SEMIQUANTITATIVE AND MULTIRESOLUTION-BASED HISTOLOGICAL ANALYSIS OF GERM LAYER COMPONENTS IN TERATOMAS DERIVED FROM
HUMAN, NON-HUMAN PRIMATE AND MOUSE EMBRYONIC STEM CELLS.
John A. Ozolek1, Carlos A. Castro2, Garrett Jenkinson3,4, Amina Chebira3, Jelena Kovacevic3,4, Christopher S. Navara2, Meena Sukhwani2, Kyle E. Orwig2, Ahmi Ben-Yehudah2, Gerald Schatten2
1Department of Pathology, Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
2Department of Obstetrics and Gynecology, Magee Womens Research Institute and Foundation, University of Pittsburgh, Pittsburgh, PA, USA
3Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
4Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
ABSTRACT
Background: The capability of cells derived as embryonic stem cells (ES) to
produce tissue components comprising the three developmental germ layers
(teratomas) is the single most important test of pluripotency. Within the
pathology literature, teratomas have been classified according to the
complexity of tissue organization such that in diagnostic terms, lesions with
two of the three germ layers are considered teratomas and lesions with high
order arrangement of tissues resembling an embryo or fetus are considered
by some to be teratomas. Little is known about the volume of distinct tissue
types produced, how tissue types are organized, variables that may influence
tissue differentiation, and species differences within teratomas. We
hypothesize teratomas derived from ES cells of different mammalian species
will exhibit species specific three dimensional tissue distribution and volumes
within teratomas.
Methods: Testes of SCID mice were injected with putative ES cells derived
from mouse (MES) and non-human primate (nhpES). A human ES line (H7) was
also used. Animals were sacrificed when visible lesions were identified. The
entire teratoma was extracted, fixed in formalin, serially sectioned and
processed by routine histological techniques. For each lesion, the amount of
each representative germ layer (ectoderm (EC; neuroglial, skin), mesoderm
(ME; mesenchyme, bone, cartilage), endoderm (EN; gastrointestinal,
bronchial, pancreas)) was semiquantified according to the following scale: 10-20%, 2-21-40%, 3-41-60%, 4-61-80%, 5-81-100%. %germ layer is given as
mode of percentage. Incubation times are given in days with standard
deviation in parenthesis. Statistical analyses were done with ANOVA and t-test
for continuous variables and Wilcoxon and Mann-Whitney tests for nonparametric variables. Results are expressed as (human vs nhp vs mouse)
where values are given.
Results: Days of ES cell incubation after mice injection were not statistically
significant between groups (71 vs 78 vs 68). However, human ES derived
teratomas were larger than nhpES teratomas, but not larger than MES
teratomas (2.6 cm vs 1.8 cm vs 1.9 cm). Teratomas derived from MES and
nhpES showed significantly higher amounts of EC (5 vs 1) than human ES
teratomas while human derived teratomas demonstrated higher amounts of
ME than nhp or mouse (4 vs 1). EN amount did not differ between groups.
Within species nhpES and MES derived teratomas had greater amounts of EC
than ME or EN while human derived teratomas had greater amounts of ME
than EC or EN.
Conclusions: We conclude that species differences exist by amounts of the
various germ layers produced in teratomas and may not be related to
incubation time or tumor size. We speculate that this may reflect basic
developmental programming differences between the species. Further
sophisticated bioimaging analysis and three-dimensional reconstruction of
teratomas will further elucidate these differences.:
INTRODUCTION
•The ability to form lesions that recapitulate
the three germ layers ectoderm, mesoderm,
endoderm) during development is one of
the assays (considered a gold standard) for
determining if potential embryonic stem cell
(ES cells) candidates are pluripotent.
•Within the pathology literature, human
teratomas are classified according to the
presence of immature and/or malignant
tissue elements as these have prognostic
significance in the pediatric and adult
populations.
•While at first glance, most teratomas
derived from ES cells appear as
disorganized tissue masses with
recognizable germ layer elements, little is
known about the contribution of each germ
layer to the lesion, the spatial organization
of germ layer elements to one another,
three-dimensional hierarchy of germ layer
contribution and whether the “final”
constitution of the teratoma is time and/or
species dependent reflecting attempts to
follow a developmental program.
