Extended Experimental Procedures Cell Culture Mouse partial iPS

Extended Experimental Procedures

Cell Culture

Mouse partial iPS cells, mouse iPS cells and mouse ES cells were maintained in ES cell

medium (DMEM containing 15% fetal calf serum (FCS), 1X non-essential amino acids

(NEAA), 5.5 mM 2-mercaptoethanol (2-ME), 50 units/ml penicillin and 50 mg/ml

streptomycin) on feeder layers of mitomycin-C-treated SNL cells stably expressing the

puromycin-resistance gene. We used conditioned medium from Plat-E cell cultures that

had been transduced with a LIF-expressing vector as a source of leukemia inhibitory

factor (LIF). MEFs were maintained in DMEM containing 10% FCS, 50 units/ml

penicillin and 50 mg/ml streptomycin. Plat-E cells were maintained in DMEM

containing 10% FCS, 50 units/ml penicillin, 50 mg/ml streptomycin, 1 mg/ml

puromycin and 10mg/ml blasticidin S. We used 13.5 d.p.c. embryos for MEF isolation.

Mouse partial iPS cell lines used in this study were 20A4 (Okita et al., 2007), 20A5

(Okita et al., 2007) and 20A16 (Okita et al., 2007). Mouse iPS cell lines used in this

study were 492B4 (Okita et al., 2008), 492B9 (Okita et al., 2008), 178B5 (Nakagawa et

al., 2008), 20D17 (Okita et al., 2007), 256H18 (Nakagawa et al., 2008), 98A-1 (Aoi et

al., 2008) and 99-1 (Aoi et al., 2008). Mouse ES cell lines used in this study were v6.5,

RF8, and 1A2 (Okita et al., 2007). MEFs used in this study were C57BL/B6 MEF (wt

MEF), Fbx-GFP reporter MEF (Takahashi and Yamanaka, 2006) and Nanog-GFP

reporter MEF (Okita et al., 2007).

RNA Sequencing

Mouse iPS cells grown on feeder cells were passaged on gelatin-coated plates twice to

remove as many feeder cells as possible, and then their RNA was isolated using the

RNeasy Mini Kit (QIAGEN). The polyA fraction was selected using a magnetic based

purification kit (Dynabeads mRNA purification kit, Invitrogen). The strand-specific

cDNA library was generated with the Whole Transcriptome Analysis Kit

(LifeTechnologies) using either adaptor mix A or B. The cDNA libraries for iPS cells

and MEFs were sequenced with the SOLiD system (LifeTechnologies) with 50-bp

single-end reads according to the instructions of the manufacturer.

Data Analysis

The sequenced reads obtained from the SOLiD 3 plus system were mapped to both the

mouse exon-exon junction sequences which were defined by three transcript databases

(i.e., Refseq, UCSC knowngenes and Ensemble transcripts) and the mouse reference

genome (mm9) using BioScope v. 1.3 (LifeTechnologies) with default mapping

parameters. Only reads that mapped to exon-exon junctions were further processed in

downstream analyses. The possible alternatively spliced regions were identified using

three transcripts databases (i.e., Refseq, UCSC knowngenes and Ensemble transcripts).

The alternatively spliced regions that were expressed in MEF and iPS cells (total

junction reads >= 10) were used to analyze the exons that were alternatively spliced

between MEF and iPS cells. The inclusion ratios were used to determine the

alternatively spliced exons by statistical analysis (a Fisher's exact-test using the 2x2

table in which junction reads were divided by cell types (i.e. MEF versus iPS cells) and

read types (i.e. inclusion versus exclusion)) followed by multiple-test correction with a

false discovery rate (Benjamini-Hochberg FDR) of less than 0.01. For clustering

analysis, we obtained an additional data set including MEF, two iPS cell lines and one

ES cell line from the SOLiD 4 system to identify the splicing patterns that differed

between the MEFs and ES cells; these differences were identified by the same method

described above. The calculation of the inclusion ratio, the statistical analysis and the

clustering analysis were performed using Microsoft SQL server and R software with

custom-made programs. The functional analyses were generated through the use of IPA

(Ingenuity Systems, www.ingenuity.com). The genes that were associated with

biological functions and pathways in the Ingenuity Knowledge Base were considered

for the analysis. Fisher's exact test was used to calculate a p-value. For motif analysis,

nucleotide sequences (300 bp in size for intronic regions and 40 bp for exonic regions)

around the skipped exons were extracted to search for overrepresented motifs between 3

to 7 nucleotides in size. The region-specific background set that includes sequences

from the set of skipped exons expressed in both MEF and iPS cells.were used to

calculate motif-enrichment p-values (Fisher's exact test) followed by multiple-test

correction with a false discovery rate (Benjamini-Hochberg FDR) of less than 0.05.

Mouse iPSC Generation

The generation of mouse iPS cells with retroviruses was performed as previously

described (Takahashi and Yamanaka, 2006) with some modifications. Briefly, Plat-E

cells were seeded at 8.1 x 10⁶ cells per 150-mm dish. On the next day, pMX-based

retroviral vectors encoding the four reprogramming factors were independently

introduced into Plat-E cells using the FuGENE 6 transfection reagent. After 24 h, the

medium was replaced with 20 ml of DMEM -containing 10% FBS. Nanog-GFP reporter

MEFs were seeded at 0.7 x 10⁶ cells per dish in 100-mm dishes coated with a layer of

gelatin. The next day, virus-containing supernatants from the Plat-E cultures were

recovered and combined. The Nanog-GFP reporter MEFs were incubated in the

virus/polybrene-containing supernatants at a final concentration of 4 μg/ml for 24 h.

