Altex 27, 1/10
Review and report series of t4 – the
transatlantic think tank for toxicology
Marcel leist, Susanne Bremer, Patrik Brundin, Jürgen Hescheler, Agnete Kirkeby, Karl-Heinz Krause, Peter Pörzgen,
Michel Pucéat, Mathias Schmidt, André Schrattenholz, Naomi B. Zak and Hannes Hentze:
The biological and ethical basis of the use of human embryonic stem cells for in vitro
test systems or cell therapy
Altex 25, 163-190.
This paper describes the derivation and use of different pluripotent stem cells, including ethical implications of
human embryonic stem cells and applications for toxicity testing.
Costanza Rovida and thomas Hartung:
Re-evaluation of animal numbers and costs for in vivo tests to accomplish REACH legislation
requirements for chemicals – a report by the Transatlantic Think Tank for Toxicology (t4)
Altex 26, 187-208.
This paper describes the status of chemical pre-registration for REACH and recalculates costs, animal needs and test resources.
Philipp B. Kuegler, Bastian Zimmer, tanja Waldmann, Birte Baudis, Sten Ilmjärv, Jürgen Hescheler, Phil Gaughwin,
Patrik Brundin, William Mundy, Anna K. Bal-Price, André Schrattenholz, Karl-Heinz Krause, Christoph van thriel,
Mahendra S. Rao, Suzanne Kadereit and Marcel leist:
Markers of murine embryonic and neural stem cells, neurons and astrocytes: reference points for
developmental neurotoxicity testing – a review by the Transatlantic Think Tank for Toxicology (t4)
Altex 27, 16-42.
This paper describes the background of DNT testing and the selection of transcription based markers for new test systems.
All reviews and reports may be downloaded from the Altex website (www.altex.ch) and AltWeb (CAAt, www.altweb.jhsph.edu).
Further in depth reviews of all toxicologically-relevant topics are encouraged. the reviews are commissioned by any member
of t4 (list). Interested authors are advised to contact a relevant t4 member directly with a review proposal. Publication is subject to a
two-step peer review process. Reviews will be published and made available to the public free of charge. Authors are eligible
for an honorarium.
t4 – transatlantic think tank for toxicology: the concept
t4 was created with the following aims:
to analyse current tools and programs and model / forecast the likely outcome with regard to safety and economical burden •?
to compare different approaches on an international scale (especially transatlantic) and support harmonization•?
to further the concept of an evidence-based toxicology (eBt) following the role model of evidence-based medicine•?
to develop and assess the conceptual needs to enable the change of approaches (predictive toxicology, •?
integrated testing, systems toxicology, organotypic and stem cell cultures)
to create and maintain the information platforms (AltWeb, Altex, testSmart workshops etc.) to further the paradigm change •?
t4 – transatlantic think tank for toxicology: the members
Alan Goldberg, Center for Alternatives to Animal testing (CAAt), Johns Hopkins University, Baltimore, USA;
thomas Hartung, Doerenkamp-Zbinden Chair for evidence-based toxicology and CAAt, Johns Hopkins University,
Marcel leist; Doerenkamp-Zbinden Chair for in vitro toxicology and Biomedicine and CAAt europe,
University of Konstanz, Germany;
Bas Blaauboer, Doerenkamp-Zbinden Chair on Alternatives to Animal testing in toxicological Risk Assessment,
IRAS, University of Utrecht, the Netherlands
Kuegler et al.
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Markers of murine embryonic and neural stem cells,
neurons and astrocytes: reference points for developmental
Table of contents
1 Introduction 17
towards new test systems for developmental neurotoxicity
lessons from the history of developmental neurotoxicity (DNt) testing
the road to a mechanism-based developmental toxicology
Markers for DNt testing
Challenges for an in vitro DNt test system
type of cells used as starting material
Pluripotency status and capacity to form any neural cell
In vitro DNt testing and validation: eSDNt V1.0 vs. eSDNt V2.0
Standardisation and statistical issues
What are stem cell genes?
5 Conclusions 34
Altex 27, 1/10
Markers of Murine Embryonic and
Neural Stem Cells, Neurons and Astrocytes:
Reference Points for Developmental
Philipp B. Kuegler1,2, Bastian Zimmer1, Tanja Waldmann1, Birte Baudis1,
Sten Ilmjärv3,4, Jürgen Hescheler5, Phil Gaughwin6, Patrik Brundin7,
William Mundy8, Anna K. Bal-Price9, André Schrattenholz10, Karl-Heinz Krause11,
Christoph van Thriel12, Mahendra S. Rao13, Suzanne Kadereit1 and Marcel Leist1
1Doerenkamp-Zbinden Chair for in vitro toxicology and Biomedicine, University of Konstanz, Germany; 2Konstanz Research
School Chemical Biology, University of Konstanz, Germany; 3Quretec, tartu, estonia; 4Department of Physiology, University
of tartu, estonia; 5Institute of Neurophysiology, University of Cologne, Germany; 6Stem Cell and Developmental Biology,
Genome Institute of Singapore; 7Department of experimental Medical Science, Wallenberg Neuroscience Center, Sweden; 8Neu-
Development, USePA, NC, USA; 9european Centre for the Validation of Alternative Methods, Institute of Health and Consumer
Protection, JRC, Ispra, Italy; 10ProteoSys, Mainz, Germany; 11Department of Genetic and laboratory Medicine, Geneva
University Hospitals, Switzerland; 12leibniz Research Centre for Working environment and Human Factors, technical University
of Dortmund, Germany; 13life technologies, Frederick, MD, USA
embryonic stem cell (eSC)-based novel test systems are amongst
the most dynamic areas of in vitro toxicology and biomedicine,
and their development is funded e.g. by a large scale eU project
(eSNAtS http://www.esnats.eu/). they may become future
alternatives to animal testing and a key element of modern risk
assessment approaches (Pellizer et al., 2005). At the start of such
systems and their performance to the maximum possible degree.
for meSC and derived cell types as a starting point for an intense
* a report of t4 – the transatlantic think tank for toxicology, reviewed by T. Hartung and A. Goldberg (Baltimore, MD, USA)
This manuscript has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, and
approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial
products constitute endorsement or recommendation for use.
Developmental neurotoxicity (DNT) is a serious concern for environmental chemicals, as well as for food
and drug constituents. Animal-based DNT models have relatively low sensitivity, and they are burdened by
high work-load, cost and animal ethics. Murine embryonic stem cells (mESC) recapitulate several critical
processes involved in the development of the nervous system if they are induced to differentiate into neural
cells. They therefore represent an alternative toxicological model to predict human hazard. In this review,
we discuss how mESC can be used for DNT assays. We have compiled a list of mRNA markers that define
undifferentiated mESC (n = 42), neural stem cells (n = 73), astrocytes (n = 25) and the pattern of different
neuronal and non-neuronal cell types generated (n = 57). We propose that transcriptional profiling can
be used as a sensitive endpoint in toxicity assays to distinguish neural differentiation states during normal
and disturbed development. Importantly, we believe that it can be scaled up to relatively high throughput
whilst still providing rich information on disturbances affecting small cell subpopulations. Moreover, this
approach can provide insight into underlying mechanisms and pathways of toxicity. We broadly discuss the
methodological basis of marker lists and DNT assay design. The discussion is put in the context of a new
generation of alternative assays (embryonic stem cell based DNT testing = ESDNT V2.0), that may later
include human induced pluripotent stem cells, and that are not designed for 1:1 replacement of animal
experiments, but are rather intended to improve human risk assessment by using independent scientific
Keywords: stem cell, development, neurotoxicity, gene ontology, astrocyte, systems biology
Kuegler et al.
Altex 27, 1/10
of methylmercury intoxication, autopsy studies revealed that
this compound targeted the fetal neural system (e.g. eto et al.,
an important endpoint in toxicology.
At the same time, the problem of developmental ecotoxicol-
ogy (e.g. reduced reproductive success of birds due to pesticides
in their food chain) was introduced by Rachel Carson in her book
“silent spring”. the above mentioned examples provide insights
tivity”. It did not cause problems when taken by pregnant women
earlier than about 20 days after conception or later than about 35
days after conception. However, within this window it caused
different effects, such as facial paralysis, when taken rather early,
malformations of arms and legs in the middle and e.g. deformi-
ties of the intestine when only taken late during the window of
sensitivity. Notably, although thalidomide acted as a sedative in
rats and mice (just as in humans), it had no teratogenic effects in
these rodent species most frequently used for toxicity testing.
In Minamata, Chisso Corporation was found responsible for
having caused the disease by introducing mercury waste into the
of the victims of the congenital disease (who had never eaten
causal relationship between their disease and the methylmer-
cury contamination. the situation was similar with other envi-
ronmental contaminants, where a cause-effect relationship was
disputed until R. Carson’s book became one of the key triggers
for a wave of public concern that resulted in the ban of dichlor-
diphenyltrichlorethane (DDt). these examples illustrate the
ships, and to identify suitable test systems. this fundamental
weakness is also evident from less dramatic and more prevalent
human poisonings that have reached the pandemic scale. the
most prominent example of such an omnipresent contaminant is
lead. It causes human developmental neurotoxicity, associated
with a reduction of intelligence estimated to have resulted in an
economic cost of > 100 billion $/year for each birth cohort born
between 1960 and 1990 (Grandjean and landrigan, 2006). the
average lead blood levels in children fell by 90% after the even-
tual ban of lead additives to gasoline (Grandjean and landrigan,
2006). However, those exposed earlier may keep suffering from
lead neurotoxicity due to its long biological half-life in addition
to the DNt effects (Cory-Slechta, 1990). In the case of the de-
velopmental toxicity of lead, the overwhelming epidemiological
able thresholds, and the availability of trustworthy human ref-
erence data helped to optimise a suitable experimental system
to improve the toxicity evaluation. there are still many other
wide-spread contaminants with effects below the threshold of a
pandemic, but with the potential to affect a large population.
