Multiplex Immunofluorescence Advancing Cancer Research

With recent advances in multiplex immunofluorescence (mIF), researchers are delving into the cytoarchitecture of tissues. The ability to label multiple proteins expressed in a single tissue section empowers cell biologists to construct cell and tissue atlases for entire organs. It also enables cancer researchers to investigate the spatial organization of the tumor microenvironment (TME), the battleground where a patient’s immune cells engage in an ongoing struggle against tumor cells. The ability to label multiple biomarkers simultaneously within a tissue while preserving morphological structures is revealing valuable information about layers of different cell types and their arrangement in the TME. This article discusses how mIF is being used to investigate cancer and improve immunotherapy.

Higher throughput mIF

Incorporating automation and robotics into mIF workflows is increasing throughput. TissueGnostics’s TissueFAXS SPECTRA images up to 8 markers at once, using tunable liquid crystal filters for greater flexibility in wavelength filtering, and the TissueFAXS Chroma system images up to 7 markers at once using an optimized filter set. “Both systems have 8-slide capacity with continuous batch mode scanning, with an option to upgrade to a 120-slide loader for high-throughput imaging,” says Anastasiia Marchuk, Junior Product Specialist at TissueGnostics. TissueGnostics offers StrataQuest software for image analysis, including phenotyping, segmentation, classification, and measurements of spatial relationships.

TissueGnostic platforms are frequently used in mIF cancer studies. For example, to improve a predictive biomarker for response to treatment of rectal cancer, researchers recently used TissueFAXS Chroma and StrataQuest to study pre-operative biopsies. Using a heat map approach, they stratified patients according to type I interferon expression and density of CD8+ T cells. They found that including these two parameters improved the predictive power of the established parameter alone (i.e., tumor-infiltrating lymphocytes), when assessing the chance that a patient responded to treatment. It is hoped that cancer immunotherapies will become more effective with predictive biomarkers to guide clinicians in choosing the best therapy for individual patients.

For higher numbers of biomarkers, Leica Microsystems’ Cell DIVE platform for multiplex IF imaging can image over 60 dye-conjugated antibodies per tissue section. Cell DIVE uses an iterative staining process that leverages the power of robotics, automated imaging, and workflow management software. The process preserves stained tissue sections, which can be used again for later experiments. Many Cell DIVE users work in immuno-oncology and use multiplex imaging to identify cell subtypes in the TME. “Researchers use this information to try to understand why some cancers progress faster than others, and why some patients respond to certain treatments and others do not,” says Michael Smith, Applications Manager, Cell DIVE, Life Science division at Leica Microsystems Americas.

A recent NIH study of follicular lymphoma (FL) used Cell DIVE together with RNA sequencing, IBEX methodology, and novel image analysis. Some FL patients experience poor prognosis and relapse early after treatment. This study investigated the TMEs within FL lymph nodes, identifying histological characteristics in high-risk FL patients that may be predictive of early relapse.

Protein expression signatures

In disease applications, using mIF in spatial biology allows entire protein expression signatures to be viewed as biomarkers in themselves. “Spatial biology is a powerful technique for quantifying protein co-expression on immune cell subpopulations and assessing their spatial arrangement within the microarchitecture of the TME,” says Ritu Mihani, Head of Product Marketing at Akoya Biosciences. “Spatial localization and quantification of tissue-specific proteins involved in immune activation enables identification of distinct cell types, their functional states, and spatial distribution, offering new insights into a tumor’s unique biology and how best to treat it.”

Spatial biology platforms such as Akoya’s PhenoCycler^®-Fusion 2.0 and PhenoImager^® HT 2.0 allow automated spatial phenotyping by multiplex IF imaging. They can simultaneously detect several to a 100 targets using molecular-barcoded antibodies for greater scalability. Researchers are applying Akoya’s spatial technology to develop cell atlases, understand cancer, and improve immunotherapy. For example, researchers used the PhenoCycler platform to construct the Human Breast Cell Atlas  with single-cell resolution, and to help define mechanisms responsible for the success of adoptive T cell therapy. In addition, a prognostic 7-biomarker panel, which stratifies early-stage skin cancer patients into high- or low-risk groups for relapse and melanoma-related death, is being transferred to the PhenoImager HT platform, according to Mihani.

Overcoming challenges

Multiplex IF comes with its own set of challenges. As with many techniques, reproducible results are highly dependent on high-quality sample preparation. “Ensuring consistent and high-quality tissue sectioning is essential for reliable multiplexed imaging,” says Mihani. “Variability in section thickness or preparation methods can affect staining and imaging outcomes.” Fixation methods, selection of antibodies, and experimental complexity are also instrumental in the success of mIF.

Whenever possible, Marchuk advises to use fluorophores that emit signals at well-separated wavelengths. “Having fluorophores with spectrally overlapping emission profiles can make it challenging to disentangle the different emission channels, even when filters isolate specific channels, as there can be unwanted emissions from another channel,” she says. Another possibility is to use fluorophores that absorb light at well-separated wavelengths instead. “Scanning systems such as TissueFAXS SPECTRA are made with these challenges in mind, reducing the risk of channel bleed-through, and an appropriate microscopy system can help to bring the number of issues down to zero,” says Marchuk.

Despite the challenges of performing multiplex IF, Smith believes that the more difficult parts occur before and after the staining, in antibody sourcing and data analysis, respectively. Sourcing and validating 60+ antibodies for a large biomarker study is a significant logistical undertaking, which Cell DIVE aims to ease by supplying a list of 350+ validated antibodies. Multiplexed imaging data analysis is a complex process involving cell types and spatial metrics. “We are proud to offer AI-driven analysis with AIVIA, to help make the process of identifying cells in tissue, their positions, and performing high-level spatial analyses easier for users,” says Smith.

Pinpointing effects of drug treatments

Despite these challenges, mIF holds the potential to deliver powerful insights when applied to clinical biomarkers. Precision for Medicine employs an automated mIF and digital pathology workflow using Akoya Bioscience’s OPAL and PhenoImager HT multispectral imaging platform, combined with HALO digital image analysis from Indica Labs. OPAL allows them flexibility to modify or design custom mIF panels, which is important for the different studies carried out by researchers who partner with them. “We often incorporate markers specific to the disease indication or etiology, the drug’s mechanism of action, or even localization of a biotherapeutic within the tissue,” says Amanda Woodrooffe, Senior VP at Precision for Medicine.

With increasing numbers of targets multiplexed, it can be challenging to establish optimal conditions for detecting all targets, when expression and conditions can vary across a sample. “We prepare for this with appropriate levels of qualification of each mIF panel based on how the assay will be applied—for example, demonstrating robust performance in multiple sections of different donor samples for each indication of interest,” says Woodrooffe. “Given the inter-individual variability in tumor target expression, being able to readily access well-annotated samples to support the development and qualification of these assays is imperative, and we can tap into our own extensive tissue biorepository to achieve this.”

Precision for Medicine often applies mIF to explore clinical biomarkers in immuno-oncology and other disease research. For example, immune-based cancer therapies (e.g., immune checkpoint inhibitors) are most effective against immune-inflamed or “hot” tumors, which contain more infiltrating T cells. “A typical research study for us involves evaluation of the immune status of tumors (i.e., hot or cold) by assessment of the extent of immune infiltration and position of immune cells in the TME, their proximity to the tumor cells, and evaluating the presence of suppressive molecules or cells,” says Woodrooffe. “Using spatial image analysis, effects of drug treatment relating to the localization and/or activation status of the immune cells can be determined.” The ability to pinpoint patient populations that will benefit from specific therapies brings us incrementally closer to more effective cancer treatments for all.