Introduction

Understanding cancer and its complexity can be advanced significantly with better tools for measuring proteins in-situ in formalin-fixed, paraffin-embedded (FFPE) tissue sections. Separation technologies, such as western blot,

microarrays, and mass spectrometry are used widely for quantitation, but lose key architecturally-specific signals that reside at the cellular level, and blend signals from multiple cell types. Laser capture micro-dissection attempts to address this issue, but is expensive and laborious to perform. Most separation approaches disaggregates tissues, blending signals from many cells and tissues.

Multiplex Biomarker Imaging

A P P L I C A T I O N N O T E

Increasing Sensitivity and Accuracy of QuantitativeImmunofluorescence in FFPE Tissue with Spectral Imaging

Highlights

• Spectralunmixingofmultispectralimagessignificantlyimprovesthesignal-to-backgroundratiocomparedwithconventionalfluorescencemonochromeimages

• Quantitatingweaklyexpressedphospho-ERKproteininFFPEtissuesections

• Autofluorescenceremovalenablesaccuratequantitationofweaklyexpressedproteins

2

Kit #8999). The kit comprises two tubes of mixed reagents, one of primaries and one of conjugated secondaries. Phospho-AKT is labeled with Alexa Fluor™ 488, phospho-ERK with Alexa Fluor 555, and phospho-S6 with Alexa Fluor 647. DAPI nuclear counterstain is included. Multispectral images were acquired, using a PerkinElmer Vectra® multispectral imaging system, and spectrally unmixed into label and autofluorescence signals (Figure 1 and 2). For comparison, conventional immunofluorescence images were acquired with fluorescence emission filters centered on the emission peak of the respective fluorophores.

For this analysis, we compared the signals for the phospho-ERK (Alexa Fluor 555) signal in a typical microarray core, as indicated in the spectrally unmixed component image and in the conventional fluorescence emission monochrome image, acquired with emission filtering centered on the Alexa Fluor 555 emission peak. Three locations in the sample were used as reference points: in cytoplasm, in a nucleus, and off the sample. A signal-to-background ratio and autofluorescence contribution were calculated for each image from the cytoplasm and off-sample signal counts.

Results

Measured signals in the conventional immunofluorescence images were significantly higher than in the spectrally unmixed component images, due to the presence of a strong autofluorescence signal, in addition to the pERK signal (table 1). In the spectrally unmixed component image, the autofluorescence is removed, and thus the pERK signal is pure and more accurate (Figure 4). A further benefit of spectral unmixing is the removal of cross-talk from overlapping fluorophores spectra, in multi-label assays. Signal-to-background in the spectrally unmixed images is 50 and in the conventional monochrome image is 3.9, a 13x improvement. More importantly, the data suggest that in this sample, 34% of the signal measured with conventional epi-fluorescence was actually autofluorescence or cross-talk from another fluorophore labels (2991 - 1959) / 2991).

Table 1. Signal counts found from the two pERK images in Figure 2. The signal-to-background ratio of each image was calculated by dividing the signal from the cytoplasm by the off-sample background signal.

Fluorescence microscopy is becoming increasingly important for these endeavors, compared to chromogenic immunohisto- chemistry, due to its higher multiplexing capability, larger and more linear signal range, and less interference among labels. However, immunofluorescence of FFPE tissues poses challenges, including the presence and effects of autofluorescence and the inability to distinguish overlapping signals due to cross-talk. Multispectral imaging eliminates these issues through the use of spectral unmixing, which enables isolation of individual biomarker signals, even when signals are substantially overlapping spatially and spectrally and are obscured by autofluorescence signal. Sensitivity can be increased by as much as 100-fold and accuracy greatly improved for weak signals1,2. The purpose of this application note is to provide an example of the advantage of multispectral imaging in the quantitation of fluorescence intensity in FFPE tissue sections.

Methods

A tissue microarray of lung cancer tissue was labeled with a multicolor immunofluorescence cocktail commercially available from Cell Signaling Technology (Pathscan Node

Figure 1. 4x survey of whole array

Figure 2. 20x multispectral images of each core

Signal (counts)

Conventional Multispectral

Cell 1 2991 1959

Cell 2 2521 1614

Stroma 701 34

Signal/Background 3.9 50

3

Figure 3. Lung tissue labeled with AlexaFluor 488, 555, and 647 and a DAPI counterstain. Panel A shows an RGB image of the sample with pAKT immunolabeled with AF488, pERK immunolabeled with AF555, and pS6 immunolabeled with AF647. Panels B through E display these unmixed component images along with the autofluorescence component in Panel F, whose border colors correspond to the pseudocolors used to form the composite image, G. The autofluorescence spectrum is unmixed in the black channel so it is not visible in the unmixed composite image.

Figure 4. Images of pERK signal generated with conventional fixed filters (above) and with spectral unmixing (below) Signal counts were found from the same three pixel locations in both images, in cytoplasm where pERK is likely expressed, in the nucleus, and a third off the sample to provide a background signal.

Conventional

Monochrome bandpass image

“Unmixed” pERK signal

Multispectral

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Conclusion

Spectral imaging enables reliable and accurate assessments of weakly expressing proteins in FFPE sections, even when fluorescence emissions are substantially weaker than background autofluorescence. The approach also automatically corrects for cross-talk between labels in multiplexed assays, thus giving pure signals of biomarkers and greatly increasing the signal-to-noise ratio compared to conventional methods. It is not sufficient to simply subtract a constant amount of autofluorescence from each pixel, as it is clear that there are varying amounts of autofluorescence across the image and in each cell. It is necessary to spectrally unmix the autofluorescence from the signals of interest. This not only improves the contrast, making the images far more legible, but it greatly improves the accuracy of any subsequent signal intensity quantitation.

References1 Tam JM, Upadhyay R, Pittet MJ, Weissleder R, Mahmood U. Mol Imaging. 2007 Jul-Aug;6(4):269-76.

2 Mansfield, J. R., Hoyt, C. and Levenson, R. M. 2008. Visualization of Microscopy-Based Spectral Imaging Data from Multi-Label Tissue Sections. Current Protocols in Molecular Biology. 84:14.19.1–14.19.15.