Abstract
Cancer research requires models closely resembling the tumor in the
patient. Human tissue cultures can overcome interspecies limitations of
animal models or the loss of tissue architecture in in vitro models.
However, analysis of tissue slices is often limited to histology. Here,
we demonstrate that slices are also suitable for whole transcriptome
sequencing and present a method for automated histochemistry of whole
slices. Tumor and peritumoral tissue from a patient with glioblastoma
was processed to slice cultures, which were treated with standard
therapy including temozolomide and X-irradiation. Then, RNA sequencing
and automated histochemistry were performed. RNA sequencing was
successfully accomplished with a sequencing depth of 243 to 368 x 10^6
reads per sample. Comparing tumor and peritumoral tissue, we identified
1888 genes significantly downregulated and 2382 genes upregulated in
tumor. Treatment significantly downregulated 2017 genes, whereas 1399
genes were upregulated. Pathway analysis revealed changes in the
expression profile of treated glioblastoma tissue pointing towards
downregulated proliferation. This was confirmed by automated analysis
of whole tissue slices stained for Ki67. In conclusion, we demonstrate
that RNA sequencing of tissue slices is possible and that histochemical
analysis of whole tissue slices can be automated which increases the
usability of this preclinical model.
Subject terms: CNS cancer, Cancer models
Introduction
Cancer constitutes an enormous burden on societies worldwide. Despite
achievements, rendering some types of cancer curable, the overall
occurrence of cancer is increasing because of growth and aging of
populations^[50]1. Research on cancer, aiming at the development of new
drugs and therapeutic strategies requires models that most closely
resemble the in vivo situation in a patient in order to have a
predictive value for future treatment. Today, most models are based on
(immortalized) cell lines grafted into immunosuppressed animals. Their
relevance is further hampered by interspecies limitations between
humans and rodents. During the last years, organotypic slice cultures
derived from human tissues, including tumors, came into focus as an
alternative model^[51]2. These models may become a valuable alternative
to animal testing not only reducing the numbers of experimental animals
but also overcoming interspecies differences. In our group, we have
already established slice cultures from human brains^[52]3,
Glioblastoma multiforme (GBM)^[53]4,[54]5, head and neck squamous cell
carcinoma^[55]6, human gastric and esophagogastric junction
cancer^[56]7, and colorectal carcinoma^[57]8. Using these organotypic
slice cultures, we tested, for example, effects of heavy ion
therapy^[58]5, polyethylenimine-based nanoparticles for siRNA
delivery^[59]9, but also novel nanostructured scaffolds for
cultivation^[60]4.
A prerequisite to use such models as clinical test system for the
outcome of therapy or the selection of the most effective drug for
individual patients is an unbiased, fast and automated cell counting
approach allowing to start treatment within a couple of days. Moreover,
whole transcriptome analysis with and without treatment would be of
help for prediction, but also to better understand mechanisms of tumor
progression and therapy resistance.
In order to address these two important issues, we focused on GBM slice
cultures which maintain their histopathological hallmarks for at least
14 days in vitro^[61]5. GBM is the most common primary brain malignancy
in adults^[62]10 with a median survival of approximately 15
months^[63]11,[64]12 despite surgical resection, X-irradiation and
chemotherapy with temozolomide (TMZ). We report that organotypic slice
cultures are suitable for automated histological analyses as well as
whole transcriptome sequencing, thereby providing an adequate
alternative with regard to individualized cancer research and therapy.
Results
Tissue integrity is maintained in slice cultures during 13 days of
cultivation
In order to see whether cultivation had an influence on tissue
integrity, hematoxylin and eosin staining of tissue slices was
performed immediately after preparation and after cultivation for 13
days. As can be seen in Fig. [65]1, the cell density of freshly cut
peritumoral brain tissue of zone III (Fig. [66]1a) decreases after 13
days of cultivation (Fig. [67]1b). In addition, we observed an increase
of apoptotic cells from 1% on day 0 to 17% on day 13 (Fig. [68]1c,
p = 0.034). Despite an obvious loss of cells, this result also
indicates that the tissue is maintained to a high degree. In
Fig. [69]1d tumor tissue after 13 days of cultivation is presented.
Unfortunately, the amount of material obtained from the patient was
very limited. Therefore, we were not able to present a comparison of
the tumor tissue from day 13 to day 0. But, it should be noted that we
have previously demonstrated that the individual histopathology of
tissue cultures derived from glioblastoma is maintained over at least
16 days^[70]5.
Figure 1.
[71]Figure 1
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Histology of freshly sliced (a) or cultivated (b) tumor-surrounding
brain tissue (peritumoral tissue of zone III) and cultivated GBM tissue
(d). Hematoxylin (nuclei) and eosin (cytoplasm) staining was done (a)
instantly after the slicing procedure or after 13 days in culture
(b,d). Apoptosis rate was determined by TUNEL staining in peritumoral
tissue on day 0 (left bar) and day 13 (right bar) (c). Scale bar:
100 µm.
RNA obtained from tissue slices is suitable for whole transcriptome
sequencing
Next, we asked whether the RNA isolated from treated and untreated
tissue slices can be further used for whole transcriptome sequencing.
Therefore, RNA was isolated from peritumoral brain (zone III) and GBM
tissue (zone I) either treated with TMZ and X-irradiation or left
untreated. For each condition, the RNA isolated from three individual
slices pooled together was collected in order to have enough material
for further analyses and to overcome the tumor’s heterogeneity. Using a
Bioanalyzer 2100, the RNA integrity number (RIN) was determined from
each sample before and after DNase digestion. The corresponding data
are presented in Table [73]1. The higher the RIN value, the better is
the RNA maintenance^[74]13. As can be seen in Table [75]1, all RIN
values were ≥ 7 before the DNase digestion which demonstrates a very
high RNA quality (Table [76]1). After DNase digestion, a severe loss of
RNA quality was observed as indicated by strongly diminished RIN values
(the reason for that is not known, but a contamination of the utilized
chemicals with RNase could be excluded in further analyses). This loss
of quality was further indicated by a loss of the characteristic peaks
of the 18 s and 28 s rRNA in the corresponding chromatograms
(Fig. [77]2). Only the peak of the 5 s rRNA still was clearly distinct
(Fig. [78]2b). Although RNA quality seemed to be insufficient for whole
transcriptome sequencing as concluded from the RIN values determined,
it should be noted that higher RINs are only necessary for
transcriptome sequencing of poly(A) RNA. In our experiments total RNA
sequencing was performed which even allows using RNA from FFPE tissue
with RINs worse than those presented in our data^[79]14–[80]16. In
fact, next generation sequencing was performed successfully. Library
preparation and sequencing resulted in sequencing depths from 243 to
368 x 10^6 reads per sample. For unknown reasons, this was not the case
for one duplicate of treated peritumoral brain tissue (zone III)
although respective RIN values were even better than those obtained
from other slices (Table [81]1). Our data clearly demonstrate that
whole transcriptome sequencing from slice cultures is possible.
