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Please cite this article in press as: Klochendler et al., A Transgenic Mouse Marking Live Replicating Cells Reveals In Vivo Transcriptional Program of
Proliferation, Developmental Cell (2012), http://dx.doi.org/10.1016/j.devcel.2012.08.009
Developmental Cell
Article
A Transgenic Mouse Marking
Live Replicating Cells Reveals In Vivo
Transcriptional Program of Proliferation
Agnes Klochendler,1,7 Noa Weinberg-Corem,3,7 Maya Moran,1 Avital Swisa,3 Nathalie Pochet,4 Virginia Savova,4
Jonas Vikeså,5 Yves Van de Peer,6 Michael Brandeis,2 Aviv Regev,4 Finn Cilius Nielsen,5 Yuval Dor,3 and Amir Eden1,*
1Department
of Cell and Developmental Biology
of Genetics
The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
3Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew
University-Hadassah Medical School, Jerusalem 91120, Israel
4Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA
5Center for Genomic Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
6Department of Plant Systems Biology, VIB, Department of Plant Biotechnology and Genetics, Ghent University, B-9052 Ghent, Belgium
7These authors contributed equally to this work
*Correspondence: [email protected]
http://dx.doi.org/10.1016/j.devcel.2012.08.009
2Department
SUMMARY
Most adult mammalian tissues are quiescent, with
rare cell divisions serving to maintain homeostasis.
At present, the isolation and study of replicating cells
from their in vivo niche typically involves immunostaining for intracellular markers of proliferation,
causing the loss of sensitive biological material. We
describe a transgenic mouse strain, expressing a
CyclinB1-GFP fusion reporter, that marks replicating
cells in the S/G2/M phases of the cell cycle. Using
flow cytometry, we isolate live replicating cells from
the liver and compare their transcriptome to that of
quiescent cells to reveal gene expression programs
associated with cell proliferation in vivo. We find
that replicating hepatocytes have reduced expression of genes characteristic of liver differentiation.
This reporter system provides a powerful platform
for gene expression and metabolic and functional
studies of replicating cells in their in vivo niche.
INTRODUCTION
The cell cycle has been extensively studied in unicellular organisms and in mammalian cells grown in culture. These models
have been invaluable for understanding the fundamentals of
cell-cycle progression (Nasmyth, 2001). However, cell proliferation in vivo is a tightly regulated process, which involves interactions with microenvironmental niches and mitogenic cues acting
through intricate signaling networks (Alberts et al., 2002; Conlon
and Raff, 1999). These interactions are difficult to model in cell
culture systems. Moreover, the biology of infrequent cell divisions, as they normally occur in vivo, might be considerably
different from that of continuously replicating, often immortalized
cells. Thus, elucidation of the transcriptional program of cell
proliferation in vivo could provide important insights into the
native regulation of cell division and the mechanisms that govern
the transition between quiescence and proliferation.
Replicating cells can be readily identified in situ based on
expression of cell-cycle markers, such as Ki67 and PCNA, or
the incorporation of thymidine analogs, such as BrdU. Although
much can be learned using such markers, they require sample
fixation and immunostaining, precluding the isolation of live replicating cells for further analysis. Methods that can in theory be
used to isolate intact replicating cells, such as vital stains for
DNA content (e.g., Hoechst 33342) and newer thymidine analogs
(e.g., EdU; Salic and Mitchison, 2008) have proven difficult to
apply for this purpose. Therefore, the study of cell proliferation
could greatly benefit from tools that enable the identification
and isolation of live replicating cells directly from mammalian
tissues.
To specifically mark live replicating cells in vivo, we developed
a transgenic mouse strain expressing green fluorescent protein
(GFP) in replicating cells as they pass through the S, G2, and
M phases of the cell division cycle. We took advantage of
a reporter construct previously characterized in cultured cells
(Hagting et al., 1998; Zur and Brandeis, 2002), consisting of
GFP fused to the N-terminal ‘‘destruction box’’ of Cyclin B1.
Cyclin B1 levels oscillate during the cell cycle. The protein is
stable from the onset of S phase until metaphase. From metaphase through G0/G1 phases of the cell cycle, an active APC/
C E3 ubiquitin ligase complex ubiquitinates cyclin B mediating
its degradation (Brandeis and Hunt, 1996; Clute and Pines,
1999; King et al., 1996; Sudakin et al., 1995). Here we show
that the CycB1-GFP reporter accurately marks proliferating cells
found in S/G2/M stages of the cell cycle in a range of tissues
in vivo. As a first application of this tool, we have isolated live
replicating and quiescent cells directly from the juvenile, rapidly
expanding liver and used expression profiling to study the transcriptional programs associated with cell replication in vivo.
We report that replicating hepatocytes have reduced expression
of genes encoding differentiated liver functions. Our results
provide a molecular view of the relationship between differentiation and proliferation in normal replicating hepatocytes and
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Developmental Cell
In Vivo Expression Program of Cell Replication
PGK promoter
A
HIV-1 CyB1
LTR N-terminus
GFP
SIN
LTR
B
0‘
18‘
54‘
72‘
36‘
Figure 1. Generation of a Lentiviral VectorExpressing GFP in S/G2/M Cells and Validation of Appropriate Expression of GFP in
Transgenic Mice Fibroblasts
(A) The 105 N-terminal residues of cyclinB1
harboring a destruction signal were fused to a GFP
reporter gene and inserted into a lentiviral vector
downstream of the constitutive PGK promoter.
(B) Time-lapse photography of live 3T3 mouse
fibroblasts transduced with the Pgk-CycB1-GFP
lentiviral vector. The arrowheads indicate mitotic
cells undergoing cytokinesis. GFP is degraded
and not visible after cell division.