For most of these hazardous compounds evidence from hu-
man epidemiology is not available. therefore, standardised
test systems, mainly rodent-based bioassays, are used to de-
rive points of departure (POD) for human health risk assess-
Murine embryonic stem cells (meSC) are pluripotent cells
able to differentiate into all cell types in the mouse, including
functional germ cells. Under appropriate conditions, meSC can
be kept as in vitro?cultures?with?an?indefinite?capacity?for?self-
renewal (evans and Kaufman, 1981; Martin, 1981). the deriva-
tion, use and properties of murine and human embryonic stem
cells (eSC) have been reviewed earlier (leist et al., 2008a),
also with the perspective of generating induced pluripotent stem
cells (iPSC) by reprogramming of somatic cells from various
species, including humans (Baker, 2010; Nagy and Nagy, 2010;
lee and Studer, 2010). Pluripotent cells are suitable for molecu-
lar biological manipulations, such as homologous recombina-
tions with exogenous DNA to alter sequences of their genome.
these properties have been used successfully for the generation
chi, Martin and Smithies, Nobel Prize 2007). Such mice stand
as in vivo proof that every stage and every cell of the nervous
system can develop from meSC under appropriate conditions,
and that the produced cells display different phenotypes accord-
ing to the genotype of the meSC used initially for generation
of the mice. It has also been demonstrated, that meSC can dif-
ferentiate in vitro to different neuronal or glial subtypes (Wobus
and Boheler, 2005). In theory, this offers the possibility to study
all steps – in detail, in real time and at the resolution of indi-
vidual cells – that lead from the multipotent meSC to the for-
mation of neuroectoderm tissue, and further to the generation of
neural stem cells (NSC), neuroblasts and various intermediate
and mature types of neural cells (Bain et al., 1995; Fraichard et
al., 1995; Strübing et al., 1995; Ying and Smith, 2003; Conti et
al., 2005). the in vitro differentiation of meSC or human eSC
(heSC), as well as of murine or human iPSC or neural precur-
interest to the understanding of developmental biology, but also
its disturbances. thus, such test systems appear useful for the
toxicity (Rt). Moreover, introduction of neural endpoints rel-
evant for developmental neurotoxicity (DNt) at different stages
of development and development of more predictive and more
strategy (Breier et al., 2009; Moors et al., 2009; Coecke et al.,
2007; lein et al., 2007).
2 Towards new test systems for developmental
2.1 Lessons from the history of developmental
neurotoxicity (DNT) testing
the area of developmental toxicology (Dt) came into public
focus 50 years ago. At that time, the drug thalidomide caused
severe birth defects, while the metal-organic contaminant meth-
ylmercury caused Minamata disease (Harada, 1995). the latter
also includes a congenital form, which is triggered by exposure
of the unborn fetus to the toxicant. It has been shown that the
cantly higher than in the maternal blood (Sakamoto et al., 2004).
Decades later, but still in consequence of this miniepidemic
Kuegler et al.
Altex 27, 1/10
substances should all be evaluated for their reproductive toxicity,
experiments involving millions of animals would be performed
to satisfy the legal requirements (Hartung and Rovida, 2009).
However, these tests of individual chemicals constitute only the
consider mixtures of compounds that humans and the environ-
ment are exposed to. Already a dozen compounds can form thou-
sands of different mixtures, which would be impossible to test by
classical toxicological approaches based on animal experiments.
even though some of the most relevant chemicals will be tested
for their effects on reproduction, these tests will most likely leave
open the safety questions concerning low dose effects on DNt.
As indicated above, testing for Dt in the low-dose range and
not impossible, in most cases. this is even more an issue for the
subarea of DNt. Within the ReACH testing requirements, DNt
ings from other studies. Dedicated studies are otherwise not re-
quired. thus, the concern remains that subtle, and predominantly
functional, DNt effects triggered by chemicals might remain
undiscovered. A comprehensive safety assessment will therefore
require alternative approaches. technical (limited test capaci-
call for new strategies in toxicology testing (leist et al., 2008b;
Hartung, 2009a; Stingl et al., 2009; Bottini et al., 2007). One
such strategy was suggested by the National Research Council
(NRC, 2007). this milestone publication has been described in
many reviews (Collins et al., 2008; leist et al., 2008c; Hartung
and leist, 2008; Hartung, 2009b), and the strategy is now often
summarised under the heading “tox21c” (toxicology for the 21st
systems would be based on cell cultures (human, where possible)
dents and other higher vertebrates; second, the essential primary
endpoints should cover disturbances of cellular (e.g. signalling,
metabolic, homeostatic, proliferation, differentiation) pathways,
and the overall resulting toxicological effect on humans would
be predicted by systems biology-based approaches from these
mechanistic data. the vision is that the new test systems would
allow a much higher throughput of compounds and would work
better in the low-dose range relevant for human exposure. the
use of a systems-based approach (e.g. omics data, quantitative
models linking cellular processes to adverse effects) is expected
to be more predictive of human toxicity (see above issue of ro-
dent testing of thalidomide). Added value may come from the
possibility to use and to compare cells of different species, in-
For this vision to become reality, the new methods must be
trusted and accepted globally (Bottini and Hartung, 2009; Bottini
to the problem of validation (Hartung, 2007), as detailed for
the areas of food safety and cosmetics safety (Hartung, 2008b;
Hartung and Koëter, 2008; Vogel, 2009). New technologies and
ideas can be imported and developed with specialists of other
disciplines (e.g. Mitterhauser and toegel, 2008; Schrattenholz
and Klemm, 2007), and teaching of alternative approaches may
be achieved in different ways (Jukes, 2008; Jukes, 2009; leist,
ment in regulatory toxicology. In the 1960s, it became evident
that developmental exposure to chemicals and drugs can alter
behavioural function in young and adult animals (e.g. Werboff
and Dembicki, 1962). As an indirect measure of neurotoxicity,
behavioural readouts have been used and validated since the
1960s. these behavioural alterations are considered as an ob-
servable expression of effects on nervous system function (Re-
iter, 1978). therefore, guidelines and test batteries have been
developed (Moser and MacPhail, 1990, 1992) and validated
for use in behavioural toxicology. In the 1980s, the U.S. en-
DNt guidelines and initiated the standardisation of this testing
strategy by the Organisation of economic Co-operation and De-
velopment (OeCD). the development of the pertinent OeCD
to yield reproducible results within and across laboratories, and
second, they must be sensitive to the effects of a range of neu-
rotoxic agents (Middaugh et al., 2003). A recent review (Makris
et al., 2009) revealed that just over 100 compounds have been
tested in studies using the OeCD 426 draft guideline. Most of
these compounds were pesticides (66%) and only 8 industrial
compounds for which neurobehavioural risk assessment had
been performed, in many cases also on the offspring of the ex-
posed animals (F1 generation). Only 1% of these compounds
were industrial chemicals (Middaugh et al., 2003). the avail-
able data for this relatively new area of toxicology of industrial
chemicals is therefore rather limited. Some of the studies indi-
cate that compounds exist for which DNt testing is the most
sensitive of all toxicity endpoints in a broad safety evaluation
battery. therefore inclusion of DNt testing in compound safety
evaluation programmes such as ReACH is likely to add impor-
tant information for regulatory decisions (Makris et al., 2009;
Middaugh et al., 2003). At present the available data is insuf-
In summary, the historical development of DNt testing strate-
gies was strongly based on the statistical concepts of reliability
and sensitivity, and biological modes of action played a relatively
minor role. In addition to the relatively low numbers of animal
studies, few human reference data are available. thus, the predic-
tive value of traditional DNt testing for human health is hard to
estimate. establishment of alternative and additional approaches
2.2 The road to a mechanism-based
the number of chemicals with potential for environmental ex-
posure is large. the new european law entitled ReACH trig-
gered an administrative procedure aiming at registration, evalu-
ation and authorisation of all chemicals produced in the eU at
> 1 t/year and not tested under the chemical safety law of 1982.
It is expected that at least 30,000 chemicals will be registered,
amongst these several thousand that are produced or used at > 100
t/year (Rovida and Hartung, 2009). A considerable percentage of
these chemicals is found in the environment or at work places,
where human exposure could potentially trigger Dt. As these
Kuegler et al.
Altex 27, 1/10
ing, standardisation of protocols and exploratory activities, and
a large variety of different approaches should be promoted and
process can be initiated with the goal of identifying a smaller set
of assays that may be used for regulatory decisions. therefore
only some general considerations are highlighted here:
For human predictivity, heSC may appear more promising than
rodent systems. However, for comparison with already exist-
ing murine and rat in vivo databases, meSC may be more suit-
able. In general, meSC presently represent a system with higher
throughput and robustness: neurons are generated much faster
and with higher yield than in the human system. As many more
laboratories have worked with meSC compared to heSC, there
is more experience in using the murine cultures. they are easier
to handle, and the tools to genetically modify these cells are
more advanced, while heSC show considerable variability in
vivo and in vitro (Parsons et al., 2009; Wu et al., 2007; Osafune
et al., 2008; Abeyta et al., 2004). It is also evident that heSC
2006; Hartung et al., 2009). However, much research in the 3R
concepts (e.g. Rothen-Rutishauser, 2008; Heindl, 2008; Wan-
ner, 2008; li, 2008a,b; Hagelschuer et al., 2009; Bahramsoltani
et al., 2009; Manzer et al., 2009; Hartung and Hoffmann, 2009;
Sauer et al., 2009). the next generation of methods (see chapter
below on eSDNt V2.0) should set its own standards instead of
aiming at a 1:1 substitution of existing animal protocols with
their own set of problems (Hartung, 2008a; Pelkonen et al.,
2009; Vedani et al., 2009; Sauer, 2009).
