Proteomic analysis of cell fate decision

COGEDE-1016; NO. OF PAGES 8

Proteomic analysis of cell fate decisionJenny Hansson and Jeroen Krijgsveld

Available online at www.sciencedirect.com

The field of proteomics is progressing at a rapid pace,

developing from primarily a specialist technology to a valuable

tool in biological research. Importantly, the establishment of

mass spectrometry as a quantitative method, miniaturisation of

liquid chromatography techniques, and improved sensitivity of

mass-spectrometric instrumentation now enable near-

complete monitoring of cellular proteome dynamics. An

increasing number of studies are therefore now applying

quantitative proteomics to study proteins and posttranslational

modifications in stem cells, to reveal molecular mechanisms

and pathways underlying pluripotency, differentiation and

reprogramming.

Addresses

European Molecular Biology Laboratory, Genome Biology Unit,

Meyerhofstrasse 1, 69117 Heidelberg, Germany

Corresponding author: Krijgsveld, Jeroen ([email protected])

Current Opinion in Genetics & Development 2013, 23:xx–yy

This review comes from a themed issue on Cell reprogramming

Edited by Huck Hui Ng and Patrick Tam

S0959-437X/$ – see front matter, # 2013 Elsevier Ltd. All rights

reserved.

http://dx.doi.org/10.1016/j.gde.2013.06.004

IntroductionStem cell biology is currently one of the most active and

fast-progressing areas in biology. This is driven by the

notion that principles of self-renewal, pluripotency and

differentiation are fundamental to the earliest stages of

human development as well as to diseases such as cancer.

The prospect of using stem cells in regenerative medicine

is another motivation explaining intense efforts to charac-

terise molecular mechanisms underlying stem cell

plasticity, including reprogramming. Numerous transcrip-

tomic and epigenetic studies have revealed transcrip-

tional profiles and chromatin states of stem cells and

differentiating cells [1–5]. However, it is becoming

increasingly clear that mRNA levels poorly correlate with

protein abundance [6], and that during early differen-

tiation expression of multiple proteins is regulated post-

transcriptionally [7,8]. In addition, the functioning of a

protein can be modulated by posttranslational modifi-

cations (PTMs), including phosphorylation, acetylation,

methylation, ubiquitination and sumoylation. Clearly, to

fully understand the mechanistic details of stem cell

dynamics there is a strong need for monitoring protein

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levels and posttranslational modifications in a compre-

hensive and quantitative manner, however the tools to

achieve this goal have been lacking until recently.

The field of mass spectrometry-based proteomics has

progressed rapidly over the past few years, now enabling

the routine characterization of �5000 proteins in single

samples [9,10]. This even extends to >10,000 proteins in

cases where availability of sample and mass spectrometry

time are not restricted, thus creating data sets that are

thought to represent complete cellular proteomes [11,12].

This evolution is due to several key advances in instru-

mentation and methodology (Box 1) and their combi-

nation into integrated workflows (Figure 1). Although

there are multiple variations in this basic workflow

[13], the key point is that they all aim to generate dense

and unbiased data sets representing a large proportion of

the proteome. This is an important prerequisite for sub-

sequent bioinformatic analyses to derive biologically

meaningful information.

In this review, we discuss recent advances in the field of

stem cell proteomics, with an emphasis on studies of the

last two years that have taken a quantitative time course

analysis approach to understand changes in cell fate.

Proteomic analysis of cell differentiation andreprogrammingQuantitative proteomics has been extensively used to

compare cellular states in a binary fashion [9,17–20].

For instance, a number of studies have compared iPSCs

and ESCs to assess if the functional similarity of these

cells is also reflected in their proteomes. Kim and col-

leagues compared human newborn foreskin fibroblasts

(hFFs), hiPSCs and hESCs by 2D-gel electrophoresis

[21]. Although this approach suffers from low sensitivity

thus excluding many low-abundant proteins, the study

showed that 15 proteins were differentially expressed

between hESCs and hiPSCs. Two other groups have

achieved much greater proteome coverage of hESCs

and hiPSCS, using stable isotope labelling, SCX chroma-

tography for peptide fractionation and high-mass-

accuracy MS [10,22]. Phanstiel and colleagues compared

four hESC and four hiPSC lines, while Munoz et al.

performed two experiments each comparing an hiPSCs

cell line to its precursor cell line and to hESCs. Although

both studies achieved great depth identifying 6761 [10]

and 10,628 proteins [22] only 2234 and 2683 proteins were

quantified in all replicates in the respective studies. This

represents only 25–33% of the proteins identified, thus

excluding the majority from the statistical analysis.