(C) Ear fibroblasts isolated from eight transgenic
mice were labeled with BrdU for 3 hr before fixation and then immunostained for BrdU (red), GFP
(green), and DAPI (blue).
(D) FACS analysis of transgenic ear fibroblasts.
GFP fluorescence was assessed in wild-type and
CyB-GFP fibroblasts. CyB-GFP cells were stained
with Hoechst to analyze DNA content of GFPpositive (green) and GFP-negative (blue) cells (red,
total living cells).
C
Counts
GFP
responsible for its specific degradation
in G0/G1 cells. We transduced mouse
3T3 fibroblasts with the recombinant
lentivirus and monitored GFP levels using
time-lapse microscopy. As expected,
Dapi
GFP
BrdU
Merge
GFP gradually accumulated as cells progressed toward mitosis but was no longer
detected after cell division (Figure 1B).
D
To generate Cyclin B1-GFP transgenic
mice, we injected the concentrated virus
G1
into the perivitelline space of single-cell
mouse embryos (Lois et al., 2002). PCR
GFP-positive
genotyping revealed that almost 100%
of the founders were positive for the
transgene, which was efficiently transS G2/M
mitted through the germline. To confirm
GFP-negative
expression of the transgene specifically
in the S/G2/M phases of the cell cycle,
PE
we first analyzed ear fibroblasts prepared
PE
Hoechst
from eight founders. We labeled cells for
Wild-Type
PGK-CyclinB1-GFP
Cell cycle analysis of
3 hr with BrdU and performed coimmuPGK-CyclinB1-GFP MEFs
nostaining to detect GFP and BrdU-positive cells (Figure 1C). Ninety-four percent
of BrdU-positive cells (n = 790) stained
demonstrate the utility of the cell-cycle reporter in studies of positive for GFP, and 90% of GFP-positive nuclei stained posiproliferation in vivo.
tive for BrdU. This tight association between BrdU and GFP in
cycling transgenic fibroblasts indicates that CycB1-GFP specifically marks replicating cells. The cells were further analyzed by
RESULTS
flow cytometry to detect both cellular DNA content and GFP (FigCultured Cells Express the CyclinB1-GFP Reporter Only
ure 1D). This analysis confirmed that G1 cells are GFP-negative,
in S/G2/M Phases of the Cell Cycle
whereas the GFP-positive cells represent cells in the S/G2/M
We constructed a lentiviral vector with the constitutive promoter phases of the cell cycle.
of the mouse Pgk1 gene driving the cell-cycle reporter gene,
composed of the 105 N-terminal residues of cyclin B1 fused to CycB1-GFP Is Absent from Quiescent Cells In Vivo
GFP (Figure 1A). This protein fragment does not contain func- Endogenous mitotic cyclins are regulated both at the transcriptional cyclin B1 but harbors the APC/C ubiquitination site tional level and posttranslationally. In our system, CycB1-GFP
2 Developmental Cell 23, 1–10, October 16, 2012 ª2012 Elsevier Inc.
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Please cite this article in press as: Klochendler et al., A Transgenic Mouse Marking Live Replicating Cells Reveals In Vivo Transcriptional Program of
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Developmental Cell
In Vivo Expression Program of Cell Replication
is constitutively transcribed and translated, and its downregulation in G0/G1 cells depends entirely on ubiquitination-mediated
degradation induced by APC/C. To verify that CycB1-GFP was
indeed degraded in quiescent cells of adult tissues, we costained for GFP and DNA replication in tissue sections from adult
transgenic mice injected with BrdU 3 hr prior to sacrifice. As expected, staining sections of the intestine revealed the presence
of GFP-positive cells in the intestinal crypts. Furthermore, GFP
correlated with BrdU staining in the small intestine, exocrine
pancreas, and the islets of Langerhans, confirming that GFP
expression is confined to proliferating cells in these tissues
(Figures 2A–2I). We then extended the analysis to adult mouse
liver. Consistent with the known quiescent state of adult hepatocytes, almost no proliferating cells were detected (as evaluated
by PCNA staining) and the cells were GFP-negative (Figures 2J
and 2K). Impressively, 48 hr following partial hepatectomy
(PH), massive proliferation of liver cells was observed with costaining for PCNA and GFP (Figures 2L and 2M). Taken together,
these results confirm that the CycB1-GFP reporter faithfully
marks replicating cells in various tissues. They also imply that
APC/C-mediated proteolysis persists in quiescent cells in vivo.
Expression Profile of Replicating Juvenile Liver Cells
To isolate replicating and nonreplicating cells directly from the
liver, we dissociated the livers of 3.5-week-old, weaned
CycB1-GFP mice into single cells. At this age the liver is still
growing and flow cytometry analysis of isolated cells indicated
that !10% of the cells were cycling (Ki67-positive). As expected,
nearly all GFP-positive cells stained positive for Ki67 (>90%;
Figure 3A). Using Affymetrix expression microarrays, we characterized gene expression profiles of sorted GFP-positive and
GFP-negative liver cells isolated from three individual mice. We
first focused on a set of 744 genes that were significantly
induced in GFP-positive cells compared to GFP-negative cells
(p % 0.05; Table S1A available online). Pathway analysis revealed significant enrichment for functions that are associated
with cell cycle, mitosis, DNA replication, and DNA repair, as
well as DNA packaging and mRNA splicing (Figure 3B). Among
the most upregulated genes were many established markers of
proliferation, including Ccnb1 (cyclinB1) and mKi67 (Figure 3C).
Quantitative RT-PCR analysis revealed a 100-fold increase in
Top2A and Ki67 mRNA levels in the GFP-positive cells (Figure 3D). In summary, the enrichment for major cell-cycle categories in genes induced in GFP-positive cells provides a strong
confirmation that the CycB1-GFP reporter indeed marks replicating cells in the S/G2/M phases of the cell cycle. Furthermore,
the list of upregulated genes provides a comprehensive view of
the transcriptional program of in vivo cell division during normal
liver expansion in juvenile animals.