3 Markers for DNT testing
3.1 Challenges for an in vitro DNT test system
A number of questions arise when one considers develop-
ing meSC, iPSC or heSC as potential test systems for DNt.
these involve species, source, genotype, developmental status,
throughput and endpoints of the model system. At the present
stage, all different options and their combinations require test-
Tab. 1: Marker genes for mESC
full name comment ref
HLA-B-assoc. transcript 1A
Cgbp, Cxxc finger 1 (PHD domain) Cgbp knock-out cells are viable but unable
to differentiate upon removal of LIF
C-myc, myelocytomatosis oncog.
dev. plurip.-assoc. 2 expressed in human pluripotent stem and
Stella, dev. plurip. Assoc. 3
dev. plurip. assoc. 4 inner cell mass
Esg1, dev. plurip. assoc. 5
Ecat1, ES cell assoc. transcript 1 also called Oeep 48
Ecat5, ES cell-expressed Ras involved in the control of ES cell proliferation
estrogen receptor, beta activates Oct4 transcript., sustains self-
renewal and plurip.
ecat3, F-box only protein 15 target of Oct4/Sox2
fibroblast growth factor 4 target of Oct4/Sox2, activates Erk
GRB2-assoc. binding protein 1 expressed in blastocyst
Cx31, Connexin 31 gap junction protein, specific for mESC
Nucleostemin low in EB, but also expressed in NPC
KH domain containing 1A member of the Khdc1/Dppa5/Ecat1/Oeep family
Khdc1c, KH domain cont. 1C member of the Khdc1/Dppa5/Ecat1/Oeep family
Kruppel-like factor 4 inhibits cell differentiation, target of Oct4/Nanog
Kruppel-like factor 5 related to Klf4
left-right determination factor 2 antagonistic Tgfbeta ligand, sometimes called Leftb 
Left-right det. factor 1 target of Klf4/Oct4/Sox2
ln-28 homolog reprogramming factor, RNA-binding protein
mutS homolog 2 DNA repair protein, downregulated during diff.
mutS homolog 6 DNA repair protein, downregulated during diff.
polyhomeotic-like 1 regulation of Hox genes via Polycomb
NM_172303h, i, j) Phd finger protein 17
NM_013633 Oct4, POU domain, class 5,
transcription factor 1
cell migration and adhesion
, , 
, , 
transcription factor regulating plurip.
Kuegler et al.
Altex 27, 1/10
behave differently from meSC concerning the pathways that
control stemness. It has been suggested that they correspond to
epiblast stem cells rather than to inner cell mass-derived cells,
as do meSC, and they may not be able to form chimeras and an
organism (li and Ding, 2009). Continuing basic research on ro-
bust and more rapid heSC protocols is still needed to eventually
provide a model system that avoids the species differences and
the necessity for an interspecies extrapolation.
3.1.2 Type of cells used as starting material
Different cell types have been used to study aspects of DNt.
eSC are derived from the inner cell mass of blastocysts (Mar-
tin, 1981; evans and Kaufman, 1981; reviewed in leist et al.,
2008a), and, using eSC-based models, all developmental steps
are accessible for examination (Winkler et al., 2009). the down-
side of this approach is that the cells need to be directed through
all differentiation steps, preferably in a synchronised way, even
under circumstances when only information on the last step is
of interest. to avoid this problem, various other cell types have
been used to study particular stages of DNt. For instance pri-
mary neurons or certain neuroblastoma, phaeochromocytoma or
teratoma cells can differentiate to a partially neuronal pheno-
type (e.g. axonal elongation and maturation), and this forms the
basis for many test systems, which are of more limited scope but
often of high reproducibility and throughput (Radio and Mundy,
2008; Radio et al., 2008, 2009; Hogberg et al., 2009, 2010). An
intermediate solution would be the use of neural stem cells or
neuroblast-like cells, which may be developed from eSC and
that do not necessitate the initial differentiation steps required
for eSC but still have the potential to develop into a number
of different, morphologically and functionally mature neuronal
and glial cell types (Buzanska et al., 2009; Breier et al., 2008;
Wang et al., 2007). the advantages and disadvantages of such
systems illustrate an important issue of DNt testing. the down-
side is that such NSC-based systems cannot model the initial
effect of compounds on this developmental period, associated
with an important coordinated wave of gene transcription, can-
not be tested. the upside of the use of NSC is that other phases,
e.g. the step from NSC or neuroblasts, can be examined with
full name comment ref
RE1-silencing transcription factor
SRY-box containing gene 2
signal transducer and activator
of transcription 3
transcription factor CP2-like 1
growth factor 1
TEA domain family member 4,
TEF-1-related factor 1
tissue inhibitor of metallo-
undifferentiated embryonic cell
transcription factor 1
Rex1, zinc finger protein 42
zinc finger protein of the
maintains self-renewal and plurip.,
(also NSC), discussed
transcription factor regulating plurip., (also NSC)
involved in LIF signaling
role in plurip. signaling
target of nanog, Oct4, SMAD
expressed from 2 cell stage on to blastocyst
also germ line
target of Oct4/Sox2
required for maintenance of plurip. in ES cells
and neural crest development
Additional accession numbers:
a) NM_001018002, b) NM_001159500, c) NM_001160012, d) NM_153547, e) NM_001033904, f) NM_001080945, g) NM_001042623,
h) NM_001130184, i) NM_001130185, j) NM_001130186, k) NM_213659, l) NM_213660, m) NM_001044384
1. Sharov et al., 2003; 2: Abranches et al., 2009; 3. Carlone et al., 2005; 4. Lewitzky and Yamanaka, 2007;
5. Maldonado-Saldivia et al., 2007; 6. Bortvin et al., 2003; 7. Imamura et al., 2006; 8. Mitsui et al., 2003;
9. Takahashi et al., 2003; 10. Sorrentino et al., 2007; 11. Zhang et al., 2008; 12. Feng et al., 2009;
13. Tokuzawa et al., 2003; 14. Okumura-Nakanishi et al., 2005; 15. Kunath et al., 2007; 16. Schaeper et al., 2007;
17. Xie et al., 2005; 18. Worsdorfer et al., 2008; 19. Tsai and McKay, 2002; 20. Beekman et al., 2006; 21. Pierre et al., 2007;
22. Li et al., 2005; 23. Wei et al., 2005; 24. Ema et al., 2008; 25. Hamada et al., 2001; 26. Farthing et al., 2008;
27. Nakatake et al., 2006; 28. Hagan et al., 2009; 29. Hanna et al., 2009; 30. Roos et al., 2007; 31. Mason et al., 2009;
32. Chambers and Tomlinson, 2009; 33. Isono et al., 2005; 34. Tzouanacou et al., 2003; 35. Singh et al., 2008;
36. Canzonetta et al., 2008; 37. Johnson et al., 2008; 38. Buckley et al., 2009; 39. Jørgensen et al., 2009;
40. Kues et al., 2005; 41. Longshaw et al., 2009; 42. Liu et al., 2005; 43. Wang et al., 2009; 44. Nishioka et al., 2009;
45. Armstrong et al., 2005; 46. Kuntz et al., 2008; 47. Singla and McDonald, 2007; 48. van den Boom et al., 2007;
49. Nishimoto et al., 2005; 50. Okuda et al., 1998; 51. Lim et al., 2007; 52. Nakata et al., 1998
Kuegler et al.
Altex 27, 1/10
nation of the role of certain genes in diseases and pathologies.
especially the availability of meSC with reporter constructs has
been broadly applied to high-throughput screens, e.g. for com-
pounds affecting DNt (Suter and Krause, 2008; Suter et al.,
2009; Conti et al., 2005). Similar reporter constructs have been
introduced and used in heSC or iPSC, but there is still ample
room for further development and improvement.
3.1.5 Pluripotency status and capacity to form any
the use and culture of eSC is a demanding technology requir-
ing high standards of good cell culture practice. the lack of
standardised protocols used for cell differentiation appears to
be a main source of low reproducibility. Additionally, at present
no single marker can indicate conclusively that a cell has left
the developmental status of meSC or heSC and that this cell
may therefore not be suitable for DNt testing any longer. Only
groups of markers can be used (tab. 1). Similar questions ap-
ply when iPSC are generated but need to be evaluated for their
“real” pluripotency. this practical problem is illustrated by data
shown in Figure 1. the cells from different passages (meSC,
CGR8 strain) behaved similarly when they were maintained in
culture (similar growth rate and morphology). Only when the
differentiation potential was tested did dramatic differences be-
that expressed similar levels of a small set of markers (Nanog,
Oct4, tdgf1) but had dramatically different differentiation po-
tentials (Osafune et al., 2008).
highly synchronised cells and therefore less experimental noise.
Ideally, many different test systems will be used to optimally
test potential DNt during all important phases of nervous sys-
3.1.3 Culture quality
the particular setup of the cultures is a major factor for the suc-
cess of a DNt test system, independent of the endpoint chosen.
be important for transcription markers in eSC-based systems.
Some cultures are grown on feeder cells, which might affect
the pattern of RNAs detected as well as the differentiation proc-
ess and the effect of chemicals on the overall culture system.
biotics or the adhesion matrix might have complicating effects.
Most importantly, the quality of the cells is a major factor for
system would be infection or genetic alteration. However, also
mycoplasma-free, genetically intact cells may be altered epige-
netically, and this may be a major source of experimental vari-
ation (Fig. 1). Only frequent and stringent controls and efforts
to avoid uncontrolled factors as listed above can lead to robust
experimental test systems.