Table 1.
RNA integrity number before (left) and after (right) DNase digestion.
Sample RNA Integrity Numbers
before DNase digestion after DNase digestion
Tumor_untreated_1 9.40 1.80
Tumor_untreated_2 8.60 1.20
Tumor_TMZ+4Gy_1 9.20 2.40
Tumor_TMZ+4Gy_2 9.20 2.40
Peritumoral brain_untreated_1 8.00 2.10
Peritumoral brain_untreated_2 7.80 1.20
Peritumoral brain_TMZ+4Gy_1 8.40 2.40
Peritumoral brain_TMZ+4Gy_2 9.10 2.50
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Figure 2.
[83]Figure 2
[84]Open in a new tab
RNA quality of cultivated tissue slices. RNA quality was determined by
a Bioanalyzer 2100 using the RNA 6000 Nano-Kit (Agilent Technologies)
and revealed good quality before the DNase digestion was performed (a).
After the DNase digestion, the RNA quality was strongly reduced (b).
The left graphs show untreated peritumoral brain tissue, the right
graphs the corresponding GBM tissue.
Technical replicates reveal a high consistency of sequencing data
As described in the preceding paragraph, the whole transcriptome
sequencing from RNA isolated from tissue slices was successful. The
next question to be answered was how consistent the results were among
individual experimental replicates. To this end, the data obtained by
separate sequencing experiments from two slice pools (three slices were
pooled in each approach) for each condition and tissue type were
compared. The linear correlation coefficient R^2 of variance-stabilized
counts was calculated for each pair (Fig. [85]3a). For all three sample
pairs, the correlation coefficient was close to 1, so that a linear
correlation between the duplicates could be assumed. As expected, the
variance within the GBM samples was slightly higher than in the
peritumoral brain samples (Fig. [86]3a,d) probably due to high
intra-tumor heterogeneity which is well-known for GBM^[87]17. The
heatmaps (R package “pheatmap” with default parameters) of the pairwise
Euclidean distances of variance-stabilized counts show that the sample
duplicates cluster together but clearly separate from the other tissue
samples and conditions (Fig. [88]3b,c). The principal component
analysis of the variance-stabilized counts confirmed these findings
(Fig. [89]3d,e). The variance between peritumoral brain and GBM tissue
was higher (Fig. [90]3d) than between treated and untreated GBM tissue
(Fig. [91]3e).
Figure 3.
[92]Figure 3
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Comparison of gene expression between peritumoral brain and GBM tissue.
(a) Correlation plots of variance-stabilized counts in sample
duplicates (peritumoral untreated = untreated peritumoral brain tissue
of zone III, GBM untreated = untreated GBM tissue of zone I, GBM
TMZ + 4 Gy = GBM tissue treated with radiochemotherapy). The
correlation coefficient represents low variability between duplicates.
(b) Distance heatmap of Euclidean distances between untreated
peritumoral brain (peritumoral) and GBM tissue (GBM). (c) Distance
heatmap of Euclidean distances between untreated and treated
(TMZ + 4 Gy) GBM tissue. (d) Principal Component Analysis. Untreated
sample duplicates cluster together with a high variability between
peritumoral brain and GBM tissue. (e) Principal Component Analysis. GBM
sample duplicates show differences between untreated and treated
(TMZ + 4 Gy) GBM tissue.
Differential gene expression between peritumoral brain (zone III) and GBM
tissue (zone I) and between treated and untreated GBM tissue
By the experiments presented in the preceding paragraphs it could be
confirmed that the data obtained by whole transcriptome sequencing are
reliable, since expression variation was reproducible between
duplicates of two different tissue cultures of the same patient. To
gain further insight into differential gene expression between
peritumoral brain (zone III) and GBM tissue (zone I) and between
treated and untreated GBM tissue, a differential gene expression
analysis was done.
A calculation with DESeq2 revealed 4270 significantly differentially
(FDR < 0.01) regulated transcripts between untreated peritumoral brain
(zone III) and GBM tissue (zone I, Fig. [94]4a). 1888 of these DEGs
were found to be significantly downregulated, and 2382 genes were
significantly upregulated in the tumor tissue (zone I) in comparison to
the peritumoral brain (zone III, Fig. [95]4b). The vast majority of all
DEGs belonged to the protein-coding fraction of transcripts
(Fig. [96]4c). In addition, known human pseudogenes and non-coding RNAs
represented approximately 100 DEGs both in the downregulated and in the
upregulated transcripts. A corresponding comparison of untreated versus
treated GBM tissue (Fig. [97]4f) revealed 3470 significantly regulated
(FDR < 0.01) transcripts. Here, 2071 DEGs were found to be
significantly downregulated and 1399 significantly upregulated in GBM
tissue which had been treated in contrast to untreated samples
(Fig. [98]4d,e).
Figure 4.
[99]Figure 4
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Differentially expressed genes (DEGs) between peritumoral brain and GBM
tissue. Analysis of differentially expressed genes (DEGs) between
untreated peritumoral brain tissue of zone III and GBM tissue samples
of zone I (a–c) and between untreated and treated GBM samples (d–f).