Reduced Expression of Liver Function Genes in
Replicating Liver Cells
A set of 478 genes showed reduced expression in replicating
(GFP-positive) cells compared to nonreplicating cells (p %
0.05; Table S1B). Functional analysis showed that this set of
genes was significantly enriched for genes involved in lipid and
amino acid metabolism and for synthesis of bile acids and bile
salts (Figure 4A). We used qRT-PCR to validate the reduced
expression of selected genes in three independent RNA samples
A
C
E
B
D
F
G
H
I
J
K
L
M
Figure 2. GFP Is Expressed Specifically in Proliferating Cells In Vivo
(A–I) CycB1-GFP mice were sacrificed 3 hr after BrdU injection. Paraffin
sections of small intestine (A–F) and pancreas (G–I) were immunostained with
BrdU (red), GFP (green), and insulin (white, G–I), and nuclei were counterstained with DAPI. Original magnification: 200 (A–F), 400 (G–I).
(J–M) Proliferating liver cells after partial hepatectomy (PH) express GFP.
Immunohistochemical staining of proliferating cell nuclear antigen (PCNA) (red)
and GFP (green) in liver sections of 2-month-old mice at 0 (J and K) and 48 hr (L
and M) after PH. Nuclei were counterstained with DAPI (blue). White nuclei
depict costaining of PCNA, GFP, and DAPI. Original magnification: 200 (J and
L), 400 (K and M). PCNA-positive GFP-negative cells most probably depict
cells in late G1 or early S-phase wherein GFP is absent or expressed at a low
level.
(Figure 4B). Induction or repression of specific pathways in
GFP-positive compared to GFP-negative cells was further
studied using gene set enrichment analysis (GSEA; Subramanian et al., 2005): GFP-positive cells showed significant repression of gene sets associated with glycolysis/gluconeogenesis,
TCA cycle, and drug metabolism via cytochrome P450 (Figure 4C). Because lipid and amino acid metabolism and metabolism of xenobiotic substances are key liver functions, we
wondered whether the transcriptional program of replicating
cells reflects downregulation of liver-specific functions. Using
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Developmental Cell
In Vivo Expression Program of Cell Replication
A
5
Cy5-Ki67
10
6.5%
3%
4
10
3
10
2
10
1
-10
1
-10
B
0.3%
90.2%
2
10
3
4
10
Cy2-GFP
10
5
10
Cell cycle
M phase
DNA repair
DNA replica!on
Mito!c cell cycle checkpoint
DNA packaging
RNA splicing
0
C
10
20
30
40
50
Significance (-Log P)
Gene
Symbol
Ra!o (GFPpos
vs. GFPneg )
cyclin B1
kinesin family member 11
hyaluronan mediated mo!lity
receptor (RHAMM)
topoisomerase (DNA) II alpha
non-SMC condensin I complex,
subunit G
protein regulator of cytokinesis 1
tripar!te mo!f-containing 59
NUF2, NDC80 kinetochore complex
component
centromere protein E
cancer suscep!bility candidate 5
kinesin family member 20B
shugoshin-like 2 (S. pombe)
centromere protein F
anillin, ac!n binding protein
proline rich 11
nucleolar and spindle associated
protein 1
an!gen iden!fied by monoclonal
an!body Ki 67
TPX2, microtubule-associated
protein homolog (Xenopus la
Shc SH2-domain binding protein 1
establishment of cohesion 1
homolog 2 (S. cerevisiae)
Ccnb1
Kif11
Hmmr
Top2a
Ncapg
Prc1
Trim59
Nuf2
Cenpe
Casc5
Kif20b
Sgol2
Cenpf
Anln
Prr11
Nusap1
Mki67
Tpx2
Shcbp1
Esco2
D
Descrip!on
33
24
22
22
22
22
20
19
19
18
18
16
16
15
15
15
15
14
14
14
Rela!ve expression
(Log10 scale)
1000
100
**
**
GFPneg
GFPpos
10
1
Top2A
several definitions for liver-specific gene sets (Table S1D), we
found that the genes repressed in replicating cells were highly
enriched for liver-specific signatures (Figure 4D).
A possible explanation for reduced expression of liver functions in the replicating cells is contamination of the GFP-positive
fraction by nonhepatic cell types with high proliferation capacity.
However, immunostaining of isolated liver cells argued against
this possibility because 100% of GFP-positive and 93% of
GFP-negative liver cells stained positive for albumin (Figure S1).
To further establish the identity of replicating cells, we stained for
Hnf6 (Onecut1) and Hnf4a, two key hepatocyte transcription
factors. GFP-negative cells were 78% and 66% positive for
Hnf6 and Hnf4a, respectively. For both transcription factors,
the fraction of positive cells was higher than 85% in replicating
(GFP-positive) cells (Figure S1). These results exclude the possibility that contaminating proliferating nonhepatocyte cells
contribute to the repression of hepatocyte markers observed in
replicating liver cells.
