Elucidating Signaling Networks in Clinical Tissues with Multispectral Imaging
T Hope, J Ruan, D Wang, R Levenson, H Gardner, C Hoyt. CRi, Inc., Woburn, MA; Novartis TBG, Cambridge, MA
Background: A common goal in clinical research is revealing correlations between outcomes and complex protein expression patterns in tissue sections. Correlations inform target validation, trial design, patient selection, response assessment, and, if trials are successful, the diagnostic component of theranostics. However, to successfully detect multiple, often weakly-expressed targets in clinical tissue sections requires appropriate staining protocols, advanced instrumentation and powerful software. After developing multi-label immunohistochemical staining methods that were quantitative, independent, and specific, the goal was to create and validate an automated, whole-slide scanning imaging system to capture and distinguish multiple labels. Such a system would have an immediate application to signal-transduction research applied to conventional tissue sections. Here we describe this platform, and present results obtained from analysis of cancer tissue microarrays (TMAs).
Design: Multispectral imaging, using a spectrally enabled whole-slide scanning system, was performed on two triple-stained TMAs: the first stained with QDots targeting pMEK and pAKT; and the second targeting p53 and stathmin, both counterstained with a Dapi. Immunofluorescence (IF) signals were spectrally unmixed and isolated from each other. Image analysis algorithms were used to differentiate relevant tissue regions (e.g., malignant and normal epithelia, stroma, necrosis, etc.) and segment cellular compartments (nuclei, cytoplasm, and membrane) to extract IF signals on a per-cell basis. Per-cell relative stain intensities were analyzed with flow-cytometry analysis software.
Results: Multispectral 20x images obtained of each TMA core were acquired and spectrally unmixed at a rate of three cores per minute. Automated image analysis, using algorithms developed by end-users in under 1hour, took 10 seconds per core, segmenting cancer-containing regions and extracting signals from relevant cell compartments. Protein expression levels resulted in relatively weak but specific QDot signals, being 10-fold lower than the nuclear label, and 2-fold lower than tissue autofluorescence.
Conclusions: Multiplexed staining and detection, coupled with flow-cytometry analysis tools can reveal multiple protein expression patterns on a cell-by-cell basis, not possible with serial single stains. The innovative multispectral platform and software can capture cellular and subcellular expression details in an intact tissue architectural context.
Tuesday, March 10, 2009 1:00 PM
Poster Session IV # 213, Tuesday Afternoon