Three days after infection, the medium was changed to ES cell medium supplemented

with LIF. In knockdown experiments during iPS cell induction, retrovirus for shRNAs

and the four reprogramming factors were produced in Plat-E cells. Nanog-GFP reporter

MEFs were seeded at 1.0 x 104 cells per well in 24-well plates coated with a layer of

gelatin one day before infection. The next day, virus-containing supernatants from the

Plat-E cultures were recovered and combined. The Nanog-GFP reporter MEFs were

incubated in the virus/polybrene-containing supernatants at a final concentration of 4

μg/ml for 24 h. Then, the feeder cells are seeded on the infected fibroblasts. The

following day, the medium was changed to ES cell medium containing KSR

(Invitrogen) instead of FCS. At day 14 after infection, the area of AP positive cells and

Nanog-GFP intensity per well were scored. GFP intensity was measured using

Powerscan4 (DS Parma Biomedical).

Flow Cytometry

Cultures were harvested by incubation in 0.25% trypsin/1 mM EDTA for 5 min at 37ºC,

and single-cell suspensions were obtained by repetitive pipetting and transfer through a

70 μm cell strainer. Cells were incubated with PE-conjugated rat anti-Thy1 (sc-52616

PE, Santa Cruz) and Alexa Flour 647-conjugated mouse anti-SSEA-1 (sc-21702 AF647,

Santa Cruz) antibodies and analyzed on a FACSAria II instrument (BD Biosciences).

Dead cells were excluded by staining with DAPI. The data were analyzed using FlowJo

software (Tree Star).

RNA Isolation, qPCR and Absolute qPCR

RNA was isolated from MEF, partial iPS cell lines and iPS/ES cell lines using the

RNeasy Mini Kit (QIAGEN) following the manufacturer’s instructions, and cDNA was

produced using the QuantiTect Reverse Transcription Kit (QIAGEN). Real-time

quantitative PCR reactions were set up with SYBR Premix Ex Taq II (Perfect Real

Time) (TaKaRa) and run on a StepOne Plus QPCR System (Applied Biosystems). RNA

was isolated from tissues and FACS-sorted cells using the RNeasy Mini Kit (QIAGEN)

following the manufacturer’s instructions, and cDNA was produced using the

QuantiTect Reverse Transcription Kit (QIAGEN). For high-throughput qPCR, cDNA

was pre-amplified, and real-time quantitative PCR reactions were loaded on 96.96

qPCR Dynamic array, and run using BioMark System (Fluidigm) following the

manufacturer’s instructions. For digital PCR, cDNA was diluted on dqPCR 37K chips,

and PCR reactions were run using the BioMark System (Fluidigm). For determining

inclusion ratios, the expression levels of splicing variants were separately detected; for

inclusion variants, one of the primer pair were designed within alternative exons and for

exclusion variants, one of the primer pair were designed for cross exon-exon junctions.

The primer sequences for qPCR are listed in Supplementary Table S3. Moreover, for the

data obtained from each primer pair, we checked them by gel electrophoresis to confirm

that only a single band is detected and the size of the qPCR product is correct. Further,

we purified a part of the PCR products and confirmed their sequences. Finally, we

cloned both inclusion and exclusion isoforms of almost all the genes, whose splicing

ratio was examined, into plasmid vectors. For a part of the genes, not the whole coding

sequence but the several hundred bp long region containing the alternative exon was

cloned. With these plasmids as templates, we tested whether PCR primers specifically

detected only their intended targets (data not shown).

Microarray Experiments

Total RNA was prepared using the RNeasy Mini Kit (Qiagen) according to the

manufacturer’s instructions. cDNA synthesis and transcriptional amplification were

performed using 200 ng of total RNA following the "Whole Transcript (WT) Expression

kit" (Ambion/Affymetrix). The fragmented and biotin-labeled cDNA targets were

hybridized to mouse Gene 1.0 ST arrays (Affymetrix) according to Affymetrix protocols.

Hybridized arrays were scanned using an Affymetrix GeneChip Scanner. The data

analyses were performed using GeneSpring GX software (Agilent Technologies).

siRNA Screen

For RNAi screen, the target genes (92 genes in total) were selected under the following

criteria; (1) Their expression levels were increased by 2-fold or more in both iPS cells

and ES cells compared to MEF based on our microarray data, and (2) they were in

the ”RNA binding” biogroup in the NextBio database (October 2010). Three Silencer

siRNA (Life Technologies) for a single target gene were pooled. Then the siRNA pools

were transfected into murine iPS cells and ES cells by using Lipofectamine 2000 (Life

Technologies) by reverse transfection according to manufacturers’ instructions. siRNAs

were used at 25nM. Transfection efficiency was examined using Alexa Fluor

555-labeled, double-stranded RNA oligomer (Invitrogen). After siRNA treatment for 48

hr, cDNA was synthesized using Cells-to-Ct kit (Life Technologies). Then, cDNA was

pre-amplified, and real-time quantitative PCR reactions were run on the BioMark

System (Fluidigm) following the manufacturer’s instructions. Each of the siRNA pools

for U2af1 and Srsf3 was tested under the same condition as the above.