It has been shown that hepatocytes differ in their gene expression profile and biochemical activities depending on their
physical location (periportal or pericentral), a phenomenon
known as metabolic zonation (Jungermann and Kietzmann,
1996). Theoretically, the differences in expression of liver genes
between replicating and nonreplicating cells could be related to
zonation if replicating cells were preferentially associated with
one compartment and not the other. The ‘‘streaming liver’’
hypothesis proposes that young hepatocytes migrate from the
portal zone toward the central vein and that this transit represents opposite gradients of differentiation and proliferative
capacity (Zajicek et al., 1985). However, experimental support
for this concept is scant (Bralet et al., 1994) and a recent genetic
lineage tracing analysis strongly argues against this model
(Malato et al., 2011). Using immunostaining for Glutamine
synthetase (Glul), a marker of pericentral hepatocytes (Moorman
et al., 1989), we examined whether replicating hepatocytes preferentially reside in a specific zone. As shown in Figure S2, hepatocyte replication occurs at a comparable level in both parts of
the lobule. Thus, our results support the view that replicating
cells in the growing liver are hepatocytes that transiently repress
liver-specific programs.
Ki67
Figure 3. Sorted GFP-Positive Hepatocytes Are Replicating
(A) Flow cytometry analysis of hepatocytes isolated from a perfused liver of
a 25-day-old mouse. The cells were stained for Ki67 (y axis) and GFP (x axis) to
evaluate the percent of cells in the different phases of the cell cycle. GFPpositive cells are Ki67 positive, and 93% of GFP-negative cells are quiescent
(G0, Ki67 negative). Note that Ki67 marks cycling cells during all stages of cell
cycle, including G1. Hence, Ki67-positive-GFP-negative cells are likely cycling
cells in G1 phase.
(B) Genes induced in GFP-positive hepatocytes show significant enrichment in
proliferation-associated gene ontology (GO) biological processes categories
(hypergeometric test p % 0.01, FDR (BH) % 0.05). The statistical significance
of enriched biological pathways is indicated, as determined by overlap analysis of genes upregulated in three independent preparations of GFP-positive
cells (p % 0.05) with MSigDB C2 curated and C5 GO collections (Subramanian
et al., 2005).
(C) Top 20 genes induced in GFP-positive cells (sorted according to fold
change) confirm induction of cell-cycle genes in the GFP-positive fraction.
(D) Quantitative RT-PCR analysis (n = 3; **p < 0.01). Results were normalized to
b-actin (endogenous control) and are presented relative to the average
expression of the GFP-negative samples. Error bars indicate standard deviation of the mean (note log scale).
See also Table S1A.
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Developmental Cell
In Vivo Expression Program of Cell Replication
A
Peroxisome
Lipid metabolism
PPAR signaling pathway
Amino acid metabolism
Bile acid and bile salt
synthesis
0
Relative expression
B 1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
2
4
6
8
Significance (-Log P)
10
*
*
*
*
**
GFPneg
GFPpos
Replicating
(GFPpos)
C
Quiescent
(GFPneg)
Cyp27a1Decr2 Aldh9a1 Acsl1 Hsd17b2
Set category
Lipid metabolism
Metabolism of amino acids
Ppar signaling pathway
Cellular ketone and organic
acid metabolic process
Transmembrane transporter activity
Glycolysis and gluconeogenesis
Metabolism cytochrome P450
GFPpos
D
GFPneg
TCA cycle
Liver-specific
-0.5
0.5
Figure 4. Liver-Specific Functions Are Repressed in Replicating
Hepatocytes
(A) Biological themes enriched in genes downregulated in replicating versus
quiescent liver cells (p values indicated). See also Figure S1.
(B) Quantitative real-time PCR analysis (n = 3; *p < 0.05; **p < 0.01) was
used to confirm downregulation of genes involved in lipid metabolism, in
To better understand the relationship between quiescent and
replicating hepatocytes and phases of hepatic differentiation, we
studied the induction and repression of pathways and genes
associated with liver development, in replicating (GFP-positive)
and nonreplicating (GFP-negative) cells. Using data on gene
expression during mouse liver development (Cairo et al., 2008;
Otu et al., 2007), we generated gene sets/signatures that reflect
transcriptional changes taking place during embryonic and final
stages of liver development (between early and late embryonic
liver and between embryonic day 18.5 [E18.5] and postnatal
day 42 [P42]). Interestingly, we find that genes induced in replicating cells are associated with genes that are activated in less
differentiated cells (E18.5 versus P42 and early embryo versus
late embryos). By contrast, genes that are repressed in replicating cells are associated with genes that are induced upon liver
maturation (Figure 5A). Although genes upregulated in GFPpositive cells and in early phases of liver development were enriched for cell-proliferation-associated functions, such as DNA
replication, mitosis, and DNA repair (p % 0.001, FDR % 0.001)
(data not shown), genes downregulated in GFP-positive cells
and upregulated through liver development were enriched for
liver-specific functions (Figure 5B). Thus, the transcriptional
changes occurring in replicating cells are consistent with
reversal or incompletion of late events of hepatocyte maturation.
Other nonhepatocyte cells were reported to be activated under
special conditions and contribute to the pool of proliferating liver
cells (hepatic progenitors/oval cells [Dorrell et al., 2011; Shin
et al., 2011], stellate cells [Moreno et al., 2010], and fetal hepatoblasts [Otu et al., 2007]). However, markers of these cell types
were not induced in replicating (GFP-positive) liver cells (Figure 5C). Thus, our results suggest that hepatocyte replication
during postnatal liver growth involves reduced expression of
liver-specific genes.
Common Repression of Liver Function in Replicating
Liver Cells and in Liver Cancer
Studies comparing gene expression profile between Hepatocellular Carcinoma (HCC) and normal liver reported repression of
liver-specific genes in the tumors and proposed this as evidence
for dedifferentiation in cancer (Acevedo et al., 2008; Cairo et al.,
2008; Lee et al., 2006; Xu et al., 2001). The fact that we observed
a similar change when analyzing cell replication in vivo during
normal postnatal liver growth suggests that this is an inherent
characteristic of cell proliferation and not a feature specific
to tumorigenesis. Consistent with this possibility, GSEA analysis
using the MSigDB C2-curated gene set collection, which
includes gene sets representing the molecular signature for
replicating cells. Error bars indicate standard deviation of the mean. See also
Figure S3.
