoma, A549, ATCC CCL-185™) in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% FBS and 50U/mL penicillin and streptomycin (Pen/Strep). To create quiescent cells, we grew primary fibroblast cells in DMEM with Pen/Strep, without FBS for seven days. We cultured WM983b-GFP-NLS cells (WM983b is a human melanoma from the lab of Meenhard Herlyn) in Tu2% media (78% MCDB media, 20% Leibovitz’s L-15 media, 2% FBS, and 1.68 mM CaCl2). The WM983b-GFP-NLS contains EGFP fused to a NLS driven by a cytomegalovirus promoter that we stably transfected into the parental cell line. Before imaging, we plated cells on two-well Lab-Tek chambered coverglasses. RNA fluorescence in situ hybridization and imaging: We performed single molecule RNA FISH on the samples as described previously (Femino et al., 1998; Raj and Tyagi, 2010; Raj et al., 2008). Briefly, we fixed the cells in formaldehyde or methanol, performed RNA FISH using the specified pools of oligonucleotides, then washed and stained nuclei with DAPI. We fixed most cells in this study using formaldehyde, but used methanol for the experiments involving transcription site quantification because it resulted in more accurate transcription site detection. We stained the actin cytoskeleton with Phalloidin-Alexa 488 (Life Technologies) to detect cell boundaries. We typically co-stained with sets of probes targeting many different mRNA. Typically, we used exon probes labeled in Alexa 594, introns with Cy3, Cyclin A2 with Atto 647N (which labels cells in S, G2 and M phase (Eward et al., 2004)) and GAPDH with Atto 700. To distinguish cells in S phase from G2 (Robertson et al., 2000; Whitfield et al., 2002), we labeled Histone H4 mRNA with Atto 647N and Cyclin A2 mRNA with Atto 700 (see Supplemental Fig. 3). Supplemental data file 1 lists all sequences of oligonucleotide probes. We imaged the cells with a Nikon Ti-E equipped with appropriate filter sets. We took a series of optical z-sections, each 0.2-0.35 microns high, that spanned the vertical extent of the cell. Image analysis and quantification: We manually identified cell boundaries and counted and localized RNA spots using custom software written in MATLAB (Raj and Tyagi, 2010; Raj et al., 2008). We estimate the technical error in our RNA count determination to be at most 15%. To compute the volume of a cell, we detected the 3D positions of a highly abundant mRNA by RNA FISH. We selected only the points that defined the outer boundary of the cell by examining each point and its neighbors within a 4µm radius. We kept only the points that had a higher z-position than their neighbors (signifying the top of the cell) or points that had no neighbors within 180 degrees (signifying the side of the cell). Once we had the points, we interpolated the points to identify a smooth representation of the cell surface. We repeated this in both an upward and

downward direction to identify the top and bottom of the cell. We calculated the volume of the cell by summing the heights between the top and bottom. Calculating the volume in this manner will always result in an underestimation of the actual volume. To correct for this bias, we first computed the outline of the cell as described above. We then dilated this hull, filled it with the same number of randomly distributed points, and then repeated the algorithm on this new set of points. If this volume matched that computed with the actual spots in the cell, we then computed the volume by integrating between the top and bottom boundaries of the dilated hull. We used GAPDH mRNA as the primary mRNA for our volume determinations, but the volume computation did not depend on the number of spots identified nor on the choice of volume-filling gene (Supplemental Fig. 1). We limited ourselves to the cytoplasmic volume by removing a vertical cylinder corresponding to the nuclear outline. This procedure does exclude the cytoplasmic volume above and below the nucleus, but that region only comprised a very small proportion of the total cytoplasmic volume. We identified transcription sites through intron/exon probe colocalization. We manually annotated transcription sites by visually inspecting images of intron and exon probes to determine instances of colocalized signal. To determine spot intensity, we identified the z-plane of maximum intensity in a 0.375µm-square region around the manually selected spot. We defined the intensity as the difference between this maximum value and a background value. For the background value, we used the median intensity in a 3.75µm-square annular region around the maximum intensity point. Note that transcription site intensity need not necessarily linearly relate to transcriptional burst size (Senecal et al., 2014). RNA degradation: We measured RNA degradation by inhibiting transcription for four hours by applying actinomycin D at 1ug/ml. We measured degradation of UBC and IER2 mRNA because they exhibited a strong correlation with volume while having a half-life short enough to enable us to observe substantial degradation within four hours of actinomycin D treatment while avoiding non-specific effects at longer times. We used a model to determine whether degradation was volume-dependent (degradation ~ 1/V) or volume-independent (degradation ~ constant). We first fit the untreated mRNA vs. volume data with a line having zero intercept. If degradation is volume-independent, we expect the treated cells to also be well-fit by a line having zero intercept, where the slope is determined by the untreated fit and an exponential decay term:

!!"(!) = !!!!!!" , where !!" is the mRNA count after 4 hours of treatment, !! is the slope of the untreated data (! = 0), ! is the decay constant (degradation rate), and ! is the treatment time. Note that here ! is the only fit parameter and is independent of volume. If degradation is volume-dependent, the equation becomes:

!!"(!) = !!!!!!"/! . Here, !/! is the decay constant (degradation rate), but ! itself is independent of volume and is the fit parameter. The line and curve described by these equations are the fits to the raw data, and the decay constants ! and !/! are the fits we show to the calculated decay constants that we show in Fig. 3A-B. We calculated the actual decay constant (Fig. 3A-B) for each cell measured at the 4 hour timepoint assuming exponential decay:

!!"(!) = !!"(!)!!!" , where we approximate !!" = !!!, and ! could in principle be either volume-dependent or -independent. LMNA siRNA knockdown: We used an siRNA targeting LMNA (Cat. #: AM16708, ID: 40502) at 30nM and a “scramble” control siRNA (Cat. #: AM4611) at 30nM. We incubated primary fibroblast cells with the siRNA for 72 hours. We verified protein knockdown via Western blot, using the SC-20680 (rabbit) antibody and a goat-anti-rabbit 680 RD secondary (Licor 926-68071). Heterokaryon formation: We created heterokaryons by separately culturing primary fibroblast cells and WM983b-GFP-NLS cells. Once the plates were 70-90% confluent, we trypsinized the cells, resuspended them in DMEM Complete media, and combined half of each plate of cells in a 15ml tube. We pelleted the cells and resuspended in PEG for 2 minutes. We added media over the course of five minutes to allow cells to fuse, then plated the cells onto two-well chambered coverglasses (Lab-Tek) and fixed the cells after 12 hours. We identified heterokaryons as cells with two nuclei that expressed both GFP (WM983b-GFP-NLS only) and GAS6 mRNA (primary fibroblast) by RNA FISH. We eliminated all homokaryons from our analyses. Fractionation and RNA polymerase II Western blot: We performed cell fractionation as described in (Bhatt et al., 2012) and based on (Wuarin and Schibler, 1994) with modifications. We conducted all subsequent steps on ice or at 4°C and in the presence of 25 µM α-amanitin (Sigma, A2263) and Protease inhibitors cOmplete (Roche, 11873580001) according to manufacturer’s instructions. We pre-chilled all buffers on ice before use. We grew primary fibroblast cells to a confluency of 90%. We removed media and washed plates twice with 1x PBS before scraping cells into 1x PBS. We collected cells by centrifuging at 500 g for 10 min. We gently resuspended the cell pellet corresponding to 1x107 cells in 200 µl cytoplasmic lysis buffer (0.15% NP-40, 10 mM Tris-HCl pH 7.0, 150 mM NaCl). We incubated the cell lysate for 5 min on ice, layered it onto 500 µl sucrose buffer (10 mM Tris-HCl pH 7.0, 150 mM NaCl, 25% sucrose) and centrifuged at 16,000 g for 10 min. We carefully removed the