(a,d) Significantly regulated transcripts are indicated in red
(p < 0.01). (b,e) Number of down- and upregulated genes in both
comparisons. (c,f) Biotype of down- (red) and upregulated (blue)
transcripts in both comparisons. TEC = to be experimentally confirmed,
NA = not available.
A pathway enrichment analysis by the Ingenuity® Pathway Analysis
software tool (Qiagen) revealed that the vast majority of the
protein-coding genes which are significant differentially expressed
between untreated peritumoral brain and GBM tissue and between
untreated and treated GBM tissue are known to be associated with
certain diseases and/or biological functions. Tables [101]2 and [102]3
show an excerpt of these diseases and functions with the corresponding
p-values and the numbers of molecules present in both datasets of
differentially expressed protein-coding genes. In peritumoral brain
versus GBM tissue, 3040 of the 3280 differentially expressed
protein-coding transcripts were found to be associated with the
tumorigenesis of tissue (Table [103]2). 511 transcripts are known to
play a role in cellular growth and proliferation (Table [104]2). In
untreated versus treated GBM tissue, 2189 of the 2527 protein-coding
transcripts are associated with tumorigenesis of tissue and 778 were
found to be associated with cellular function and maintenance
(Table [105]3). Further significantly enriched functions are, among
others, cell death, cell and organismal survival, proliferation of
tumor cells, progression of cell cycle, and cell-to-cell signaling
(Tables [106]2 and [107]3).
Table 2.
Top diseases and functions of significant differentially expressed
genes in untreated peritumoral brain vs. GBM tissue.
Diseases and disorders p-value molecules of 3280 in total
Cancer 1.64 × 10^−09 − 1.29 × 10^−150 3101
- Tumorigenesis of tissue 3.29 × 10^−145 3040
- Malignant solid tumor 1.90 × 10^−139 3084
Organismal injury and abnormalities 1.64 × 10^−09 − 1.29 × 10^−150 3135
Gastrointestinal disease 8.04 × 10^−10 − 1.88 × 10^−130 2822
Endocrine disorders 1.47 × 10^−09 − 3.03 × 10^−112 2641
Dermatological diseases and conditions 6.23 × 10^−11 − 3.08 × 10^−90
1926
Molecular and cellular functions
Cellular development 9.97 × 10^−10 − 1.91 × 10^−44 598
Cellular growth and proliferation 9.97 × 10^−10 − 1.91 × 10^−44 511
- Proliferation of neuronal cells 3.25 × 10^−15 198
Cellular assembly and organization 4.76 × 10^−10 − 1.90 × 10^−41 747
Cellular function and maintenance 9.97 × 10^−10 − 1.90 × 10^−41 973
Cell-to-cell signaling and interaction 1.65 × 10^−09 − 1.60 × 10^−37
677
Physiological system development and function
Nervous system development and function 1.65 × 10^−09 − 1.91 × 10^−44
994
Tissue development 1.65 × 10^−09 − 1.91 × 10^−44 999
Embryonic development 1.61 × 10^−09 − 8.66 × 10^−40 768
Organismal development 1.65 × 10^−09 − 8.66 × 10^−40 1198
Tissue morphology 1.61 × 10^−09 − 7.98 × 10^−33 804
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Table 3.
Top diseases and functions of significant differentially expressed
genes in treated vs. untreated GBM tissue.
Diseases and disorders p-value molecules of 2527 in total
Cancer 6.38 × 10^−04 − 1.00 × 10^−63 2306
- Tumorigenesis of tissue 1.29 × 10^−62 2189
- Malignant solid tumor 4.32 × 10^−58 2263
- Glioma 5.59 × 10^−04 216
Organismal injury and abnormalities 6.38 × 10^−04 − 1.00 × 10^−63 2327
Gastrointestinal disease 4.31 × 10^−04 − 2.00 × 10^−56 2148
Hepatic system disease 4.31 × 10^−04 − 2.09 × 10^−39 1632
Reproductive system disease 1.93 × 10^−04 − 2.98 × 10^−36 1514
Molecular and cellular functions
Gene expression 5.07 × 10^−08 − 6.05 × 10^−13 514
Cellular assembly and maintenance 6.38 × 10^−04 − 1.13 × 10^−12 525
Cellular function and maintenance 3.19 × 10^−04 − 1.13 × 10^−12 406
Cell death and survival 6.22 × 10^−04 − 1.36 × 10^−10 778
Cell cycle 5.47 × 10^−04 − 5.51 × 10^−10 348
- Cell cycle progression 5.51 × 10^−10 251
- Proliferation of tumor cells 6.38 × 10^−04 99
Physiological system development
Organismal survival 9.88 × 10^−13 − 1.30 × 10^−13 554
Nervous system development and function 5.53 × 10^−04 − 9.90 × 10^−12
380
Tissue morphology 5.53 × 10^−04 − 1.18 × 10^−09 268
Organ morphology 4.31 × 10^−04 − 1.10 × 10^−08 255
Organismal development 4.35 × 10^−04 − 1.10 × 10^−08 551
[109]Open in a new tab
Knowledge base analysis of expression data predicts reduced proliferation in
slices after treatment which could be confirmed by automated histochemical
analysis
In the previous sections it was demonstrated that whole transcriptome
sequencing can be performed with tissue slices in order to reveal
differences in gene expression. Now it was of interest, whether these
data can be used to make predictions about possible physiological
responses to treatment that can be confirmed by a second method.
Therefore, we performed a knowledge base data analysis using the
Ingenuity® Pathway Analysis (IPA®) software tool (Qiagen). An
IPA®-generated list of genes which are described to be associated with
proliferation of cancer and/or neuronal cells was compared to the
significantly regulated transcripts that were found between treated and
untreated GBM tissue. The analysis revealed 190 genes that were present
in both lists. Further analysis indicated reduced proliferation under
treatment conditions (Fig. [110]5b). Among the most prominent genes we
identified down-regulation of MKI67, SPP1, PDGFRA, FGF1, CXCR4, CD44,
HGF and KIT under the influence of treatment (Fig. [111]5a).
Figure 5.