(C and D) Gene set enrichment analysis (GSEA) of quiescent versus replicating
hepatocytes, using MSigDB C2 curated gene set collection (C) or liver-specific
gene sets (D). Gene sets (rows) that were significantly downregulated in GFPpositive liver cells are shown (nominal p v% 0.01, FDR q % 0.05), labeled on
the right by general categories. For each gene set (row), the mean expression
of the ‘‘leading edge’’ genes (which supported the differential expression
signature) in each experiment (column) is shown, after logarithmic transformation and row centering. The description of significantly differentially expressed gene sets is contained in Tables 1C (C) and 1D (D).
See also Table S1B.
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Developmental Cell
Replicating
(GFPpos)
-0.5
B
Genes downregulated in P42 versus E18.5 -liver
Genes downregulated in late versus early
embryonic liver
Genes upregulated in P42 versus E18.5 -liver
Genes upregulated in late versus early
embryonic liver
0.5
Liver-specific genes
Peroxisome
Metabolism of lipids and
lipoproteins
Pyruvate metabolism
Tryptophan metabolism
Fatty acid metabolism
Ppar signaling pathway
Quiescent
(GFPneg)
C
0
2
4
6
8 10 12 14 16
Significance (-Log P)
Sox9
Foxj1
Prom1
Epcam
Cd44
Tacstd2
Ncam1
Cldn3
Onecut2
Acta2
Des
Vim
Col1a2
Mmp2
Hepatic Progenitor Markers
Hepatic Stellate Cell Markers
Afp
Krt7
Krt19
Epcam
Vim
Igf2
H19
Fetal Hepatoblast Markers
3
5.5
a variety of phenotypes extracted from published studies
(including HCC), showed significant association between genes
repressed in replicating (GFP-positive) hepatocytes and genes
repressed in HCC (Figure 6A). This observation suggests that
many of the changes observed in HCC reflect normal changes
in gene expression in replicating cells. Furthermore, genes
repressed in replicating versus quiescent hepatocytes and in
HCC versus normal liver are indeed involved in liver function
(Figure 6B).
Another fundamental feature of cancer is the acquisition of
embryonic markers, considered to be part of the dedifferentiation process or to pinpoint the precursor origin of the tumor cells.
Indeed, markers of liver precursor cells (hepatoblasts), which are
absent from adult hepatocytes, are often expressed in HCC
(Libbrecht et al., 2000; Roskams et al., 2003). Notably, this
aspect of gene expression in cancer was completely missing in
the normal program of replicating cells because none of the
classic hepatoblast markers (Afp, Krt7, Krt19, Epcam, Vim,
Igf2, and H19) were upregulated in normally replicating liver cells
of juvenile mice (Figure 5C).
DISCUSSION
Replicating
(GFPpos)
A
Quiescent
(GFPneg)
In Vivo Expression Program of Cell Replication
8
Figure 5. Gene Set Enrichment Analysis Reveals that Genes Downregulated in Replicating Hepatocytes Are Induced throughout Liver
Development and Are Involved in Liver Function
(A) Gene set enrichment analysis of genes repressed and induced in replicating
versus quiescent hepatocytes using gene sets representing changes in
expression during liver development (p % 0.0001). The heatmap was generated as described in Figure 4C. See also Table S1D.
(B) Graphs showing p values of biological processes enriched in ‘‘leading
edge’’ genes (which supported the differential expression signature) repressed
in replicating liver cells and induced through liver development. The list and
description of significantly differentially expressed liver development gene
sets is contained in Table S1D.
(C) Markers of hepatic progenitors/oval cells (Dorrell et al., 2011; Shin et al.,
2011), stellate cells (Moreno et al., 2010), and fetal hepatoblast (Otu et al.,
2007) are not elevated in replicating (GFP-positive) liver cells. Expression of
characteristic markers of the different liver cell populations (rows) are shown in
A Transgene-Enabling Dissection of the Gene
Expression Program of Replicating Cells In Vivo
Proper tissue homeostasis requires precise control over replication and quiescence states, and impairment of these processes
may result in degenerative or neoplastic diseases. It is therefore
important to understand in detail how and why cells engage in or
exit from the cell cycle, in their natural context in vivo. To address
this fundamental challenge we have developed a transgenic
mouse model, which marks in situ live replicating cells and
allows their isolation. Using these mice we have isolated replicating and nonreplicating cells directly from the liver and
determined their gene expression profile. As expected, the transcriptome of replicating liver cells shows enrichment for genes
known to be involved in the cell cycle and identifies genes not
associated previously with replication. The data generated
here provides a unique perspective of the features of normal
cells replicating in their natural environment, which was not available before for most tissues.
Repression of Liver Differentiation Programs in
Replicating Juvenile Hepatocytes
Analysis of gene expression in replicating liver cells revealed
a surprising decrease in the expression level of genes associated
with liver function. Our analysis suggests that, during liver growth
in adolescent mice, the replicating cells are hepatocytes and not
hepatic progenitors or other nonparenchymal cells and that the
gene expression pattern in these cells reflects active replication
and a less differentiated state. Although it is formally possible
that replication is carried out by a yet-to-be-discovered subpopulation of less differentiated hepatocytes, our results support the
view that replicating cells in the growing liver are hepatocytes
that transiently repress liver-specific programs. The model of
each experiment (column). Blue and red reflect low and high expression levels,
respectively, as indicated in the log2-transformed scale.