Nevertheless, 293 and 58 proteins were found to be

on, Curr Opin Genet Dev (2013), http://dx.doi.org/10.1016/j.gde.2013.06.004

Current Opinion in Genetics & Development 2013, 23:1–8


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2 Cell reprogramming


Box 1 Major advances in proteomic technologies

Achieving full coverage of cellular proteomes is challenged by two

major factors inherent to most biological samples: proteome com-

plexity and dynamic range. Facing this problem, a number of

developments in methodology and instrument design, ranging from

sample preparation to chromatography and mass spectrometry have

contributed to the improved analytical power of proteomic workflows.

When used in combination, they now permit to dig deep into the

proteome identifying and quantifying proteins even in the lowest

abundance range and in small sample sizes (10,000–100,000 cells).

The most salient advances are listed below, the following reviews and

references therein provide an excellent entry point for a more detailed

overview: [13–16].

Sample preparation

� Protocols for generic and unbiased extraction of proteins from

cells.

� Integration of protein isolation and digestion in miniaturized

devices (on-filter, on-column).

� Stable isotope labelling approaches for protein quantification (e.g.

SILAC, TMT, iTRAQ).

� Peptide fractionation techniques (e.g. SCX, IEF, HILIC) to reduce

sample complexity thereby increasing overall sampling depth.

� Enrichment of posttranslationally modified proteins (e.g. TiO2 and

IMAC for phosphopeptides; modification-specific antibodies).

Chromatography

� Miniaturization of liquid chromatography columns (20–75 mm

internal diameter) to enhance sensitivity.

� Decreased particle size of chromatographic sorbents (1.5–5 mm)

and extended column length (30–50 cm) to improve peak shape

and peak capacity.

� Ultra performance liquid chromatography (UPLC) systems deli-

vering consistent low flow rates (�100–300 nl/min).

Mass spectrometry

� Efficient ion transfer optics improving overall sensitivity.

� Fast peptide fragmentation (�10 Hz) boosting the number of

peptide identifications per time unit.

� Availability of various modes of peptide fragmentation (CID, HCD,

ETD) with overlapping performance for peptide subsets (e.g. with/

without PTMs).

� Sensitive detection with high resolution and high mass accuracy

for protein identification at low false discovery rate (e.g. Orbitrap,

time-of-flight).

Bioinformatics

� Integrated workflows for raw data processing, peak detection,

protein identification and quantification, and data quality evalua-

tion (e.g. MaxQuant, Proteome Discoverer).

differentially expressed between hESCs and hiPSCs with

statistical significance, respectively, however with low

overlap between the two studies [23]. Importantly, the

study by Phanstiel and colleagues showed that expanding

the number of replicate analyses from 1 to 3 increased the

number of differentially expressed proteins detected with

statistical significance from 5 to 293 proteins [22]. This is a

clear demonstration of how experimental design enables

more powerful statistical evaluation, thus determining the

overall outcome of the study especially when proteomic



differences are small. Another remaining question emer-

ging from these studies is whether the small overlap

between the data sets is due to the use of different cell

lines, different technologies and instrumentation, or both.

This touches on a general conundrum in the field where

data produced in one lab cannot always be fully repro-

duced in another lab, because of differences in data

collection or processing.

Determining proteome changes in cells during their tran-

sition from one state to another is best done in a time-series

to capture temporal dynamics of protein expression. Sev-

eral such studies have been conducted recently, in differ-

ent contexts of cellular differentiation and development

[24–26,27�,28–31,32��]. To save in mass spectrometry

time, multiplexed approaches using isobaric mass tags

(e.g. TMT or iTRAQ) have been popularly used, offering

the capability to compare several proteomic states (e.g. up

to eight time points) in a single experiment. However,

these approaches still suffer from decreased proteome

sampling depth [33] and quantification accuracy [34], for

which improvements have started to emerge [35] but are

not yet fully resolved [36]. iTRAQ has been used to

investigate proteome dynamics during ESC differentiation

by embroid body formation [37] or by transfer of ESCs to

medium that lacked the factors needed for stem cell

maintenance [38]. While these studies led to the charac-

terisation of 575 and 1032 protein expression profiles,

respectively, a greater depth has been achieved in the

analysis of differentiation of ESCs into oligodendrocyte

progenitor cells [39�]. Here, 4-plex iTRAQ combined with

SCX fractionation and analysis by high-resolution nano

LC–MS/MS resulted in the quantification of about 3000

proteins, including novel markers (e.g. AMBRA1, NID1

and CRYAB) of different steps during oligodendrocyte

differentiation.