supernatant (600 µl) corresponding to the cytoplasmic fraction. We gently resuspended the nuclei pellet in 400 µl nuclei wash buffer (0.1% Triton-X-100, 1 mM EDTA, in 1x PBS) and centrifuged it at 1,500 g for 1 min. We removed the supernatant and gently resuspended the pellet in 200 µl glycerol buffer (20 mM Tris-HCl pH 8.0, 75 mM NaCl, 0.5 mM EDTA, 50% glycerol, 0.85 mM DTT). Next, we added 200 µl nuclei lysis buffer (1% NP-40, 20 mM Hepes pH 7.5, 300 mM NaCl, 1M Urea, 0.2 mM EDTA, 1 mM DTT), vortexed, incubated on ice for 2 min and centrifuged at 18,500 g for 2 min. We carefully removed the supernatant corresponding to the nucleoplasmic fraction (350 µl) and added 250 µl 1x PBS/Protease inhibitors cOmplete to adjust the volume for Western blot experiments (described below). We resuspended the chromatin pellet in 600 µl chromatin resuspension solution (25 µM α-amanitin, Protease inhibitors cOmplete, in 1x PBS). We monitored the success of cell fractionation by Western blot analyses. For Western blot analyses, we probed membranes with the following primary antibodies: Pol II (F-12, Santa Cruz Biotechnology; directed against the N-terminal region of Rpb1), Pol II Ser2-P (3E10, Active Motif), Pol II Ser5-P (3E8, Active Motif), Histone 2B (FL-126, Santa Cruz Biotechnology), U1 snRNP70 (C-18, Santa Cruz Biotechnology) and GAPDH (6C5, Applied Biosystems). Next, we probed membranes with Cy5- and Alexa Fluor 647-conjugated secondary antibodies (Cy5 goat anti-mouse, A10524; Cy5 goat anti-rabbit, A10523; Cy5 goat anti-rat, A10525; Alexa Fluor 647 rabbit anti-goat, A21446; Life Technologies), and scanned using a Typhoon 9400 scanner (GE Healthcare). We quantified fluorescent signals with ImageJ 1.47v software. Triptolide: We degraded RNA polymerase II in primary fibroblast cells by incubating cells in 100nM triptolide for one hour, then fixed cells in methanol (control cells remained untreated). Cell size verification: To check that fixation did not alter cell size, we monitored the size of cells through the fixation and permeabilization process by fixing cells while on the microscope stage. We monitored cell area by taking images in brightfield, and we monitored cell height by coating the cells with fluorescent beads and imaging them in a series of optical z-sections. We took images of the same cells after 10 minutes of fixing in 4% formaldehyde and after 30 minutes of permeabilization in ethanol. We calculated cell area by segmenting the cells as usual, and we determined height by identifying the plane of the bottom of the cell and the plane of the top of the cell (the last plane where beads remain motionless) and subtracting the two values. Quantification of cell-to-cell variability: We developed a phenomenological metric for cell-to-cell variability that takes into account both volume-correlated and volume-independent contributions to mRNA numbers per cell (see supplemental note for derivation and further information). We also used a model of transcriptional bursting with volume-dependent transcription that enabled us to quantify transcriptional parameters from population distributions of mRNA counts and volumes. Repli-seq analysis:

We accessed Repli-seq data from Hansen et al. 2010 (Hansen et al., 2010) using the UW Repli-seq track on the UCSC Genome Browser. Bulk RNA Sequencing: We sequenced total RNA from primary fibroblast cells. We used the NEB Next Ultimate Library Preparation Kit for Illumina and the Ribo-Zero Magnetic Gold Kit. We used 50b single-end reads and sequenced each of two replicates at a depth of 10-15M reads. We aligned reads to hg19 using STAR’s included annotation. We quantified reads per gene using HTSeq and a RefSeq hg19 annotation. We calculated FPKM for each gene using R. All sequencing data is available at GEO accession number GSE66053. Single-cell RNA Sequencing: We isolated 96 single cells, lysed, and performed first- and second-strand synthesis on a Fluidigm C1 Single-Cell Auto Prep System using a large size chip. We spiked in ERCC (External RNA Controls Consortium) RNA controls, Mix 1 (Ambion 4456740) at a concentration of 1:10,000 before adding the cells to the C1. We prepared cDNA libraries using the Nextera XT DNA Sample Preparation Kit (Illumina, PN FC-131-1096) and used 96 paired barcodes from the Nextera XT DNA Sample Preparation Index Kit (96 Indices, 385 Samples) (Illumina, PN FC-131-1002) following the abbreviated Fluidigm protocol for the Nextera XT kit. We sequenced the samples on a NextSeq 500 using 75b paired-end reads to a depth of ~1-2M reads per sample. To quantify sequencing data, we aligned reads to hg19 (using STAR’s included annotation) and the ERCC reference transcripts. We quantified reads per gene using HTSeq and a RefSeq hg19 annotation. All sequencing data is available at GEO accession number GSE66053. Single-cell RNA Sequencing Calibration and Analysis: We independently calculated ERCC and genomic FPKM for each sample, normalizing to the total number of reads mapped to ERCC loci or genomic loci, respectively. All FPKM data shown for endogenous genes is this genomic FPKM. For each cell, we considered the ratio of total genomic reads to total ERCC reads to be proportional to the total starting amount of mRNA in that cell. We sequenced 96 wells total, of which 5 were “control” wells that contained no cells and 14 were wells containing fixed cells. We excluded these 19 cells from the analysis. Further, we excluded 12 cells that had fewer than 1 million total reads, and 21 cells that had a genomic/ERCC read ratio of less than 30. We performed all further analyses on the 44 remaining cells. Transform read ratio to volume: We assumed that the ratio of genomic/ERCC reads for each sample was proportional to the total mRNA in each cell. We also assumed that, for our RNA FISH measurements, total GAPDH mRNA counts were proportional to the total amount of mRNA in each cell. The distributions for total mRNA obtained in this manner were similar between RNA FISH and single-cell RNA sequencing, but had different means. We therefore normalized the sequencing data to have the same mean as the RNA FISH distribution. From our RNA FISH dataset, we have many co-measurements of GAPDH mRNA (total mRNA) and