[112]Figure 5
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mRNA expression indicates an inhibition of proliferation after
treatment. The differentially expressed transcripts in treated versus
untreated GBM tissue were compared to a list of
proliferation-associated genes obtained from the Ingenuity® Pathway
Analysis (IPA®, QIAGEN). 190 genes were found to be present in both
lists. Transcripts per million of some of these genes are displayed in
(a). Knowledge base analysis with IPA® indicates an inhibition of
proliferation of neuronal and cancer cells (b, blue lines). Green
symbols represent a decreased measurement of the respective transcript.
In order to confirm a negative effect on proliferation in the tumor
slices of this patient under treatment, as predicted by gene expression
analysis, we performed immunohistochemistry on paraffin sections
derived from slices. For the analysis, a quantitative image analysis
was implemented. In the experiment presented in Fig. [114]6, slices
from peritumoral brain (zone III, Fig. [115]6a) and from GBM tissue
(zone I, Fig. [116]6b) were labeled with an antibody directed against
Ki67 (untreated samples are shown as example). Ki67 is a commonly used
proliferation marker which is present during G1, S, G2, and mitosis but
absent in G0 phase^[117]18. In addition, DAPI was used to counterstain
nuclei in order to evaluate whether a Ki67-positive signal is indeed
localized to a nucleus to prevent counting of unspecific signals.
Figures [118]6a,b show the original pictures recorded by the slide
scanner. In a first step, the pixel area of the whole tissue was
calculated (gray masks in Fig. [119]6a’/’’,b’/’’) as well as the
DAPI-positive area (Fig. [120]6a’,b’) representing the nuclei. To
determine the proliferation capacity of peritumoral brain
(Fig. [121]6a) and GBM tissue (Fig. [122]6b), double-positive nuclei
were analyzed (Fig. [123]6a’’,b’’). Consecutive H/E-stained sections of
the tissue are shown in Fig. [124]6a’’’,b’’’ to demonstrate the native
condition of the analyzed tissue slices. The automatic quantification
revealed a statistically significant decrease of proliferating cells in
treated peritumoral brain and GBM tissue compared to the untreated
controls (Fig. [125]6c). Furthermore, GBM tissue has a high nuclei
density and a small tissue area, whereas peritumoral brain tissue
exhibits a larger tissue area combined with a smaller cellular density
(Fig. [126]6d).
Figure 6.
[127]Figure 6
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Histological finding of reduced proliferation after treatment supports
mRNA expression data. Paraffin-embedded treated and untreated
peritumoral brain (a) and GBM tissue (b) was stained with a Ki67
antibody as proliferation marker (red) and DAPI as nuclei marker (blue)
and recorded by a slide scanner. Representative images of untreated
samples are presented. (Note: green signals are attributed to
autofluorescence of the tissue). For quantification, the total tissue
area (a’/”, b’/”, gray), the nuclei area (a’, b’) and the Ki67-positive
nuclei area (a”, b”) were determined. H/E stainings of consecutive
tissue sections are shown in a”’ and b”’. (c) Ratio of proliferating
area (Ki67- and DAPI-positive pixel area) per DAPI area in untreated
and treated (TMZ + 4 Gy) peritumoral brain (left) and GBM tissue
(right). (d) Ratio of DAPI area per total tissue area compared to total
tissue area in pixels in untreated and treated (TMZ + 4 Gy) peritumoral
brain (red circles, green squares) and GBM tissue (blue diamonds, black
triangles). Biological replicates: 1; Technical replicates: 3; Scanned
sections: 33 (untreated peritumoral brain), 32 (treated peritumoral
brain), 13 (untreated GBM), 8 (treated GBM). Scale bars: 100 µm (a),
50 µm (b).
The results of the automated analysis were confirmed by manual
analysis. Segmented areas of total tissue and DAPI were highly
correlated (R^2 = 0.998 and R^2 = 0.876, respectively; all p < 0.001)
while values for the proliferating area showed moderate correlation
(R^2 = 0.616, p < 0.001) (Fig. S3).
Discussion
Despite intense research during the last decades, many cancerous
diseases are still associated with a poor prognosis and a low median
overall survival, e.g. 14 months for advanced non-small cell lung
cancer^[129]19, 12 months for advanced gastric cancer^[130]20, and 15
months for GBM^[131]12. Therefore, the establishment of preclinical
models to test newly developed drugs and treatment strategies is an
important step in oncological research. As outlined in the
introduction, the frequently used animal models often fail because of
interspecies differences that impede clinical translation. Cell culture
models, on the other hand, are far away from the in vivo situation as
tumor tissue can be composed of a bulk of many other cell types aside
from tumor cells, e.g. endothelial cells^[132]21, pericytes^[133]22,
tumor-associated immune cells^[134]23, and cancer stem-like
cells^[135]24 which is not reflected by cell culture models. As a more
realistic system patient-derived xenograft models have been developed,
injecting patient-derived tumor cells into immunodeficient
mice^[136]25. Thus, the animals generate tumors which are supposed to
maintain the original tumor’s biology thereby mimicking the human
patient. This is, among others, well described for breast
cancer^[137]26, non-small cell lung cancer^[138]27, or melanoma
metastasis^[139]28. Besides the great burden for the animals, the
production of patient-derived tumors within rodents is a time-consuming
method which is therefore unlikely to find its way into a clinical
setting with regard to personalized cancer therapy. The
immunodeficiency of these mice, which is required to inhibit the
rejection of injected human tumor cells^[140]29,[141]30 further impedes
the successful translation into the clinics.
As an alternative to animal and cell culture models, human tissue slice
cultures are now increasingly employed in cancer
research^[142]2,[143]31–[144]37. One of the major advantages of tissue
slice cultures is the maintenance of the tissue topology and
composition of different cell types including immune cells, as
represented by microglia which play a crucial role in GBM
progression^[145]38–[146]40. Therefore, slice cultures may reflect the
tumor’s heterogeneity far better than conventional cell culture and
animal models. Yet, tumor heterogeneity is not only defined by the
general presence of different cell types, but also by different
characteristics of the tumor cells in different areas of the
tissue^[147]41. This impedes the reproducibility of such ex vivo
experiments and increases the difficulty of successful translation into
a clinical setting for human patients. For that reason, the slices
obtained from one patient are pooled together and are randomly
distributed in triplicates to the membrane inserts. For RNA analysis,
these slices are pooled again to diminish the possibility that the
differences observed here are just resulting from a different
localization within the original tumor.