The first proteomic study of cellular reprogramming was

recently reported [40��], monitoring the dynamics of

protein expression over time, from fibroblasts to the

induced pluripotent state (Figure 2). Specifically, FACS-

sorting on the basis of Thy1, SSEA1 and Oct4-GFP

expression was used to collect cells destined to reach

the pluripotent state [41], at 3-day intervals over the entire

15 days of reprogramming. Stable isotope labelling via

reductive dimethylation was then used to quantify pro-

teome changes between each of the consecutive time

points. Peptide fractionation by isoelectric focusing and

analysis by high-resolution nano LC–MS/MS led to the

quantification of close to 8000 proteins. The data revealed a

two-step resetting of the proteome during the first and last

three days of reprogramming, indicating that the proteome

composition of the intermediate states is remarkably differ-

ent from either fibroblasts or iPSCs. In addition, the study

showed that proteins within complexes and protein

families change in abundance in a highly synchronous

fashion, suggesting a shared regulatory mechanism.




Proteomics of cell fate decision Hansson and Krijgsveld 3


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Figure 1

Cell sample preparation Tissue extraction Cell culture FACS sorting

Proteolysis TrypsinLysC

Peptide fractionationand/or enrichment Ion exchange chromatography Reversed-phase chromatography Isoelectric focusing Phoshopeptide enrichment

LC-MSMS (U)HPLC High-resolution MS MS/MS peptide fragmentation

Protein identification Mascot SequestAndromeda

Protein quantification Chemical stable isotope labellingMetabolic stable isotope labelling (SILAC) Isobaric tagging (TMT/iTRAQ) Label-free quantification

Data analysis Statistical analysis Pathway analysis Clustering analysis Protein interaction analysis

Differentiation

Marker 1

Mar

ker

2

Charge/hydrophobicity/ isoelectric point

Retention time m/z

m/z

PEPTI DE

Rel

ativ

e ab

unda

nce

Differentiation

Rel

ativ

e ab

unda

nce

MS MSMS

p

p

m/z

PEPTI DEp

Rel

ativ

e in

tens

ity

m/z

Current Opinion in Genetics & Development

Quantitative proteomic workflow. A typical proteomic experiment for comparison of cellular proteomes consists of lysis of collected cells, protein

extraction, enzymatic digestion of proteins into peptides, followed by a fractionation step to reduce the complexity of the peptide mixture. If a specific

class of peptides is targeted, for example, phosphorylated peptides, enrichment strategies are employed. Each peptide fraction is then analysed by

reversed-phase liquid chromatography coupled to mass spectrometry (LC–MS), in which peptides are fragmented by tandem MS (MS/MS). Search

algorithms are used to match experimental to theoretical fragmentation patterns of peptide sequences predicted from the genome sequence, thus

leading to peptide and protein identity. Protein quantification is typically achieved through the use of stable isotope labels, incorporated either

metabolically at the protein level or chemically at the peptide level. A multitude of bioinformatic approaches can then be used to interpret the data and

to infer biological insight by expression, cluster and network analysis.

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Figure 2

iPSC

Pluripotency

FACS

LC-MS/MS

iPSCFibroblast

Protein abundance change

time

abun

danc

e

Complex 1

time ab

unda

nce Complex 2

time

abun

danc

e Complex 3

Day0 Day3 Day6 Day9 Day12 Day15

Nup210

Other Nups

time

abun

danc

e

iPSC

Pluripotency

FACS FF

LC-MS/ MS

iPSCroblast

Protein abundance change

time

abun

danc

e

Complex 1

time ab

unda

nceComplex 2

time

abun

danc

e Complex 3

Day0 Day3 Day6 Day9 Day12 Day15

Nup210

Other Nups

time

abun

danc

e

+ Nup210

- Nup210

time

abun

danc

e

time

abun

danc

e

time

abun

danc

e

time

abun

danc

e

(a) Fibroblast

(b)

(c) (e)

(d)


Quantitative proteomic analysis of cellular reprogramming. (a) Cells undergoing reprogramming to pluripotency were FACS-sorted at three-day

intervals. Extracted proteins were digested, labelled by stable isotopes, fractionated via isoelectric focusing and analysed by LC–MS/MS. Close to

8000 proteins were identified, which were analysed bioinformatically to derive insights at various levels: (b) grouping all proteins in a heatmap revealed

large protein abundance changes early and late during reprogramming, while small changes occurred in the intermediate phase. (c) Clustering of

proteins with common expression profile along reprogramming revealed enrichment of biological processes within each cluster. (d) Proteins within

protein complexes and families showed strongly coordinated expression changes during reprogramming. (e) In stark contrast to the rest of the 26

quantified nuclear pore proteins, Nup210 was strongly increased, a phenomenon that was demonstrated to be essential for reprogramming (adapted

from Ref. [40��]).