volume from which we establish a transformation equation between total mRNA and volume. We obtained this transformation equation using PCA, or orthogonal regression. Using this equation, we transformed total mRNA obtained through sequencing into actual volume in picoliters. Transform FPKM to molecule count: FPKM is more a measure of mRNA concentration than mRNA count, as it is normalized to total reads. To get a measure more similar to mRNA count, for each cell, we multiplied each gene’s FPKM by the genomic/ERCC count ratio (“volume”) of the cell. For each gene in our RNA FISH dataset, we fit the log of the seq “counts” and the log of the actual counts from RNA FISH by orthogonal regression. We then used this transform to convert the FPKM of all genes to their RNA FISH count equivalent. Note that, because we fit in log space, the transform between FPKM and count is nonlinear, and actually scales as approximately FPKM ~ (RNA FISH)1.7. Once we had our single-cell sequencing data in terms of RNA FISH count and volume in picoliters, we calculated Nm as described for RNA FISH. We performed all of our sequencing analysis in R. C. elegans growth and imaging: We grew N2 (wild type) and CB502 (sma-2 mutant) C. elegans on NGM agar plates with OP50 lawns, kept at 20º C. Every 2-3 days, we transferred a small portion of each strain to new plates to prevent overgrowth. We released the worms off of the plates using phosphate buffered saline (PBS) solution, then fixed with 4% formaldehyde for 45 minutes. We permeabilized and stored the worms in 70% ethanol. We performed the RNA FISH protocol, then mounted the sample between a slide and coverglass before imaging. We manually identified head and gonad boundaries and counted and localized RNA spots using custom software written in MATLAB. To compute the volume of each worm segment, we multiplied the area of the segment by the height of the segment (thus approximating the segment as a prism). We determined the height by taking the vertical difference between the highest and lowest RNA spots’ positions, as determined by our software. We determined the number of cells in each segment through manually counting the DAPI-stained nuclei. We obtained data from multiple segments. When combining the data (number of mRNA spots per volume or per nucleus), we weighted each segment by its volume.

Model of di↵usible trans factor for volume:DNA ratio sensing

Here, we outline a fairly generic model for how a di↵usible trans factor may transmit informationon the ratio of volume to DNA to lead to increased transcription in larger cells irrespective of DNAcontent. The primary assumptions are that the factor is predominantly localized to the nucleus, thefactor is required for mRNA transcription, and the cellular concentration of the factor is roughlyconstant irrespective of cellular volume (i.e., the total amount of factor is proportional to cellularvolume). RNA polymerase II holoenzyme satisfies these conditions, although we do not claim thatRNA polymerase II is the factor.

