Caliper Life Sciences, Inc. Hopkinton, MA
Application Note 004
Measuring Double-Positivity in Individual
Tumor Cells in FFPE Tissue Sections
Introduction
Discoveries made in the fields of genomics and proteomics have shown that cellular behavior is determined by complex
networks of protein interaction. The ability to reveal which signaling networks are active in FFPE tissue offers the opportunity
to further our understanding of how disease cells behave in-situ, how this behavior is affected by and reflected in local
microenvironments, and how diseased cells respond to therapies.
These kinds of questions can be answered in a limited way by methods that disaggregate or homogenize tissue, or that
utilize conventional single-stain IHC. Simultaneous assessment of two proteins in intact FFPE tissue sections enables more
accurate determination of cell state. Double positivity assessments reveal the percentages of cells that express both, just
one, or neither of two proteins. Double positivity assessment can be used to answer many demanding analytical questions
about cells in disease-related tissues. Some examples are: a) how many EGFR-positive cells are actively signaling, b) what
percentage of tumor cells are proliferating or dying, c) are there any cancer stem cells present, or d) how many cells have
had a phospho-epitope travel across the nuclear membrane?
With Caliper’s new double positivity tool, both spatial and proteomic information are retained, and varying combinations
of biomarkers can be investigated, including nuclear-to-cytoplasmic expression of a single marker, co-localization of
two markers in a given cellular compartment (e.g., the nucleus), or cell co-expression of markers in different cellular
compartments. The tool treats each cell as an assembly of associated compartments, nucleus, cytoplasm, and membrane,
thus retaining per-cell, object-based data.
The tool works on multi-color immunohistochemical or immunofluorescence tissue samples by spectrally unmixing each
stain or label into separate but completely co-registered component images, enabling each marker to be accurately
measured even when co-localized. Still further, the tool allows for the isolation of immunofluorescence signals from
autofluorescence, thereby dramatically improving sensitivity and signal-to-background ratio.
Multispectral Imaging and Spectral Unmixing
Today, we are experiencing a rapid adoption of multi-marker methods in tissue imaging, with advances in both chromogenic
(brightfield) and fluorescence methods. In brightfield, each target is stained with a unique chromogenic color. For example,
breast tissue can be stained for estrogen receptor (ER) with Fast Red and progesterone receptor (PR) with DAB. Counterstains
such as hematoxylin provide morphological context.
However, overlapping multiple stains, in the same cellular compartments or regions of tissue, are very difficult to assess
visually - which stains are present and what are their intensities? This limitation is the same whether doing a visual
assessment down the eyepieces of the microscope or using a color (RGB) camera. An effective means of overcoming
this limitation is to use spectral imaging and perform linear unmixing to separate and quantitate accurately each marker,
regardless of whether they overlap spatially. [1]
An example is shown in Figure 1, with ER labeled with Vector SG, PR with Vector VIP, Her2 with DAB and a hematoxylin
counterstain. A color (RGB) image of this sample is shown in Fig 1, panel A. It is obvious that many of the nuclei have been
stained with ER, PR or both, but it is extremely difficult, even on close inspection, to assess stain levels. Panels B-E of Fig 1
show the quantitative, unmixed images for each of the chromogens. Unmixed component images can be used for further
analyses, as will be shown later. To improve legibility of the sample, component images can be recombined in a pseudocolor composite image, either with a fluorescence appearance (Fig 1F) or with a conventional, but re-colored, brightfield
appearance. There are a wide range of visualization techniques that can be applied to these unmixed and composite
images. [2]
Application Note 004
Immunofluorescence is often considered more quantitative than
brightfield immunohistochemistry for determining the amounts of
protein in tissue, primarily because fluorescence does not include
a non-linear enzymatic multiplication step, fluorescence detection
offers a larger dynamic range (4 versus 2.5 logs) and detected
fluorescence signal is a linear function of label quantity. However,
it suffers from a serious drawback: tissue, particularly formalinfixed paraffin-embedded tissue, is extremely autofluorescent,
reducing the contrast and quantitatability of the method. However,
in addition to being useful for quantitating spectrally overlapping
signals from multiple labels, spectral imaging separates interfering
A.
F.
B.
C.
D.