Immunoblotting

Cells were lysed in 1 x Cell Lysis Buffer (Cell Signaling Technology) with protease

inhibitors (Complete; Roche) and 0.5% deoxycholate. Cell lysates were separated by

SDS-PAGE and analyzed by immunoblotting with anti-U2AF1, anti-SRSF3 antibodies

(Santa Cruz Biotechnology) and anti-GAPDH antibody (Ambion).

Public Microarray Data Analysis

The expression profiles of the 92 RNA-binding genes in tissues and cell lines were

obtained from the BioGPS public database (http://biogps.gnf.org).

Extended Discussion

In this study, we performed global analysis of alternative splicing and identified several

hundred genes whose splicing patterns are changed during the reprogramming process.

Moreover, our data indicate that molecular properties of somatic cells revert to those of

embryonic stem cells in terms of isoform expression. Although the functional

significance of each splicing variant in the reprogramming process remains to be

elucidated, our analysis reveals that cellular reprogramming accompanies the drastic

changes in splicing regulation of genes that are expressed in somatic cells and

pluripotent stem cells.

Our analyses identified several hundreds of genes which undergo alterations in

splicing patterns during somatic cell reprogramming. We also found that about a half of

the genes are changed in their expression by more than two fold based on our RNA-seq

data. Alternative splicing can induce nonsense-mediated RNA decay (NMD) and

modulate transcript levels. However, only approximately 10% of the alternative splicing

changes are predicted to modulate transcript levels by NMD (data not shown).

Therefore, the changes in the gene expression could not be explained simply by NMD

alone. It is assumed that gene expression levels are determined not only by NMD but

also by transcriptional regulation and/or modulation of mRNA stability. The relationship

between alternative splicing and transcriptional regulation should be investigated in a

future study. Our computational analyses provided mechanistic insight into the

differences in splicing patterns between somatic cells and pluripotent stem cells. First,

overrepresented motifs were identified in and around the alternative exons, implying

that particular RNA-binding proteins recognize these sequences to control splicing

outcomes during the iPS cell induction. Our analysis also showed that the lengths of the

introns around exons, which are preferentially included in iPS cells, are shorter than

those in MEF, whereas the lengths of the exons are longer. We also found that there is

difference in distribution of the inclusion ratios in two classes of AS events, alternative

last exon (ALE) and alternative 3' splice site (A3SS), between iPS cells and MEFs,

indicating that shorter introns tend to be removed more easily in iPS cells than in MEFs

in those two classes of AS events. The lengths of exons and their surrounding introns

have been proposed to be associated with the exon recognition efficiency (Pandit et al.,

2013). Thus, our analysis suggest the drastic change in the molecular machinery of

splicing regulation, which occur during somatic cell reprogramming, are associated with

the change of the molecular repertoire of RNA-binding proteins.

Our clustering analysis based on our absolute qPCR data demonstrated that overall

splicing patterns were similar among the iPS and ES cell lines, and that the splicing

patterns in iPS cell lines used in our study were clearly different from those in partial

iPS (piPS) cells. On the contrary, the inclusion ratios of several genes varied among

pluripotent stem cells. However, we could not find any splicing events that clearly

distinguish iPS cell lines from ES cell lines, or any attributes, such as cell origin, which

could account for the differences in splicing patterns among iPS cell lines. It will be

interesting to explore the mechanisms that cause the differences in splicing among iPS

cell lines for elucidating the nature of reprogramming by transcription factors.

We found that the splicing patterns of genes in pluripotent stem cells are similar to

those in testes. This may imply that alternative splicing regulation in pluripotent stem

cells uses the same mechanisms as those in the testes. Previous studies showed that

pluripotent cells can be derived from neonatal and adult testes in mice and humans

(Conrad et al., 2008; Guan et al., 2006; Kanatsu-Shinohara et al., 2004; Kee et al., 2010).

Thus, the similarity of the splicing characteristics may reflect the potential capacity to

derive pluripotent cell lines.

Our results also indicate that the timing of splicing pattern transitions during

somatic cell reprogramming significantly varies from gene to gene. This result may

support the hypothesis that induced reprogramming is a gradual process that involves

several intermediate states (Stadtfeld et al., 2008). This sequential splicing switching,

along with the sequential events of gene expression and repression, may provide clues

to dissect the intermediate states. So far, a few genes have been proposed as markers of

cells that are committed to reprogramming (Brambrink et al., 2008; Stadtfeld et al.,

2008). However, most of them, despite being enriched in committed cells, cannot be

used to distinguish committed from non-committed cells. The splicing patterns of the

genes we identified may be promising to use as early reprogramming markers or for the

verification of pluripotency by developing reporter constructs to visualize their splicing

patterns.

Our siRNA screen experiment has identified candidate RNA-binding proteins that

function as splicing regulators in pluripotent stem cells. Moreover, our analysis showed

that U2af1 and Srsf3 play a role in somatic cell reprogramming. U2af1 is one of two

subunits of U2 snRNP auxiliary factor (U2af) and binds to the 3’ splice site of the

pre-mRNA intron to enhance splicing (Webb and Wise, 2004; Wu et al., 1999). It has

also been shown that the abundance of U2af1 could affect alternative splicing (Fu et al.,

2011; Pacheco et al., 2006). Thus, it is likely that the splicing pattern in pluripotent stem

cells is maintained by the abundance of U2af1. Srsf3 is one of the SR protein family

genes that are known to be a regulator of alternative splicing (Zahler et al., 1992). It has

been reported that Srsf3 knockout mice fail to form blastocysts, and die at the morula

stage (Jumaa et al., 1999). Moreover, we found that siRNAs against Srsf3 suppressed

the expression of pluripotency genes, including Nanog and Oct4 (Figure S4D),

indicating that Srsf3 is also important for the maintenance of pluripotency. The splicing

regulation by particular RNA-binding proteins is known to be critical in a number of

biological processes, such as postnatal heart development (Kalsotra and Cooper, 2011;

Kalsotra et al., 2008). After completion of our work, there appeared a paper of Han et al.

