Detailed Review of Analyzing Methods

Abstract

Multiplex immunofluorescence (mif) – immunofluorescence technology based on tyramide signal amplification and multispectral imaging, which allows the simultaneous detection of multiple markers on a single tissue section without compromising the tissue architecture.

Introduction

Mif staining consists of following steps:

• Slide preparation

• Epitope retrieval

• Blocking

• Primary antibody incubation

• Incubation with secondary antibody conjugated with horseradish peroxidase (HRP), which catalyzes the formation of tyramide into highly reactive tyramide radicals that covalently bind to tyrosine moieties close to the epitope of interest on formalin fixed paraffin embedded (FFPE) tissue

• Tyramide signal amplification (TSA) with fluorophore incubation

• Primary and secondary antibody washout

• Repeating the above steps for each primary antibody and corresponding TSA fluorophore

• Counterstain with DAPI and mount

Multiplex immunofluorescence images are read digitally. Slides with region of interest are scanned with pathologists' expertise.

Image analysis:

• Spectral library: collection of spectral references matching the intensities contained in the multispectral image used to decode the information in the image at each pixel.

• Linear unmixing: for the purpose of analysis of a multispectral image, it needs to be unmixed into its component fluorochromes, which is done using a software constructed algorithm which keeps spectral library and tissue autofluorescence as reference. Basic principle of analysis of the generated data is segmentation of tissue into tumor and stroma, segmenting individual cells and then phenotyping markers based on marker of interest.

  
  

Software and platforms to analyze multiplex data

• Image J 

• Qupath 

• Inform 

• Cell Analyst 

• Image Pro 

• Aperio eslide Manager analysis 

• Icy 

• Tissue Studio

  
  

Educational Content

In this case in the article in the whole section mesothelioma cohort, the individual cells, defined by nuclei staining (DAPI+), were subjected to phenotyping to characterize co-localization of the multiple cell populations detected using individual algorithms from the inform 2.4.8 image analysis software under pathologist supervision. The individual image marker analyses from the panel were merged at the end using the X and Y coordinates of each cell by the program phenoptr script from R studio (Akoya Biosciences). Using SAS Enterprise Guide 7.1 software, the final report consolidation presented the average of the density per mm2 of the different cell phenotypes found from the rois analysed for the tumor epithelial and stroma compartments combined (tumor-epithelial-stroma compartment) and by compartment (tumor-epithelial and tumor-stroma compartments).

Advantages:

• Many options for mixing different markers on a single tissue section for completely different tasks

• Can analyze 2 - 50 markers at one time, expressed on a single cell level with high precision

• Can study the cell biology through capturing multidimensional data related to tissue architecture, spatial distribution of multiple cell phenotypes and co-expression of signaling and cell cycle markers at the same time

• Study the tumor microenvironment in detail, including spatial configuration

• Powerful tool for immune profiling to achieve a targeted therapy

• Discover new therapeutic biomarkers and possible drug targets, thus helping to discover new immunotherapies

• Immunophenotypic findings of the tumor associated immune infiltrate determined by multiplex immunofluorescence can further be correlated with genomic or transcriptomic data from the same patients

• Spectral imaging combines spectroscopy and imaging

• Can acquire 2 dimensional, spatial (X and Y) and temporal data information from objects depending on parameters like spatial resolution, field of view, dynamic range and lowest detectable signal based on their emitted wavelength

• Multispectral imaging can capture approximately 10 - 30 wavelengths at a time at each pixel of an image, providing the intensity wavelength spectrum at every pixel of the image, in addition to the typical 2 dimensional position of every pixel

• Subsequent data can be used to generate multiple datasets elaborating cell phenotypes, nearest neighbor distances, which can be used for spatial position of single cells in a tissue

• 3D generation of an image

  
  

Disadvantages:

• Using panCK for selection of tumor epithelium, because the normal epithelium also panCK + (in the case in the article)

• Different histological types of selected mesothelioma cases – 11 cases of epithelioid mesotheliomas and 1 of biphasic mesothelioma (in the case in the article)

• Subjectivity – the pathologist selects 10 regions of interest of the tumor for scanning (in the case in the article)

• 3 time 1-week interval between stainings (in this case) (in the case in the article)

• Low number of cases of mesothelioma (in the case in the article)

• Not all antibodies used in miF are from the same manufacturer (in the case in the article)

• Close tones of colors in a 9-color MIF staining panel can create a challenge for image analysis (in the case in the article)

• Possible errors in the analysis at the stage of separation into the stromal and epithelial compartments

• Possible errors in the analysis at the stage of separation into tumor stromal and non-tumor stromal compartments

• Cross-talking reaction between the fuorophores or blocking of the expression of one antibody by another when it was expressed in the same cell compartment (umbrella effect)

• It is necessary to excite all the fluorophores contained in a single slide after the staining, but the emission spectral range cannot overlap with the excitation range

• Use of automated staining is preferred

• A serial of at least 10 different tissues must be tested to determine the assay's operating characteristics (sensitivity and specificity) for each antibody

• Multiplex immunofluorescence images are read digitally on specially built software, which needs expertise to build an algorithm to analyze each case

• Requires a significantly longer time to acquire, process and analyze

• Needs costlier equipment and special expertise

• For read generated data need computer programming languages viz R, Python and C to generate meaningful results

  
  

Practice Implications

This detailed review provides pathologists and researchers with a comprehensive understanding of multiplex immunofluorescence methodology, highlighting both its powerful capabilities for immune profiling and targeted therapy development, as well as its technical challenges and limitations. Understanding these advantages and disadvantages is crucial for appropriate implementation and interpretation of this advanced diagnostic technique in clinical and research settings.

Conclusion

Multiplex immunofluorescence represents a powerful tool for immune profiling and biomarker discovery, offering simultaneous detection of multiple markers while preserving tissue architecture. However, its implementation requires significant expertise, specialized equipment, and careful consideration of technical limitations to ensure accurate and reproducible results.

References

"Identification of distinct immune landscapes using an automated nine‑color multiplex immunofluorescence staining panel and image analysis in paraffin tumor tissues"