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Figure 3

UPDOWN

Phosphorylationstatus

DNMT

p p p p

DNMT

PAF1C

Differentiation

0 min 30min 1 hour 6 hours 24 hours SCX

TiO2

LC-MS/MS

(a)

(b) (c)

(d)

UPDOWN

Protein abundance

Pro

tein

#


Phosphoproteomic analysis of early ESC differentiation. (a) ESCs were cultured in SILAC medium and then induced to differentiate in a nondirected

manner. After protein extraction and digestion, peptides were fractionated with SCX, followed by phosphopeptide enrichment using TiO2 and analysis

by LC–MS/MS. (b) Among 6500 proteins and 15,000 phosphopeptides, changes in the phosphoproteome were more extensive than the changes in

protein abundance. (c) Extensive changes in phoshorylation was found in the N-terminal regions of DNA-methyltransferases (DNMTs). (d) A specific

interaction of DNMTs with the PAF1 transcriptional elongation complex (PAF1C) was found during early differentiation (adapted from Ref. [32��]).

Exceptions to this pattern, where the expression profile of a

single protein deviates from the rest of the proteins within a

complex, was proposed to indicate a specific functionality

during reprogramming. Such a deviating pattern was

observed for the nuclear pore protein Nup210, which

was indeed demonstrated to have a critical regulatory role

in cell cycle progression and reprogramming, evidenced by

the failure to generate iPSCs upon knock-down of Nup210

(Figure 2) [40��].

Posttranslational modifications during stemcell differentiationA major benefit of mass spectrometry-based proteomics is

its potential to identify posttranslational modifications.

Phosphorylation is among the most prominent examples,

fulfilling an important role in cell signalling. Since the

stoichiometry of protein phosphorylation is typically very

low, this has triggered the development of enrichment

strategies to facilitate the detection of phosphorylated

peptides, finding their way to phosphoproteome analysis

in stem cells [22,42–45].



First insight into global temporal phosphorylation

dynamics in hESCs was provided in a study applying

quantitative MS to analyse the phosphoproteome of hESCs

during the first four hours of bone morphogenic protein

(BMP)-induced differentiation [45]. Amongst others, this

revealed that Sox2, a key pluripotency factor, is itself

subject to phosphorylation. An expanded understanding

was provided more recently by Rigbolt and colleagues, who

performed quantitative proteomic and phosphoproteomic

analyses of hESCs during the first 24 hours after induction

of nondirected hESC differentiation [32��]. Importantly,

two distinct differentiation protocols allowed for discrimi-

nation between treatment-specific and common events

associated with induction of differentiation. Using a

SILAC-approach combined with SCX fractionation,

TiO2 phosphopeptide enrichment and high-resolution

LC–MS/MS, an in-depth phosphoproteomic dataset was

achieved, identifying 6521 proteins and mapping 14,865

phosphopeptides (Figure 3). In addition to highlighting

dynamic phosphorylation on DNA methyltransferases, the

data revealed that alteration of the phosphoproteome was






more extensive than the changes in protein abundance (30–45% sites showed a change in phosphorylation status while

17–19% proteins changed in abundance) (Figure 3).

Indeed, of 216 identified transcription factors, including

many with an established role in stem cell pluripotency and

differentiation, only about 10% changed in abundance

within 24 hours of differentiation, while almost half of all

714 phosphorylation sites on transcription factors changed

more than twofold over the same period. Interestingly, a

study investigating FGF-2-assisted stem cell maintenance

also identified transcription factors as one of the largest

classes of regulated phosphoproteins [46], suggesting that

dynamics of phosphorylation intersects with transcription

factor activity.

Beyond phosphorylation, other posttranslational modifi-

cations that may regulate stem cell properties should not

be neglected [47]. Recently, quantitative mass spectrom-

etry was successfully employed to map the global changes

in ubiquitination in pluripotent and differentiated ESCs,

identifying multiple members of the core pluripotency

machinery to be ubiquitinylated [48�]. The authors there-

fore suggest that the ubiquitin-proteasome system is an

additional mode of regulation of stem cell pluripotency.

Conclusions and outlookQuantitative proteomics is still a young field where con-

tinuous technical and methodological advances indicate

that full maturation has not been reached yet. Future

developments will continue to focus on increasing sen-

sitivity and throughput to maximize the information

content obtained from small cell populations. Future

trends facilitated by increased speed and sensitivity of

mass spectrometers will likely include minimizing the

number of sample handling steps [49] to avoid protein

losses along the way. Importantly, this will permit a

decrease in the number of cells required for in-depth

proteome analysis, opening the way to analyse highly

purified rare cell populations obtained by FACS-sorting

or tissue micro-dissection [50]. In addition, further de-

velopment of approaches for multiplexed data collection

will facilitate dynamic proteome profiling across multiple

time points or conditions [51,52]. All of this will help to

bridge proteomic methodologies to in vitro and in vivosystems used in stem cell biology, thereby enabling the

identification of markers and mechanisms pertinent to

many facets of cell fate decision.

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� of special interest

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Proteomic analysis of cell fate decision

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