(2013) reporting that two RNA-binding proteins, MBNL1 and MBNL2, whose

expression levels are much lower in pluripotent stem cells than in many differentiated

cells, are involved in the maintenance of pluripotency and cellular reprogramming by

negatively regulating alternative splicing specific for pluripotent stem cells. Hence, the

RNA-binding proteins might have a role in the coordinated splicing transition during

reprogramming, and be integrated into the molecular mechanisms underlying

reprogramming.

In summary, our study describes the drastic change in splicing isoform expression

and its regulatory mechanisms during reprogramming, and suggests that alternative

splicing regulation represents part of the mechanisms of cellular reprogramming and has

important roles in pluripotency, although the functional relevance of splicing during

cellular reprogramming remains to be elucidated.

Inclusion ratio

0.0 0.5 1.0

Alte

rnat

ive

5'sp

lice

site

(A5S

S)

Alte

rnat

ive

3'sp

lice

site

(A3S

S)

Alte

rnat

ive

first

exon

(AFE

)

Mut

ually

exc

lusi

veex

on (M

XE

)A

ltern

ativ

e la

stex

on (A

LE)

0 5 10-log10(p-value)

Embryonic Development

Cellular Assembly and Organization

Cellular Function and Maintenance

Gene Expression

Organismal Survival

RNA Post-Transcriptional Modification

Post-Translational Modification

Cell Morphology

Connective Tissue Development and Function

Renal and Urological System Development and Function

Tissue Morphology

Cell Cycle

Figure S1

iPS

(492

B4)

iPS

(178

B5)

MEF ES

(v6.

5)

iPS

(492

B4)

iPS

(178

B5)

MEF ES

(v6.

5)

iPS

(492

B4)

iPS

(178

B5)

MEF ES

(v6.

5)

iPS

(492

B4)

iPS

(178

B5)

MEF ES

(v6.

5)

iPS

(492

B4)

iPS

(178

B5)

MEF ES

(v6.

5)

A

B C

Region 1(40bp)

Region 4(40bp)

Region 2(300bp)

Region 3(300bp)

Region 5(40bp)

Region 8(40bp)

Region 6(300bp)

Region 7(300bp)

Group_iPSincGroup_MEFinc

ACAA (p=2.0×10 )-2

ACAAA (p=2.6×10 )-3

AACUA (p=4.8×10 )-2

Region 4

Region 5

Region 7

CAAU (p=4.0×10 )-2Region 8

Region 6UCG (p=3.2×10 )-2

CAA (p=1.1×10 )-2Region 3

UUUGC (p=3.7×10 )-2

Figure S1. Clustering Analysis for Each Splicing Pattern, the Functional analysis

and Motif Identification, Related to Figure 1

(A) Clustering analysis of the splicing profiles for each splicing pattern determined by

inclusion ratios are shown as described in Figure 1B.

(B) The functional analysis using IPA identified the biological functions and pathways

that were significantly enriched in the genes whose splicing patterns are different

between MEFs and iPS cells (Figure 1A). Fisher's exact test was used to calculate a

p-value.

(C) Nucleotide sequences (300 bp in size for intronic regions and 40 bp for exonic

regions) in eight regions (from Region 1 to Region 8) around the skipped exons were

extracted to search for overrepresented motifs in Group_MEFinc and Group_iPSinc

(Figure 1C) between 3 to 7 nucleotides in size. The region-specific background set that

includes sequences from the set of skipped exons expressed in both MEFs and iPS cells

were used to calculate motif-enrichment p-values (Fisher's exact test). Overrepresented

motifs in each group with p-value (Benjamini-Hochberg FDR) < 0.05 are presented.

Csda Csnk1d Dclk2 Ezh2 Fgfr1 Foxm1

Trim33 Ubn1 Ubtf

Trim33 Ubn1 Ubtf

ExclusionInclusion

ExclusionInclusion

ExclusionInclusion

Sam

ple

123456789

10111213141516

0 0.25 0.5Inclusion ratio

0.75 1.0 0 0.25 0.5Inclusion ratio







0.75 1.0


0.75 1.0













0.75 1.0













0.75 1.0




0.75 1.0



0.75 1.0









0.75 1.0

MEF

piPSC

iPSC

ESC

Sam

ple

123456789

10111213141516

MEF

piPSC

iPSC

ESC

Sam

ple

123456789

10111213141516

MEF

piPSC

iPSC

ESC

Sam

ple

123456789

10111213141516

MEF

piPSC

iPSC

ESC

Sam

ple

123456789

10111213141516

MEF

piPSC

iPSC

ESC

Figure S2

Hmgxb4 Hsf2

Csda Csnk1d Dclk2 Ezh2 Fgfr1 Foxm1 Mark2Hmgxb4 Hsf2 Map3k7 Map4k4 Mapk9

Golga2 Mpzl1 Nasp Palm Plod2 Prrc2b Smg7 Ubqln1

Mark3 Max Maz Mlh3 Mta1 Myef2 Tfdp2Nek1 Nrf1 Prpf4b Tbx3Rnps1

Mark2





0.75 1.0

Map3k7 Map4k4 Mapk9 Mark3 Max Maz Mlh3





0.75 1.0

Mta1 Myef2 Tfdp2









0.75 1.0

Nek1 Nrf1 Prpf4b Tbx3Rnps1

ES

(1A

2)E

S (v

6.5)

iPS

(98A

-1)

iPS

(99-

1)iP

S (1

78B

5)iP

S (2

56H

18)

iPS

(492

B9)