The model assumes binding of the factor to the DNA, and that only bound factor can result inproductive transcription. The goal of the model is to provide a basis for the empirical finding thatlarger cells have increased transcription from the same absolute number of DNA molecules. Ourmodel encompasses two broad categories of mechanism that would lead to a perfectly linear scalingof transcription with cytoplasmic volume: (1) The factor is sequestered entirely in the nucleus, andso if the nucleus doesn’t change with cellular volume, the concentration of the factor in the nucleuswill be proportional to the total amount of factor. Thus, the factor will be proportionally morebound to the DNA in a larger cell than a smaller cell, producing more transcription. (2) The factoris a purely “limiting” factor in the sense that it has a very high a�nity for DNA and the number ofbinding sites exceeds the amount of factor. In this situation, essentially all available factor will bebound to the DNA, and so for each gene, there would be proportionally more transcription in largercells because more factor would be bound to DNA. These mechanisms are not necessarily mutuallyexclusive. The model incorporates a�nity and nuclear volume as parameters, and so encompassesboth of these potential mechanisms.

Briefly, the conclusion we derive from our model is that both scenarios pose viable mechanismsfor scaling transcription with cellular volume. That said, we overall mildly favor scenario 1. Ourdata show that nuclear volume increases somewhat with nuclear size, which the model predictsshould lead to a slight decrease in transcription in larger cells, and thus a higher concentration ofmRNA in smaller cells, which is precisely what we observe. Moreover, there is a rough quantitativeagreement between the degree of increased nuclear volume and the higher concentration of mRNAin smaller cells. Definitively proving that the cell follows scenario 1 of our model will require furtherexperiments.

We begin with a few definitions. We use quantities within brackets to denote concentration(molecules per volume) and quantities without brackets to denote number of molecules per cell.For instance, factor

free

is the number of free molecules of the factor, while [factorfree

] is the number offree molecules per unit volume. factor

DNA

denotes the number of factor molecules instantaneouslybound to DNA, factor

total

is the total amount of factor in the cell/nucleus, and DNA is the number ofbinding sites on the DNA for the factor in the nucleus. K

DNA

is the binding a�nity of the factor fora particular gene. The cellular volume is given by V , and the nuclear volume by V

nuclear

. Thus, givenour assumption of proportionality, we define p

factor

to be a constant such that factortotal

= p

factor

V .The total factor is given by

factortotal

= factorfree

+ factorDNA

, (1)

Dividing by the nuclear volume, we arrive at a relationship between concentrations:

[factortotal

] = [factorfree

] + [factorDNA

] . (2)

The binding a�nity is defined via mass action as

K

DNA

=[factor

free

] [DNA]

[factorDNA

]. (3)

and may be di↵erent for di↵erent genes owing to promoters having di↵erent numbers of bindingsites for the factor or di↵erent binding a�nities.

Thus, the total concentration of the machinery bound to DNA is

[factorDNA

] =([factor

DNA

]� [factortotal

]) [DNA]

K

DNA

. (4)

Solving for [factorDNA

], we find:

[factorDNA

] =[factor

total

] [DNA]

K

DNA

+ [DNA]. (5)

In the limiting case where K

DNA

= 0, we expect all of the factor to be bound to DNA, and inthat case, we find [factor

DNA

] = [factortotal

], as expected.Relating concentrations to volumes yields

[factortotal

] =p

factor

V

V

nucleus

, (6)

where p

factor

is the proportionality constant defined earlier. Similarly,

[DNA] =DNA

V

nucleus

. (7)

Hence,

[factorDNA

] =p

factor

V

Vnucleus

DNA

Vnucleus

K

DNA

+ DNA

Vnucleus

. (8)

Simplifying,

[factorDNA

] =

✓1

V

nucleus

◆p

factor

·V ·DNA

K

DNA

·Vnucleus

+DNA. (9)

Because [factorDNA

] = factorDNA

/V

nucleus

, we can solve for the total amount of transcriptionalmachinery bound to DNA:

factorDNA

=p

factor

·V ·DNA

K

DNA

·Vnucleus

+DNA. (10)

In the limiting case K

DNA

= 0 here, we find that factorDNA

is directly proportional to volume,and equal to the total amount of factor in the nucleus irrespective of nuclear volume. However,in the case where K