In the 21st century we can begin to ask whether there is an inter-
action of genetic and environmental factors (gene x environment
effect) for DNt and whether our test systems could also yield
ample, heSC from different ethnicities, genders and genotypes
can now be compared. New opportunities have arisen from the
general availability of the technique to generate human induced
pluripotent stem cells (hiPSC), which behave like heSC but can
be generated from presumably all somatic cell types including
skin samples of individuals. Soon, libraries will be available
human individuals. the use of such cells for more genotype-
related information in safety sciences appears very attractive.
the murine counterpart is the availability of over 20,000 gene
boundaries that may be further genetically engineered e.g. to
generate transgenic reporter or selection lines for many endog-
enous promoters) (Singla et al., 2010) and of thousands of trans-
genic and knock-out mice with the corresponding meSC de-
rived therefrom. Such meSC may be generated by targeting of
the second allele of heterozygous knock-out meSC (Madan et
al., 2009) or from the mice in two different ways. traditionally,
meSC would be derived from blastocysts of homozygous mat-
molecule chemicals that support meSC generation (Ying et al.,
2008; li and Ding, 2009). An emerging technology promises
the generation of pluripotent stem cells from cells of mice by
different techniques of reprogramming (lewitzky and Yamana-
ka, 2007; Kim et al., 2009a; Stadtfeld et al., 2010; Carey et al.,
2010). transgenic approaches, that also allow expression of hu-
man proteins in mice, have already been applied to the exami-
Fig. 1: Different neuronal differentiation potential of mESCs
from the same strain.
CGR8 mESC were kept under routine culture conditions (details
available from Leist lab). High (passage 115, p115) and low
(passage 39, p39) passage cells were triggered to differentiate
towards the neuronal lineage in parallel. After 20 days of
differentiation, total mRNA was extracted and analysed by
quantitative real time PCR for marker genes of mESC (Oct4),
NSC (Nestin) or neuronal (βIII Tubulin, MAP-2, Synaptophysin).
Gene expression levels were first normalised to the housekeeping
gene GAPDH and then to the expression in undifferentiated
mESCs (day 0), which was arbitrarily set to 1. Data represent
means ± SD from triplicates. *** p < 0.001
Kuegler et al.
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predictor of teratogenicity (Marx-Stoelting et al., 2009; Seiler et
al., 2006; Genschow et al., 2004; laschinski et al., 1991). the
blasts under different growth and differentiation conditions, one
PROtOCOl no. 113 (DB-AlM data base; http://ecvam-dbalm.
jrc.ec.europa.eu/). the presence of foci of beating cells is the end-
point for cardiac differentiation. the prediction model involves
mathematical comparisons between different endpoints (e.g.
translation of these classes into potential human toxicity classes.
As evident from this example, each of the three main elements
can be developed and optimised relatively independently from
the others. A number of developmental neurotoxicants are also
potential (Chapin and Stedman, 2009; Buesen et al., 2009).
For the validation of each test system, three major domains
need to be considered (Hartung et al., 2004; Hoffmann and Har-
this includes parameters like robustness of the test system,
comparability of data obtained in different laboratories or by
different operators, on different days or in parallel replicates. It
is related to technical features of the assay.
the correlation of the in vitro results with the known human data
or a corresponding “gold” standard (often in vivo animal data).
the test procedure or the prediction model. However, it remains
in the end a mathematical-correlative exercise, which neither re-
quires, nor indicates, relevance. Correlations may also be gener-
ated easily by simple mathematical tricks (Fig. 7 in leist et al.,
has some implicit consequences. As the set of compounds used
for the correlations is necessarily small, compared to all possible
compounds that may be used in the test system, it may not be
representative to the same degree for all classes of compounds.
therefore, the prediction model has a certain applicability do-
main, e.g. it applies to a certain group of compounds used for the
validation process (e.g. genotoxic carcinogens for the Ames test).
It may fail completely when different compounds (e.g. epigenetic
carcinogens in the above example) are used.
For the above reason, this third domain is highly desired in a
test system. It has been given less priority than the two other
methods. With the rise of the tox21c idea, this should become
the dominant domain in the near future. Biological relevance
should be the basis of predictive systems biology. this has a
major impact on the design of new test systems for DNt.
optimised for predictivity based on correlation. With respect
It has been shown beyond doubt that intact meSC have the full
potential of a pluripotent stem cell, i.e. to generate every cellular
phenotype (including every neural cell) in the organism. If DNt
assays were to be developed on the basis of heSC, one objection
may be that formation of complete brains has not been demon-
of pluripotency is unlikely ever to be provided. However, many
relevant neural cell types can be formed from heSC. For instance,
cells derived from heSC have been used for transplantation into
et al., 2009). Also, 3-dimensional “brain-like” engineered neural
tissue (eNt) has been generated in vitro from heSC (Preynat-
Sauve, 2009). thus it appears that heSC should be also suitable
as a test system to cover the full range, or at least most aspects, of
DNt once simple and robust protocols and a full characterisation
of the functionality of resultant cultures are available.
3.1.6 DNT specific processes and endpoints
Neurodevelopment is a highly complex biological process that
involves proliferation, migration, apoptosis, differentiation, syn-
aptogenesis, neurite and network formation, as well as gliogen-
esis and myelinisation. All these processes need not only to be
functional, but also require correct timing and complicated bal-
ances within a microenvironment often referred to as a “niche”.
for a comprehensive description of the overall outcome. experi-
mental endpoints that have been tested comprise electrophysiol-
ogy, neurotransmitter release, immunostaining and other methods
evaluations of cellular morphology. In general, endpoints that
have been shown to be suitable for other cellular test systems
should also be useful for meSC or heSC. However, there can be
practical limitations. these are mainly due to the heterogeneity of
this heterogeneity may be desired, e.g. for generation of “organ
simulating tissues”. In most cases it is accidental or stochastic,
as currently-used protocols lead to the generation of different
cell populations that are not homogeneously distributed but may
rather grow in patches or islands within a dish. Moreover, some
cells grow preferentially on top of or under other cells. In this situ-
ation it is particularly important to select endpoints that guarantee
robustness (reproducible results, also when experimental condi-
tions vary slightly), are biologically plausible and allow optimal
predictivity. It is beyond the scope of this review to evaluate the
usefulness of all different endpoints for DNt testing, and the ex-
perimental evidence for this. Instead, general principles of assay
set-up will be discussed below in more detail for embryonic stem
cell-based developmental neurotoxicity testing (eSDNt) testing.
3.2 In vitro DNT testing and validation:
ESDNT V1.0 vs. ESDNT V2.0
every in vitro toxicity test system consists of three elements:
the biological system, the endpoint/test procedure and the pre-
well-established embryonic stem cell test (eSt) used as a general
Kuegler et al.
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the eSt may be adapted in different ways for DNt testing.
However, in all cases a fundamental difference between cardio-
teratogenicity and neuroteratogenity needs to be considered: the
heart consists of a limited number of cell types in a relatively
homogeneous tissue arrangement, and most developmental ef-
fects on the heart have some form of histological or morpho-
logical correlate. the nervous system consists of many different
cell populations, and DNt, as well as many CNS diseases, can
have predominantly behavioural and functional consequences
(e.g. on regulation of mood, intelligence, attention, concentra-
tion, motor activity) without obvious morphological correlates.
this needs to be taken into account when test systems are be-
eSDNt V1.0 (embryonic stem cell based developmental neu-
rotoxicity test, version 1.0). It operates predominantly as a
black box system, similar to reproductive toxicology studies
in animals. Understanding of the mechanisms is not required
to derive the results and the regulatory consequences in both
tain information from this system on why positive compounds
are positive and why negative compounds are negative. How-
ever, as this information is not required for regulatory testing
Tab. 2: Neural stem cell markers*
name accession number full name comment
Prtg protogenin homolog
Polycomb complex protein BMI-1
Chromodomain-helicase-DNA-binding prot. 1
Chrodin like protein 1
Cellular retinoic acid-binding protein 2
C-X-C chemokine receptor type 4
Developing brain homeobox protein 1
developing brain homeobox 2
Delta-like protein 3
Fatty acid-binding protein, brain
Fibroblast growth factor 5
Fibroblast growth factor receptor 2
Forkhead box protein B2
Forkhead box protein D3
Endothelial transcription factor GATA-2
lysophosphatidic acid receptor 4
GS homeobox 2
Hairy and enhancer of split 5
Hairy and enhancer of split 6
Inhibitor of DNA binding 2
Iron-responsive element-binding protein 2
LIM/homeobox protein Lhx1
IM/homeobox protein Lhx9
low density lipoprotein receptor-related protein
Myeloid ecotropic viral integration site 1
Msh homeobox 1-like protein
transient neuroepithel. progenitor
important for proliferation
adult and foetal NSC, signalling
also adult NSC
foetal NSC; Notch ligand
assoc. with nestin
inhibits neuronal differentiation
Kuegler et al.
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name accession number full name comment
Enhancer of filamentation 1
Neurogenic differentiation factor 4
Nuclear factor erythroid 2-related factor 2
Helix-loop-helix protein 2
COUP transcription factor 1
Nuclear receptor subfamily 6 group A memb 1
NT-3 growth factor receptor
Protein numb homolog
Orthodenticle homolog 2
Paired box protein Pax-3
Paired box protein Pax-6
Protein kinase C zeta
Peroxisome assembly factor 1
Tyrosine-protein kinase transmembrane R.