ES

(RF8

)iP

S (4

92B

4)iP

S (2

0D17

)

wt M

EF

Ng

ME

FFb

x M

EF

piP

S (2

0A4)

piP

S (2

0A5)

piP

S (2

0A16

)

BA

D

E

C

Sam

ple

MuscleOvaryTestis

KidneyIntestine

SpleenStomach

LiverLungHeart

ThymusBrainiPSCMEF

Sam

ple

MuscleOvaryTestis

KidneyIntestine

SpleenStomach

LiverLungHeart

ThymusBrainiPSCMEF

Sam

ple

MuscleOvaryTestis

KidneyIntestine

SpleenStomach

LiverLungHeart

ThymusBrainiPSCMEF

Inclusion variantiPSC

iPSC

Exclusion variant

Inclusion variant

Exclusion variant

800

Estimated number of molecules

2) The number of chambers in which a PCR reaction occurs are counted up.

200

Inclusion variantExclusion variant

iPSC

ESC

MEF

iPSC

ESC

MEF

0.4 0.0 0.40.8 0.0 0.5 1.0

Inclusion ratioAbsolute expression (A.U.)

digital PCR

3) The actual number of molecules in a unit volume are estimated based on the poisson distribution.

4) By combining digital PCR data of a single reference sample and high-throuput qPCR data, absolute expression values are determined to directly compare expression levels of different targets (”absolute qPCR”, the panel above, left). Then, the inclusion ratios for each sample are calculated based on the absolute qPCR data (the panel above, right).

1) Each PCR reaction solutions is diluted into 770 chamberson “dq PCR 37K chips” (Fluidigm®). PCR reactions are run

using the BioMark™ real-time PCR reader (Fluidigm®).

Figure S2. Characterization of Splicing Patterns in MEFs and iPS Cells, Related to

Figure 2

(A) Workflow schematic of the absolute qPCR method.

(B) Splicing patterns of 27 genes that belong to GO categories, “DNA-binding” and

“Protein-kinase” in MEFs, three piPS cell lines, seven iPS cell lines and three ES cell

lines using absolute qPCR. Experiments were carried out as in Figure 2A.

(C) Clustering analysis of splicing patterns of MEF, partial iPS (piPS) cells, iPS cells

and ES cells. This analysis was based on the inclusion ratios obtained in Figure 2A and

S2B.

(D) Splicing patterns of 8 genes, that are not related to “DNA-binding” and

“Protein-kinase”, in MEFs, three piPS cell lines, seven iPS cell lines and three ES cell

lines using absolute qPCR. Experiments were carried out as in Figure 2A.

(E) Splicing patterns across multiple adult mouse tissues. Experiments were carried out

as in Figure 2B.

Csda Csnk1d

Dclk2

Ezh2

Fgfr1 Hsf2

Foxm1 Hmgxb4

Map3k7

Thy1 Nanog

Map4k4 Mark2Mark3 Mlh3 Myef2 Nrf1 Rnps1 Ubn1

Exon Exclusion

Exon Inclusion

1

early middle

late

23456


0.75 1.0




0.75 1.0




0.75 1.0










0.75 1.0


0.9 0 0.25 0.5Inclusion ratio










0.75 1.0

Sam

ple

123456

Sam

ple

123456

Sam

ple

Sample number

1 2 3 4 5 6Day -1 Day 4 Day 8 Day 14Thy1 +

Nanog +

iPSThy1

SSEA-1 +SSEA-1 Nanog

MEF

Max Maz Mta1 Nek1 Prpf4b Tbx3Mapk9

Tfdp2 Trim33 Ubtf

Figure S3

A

B

C

Rel

ativ

e ex

pres

sion

1 2 3 4 5 6 1 2 3 4 5 60

0.2

0.40.6

0.81.0

1.2

1.0

0

1.21.41.61.8

0.20.40.60.8

Sample Sample

iPS

(492

B4)

Ng

ME

F

Day

-1 T

hy+

Day

4 T

hy-

piP

S (2

0A-4

)

piP

S (2

0A-5

)

piP

S (2

0A-1

6)

Day

8 S

S+

Day

14 N

anog

+

Figure S3. Splicing Pattern Transitions during Somatic Cell Reprogramming,

Related to Figure 3

(A) qPCR analysis for marker genes. Larger sample numbers correspond to more

reprogrammed samples. The expression levels of Nanog and Thy1 were normalized to

Gapdh expression. For Thy1, the expression levels in the day -1 sample was set to 1.

For Nanog, the expression level in the day 14 sample was set to 1. The sample numbers

are the same as in Figure 3B.

(B) Determination of splicing switch times during reprogramming using absolute qPCR.

Experiments were carried out as in Figure 3B.