For histology, single slices are embedded in paraffin and stained
individually. In conventional microscopy, only parts of the whole
tissue can be recorded and analyzed. Furthermore, most histological
analyses are still performed “manually” which is time-consuming and
investigator-dependent. In this study, as exemplified by tissue from
one GBM patient, we present that whole slices can be recorded and
analyzed automatically (Fig. [148]6). Therefore, it is possible to
retrospectively draw conclusions about the extent of heterogeneity in
the original tissue. The automation of the histological analysis is
time-saving, objective and reproducible. That in turn increases the
suitability for a clinical application of this method with regard to
individualized cancer therapy. By designing the experiments in
duplicate or even triplicate approaches (depending on the available
amount of tissue) the results are getting even more reproducible. In
addition, the RNA expression analyses presented here were performed in
replicates and exhibited a very good correlation and only slight
differences within each sample pair (Fig. [149]3). Therefore, it can be
concluded that the random distribution of three slices may be
sufficient to depict the intratumoral heterogeneity. Further
investigations on more GBM slice cultures are currently being analyzed
to confirm this finding and to verify whether this is consistent among
patients.
The histological finding of reduced proliferation in treated GBM tissue
is consistent with RNA expression data obtained from the same samples.
Here, the same treatment-mediated effect was observed (Fig. [150]5a).
Eight genes, which were found to be downregulated in treated compared
to untreated GBM tissue and are known to be associated with
proliferation of neuronal and/or cancer cells, were chosen for further
analysis. This analysis revealed a downregulation of SPP1 which has
been shown to be overexpressed in grade IV gliomas and which is related
to worse overall survival also in patients with lower-grade
glioma^[151]42. Some isoforms of SPP1 are in fact known to promote
glioma cell invasion^[152]43. In addition, we identified a
down-regulation of CD44 under treatment (Fig. [153]5c). This
down-regulation may be caused by down-regulation of SPP1 which was
shown to increase the synthesis of the CD44 variant CD44v6 in liver
cancer cells^[154]44. CD44 itself is known as a marker of GBM
invasiveness and was shown to promote stem cell-like properties in
glioma and to play a role in the mediation of resistance to radiation
and chemotherapy with temozolomide^[155]45,[156]46. An increased
expression of CXCR4 is associated with the recurrence of glioblastoma
after radiochemotherapy and could indicate an activation of the
CXCL12-CXCR4 pathway representing an alteration in the angiogenic
pattern within the tumor^[157]47. FGF1 and other members of the FGF
family are involved in cell proliferation, differentiation, and
migration^[158]48. Therefore, down-regulation of these family members
is in agreement with the histologically observed decrease of
proliferation. At this point, it is also interesting to note that
FGF1/FGFR signaling activates Aurora A, a kinase which is involved in
the maintenance of the stem cell characteristics of GBM cells^[159]49.
We further found down-regulation of PDGFRA and c-KIT which is
especially interesting as these receptor tyrosine kinases have long
been suggested as GBM therapeutic targets^[160]50,[161]51. In
conclusion, the treatment-induced changes in mRNA expression are in
agreement with the histological analysis which demonstrated inhibition
of proliferation, as determined by a statistically significant decrease
in the Ki67-positive pixel area under treatment (Fig. [162]6c).
The confirmation of the automatic analysis procedure was done by manual
segmentation by three independent observers and both approaches were
correlated with each other. A certain divergence of values among the
three observers was noticed. While the results for total tissue area
were very consistent, there was a notable spread in results for DAPI
area which could be attributed to blooming around the stained nuclei.
These minimal blooming artifacts appear during image acquisition and
have no impact on the automatic analysis. Nevertheless, they proved to
be interfering for observers during manual analysis. The large spread
for the proliferating area was mainly caused by low signal intensities,
poor image contrast and faintly remaining background fluorescence.
These factors generally impede manual analysis and observers tend to
underestimate threshold values. Overall, there was a very good
correlation between manually and automatically obtained results for the
total tissue area, which could be easily segmented by the three
observers. The comparison of manual and automatic analysis of the DAPI
area also showed very good correlation, although a manual
under-segmentation was noted. The corresponding comparison of the
proliferation area determination exhibited a moderate correlation and
results indicated a manual over-segmentation. Values from individual
images showed notable dispersion between automatic and manual analysis.
In conclusion, our data, in compliance with former
studies^[163]4–[164]7 demonstrate that organotypic slice cultures
provide a suitable model for mimicking the in vivo situation within the
patient thereby allowing insights into tumor biology that would not be
possible by the use of conventional cell culture or animal models. By
this means, it helps to reduce the numbers of animals used in cancer
research. Furthermore, it may promote the way to individualized cancer
medicine which is the current goal for therapeutic approaches. In the
future and with the simultaneous development of new drugs it could be
conceivable to prepare slice cultures for each patient, test possible
chemotherapeutics and assist the physicians concerning the individual
treatment strategy^[165]2,[166]36,[167]52.
Material and Methods
Patient and samples
Glioblastoma tissue was obtained by surgery of a 51 year old male
patient diagnosed with primary glioblastoma (GBM, WHO grade IV).
Surgery and diagnosis were performed at the Department of Neurosurgery
and the Department of Neuropathology, University Hospital Leipzig,
Germany, according to the EANO guideline for the diagnosis and
treatment of anaplastic gliomas and glioblastoma^[168]53. To get
surgical access to the MRI contrast-enhanced tumor tissue ( = zone I),
also tumor-surrounding brain tissue had to be removed. In the
following, we refer to the tumor-surrounding tissue as peritumoral
tissue ( = zone III), which is basically normal brain tissue with only
very few tumor cells^[169]54. Both tissue types were subjected to
organotypic tissue slice cultures in duplicates. Tissue acquisition and
experimental procedure were approved by the institutional research
ethics board (Ethical Review Committee of the Medical Faculty of the
University of Leipzig, #144-2008; registration numbers: IORG0001320,
IRB00001750) in accordance with the Helsinki Declaration
([170]https://www.wma.net/policies-post/wma-declaration-of-helsinki-eth
ical-principles-for-medical-research-involving-human-subjects/). The
patient provided written informed consent for experimental usage of his
tissue samples and retrospective analysis of the data according to the
General Data Protection Regulation of the European Community
([171]https://gdpr-info.eu/).