Runt-related transcription factor 1
Retinoic acid receptor RXR-alpha
ryanodine receptor 3
Secreted frizzled-related protein 2
Transcription factor SOX-1
Transcription factor SOX-11
Transcription factor SOX-2
T-cell acute lymphocytic leukemia protein 2
Transcription factor 4
Zinc finger protein 1
Gold standard, broad profile
also in some cells at later stages
also at later stages
also other (haematopoietic) SC
in foetal NSC
also in ESC
RG: radial glia; NSC: neural stem cells; OG: oligodendrocytes; SC: stem cells
Additional accession numbers:
a) NM_001042725, b) NM_031258, c) NM_010207, d) NM_001042577, NM_010714, e) NM_017464,
f) NM_010264, NM_001159549, g) NM_182809, h) NM_010949, i) NM_001159520, j) NM_001039079, k) NM_008994,
l) NM_001007596, m) NM_009821, NM_001111021, NM_001111023, n) NM_001083967
* Abranches et al., 2009; Maisel et al., 2007; Kelly et al., 2009; Vogel et al., 2009; Gaspard et al., 2008; Liu et al., 2004;
Barberi et al., 2003; Ghosh et al., 2008
that describe neuronal subpopulations and differentiation states
are required. the use of RNA-based markers is suggested here
as one possible approach to be explored.
Moreover, to make the test systems independent of narrow
applicability domains and to design them for broad testing right
from the start, the tox21c strategy suggests a toxicity pathway
and mechanism-based approach (NRC, 2007). Such assays
would examine quantitative cause-effect relationships with ref-
erence to relevant toxicity pathways, and the prediction model
would integrate the rich information from multiple endpoints.
Such future assay systems may then be labelled eSDNt V2.0.
ing developed. For instance, the difference in the ratio between
different neuronal populations needs to be detectable in the ab-
sence of an overall loss of cells. As different brain regions de-
velop during different time windows, they display different sen-
sitivities to neurotoxicants at different times. For instance, the
DNt compound methylazoxymethanol (MAM) has different
effects on the brain when given on different days of embryonic
development (Penschuck et al., 2006 and references therein).
thus DNt test systems must also provide the option to apply
potential toxicants in different phases of development.
Simple endpoints (for instance the number of all neurons or of
functional neurons – similar to those used in the eSt) are likely
Kuegler et al.
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from a non-homogeneous mixed cell population and can give
information on its relative composition. the transcriptional
studies in inhomogeneous populations of adherent cells or for
and reference genes are available (see below – point (3)). the
big disadvantage of the technology is that co-localisation stud-
population that undergoes changes in response to the toxin can
the use of transcription based endpoints (e.g. Northern blot,
gene microarrays and PCR) also requires some technical con-
forms may indicate relative expression differences with varying
sensitivities and accuracies for different genes. Without detailed
background data, information on a single gene may not be reli-
expression changes by quantitative real-time PCR methods. If
technology is available in most laboratories at reasonable cost
derived from online databases (RtPrimerDB, http://medgen.
As this review focuses on the compilation of gene lists that
should be useful as background description of cellular states in
DNt assays, three major technical issues of gene selection and
4.1.1 Gene annotation
First, the literature, including also relatively recent publications,
this is due to the discovery and cloning process, which often
occurred in parallel in different places, initial discovery in dif-
ferent species, protein and antigen names that differ from the
Here an initial basis is provided for the characterisation of the
cells used in such assays.
4 The definition of stem cell genes
4.1 Transcription-based markers
type to another, different sets of markers may be applied. these
tigen-based), and include the metabolome, functional character-
istics (e.g. electrophysiological responses) and characterisation
of the transcriptome (mRNAs and miRNAs). these approaches
ple requirements, technical requirements and throughput.
the most frequently used approaches are antigen based meth-
been performed e.g. by BD Biosciences (www.bdbiosciences.
terisation and for sorting cells, only limited by antibody avail-
ability (works best for surface antigens). Quantitative evalua-
and work particularly well in non-adherent cultures or with cells
that can be detached by enzymatic treatment without affecting
the epitope. Use on adherent cells requires advanced imaging
technologies and is often harder to quantify and to control. On a
semi-quantitative or qualitative level, antigen staining offers an
easy option to characterise mixed cell populations and to deter-
mine co-localisation of different markers within a given cell.
RNA-based measurements have been suggested to be par-
ticularly useful to characterise the differentiation of eSC
(Noaksson et al., 2005) and to detect neurotoxicity and DNt
(Hogberg et al., 2009; Bal-Price et al., 2009; Stummann et al.,
for instance to indicate cellular activation states (Henn et al.,
2009; lund et al., 2006; Falsig et al., 2006). the method is fre-
quently used successfully for quantitative studies in homogene-
ous populations of cells. More or less every gene transcript can
be examined (few exceptions due to highly repetitive or highly
GC-rich sequences). the expression pattern can be interpreted
as a “signature” of the status of the tested cell population. the
gene interaction networks. For instance, different types and
differentiation stages of neurons and glial cells differ in their
detection of subtle effects of developmental neurotoxicants and
give information on the affected pathways. Deviations from the
“default transcription signature” may permit the detection of
subtle effects of developmental neurotoxicants, and give infor-
mation as to the pathways affected. they may also occur as
a consequence of cell cycle progression or cellular activation
state. Such signatures and their alterations can also be obtained
Tab. 3: Issues concerning identification and selection
of transcription-based markers
Definition of applicability domain
Selection of criteria for appropriate markers (assay dependent)
Method for identification/qualification of markers
Selection of negative (exclusion) and positive markers
Assembly of set of markers (no single marker is adequate)
(Semi-)Quantitative relationship of markers
(ratios; thresholds; yes/no)
Definition of differentiation status
Composition of culture over time
Selection of control population(s) for cell type specific endpoints
Biological validation of endpoint-markers with (positive and
Timing of chemical exposure (duration and differentiation status)
Use of reference databases for cross-validation of data
Statistical and standardisation issues within and between
Known species differences
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related functions of genes. therefore meSC genes as endpoints
4.1.3 Standardisation and statistical issues
this applies in different ways to individual studies as well
as to meta-analyses. In the former, normalisation, standardi-
sation and cut-off procedures are mostly hidden in materials
and methods in a way that makes them hard to control or to
reproduce by peers. Alterations of expression levels are often
calculated relative to housekeeping genes, but the stability and
variance of these reference points is only very rarely indicat-
ed. However, these data and procedures have a large impact
keeping genes may be selected based on various criteria. Most
importantly, the gene needs to be expressed in equal amounts
relative to the total amount of cellular mRNA. In many cell
types,?this?condition?is?fulfilled?for?Gapdh, 18S ribosomal RNA
(18S rRNA), and β2 microglobulin (b2m). Other markers that
are also used frequently comprise Hprt, 28S ribosomal RNA
(28S rRNA), Actb or Acta1. More rarely found options are
Ribosomal protein L32 (RPl32) or Phosphoglycerate kinase
1 (PGK1). However, these housekeeping mRNAs do not al-
ways behave according to the criteria set above (e.g. Der et al.,
1998). this problem is particularly pronounced in differentia-
type in the dish can be very different (overall phenotype, size,
cell cycle status, metabolic activity, etc.) from the starting cell,
and therefore express housekeeping genes at different levels.
Similar problems may occur upon exposure to toxicants. An-
other type of problem lies in the heterogeneity of cells in DNt
test systems. the cultures may contain different subpopulations
that express house-keeping genes at different levels. Upon dif-
ferentiation, the relative amounts of these subpopulations may
change dramatically, leading to enormous practical challenges
concerning the standardisation of gene expression levels. to
circumvent this, samples are often referenced to a group of
housekeeping genes instead of a single gene only. In other cas-
points, such as B3 tubulin or Fox-3 (NeuN) for neurons, and
e.g. Doublecortin or Neurogenin to refer shifts in patterning
overall population. Concerning meta-analysis (e.g. Assou et al.,
2007; International Stem Cell Initiative, 2007; Bhattacharya et
al., 2005; Bhattacharya et al., 2009), additional problems need
to be considered. the statistical criteria and quality of the stud-
ies included in the meta-analysis might vary strongly, and the
initial conditions and rules set within these analyses might be
hard to trace. therefore, it is dangerous to rely blindly on the
summary of the outcome. this applies also to the table compi-
lations presented here. If they are put to experimental scrutiny
and trigger a constructive discussion and an improved second
version, then a major goal of this review will already have been
reached. Possibly subsets will have to be selected, according
gene name, and changes of names upon consolidation of the
fully sequenced mouse and human genomes. We have chosen
retrieved from PubMed (http://www.ncbi.nlm.nih.gov/pubmed)
in addition to various other names in common use. In addition,
is listed. Notably, these accession numbers do not refer to the
particular transcripts of genes with multiple splicing variants.
thus, one gene can have more than one accession number. this
is highly important for expression analysis and corresponding
database searches, as a given gene can form different transcripts
in different cell types or at different differentiation stages.
therefore, problematic situations might arise where analysis of
gene regulation by different methods (different PCR primers,
different hybridisation oligos, etc.) yields different results. In
such situations, different transcripts might have been analysed.
to cover this situation, accession numbers for different splice
and annotation variants of the same gene are also included in the
tables. the NCBI RefSeq database provides annotated individu-
al transcripts and protein sequences (derived from its predeces-
sor, Genbank) with accession numbers that are distinguished
by? a? two-letter? prefix? (http://www.ncbi.nlm.nih.gov/RefSeq/
key.html). Curated transcripts for mRNA, noncoding RNA and
annotations? (two-letter? prefixes? without? a? following? under-
score) or Refseq sequences that are undergoing annotation or
an alternative informative annotation and curation effort by the
european Bioinformatics Institute (eBI) also curates sequences
and splice variants derived thereof (www.ensembl.org). typi-
cally, it is helpful to design gene expression strategies against
the curated sequences, although it is important to be aware of
(and design around) the potential for underlying variation in that
transcript. the collective variation in gene expression can be
viewed with the aid of online genome browsers as provided by
the University of Santa Cruz (http://genome.ucsc.edu/) or the
4.1.2 GO categories
Gene Ontology Project is an initiative to classify genes and
gene products according to known molecular functions with a
defined? and? finite? vocabulary? (http://www.geneontology.org).?
egories in the three principal areas “cellular component”, “bio-
logical process”, and “molecular function”. they are organised
by a hierarchical relationship between these groups. When the
transcripts often cluster to certain GOs, and these GOs can give
useful information on the types of changes that are occurring
(structural, signalling, differentiation). thus, it may be useful
starting population of DNt experiments and the changes of
genes characteristic for this population. Unfortunately such a
Kuegler et al.