(C) Clustering analysis of splicing patterns of FACS-sorted cells, piPS cells, iPS cells

and MEF. This analysis was based on the inclusion ratios obtained in Figure 2A, 3B,

S2B and S3B.

Figure S4

N.C

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m44

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Dim

t1Cc

nt1

Dazl

Celf1

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Mrp

l1Dd

x18

Rpp2

5Dk

c1Ei

f2d

Nhp

2Ig

f2bp

1Dd

x41

Srsf

2Sr

sf7

Mrp

s5Ra

ver2

G3b

p2Ra

d51a

p1Rr

p9N

r0b1 Bo

llG

3bp1

Brca

1Dd

x4Sr

sf3

Srbd

1Rb

m38

Srsf

10O

as1g

Exos

c5Ex

osc7

C Bard

1Yb

x2Ei

f4a3

Nufi

p1Ls

m5

Dcp2

Strb

pEs

rp1

Oas

1aSn

rpa1

Elav

l3N

xf7

Ncl

Ddx5

2Ei

f1a

Hnrn

pcDd

x11

Rnm

tAq

rEx

osc3

Thum

pd3

Snrp

nDd

x25

Krr1

Ddx5

6N

ol8

Dis3 Fb

lDn

d1Fu

bp1

Elav

l2Rp

s4y2

Rbpm

sDd

x10

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1N

pm1

Ddx4

6La

rp4

U2a

f1Rb

m14

Exos

c8Ta

rdbp

Tra2

aAd

at1

Rbm

x2Xp

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emin

5N

sun2

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pa1

Cdc5

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Hnrn

pa3

N.C

.1N

.C.2

Oct

4Rb

m44

Pnpt

1Er

al1

Dim

t1Cc

nt1

Dazl

Celf1

Rpf1

Mrp

l1Dd

x18

Rpp2

5Dk

c1Ei

f2d

Nhp

2Ig

f2bp

1Dd

x41

Srsf

2Sr

sf7

Mrp

s5Ra

ver2

G3b

p2Ra

d51a

p1Rr

p9N

r0b1 Bo

llG

3bp1

Brca

1Dd

x4Sr

sf3

Srbd

1Rb

m38

Srsf

10O

as1g

Exos

c5Ex

osc7

C Bard

1Yb

x2Ei

f4a3

Nufi

p1Ls

m5

Dcp2

Strb

pEs

rp1

Oas

1aSn

rpa1

Elav

l3N

xf7

Ncl

Ddx5

2Ei

f1a

Hnrn

pcDd

x11

Rnm

tAq

rEx

osc3

Thum

pd3

Snrp

nDd

x25

Krr1

Ddx5

6N

ol8

Dis3 Fb

lDn

d1Fu

bp1

Elav

l2Rp

s4y2

Rbpm

sDd

x10

Ddx2

1N

pm1

Ddx4

6La

rp4

U2a

f1Rb

m14

Exos

c8Ta

rdbp

Tra2

aAd

at1

Rbm

x2Xp

o1G

emin

5N

sun2

Hnrn

pa1

Cdc5

lEx

osc2

Gar

1Ja

kmip

1Ee

fsec Ilf3

Hnrn

pa3

-4

-3

-2

-1

0

1

2

3

4

-4

-3

-2

-1

0

1

2

3

4

Nanog

Oct4

Nanog

Oct4

Rel

ativ

e ex

pres

sion

(lo

g2)

Rel

ativ

e ex

pres

sion

(lo

g2)

CA

B D

E F

G

I

H

J

iPSC

ESC

Rel

ativ

e ex

pres

sion

0

2

4

6

8

10

12

CSECSPi

N.T

.

N.C

.1

siOct4

1 2 3

N.T

.

N.C

.1

siOct4

1 2 3

2xdC

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2

CSECSPi

N.T

.

N.C

.1

siOct4

1 2 3

N.T

.

N.C

.1

siOct4

1 2 3

4tcO

0.5

0.0

-0.5

1.5

2.0

2.5

1.0

Mark3 Foxm1 Trim33

0

-1

-2

2

3

4

5

1

-0.8-1.0-1.5

-0.4-0.20.0

0.60.40.2

-0.6

iPS

C

N.C

.

siU

2af1

1 32

Inc/

Exc

(log

2)

Inc/

Exc

(log

2)

Inc/

Exc

(log

2)

iPS

C

N.C

.

siU

2af1

1 32

iPS

C

N.C

.

siU

2af1

1 32

Ezh2

-2.0-2.5-3.0

-1.0-0.50.0

1.00.5

-1.5

Inc/

Exc

(log

2)

iPS

C

N.C

.

siU

2af1

1 32

Mta1 Myef2 Nrf1

-1.0-1.2-1.4

-0.6-0.4-0.2

0.20.0

-0.8-0.8-1.0-1.2

-0.4-0.20.0

0.40.2

-0.60.0

-1.0

2.0

3.0

4.0

1.0

iPS

C

N.C

.

siU

2af1

1 32

iPS

C

N.C

.

siU

2af1

1 32

iPS

C

N.C

.

siU

2af1

1 32

Inc/

Exc

(log

2)

Inc/

Exc

(log

2))2gol( cxE/cnI0.4

0.2

0.0

0.8

1.0

U2af1

0.6

iPS

C

Rel

ativ

e ex

pres

sion

N.C

.

siU

2af1

1 32

iPS

C

N.C

.