DNA

is not zero, then the volume of the nucleus will result in deviations fromperfect scaling of transcription with cellular volume. Intuitively, if the volume of the nucleusincreases somewhat in larger cells, then the concentration of the factor and the DNA will decreaseand hence the amount of factor bound to DNA will be somewhat less than it would be otherwise.In that case, larger cells would have somewhat less transcription than would be expected in the caseof perfect scaling of transcription with cellular volume, which fits with our experimentally observed

volume-independent transcript abundance (i.e., decreased mRNA concentration in larger cells).We also observed that nuclear volume is somewhat greater in larger cells. Thus, it was possible,at least qualitatively, that the increase in nuclear volume could explain the apparent decrease inmRNA concentration in larger cells. We thus wanted to check whether there is a quantitativeagreement between our observed relationship between nuclear volume and cytoplasmic volume andthe increased mRNA concentration in smaller cells, which would establish the plausibility of sucha model.

As mentioned, our measurements show that nuclear area and cellular volume positively correlate.Approximating nuclear volume by raising nuclear area to the 3/2 power, we find a linear relationshipbetween nuclear “volume” and cellular volume (V

nucleus

/ a + bV ), with y-intercept a = 2169femtoliters (95% C.I. = (1923, 2381)), and slope b = 0.9354 (95% C.I. = (0.8313, 1.042)). It isimportant to note that the while the relationship is well-fit by a line, the line does not pass throughzero, and so nuclear volume is not directly proportional to total cellular volume. Using this linearrelationship, we can express the total amount of factor bound to DNA as a function of cellularvolume:

factorDNA

(V ) =p

factor

·V⇣a+ bV

⌘+ 1

, (11)

where a = KDNADNA

· a and b = KDNADNA

· b. The ratio a/b (= a/b) has units of volume, and is geometricallyequivalent to the x-intercept of the line of best fit between nuclear volume and cellular volume.

We now wanted to check whether the volume-independent transcription we observed in ourmRNA-volume plots would quantitatively agree with this model. Because the factor is requiredfor transcription and only transcribes when bound to DNA, then each gene essentially grabs afixed proportion of the amount of factor bound to DNA. (This fraction will depend on the specificregulation of the gene.) So the total transcription of a gene will be proportional to factor

DNA

. Thus,lumping together this proportionality constant along with mRNA production and degradation andother associated constants into a constant c, the relationship between RNA and volume is givenby:

RNA(V ) = c · factorDNA

(V ) . (12)

We should be able to fit our RNA vs. volume data using the above equation to obtain estimatesfor a and b, in particular their ratio, which is directly comparable to the ratio a/b.

We did so for three genes and found fitting parameters:

UBC ZNF444 EEF2

a (fL) 1.578 95.68 0.274795% C.I. (a) (fL) (0.8524, 2.461) (70.23, 127.9) (-0.1518, 0.5630)

b 1.936⇥10�4 0.01495 0.0003658

95% C.I. (b) (-7.617⇥10�5, 4.595⇥10�4) (0.004163, 0.02394) (0.0002680, 0.0005879)

a/b (fL) 7044 6446 744.4

95% C.I. (a/b) (fL) (-62743, 111200) (2988, 28110) (-253.5, 2064)

For the fit of nuclear area to volume, we find the ratio a/b = 2329 femtoliters, with a 95%confidence interval of (1844, 2851). We note that the ratios a/b for all of our genes are of thatsame order of magnitude, albeit with large error. This result suggests that the above equationfor factor

DNA

(V ) may be the equation governing the production of mRNA in cells. This model

provides an explanation that is quantitatively consistent with our data for why smaller cells exhibitproportionally slightly more transcription than larger cells—nuclei in small cells are slightly smallerthan those in large cells, increasing the concentration of factor

DNA

(V ), and therefore increasingtranscription.

Our results are consistent with RNA polymerase II holoenzyme being the factor. RNA poly-merase II is required for transcription, transcribes when bound to DNA, and is almost exclusivelylocalized to the nucleus. Also, most reports indicate that most RNA polymerase II in the nucleusis not specifically bound to DNA. Based on that fact, one would expect that increased nuclear sizeshould lead to slight under-transcription, as we observed. Our analysis shows that this relation-ship is quantitatively plausible. Further studies will be required to rigorously establish that RNApolymerase II holoenzyme is the factor that connects volume/DNA ratio to transcription.

