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37 Signal amplification strategies for in situ detection of low-expressed markers in multiplex immunofluorescence
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  1. Pino Bordignon,
  2. Paula Juričić,
  3. Alexandre Kehren,
  4. Victor de Gautard,
  5. Saska Brajkovic and
  6. Diego Dupouy
  1. Lunaphore Technologies, Tolochenaz, Switzerland

Abstract

Background The characterization of the tumor microenvironment is essential for a deep understanding of tumor biology and the identification of personalized targeted therapies. By means of new spatial proteomic approaches, a detailed mapping of the different immune cells has been made possible1. Among them, multiplex immunofluorescence has emerged as a key technology, maximizing the amount of extractable information from a cancer tissue section2. While lineage markers can usually be easily detected via canonical indirect immunofluorescence, functional markers often show much lower levels of expression and would benefit from a signal amplification strategy3. Combining the possibility to amplify low-expressed markers with a system capable of performing multiplex staining could provide significantly improved tumor immunoprofiling with detailed information on immune cell activation and exhaustion.

Here, we aim to implement amplification technologies on the COMET™ system, capable of performing automated multiplex immunofluorescence, to improve the detection of low-expressed markers.

Methods The COMET™ platform is a microfluidic-based system that enables fully automated sequential immunofluorescent (seqIF™) assays based on iterative series of fast tissue staining, imaging, and antibody elution cycles on the same tissue section. Two amplification technologies were automated: Fluorescent Signal Amplification via Cyclic Staining of Target Molecules (FRACTAL)4, and enzymatic Tyramide-based Signal Amplification (TSA)5, also testing different commercially available TSA kits. The effects of amplification on low-expressed markers were evaluated with respect to standard assays on multiple human FFPE tissues. Normalized Mean Intensity and Signal to Background ratio have been used as parameters for the evaluation.

Results The two amplification technologies, FRACTAL and TSA, were successfully automated and optimized on COMET™. The signal intensity of multiple challenging markers, including FoxP3, PD1, PD-L1, and IDO-1 – though being detectable by standard seqIF™ – was significantly amplified by the two techniques. Overall, FRACTAL appeared to be the most suitable technology as shown by the better performance in terms of signal amplification (by NMI or SBR quantification) over the TSA amplification tested on COMET™. Importantly, optimization of the elution step resulted in efficient removal of all antibody layers employed in the FRACTAL method from the stained tissues, therefore allowing its use in sequential staining cycles on the same section.

Conclusions The implementation of amplification technologies in the COMET™ workflow for hyper-plex panel development will allow the detection of difficult-to-target biomarkers, such as functional markers with low-expression levels. This strategy can provide a significant improvement in immune cell profiling of tumor samples.

References

  1. Lundberg E, Borner GHH. Spatial proteomics: a powerful discovery tool for cell biology. Nat Rev Mol Cell Biol. 2019; 20(5):285–302.

  2. Francisco-Cruz A, Parra ER, Tetzlaff MT, Wistuba II. Multiplex Immunofluorescence Assays. Methods Mol Biol. 2020; 2055:467–495.

  3. Stack EC, Wang C, Roman KA, Hoyt CC. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods. 2014; 70(1):46–58.

  4. Cho Y, Seo J, Sim Y, Chung J, Park CE, Park CG, Kim D, Chang JB. FRACTAL: Signal amplification of immunofluorescence via cyclic staining of target molecules. Nanoscale. 2020; 12(46):23506–23513.

  5. Wang G, Achim CL, Hamilton RL, Wiley CA, Soontornniyomkij V. Tyramide signal amplification method in multiple-label immunofluorescence confocal microscopy. Methods. 1999; 18(4):459–64.

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