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Developmental Cell
Replicating
(GFPpos)
A
Quiescent
(GFPneg)
In Vivo Expression Program of Cell Replication
Genes downregulated in human HCC
Genes downregulated in mouse HCC
-0.5
0.5
B
Fatty acid metabolism
PPAR signaling pathway
Peroxisome
Metabolism of amino acids
Lipid metabolism
Steroid metabolism
0
5
10
15
Significance (-Log P)
20
Figure 6. Gene Set Enrichment Analysis Reveals that Genes
Repressed in HCC Are Involved in Liver Function and Are Downregulated in Replicating Hepatocytes
(A) Gene set enrichment analysis of genes repressed in replicating versus
quiescent hepatocytes using gene sets representing genes repressed in HCC
(p % 0.0001). The heatmap was generated as described in Figure 4C.
(B) Graphs showing p values of biological processes enriched in ‘‘leading
edge’’ genes (which supported the differential expression signature) repressed
in replicating liver cells and in HCC. The list and description of significantly
repressed HCC gene sets is contained in Table S1D.
transient repression of differentiation programs is consistent
with a previous report on transient downregulation of liver function genes during recovery from partial hepatectomy (White
et al., 2005). In this study, downregulation of liver metabolic
genes was one of the first changes in gene expression, evident
already 2 hr following partial hepatectomy and preceding cell
duplication, further supporting the idea that replicating cells
downregulate expression of liver-specific functions. However,
to the best of our knowledge, rare homeostatic divisions of hepatocytes in adult mice in healthy conditions were not shown
before to involve transient ‘‘transcriptional dedifferentiation’’ as
we describe here for juvenile hepatocytes in the growing liver.
The pioneering observation of Charles Leblond that in many
tissues, dividing cells were identical in morphology to adjacent,
differentiated cells (Leblond, 1964, 1981) challenged the classical view that terminally differentiated cells are postmitotic.
More recent genetic lineage tracing studies further showed
that this so-called ‘‘simple duplication’’ was responsible for
tissue homeostasis in additional tissues, such as the endocrine
(Dor et al., 2004; Nir et al., 2007; Teta et al., 2007) and exocrine
pancreas (Desai et al., 2007). Our results may provide an interesting clue regarding this fundamental question. We hypothesize
that the observed repression of liver function genes in replicating
liver cells reflects temporal compartmentalization of differentiation and proliferation, reminiscent of the classically perceived
mutually exclusive nature of these processes. We note that in
cell types that undergo a single division followed by prolonged
quiescence, such as pancreatic beta cells (Salpeter et al.,
2010; Teta et al., 2007), this transient transcriptional response
is unlikely to be manifested at the protein level or to impact cell
function. However, multiple consecutive cell divisions may
cause eventually an apparent loss of the differentiated state,
when preexisting stores of proteins will be depleted. More
studies will be needed to examine this idea.
Our data show that many of the transcriptional changes in
HCC, including repression of liver-specific genes, reflect the
normal program of cell replication (Figure 6). Interestingly, the
presence of embryonic markers, often discussed in conjunction
with dedifferentiation in cancer, is not seen in normal adult liver
replication, indicating that this is a genuine trait of transformed
cells, which may reflect a different cellular origin (stem or progenitor cells) or may be acquired by the tumor following genetic or
epigenetic change. More work will be required to determine
the extent to which dedifferentiation in liver cancer (or cancer
in general) is a mere consequence of hyperproliferation.
Future experiments will examine if and how key liver transcription factors (Zaret and Grompe, 2008) are affected during the cell
cycle in vivo. Examination of the transcriptome of GFP-positive
hepatocytes revealed downregulation of Tbx3, a transcription
factor regulating proliferation and differentiation of hepatoblasts
(Suzuki et al., 2008), and Ahr, a nuclear receptor involved in
Cytochrome P450 regulation. The downregulation of Tbx3 and
Ahr was confirmed by quantitative RT-PCR (Figure S3). No
decrease in mRNA levels of other major transcription factors
involved in hepatic cell differentiation (Hhex, Onecut1 [Hnf6],
Onecut2, Prox1, and Hnf4a) was detected in the microarray
data. qRT-PCR did reveal a statistically significant decrease in
the mRNA level of Hnf4a, but not Hnf6 or Onecut2, in replicating
hepatocytes (Figure S3; data not shown). These results provide
a potential molecular mechanism for the downregulation of liver
function genes in replicating hepatocytes. However, more work
will be required to assess the protein level, DNA binding, and
transcriptional activity of relevant transcription factors in replicating hepatocytes to determine the underlying mechanism. It
also remains to be determined how general is the phenomenon
of transcriptional dedifferentiation during replication of terminally
differentiated cells. Studies to address this question using other
tissues from CycB1-GFP mice are underway.
Applications of the Live Reporter for Cell Replication
The system described here provides a powerful platform for
the study of cell replication and quiescence in vivo. Another
live reporter system for replicating cells based on stabilization
of Geminin protein was recently described and used for in vivo
time-lapse microscopy in zebrafish and mouse embryos
(Sakaue-Sawano et al., 2008; Sugiyama et al., 2009). Cell sorting
and gene expression applications in adult organisms were not
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Developmental Cell
In Vivo Expression Program of Cell Replication
explored in these studies. In addition to delineation of tissuespecific transcriptomes for the understanding of cell proliferation, as exemplified here, metabolic and proteomic profiling of
replicating cells can be applied to unravel posttranscriptional
levels of regulation, activity of signaling pathways, and metabolic
state of replicating cells. The specific effects of drugs and other
factors on the biology of replicating cells in a tissue can be
analyzed. Insights from such experiments may provide important
clues for regenerative medicine.
The replication reporter presents also interesting opportunities
in cancer biology. Expression profiles of replicating cells from
tumors and normal tissues could help distinguishing between
abnormally expressed genes in tumors and genes associated
with normal cell divisions. Such an approach may reveal
cancer-specific replication markers with a potential to become
new drug targets. Replicating cells isolated from normal or tumor
tissues can also be utilized for functional analysis. For example,
determining the tumorigenic potential of replicating versus
quiescent cells in tumors may bear relevance for the concept
of cancer stem cells.