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2007). In addition, much of the variation may be due to real spe-
cies differences. In fact, the biology of meSC and heSC shows
distinct differences with regard to signals required to maintain
pluripotency (Wei et al., 2005; eckfeldt et al., 2005; Wang et al.,
2009). In this situation, it is tempting to conclude quickly that
heSC are more relevant for human physiology. However, strong
evidence indicates that meSC may resemble cells of the human
inner cell mass of the blastocyst more closely than heSC (li and
Ding, 2009). Moreover, it is not known whether some differences
of eSC in culture have any effects on readouts for DNt. this can
only be determined experimentally, and should be done so.
identify eSC markers has some conceptual shortcomings: First,
the factor of differential expression that is used as cut-off is often
relatively low (e.g. 2-3 fold). this means that it would be very
hard to identify an eSC contamination of around 30% within
an otherwise fully differentiated cell population. this low cut-
Second, the “differentiated population” used for comparison
was frequently obtained from embryoid bodies (eBs), i.e. 3-di-
mensional spheroids formed from eSCs when they are left to
differentiate “wildly” (in a non-guided way, only triggered by
withdrawal of pluripotency factors). this population contains
cells from all three germ layers, and may not be relevant for the
identification? of? differentially-expressed? genes? between? ESC?
and differentiating neurons. thirdly, this approach is bound to
identify many “false positives”, as two populations with differ-
ent proliferation characteristics are being compared. thus, genes
involved in DNA synthesis, chromatin structuring and cell cy-
cle regulation would be selected as putative stem cell genes. A
variant of this approach was taken by the International Stem Cell
according to the similarity of their behaviour to that of Nanog
when over 50 heSC lines were differentiated to eBs. the top 6
group comprises Nanog, Tdgf1, Gabrb3, Dnmt3b, Gdf3, Pou5f1/
Oct4 and the top 20 group additionally contains Fgf4, Gal, Leftb,
Ifitm1, Nodal, Tert, Utf1, Foxd3, Ebaf, Lin28, Grb7, Podxl, Cd9
and Brix (the International Stem Cell Initiative, 2007).
A third approach to identify stem cell genes is based on the
concept that genes qualify for inclusion when they are required
for the function and maintenance of eSC. these genes would be
also form the basis for the opening of a GO category under the
such genes are Pou5f1/Oct4 and Nanog (Mitsui et al., 2003) or
the Klf (Krüppel-like factor) genes. However, Oct4 is also found
in germ stem cells or cardiac differentiation (Stefanovic and
Puceat, 2007), Nanog plays a role in neuronal differentiation
(Molero et al., 2009) and Klf-4 is also an oncogen (Rowland et
al., 2005). the Wnt, FGF and BMP/tGF-ß pathways – and as-
sociated genes – are clearly involved in the maintenance of stem-
ness, but they also play a role in dozens of other processes. the
same type of ambiguity is found when one examines the genes
that can be used for reprogramming. In addition to Oct4, Na-
nog and Klf-4 above, for instance Sox2, Lin28 and Myc are used.
Sox2 and Myc play roles not only in reprogramming but also in
4.2 What are stem cell genes?
the existing literature. Dozens of papers have dealt with such
genes and large numbers of microarray studies have been per-
formed to identify such genes, but also doubt has been voiced
er they rather represent one of many possible transient states
(efroni et al., 2009; Zipori, 2004). the expression pattern as-
sociated with such states may vary between different stem cell
lines. Such effects may be linked to higher dynamics of the
genome than commonly expected. For instance, non-protein
coding line elements, which make up a large proportion of the
human genome, have been shown to be active as transposons in
eSC and, even more commonly, in NSC. Such activity might
affect the activity of classical genes directly, e.g. by insertion, or
al., 2009; Muotri et al., 2007; Garcia-Perez et al., 2007; Muotri
and Gage, 2006; Muotri et al., 2005).
For assay development we have to take a closer look at the
important distinction between “stemness genes” and “stem cell
of the discussion will be on eSC (and NSC) markers.
Unfortunately, the term “stem cell marker gene” is less clear
for instance be “a gene that is only expressed in meSC, and in
no other cell type”. Unfortunately, no such gene exists. the re-
ers” are genes that are by no means expressed in meSC. this
ambiguity, as meSC cultures may often be contaminated with
more differentiated cells. Upon transcriptome analysis it may
then appear that apparently pure stem cells “express” certain
genes usually not associated with meSC, such as B3 tubulin,
Keratins-8 and -18 or Alpha cardiac actin (Ginnis et al., 2004;
Bhattacharya et al., 2005). to establish an eSC database free of
contaminations, cells may be sorted prior to analysis or selected
on the basis of the activity of a sharply-regulated stemness gene
like Utf-1 (tan et al., 2007).
characterise transcriptome changes when eSC differentiate and
eSC as stem cell genes. this approach has been taken many
times in many variations (reviewed in Bhattacharya et al., 2009
and efroni et al., 2009). the result was that these approaches
eSC, such as Lefty2, Oct3/4, Nanog, Utf-1 and Tdgf1. However,
astonishingly large differences were observed between the stud-
ies. It was surprising that some studies found that meSC genes
overlap with heSC genes only to a low degree, i.e. between 15
and 35% (Bhattacharya et al., 2005; Ivanova et al., 2002; Ram-
alho-Santos et al., 2002). this may indicate some intrinsic weak-
nesses of these studies (see e.g. paragraph on standardisation and
statistics issue). An alternative explanation may be that the deri-
vatisation of the lines affects their later phenotype (Navara et al.,
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genes are not included when their expression is relatively low
compared to neural tissue expression.
(e) Only mRNAs coding for proteins have been considered for
this analysis. Information on micro RNAs (miRNA) is still rela-
tively limited. However, it appears that expression of miRNA
tively high expression levels in meSC compared to other cell
therefore also include miRNAs. A further step may be a more
detailed analyses of the promoters themselves and their epige-
netic state by chip-on ChIP experiments (microarray analysis of
tion of methylcytosine as altered base in the DNA structure) or
one of the many related new technologies. For instance, it has
been suggested that the eSC genome may be characterised and
eckfeldt et al., 2005). this has been corroborated on the mo-
lecular level by genome-wide mapping of the chromatin state
of eSC and other cells and indeed has functional consequences
(Mikkelsen et al., 2007). the resultant pervasive transcription is
particularly prominent in eSC, and a major difference between
eSC state and more lineage committed differentiation stages may
be the extent of this genome wide transcriptional activity (efroni
et al., 2009), that involves many non-protein coding RNAs (Ber-
retta and Morillon, 2009; Dinger et al., 2009; Jacquier, 2009;
Mikkelsen et al., 2007). to transform this knowledge into robust
over a thousand conserved large intervening non-coding RNAs
100 were regulated by Oct4 and Nanog and functionally impli-
cated in a stemness network, and at least one was only expressed
in eSC. thus, lincRNAs are candidates for future lists of differ-
in transcription-based cell characterisations. the meSC table
contains only positive markers, as naturally all genes listed in
table 2 (or other tables presented here) represent the correspond-
ing negative markers. typical markers for endodermal differen-
tiation (e.g. intestine, glands, liver) would be VegfR2, Sox17, Ttr,
ApoA1, Lim1, Cytokeratin19, FoxA2, Alphafetoprotein or Gata-4
(also mesendoderm and cardiac mesoderm); for mesoderm (e.g.
muscles, bones, heart, blood): Hand1, Brachyury, Smooth mus-
cle actin, Cd31, Cd34, Cd325 or Eomes (also trophoblast), and
e.g. Ncam1 or certain keratins (Krt 18) indicate ectoderm. Other
useful and sensitive markers for initial differentiation away from
eSC may be Fibronectin-1, Naalad2, Profilin-1 and Slc40a1.
4.4 Neurodevelopmental biology and definition
of neural stem cell markers
Differentiation of meSC towards neurons triggers coordinated
cluster analyses (Abranches et al., 2009; Schulz et al., 2009).
Accordingly, the cells move from the multipotent stem cell state
as e.g. Sox2 is highly expressed (and functional) in NSCs, and
Myc is upregulated in many tumours and rapidly dividing cells.
In conclusion, simple rules for the selection of eSC marker
genes cannot be applied. More advanced algorithms based on
multiple markers are required as described below.
4.3 Definition of mESC markers
according to the following criteria:
(a) the gene needs to be expressed in meSC (differences be-
tween meSC and heSC need to be taken into account).
(b) the gene needs to be expressed in meSC considerably high-
er than in most other cell types. Frequently, eSC were com-
pared to embryoid bodies (eBs). In other approaches meSC
were compared to mNSC and other stem cell types (haemat-
opoietic) to identify unique marker genes (Ivanova et al., 2002;
Ramalho-Santos et al., 2002). An interesting approach in that
direction was also taken by groups at the NIH (Bhattacharya et
al., 2004; Bhattacharya et al., 2005; Bhattacharya et al., 2009;
Ginis et al., 2004), when eSC were compared to RNA pools
from normal differentiated tissue. this approach was taken one
step further in a large meta-analysis, in which heSC expression
100 tissue analyses (Assou et al., 2007). For the compilation of
table 1, especially co-expression at similar levels in NSC was
a group of genes, the criterion of absence of expression in other
cells needs not be applied stringently, providing that it refers to
large group of meSC marker genes is selected, it is likely that
expression in other cells is cancelled out (averaged), while each
of the genes should be expressed in meSC.