E.
autofluorescence from fluorophore signals of interest, a technique
which has been well-documented. [3,4] Figure 2 shows an
example of the improvements in contrast, signal-to-noise ratio and
quantitative accuracy that is obtained using spectral imaging of
tissue sample. This sample was stained for EGFR using a 585-nm
QDot and counterstained with DAPI. Fig 2A shows a color image
of the sample. Three regions of the sample have been chosen
for quantitative comparison, each shown with a yellow dot: one
where there is no sample, one inside a nucleus and one over a
membranous region. Fig 2B shows the monochrome image of the
sample at 585 nm, the peak of the QDot emission, and while some
membrane staining for the EGFR can be seen, the majority of it is
difficult to see due to interference from tissue autofluorescence
as well as bleed-through of the DAPI counterstain. On the other
hand, Fig 2C, which shows the unmixed QDot 585 image, shows a
much cleaner membrane staining, with a greatly improved signalto-noise ratio.
Monochrome
MSI
Membrane
69.1
23.4
Nuclear
99.9
1.3
No sample
6.9
0.4
Table 1. Spectral imaging vs monochrome quantitation results
Figure 1. Multiplexed immunohistochemical image series of a formalinfixed, paraffin-embedded breast section containing regions of breast
cancer stained for three proteins, including ER (SG), PR (VIP) and Her2
(DAB), and a hematoxylin counterstain. Imaging and spectral unmixing
was accomplished using a Caliper Nuance multispectral imaging system.
Panel A: A color image prior to spectral unmixing. Most of the stain has
combined to produce a brownish, muddy appearance, making it difficult
to disercn individual labels. Panels B - E: Spectrally unmixed component
images for each of the labels, ER, PR, Her2 and Hematoxylin, resepectively. Panel F: Resulting composite images shown in pseudo-colors in either
a pseudo-fluorescence mode or a brightfield mode. Sample provided by
Dr. Michael Feldman, UPenn.
A.
B.
No Sample
6.9
Membrane
69.1
99.9
Nuclear
C.
0.4
23.4
1.3
Figure 2. Quantitation of signal intensities from an immunofluorescence
image labeled for EGFR and Dapi. Panel A: a color RGB image identifying the locations of three measurements taken off the sample, on the
membrane and in the nucleus. Panel B: a monochrome image acquired
at 585nm (peak emission for EGFR-labeled QDot). Quantitation of EGFR
signal includes background as clearly shown by the erroneous presence
of signal in the nucleus, and by the high values both on and off the
sample. Panel C: a Nuance unmixed component image of EGFR with the
same three points measured. Data correctly indicate that the background
has been unmixed and there is little to no EGFR signal in the nucleus.
Comparing the signal intensities of the three chosen regions
in the monochrome and unmixed EGFR images shows a drastic
improvement in quantitation. Table 1 shows the intensities
extracted from these regions. In the monochrome image, the
“EGFR” signal in the nuclear region is actually higher than from
the membrane region, due to interference from autofluorescence
and DAPI, and the ratio between the membrane region and the
region with no sample is only 10 to 1. The unmixed image, on the
other hand, shows a far stronger signal in the membrane region
compared to the nuclear region (as is biologically expected), and
the ratio between the membrane region and the region with no
sample is nearly 60 to 1. Studies have shown that the improvement
in signal-to-noise obtained from unmixing ranges from between 80
and 300 times, and has been used for both fluorescence microscopy
of tissues and for fluorescence imaging of small animals such as
mice. [5, 6]
Multispectral imaging capability comes in two levels of product –
the award winning Nuance, which is a manual system that can be
mounted on research-grade microscopes, and the Vectra, which
is a fully integrated system consisting of robotic slide handling,
automated image acquisition, and tools for automated image
analysis. Caliper’s Vectra system can acquire images and analyze
200 slides at a time once it has been trained with a small number
of representative images. Like the Nuance system, it can be used
for both brightfield and fluorescence imaging.
Automated “per-cell” analysis of tissue compartments
Often it is important not only to quantitate signal intensities on a
pixel basis as shown in Figure 2, but to quantitate the amount of
each marker on a per-cell basis. In flow cytometry, this kind of percell quantitation is easily achieved as each cell is passed through
the analysis chamber and individually assessed, and in high-content
Application Note 004
imaging systems, which image cells in multiwall plates, it is also
relatively easily achieved, as each cell imaged is a whole cell, is
of interest, and there is no autofluorescence to interfere with
quantitation results. In tissue sections, however, it is a lot more
difficult to quantitatively determine the signal levels of each marker
on a per-cell basis. One of the primary difficulties is that not every
cell is of interest. Tissue sections can contain cells from many tissue
types (cancer, inflammation, stroma, other organ tissue, blood
vessels, etc.) and only some of those represent the cells-of-interest.