In summary, we present a transgenic mouse system that
allows for the isolation of live replicating cells from their in vivo
niches, even in situations in which cell divisions are rare. We
expect this tool to have broad utility in the study of cell replication
in the context of cancer and regenerative biology.
EXPERIMENTAL PROCEDURES
Recombinant Lentiviral Vector Construction and Lentivirus
Production
Vesicular stomatitis virus G protein (VSV-G)-pseudotyped, third-generation
LVs were constructed to express cycB-GFP under the human phosphoglyserate kinase (hPGK) promoter. Woodchuck hepatitis virus pre-element (wPRE)
and central polypurine tract (cPPT) were used in the vector backbone.
Lentiviral stocks were produced by transient transfection of 293T cells with
the pHRH encapsidation plasmid, a VSV glycoprotein-G-encoding plasmid
and the pLTCycB1-GFP recombinant vector. After 48 hr, the virus supernatant
was harvested, treated with DNase I (Sigma-Aldrich, St. Louis, MO, USA), and
filtered through a 0.45 mM filter prior to ultracentrifugation. Virus was concentrated by ultracentrifugation at 22,000 rpm for 1.5 hr using Sorvall SW28 rotor
and resuspended in 200 ml, and the resulting pellet was resuspended in PBS,
separated into aliquots, and frozen at "80# C until use. Virus titer was determined by transduction of NIH 3T3 cells.
we used the Amersham Cell Proliferation Fluorescence Kit and processed the
coverslips in accordance with the manufacturer’s instruction. After counterstaining with DAPI and mounting using Vectashield (Vector Laboratories,
Burlingame, CA, USA), immunofluorescence was monitored and images
were digitally recorded. For analysis of DNA content, fibroblasts were stained
with Hoechst 33342 (5 mg/ml) for 30 min at 37# C, washed in ice-cold PBS, and
subjected to flow cytometric analysis using BD LSR-II flow cytometer.
Liver Cell Purification, FACS Analysis, and Sorting
Adult mice were subjected to standard two-step collagenase perfusion for
isolation of primary hepatocytes. Briefly, the liver was preperfused with
Krebs-Ringer buffer with 20 mM glucose and 0.5 mM EGTA and then perfused
with Liver Digestion Buffer (Invitrogen). Parenchymal cells were purified by
Percoll buffer (90% Percoll [Sigma-Aldrich], 1XPBS) at low-speed centrifugation (100 g, 10 min). Viability of isolated hepatocytes was around 90% as determined by Trypan blue. Isolated liver cells were cytospun for immunostaining
and fluorescence-activated cell sorting (FACS) was used to collect GFP-positive and GFP-negative cell populations directly into TRI Reagent (SigmaAldrich) for RNA extraction.
RNA Preparation, Amplification, and Microarray Hybridization
Methods
For gene expression profiling, total RNA was prepared using QIAGEN RNeasy
microkit in accordance with the manufacturer’s protocol. Ten nanograms of
total RNA from each sample was amplifyed using the WT-Ovation Pico RNA
Amplification System (Nugen, San Carlos, CA, USA). Double-stranded cDNA
was generated using the WT-Ovation Exon module (Nugen), followed by biotin
labeling with the FL-Ovation cDNA Biotin Module V2 (Nugen). The labeled
samples were hybridized to the Mouse gene1.0ST GeneChip array (Affymetrix,
Santa Clara, CA, USA). The arrays were washed and stained with phycoerytrin
conjugated streptavidin (SAPE) using the Affymetrix Fluidics Station 450, and
the arrays were scanned in the Affymetrix GeneArray 3000 scanner to generate
fluorescent images, as described in the Affymetrix GeneChip protocol.
Immunohistochemistry
Paraffin sections (5 mm thick) were rehydrated, and antigen retrieval was
performed using a PickCell pressure cooker. The following primary antibodies
were used: guinea pig anti-insulin (1:200; DAKO, Glostrup, Denmark), mouse
anti-BrdU (cell proliferation kit, 1:300; Amersham, Piscataway, NJ, USA),
rabbit anti-ki67 (1:200; Neomarkers, Fremont, CA, USA), goat anti-GFP
(1:400; Invitrogen, Carlsbad, CA, USA), mouse anti-PCNA (1:400, DAKO),
mouse anti-albumin (1:100, R&D Systems, Minneapolis, MN, USA), mouse
anti-Hnf4a (1:100, Abcam, Cambridge, UK), rabbit anti-Hnf6 (1:50, Santa
Cruz Biotechnology, Santa Cruz, CA, USA), and mouse anti-glutamine synthetase (1:200, BD Bioscience, Franklin Lakes, NJ, USA). For DNA counterstain
we used DAPI (1:100; Sigma-Aldrich) or Sytox (Invitrogen). Secondary antibodies were all from Jackson Immunoresearch Laboratories (West Grove,
PA, USA). For double staining, we used only affinity-purified secondary antibodies, suitable for multiple labeling. All immunofluorescence images were
captured on an Olympus FV1000 confocal microscope.