•The ability to accurately detect and quantify
specific tissue types will begin to allow the
ability to detect species specific differences
in developmental programming and enable
accurate three-dimensional reconstruction
of teratomas and comparison to highresolution magnetic resonance imaging.
AIMS
•Compare using a semiquantitative
approach the contribution of each germ
layer to teratoma formation within species
and between species.
•Determine whether germ layer
contribution is based on the size of the
teratoma or incubation time.
•Determine the accuracy of
multiresolution based imaging analysis
techniques in identifying specific tissue
types derived from each germ layer.
Figure 2
METHODS
Semiquantitative Analysis
•Testes of NOD-SCID mice (Jackson
Laboratories, Bar Harbor, Maine) were
injected with putative ES cells derived
from mouse (MES), non-human primate
(nhpES [60,000-120,000cells/testes]), and
a human (hES;H7 [3,000-4,500
cells/testes]) source. A total of 8, 3, and 2
teratomas were derived from non-human
primate, murine, and human ES cells
respectively.
•Mice were sacrificed when visible lesions
were identified.
•The entire teratoma was carefully
dissected and removed in its entirety and
fixed in 10% phosphate buffered formalin
(3.6% formaldehyde).
•After fixation, lesions were measured,
serially sectioned and processed by
routine histological methods.
•For each lesion, the amount of each
representative germ layer (i.e. ectoderm,
mesoderm, and endoderm) was
estimated on each slide of the serially
sectioned teratoma using the following
scale: 1-[0-20%], 2-[21-40%], 3-[41-60%],
4-[61-80%], and 5-[81-100%]. Tissue
components of each germ layer were
identified according to the following table
(Table 1).
•Size (greatest dimension) of lesions is
given in centimeters. Incubation times are
given in days. The percentage of germ
layer present is given as median of
percentage. Statistical analyses were
done with ANOVA and t-test for
continuous variables and Wilcoxon and
Mann-Whitney tests for non-parametric
variables.
METHODS
Multiresolution Classification
•Multiresolution techniques have been
developed over 20 years ago.
•Multiresolution classification new--First attempt to apply it to this type of
data.
•If feasible, will allow accurate
classification and quantification of tissue
types throughout the entire teratoma.
•Results can be correlated with threedimensional high-resolution magnetic
resonance image renderings.
•Generic classifier: Typically feature
extraction (numerical) followed by
classification (Figure 1).
•Large multi-class images are separated
into small single class images.
•Texture features are used in neural net
classifier using 10-fold cross validation.
•Classification of tissue type achieved
through multiresolution classification
(Figure 2). It uses multiresolution
decomposition---discrete wavelet
transformation (DWT) or stationary
wavelet transformation (SWT) (Figures 3,
4), followed by texture feature
classification as well as weighting to
combine local decisions into a global one.
S1
GENERIC
CLASSIFIER
S1
GENERIC
CLASSIFIER
MR
WEIGHTING
DECOMPOSITION
S1
GENERIC
CLASSIFIER
S1
GENERIC
CLASSIFIER
IMAGE
SWT transformation provides highest accuracy of
identifying specific tissue types compared to using
DWT or the generic classifier alone.
Figure 3
CONCLUSIONS
RESULTS
Semiquantitative Analysis
Discrete wavelet transform (DWT) with two levels of
decomposition and reconstrcution. g and h are orthogonal
lowpass and highpass filters
Figure 4
Figure 4: A) Original image without hue or saturation data. B)
Image in A after 2-stage discrete wavelet transformation.
TABLE 1
Tissue components of germ layers
ECTODERM
MESODERM
Central nervous
Skeletal muscle
system
Bones
Retina
Dermis Connective
Cranial,
tissues Urogenital
sensory,enteric
system
ganglia and nerves
Heart
Epidermis
Hematopoietic
Hair
ENDODERM
Stomach
Colon
Liver
Pancreas
Epithelium of
Trachea
Lungs
Pharynx
Thyroid Intestine.
Figure 5
•No differences were seen for incubation
days between teratomas derived from
nhpES, MES, or HES. The human
teratomas sampled were significantly
larger than either nhp or mouse derived
lesions (Table 2).