Note, however, that in many situations such as in early embryogenesis, nuclear size does changedramatically as a function of cellular volume. In these situations, the mechanism we describe wouldface a challenge because the concentration of RNA polymerase II holoenzyme in the cell’s nucleuswould remain the same after division (and the associated decrease in cellular volume), leading toover-transcription. The limiting factor model (scenario 2, with K

DNA

very small) would not su↵erfrom these issues. It is possible that some intermediate scenario is at play in early embryogenesis.

Another problem with these models is the potential for runaway positive feedback, in which arandom increase in the factor would lead to more production of the factor, thus leading to runawaytranscription. For this reason, we expect that the cell maintains strong control of factor levelsto avoid these issues. Ultimately, a complete understanding of factor dynamics will likely requireadding growth to models of transcriptional homeostasis.

Computing volume-corrected noise measure from single-cell mRNA

and volume measurements

We define volume-corrected noise measure as the cell-to-cell expression variability in mRNA levelsthat cannot be accounted for by cell-to-cell di↵erences in volume. Throughout the section, wedenote the variance of a random variable X by �

2

X

.

Let m and V be random variables denoting single-cell mRNA level and volume, respectively. Theexpected number of mRNA transcripts in a cell given its volume V is assumed to increase linearlywith V , i.e.,

hm|V i = a+ bV =) hmi = a+ bhV i, (13)

where h.i represents the expected value, and a, b are gene-specific constants (volume-independentand volume-correlated transcript abundance, respectively). From (13), the covariance between m

and V is given by

Cov(m,V ) = hmV i � hmihV i = h(a+ bV )V i � (a+ bhV i)hV i = b�

2

V

. (14)

The extent of cell-to-cell variability in mRNA counts that can be accounted for by volume is

�

2

hm|V i = �

2

a+bV

= b

2

�

2

V

, (15)

which using (14) can be written

�

2

hm|V i = bCov(m,V ). (16)

Volume-corrected noise measure Nm defined as

Nm :=�

2

m

� �

2

hm|V ihmi2 (17)

is obtained as follows using (16)

Nm = CV

2

m

� bCov(m,V )

hmi2 = CV

2

m

� S

Cov(m,V )

hmihV i , (18)

where CV

2

m

represents the total variability in mRNA levels measured by its Coe�cient of Variation(CV ) squared and

S =bhV ihmi =

bhV ia+ bhV i . (19)

Noise measure in a two-state promoter model

Consider two alleles, where each allele transitions independently between active and inactive stateswith rates k

on

and k

off

. We assume that the transcription rate from the active state increaseslinearly with cell volume V . We first compute CV

2

m

(mRNA coe�cient of variation squared) forthe case where transcription is independent of volume, and then extend it to the volume dependentcase.

Transcription rate independent of volume

Let the transcription rate from active state be k

m

. Then, the steady-state first and second-ordermoment of the mRNA level m is given by

hmi = 2Gon

k

m

�

m

, hm2i = hmi+ �

m

(1�G

on

)hmi2

2(Gon

�

m

+ k

on

)+ hmi2, (20)

where

G

on

=k

on

k

on

+ k

off

(21)

is the fraction of time an allele is in the active state, and �

m

is the mRNA degradation rate. Notethat the factor two in (20) arises due to the presence of two alleles. This results in

CV

2

m

:=hm2i � hmi2

hmi2 =1

hmi +�

m

(1�G

on

)

2(Gon

�

m

+ k

on

). (22)

Transcription rate dependent on volume

We assume k

m

= a + bV , where volume V is a random variable with mean hV i and variance �

2

V

.Based on (20),

hm|V i = 2Gon

(a+ bV )

�

m

, hm2|V i = hm|V i+ �

m

(1�G

on

)hm|V i2

2(Gon

�

m

+ k

on

)+ hm|V i2. (23)

Unconditioning on the volume we obtain

hmi = 2Gon

(a+ bhV i)�

m

(24a)

hm2i = hmi+�

m

(1�G

on

)⌦hm|V i2

↵

2(Gon

�

m

+ k

on

)+⌦hm|V i2

↵. (24b)

Using (23)

⌦hm|V i2

↵=

*✓2G

on

(a+ bV )