(c) the marker gene should not be expressed in neural stem
cells and neuroectodermal cells and thus be different from the
condition 2 and applies particularly for meSC markers used in
DNt experiments. For instance, Galanin is a frequently-identi-
neurons. Genes with such behaviour may not be downregulated
upon meSC differentiation towards the neuronal lineage and are
therefore useless as meSC markers for this particular purpose.
A vast amount of gene expression data is available to identify
relevant genes. Here, both individual papers (e.g. Abranches et
clusion? of? candidates.? For? instance,? the? EU? fifth? framework?
research programme (FP5)-consortium FunGeneS provides
extensive? transcriptome? profiling? information? on? the? differ-
entiation of meSC to neurons, coupled to web-based analysis
software (FunGeneS consortium http://www.fungenes.org/)
(Schulz et al., 2009). Similar approaches are taken for instance
by the StemBase of the Ontario Genomics Innovation Center
(StemBase http://www.stembase.ca/?path=/) (Perez-Iratxeta
et al., 2005; Porter et al., 2007).
(d) Genes with a known functional role for the maintenance of
meSC (e.g. loss of stemness upon their knockdown or knockout
(Misui et al., 2003)) are included as markers if they do not have
multiple roles also in other cell types. the reasoning is simi-
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phenotypes depending on the differentiation protocol (Bouhon
ing basic criteria for NSC can still be differentiated to all CNS
cell types. Differences also exist between eSC-derived NSC,
and brain-derived NSC (both can only be obtained by extensive
in vitro culturing, potentially leading to artefacts), for instance
in the readiness to generate astroglial cells, or between spinal
cord NSC and cortical NSC in the expression of many pattern-
ing marks and genes with broadly varying biological function
(Kelly et al., 2009). thus, it is not a straightforward and unam-
given NSC population that can be maintained in culture.
growth conditions, followed by withdrawal of growth factors
over an early neuroectoderm state to a state in which they can
form rosettes that still have the potential to develop to central and
peripheral neurons. this state is closely linked to the production
of neural precursor cells or NSC. Such NSCs (human or murine)
may be enriched and clonally expanded under appropriate cul-
ture conditions (Ying and Smith, 2003; Conti et al., 2005; Bar-
beri et al., 2003; Koch et al., 2009; elkabetz et al., 2008; Okabe
et al., 1996). NSC markers may be derived from gene expres-
instance been done for human rosette-type cells vs. heSC (elk-
abetz et al., 2008), but multiple comparisons against different
populations (including more mature neurons) would be required
properties of undifferentiated progenitors, may exhibit distinct
or neurotransmitter phenotypes (Klein and Fishell, 2004). Simi-
Tab. 4: Markers for fine mapping of DNT effects in developing neural cells
category name accession number full name comment
forkhead box G1 (Bf1)
empty spiracles homolog 1
empty spiracles homolog 2
distal-less homeobox 1
Titf1, NK2 homeobox 1
GS homeobox 2
orthodenticle homolog 1
orthodenticle homolog 2
Math1, atonal homolog 1
Iroquois related homeobox 1
GLI-Kruppel family member
OC transcription factor 2
ISL1 transcription factor
NK2 transcription factor
nuclear receptor subfamily 2
Mash1, achaete-scute complex homolog 1 ventral forebrain
homeobox msh-like 1
glutamic acid decarboxylase 2
solute carrier family 6a13
calbindin 2, calretinin
solute carrier family 17a6
tryptophan hydroxylase 1
HB9, motor neu. And panc. homeobox 1 motor neurons
dorsal fore- and midbrain
dorsal fore- and midbrain
Kuegler et al.
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category name accession number full name comment
non ectodermal germ layers
SRY-box containing gene 10
neurogenic differentiation 4
tubulin beta 3
emb. lethal abnormal vision-like 4
Eph receptor A7
SRY-box containing gene 17
SMA, actin, alpha 2, smooth muscle
GATA binding protein 4
collagen type IV alpha 1
collagen type I, alpha 1
microtubule-associated protein tau
RNA binding protein
early marker in diff.
early marker in diff.
late marker in diff.
late marker in diff.
synaptic vesicle assoc.
ionotropic glutamate R.
autism, schiz. assoc.
OC: oligodendrocyte, panc: pancreas, neu: neuron, NCC: neural crest cell, emb: embryonic, R: receptor, schiz: schizophrenia,
Additional accession numbers: a) NM_001160112, b) NM_001077632, c) NM_001110222, NM_001110224, NM_001110223, d)
NM_001038698, e) NM_001122889, f) NM_183274, g) XM_913832, h) NM_001042451, i) NM_001038609
regional patterning is thought to be achieved by cell-extrinsic,
contrasting gradients of morphogens and growth factors, includ-
ing Bone Morphogenetic Proteins (BMPs), Sonic Hedgehog
(Shh), Retinoic acid, Fibroblast Growth Factors (FGFs), etc.
these chemical gradients establish a positional axis that confers
pile the list of markers for in vitro differentiation (tab. 2). the
translation of knowledge from developmental gene expression
to in vitro gene expression is not without caveats. For instance,
the gradients formed in vivo are complex and not stable over
time. For instance, NSC formation in the neural tube structure
begins rostrally, and zones of NSC formation and patterning are
moving in a rostro-caudal (from head to tail) way along the neu-
ral tube (Wilson and Maden, 2005). In vivo neurulation is also a
desynchronised process. Homogenates used for transcriptional
and neuronal differentiation. the differentiation process is usu-
ally not 100% synchronised, and cellular differentiation stages
form a continuum. therefore, the wave of NSC gene expression
may overlap with the antecedent meSC gene expression and
with the following wave of NSC-derived neuronal/glial gene
patterns of gene expression solely within the context of an in
vitro differentiation system. For this reason, changes in gene ex-
pression are interpreted with reference to those observed during
neural?specification?and?lineage?progression?in vivo (Rubenstein
and Puelles, 1994; Rubenstein et al., 1998).
A basic characteristic of the nervous system is the high diver-
sity of different cell types, which is necessary for appropriate
manner, depending on the position of neuroepithelial progeni-
tors along the rostrocaudal or dorsoventral axes (Fig. 2). this
Kuegler et al.
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Similar processes occur also in in vitro culture, which ex-
plains that e.g. the density of cells has a major impact on the
end result of the differentiation. Chemicals can act in this phase
on the cells or their signalling molecules, and the exposure
may result in a shift of the balance between neuronal subtypes
(Gaspard et al., 2008) or between glia and neurons (Fritsche et
al., 2005; Steinhart et al., 2007). Such early events have been
speculated to have a late impact e.g. on development of neuro-
degenerative disease (landrigan et al., 2005), and it has been
demonstrated experimentally that e.g. exposure to polychlorin-
therefore, NSC and their progeny can be hard to disentangle
at the level of transcription without reference to cellular, spatial
high resolution in situ hybridisation).
Ideally, meSC-derived NSC gene expression should broadly
recapitulate developmental patterns of gene expression ob-
erative progenitor zones at later stages. Such information has
marker genes (Fig. 2). For our compilation, we used the strong
marker, even though the gene may be expressed in NSCs. this
tion will require adaptations.
A different approach would be to look at functional impor-
tance. While some of the most frequently used meSC markers
also have a functional role in stemness, many of the typical NSC
and radial glia markers are e.g. cytoskeletal elements (Nestin,
Gfap, Vimentin) without known function in NSC maintenance.
Others, with known and important functions, such as the tran-
scription factors Sox2 or Zic1, or signal transduction molecules
like Jak2, Hes5 and Fgfr3 also have roles in other cells. thus, at
present, it appears necessary to combine all different approach-
manual cherry-picking based on literature studies. Such at-
tempts have been made repeatedly, and the table (tab. 2) pre-
sented here is strongly based on the publications of Abranches
et al., 2009; Maisel et al., 2007; Kelly et al., 2009; Vogel et al.,
2009; Gaspard et al., 2008; liu et al., 2004; Barberi et al., 2003;
Ghosh et al., 2008.
4.5 Definition of differentiation markers for
different neuronal stages
After the generation of NSC, neuronal differentiation proceeds.
the currently accepted model of neural developmental pro-
poses that extracellular signalling molecules act on NSC and
their progeny and determine what type of neurons or glia they
will become. this would be accompanied by migration of the
neuroblasts and by generation of new signalling gradients due
to factors secreted from neural cells themselves. Cells at a dif-
ferentiation stage after the NSC stage that are not yet mature
neurons are frequently referred to as neuroblasts. However, the
opment, the neuroblast is essentially a post-mitotic neuron that
is distinguished from a maturing or mature neuron by its spe-
cialisation for migration rather than for functional integration.
Postnatally, neuroblasts are a distinct population of cells, which
are capable of proliferation and are neuronally committed.
therefore, we use the expression NSC in this review to signify
proliferative cells with self-renewal capacity, the ability to form
neuronal and glial cells, and a dependence on eGF and bFGF
for optimal proliferation (meSC depend on lIF). the neuronally
committed progeny of NSC includes maturing neurons at differ-
ent stages, which we denote as “neuroblasts” when referring to
early stages and as neurons when referring to late stages.
Fig. 2: Basic concepts of neurodevelopment
Very early during embryonic development (about day 7.5) the
neural plate forms as an area of early neuroectodermal tissue,
whereas flanking regions form ectoderm (ED).