siS

rsf3

1

0.0

-0.4

-0.8

0.8

1.2

0.4

Hmgxb4 Rnps1 Map4k4 Mark2Srsf3

0.4

0.2

0.0

0.8

1.0

1.4

1.2

0.60.0

-0.4

-0.8

0.8

0.40

-1

-2

-3

-4

1

0.0

-0.5

-1.0

-1.5

-2.0

0.5

1.0

32

iPS

C

N.C

.

siS

rsf3

1 32

Inc/

Exc

(log

2)

Inc/

Exc

(log

2)

Inc/

Exc

(log

2))2gol( cxE/cnI

Rel

ativ

e ex

pres

sion

iPS

C

N.C

.

siS

rsf3

1 32

iPS

C

N.C

.

siS

rsf3

1 32

iPS

C

N.C

.

siS

rsf3

1 32

Snrpa1

shNC #1

**

*

#2

shSnrpa1

#3

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2Nsun2

* *

*

shNC #1 #2

shNsun2

#3

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2Ddx46

* **

shNC #1 #2

shDdx46

#3

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2Hnrpa1

**

shNC #1 #2

shHnrpa1

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2

Snrpa1

shNC #1 #2

shSnrpa1

#3

GFP

inte

nsity

0

0.2

0.4

0.6

0.8

1

1.2Nsun2

shNC #1 #2

shNsun2

#3

GFP

inte

nsity

00.20.40.60.8

11.21.4

Ddx46

shNC #1 #2

shDdx46

#3

GFP

inte

nsity

00.20.40.60.8

11.21.4

* *

Celf1

shNC #1 #2

shCelf1

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2

** *

2phN

shNC #1 #2

shNhp2

#3

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2

* **

Larp4

shNC #1 #2

shLarp4

#3

Rel

ativ

e ex

pres

sion

0

0.2

0.4

0.6

0.8

1

1.2

2phN

GFP

inte

nsity

00.20.40.60.8

11.21.4

shNC #1 #2

shNhp2

#3

*

GFP

inte

nsity

00.20.40.60.8

11.21.4

Larp4

shNC #1 #2

shLarp4

#3

*

GFP

inte

nsity

Celf1

shNC #1 #2

shCelf1

00.20.40.60.8

11.21.41.6 *

Hnrpa1

shNC #1 #2

shHnrpa1

GFP

inte

nsity

00.20.40.60.8

11.21.41.6

MEFTestis

ES cell 1ES cell 2

Samples

92 R

NA

-bin

ding

gen

es

ExressionLow High

-3 0 3(log2)

MEF (log10) MEF (log10) MEF (log10)

iPS

492B

4 (lo

g10)

iPS

178B

5 (lo

g 10)

ES

V6.

5 (lo

g 10)

Figure S4. siRNA Screen for RNA-binding Proteins which Regulate Splicing

Patterns in Pluripotent Stem Cells, Related to Figure 4

(A) Scatter plots of gene expression profile in MEF and ES/iPS cells. Only the 92

RNA-binding protein-encoding genes, which were selected based on the criterion that

their expression level is at least 2-fold higher in iPS/ES cells than in MEF, are shown in

the scatter plots.

(B) The expression profiles of the RNA-binding genes which are highly expressed in

pluripotent stem cells across various tissues and cell lines. Hierarchical clustering of

expression profiles of the 92 RNA-binding genes based on the data sets registered in the

BioGPS database.

(C) qPCR analysis in iPS cells (492B4) and ES cells (RF8) treated with siRNAs against

Oct4. An siRNA against Oct4 effectively downregulated its target expression by ~90%

relative to negative control siRNA, and the treatment with the siRNA against Oct4 for

48 hr was sufficient to induce Cdx2, a trophectoderm marker gene. The expression

levels of Oct4 and Cdx2 were normalized to Gapdh expression. The expression levels in

the N. T. sample were set to 1. N.T.: no treatment.

(D) qPCR analysis in iPS cells (492B4) and ES cells (RF8) treated with siRNAs against

RNA-binding protein-encoding genes. Relative expression of pluripotency genes,

Nanog and Oct4, in each siRNA treated cell. The expression levels of Nanog and Oct4

were normalized to Gapdh expression. The expression levels in the sample treated with

N.C.1 siRNA were set to 0 (log2). N.C.: negative control.

(E) (G) The expression levels of U2af1 (E) and Srsf3 (G) were determined by qPCR in

iPS cells (492B4) after treatment with each of siRNA pools against U2af1 and Srsf3,

respectively. The expression levels were normalized to Gapdh. The expression levels in

the N.C. sample were set to 1. N.C.: negative control.

(F) (H) The graphs indicate fold-changes in relative inclusion ratios for each gene after

treatment with each of siRNA pools against U2af1 and Srsf3. The inclusion ratios in the

N.C. sample were set to 0 (log2). Experiments were performed in biological triplicates.

The error bars represent standard deviations.

(I) Knockdown efficiencies of each shRNAs in MEFs. The expression levels of each

RNA-binding protein-encoding genes were analyzed by qPCR and normalized to Gapdh.

The expression levels in the shNC-treated sample were set to 1; shNC, negative control

shRNA. Mean ± SD; n=3. *p < 0.05 for Student’s t test comparing to control

shRNA-expressing MEF.

(J) The effects of each shRNA expression on somatic cell reprogramming. Retroviruses

expressing Oct4, Sox2, Klf4, c-Myc and shRNAs were used to infect Nanog-GFP

reporter MEFs on day 0. Nanog-GFP reporter activity was measured by microplate

reader on day 14 after infection. Non-infected MEF was used as a GFP negative control.