Tissue slice preparation
Tissue slices that can be maintained in culture for at least 14 days
were prepared using a previously described protocol^[172]5. In brief,
surgically removed tissue not required for neuropathological diagnostic
was transferred to Dulbecco’s Modified Eagle Medium (DMEM, Gibco)
supplemented with glucose (4.5 g/l, Gibco), fetal calf serum (10%,
Biochrom), Glutamax (1%, Gibco) and penicillin/streptomycin (1%,
Gibco). Organotypic tissue slices were prepared using a tissue chopper
(McIlwain TC752) under sterile conditions (Fig. [173]7). Before
preparation, a razor blade was sterilized by autoclaving. A normal
glass pipette as well as a glass pipette with the fine tip broken off
and appropriate forceps were autoclaved. The tissue was washed twice
with fresh Minimum Essential Medium (MEM, Gibco) and was put on a stack
of sterile filter membranes, cut into ~ 350 µm thick slices and
transferred into ice-cold MEM. The slices were separated from each
other by pipetting up and down with the wide opening of the broken-off
glass pipette. Using this pipette they were randomly transferred onto
membrane culture inserts (Millipore) in triplicates. The inserts were
put into six-well plates equipped with 1 ml medium per well. The
culture medium was composed of MEM, 25% Hank’s Balanced Salt Solution
(with Ca^2+ and Mg^2+, ThermoFisher Scientific), 10% heat-inactivated
horse serum (Gibco), 1% L-glutamine (Gibco), 1% glucose (Mediatech
Inc.) and 1% penicillin/streptomycin (Gibco). The slices were
cultivated on a liquid/air interface in a humidified incubator at 37 °C
and 5% CO[2] for 13 days in total. During cultivation, slices were
provided with fresh medium every 2 to 3 days.
Figure 7.
[174]Figure 7
[175]Open in a new tab
Experimental setup. Freshly resected glioblastoma (zone I) and
peritumoral brain tissue (zone III) was transported into the lab in
sterile transport medium and stored at 4 °C. The production of 350 µm
tissue slices was performed with a tissue chopper. The slices were
separated from each other by the wide opening of a glass pipette and
randomly allocated to membrane inserts and put in the wells of sterile
6-well plates, previously filled with 1 ml of cultivation medium. The
slices were cultivated 10 days before treatment with radiochemotherapy
was implemented. 24 hours prior to irradiation with 4 Gy the slices
were pretreated with 200 µM temozolomide (TMZ). After a total treatment
time of 72 hours the slices were either fixed in 4% paraformaldehyde
for histological analyses or processed for RNA and protein isolation to
perform whole transcriptome sequencing and protein analyses. We
acknowledge Dr. Sonja Kallendrusch (Institute of Anatomy, University of
Leipzig, Faculty of Medicine, Germany) who kindly provided the
photograph of the tissue chopper.
Treatment of tissue slices
After 10 days in culture, slices were treated with temozolomide (TMZ,
200 µM). Control slices were incubated with the corresponding amount of
dimethyl sulfoxide (DMSO, 0.2% v/v) used as vehicle. 24 hours after
initial treatment, slices were X-irradiated (4 Gy) or sham-irradiated
(control slices), and provided with fresh TMZ- or DMSO-supplemented
medium the other day. For X-irradiation, a 200 kV irradiation machine
(Gulmay Medical D3000, Gulmay, Surrey, UK) with a copper filter was
used. The dose rate was 1.156 Gy/minute and each sample was irradiated
3.46 minutes to reach the target dose of 4 Gy. After a total treatment
time of 72 hours, slices were processed for further analyses
(Fig. [176]7).
Histology
Slices were fixed in 4% paraformaldehyde at 4 °C overnight and washed
with phosphate-buffered saline (PBS). Slices were dehydrated and
embedded in paraffin. Paraffin sections (7 µm) were cut with a sledge
microtome and collected on glass slides (3 sections per slide).
Hematoxylin and eosin staining was performed to evaluate the tissue
maintenance. Photographs were taken with a digital slide scanner
(Pannoramic Scan II, 3D HISTECH Ltd., Budapest, Hungary).
For immunological staining, every third slide per condition was dewaxed
in xylene and rehydrated in decreasing concentrations of ethanol.
Before immunostaining, the slides were pretreated two times for
20 minutes with citrate buffer (pH 6) in a microwave. Slides were
washed with PBS and permeabilized/blocked with 0.3% Triton/PBS and 10%
normal goat serum for 30 minutes. The primary antibody against Ki67
(MIB1 clone, mouse, 1:100, Dako, code number: M7240) was diluted in
0.3% Triton/PBS with 1% normal goat serum and incubated overnight at
4 °C. The Alexa 568-labeled secondary antibody (goat anti-mouse, 1:800,
Gibco, catalog number: A-11004) was diluted in PBS and slides were
incubated for 1 hour at room temperature. To stain the nuclei, slides
were incubated with DAPI (ThermoFisher Scientific) for 15 minutes at
room temperature. Slides were thoroughly washed with PBS and aqua dest.
and covered with Fluorescence Mounting Medium (Dako) and coverslips.
For apoptosis detection, five to six slides per condition were dewaxed
as described above. A TUNEL assay was performed according to the
manufacturer’s protocol (Click-iT™ Plus TUNEL Assay, Alexa Fluor™ 594,
Invitrogen™, order number [177]C10618). To stain the nuclei, slides
were incubated with DAPI, washed, and covered with coverslips as
described above.