The standard means of overcoming this tissue region problem is to
either simply visually assess only those cells that are wanted, or to
laboriously draw regions-of-interest around those tissue types of
interest and then assess only the cells within those regions. Neither
of these solutions is amenable to studies with more than a few
samples, and both are prone to error and can take quite a bit of
time to complete.
A.
C.
B.
marker IHC images, or on spectral images acquired from multilabel
or fluorescence samples. Further, double-positivity scoring can be
assigned to each sample as described below.
Double-positivity Scoring in IHC
An example of this combination of morphologic and cellular analysis
can be seen in Figure 3. This breast cancer sample was stained
for ER using Vector VIP, PR using Vector SG and counterstained
with hematoxylin. In this example it is important both to be able
to correctly quantitate the amount of chromogen in each nucleus,
but to be able to select which nuclei in which morphologic region
are going to be quantitated. Fig 3A&B shows a color image of
the sample and Fig 3 C&D show the analysis results. The inForm
morphologic segmentation is shown by superimposing a green
(stroma), red (tumor) or blue (empty) color overtop of the RGB
image. In addition, the analysis generates the total percent area
of each of the tissue types (stroma, cancer and blank) as well as
the number and size of contiguous portions of each. After the
morphologic segmentation, the unmixed hematoxylin counterstain
image is then used as the basis for a cellular segmentation, finding
each nucleus in the image. The found nuclei as shown as green
circles superimposed onto the morphologic segmentation in Fig 3B.
A.
B.
C.
D.
D.
Figure 3. Immunohistochemical breast section labeled for ER (VIP), PR
(SG) and a hematoxylin counterstain. Panel A: color image. Panel B: unmixed composite with inForm tissue and cell segmentation overlay. Panel
C: scatter plot of four phenotypes, including PR single-positive, PR/ER
double-positive, PR/ER double-negative and ER-positive. Panel D: percentages for the above combinations.
To overcome this tissue morphology problem, Caliper developed a
learn-by-example interface, the inForm software, which provides
a simple, easy-to-use means of finding morphological regions of
interest followed by automated cell analyses. In brief, the software
works by utilizing the user’s own knowledge of the morphology of
their samples. The user draws a number of representative training
regions on images of their samples, and the software then develops
an algorithm to separate, or segment, the various morphologic
regions on which it was trained. Training sets should include
images that represent the heterogeneity across images. When
performing a clinical research study, it is recommended to use
between twenty to thirty images versus five to ten on animal-based
studies. Although this sounds complicated, it is, in practice, quite
simple to accomplish, with each algorithm development taking
only seconds after the training regions have been drawn. Once the
software has been trained to recognize morphologic regions, then
automated cellular analyses including nuclear, cytoplasmic and
membrane measurements can be performed on the cells within
each region. This kind of analysis can be done on standard single-
E.
Figure 4. Images from an IF tissue microarray labeled for pAKT, pERK,
and pS6. TMA cores were imaged with Vectra automated slide analysis
system. Signals were measured using spectral imaging and associated
unmixing algorithms. Signals were extracted from objects in images using inForm to determine co-expression of various combinations of phospo-proteins. Panel A: conventional color imaging before spectral unmixing. Panel B: composite spectrally unmixed image. Panel C: tissue mask
overlay. Panel D: Cell segmentation mask along with double-positivity
data for pS6 (signal 1) versus pERK (signal 2). Panel E: two scatter plots
of two different phenotypes, respectively, including pS6 versus pERK,
and cytoplasmic pERK versus nuclear pERK. The ability to determine coexpression of a single phospho-epitope across the cellular compartments
is particularly useful in being able to determine the state of a given signaling pathway.
Once the nuclei are found, the total amount of each marker within each nucleus is determined, providing a per-cell amount of each
marker for each cell in the image. The resulting per-cell quantitative data can then be displayed as a scatter plot of in a manner similar
to flow cytometry. Fig 3C shows the scatter plot obtained from plotting the per-cell amount of the ER and PR markers in the tumor
morphologic region. By applying a threshold (again, similar to flow cytometry), one obtains the number of cells that are expressing each
of the four possible phenotypes: double negative; single positive for ER; single positive for PR; and double positive. The results for this
sample can be seen in Fig 3D.