The GFP intensity in the shNC-treated sample was set to 1. Mean ± SD; n=3. *p < 0.05

for Student’s t test comparing to control shRNA-expressing cells.

Table S1. Summary of the Mapping of SOLiD Reads by RNA-seq, Related to Figure 1

SOLiD3plus

MEF

(Adaptor A)

MEF

(Adaptor B)

iPS, 492B4

(Adaptor A)

iPS, 492B4

(Adaptor B)

Total reads 99,678,123 94,618,693 105,705,946 80,531,414

Mapped reads 74,371,900 74.61% 65,943,715 69.69% 79,926,847 75.61% 56,713,380 70.42%

Junction reads 6,024,795 6.04% 5,015,361 5.30% 5,956,049 5.63% 3,787,470 4.70%

Total sequenced reads

194,296,816

186,237,360

Mapped reads

140,315,615 72.22%

136,640,227 73.37%

Junction reads 11,040,156 5.68% 9,743,519 5.23%

SOLiD4

MEF

(Adaptor A)

iPS, 492B4

(Adaptor A)

iPS, 178B5

(Adaptor A)

ES, V6.5

(Adaptor A)

Total reads 119,542,102 125,608,031 92,175,180 120,056,116

Mapped reads 104,563,190 87.47% 107,612,507 85.67% 80,140,580 86.94% 103,025,982 85.81%

Junction reads 8,513,309 7.12% 8,204,982 6.53% 6,178,685 6.70% 7,970,114 6.64%

Table S2. The List of Genes Whose Splicing Patterns Differ between MEFs and iPS

Cells by More Than 0.2 with Respect to the Inclusion Ratio with Statistical

Significance, Related to Figure 1

See separate Excel file.

Table S3. PCR Primers List, Related to Figures 2, 3, and 4

See separate Excel file.

Supplemental References

Aoi, T., Yae, K., Nakagawa, M., Ichisaka, T., Okita, K., Takahashi, K., Chiba, T., and

Yamanaka, S. (2008). Generation of pluripotent stem cells from adult mouse liver and

stomach cells. Science 321, 699-702.

Conrad, S., Renninger, M., Hennenlotter, J., Wiesner, T., Just, L., Bonin, M., Aicher, W.,

Buhring, H.J., Mattheus, U., Mack, A., et al. (2008). Generation of pluripotent stem cells

from adult human testis. Nature 456, 344-349.

Fu, Y., Masuda, A., Ito, M., Shinmi, J., and Ohno, K. (2011). AG-dependent 3'-splice sites are

predisposed to aberrant splicing due to a mutation at the first nucleotide of an exon. Nucleic

Acids Res. 39, 4396-4404.

Guan, K., Nayernia, K., Maier, L.S., Wagner, S., Dressel, R., Lee, J.H., Nolte, J., Wolf, F., Li,

M., Engel, W., et al. (2006). Pluripotency of spermatogonial stem cells from adult mouse

testis. Nature 440, 1199-1203.

Jumaa, H., Wei, G., and Nielsen, P.J. (1999). Blastocyst formation is blocked in mouse

embryos lacking the splicing factor SRp20. Curr. Biol. 9, 899-902.

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alternative splicing. Nat. Rev. Genet. 12, 715-729.

Kalsotra, A., Xiao, X., Ward, A.J., Castle, J.C., Johnson, J.M., Burge, C.B., and Cooper, T.A.

(2008). A postnatal switch of CELF and MBNL proteins reprograms alternative splicing in

the developing heart. Proc. Natl. Acad. Sci. USA 105, 20333-20338.

Kanatsu-Shinohara, M., Inoue, K., Lee, J., Yoshimoto, M., Ogonuki, N., Miki, H., Baba, S.,

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Nakagawa, M., Koyanagi, M., Tanabe, K., Takahashi, K., Ichisaka, T., Aoi, T., Okita, K.,

Mochiduki, Y., Takizawa, N., and Yamanaka, S. (2008). Generation of induced pluripotent

stem cells without Myc from mouse and human fibroblasts. Nat. Biotechnol. 26, 101-106.

Okita, K., Nakagawa, M., Hyenjong, H., Ichisaka, T., and Yamanaka, S. (2008). Generation

of mouse induced pluripotent stem cells without viral vectors. Science 322, 949-953.

Pacheco, T.R., Coelho, M.B., Desterro, J.M., Mollet, I., and Carmo-Fonseca, M. (2006). In

vivo requirement of the small subunit of U2AF for recognition of a weak 3' splice site. Mol.

Cell. Biol. 26, 8183-8190.

Pandit, S., Zhou, Y., Shiue, L., Coutinho-Mansfield, G., Li, H., Qiu, J., Huang, J., Yeo, G.W.,

Ares, M., Jr., and Fu, X.D. (2013). Genome-wide analysis reveals SR protein cooperation and

competition in regulated splicing. Mol. Cell 50, 223-235.

Webb, C.J., and Wise, J.A. (2004). The splicing factor U2AF small subunit is functionally

conserved between fission yeast and humans. Mol. Cell. Biol. 24, 4229-4240.

Wu, S., Romfo, C.M., Nilsen, T.W., and Green, M.R. (1999). Functional recognition of the 3'

splice site AG by the splicing factor U2AF35. Nature 402, 832-835.

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Documents

Extended Experimental Procedures Cell Culture Mouse partial iPS