Imaging and image analysis
The immunofluorescently stained microscope slides were fully digitized
at 20x magnification using a digital slide scanner (Pannoramic Scan II,
3D HISTECH Ltd., Budapest, Hungary) equipped with a quad band
(DAPI/FITC/TRITC/Cy5) filter set. DAPI filter was used for blue DAPI
channel, FITC filter was used for green tissue autofluorescence
channel, and TRITC filter was used for Ki67 channel. Images of the
stained tissue slices were exported from slide scanner data sets
(Pannoramic Viewer, Version 1.15.4, 3D HISTECH Ldt., Budapest, Hungary)
as PNG images with pixel dimensions of 0.325 µm. Some regions in the
exported images had to be masked by hand (Adobe Photoshop CS6, Adobe
Systems Inc., San Jose, USA) in order to remove artifacts (i.e. tissue
overlaps, air bubbles, unspecific staining, dirt/fluorescent particles,
blooming, etc.). Spectral bleedthrough between different color channels
was corrected using the “Spectral Unmixing” plugin for ImageJ (Version
1.51n, [178]http://imagej.hih.gov/ij). Image analysis was performed
with Mathematica (Version 11.1, Wolfram Research, Inc., Champaign, IL,
USA). Corrected fluorescence images were imported and split into
separate color channels. In order to obtain tissue masks (almost
entirely represented by DAPI and autofluorescence signals), all images
were smoothed with a 5 pixel wide Gaussian filter and binarized using
Otsu’s (cluster variance maximization) thresholding method^[179]55
prior to color channel separation. DAPI signals within blue image
channels were also binarized using Otsu’s thresholding method while
proliferation marker (Ki67) signals within red image channels were
binarized using Kapur’s (histogram entropy minimization) thresholding
method^[180]56. Since specific proliferation marker staining can only
occur within the nuclei, the binarized DAPI and Ki67 images were
multiplied in order to omit unspecific staining outside of nuclei. The
resulting masks were further cleared of very small segments (up to 20
pixels) to eliminate specks of fluorescent particles within nuclei.
Finally, the areas of total tissue, DAPI and Ki67 masks were determined
and ratios were computed. Numbers of analyzed images were as follows:
33 for untreated peritumoral brain tissue, 32 for peritumoral brain
treated with TMZ + 4 Gy, 13 for untreated GBM tissue, 8 for GBM tissue
treated with TMZ + 4 Gy.
To verify the result of the automated image analysis approach we
performed an additional interactive analysis by three independent
observers using ImageJ. Corrected fluorescence images were imported and
split into separate color channels (DAPI, Ki67, autofluorescence).
Subsequently, all color channels were segmented by interactive
thresholding. Manually generated masks were imported in Mathematica and
analyzed corresponding to the automatically segmented masks. Calculated
parameters of the three observers’ segmentations were averaged and
ratios were computed.
Tissue slices with apoptosis staining underwent the same imaging and
image preprocessing procedures as the microscope slides stained against
Ki67, as mentioned above. Apoptosis was captured using the TRITC filter
of the digital slide scanner. Spectral unmixing was performed and
apoptosis signals within red image channels were bianrized using
Kapur’s (histogram entropy minimization) thresholding method. Binarized
DAPI and apoptosis images were multiplied in order to omit unspecific
staining outside of nuclei. Subsequently, segmented images were
inspected and masked by hand if necessary (e.g. vessels, artifacts).
Finally, the areas of total tissue, DAPI, and apoptosis masks were
determined, ratios were computed, and results were averaged for all
slices originating from the same tissue slice.
RNA sequencing
Total RNA from cultivated tissue slices was isolated using the miRNeasy
mini Kit (Qiagen) following the provided manufacturer’s protocol. RNA
yield was measured with the Qubit 2.0 instrument (Life Technologies)
using the RNA Broad Range Assay. Total RNA amount per sample ranged
from 1.5 to 2.9 µg. RNA quality was determined by the Bioanalyzer 2100
using the RNA 6000 Nano-Kit (Agilent Technologies). All samples had RNA
integrity numbers of ≥ 7.6 (Table [181]1, before DNase digestion). RNA
was DNase-digested twice using the TURBO DNA free Kit (Ambion®,
ThermoFisher Scientific).
For library preparation with the Truseq-Stranded Total RNA Sample Prep
Kit (Illumina) up to 200 ng RNA per sample were used. A ribosomal RNA
(rRNA) depletion step using the Ribo-Zero Gold rRNA Removal Kit
(Illumina) was conducted according to the manufacturer’s protocol and –
depending on the quality of each sample – a fragmentation was done.
Every library was equipped with two barcodes to allow multiplexing of
the samples. Concentrations were determined using the Qubit DNA Kit and
the DNA quality was detected by the Bioanalyzer 2100 (DNA1000 Kit).
According to the average size, which is determined by the Bioanalyzer,
and the exact concentration of the samples, the molarity of each
library was calculated.
The samples were sequenced at the HiSeq2500 with 2 × 126 bp paired-end
reads. 12 pM of DNA were put on the flowcell using one lane per sample.
The number of reads obtained was between 243 and 368 × 10^6 reads per
sample, except for one sample (“peritumoral brain TMZ + 4 Gy 2”) with
less than 50,000 reads.
Data analysis and statistics
Primary and secondary data analysis
Postprocessing of obtained raw reads per sample included demultiplexing
using Illumina bcl2fastq v1.84 and secondary data analysis covering
adaptor trimming, read mapping and expression quantification. Data
processing of the secondary data analysis was invoked and monitored by
the universal analysis pipeline
([182]http://uap.readthedocs.io/en/master/), ensuring consistent and
reproducible execution of each single analysis step per sample. The
according configuration files are available as Supplementary File S1.