In addition to quantitating the amount of each marker in the nucleus, the inForm software also has algorithms for quantitating the
amount of marker in the cytoplasm or in the cell membrane. The sample used in Figure 3 does not need cytoplasm or membrane marker
analyses (as ER and PR are both nuclear markers). However, for markers that are present in more than just the nucleus, one can determine
the amount in each cellular compartment (nucleus, cytoplasm or membrane) as well as the total amount. These values can each be used
for scatter plots like the one in Figure 3C. This kind of flow-cytometry-like data analysis is often termed “tissue cytometry” because
it provides the per-cell quantitative analysis, scatter plot display and thresholding associated with flow cytometry from entirely tissuesection-based immunohistochemistry, immunofluorescence or in-situ hybridization data.
Double-positivity Scoring in IF
Measuring double-positivity in fluorescently-labeled tissue sections tends to be more difficult due to the masking effects of autofluorescence.
Caliper’s multispectral imaging approach, however, overcomes this limitation in being able to spectrally unmix the autofluorescence
from the other labels of interest. As with IHC, inForm can be used to analyze each component image (e.g., representing the labels of
interest) and measure the various marker combinations of interest. Figure 4 illustrates an example where expression of phospho-AKT
(pAKT), phosho-ERK (pERK) and phospho-S6 (pS6) are unmixed and subsequently measured from a lung cancer TMA. This particular
study involved a 3000-core tissue micro array, where the double positivity tool was used to explore several of the possible 36 different
marker pairings (3 labels x 3 compartments = 9 parameters, n(n-1)/2 permutations). Example data are shown in Figure 4b. A commercially
available four color kit from Cell Signaling Technologies was used to label the respective markers. The imaging and analyses were done
using Caliper’s Vectra slide analysis system.
Summary
A common goal in pharmaceutical and life science research is to reveal correlations between protein expressions in intact tissue and clinical
outcomes. Correlations support many activities in research and ultimately in the clinical setting: target validation, trial design, patient
selection, drug response assessment, and if trials are successful, the diagnostic component of theranostics. However, the predictive
power of measurement of protein expression depends on the precision and accuracy of tools to extract data from tissue samples, which
are inadequate in many cases. For example, many techniques deployed today, such as those based on microarray detection or analysis of
sample lysates provide data that are averages from volumes of tissue, including many cells not of interest. These methods blur out key
proteomic information that reside at the cellular level, relating to the signaling states of individual cells.
Quantitating the expression of multiple proteins in intact tissue sections provides valuable information about how cancer-related cells
function, behave, and respond in context, and how their presence, frequency, and spatial distribution correlate with drug efficacy and
clinical outcomes. The ability to determine co-expression levels of interacting proteins in the morphological context of the tissue is a
valuable new method that should help answer more complex biological questions.
References
1
Van der Loos, C. “Chromogens in Multiple Immunohistochemical Staining Used for Visual Assessment and Spectral Imaging: The Colorful
Future” Journal of Histotechnology, 33:1, March 2010, 31-40.
2
James R. Mansfield, Clifford C. Hoyt, Richard M. Levenson “Methodologies for visualization of microscopy-based spectral imaging data of
multi-label tissue sections” Current Protocols in Molecular Biology, 14.19.1-14.19.15, October 2008.
3
Richard M. Levenson and James R. Mansfield, “Spectral Imaging in Biology and Medicine: Slices of Life” Cytometry A. 2006 Aug; 69(8):748-58.
4
Liu, J.; Nie, S. “Molecular Mapping of Tumor Heterogeneity on Clinical Tissue Specimens with Multiplexed Quantum Dots” ACS Nano 4:5,
2010, 2755-2765.
5
Tam JM, Upadhyay R, Pittet MJ, Weissleder R, Mahmood U. “Improved in vivo whole-animal detection limits of green fluorescent proteinexpressing tumor lines by spectral fluorescence imaging.” Mol Imaging. 2007 Oct-Dec;6(4):269-76.
6
James R. Mansfield, Clifford C. Hoyt, Peter J. Miller, Richard M. Levenson “Distinguished photons: increased contrast with multispectral in-vivo
fluorescence imaging”, Biotechniques, Vol. 39 Supplement: pp S25-S29 (Dec 2005).
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TIS-AP-004 Mar 11