•Both nhp and mouse derived teratomas
demonstrated higher median percentage
of ectoderm derived tissue present in their
teratomas compared to hES derived
teratomas. However, hES cell derived
teratomas demonstrated higher
percentages of mesoderm derived tissues
than nhpES or MES derived teratomas
(Figure 5). No differences were seen for
percentage of endoderm derived tissues
between nhp and mouse ES teratomas.
Significant differences at a p-value of 0.02
were seen between endoderm derived
tissues from nhpES and MES compared
to hES teratomas (Table 2).
Classification with and without
Multiresolution
•For tissue types selected for analysis
(mesenchyme, skin, myenteric plexus,
bone, necrosis, and striated muscle), the
multiresolution classifier improved
accuracy of detecting a particular tissue
type over the use of a generic classifier.
Stationary wavelet transform produced a
mean of 83% accuracy compared to 75%
and 68%, for discrete wavelet transform
and no multiresolution respectively
(Figure 6; Table 3).
TABLE 2
% of tissue types in species-specific teratomas
Incubation (d)
Size (cm)
EC (median)
ME (median)
EN (median)
NHP
MOUSE
HUMAN
77.8 (13.8) 68.3 (26.3) 70.5 (6.4)
1.8 (0.3)*
1.9 (0.6)
2.6 (0.1)*
3
5
2
2
1
4
1
1
1
SD in ( ), *-p<0.01, See text in Results section for statistical
analysis of median percentage of germ layers
TABLE 3
Accuracy of tissue classification using a
multiresolution classifier
GENERIC CLASSIFIER
FEATURE
EXTRACTOR
GLOBAL
DECISION
Figure 2: Multiresolution classifier where an image is “decomposed” (see Figure 3) and the resultant subbands
subjected to the generic classifier (Figure 1) until assigned class label (global decision) is achieved.
Figure 1
INPUT
IMAGE
Figure 6
CLASSIFIER
CLASS
LABEL
Neural network based generic classifier where image features (texture, shape, color) are subjected to
neural network until output matches desired class label
Teratoma derived from nhpES cells with representative tissues derived from
ectoderm: A) Neuroepithelium and B) skin. Tissues derived from mesoderm
include C) bone and D) skeletal muscle. Tissues derived from endoderm are
highlighted by E) respiratory epithelium and F) intestinal epithelium.
ACCURACY%
MEAN
SD
MAX
MIN
NO MR
68.0
4.1
73.3
59.2
DWT
74.7
2.1
77.4
71.6
SWT
83.2
1.2
84.9
81.7
•In general, ectoderm and mesoderm
predominate within teratomas derived from
ES cells regardless of the species.
•Teratomas derived from hES cells tend to
be larger than those derived from nhpES or
MES cells.
•Non-human primate and mouse teratomas
show a greater percentage of ectoderm
derived tissue than human teratomas.
Human teratomas show a greater
percentage of mesoderm than non-human
primate or mouse. For all species
endoderm derived tissues are present in the
least amount.
•Using the multiresolution classifier with
texture features only computed on the
multiresolution-decomposed digital images
of tissue types within teratomas, we obtain
accuracy of 83%.
SPECULATIONS
•We speculate that developmental
programs and/or timing differ between
species such that mammals with shorter
gestational ages show greater prevalence
of ectoderm derived tissues (particularly
neural tissue which is first to develop)
even in a seemingly disorganized
conglomerate of tissues comprising the
teratoma.
•The ability to recognize and quantify
tissue types using digital imaging analysis
tools including MR transforms and then
reconstruct these lesions in three
dimensions will allow us to understand the
spatial relationships of tissue types and
correlate with high-resolution imaging
studies.
•Higher accuracy of tissue typing using
these and other MR transforms can be
achieved using color, shape, and location.
CORRESPONDENCE
•John A. Ozolek, M.D.
Assistant Professor of Pathology
Children's Hospital of Pittsburgh
3705 Fifth Avenue
Pittsburgh, PA 15213
412-692-5641/412-251-2248 (office/cell)
412-692-5650 (Department)
412-692-6550 (fax)
ozolekja@upmc.edu
•Carlos A. Castro, D.M.D., M.D.
Research Associate
Department of Obstetrics, Gynecology
and Reproductive Medicine
Magee Womens Research Institute
204 Craft Avenue
Pittsburgh, PA 15213
412-641-6086/412-310-3091 (office/cell)
412-641-2410 (fax)
pdccac@pdc.magee.edu