A. Within the next 24 h this plate invaginates and closes to form
the neural tube, which is the precursor stage of the central nervous
system. Cells at the lateral margins of the neural tube form the
neural crest cells (NC) that migrate to various locations and form
parts of the peripheral nervous system among other cell types.
B. The neural tube (light gray) is flanked by non-neural tissue (dark
grey) and extends from the head region towards the prospective
tail region. At this stage, clear patterns of neurons along
different axes are established, which lead to different neuronal
subpopulations in the adult. The major axes are from back
(dorsal = d) to belly-side (ventral = v) and from head (rostral = r)
to tail-side (caudal = c).
C. lateral view of a day 10.5 embryo (E10.5): the caudal end (c)
represents the spinal cord (SC), the rostral end (r) develops into
the brain, where forebrain (FB), midbrain (MB) and hindbrain
(HB) can be distinguished. The dorso-ventral axes (d-v) remains
present both in the spinal cord (motor neurons in the ventral part)
and in the brain (e.g. the dorsal forebrain differentiates to cortical
D. Embryonic stem cells (ESC) can differentiate to neural stem
cells (NSC) with characteristics resembling those of proliferating
cells found in the early development of the nervous system.
The mESC-derived NSC-like cells, like their in vivo counterparts,
retain the capacity to acquire region-specific identities and
differentiate into neurons and/or glia via intermediate lineage-
restricted progenitor cell stages in vitro.
Kuegler et al.
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4.6 Astrocyte markers
A discussion of all markers relevant for the different develop-
mental phases is beyond the scope of this review. As one ex-
ample for the complexity, we chose a relatively simple neural
population: astrocytes. Although these cells make up more than
half of the brain mass, they have been relatively neglected as
potential targets of toxicity or DNt. It is generally assumed that
GFAP. However, recent research has shown that antibodies to
this protein also label radial glia (NSC-related cells) (Seri et al.,
2001; Ganat et al., 2006; Götz and Steindler, 2003; Buffo et al.,
2008), and that about 50% of astrocytes in the brain may not ex-
et al., 2008). Moreover, knockout of Gfap has no major effects
on astrocyte development or brain function (Pekny et al., 1995;
Gomi et al., 1995). thus, a broader panel of astrocyte mark-
ers, as compiled here (tab. 5), is urgently needed, similar to the
markers for meSC and mNSC presented in tables 1 and 2.
ated biphenyls (PCBs) in utero can affect the outcome of stroke
in later life without major effects on brain development (Dzien-
nis et al., 2008). thus, we have to assume that DNt does not
necessarily affect the number of neurons or other major cell
lationships between neuronal populations. For such endpoints,
pears to be a useful approach to detect deviations from the nor-
tion of differentially spliced genes that form highly cell-type or
et al., 2009). New microarray platforms that allow reliable de-
tection of exon splicing may enable detailed analysis of post-
mitotic neuronal differentiation.
Tab. 5: Marker genes for astrocytes
name accession number full name comment reference
Aldehyde dehydrogenase family 1 member L1
Fructose-bisphosphate aldolase C
Carbonic anhydrase 2
Cysteine sulfinic acid decarboxylase
Glial fibrillary acidic protein
ATP-sensitive inward rectifier potassium channel 10, Kir4.1 absent in immature AC
Monoamine oxidase type B
Nuclear factor 1 A-type
Nuclear factor 1 B-type
Nuclear factor 1 X-type
Glycogen phosphorylase, brain form
GLT-1, excitatory amino acid transporter 2
Glast-1, excitatory amino acid transporter 1
SC1, SPARC-like protein 1
also in smooth muscle
also in GFAP-neg AC
also synthesised by MG
also RG, endfeet at vessels
in reactive AC
also in OC
labels subset of AC
AC specific in the brain
also in GFAP-neg AC
also in OC, NSC
also in OC, NSC
also in OC
useful RNA marker
specific for AC in brain
also in early AC, NSC
early AC, also NSC, RG
also RG, NSC, early AC
AC: astrocytes; RG: radial glia; NSC: neural stem cells; OG: oligodendrocytes; MG: microglia
Additional accession numbers:
a) NM_144855, b) NM_010277, c) NM_008128, d) NM_001122952, NM_001122953,
e) NM_008687, NM_001113210, f) NM_010906, NM_001081981, g) NM_011393, NM_001077514
1: Lecain et al., 1991; 2: Cahoy et al., 2008; 3: Hatada et al., 2008; 4: Gee et al., 2005; 5: Nakahama et al., 1999; 6: Fatemi et al., 2008;
7: Sheng et al., 2004; 8: Rodnight et al., 1997; 9: Ghandour et al.,, 1979; 10: Lovatt et al., 2007; 11: Steffek et al., 2008; 12: Wu et al., 2005;
13: Wilczynska et al., 2009; 14: Pfeiffer et al., 1992; 15: Burette et al., 1998; 16: Chaudhry et al., 1995; 17: McKinnon et al., 1996;
18: Dahl et al., 1981; 19: Zamora et al., 1988
Kuegler et al.
Altex 27, 1/10
the transcription-based markers discussed in this review
represent an effort to characterise subtle disturbances in the
waves of gene inductions leading from meSC to differentiated
tions, possibly small subgroups of markers can be selected
to obtain relevant information. A step further would be the
secreted alkaline phosphatase (Suter et al., 2009; Volbracht
et? al.,? 2009),? driven? by? cell-? and? stage-specific? promoters?
would be used as endpoints of gene induction, also in very
complex cell mixtures. As with all transcription-based assays,
the endpoints suggested here do not necessarily correlate with
protein or function, and this issue will require further charac-
the end, the proof of the pudding is in the eating. In extreme
cases, certain well-established markers cannot be detected
at all on the RNA-level for technical reasons (e.g. highly re-
these comprise the early astrocyte precursor marker A2B5,
the meSC marker SSeA-1 or the NSC marker polysialylated-
glycosylations or keratin sulfates, and in extreme cases gly-
cosylation itself (GalNAc-epitopes on multiple proteins) can
be an excellent marker for meSC (Nash et al., 2007). Such
antigenic markers are ideally combined with RNA markers
and, in the future, RNA marker sophistication will increase
by the inclusion of miRNAs, lincRNAs or other non-coding
differentiation capacity of eSC, also to germ layers other than
neuroectoderm, and it should therefore be a suitable substitute
for old-fashioned assays testing teratoma formation in vivo to
establish pluripotency of a cell population. this review fo-
cussed mainly on meSC differentiation, but the underlying
principles also apply to heSC. For the human counterparts,
some excellent compilations of stem cell markers exist (Assou
et al., 2007; International Stem Cell Initiative, 2007; Bhatta-
charya et al., 2005, 2009), and it needs to be noted in this con-
text that clear species differences may exist (Ginis et al., 2004;
Sato et al., 2003). For instance, the above mentioned marker
SSeA-1 does not work for heSC, while those are character-
ised by SSeA-3/4, which do not work for meSC. Genes like
threonine dehydrogenase (tdh) (Wang et al., 2009), FoxD3
or the genes coding for the receptor of the meSC growth fac-
tor lIF (which is dispensable for heSC) are regulated in a
taken into account, meSC still remain a very robust system for
studying neural development and are possibly able to provide
human DNt relevant information on compounds more sensi-
tively than the currently used animal models.
trocyte markers differs from that of stem cell markers. All of the
stem cell markers are expected to be expressed in all stem cells.
In contrast to this, not all “astrocyte markers” are expressed in
and different developmental stages of such subpopulations.
Only this comprehensive picture based on multiple markers will
yield meaningful information on the fate of the diverse group of
ences on their development.
4.7 Toxicity pathways
In this review we have focussed on markers useful for the des-
cription of subtle phenotypic effects caused by toxicants – in-
dependent of their mode of action. An interesting additional
aspect of transcription-based endpoints may be the possibility
ready established for other organ toxicities, in particular hepa-
totoxicity (Ruepp et al., 2005; Steiner et al., 2004; Blomme
et al., 2009). these two different approaches may be applied
independently or be combined. An example may best demon-
strate the underlying principle: For instance, a chemical may be
shifts in the patterning markers presented in table 5. It may e.g.
increase dorsal markers and decrease ventral markers relative
to house keeping genes. On closer (mechanistic) examination,
one may notice, that in particular sonic hedgehog (Shh) target
genes were down-regulated upon exposure to the chemical.
the mechanism of toxicity may thus involve inhibition of Shh
cally for disturbances of key signalling pathways by reporter
assays or transcriptome analysis coupled with systems biology
signalling pathway, and upon subsequent examination of DNt
effects, it would lead to a dorsalisation of the developing neu-
rons. Cyclopamine is a substance that behaves as described
a similar manner, involve e.g. retinoic acid synthesis, notch
processing or Wnt, tGF-beta or Ah-receptor signalling. the
above examples show the independence of mechanistic and
phenotypic approaches and the huge potential of using and
combining both. In the context of eSC-based neurodevelop-
mental test systems, it is important to note that the phenotypic
mental test system (differentiating eSC). In contrast to this,
the mechanistic approach may also be applied to (and work
much better in) much simpler systems involving the respec-
tive pathways. Differentiating eSC are in fact, due to their
complexity, not very suitable as a mechanistic screen system.
this review has predominantly focussed on the markers that
may be useful for DNt/teratogenicity screening approaches
in the nearer future.
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this work was supported by the Doerenkamp-Zbinden Founda-
tion, the FP7 eSNAtS project, the DFG international research
training school IRtG1331, and the Konstanz Research School
Chemical Biology of the German excellence Initiative. We are
grateful to many who gave useful advice and help on many oc-
casions and especially to Brigitte Schanze for excellent secre-
Prof. Dr. Marcel leist
Doerenkamp-Zbinden Chair for in vitro toxicology
University of Konstanz,
Department of Biology, Box 657
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