Gene Expression Analysis
Microarray data on gene expression in replicating liver cells is available with the
Gene Expression Omnibus (GEO) accession number GSE39966. Gene expression profiles were processed with RMA (Robust Multichip Average) using the
RMAExpress implementation (Irizarry et al., 2003). Affymetrix probes were
collapsed to 22,349 unique genes (Gene Symbol) by selecting the probe with
the maximal average expression for each gene across all six samples. Differential expression was analyzed using the ComparativeMarkerSelection and ExtractComparativeMarkerResults modules in GenePattern (Gould et al., 2006;
Reich et al., 2006). We selected the 1,222 genes significantly differentially expressed between replicating and nonreplicating cells based on the two-sided
paired t test (asymptotic p values % 0.05). We defined two sets of genes depending on whether genes were induced (744 genes) or repressed (478 genes)
in replicating cells compared to nonreplicating cells. We identified specific
pathways from the MSigDB collections (12) that were significantly enriched in
both sets of genes based on the hypergeometric test (p values % 0.05). Furthermore, we studied the significant induction and repression of these pathways in
replicating cells compared to nonreplicating cells using the GSEA module in
GenePattern (12, 38). In our analysis we considered gene sets with a nominal
p value below 0.01 and a false discovery rate (FDR) below 0.05 to be significant.
For each gene set, the mean expression of the ‘‘leading edge’’ genes (which
supported the differential expression signature) in each of the replicating and
nonreplicating cells was visualized in GENE-E (http://www.broadinstitute.
org/cancer/software/GENE-E/), after logarithmic transformation (log2) and
centering (zero mean). Publicly available liver expression data at embryonic
time points E11.5, E12.5, E14.5, and E16.5 days of gestation (Otu et al.,
2007) were downloaded from the GEO (accession number GSE6998). Liver
expression data at E18.5 days of gestation and at 42 days after birth (P42) (Cairo
et al., 2008) were downloaded from ArrayExpress accession number E-MEXP1853. Data normalization and analysis was performed as described above.
Quantitation of Cell Proliferation and Cell-Cycle Analysis
Transgenic MEFs were plated on coverslips and labeled with 10 mM BrdU for
3 hr. To monitor cell proliferation and GFP expression in transgenic fibroblasts,
Quantitative Real-Time PCR
Total RNA (1 ng) was used for first-strand cDNA synthesis using random
primers (Roche, Indianapolis, IN, USA) and reverse transcriptase (ImProm-II,
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Developmental Cell
In Vivo Expression Program of Cell Replication
Promega, Madison, WI, USA). Real-time PCR was performed with SYBR
Green PCR master mix (Applied Biosystems, Foster City, CA, USA) in 96well plates using the 7900HT instrument (Applied Biosystems). All reactions
were performed in triplicates with three biological replicates. The relative
amount of mRNA was calculated using the comparative CT method after
normalization to b-actin. Briefly, we calculated DCt values between each
gene and b-actin, and DDCt values were calculated between the DCt of
each replicate and the average DCt for GFP-negative replicates. We used
the following primers: b-actin forward (50 -cacagcttctttgcagctcct-30 ) and
reverse (50 -gtcatccatggcgaactgg-30 ), Top2A forward (50 -agcagattagcttcgt
caacagc-30 ) and reverse (50 -acatgtctgccgcccttaga-30 ), Ki67 forward (50 -tt
gaccgctcctttaggtatgaa-30 ) and reverse (50 -ttccaagggactttcctgga-30 ), Ald9h1
forward (50 -tgcatccatggcaggggagc-30 ) and reverse (50 - cacacacaccccaa
gcggct-30 ), Decr2 forward (50 - agcagctgtggatgctatgacgc-30 ) and reverse (50 ggagccaggctgttgacacgg-30 ), Cyp27a1 forward (50 - ggagaccctgcgcctctac
cc-30 ) and reverse (50 - cccgggacaccacgtagtgg-30 ), Acsl1 forward (50 cgggcagcggaggagaattct-30 ) and reverse (50 - ggtgcgcacgtactgccgaa-30 ),
Hsd17b2 forward (50 - actgtcgaggtcacc aaggcgt-30 ) and reverse (50 - tgga
accgtgcctcccatgc-30 ), Hnf4a forward (50 -cctgcaggtttagccgacaa-30 ) and
reverse (50 -gaagcacttcttaagcctgcagta-30 ), Tbx3 forward (50 -gaagtcaggaaggc
gaatgttc-30 ) and reverse (50 -gtcgtcagcagctataatgtccat-30 ), Ahr forward
(50 -ggcagcttattctgggctataca-30 ) and reverse (50 -taatcatgcggatgtgggatt-30 ),
Hnf6 forward (50 -ggcgctccgcttagcagcct-30 ) and reverse (50 -gcctgg
gctttttgggggtgt-30 ), Oc2 forward (50 -tacgcctggcagcatgcaaa-30 ) and reverse
(50 -ccgtgaacaccaggcgggat-30 ).
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ACCESSION NUMBERS
The GEO accession number for the microarray data on gene expression in
replicating liver cells reported in this paper is GSE39966.
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SUPPLEMENTAL INFORMATION
Supplemental Information includes three figures and one table and can
be found with this article online at http://dx.doi.org/10.1016/j.devcel.2012.
08.009.
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ACKNOWLEDGMENTS
We thank Ittai Ben-Porath, Shmuel Ben-Sasson, Eithan Galun, Eli Pikarsky,
and Gustavo Mostoslavsky for a critical reading of this manuscript, Abed Khalaileh for help in performing partial hepatectomy, and Benzi Zuberi for zygote
infection. This work was funded by the Sixth Framework Programme of the
European Union and the JDRF Center for Beta Cell Therapy in Diabetes and
by an ERC starting grant (to Y.D.). This work was supported in part by a grant
from USAID’s American Schools and Hospitals Abroad (ASHA) Program for the
upgrading of the Flow Cytometry Laboratory at the Hebrew University Medical
School, I-CORE Program of the Planning and Budgeting Committee, and The
Israel Science Foundation #41.11.
Received: April 6, 2012
Revised: July 15, 2012
Accepted: August 14, 2012
Published online: September 20, 2012
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