�

m

◆2

+= hmi2(1 + S

2

CV

2

V

), (25)

where the mean mRNA count hmi is given by (24a), S is given by (19) and CV

2

V

is the volumeCV

2. Substituting (25) in (24b)

hm2i = �

m

(1�G

on

)hmi2(1 + S

2

CV

2

V

)

2(Gon

�

m

+ k

on

)+ hmi2(1 + S

2

CV

2

V

) + hmi. (26)

Above equation yields

CV

2

m

:=hm2i � hmi2

hmi2 =1

hmi +�

m

(1�G

on

)(1 + S

2

CV

2

V

)

2(Gon

�

m

+ k

on

)+ S

2

CV

2

V

. (27)

As expected, (27) reduces to (22) when CV

2

V

= 0. In (27), the first term represent Poissonian noisein mRNA level due to random-birth death of individual mRNA molecules. The second term is thenoise contribution from stochastic promoter switching. Variation in mRNA levels due to cell-to-celldi↵erences in cell volume is represented by the last term. Removing the last term, we obtain thenoise measure as

Nm =1

hmi +�

m

(1�G

on

)(1 + S

2

CV

2

V

)

2(Gon

�

m

+ k

on

). (28)

Estimating promoter transition rates between active and inactive

states

Noise measures obtained from single-cell mRNA count and volume measurements are used toestimate promoter transition rates k

on

and k

off

using (28). To correct for measurement noise,we take into account a 15% error in mRNA counting. From (21) and (28)

G

on

=k

on

k

on

+ k

off

(29a)

Nm =1

hmi +�

m

(1�G

on

)(1 + S

2

CV

2

V

)

2(Gon

�

m

+ k

on

)+ CV

2

count

, (29b)

Average promoter dwell-time (30) obtained from the noise measure (Eq. (29)) or the total mRNAexpression variability (Eq. (31)). mRNA half-lives and dwell times are reported in hours.

Gene G

on

CV

2m

Nm/CV

2m

mRNA T

on

, Toff

T

on

, Toff

half-life from Nm from CV

2m

ACTN4 0.65 0.14 0.4 13 6.1, 3.3 40.5, 21.8GAPDH 0.65 0.23 0.17 24 4.9, 2.6 331, 178EEF2 0.75 0.18 0.17 16 3.2, 1.1 1443.6, 481.2FTL 0.3 0.58 0.32 24 6.3, 14.6 45.4, 105.9

ICAM1 0.05 0.8 0.8 6.5 0.5, 9.7 0.8, 15.4ACTA2 0.2 1.18 0.9 3 5.1, 20.3 7.9, 31.6LUM 0.15 0.7 0.75 24 7.8, 44.3 12.7, 72.5

SUPTH5 0.3 0.12 0.58 10 0.45, 1.1 1.4, 3.3

where CV

2

count

= 0.152 = 0.0225 represents the mRNA counting error. In the above equations,quantities Nm, S (defined in (19)), CV

2

V

(volume CV

2), hmi, Gon

are computed from data for agiven gene. Using mRNA half-life information from literature, rates k

on

and k

off

can be estimatedby solving (29). The average promoter dwell time in the active and inactive state is given by

T

on

=1

k

off

, T

off

=1

k

on

, (30)

respectively, and reported in Table I under the column “Ton

, Toff

from Nm”. Since there maybe other unaccounted sources of noise in gene expression, these dwell time estimates should beconsidered an upper bound on their actual values.

We contrast the above estimates to the scenario where all the mRNA expression variability isassumed to arises from transcriptional bursting. From (22), k

on

and k

off

in that case would beestimated by solving

G

on

=k

on

k

on

+ k

off

(31a)

CV

2

m

=1

hmi +�

m

(1�G

on

)

2(Gon

�

m

+ k

on

)+ CV

2

count

. (31b)

Since CV

2

m

> Nm, dwell times obtained from (31) (see “Ton

, T

off

from CV

2

m

” in Table I) aresignificantly larger than those obtained from (29). For example, using the GAPDH noise measure,we estimate T

on

= 4.9 hours. However, if one ignores the contribution of cell volume in drivingintercellular variation in GAPDH mRNA, the mean dwell time in the active state is obtained to be13� 14 days (331 hours) from (31).

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