In detail, adaptor sequences (adaptor 1: AGATCGGAAGAGCACACGTCT, adaptor
2: AGATCGGAAGAGCGTCGTGTA) were removed from raw reads by utilizing
AdaptorRemoval v.2.2.0^[183]57 with additional parameters –trimns
–trimqualities –minquality 20, and –minlength 20 in order to trim
terminating ambiguous bases or bases with a quality score less than 20
and to discard reads shorter than 20 bases. Trimmed reads were mapped
to the human reference genome version GRCh38/hg38 by segemehl
v0.2.0^[184]58 in split read mode (option –splits) and with additional
parameters –hitstrategy 1 and –differences 1 to report the best
alignment with at maximum one indel or mutation in the initial seed and
passing the default minimal alignment accuracy. Expression
quantification for the human reference gene annotation Gencode
v25^[185]59 was obtained by using HTSeq v0.6.1^[186]60 with parameters
–stranded = reverse, –type = exon, –idattr = gene_id and
–mode = intersection-strict. The number of reads assigned to a gene is,
thus, defined by the number of paired reads that completely map to the
exons of this gene and that do not map to any other gene. For assessing
expression variation among samples raw counts were variance-stabilized
by using the R library DeSeq2 version 1.10.1^[187]61. For visualization
of expression, data raw gene counts were transformed to transcripts per
million (TPMs) in order to correct for different sequencing depths of
RNA libraries and gene length.
Quality control of obtained deep sequencing data
In order to assess the overall quality of the RNA sequencing for each
tissue specimen a subsample of 1 million raw paired-end reads was
randomly chosen by fastq-sample v0.0.14
([188]http://hannonlab.cshl.edu/fastx_toolkit/) using default
parameters ([189]https://github.com/dcjones/fastq-tools). Each sample
was evaluated according to the following criteria using FastQC v0.11.5
([190]https://www.bioinformatics.babraham.ac.uk/projects/fastqc/),
FastQ Screen v0.11.1a
([191]https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/)
, and self-developed scripts (for details see Supplemental Methods):
(i) minimal Illumina Phred Quality Score of 30 reflecting minimal base
call accuracy of 99.9%, (ii) no adapter sequence remnants detected,
(iii) a negligible number of reads mapped to reference genomes other
than human, and (iv) more than 90% of reads mapped to the human
reference genome GRCh38/hg38 (Fig. [192]S1). A manually assorted list
of human rRNA sequences (see S1 Table for NCBI RefSeq identifiers) was
used to calculate the fraction of reads mapping to human rRNA
transcripts, resulting in fractions ranging from 17% to 66%
(Fig. [193]S2).
All samples except one (“peritumoral brain TMZ + 4 Gy 2”) passed all
quality criteria (Figs. [194]S1 and [195]S2). For the remaining
samples, a high fraction of reads mapping to rRNA transcripts was
observed. However, reads corresponding to endogenous rRNA resulted in a
maintainable number of reads. The fraction of high reads mapping
antisense to rRNA genes resembled rRNA antisense probes from the rRNA
depletion step, and thus do not affect assessment of transcriptome
variation (Fig. [196]S2).
Differential expression analysis
Differential expression was assessed with negative binomial models by
using the R library DESeq2 version 1.10.1^[197]61 and RStudio version
1.1.442^[198]62. Both Samples of the treated peritumoral brain tissue
(“peritumoral brain TMZ + 4 Gy”) were excluded from differential
expression analysis because minimal number of required sample size was
not reached due to sequencing failure of one sample of this group. The
linear term for the negative binomial model to obtain significant
changes in gene expression between two selected contrasts of interest
(untreated peritumoral brain vs. untreated GBM tissue, untreated GBM
vs. treated GBM tissue) is:
[MATH:
logλg<
/mi>i=β0+β
1⋅groupk :MATH]
with λ[gi] denoting the relative abundance of gene g in sample i. The
group parameter group[k] reflects a vector specifying the contrasts
used for expression variation assessment. It assigns samples to the
groups “untreated peritumoral brain” and “untreated GBM tissue” or to
the groups “treated GBM” and “untreated GBM”, respectively. For both
contrasts, expression variation was assessed for all genes with at
least one read count in all regarded samples. Default settings of
independent filtering of the DeSeq2 R library were used. All genes with
a false discovery rate (FDR) < 0.01^[199]63 were classified to be
significantly differentially expressed.
Ingenuity® pathway analysis (IPA®)
The pathway enrichment analysis was done with the Ingenuity® Pathway
Analysis software tool version 44961306 (IPA®, Qiagen). A table
containing all the significant differentially expressed transcripts of
the protein-coding fraction between treated and untreated GBM samples
(2527 transcripts) and between untreated GBM versus peritumoral brain
samples (3280 transcripts) was uploaded. A core analysis was run with
default parameters based on expression log ratio. To link the
histological data to the expression analysis data, a list of genes
which are well-known to be associated with the proliferation of cancer
and/or neuronal cells, was generated by IPA®. This IPA® list (1678
genes) was compared to the list of significant differentially expressed
protein-coding genes between treated and untreated GBM tissue and the
number of transcripts present in both lists was calculated. Of the 190
genes which were found in both lists, 7 of the most prominent ones were
chosen for further analyses. They were extracted from the list of
differentially expressed genes (DEGs) between treated and untreated GBM
tissue, another core analysis was run with default parameters and the
z-score was calculated. The z-score indicates whether an associated
disease, function or pathway is predicted to be inhibited or activated
under the given expression values^[200]64. Figure [201]5 shows the
results of this analysis. Green gene symbols in the figure illustrate
the measured downregulation of the gene and blue arrows indicate the
inhibition of the corresponding biological function, representing
negative z-scores calculated by IPA®.
Statistical analysis of image quantification data
Statistical analysis was performed with IBM SPSS Statistics (version
22; IBM Corp.; Armonk, New York, USA). Data were tested for normal
distribution using the Shapiro-Wilk test. Group comparisons were
performed using Kruskal-Wallis test with Dunn’s post hoc tests to
adjust the p-value for multiple comparisons. Correlation analysis of
manually and automatically calculated values was performed by computing
Spearman’s rank correlation coefficient. Significance for all tests was
set at p < 0.05. Data were expressed as median and interquartile range,
boxplots and scatterplots were generated using Mathematica.
Supplementary information
[202]Supplementary Figures^ (932.2KB, pdf)
[203]Supplementary Tables^ (6.8KB, pdf)
[204]Supplementary Methods^ (92KB, pdf)
[205]Supplementary Information^ (16.5KB, pdf)
Acknowledgements